Melanoma image segmentation has important clinical value in the diagnosis and treatment of skin diseases. However, due to the difficulty of obtaining data sets, and the sample imbalance, the quality of melanoma image data sets is low, which reduces the accuracy and the effectiveness of computer aided diagnosis of melanoma image.In this work, a method of melanoma image segmentation by incorporating medical prior knowledge is proposed to improve the fidelity of melanoma image segmentation.Anatomical analysis of the melanoma image reveal the star shape of the melanoma image, which can be encoded into the loss function of the UNet model as a prior knowledge.Our experimental results on the ISIC-2017 data set demonstrate that the model by incorporating medical prior knowledge obtain a mIoU (Mean Intersection over Union) of 87.41%, a Dice Similarity Coefficient of 93.49%.Therefore, the model by incorporating medical prior knowledge achieve the first rank in the segmentation task comparing to other models and has high clinical value. Melanoma is the most common type of skin cancer and has the highest death rate of all skin cancers. However, melanoma can be cured with a minor surgical operation detected at an early stage. Therefore, segmentation of melanoma images can be used to help dermatologists evaluate and take treatment means as soon as possible.Initially, basic methods with low segmentation accuracy such as edge detection, threshold segmentation and region segmentation were introduced to segmentation of medical images. With the rapid development of deep learning technology, deep learning models have been widely leveraged in segmentation of skin lesions. However, with the improvement of model accuracy, complex structures and models are increasingly dependent on the number of images and high-quality of them, which is extremely difficult to get. The most common techniques to increase the number of images are data augmentation, such as traditional visual augmentation methods or generative models.In the context of applying models on low-quality data sets , the mosSince real physicians often do not need a large amount of data to make diagnoses, this method can bypass the problem of reliance on high-quality medical image data sets. However, while the above methods are applied to the segmentation of melanoma images, segmentation results that are obviously inconsistent with the anatomical structure of skin lesions, internal hole errors and external island errors occur in the segmentation map. The use of loss functions that do not encode anatomical priors led to these errors.To solve this problem, specific shape constraints can be designed according to the speckle shape of the melanoma images and utilized to the model can be trained using that. Since the seminal work of We aim to proposes a melanoma image segmentation method based on star shape prior, and encode it in loss function as a regularization term. To penalize non-star shape segment, including external and internal errors area. our experimental results demonstrate the improvement of segmentation performance.Comparing with the segmentation of ordinary images, there is a lot of many anatomical prior informations in medical images such as the position, shape and topological structure of organs or lesions. There are several methods and techniques to incorporating anatomical priors of lesions or organs into the model of medical image segmentation. It is one of the methods by learning prior knowledge with generative model and then integrating it into the network . Alternaetc.An end-to-end training model is to encoding anatomic prior knowledge to the loss function as a regularization term. The prior features can be divided into the following categories according to the types of regularization term: topological regularization term, which ignore the shape of organs and extract abstract connections between organs; dimensional regularization terms, which combine dimensional information about the size of organs or lesions in the model; interregional regularization terms are to find the geometrical and distance interaction between image regions; Shape regularization term, such as geometric shape feature, polygon feature, star feature, Different regularization terms should be designed for different organ and lesion when encoding medical prior into the loss function. Topological priors should be used when encoding the features of thin films and curved objects. For the tasks of whole-body segmentation, interregional priors are needed; For skin lesions, such as melanoma segmentation, star priors can be used to improve the convexity of organs or lesions to enhancing the model performance.Melanoma skin lesions have a speckled shape. The star-shaped prior is suitable for the segmentation of skin lesions. The star-shaped prior is derived from the concept of star convex set in mathematics, as shown in There are usually only a few dozens to a few hundreds of malignant melanoma images in a melanoma image data set, which is too small. If only the data