Daily life activities often require humans to perform locomotion in challenging scenarios. In this context, this study aimed at investigating the effects induced by anterior-posterior (AP) and medio-lateral (ML) perturbations on walking. Through this aim, the experimental protocol involved 12 participants who performed three tasks on a treadmill consisting of one unperturbed and two perturbed walking tests. Inertial measurement units were used to gather lower limb kinematics. Parameters related to joint angles, as the range of motion (ROM) and its variability (CoV), as well as the inter-joint coordination in terms of continuous relative phase (CRP) were computed. The AP perturbation seemed to be more challenging causing differences with respect to normal walking in both the variability of the ROM and the CRP amplitude and variability. As ML, only the ankle showed different behavior in terms of joint angle and CRP variability. In both tasks, a shortening of the stance was found. The findings should be considered when implementing perturbed rehabilitative protocols for falling reduction. Humans have to constantly deal with the challenge to adapt their gait to several environmental conditions; thus, one of the main roles of the central nervous system (CNS) is to assure the maintenance of dynamic stability during locomotion on different types of surfaces . DynamicAs concerns spatio-temporal parameters, Madehkhaksar et al.  evaluateMoving to kinematics, the analysis of trunk and upper body segments was deeply conducted in previous studies. As an example, Rum and colleagues  quantifiFocusing on this literature overview, it is clear that the analysis of all the cited variables can lead to the identification of useful information to better understand the mechanism of the Central Nervous System (CNS) in controlling perturbed locomotion. To the best of the authors’ knowledge, no studies have explored the effects induced by perturbation on lower limb kinematics in terms of joint angle variations, as well as all the previous studies applying unexpected perturbations to understand human strategies in balance responses. However, it is important to analyze the changes in lower limb joint angles, which represent the most widespread analyzed parameters when performing gait analysis, as well as understanding the reactive response to expected perturbations. In addition, lower limb joints are the most important ones when considering the response of a subject after perturbations of the support base in order to avoid falls; in fact, it is well known that ankle- and/or hip-based strategies are mandatory for maintaining equilibrium . As a coDue to the expected nature of the perturbations, such analysis permitted us to evaluate the modification of the CNS behavior focusing only on the perturbation rather than on other external factors that could influence the gait patterns in case of unexpected perturbations. In addition, humans daily face expected perturbation of their walking patterns, such as walking on uneven ground, ascending or descending stairs, etc. Consequently, the results of this paper can point to important features to consider when implementing gait perturbation paradigms for rehabilitation purpose, i.e., reducing falls among the elderly. Twelve young, healthy participants  were recruited in the experimental protocol. The dominant leg, which was identified by asking participants to kick a ball , was rigThis study was approved and reviewed by the Ethics Committees of the Heidelberg University (AZ Schw 2018 1/2).The perturbed-locomotion protocol was performed by using a treadmill  that was able to provide sudden external perturbations in both the anterior-posterior (AP) and medio-lateral (ML) directions. AP perturbation was achieved by providing a rapid acceleration of the treadmill belt, whereas a sudden displacement of the belt-supporting platform was performed to provide ML perturbation. The kinematics of the lower limb was gathered through commercially available inertial measurement units (IMUs) provided by Xsens Technologies . Each sensor was equipped with a triaxial linear accelerometer, triaxial gyroscopes, and a triaxial magnetometer able to respectively acquire the linear acceleration, the angular velocity, and the magnetic field of the body segment it was attached to. The acquisition frequency of IMUs ranged from 40 Hz to 120 Hz. In this study, the acquisition frequency was set to 60 Hz, in accordance with previous studies on gait analysis ,25; in aBefore the experimental session, each participant was sensorized with seven IMUs placed on the pelvis, right and left thighs, right and left shanks, and right and left feet. The placement was performed by the same operator using ad hoc elastic belts that limited the movement artifacts during the execution of the motor tasks. After the sensorization phase, each participant was asked to perform two static tasks; specifically he/she had to maintain a standing upright position and a sitting position for at least 5 seconds each in order to acquire the data mandatory for the functional calibration of the sensors . SuccessBefore starting with the motion tasks, each participant performed several strides in order to familiarize with both the treadmill speed and the presence of sensors to avoid gait pattern alterations. The familiarization period was stopped when the participants felt her/himself comfortable with the treadmill speed and lasted approximatively 3 min per each participant. No data were acquired during the familiarization phase.The experimental protocol consisted of three walking tasks. During the first task, hereinafter named as Normal Walking (NW), each participant was asked to walk on the treadmill for 6 minutes. The speed of the treadmill was equal for all the participants and set at 4.3 km/h. The value of the walking speed was in line with average walking speed of pedestrians . Data gaAP), consisted of 3 minutes of normal walking interspersed with three expected perturbations in the anterior-posterior direction. The perturbation consisted of a sudden acceleration of the treadmill belt in the anterior-posterior direction, set at 0.5 m/s2 for a duration of 0.5 s. Similarly, the third task, hereinafter named as Expected perturbation in Medio-Lateral (EML), consisted of 3 minutes of normal walking interspersed with three expected perturbations in the medio-lateral direction. In this case, the perturbation, which lasted 0.5 s, consisted of a rapid displacement of the treadmill base in the medial direction with the values of displacement and acceleration equal to 16 cm and 0.18 m/s2, respectively. All the perturbation parameters were selected during a pilot study conducted on three participants in order to optimize the perturbation amplitude avoiding the subject’s fall. The second task, hereinafter named as Expected perturbation in Anterior-Posterior  technique was adopted . The CRPFinally, CRP was computed by subtracting the distal and proximal joint phase angles. CRP values can range between −360° to 360°, with values close to 0 ± 360° indicating that the two segments were moving following an in-phase coupling, whereas −180° and 180° represent an out-phase coupling behavior . CRP wasAs synthetic indices, the Mean Absolute Relative Phase (MARP) and Deviation Phase (DP) were used. Firstly, we computed the ensemble CRP curve by averaging the CRP across the stride of the same walking condition for each participant. Then, the MARP was determined by averaging the absolute value of the ensemble curve points for the overall duration of the task T, as in the following equation :MARP=∑i=i) for the overall duration of the task T, as in the following equation:Instead, DP was evaluated by averaging the standard deviations of the ensemble CRP points  and the knee and ankle joints also in the first subphase of the stance period. Furthermore, only the ankle joint between 50% and 60% of the gait cycle showed differences between NW and EML, whereas no difference was found when comparing the two types of perturbed locomotion.By analyzing Considering the synthetic indices, the mean mROM and the mean CoV across participants with their relative standard deviations are reported in By moving the inter-joint coordination analysis, the mean CRP curves and related standard deviations are reported in AP, whereas no difference was noticed in the other compared pairs. More specifically, the differences were observed (1) at the first phase of the stance both for the couple hip-knee and the couple knee-ankle; (2) at the last phase of the stance both for the couple hip-ankle and the couple knee-ankle; (3) immediately after the start of the swing phase, i.e., approximatively 60% of the gait cycle, in all the examined inter-joint couples; (4) at the midswing only in the couple hip-knee; and (5) immediately before the heel strike both for the couple hip-ankle and the couple knee-ankle.It was found that ANOVA tests always revealed statistical differences. In particular, the differences were found only when comparing NW and EThe results obtained for the synthetic indices computed on the CRP curves and the relative statistical findings are reported in AP for all the three examined joint pairs , whereas the index related to EML was not different from NW in the couple hip-knee. Generally, the indices computed for the normal walking tasks achieved the greatest values of MARP with 62°, 110°, and 110° for the pairs HK, HA, and KA, respectively. As far as DP, the two motion tasks in which a participant was subjected to external perturbations were associated with values statistically greater than the ones related to the normal walking tasks . The lowest value of DP was related to the HK in normal walking, equal to 14°, whereas the greatest was associated with KA in EML task, equal to 33°.By considering the MARP, statistical differences were found between NW and EThrough the aim to assess the effects induced by external expected perturbations to level locomotion in healthy subjects, we analyzed lower limb joint angles and inter-joint coordination parameters in a cohort of 12 participants when performing three motions, i.e., normal walking, walking with perturbations in the AP direction, and walking with perturbations in the ML direction.The findings revealed that the curves related to all the three examined joints were characterized by a topological similarity among the unperturbed and the perturbed locomotion. Focusing on perturbed locomotion, it was clear how the differences with respect to normal walking were mainly due to a different distribution of the timing events within the stride. In fact, a shortening of the stance period was observed in both AP and ML data by considering the early ankle plantarflexion and hip-knee flexion immediately before the transition between the stance and the swing phases. The SPM outcomes also confirmed this consideration since the differences were not related to all the gait cycles but only at one specific time interval, i.e., the transition between stance and swing phases, allowing assessing the presence of a phase-dependent compensatory responses . These rDespite the differences during the gait cycle, absence of differences in terms of range of motion was observed. However, such a finding confirmed that the range of motion can be considered a kinematic parameter invariant across several locomotion-related tasks, as also reported for other parameters related to kinematics and muscle activity . Thus, wFinally, since the absence of statistical differences between the two perturbed tasks was mainly due to the high inter-subject variability related to ML, some qualitative speculations on different strategies can be noted. The differences with respect to normal walking observed for all the examined joints when analyzing AP data allowed us to affirm that the entire biomechanical chain is influenced by the anterior-posterior perturbation and, consequently, we can speculate that the ankle is not able to absorb the disturbance. Such a result, combined with the already discussed shortening of the stance, suggests that participants are forced to change gait from walking to running in order to avoid trips and/or falls. This aspect is well recognized as one of the main strategies adopted by the central nervous system when an anterior-posterior perturbation is provided . In addiBy summarizing, the main effects caused by expected anterior-posterior and medio-lateral perturbations are the shortening of the stance and an increment of the stride-to-stride variability. Such effects are probably due to two distinct strategies that are a walking–running transition in case of anterior-posterior perturbations and stepping strategy when inward perturbations are provided.As for the joint angle curves, the trend of the continuous relative phase is in accordance with the ones reported for normal walking of healthy subjects in previous studies ,31,33. CFocusing on the synthetic indices, both perturbed tasks are generally characterized by an increment of the in-phase coupling of the lower limb joint, attesting from the significant reduction of the CRP amplitude (MARP). Contrary to what is thought, a more in-phase coordination does not mean a better working of the central nervous system but only a reduction of independent action associated with each segment, which is the physiological behavior that guarantees the prevention of falls . The incThese findings, combined with the ones related to the joint angles, should be taken into account for implementing perturbation paradigm programs addressed to increase the adaptation of a subject to challenging walking scenarios , using tUnderstanding how the central nervous system modifies the gait patterns in response to external perturbation of locomotion can lead to useful information for implementing perturbation rehabilitative programs, as well designing control systems for exoskeletons to use in daily life. The findings of the present study reveal that different physiological gait patterns in terms of joint angles and inter-joint coordination are found when anterior-posterior and inward expected perturbations are provided. Both perturbations lead to a shortening of the stance, an increment of the gait variability, and modifications of amplitude and variability of the inter-joint coordination. No differences were statistically appreciable between both pattern and synthetic indices associated with the two perturbed tasks; however, two different balance recovery strategies were adopted, which were walking–running transition in the case of anterior-posterior perturbations and a stepping strategy when locomotion was perturbed in an inward direction. Further studies should apply the same methodology in order to investigate the effects of aging; as well, the analysis of out-of-sagittal planes should be performed to fully characterize the compensatory mechanism adopted by CNS.


The liver is an irreplaceable organ in the human body, maintaining life activities and metabolism. Malignant tumors of the liver have a high mortality rate at present. Computer-aided segmentation of the liver and tumors has significant effects on clinical diagnosis and treatment. There are still many challenges in the segmentation of the liver and liver tumors simultaneously, such as, on the one hand, that convolutional kernels with fixed geometric structures do not match complex, irregularly shaped targets; on the other, pooling during convolution results in a loss of spatial contextual information of images. In this work, we designed a cascaded U-ADenseNet with coarse-to-fine processing for addressing the above issues of fully automatic segmentation. This work contributes multi-resolution input images and multi-layered channel attention combined with atrous spatial pyramid pooling densely connected in the fine segmentation. The proposed model was evaluated by a public dataset of the Liver Tumor Segmentation Challenge (LiTS). Our approach attained competitive liver and tumor segmentation scores that exceeded other methods across a wide range of metrics. Since the liver is a reasonably significant organ for abdominal metabolism, liver tumors in particular, malignancy certainly poses a serious threat to human health. Statistics from the World Health Organization have also shown that commonly occurring liver cancer was accompanied by a high mortality rate worldwide. How to accurately identify, locate and segment lesions has become the primary step in the development of subsequent precision treatment and individualized protocols. Accurate diagnostic treatment has a significant positive effect on reducing the number of patients suffering from diseases and improving disease prognosis . The comMedical image segmentation algorithms based on deep-learning convolutional neural networks are especially representative in fully automated segmentation of the liver and liver tumors. Long et al.  from BerHowever, the locations and shapes of the liver and liver lesions are highly variable in the different CT images . It is wIn this work, we propose a cascaded U-ADenseNet network to segment CT images for the liver and its lesions. The network is designed to reduce the difficulty of segmenting areas concerning targets that involve diverse locations and complex shapes. So as to minimize the loss of spatial information during down sampling from feature extraction, U-ADenseNet replaces down sampling with dilated convolution. We innovate a multi-layered channel attention module, subtly combining atrous spatial pyramid pooling. The structure uses U-Net to initially extract the feature information of the liver and tumors, cascading our designed ADenseNet for fine segmentation afterward. The model makes use of densely connected modules to realize the feature interaction of multiple receptive fields, effectively unifying global spatial information with local depth semantic information. Our experiments applied the dataset from the Liver Tumor Segmentation Challenge competition (LiTS) . ExperimEarlier convolutional layers tend to learn lower-level concepts more, while the later convolutional layers produce higher-level feature maps. The number of feature maps needs to be increased when the network reaches deeper levels. A simple way to build the neural network architecture for this task is to simply stack many convolutional layers and output the final segmentation mappings. Corresponding segmentation mappings are learned from the input images by direct, successive transformations of the feature maps . TherefoConvolutional networks can be significantly deeper, more accurate and easier to train if they contain shorter connections between the layers closer to the input and the layers closer to the output. The traditional structure is delivered from layer to layer, and some information is changed whilst some information is retained in the delivered information. DenseNet starts with features and achieves better results and fewer parameters by making the best use of features. DenseNet improves the efficiency of information and gradient transfer in the network. Each layer obtains the gradient directly from the loss function and obtains the input signal directly, so that a deeper network can be trained, and this structure also has the effect of regularization . This coThe benefit of down sampling the feature mapping is the extended perceptual field for the fixed, constant convolutional kernel size. The approach makes more sense than increasing the size of the convolutional kernel because of the lower parametric efficiency of large-size convolutional kernels. However, this expansion is involved in the reduction in spatial resolution. Dilated convolution provides an alternative method to obtain a wide field of view while preserving the full spatial dimensionality. Dilated convolution separates the space with values based on a specified dilation rate. The pooling layers are replaced by dilation convolutions with continuously increasing dilation rates, which can prevent the loss of spatial details while maintaining the same perceptual field . Atrous In order to extract more effective features from the liver and liver tumors, this work is conducted by using the method of coarse-to-fine segmentation. Multiple network architectures are utilized to implement liver and tumor image segmentation tasks. The general steps of such a coarse-to-fine segmentation can be illustrated in In detail, pooling will lead to the excessive loss of spatial information of images. A mismatch between convolutional kernels with a fixed geometric structure and complex, irregularly shaped targets of the liver and tumors limits segmentation performance. In order to address these problems, this new network is designed with a coarse-to-fine principle and applies to the segmentation of the liver and tumors. HU windowing and Gaussian filtering on the images. The formula for HU windowing is shown below:The purpose of image pre-processing is to reduce the interference of complex backgrounds on the target area. The publicly available dataset used in our experiments of this study was CT images from the Liver Tumor Segmentation Challenge (LiTS) . These CThese images have different slope and intercept values. The parts we want are filtered according to HU, and all other parts are blacked out or whitened. This reduces the interference of the complex background on the target areas. In addition, Gaussian filtering is a linear smoothing filter that is suitable for eliminating noise. Gaussian filtering is realized by a discretization window and sliding window convolution with a Gaussian template.The information on multiple image resolutions is critical to capturing target location details, so the interactions between multiple image resolutions need to be learned to obtain more feature information. The second stage of the cascaded U-ADenseNet network in the study of the segmentation of the liver and its tumors is demonstrated in In order to increase the receptive field, the extracted feature map is often pooled to achieve the effect of increasing the receptive field, combined with the multi-scale information by jumping connections. Since pooling is conducted in a direct and crude way, the spatial resolution will be sacrificed after each pooling, leading to the loss of spatial information after multiple pooling, even doing harm to the segmentation effectiveness. The emergence of dilated convolution is to solve the problem of enhancing the receiver field without a loss of information. Atrous spatial pyramid pooling will parallel or cascade the convolution of different dilation rates to obtain multi-scale information gain. Due to its mechanism of dilated convolution, only a small number of pixels are selected for each calculation and the sampling is not intensive, resulting in a large amount of discarded spatial information. Therefore, Adense_block applies the ideology of dense connection to atrous spatial pyramid pooling.For the purpose of reducing the loss of important features of the CT images in the training process and causing segmentation results to be more accurate, it must enhance the features of complex segmentation targets via multi-layered channel attention. The design of multi-layered channel attention is aimed at reducing the difficulty of segmenting the complex liver and tumors, by selecting information of different spatial scales across channels. Since this article has dealt with the task of multi-class segmentation of the liver and liver tumors, the loss function of adaptive, weighted cross-entropy was used. The error value of the loss function was calculated at the endpoint of the forward propagation, and the loss layer was also the starting point of the back propagation. Different weights can be assigned to the different categories when the unbalanced categories were in the samples. Cross-entropy measures the degree of difference between three different probability distributions for the same random variable, and can be expressed in machine learning as the difference between the true probability distribution and the predicted probability distribution. The smaller the value of cross-entropy, the better the model prediction. The formula of the adaptive weighting method was as follows:i, falls into category, c, which included background, liver or liver tumors. i. Moreover, the weighting of the categories was defined by the following formula:Among the formula, t, which was a hyper-parameter, to 1.02. Then, we limited Here, We adopted Pytorch to implement this model for medical CT images segmentation of the liver and tumors through running the Linux Ubuntu 16.04 64-bit operating system. Our experimental instrument was equipped with an NVIDIA RTX 2080 Ti 11 GB GPU on an Intel Core i7-7700K 4.20 GHz with 16 GB RAM. The cascaded U-ADenseNet network training performed by 300 epochs on a single GPU with a constant initialized learning rate was 0.0001 and the initialized alpha was 0.33.The LiTS dataset contained 131 cases of enhanced abdominal CT scans with labels in the formal way, originally in a neuroimaging informatics technology initiative (NIFTI) format. Because these CT volumes were acquired from multiple clinical sites by different scanners and protocols, the images varied widely in their appearance. The data were divided into a training set of 105 cases and a testing set of 26 cases, which were processed into axial slices in a portable network graphics (PNG) format. Each CT slice has a resolution of 512 × 512. The all-black images that did not contain the liver and tumors were removed from the training set, speeding up the training process for the liver and tumors in order to reduce the training burden. Considering the problems of blurred boundaries and low contrast between the targets and other surrounding organs in the medical images of the LiTS dataset, the images needed to be processed to reduce the interference of extraneous noise and enhance the contrast. To validate and measure the accuracy of predicted results and the effectiveness of the segmentation algorithm, this section used three evaluation metrics to validate the segmentation results of the liver and liver tumors. The Dice similarity coefficient (DSC) can be seen as the most common evaluation metric in the field of segmentation algorithms. The Dice similarity coefficient can be calculated by the following formula:Here, A denotes the predicted segmentation result and B indicates the ground truth. Furthermore, volumetric overlap error (VOE) and relative volume difference (RVD) were used as evaluation metrics. VOE represents the overlap between the segmentation result and the actual segmentation result. RVD indicates the difference between the splitting results and the volume between markers. The specific calculation formulas are as follows:Here, TP, FP and FN denote the number of pixels that are predicted with the correct foreground label (true positive), the number of background pixels that are erroneously predicted as foreground