set itself is used for image segmentation, the segmentation results will not be able confirm the anatomical characteristics of melanoma skin lesions. Therefore, the UNet model is first constructed in this article. And then segmented star prior loss function was designed for training to enhance the performance of the model.etc. The cross-entropy loss function is the most commonly used loss function in segmentation of skin lesions. However, it ignores the quantity ratio of different types of pixels in the sample space. Therefore, it is easy to produce the problem of category imbalance, and the category with the most pixels leads the training inevitably. The Dice loss function can be used to balance positive and negative samples. It has good performance when positive and negative samples are extremely uneven, and the loss of each pixel is strongly correlated with the adjacent region. However, the Dice loss function has the problem of insufficient mining of background area. It will adversely affect the back propagation process, making training process unstable. Therefore, There are different type of loss functions without coding anatomic priors, such as cross-entropy loss, Dice loss and Kappa loss, Although these attempts and improvements on loss function have enhanced the segmentation model performance recently, there are still shortcomings as follows : First, The malignant sample data in the original data set is selected for training. The UNet model is built with ResNet34 as the backbone for image segmentation of malignant samples. Its structure is shown in In In order to encode the anatomical structure of melanoma skin lesions into the UNet model for training. Shape prior can be fused into the loss function. When the star prior is encoded into the loss function, it can be designed by numerical superposition, as shown in In According to the features of the internal hole errors and external island errors in melanoma image segmentation, the star prior formula regularization term In the medical image segmentation tasks, the errors are divided into two kinds, namely the false positive errors and false negatives errors. Then further subdivided into four categories: the internal false negative, the edge false negative, the external false positive and the edge false positive, as shown in The optimization goal of the star prior loss function The Society for Medical Impact Informatics (SIIM) has collaborated with the International Skin Imaging Collaboration , has conIn our work, the model is applied to the preprocessed ISICM data set to build a three-stage processing structure. And the ISICM was finely segmented. Finally, our experiment is demonstrated in images, then the performance of the model is analyzed. 220 malignant sample images were segmented in the proportion of 6:2:2 to the training set, the evaluation set and the test set.First, the images is scaled to a size of 256 × X or Y × 256 in accordance with the length and width ratio of the image . The edges are then filled and the image size is 256 × 256. In addition, the image is flipped and brightness adjusted. Three malignant samples after data pretreatment are shown in The Pytorch framework  was usedFor the core experiment, three groups of ablation experiments were designed, namely, the non-regular term group, the unsegmented star prior group and the non-central distance scaling group. The experimental results are shown in The segmentation results are displayed, as shown in As the baseline experimental group, the loss function with star priori was not used, and only Dice loss function was used for training. As shown in Unsegmented basic star prior formula. Item As shown in The segmented star prior loss function in The segmented star prior loss function in Formulas Compared with UNet fused star prior method SSPFCN , AttentiPrevious methods performed well on large, high-quality data sets, but performed poorly on ISIC data set with only 220 skin images of malignant melanoma. Comparing to the recent methods, the star priority-based melanoma segmentation method has further improved the performance of the segmentation model on the data set with fewer samples. An advantage of the proposed approach is that a separate intensity normalization stage , which uIn this work, a melanoma segmentation method incorporating medical prior is proposed, and experimental verification is carried out on ISICM data set. Piecewise loss function of star prior is designed and applied to melanoma image segmentation teak. Our experiment demonstrate that the melanoma segmentation model based on the star prior enhances the performance of the model, and improves the mIoU value, mAP value, Dice value and Jaccard value.10.7717/peerj-cs.1122/supp-1Supplemental Information 1Data of the Non regularization term group (baseline).Click here for additional data file.10.7717/peerj-cs.1122/supp-2Supplemental Information 2Data of the Unsegmented star prior group.Click here for additional data file.10.7717/peerj-cs.1122/supp-3Supplemental Information 3Data of the non-Center distance scaling group.Click here for additional data file.10.7717/peerj-cs.1122/supp-4Supplemental Information 4Click here for additional data file.