pixels  and the number of foreground pixels that are erroneously predicted as background pixels.To verify the effectiveness of the algorithm concerning the simultaneous segmentation of the liver and liver tumors in this paper, we implemented several experiments. The ablation results are shown in We propose the model of cascaded U-ADenseNet for the coarse-to-fine segmentation with the addition of dilated convolution and a channel attention mechanism. The goal is to increase the receptive field and reduce the loss of spatial resolution information. We implemented the ablation experiments to study the effect of different technical points of this cascade network on the performance of the segmentation network. Specifically, We have implemented experiments for different patterns of liver tumors. Pattern one refers to predicting large liver tumors, pattern two represents the prediction of small liver tumors and pattern three indicates predicting multiple liver tumors.In this paper, a pixel ratio of liver-to-tumor greater than 10% is defined as a large tumor and one less than 10% is defined as a small tumor. The threshold value is selected according to the pixel ratio of liver-to-tumor, which can be adjusted according to specific, actual scenes. Some typical examples of ground truths and the segmented predicted images of the training model for several cases are shown in the following figures. These cases refer to those from Moreover, Furthermore, in In pattern one, the pixels of each tumor account for more of the total pixels of the liver. The difference between U-Net + ADenseNet2 and U-ADenseNet is whether the input is performed in the way of multi-resolution in the second stage, and a single-resolution input will be better than a multi-resolution input when the liver is large, so the Dice metrics using the cascaded U-ADenseNet in pattern one are slightly lower than U-Net + ADenseNet2. It can be seen clearly in Meanwhile, the algorithm proposed in this work was compared to other typical segmentation algorithms, such as Densely-CNN , USE-NetThe segmentation accuracy has been improved to a certain extent, which can verify the effectiveness of the cascaded U-ADenseNet. Some examples of results, referring to the images that were predicted by the processing of our proposed network. It can be demonstrated in Results of the liver segmentation contours are marked in light-green and the ground truths are marked in light-red. The predicted results of liver tumor segmentation contours are marked in dark-green and the ground truths are marked in dark-red. In Fully automated medical image segmentation has become a key research technology for clinical disease diagnosis and treatment. In this study, patterns concerning the accuracy of various segmentation methods for different morphological cases of liver tumors were found. This study presented the novel U-ADenseNet network in a cascade. The techniques of dilated convolution and the multi-layered channel attention module enabled the network to extract more discriminative image features, which helps to improve the overall segmentation performance of the liver and tumors. At the same time, this two-step cascade method also solved the problem of unbalanced tumor segmentation data caused by the small proportion of liver tumors in the whole image. These experimental results on the publicly available LiTS dataset confirmed the effectiveness of the liver and tumors segmentation. For precision medicine, suitable automatic segmentation methods can be selected in the future based on the general characteristics of the liver lesions. The method of the cascaded U-ADenseNet is the most suitable one for the segmentation of multiple liver lesions, with the highest accuracy compared to some other methods. This method has some gaps in the performance of the state-of-the-art method of segmenting the liver and tumors separately, but this method has the advantages of simultaneous segmentation. Our method can be efficiently used for the segmentation of multiple lesions. Since the algorithm in the paper may cause the loss of some information during pre-processing for the given CT images, more pretreatment before feature extraction will be incorporated in future work, which can better assist the diagnosis of liver tumors together with treatment and other clinical application tasks in the liver.


Internet of Things (IoT) is a successful area for many industries and academia domains, particularly healthcare is one of the application areas that uses IoT sensors and devices for monitoring. IoT transition replaces contemporary health services with scientific and socioeconomic viewpoints. Since the epidemic began, diverse scientific organizations have been making accelerated efforts to use a wide range of tools to tackle this global challenge and the founders of IoT analytics. Artificial intelligence (AI) plays a key role in measuring, assessing, and diagnosing the risk. It could be used to predict the number of alternate incidents, recovered instances, and casualties, also used for forecasting cases. Within the COVID-19 background, IoT technologies are used to minimize COVID-19 exposure to others by prenatal screening, patient monitoring, and postpatient incident response in specified procedures. In this study, the importance of IoT technology and artificial intelligence in COVID-19 is explored, and the 3 important steps discussed such as the evaluation of networks, implementations, and IoT industries to battle COVID-19, including early detection, quarantine times, and postrecovery activities, are reviewed. In this study, how IoT handles the COVID-19 pandemic at a new level of healthcare is investigated. In this research, the long short-term memory (LSTM) with recurrent neural network (RNN) is used for diagnosis purpose and in particular, its important architecture for the analysis of cough and breathing acoustic characteristics. In comparison with both coughing and respiratory samples, our findings indicate poor accuracy of the voice test. COVID-19 has been reported as a global epidemic by the World Health Organization (WHO), and it travels out across globe. 8.8 million cases were registered globally on June 30, 2020. With a record of 2.64 million cases and 128 lakhs of accidents, the United States is perhaps the most afflicted country in the region [The human brain system illustrates the complex issues, but the lot of concerns increases the cost of solving that issue. AI is capable to concurrently quantify a range of issues and remedies. Many people just assume that AI is just about automation, but it is a computer that imitates human consciousness . IoT conIoT is an emerging technology that consists of the set of links provided to interconnects with anything, anywhere, any connection, any device, and any period. IoT technology may affect the whole company range, since every device and entity in the digital Internet network might be identified as having huge benefits. Such benefits often include enhanced connection between services, devices, and systems that go beyond the machine status . IoT delThe emphasis can be made by an IoT framework, integrated with AI. Enhance social protection by using monitoring technologies, image-recognition technology, and the use of AI-enabled software and networks to provide, distribute, trace, and control people's access to the public places . UsuallyA further helpful function might be monitoring people's geolocation. The difference between people could provide helpful info during epidemic of a deadly virus . We willIt is also important to understand the protection and information security extensively during the implementation of certain applications in order to avoid the misuse of confidential data . AuthoriThe area of eHealth used by IoT and AI to healthcare has mainly become interdisciplinary and offered new horizons to problems during the COVID-19 pandemic [AI algorithms in particular forecasts play an important role in the study of epidemics. With enormous information on this pandemic, the machine learning methods assist to discover ways to prevent the transmission of the virus from taking prompt action . This seAI is data-driven and makes choices based on the data on which it has been trained. AI is needed to cope with the present epidemic since it has the advantage over the previously mentioned conventional methods. Although AI cannot supplant physicians, it helps them to make diagnostic choices efficient and accurate. Techniques may be used to change the healthcare sector. In pandemic conditions, the healthcare system is overburdened with the enormous number of patient demands, and AI can support them as quickly as practicable with minimal cost for diagnosis and treatment. AI algorithms, such as sensors and cameras, may be readily incorporated into the current infrastructure to automate the contact tracking process. AI helps to do simulations for the prospective medication during the vaccine development process to estimate its efficacy.The WHO has reported the pivotal symptoms of COVID-19, including body pains, increased heart rate, strong coughing, and severe breathing difficulties . ScientiBrem et al.  infectioBayindir  suggesteDong and Yao  discusseThey identified the capability of the wireless connection and evaluated the network transfer to diagnose the area at greater hazard. They will also obtain greater attempts to monitor the spreading in some areas. Gupta and Johari  planned Siriwardhana et al.  addresseHussain et al.  emphasizWu et al.  suggesteAfshar et al.  claimed Monitoring of illnessRisk forecastingMonitoring of surgical examinationHealing studiesStudy and simulation of the virusSite recognitionFalse news for breakingImplement the steps to shut downAI has proven to be a scientific development. It is an incredibly powerful method to solve the COVID-19 epidemic if adequately used . The folMany of the above methods are discussed in depth in the following.On December 31, 2019, a Toronto-based city health firm, Blue Dot, announced an imminent coronavirus outbreak. The Blue Dot AI system includes various machines learning and natural language processing (NLP) instruments to locate signs of infections that are developing. This ability allows Blue Dot to control the SARS-CoV-2 spread and predict that it would evolve long before pathologists. But this would not go so far as to suggest that there was no human experience to do so as well. Although your model of AI might forecast the epidemic, human knowledge of the model performance remained key. An important field of study was the use of prediction techniques to support hospital resource planning. The time-series analysis has been one of the most common methods used to generate short-term demand predictions since it offers thorough treatment for seasonality and serial correlations. For example , the preRapid COVID-19 diagnoses can permit authorities to take appropriate action to restrict the greater dissemination of the infection. Although, the scarcity of reagent kits internationally has complicated the success of huge diagnostic imaging by the officials. Most established AI techniques have been recast, whilst others will be developed to reduce this issue.As new, the lack of appropriate virus diagnostic and counseling procedures is one of the largest challenges with SARS-CoV-2. However, AI is known to have an achieve design to accelerate the manufacturing process by evaluating the latest COVID-19 instances as well as ongoing studies into viral infections. A variety of organizations and research laboratories have also used AI to classify new COVID-19 therapies. AI not only can speed things up in clinical trials but can also help to diagnose the established medicines.Knowing the infection is just the secret to establishing the appropriate cure of COVID-19. Pathogens never replicate on their own, so that they can generate versions of their DNA using cell membranes. For this reason, an infection normally destroys the cell wall through a Keychain system by connecting itself with the host receptors. For most antibody substances, the