A novel approach for multichannel epilepsy seizure classification which will help to automatically locate seizure activity present in the focal brain region was proposed. This paper suggested an Internet of Things (IoT) framework based on a smart phone by utilizing a novel feature termed multiresolution critical spectral verge (MCSV), based on frequency-derived information for epileptic seizure classification which was optimized using a flower pollination algorithm (FPA). A wireless sensor technology (WSN) was utilized to record the electroencephalography (EEG) signal of epileptic patients. Next, the EEG signal was pre-processed utilizing a multiresolution-based adaptive filtering (MRAF) method. Then, the maximal frequency point at which the power spectral density (PSD) of each EEG segment was greater than the average spectral power of the corresponding frequency band was computed. This point was further optimized to extract a point termed as critical spectral verge (CSV) to extract the exact high frequency oscillations representing the actual seizure activity present in the EEG signal. Next, a support vector machine (SVM) classifier was used for channel-wise classification of the seizure and non-seizure regions using CSV as a feature. This process of classification using the CSV feature extracted from the MRAF output is referred to as the MCSV approach. As a final step, cloud-based services were employed to analyze the EEG information from the subject’s smart phone. An exhaustive analysis was undertaken to assess the performance of the MCSV approach for two datasets. The presented approach showed an improved performance with a 93.83% average sensitivity, a 97.94% average specificity, a 97.38% average accuracy with the SVM classifier, and a 95.89% average detection rate as compared with other state-of-the-art studies such as deep learning. The methods presented in the literature were unable to precisely localize the origination of the seizure activity in the brain region and reported a low seizure detection rate. This work introduced an optimized CSV feature which was effectively used for multichannel seizure classification and localization of seizure origination. The proposed MCSV approach will help diagnose epileptic behavior from multichannel EEG signals which will be extremely useful for neuro-experts to analyze seizure details from different regions of the brain. Epilepsy is the most common neurological illness, impacting 50 million people globally . EpilepsIn view of this, continuous monitoring of real-time signals, such as EEG, is performed by employing WSN technology, one of the most promising emerging techniques for real-time monitoring of patients remotely. WSN utilizes body sensor network (BSN) that records vital signs of an epileptic patient such as EEG. A huge amount of data is generated with the rise in the use of BSN. Cloud computing (CC) facilitates storing and analyzing such rapidly produced EEG sensor data in real time from different patients located in distinct geographic regions. CC combined with BSN facilitates a framework to monitor and analyze the EEG data efficiently, in real time .The main aim of this work was to develop an efficient cloud-based epileptic seizure monitoring system to help neuro experts automatically classify epileptic seizures. The proposed automated seizure monitoring system will assist neuro experts as an additional tool to take decisions with more precision. The objectives of this paper were to capture high frequency oscillations (HFO) related to seizure information for seizure classification using the CSV feature in real time; and employ the FPA technique for optimization of the CSV feature, thereby facilitating localization of the brain focal region where the seizure has occurred. In order to obtain these objectives, we have developed a model employing BSN, mobile phone, and cloud infrastructure.The proposed work is presented as given below: The literature work is divided into two sections. The first section discusses various feature extraction techniques used for the classification of epileptic seizures. The second section details the use of sensor technology by utilizing WSN and CC in the seizure classification.Various EEG analyses show that dynamic variations of brain activity occur in relation to time, frequency, and space parameters. In regard to a non-linear signal, frequency-related techniques are employed to analyze the working of the epileptic EEG data processing ,5,6.Research has been carried out on the diagnosis of seizures from long-term EEG recordings . Time-vaAutomated seizure detection methodology can be carried out either on a single channel data or on multichannel information ,15. In aIn order to interpret seizure information effectively and efficiently, a segment-wise analysis is proposed in literature. Kiranyaz et al. have used a segment-wise seizure detection strategy in lengthy EEG signals along with Particle Swarm Optimization (PSO) . This apA majority of the state-of-the-art studies that have been undertaken perform automatic diagnosis on single channel EEG signals. These techniques have overlooked correlations between the various channels which is clinically baseless . RecentlThe work presented in this research paper has come up with a technique to detect epileptic seizures automatically from multichannel EEG recordings segment-wise and to localize the seizure region of the brain accurately. The proposed approach extracts the actual seizure information using the optimized MCSV feature by employing a bio-inspired (BI) algorithm, namely, FPA.Clinically, visual interpretation of the epileptic EEG data is performed by brain experts. However, the task of examining these long-term seizure signals is very exhausting, time consuming, and difficult to diagnose. EEGs need to be analyzed by a fully automated computer-based system so that such limitations can be eliminated. It is also challenging to classify the exact epileptic seizure region in long duration EEG recordings. In this paper, multichannel segment-wise seizure classification was performed using FPA  which alCurrently, many research studies have explored the usefulness of integrating the WSN with the CC framework. However, very few research efforts have been undertaken to establish the feasibility of integrating cloud-enabled frameworks with BSN, facilitating online and offline monitoring of epileptic seizure events. Forkan et al. employed a model using a service-oriented framework to enable an assisted living service in real time. It employed a middleware layer that reduces the complexity of real time data from various distinct sensors and contextual information . The autThe block schematic of the proposed multichannel seizure classification using the cloud storage and processing, and FPA approach is represented in In the proposed approach, cloud storage and the processing part consist of distinct modules such as pre-processing and data management, feature extraction and dimensionality reduction, and multichannel classification. The IoT part comprises a smart phone and cloud to capture and transmit data blocks. The smart phone is connected through a proper communication protocol to the cloud server.In the data processing module, initially the multichannel epileptic EEG data is pre-processed segment-wise using MRAF . It is fThe input EEG signal read from the multichannel is split into segments. For segmentation, a sliding window approach is used which breaks up the raw EEG data into sections or segments for feature extraction . In the Frequently, EEG seizure details are affected by the presence of physiological artifacts . So, it MRAF is implemented in three main steps:(i)To begin with, the EEG signal that is input is decomposed into multiple frequency levels making use of DWT;(ii)Next, soft thresholding is brought into use with the DWT coefficients to generate a signal with minimized abrupt changes;(iii)Finally, MRAF is undertaken to produce an EEG signal that contains minimal physiological EEG artifacts.Here, soft thresholding is executed on the obtained high frequency wavelet coefficients. Soft thresholding is rendered by (1) as follows :(1)Tl=σlMRAF is then performed to produce the pre-processed information of the seizure zone. The data thus obtained is expressed by the MRAF expression :(2)Xo(n)After pre-processing the EEG signal using MRAF, features are extracted for each EEG segment. The first step of feature extraction is the computation of the average of spectral values (average PSD) for each frequency band such as delta, theta, alpha, beta, and gamma. For each frequency band, for each segment, the PSD is calculated. Then the maximal frequency point at which the PSD of the segment is greater than the average PSD of the corresponding frequency band is computed which is termed as spectral verge (SV). However, due to the involvement of undesired glitches in the low frequency region most of the seizure related information is missed. In order to ensure that no seizure details are missed, the SV is optimized further using the FPA. Then, by considering the SV point as the seed point, optimization is performed by the FPA utilizing Levy’s path. The optimized point thus obtained is called the CSV. The CSV is obtained for each segment of the multichannel EEG signal.Step 1:Process segment k of an EEG signalStep 2:Process band b of an EEG signalStep 3:Calculate power spectral values of all segments and all bands.Step 4:Take the average of the calculated power spectral values.Step 5:Apply the FPA to optimize and calculate the CSV.Step 6:Add values of each calculated CSV to the feature map.Important steps for the computation of CSV given below:x (n), for simplicity. Then x (n) is split segment-wise for feature extraction. x (n), is represented as ‘x’ where ‘n’ represents the sample index which is omitted. Now we represent the signal ‘x’ in the form of segments as:As per Equation (2), we have achieved multiresolution filtering. The filtered output Let The filtered signal in each band can now be spilt in a similar manner and the process is repeated for all frequency bands.Now, the average spectral power is computed for each segment as uniform distribution of spectral power is observed for a high frequency range for the epileptic seizure segment.Let Now, we are interested in frequency point ‘m’ between 0 to Condition 1: Power spectral density of that frequency point is greater than Mathematically, if Condition 2: The frequency point ‘m’ itself is greater than any other point that satisfies condition 1.Mathematically, if In the presented analysis, we have considered this spectral verge point as a criterion for classifying seizure activities. However, due to patient-specific frequency variations present in the seizure region, misleading frequency points in some segments lead to misclassification. In order to optimize this, we chose FPA as a swarm-based intelligence technique inspired by the pollination behavior of flowering plants. In order to solve this optimization problem, the primary requirement for FPA is initialization of the objective function f(x) and seed solution. We have considered a combination of (5) and (6) as our objective function which is as follows:A seed solution was obtained from the objective function and used as input for the first iteration as the best fit solution.We can state the solution function in a generalized form as Now we need to optimize this seed solution to get the best fit using the pollination method as explained in the algorithm below:FPA uses the concept of a biotic process of global and local pollination to search for the best fit, which we have used anonymously as global and local seizure abnormalities. In order to differentiate between the local and global seizure abnormalities, FPA iterates through local and global abnormalities effectively and optimizes the solution to correctly identify the epileptic seizure segments using the pollination algorithm.The optimal solution is defined by:So, the resultant solution will be in the upper frequency bound The criterion for the best fit is the solution for which stability is achieved over possibilities of all solutions or maximum iterations are reached for solution generation.In order to reach the best solution, define the population function map given as:FPA can now be applied to the population of solutions above to get the optimized solution.A biotic or abiotic process of search for the optimized solution from the map of g* is the current best solution; The biotic process is given as:γ(λ) is the gamma function with λ = 0.5.Levy’s distribution is expressed as:The abiotic process is given as:At each iteration the probability switch is calculated using random distribution to define which path to follow, local or global pollination.There can be various stopping criteria for stopping the iteration. Here, we have chosen a finite number as the maximum iteration to achieve stability.Finally, we get the best solution as the CSV which is the optimized solution of all the solutions in the map.Now the CSV can be obtained similarly for all the EEG segments as α), beta (β), gamma (γ), theta (θ), delta (δ), and full band (fb), the CSV can be obtained in a similar fashion. The feature map thus formed can be represented by (13) as:For each band, alpha  was developed which performs the EEG capturing and transmission. Here, the TUH EEG data available in the cloud is directly interfaced with the multichannel EEG acquisition and transmission components. It was followed by the epileptic seizure data processing module which was hosted on the cloud to process the multichannel EEG. This is performed by employing Raspberry-pi 4 and Amazon Elastic Compute Cloud (EC2) using Active Directory (AD) to install the App in the processing unit. From Epileptic EEG signals are recorded by using scalp electrodes or intracranial electrodes. Various elements impede epilepsy detection. Seizure recordings use scalp electrodes and because of the presence of noises from the scalp, detection of epileptic signals is hampered. However, scalp seizure signals are easier to examine compared to intracranial signals. Hence, the epileptic seizure database used in this paper is obtained using scalp electrodes.This research work confirms that all experiments were performed following relevant guidelines and regulations. The TUH EEG dataset used for the experimentation is an open access data. Also, this work confirms that the data obtained from the local hospital has been obtained and analysed under the consent and the guidance of Dr. Nandan Yardi, Senior consultant in epileptology and President, Indian Epilepsy Association, Pune chapter, Pune.The first dataset used for the analysis of the proposed novel approach comprised records of ten patients with medically complex partial seizures obtained from an open access dataset, Temple University Hospital Electroencephalography Corpus (TUH EEG) . EEG eleIn this study, the second