operational function is to avoid this by removing the cell surface receptors. Therefore, researchers can design the consensus that exists in developing the successful inhibitors.The SARS-CoV-2 belongs to the beta-CoV tribe of attention and respect. Genotypes of such diseases usually consist of a combination of populations (bats and rodents). To present, an unexplained element is the biological host that made it easier to relay COVID-19 for humans. To correlate the bacterial vector efficiently and for recognition of differences among recognized viruses, machine learning algorithms have been used. The study proposes that the seasonal influenza species uses the decision tree algorithm. The SARS-CoV-2 can also be expanded to incorporate these versions.Currently, it is unpredictable days since the COVID-19 epidemic have produced some theories and explanations of deception. There has been a lot of disinformation on social networking sites. Technology firms including Google, YouTube, and Facebook have used AI strategies to avoid the dissemination of such false information as well as provide checked facts. Both the networks have sought to filter the material with even the smallest disinformation. In fact, YouTube has put in place tough steps to gather some false video news.A great several nations around the globe are using AI to encourage social dissociations and lock-up initiatives, including China, India, the United States, and the United Kingdom. In China, Baidu has established machine view (CV) operated infra-road monitors to search public areas, making it one of the biggest AI and network companies in the world. These devices not only recognize individuals with elevated physical heat but also distinguish residents who are not adopting the lockdown steps using their built-in face recognition system. A similar CV surveillance system was used to track the public dissociating steps taken by masses in Oxford, England.IoT offers strategically advanced forum for tackling the COVID-19 epidemic which overcomes the big lock-up difficulties [Due to the absence of every clear care plan, the greatest available method of protection against epidemic of COVID-19 was established at the time . NeverthDue to tight locking policies in many countries of the world, the car sector has undergone huge infrastructure delays to combat the epidemic. With social isolation imposed and people needed to stay at home, the use of vehicles, both commercial and personal, has diminished worldwide. The automobiles affiliated with essential services are the only cars still in use.Atmospheric sector has been hit massively by the COVID-19 epidemic. The affected countries including virtually all states were compelled to both globally and domestically to implement a travel restriction. Critical lines of distribution serving cargo and freight planes are the only operational airways.Since the epidemic of COVID-19, the tourist industry was one of the hardest hit sectors. Tourist industry sales make up to 10% of the worldwide GDP.Building businesses are expected to face serious disruptions and setbacks attributable to COVID-19 epidemic in existing ventures. As a result of the strict self-quarantine rules, most building companies will have to suspend their nonessential activities before the epidemic comes to an end. This is expected to lead a massive reprogramming of current programs that could lead to serious damages.The epidemic COVID-19 has a major impact globally upon these healthcare services, whereas the overeating induced by lockdown acts and travel restrictions has impacted other industries financially, and the safety sector is far from stagnating. Worldwide clinicians are now experiencing the need for COVID-19 patients to be managed by fans, intensive care units (ICUs), and personal protective equipment's (PPE).Alsaeedy and Chong  All extrCOVID-19 has a unique effect on society, and the economy of the effect of this epidemic nowadays has increased the use of numerous new technologies in combination with other technologies such as AI, data management, and cloud. IoT is of tremendous help in this disaster IoTs, utilized for epidemic monitoring. Important IoT, AI, and big data innovations that assist with the COVID-19 problem and the several problems stated in that effort include telemedicine's for the prevention and management of disease, the prevention of outbreaks and the minimization or even stoppage of the transmission of the virus, the use of drones to monitor for isolation, and mask use. For COVID-19, primary IoT applications are the online clinics, remote consultations, fast screenings, and the smart monitoring of sick individuals and virus prediction , the reaSuraksha Kawach is an IoT device that is utilized by the Defense Research and Development Organization (DRDO), India, to monitor corona-affected individuals and to monitor them. This gadget may be used in the arm or knee and is a GSM and GPS device for reliable monitoring in real-time basis. The prototype of Suraksha Kawach gadget is shown in In order to minimize healthcare personnel' danger, thermal sensors are used to monitor COVID-19 patients' body temperature. To prevent its fast spread, it is necessary to establish valid and realistic medications to treat SARS-CoV-2 disease in people. The nucleic acid test (NAAT) is recommended by the WHO which perhaps the most important model of SARS-CoV-2 detection for the effective SARS disease. These studies include the use of the nasopharyngeal swab procedure in which the cleaning solution is used in a specimen. This contains a combination of mucus and saliva.In the event of significant breathing problems, the WHO advises that samples be taken from the nose and throat as well. Such specimens are taken to a specialist lab, where the polymerase chain reduction method is used to measure the existence of viral RNA. AI systems observed that the epidemic of an unpredictable form of disease in the Chinese government before the world was ever conscious of the danger faced by the coronavirus (COVID-19). With the disease becoming a global epidemic, AI tools and techniques should be used to encourage actions by policy-makers. Furthermore, the health community and the society are essential to plan and control any step of the crisis: identifying, stopping, reacting to, and recovering and growing of COVID-19. X beams and CT filters [Since the main report of the 2019 coronavirus infection (COVID-19) in Wuhan, China, more than 200 countries and locations have been affected worldwide. Science and technology take on an important role in this perplexing battle. For example, as China began reacting to infection, it zeroed on brain research by monitoring polluted ancient times moving sick people, robots to convey food and medicines, automatons to clean open spots, and by watching and transmitting sound communication to the public to urge them to stay at home. Man-made knowledge was widely used to identify the new particles in transit to assist COVID-19. A number of experts uses AI to find new medicines and remedies alongside certain software engineering analysts that focus on the identification of compelling patients via the preparation of a clinical image like  filters .In any case, computer-based intelligence follows the bands to assist group the isolated rule. Advanced mobile telephones and AI-enhanced heated cameras are being used to recognize fever and tinged people . NationsIt is used in the treatment of medical claims. Automobiles and robots are used to transport food and medicines, just as they are used to sanitize public areas. AI helps to create the medicines and COVID-19 antibodies using super PCs . This prData accessionData accusationSystemizationForecastingThe program process entails the primary analyses and checks for modern sensors and intelligent systems. Research designs with AI techniques are designed to define a mechanism that underlies the number of environments in order to overcome the difficulty of the problem. The goal of the project is to explore the various sensing devices to gather the multiple information and utilizes the AI systems mostly on data sources. The key objective of the proposed work is to gather information upon this thermal and breathing level of the COVID-19 instances from various devices in real-time. In the data preprocessing, the recorded actual statistics shall be believed to reduce the error and extreme values from the principal component analysis (PCA). The third phase resulted in classifying the COVID-19 instances into three groups such as SIR, disease prone, affected and resuscitated (SIR). Then, in the fourth stage, the deep, simple RNN  predicts COVID-19 with past health data. t−1 would be the same. This o/p is the i/p of the multiplication of the 2nd matrix. The RNN is a class of the ANN that generates a graph structure for spatial and dynamic characteristics analysis. It assists to modeling the sequence information from transmission systems. The prediction findings are identical to the human brains. The processing of knowledge is clear. Assume an easy and simple given set of data feeding just one neuron. The method provides the performance in a typical neural net by increasing the input with the mass and creates strategies. An RNN returns this performance with the bunch of times. We call timestamp of the period, and the output is entered for the next multiplication of matrices. For illustration, the system consisted of one neuron in the image. The system measures the input/weight mathematical operations and applies nonlinearity with the enabling function. The performance at Data entered with the first weight setEarlier production for a second weight collectionThe proposed system consists of 6 neurons. Two important properties of the ANN are as follows.Notice that the quantities of the listed within the first flow forward which are null, and we have no quality present.S): patients immediately suspected of disease by othersSuspected (I): victims infected through whatever requiresInfected (R)Victims healed by infection, and patients accused of infection from others  is being implemented. The proposed method uses the SIR model as follows:a signifies the number of patients being tested; whereas  i represent the relative state constant determined by a1,  a2, the infection rate b parameter calculate is the rate of the victims not healed by loss of resistance, m is the normal patient's mortality rate, and T is a natural cure for the actual individuals diagnosed.The design of the outbreak is mathematically written as follows:The table describes that the total population (sample size) of 227 among that population female population of 85 members and male population size of 135 members and other population size of 7 members and 30 members are below 18 years, between 19 and 30 years are 63 members, between 31 and 59 years are 92 members, and above 60 years are 42 members and 33 members having body temperature below 370°C, and 194 members having the temperature , and 123 members are infected with COVID-19, and 123 members are in danger zone; they are having different health abnormalities such as breathing problems.The threat of the COVID-19 epidemic is still being faced by the universe that attempts complemented by numerous emerging innovations, including such IoT. These are being made by AI to mitigate their impact. Holding that, we have some recent observations of COVID-19 epidemic as the basis for this study. This study is initiated by an exhaustive analysis of COVID-19, wherein we discuss its medical characteristics, dissemination process, and the detection techniques.For the population analysis, the SIR disease process is employed to identify the events under suspicion; the information is then fed into an LSTM RNN to estimate the COVID-19 instances with past data contained in the secret layer. The two stable problems in electronic enforcement, which requires adjustment, include confidentiality and faith. The migration of data also appears as an additional obstacle to solve interference and complexity by replacing evolutionary methods.


The travel trajectory data of mobile intelligent terminal users are characterized by clutter, incompleteness, noise, fuzzy randomness. The accuracy of original data is an essential prerequisite for better results of trajectory data mining. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is one of the most effective trajectory data mining methods, but the selection of input parameters often limits it. The Sage-Husa adaptive filtering algorithm effectively controls the error range of mobile phone GPS data, which can meet the positioning accuracy requirements for DBSCAN spatial clustering having the advantages of low cost and convenient use. Then, a novel cluster validity index was proposed based on the internal and external duty cycle to balance the influence of the distance within-cluster, the distance between clusters, and the number of coordinate points in the process of clustering. The index can automatically choose input parameters of density clustering, and the effective clustering can be formed on different data sets. The optimized clustering method can be applied to the in-depth analysis and mining of traveler behavior trajectories. Experiments show that the Sage -Husa adaptive filtering algorithm proposed further improves the positioning accuracy of GPS, which is 17.34% and 15.24% higher eastward and northward, 14.25%, and 18.17% higher in 2D and 3D dimensions, respectively. The number of noise points is significantly reduced. At the same time, compared with the traditional validity index, the evaluation index based on the duty cycle proposed can optimize the input parameters and obtain better clustering results of traveler location information. The site selection and layout of the stops are an essential part of urban public transport service design. To attract more travelers to choose public transport, managers need to consider passenger travel needs, formulate reasonable stops and routes layout so that walking distance to the stops should not be too large, to maximize the advantages of the public transport. Usually, the travel needs with similar origins or destinations are grouped into one category through spatial clustering, and the appropriate location should be selected in the public transport travel area after clustering. Then, the walking distance between users and stops in the region is within a reasonable range.The popularity of intelligent mobile terminals makes it possible to obtain massive and high-precision Spatio-temporal data. Meanwhile, the development of mobile internet accelerates the generation and accumulation of Spatio-temporal data. Hundreds of millions of mobile location data are generated daily from different sources, such as smartphones equipped with GPS navigation devices, providing a database for research into users’ travel trajectory patterns. The travel trajectory data of mobile intelligent terminal users are characterized by clutter, incompleteness, noise, and fuzzy randomness. The accuracy of the original data is an essential premise for achieving a better result. Therefore, exploring a more accurate and efficient anti-noise GPS data preprocessing method suitable for mobile smartphone users’ trajectory based on the existing algorithm is the basis of current research on travel habits, dwell characteristics, and related application development, which is an essential prerequisite for travel trajectory data mining.In addition, based on the trajectory data of urban public transport APP, the traditional DBSCAN  Clustering algorithm was improved in the paper: a novel cluster validity index was proposed based on internal and external duty cycle to balance the influence of the distance within-cluster, the distance between clusters, and the number of coordinate points in the process of clustering. The index can automatically choose input parameters of density clustering, and the effective clustering can be formed on different data sets. The optimized clustering method can be applied to the in-depth analysis and mining of traveler behavior trajectories.This study aims to explore the extraction scheme about spatial dwell point suitable for mobile intelligent terminal users’ trajectory. A more accurate, efficient, and noise-resistant mobile phone GPS data preprocessing method makes it possible to efficiently mine travel trajectory data; The improved DBSCAN algorithm can optimize the input parameters and obtain better clustering results of traveler location information. The combination of the two can significantly improve the reliability of the identification results of bus travel areas. According to the results of travel area identification, the site settings are optimized to reduce the walking distance of travelers, improve the travel experience and service reliability, save travel costs, and provide data support for the construction of urban public transport networks of wisdom cities.Sage and Husa jointly proposed Sage-Husa adaptive filtering algorithm. It is a variance matching algorithm proposed based on the Kalman filtering algorithm. This method can estimate the first and second moments of the current noise more accurately, and the algorithm has a simple principle and high real-time performance. This filtering algorithm has been widely used in many fields. In the process of recursion and filtering, to achieve the optimal filtering performance , Sage-Husa adaptive filtering algorithm uses the observed value to modify the predicted value not only constantly but also constantly adjusts the system model parameters and the statistical parameters of the noise model.Chunmei Huang et al.  proposesFrom the above analysis, Sage-Husa adaptive filtering performs real-time estimation of the current observation noise through the recursive method. In the estimation process, updated weights are used to modify the observation noise variance in real-time. The model error can be reduced, the divergence of the filter can be suppressed, and the filter’s accuracy can be improved by the accurate real-time estimation of the variance of observation noise. After using this algorithm, the number of noise points is reduced obviously. It has the advantages of low cost and convenient use and can meet positioning accuracy requirements for DBSCAN spatial clustering.However, in most of the above studies, single positioning data is used as the processing data set. However, in the case of GPS big data of mobile smartphones, the data denoising results will be different to some extent. In this paper, mobile smartphone positioning has certain advantages in initial data collection methods and collection environment, and smartphone positioning may be more suitable for studying mobile intelligent terminal users’ trajectories. The denoising algorithm mentioned in this paper is applied to the GPS of the mobile smartphone to improve the precision of GPS positioning with low cost and good robustness.Trajectory clustering is a kind of trajectory pattern mining. Trajectory clustering aims to find the representative path or common trend of different moving objects . Many diDBSCAN is widely used in many scientific fields due to its simplicity and ability to detect clusters of different sizes and shapes. Because the traditional DBSCAN algorithm relies heavily on the user’s manual experience when selecting the clustering parameters. If the user does not have enough knowledge to determine the parameter values correctly, the improper value of input parameters may affect the quality of the clustering results. To overcome this defect, on the one hand, some researchers combined the two methods to determine parameters, Sharma et al.  combinedOn the other hand, improving the validity index of the clustering algorithm can also effectively select the clustering parameters and enhance the clustering effect. Duun Validity Index 1973) , DBI  information points of travelers can be obtained through data collection, and the transportation distribution of a single individual can be obtained by superimposing the GPS information points of a single individual.Travel has two contradictory characteristics of regularity and randomness. In most cases, most people’s travel has a frequency, usually manifested by the periodic characteristics of travel for the workday and random features of the trip for non-workday. Other groups, such as children, the elderly, and freelancers, show spontaneous travel throughout the day.The travel distribution data of residents are represented by numerous discrete information points, each representing the spatial location of residents at a specific moment. Due to the regularity and randomness of residents’ travel, different travelers will eventually form travel point groups with different densities and sizes. According to other clustering methods, the point groups formed are also different. The cluster results determine whether the user travel analysis is accurate to a certain extent, and different clustering methods can get different clustering results. How to efficiently and accurately cluster the travel point group is one of the problems we are faced with.There are six commonly used clustering methods, and their advantages and disadvantages are listed as follows:Partition-based methods: it is an older clustering method, which is widely used at present. It is simple and efficient for large data sets, with low time and space complexity. However, it is easy to form an optimal solution, which is too sensitive to noise and outliers, so it is necessary to set the K value in advance. Different K values may cause different clustering results. Secondly, this method may hide non-convex or clusters of very different sizes.Hierarchical methods: are highly interpretable and easy to understand and implement, but the time complexity is too high, usually O (n3).Grid-based methods: The clustering speed is very fast because the clustering speed is independent of the number of data objects and depends on the number of units in each dimension of the data space. Its disadvantages are being sensitive to parameters, being unable to deal with irregular distribution of data. The efficiency of this clustering algorithm is at the cost of the accuracy of clustering results.Model-based methods: The classification of clustering is expressed in probabilistic modes, and parameters can describe the features of each cluster with high adaptability. Its disadvantage is that the execution efficiency is not high, especially when the number of distributions is large and the data is small.Fuzzy clustering method: it has a good clustering effect for data satisfying normal distribution, but it cannot ensure that FCM converges to an optimal solution, and the performance of the algorithm depends on the initial clustering center.Density-based methods: They are not sensitive to noise and can find clusters of arbitrary shapes. However, the clustering results are closely related to the parameters. In the face of the different sparse degrees of clustering, the same judgment criteria will destroy the natural structure of clustering.Combined with the characteristics of residents’ travel data and compared with the features and applicability of the above six standard clustering algorithms, the density clustering algorithm is more suitable for studying urban residents’ travel original-destination clustering. The most common algorithm in density clustering is the DBSCAN algorithm, which can be adapted to data clustering of any shape, effectively remove noise interference, and generate a specified number of clustering results according to the features of data points. However, at the same time, DBSCAN has the common disadvantages of the density clustering algorithm. The clustering results are directly related to the parameter selection., selecting the input parameters carefully to obtain the optimal outcome of travel trajectory clustering and how to choose the input parameters scientifically and effectively is one of the problems that should be solved in this paper.Firstly, Sage-Husa adaptive filtering is used to reduce the error range of GPS data and improve the accuracy of mobile GPS positioning at a low cost. Then, the automatic selection of DBSCAN input parameters is realized according to the enhanced DBSCAN algorithm and the novel validity index of trajectory clustering results. This index balances the degree of cohesion, the distance between clusters, and the density within groups. It calculates the optimal input parameter of the density clustering model to avoid the limitation of manual parameters. After comparing the 2D synthetic data with the case travel data of public transport of Jinhua city, the evaluation index is superior to the traditional evaluation index in the density-based geographical location information clustering.i. Basic principle. The Sage-Husa adaptive filtering performs real-time estimation of the current observation noise by the recursive method. In the estimation process, the updated weight is used to modify the observation noise variance in real-time. The model error can be reduced, the divergence of the filter can be suppressed, and the filter’s accuracy can be improved by the real-time accurate estimation of the variance of observation noise. First, consider the following random linear offline system (see Eq ( (see Eq ):{Xt=AXt is the system state vector at time t; At,t−1 is the state transition matrix from time t-1 to time t; Ct,t−1 is the control-input matrix from time t-1 to time t, At,t−1 and Ct,t−1 can both change as time t changes; Yt is the observation vector at time t, Ht is the observation matrix at time t; wt−1 is the process noise at time t-1, and vt is the observation noise at time t, which are independent of each other, and both are normal white noise sequences with time-varying mean and covariance matrix. Among them, process noise and observation noise conform to the following statistical characteristics :{E[wtWhere the Sage-Husa adaptive algorithm can be described as Eq :{X^t=X(Where  (see Eq ):{q^t=dt is the update rate and represents the update rate of noise parameters.Where, It can be seen from the above equations that the Sage-Husa adaptive algorithm estimates and corrects the statistical characteristics of process noise and observation noise in real-time while using GPS measurement data for recursive filtering to achieve the purpose of adaptive filtering. The method is simple in principle and good in real-time, so it has been widely applied in many fields. According to the above algorithm, it is easy to implement the computer program, and the autoregressive operation can be continued.ii. Basic steps. When using Sage-Husa adaptive filtering to de-noise GPS positioning data, the basic steps are as follows:Firstly, the state equation and the observation equation are established, and then the observation vector is determined.wt−1 and vt was determined.The prior data were used to calibrate and test the model, and then the model noise covariance The model was validated with measured data.We used Sage-Husa adaptive filtering algorithm to process and analyze the data collected by the mobile phone GPS  algorithm is a representative density-based Clustering analysis algorithm, which can find arbitrary shapes of clusters and effectively shield interference from noise data.i. Working principle. DBSCAN algorithm uses the high-density connectivity of clusters to find clusters of arbitrary shape quickly. Its core idea is that for each object constituting the cluster, the number of objects in its Eps neighborhood must not be less than a given value MinPts. The following are some definitions involved in the basic idea of density-based clustering:Eps: the distance set by the user is Eps. the object is in the area with itself as the dot and Eps as the radius called the Eps neighborhood of the object.Core object: an object is a core object if the number of data points in its Eps neighborhood is greater than or equal to the given value MinPts.Direct density reachable: in sample set D, p is the core object, q is in the Eps neighborhood of p, and object q is directly density reachable from object p.Density Reachable: in sample set D, a series of sample points P1, P2, Pn,…, P1 = p, Pn = q, Pi∈D, 1≤ i ≤ n, if object Pi is direct density reachable from the Pi-1, then object p is density reachable from object q, this relationship is asymmetric.Density connected: If there is an object O in object set D, objects p and q are density-connected if object O is density-reachable to objects p and q. This relationship is symmetric.Noise: a density-based cluster is a collection of the largest density-connected objects based on density-reachability. Objects that are not contained in any cluster are called noise.Eps can represent the distance from the residence or workplace to the stops of users who choose to take urban public transport. MinPts can define the minimum number of covered passengers required to meet the stop setting conditions. Density-connected points represent all users served by any stop. The density reachability relationship is the basis for the DBSCAN algorithm to divide the travel demand of the same passengers. The density connected point represents the passenger travel cluster that can be divided into one cluster.In the practical application of urban site location selection, ii. Steps in the DBSCAN algorithm. Based on the above definition, the DBSCAN algorithm uses a method to find density reachable objects for clustering analysis. The correctness of the DBSCAN algorithm is guaranteed because a cluster is equivalent to the set O consisting of all the density connected points objects of any core object in the cluster.DBSCAN finds all density-reachable objects contained in each cluster by iteratively looking for all directly density-reachable objects. The specific methods are as follows:D, Eps, Minpts (given minimum number);Input: database Output: cluster.p in the database, and if p has not been processed (grouped into a cluster or marked as noise), check its Eps neighborhood NEps(p), if the number of objects contained is not less than MinPts, a new cluster C is created, and add all points of NEps(p) to C;Check the unchecked object q in C, check its Eps neighborhood NEps(q), if NEps(q) contains at least MinPts objects, then objects of NEps(q) that do not belong to any cluster are added to C;For all unchecked objects C until no new objects are added to the current cluster C;Repeat step (2) to continue checking for unchecked objects in Repeat steps (1) through (3) until all objects fall into a cluster or are marked as noise.The density reachable objects are obtained by continuously executing region queries to achieve a region query and return all objects in the specified region.iii. Disadvantages of the DBSCAN algorithm. DBSCAN needs to manually set two parameters, Eps and Minpts, which are set by the user and remain the same during the run. When Eps is constant and Minpts changes, Minpts is too small to treat the noise in the data as cluster data, and Minpts is too big to treat low-density data as noise. The result is that the clustering accuracy is not high, and the clustering effect is not reasonable without providing a sound decision basis and wisdom support. The clustering effect depends on the initial parameters greatly. It is an effective way to improve the clustering effect to determine the reasonable initial parameters.DBSCAN algorithm has two significant disadvantages:Eps, and only clusters with similar density can be found, which affects the quality of clustering results. In practice, it is challenging to determine before the algorithm is run.It is sensitive to the input parameter Eps and MinPts that determine the density threshold are globally unique, DBSCAN can only find clusters with approximate densities.It is challenging to find clusters with significant density differences. Because i. Clustering evaluation index based on the duty cycle. Usually, we choose the clustering evaluation index to determine the quality of clustering results, also known as clustering validity analysis. A good clustering partition should have the following characteristics: the samples in different clusters should be as diverse as possible, and the samples in the same cluster should be as similar as possible.It is found that the factors affecting the clustering results include not only the degree of clustering and the boundary distance between clusters but also the number of trajectory points in the cluster. Because the traditional evaluation index only considers the cohesion degree, clustering distance, and other coefficients, trajectory clustering has limitations. In the evaluation of clustering cohesion degree, the inner density of clusters is not considered, and the relationship between the number of internal clusters and the size of the cluster is ignored. In irregular clustering, the influence degree of a single variable is often too large, and the clustering result usually stays at the boundary point, which cannot achieve the optimal selection of parameters.Aiming at the problem that the existing evaluation index is unsuitable for location point clustering, this paper proposes a novel validity index based on the internal and external duty cycle index (IEDCI). The equation is as follows:Si,, Sj and Si+j  is proposed based on the internal and external duty cycle. Eq  is as foni、nj represents the number of points in the ith and jth cluster, and k represents the number of the current clusters. F(k) is the result of the evaluation index based on the duty cycle. The smaller the value is, the better the classification result is. F(k) differences in the number of clusters may lead to different results. The clustering parameters have the best effect (cluster threshold MinPts and neighborhood radius Eps) when the value of F(k) is the smallest.Where, A validity evaluation index based on clustering points and the clustering duty cycle is proposed, which is used to evaluate the clustering results generated by different input parameters and determine the current optimal input parameters according to the previous feedback to find the optimal input parameters and clustering results. When processing the clustering results, the Silhouette Coefficient and the Davius-Bouldin index (DBI) only consider the relationship between the cohesion degree and the clustering distance and do not fully consider the influence of the clustering points in a single clustering result on the overall clustering effect. Therefore, the clustering evaluation proposed is better than the above evaluation indexes. The specific experimental results are shown in Tables ii. A parameter-free DBSCAN algorithm. The DBSCAN algorithm requires two critical parameters, MinPts, and Eps. However, when selecting these two clustering parameters, the DBSCAN algorithm depends on users’ practical experience. The improved DBSCAN clustering algorithm proposed clusters the data and automatically determines the input parameters: the input parameters of the evaluation index are the clustering results generated during the clustering process, and then the evaluation results are obtained. The specific algorithm is shown in The input parametersD is the current input data set. D1  represents x and y of the plane coordinates in the set.■ MaxEps is the maximum distance between two plane coordinate points, which can be flexibly determined according to practical meaning.■ MinEps is the minimum distance between two plane coordinate points, which can be flexibly determined according to practical meaning.■ MinEps to MaxEps.■ E is the distance between any two points in the set, ranging from MaxNum sets the upper limit of the clustering threshold because if the number of clusters is too large, the data set may not cluster effectively.■ MinNum sets the lower limit of the cluster threshold. If the number of clusters is too small, it may lead to too many clusters, and even a point becomes a cluster without a calculated value.■ MaxNum and MinNum.■ M determines the optimal quantity threshold for a cluster, which ranges between Output parametersResultC is the clustering result. Different input parameters can be used to get different clustering results.■ MinIEDCI is the minimum duty cycle and is initially set to infinity.■ BestEps is the best value for E, initially set to 0.■ Best MinPts is the best value for M, with an initial value of 0.■ Different input parameters will produce different clustering results. The algorithm gives a range of input parameters, traverses all parameter values within the field, and generates clustering results to prevent some parameters from being lost. The optimal evaluation value can be obtained by evaluating and calculating the clustering results, and the optimal input parameters can be calculated based on the backpropagation method. The algorithm process is described as follows:STEP 1 Building the input parameter rangeEps and MinPts should be determined to construct the effective range of clustering parameters.In different application scenarios, the value of the optimal clustering input parameter fluctuates within a specific range. Since the range of input parameters determines the efficiency of algorithm execution and the possibility of finding the optimal value, it is essential to establish a suitable range of input parameters before algorithm execution. Too many clustering times, the data set may not form effective clustering; Too few clustering times and too scattered clustering are not practical. In addition, the distance between cluster points will affect the compactness within the cluster. If the distance measure is too large, the clusters are too discrete to distinguish different clusters effectively. If the distance measure is too small, the clustering distance is too close, producing too many trivial and worthless clustering results. Therefore, at the early stage of clustering, the maximum and minimum values of STEP 2 Generating clustering resultsC) were saved.Cyclic density clustering was carried out to complete the clustering calculation of all travelers’ trajectory points for six months using the neighborhood radius range in Step 1 as the input parameter. All the clustering results , and IEDCI  were used to evaluate the clustering results. The best clustering parameters STEP 4 Obtaining the optimal clustering resultsBestEps and BestMinPts in Step 3 as input parameters. The clustering result is the clustering of the actual activity trajectory of the travelers, which is all the possible travel areas of the public transport travelers in the follow-up research.The best clustering result was calculated, taking In this study, the trajectory points of individual users’ smartphones were collected as the original data set. The GPS positioning information was mainly obtained using the integrated positioning sensor in the experimental mobile phone for recording and storage. At present, Baidu Map, Tencent Map, and Google Map all provide map-based API development applications. With the authorization of smartphone users, the collection of smartphone users’ trajectory points can be realized by using the APP of mobile phones. According to the setting of the positioning software, the user’s trajectory point information is collected every 10 seconds. In an ideal state, the number of trajectory points collected in a day should be 8640. However, the number of recorded trajectory points varies under different positioning strategies due to the differences in hardware performance, operating systems, carrier networks, and signal stability of location sensors of users’ smartphones.It is necessary to preprocess the collected original trajectory point data to meet the data format requirements of the clustering algorithm. In this paper, the GPS coordinate data processed by Sage-Husa adaptive filtering is preprocessed again, including data cleaning, data centralization, and standardization. Data cleaning aims to eliminate null values in the original data set, remove or correct incorrect data, and prepare for the accurate discovery of clustering patterns. Data centralization and standardization is since all variables in the collected data often use different units of measurement, and the difference between the data is significant. The data with a large absolute value can overwhelm a small absolute value in the clustering process. The latter cannot reflect the characteristics of the existing system.1 (downtown) and location L2, the GPS integrated device is used to output positioning data every 5s for a fixed location. The 500 continuously acquired data are composed of the original data set algorithm test.The accuracy of the GPS is easily affected by environmental factors. Therefore, taking location L1 is 4.6 m. The mean error of longitude and latitude of L2 is 6.3m  D, accessibility reference set S including bus stops, subway stops, and other transportation hubs.Output: the urban public transport travel area  generated based on the input data.Steps:D of access requirement points Dw= D, cluster number k = 0, cluster partition set Crc = Ø;Set the set Perform the parameter-free DBSCAN algorithm in Ck, the points which be divided into Ck will be removed from the set Dw;The clustering result is w = Ø, then output the clustering result as the location of the alternative site, Crc = {C1C, 2C…, k}, otherwise, go back to step (2) again;If the DS∪CSite = rc = S{1S, 2S,…,iS,…N1C, 1C, 2C,…, jC,…, N2}.Using the improved parameter-free DBSCAN algorithm to solve the location problem of original-destination of public transport, we can get the travel area of public transport  2D synthetic data sets included random numbers generated for a computer simulation. Each data set has 1,200 points, each represented as a coordinate and divided into a cluster. These data sets are Distinct Cluster, Fuzzy cluster, Halo cluster, and non-cluster . A totaTo verify the reliability and accuracy of the Sage-Husa-based adaptive filtering algorithm proposed, the GPS data collected is de-noised and compared with the original GPS data without denoising. The case shows that the proposed algorithm effectively controls the error range and further improves the positioning accuracy of GPS. Positioning accuracy eastward and northward are improved by 17.34% and 15.24%, respectively, and the positioning accuracy in 2D and 3D dimensions is enhanced by 14.25% and 18.17%, respectively. After using this algorithm, the number of noise points is reduced obviously, and the data error is controlled in a smaller range and concentrated to the actual coordinate points.Eps is the walking distance of the traveler, and MinPts is the number of the dwelling of traveler in a specific area, both of which have practical significance. Therefore, the parameter range can be delimited according to the real meaning. According to the statistics of existing data, it is concluded that the walking radius of travelers is mostly between 20 meters and 110 meters. Therefore, the Eps threshold was set within  in the experiment. All subsequent experimental tests are based on this range.In traveler trajectory mining, MinPts was set within , and subsequent experimental tests were based on this range.Clustering with too few or too many points is of no practical significance because too small a clustering coordinate threshold may be a noise point, and it is not easy to find the clustering with a large threshold. The threshold value of The case data set was used, and the clustering results were generated and compared with other parameters, including empirical and statistical values, to verify the performance of the parameters automatically selected by the improved DBSCAN algorithm.Eps value is 60, MinPts value is 12). The Eps and MinPts values obtained from experience were 85 and 10, respectively. The Eps and MinPts obtained by improved DBSCAN were 65 and 12, respectively.The results for all input parameters are counted to find the most common cluster number see . StatistThe case data set contains 500 individual location points. Compactness, separation, and DBI were used to evaluate the clustering results. Compactness and DBI represent the degree of cohesion of the clusters, and the degree of separation represents the distance between the clusters. The smaller the compactness and DBI, the higher the separation value, and the better the clustering effect. As shown in 2D synthetic data sets and case data set respectively are used to generate clustering results and compare them with other validity indicators, including DBI and Silhouette Coefficient evaluation, to verify the performance of IEDCI.This paper uses compactness and separation to evaluate the clustering results of four 2-D synthetic data sets. Case data sets are to be used to evaluate the performance of the algorithm. The entire evaluation process is as follows.(1) Optimal input selection: The algorithm in x represents all possible values of Eps, the coordinate y represents all possible values of MinPts, and the coordinate z represents the corresponding values. The values of Eps and MinPts are the best when the value is the smallest. After parameter selection within the same input range, the optimal parameter value generated by the Silhouette Coefficient will be generated at the boundary point, and both DBI and IEDCI will obtain the optimal parameter value within the range, minimize the value of the evaluation index.In the figure, the axis (2) Clustering results: Three different clustering results are generated using the optimal input values of the three evaluation indexes. As shown from (3) Clustering evaluation: The compactness and separation degree of the generated clustering results were evaluated, and the evaluation results are shown in This study aims to explore the extraction scheme about spatial dwell point suitable for mobile intelligent terminal users’ trajectory.A more accurate, efficient, and noise-resistant mobile phone GPS data preprocessing method makes it possible to efficiently mine travel trajectory data. The Sage -Husa adaptive filtering algorithm can effectively determine the error range and further improve the GPS positioning accuracy: the positioning accuracy in the eastward and northward is improved by 17.34% and 15.24%, respectively, and the positioning accuracy in 2-D and 3-D dimensions are improved by 14.25% and 18.17%, respectively. After using this algorithm, the number of noise points is reduced obviously. The GPS of smartphones can improve the positioning accuracy through the proposed adaptive filtering algorithm, which has the advantages of low cost and convenient use and can meet the positioning accuracy requirements for DBSCAN spatial clustering.This paper puts forward a novel traveler trajectory mining method, using a novel evaluation index to evaluate the input parameters of DBSCAN. The evaluation index balances the distance within the cluster and the distance between the clusters to obtain the optimal input parameters of location data clustering. The problem of parameter inaccuracy caused by manual experience is avoided. Secondly, based on the travel data of Jinhua city, the method proposed is verified, and the experiment shows that the algorithm proposed can find the optimal input parameter value on the traveler trajectory data set. By calculating the compactness and separation of the clustering results, it is found that compared with the Davies-Bouldin index (DBI) and Silhouette Coefficient, the optimal parameter value found by IEDCI has a smaller cohesion value and a larger cluster spacing value. Therefore, the proposed algorithm has good performance in the regional clustering of public transport travelers.Based on the above two points, the reliability of identification results can be significantly improved. However, at the same time, there are also the following problems:Due to time constraints and privacy considerations, recruiting volunteers for mobile positioning data is difficult, so the data amount is small, and large-scale trajectory data mining and verification cannot be carried out.This paper does not consider the accuracy and reliability of mobile phone GPS single-point positioning in complex and harsh environments.Some outliers in the initial data set have not been deeply analyzed. Improving the quality of track point acquisition is the prerequisite for the clustering algorithm’s accuracy of dwell point extraction. From the perspective of the clustering algorithm, it remains further discussed to study the time interval, missing data collection, and positioning error of trajectory points.Due to the particularity of data and the diversity and complexity of requirements, future research should focus on the following points:Increase the amount of mobile phone positioning data. It is hoped that more mobile phone trajectory data can be obtained in the next step to carry out large-scale trajectory data mining and verification.An in-depth study on the mobile intelligent terminal development model and trajectory data acquisition, data preprocessing, and WebGIS system integration should be carried out. Form a more automatic and intelligent trajectory extraction application about dwell points in mobile phone APP.Big data is currently a hot topic. However, it is also a huge challenge, on the one hand, provides researchers with the data source, on the other hand, also asked to select the effective data, based on guarantee the clustering effect, and explore a suitable, lower time complexity, higher execution efficiency of the extraction and clustering algorithm of massive Spatio-temporal dwell point trajectory,Conduct sufficient research on trajectory data pattern mining and personal travel feature analysis. In this paper, the travel mode, travel distance, time period, trajectory semantics, and other issues are combined to explore the valuable potential information contained in the trajectory data of smartphone users by using dwell point data.Promotion and application: The proposed method can be used to cluster the location information of travelers to obtain the original-destination of travel and be extended to logistics and supply chain management, vehicle dynamic routing, gas station planning, and other routing problems. That is because all these problems are point clustering problems on two-dimensional maps. Unlike other clusters, the size of a cluster is limited because people or vehicles have a specific range of movement.S1 File(DOCX)Click here for additional data file.S2 File(DOCX)Click here for additional data file.


Skin lesion segmentation is a primary step for skin lesion analysis, which can benefit the subsequent classification task. It is a challenging task since the boundaries of pigment regions may be fuzzy and the entire lesion may share a similar color. Prevalent deep learning methods for skin lesion segmentation make predictions by ensembling different convolutional neural networks (CNN), aggregating multi-scale information, or by multi-task learning framework. The main purpose of doing so is trying to make use of as much information as possible so as to make robust predictions. A multi-task learning framework has been proved to be beneficial for the skin lesion segmentation task, which is usually incorporated with the skin lesion classification task. However, multi-task learning requires extra labeling information which may not be available for the skin lesion images. In this paper, a novel CNN architecture using auxiliary information is proposed. Edge prediction, as an auxiliary task, is performed simultaneously with the segmentation task. A cross-connection layer module is proposed, where the intermediate feature maps of each task are fed into the subblocks of the other task which can implicitly guide the neural network to focus on the boundary region of the segmentation task. In addition, a multi-scale feature aggregation module is proposed, which makes use of features of different scales and enhances the performance of the proposed method. Experimental results show that the proposed method obtains a better performance compared with the state-of-the-art methods with a Jaccard Index (JA) of  The skin is the largest organ of the human body. When the skin cells become disordered and grow out of control, they can develop into skin cancers and may spread to other body parts. Skin cancer is the most prevalent cancer worldwide. Among all the types of skin cancers, melanoma is the most aggressive kind of skin cancer, whose incidence has risen rapidly over the last 30 years . The besNumerous clinical metrics based on the appearance of local color and texture patterns for the detection of melanoma have been proposed using dermoscopy images, such as ABCDE rules ,7, sevenAutomatic skin lesion segmentation is still a challenging task. For some skin lesions with light pigment, the color and visual patterns of the pigment regions and the surrounding skin regions are very similar, resulting in fuzzy and unclear boundaries, which makes the skin lesion segmentation task extremely difficult. In addition, the original dermoscopic images are of high-resolution, which is resource-intensive and time-consuming for the computers to process directly. Therefore, down-sampling is used first to reduce the image size. Finer textures and subtlety are lost during this procedure, which makes it even harder to differentiate the boundaries of these skin lesions. Moreover, the skin lesions also contain items such as hairs, veins, color-makers, rulers and glues, which affect the color and texture distribution of the skin lesions and impede successful learning. Edge prediction is leveraged as an auxiliary task for the skin lesion segmentation task. The proposed method learns these two tasks simultaneously by two parallel branches (edge prediction and segmentation mask prediction). The edge prediction branch can guide the learned neural network to focus on the boundaries of the segmentation masks. Up to the authors’ knowledge, this is the first work that utilizes edge information to assist the skin lesion segmentation task. Note that the edge of a segmentation mask can be obtained automatically by applying some contour detection methods and hence no extra labeling effort is required for the proposed method.A cross-connection layer (CCL) module and a multi-scale feature aggregation (MSFA) module are proposed in this paper. The interaction of different tasks is realized by the CCL module. During the training process, the CCL module can implicitly guide the learning of the two tasks jointly, and hence boost each task’s performance in turn. Meanwhile, the MSFA module can make use of multi-scale information. Typically, a prediction head is placed at the intermediate feature maps of each resolution for both the edge prediction and segmentation prediction branch. The weights for the feature maps of each resolution can be learned automatically during training.In this paper, a novel CNN method that uses auxiliary information is proposed for skin lesion segmentation. The proposed method can be trained in an end-to-end manner without any pre-processing or post-processing steps. The contribution of this work is two-fold:The organization of the paper is as follows. Various skin lesion segmentation methods have been developed in literature, the conventional methods include the thresholding-based methods , region-The deep CNN models have achieved remarkable success in various computer vision tasks ,25,26,27Based on the aforementioned introduction, the idea of treating the segmentation as a classification task has been proposed for skin lesion analysis by Jafari et al. . The inpA recent technique named DEXTR (Deep Extreme Cut)  has showIn this section, details of the proposed method are presented. Given an input skin lesion image, the proposed method will simultaneously predict the segmentation mask and its corresponding edge (contour) during training. During the testing phase, only the segmentation mask is used for prediction. A schematic diagram of the proposed method is shown in F for the subsequent edge prediction and segmentation mask prediction. In this paper, ResNet-101 , random rotation in the degree range , ground truth cropping with zero-padding of 50 pixels, and an image deformation method named Rigid Moving Least Squares  are usedDuring the testing phase, only the segmentation mask is needed to evaluate the performance of the proposed method. Test augmentation is also performed by rotating the input test image by The skin lesion segmentation method is implemented using PyTorch. All the experiments were conducted on a server with an Intel Xeon Processor CPU and two GPUs of Nvidia Tesla V100 with 16 GB memory.ISBI2017 for skin lesion detection  is used To evaluate the segmentation results, the Jaccard Index (JA), Dice Coefficient (DC), Accuracy (ACC), Sensitivity (SE) and Specificity (SP) are used in this paper. These criteria are defined as follows: As in Eqution , there iAs we can see from To show the effectiveness of the proposed method, an ablation study about the proposed method on the ISBI2017 test data is conducted. We name the three yellow blocks in  in the sIn this section, we compare the proposed method with other state-of-the-art methods using the ISBI2017 test data. The experimental results are shown in In this section, a qualitative analysis of the performance of the proposed method is also conducted. The final outputs of the segmentation and edge prediction branches are displayed in In this paper, a novel convolutional neural networks (CNN) based method with auxiliary task learning is proposed. Edge prediction, as an auxiliary task, is performed simultaneously with the segmentation prediction to help the segmentation task. The ground truth of the edge prediction task can be obtained automatically from the ground truth segmentation masks by using a standard contour detection method, and hence no extra labeling effort is required. A cross-connected layer (CCL) module is proposed, where the intermediate feature maps of each task are fed into the other task’s subblock, which implicitly guides the neural networks to focus on the boundary region and boosts the performance of the segmentation task. A multi-scale feature aggregation (MSFA) module is proposed, which can automatically learn the final mask by aggregating the output of different scales. An ablation study has shown the benefits of these proposed modules. Experimental results with the ISBI2017 dataset have shown that the proposed method outperforms the other state-of-art methods in terms of performance measures such as the Jaccard index and accuracy.