dataset used was obtained from a local hospital in Pune, India, with the consent of the ethical committee. EEG recordings were Unipolar 21 channel recordings obtained from three different patients. The length of the EEG recordings was from one to eight hours. The international 10–20 electrode placement was utilized for placing electrodes and the data were recorded at a sampling rate of 250 Hz. A neuro-expert annotated the data as seizure and non-seizure data. A detailed explanation of the datasets used for the experimentation is displayed in Initially, the length of the analysis window is the basis for the segmentation of the EEG signals. For the purpose of this study, the authors chose 4 s as the length of the analysis window. A comparison of the outcome of seizure detection acquired by the algorithm proposed in this paper and the results decided by experienced epileptologists was conducted by making use of parameters such as average detection rate, specificity, sensitivity and accuracy . The senTP denotes the number of seizure periods that are correct;FP represents the number of incorrect seizure periods;TN denotes the number of correct non-seizure periods; andFN shows the number of incorrect non-seizure periods.The evaluation parameters are given by (14), (15), (16), and (17), respectively,The proposed approach was validated making use of a ten-fold cross validation technique . To starChoosing the appropriate length of the segment window as well as the window overlap for the segmentation of the EEG signals was critical. In order to investigate the effect of different lengths of non-overlapping windows on the result of seizure detection, three different window sizes of 2 s, 4 s and 5 s, were considered for analysis in this work.From this analysis it was observed that there was no significant effect when different segment sizes are used, with or without overlapping of segments in the epileptic identification results. However, the EEG segment should not be too small such that the analysis takes more time. A data length of 4 s with a non-overlapping window was considered as an optimal window size in this proposed work.The presence of a seizure event can be seen in the low as well as high frequency bands. It is important to retrieve the seizure information from these frequency bands by filtering out abrupt changes. This purpose was served by the MRAF technique. In addition, the technique was able to localize the epileptogenic area indicating the start and end of seizures present in the seizure signal accurately . InitialFrom this analysis it was observed that the proposed MCSV approach using the SVM classifier provided a better detection rate as compared to MCSV with K-Means and k-NN as classifiers because it was able to handle outliers. Moreover, the SVM classifier is superior to K-Means and k-NN in two ways: (i) the boundary decision that SVM determined was more accurate; and (ii) the non-linear classification accuracy for SVM was higher. Therefore, we used the MCSV-SVM classifier combination in this work to detect epileptic seizures.The proposed MCSV approach extracted segment-wise multichannel seizure information at each EEG frequency band. The cloud application was used for reading and storing the EEG sensor data that were input. The IoT system enabled by Raspberry pi 4 was used to store and process the EEG data to enable the user to view it at any time and at any place utilizing a Web browser and an Internet connection. Specific modules like pre-processing and data management, feature extraction and dimensionality reduction, and multichannel classification were processed by the Raspberry pi 4 processing unit. In the proposed work, HTTP was utilized by making use of a GET and POST request to transmit information from the Raspberry pi enabled IoT to the cloud application. In the proposed work, cloud service Storage as a Service (STaaS) was made use of as a host for the cloud application that has the function of storing the EEG signal information and visualizing it.Segmenting the signal into small sections helps to identify the seizure region accurately in the EEG signal. Segment-wise variation in the spectral power helps to track normal EEG segments from abnormal seizure segments. This research work utilized spectral changes to trace this abnormal seizure information using the MCSV approach. The proposed MCSV approach extracted the actual seizure information using the optimized CSV feature by employing a BI algorithm, namely, FPA. FPA, in comparison to other BI algorithms, has shown better results in terms of flexibility, scalability, and adaptability, especially for non-stationary signals such as an EEG signal. The proposed CSV feature was able to capture HFOs represented by the gamma band of the EEG signal. Detection of origination of HFOs from the brain region facilitated localization of the seizure activity. The proposed MCSV approach was able to capture HFOs thereby precisely localizing the seizure information in the brain region.The primary goal of this work was to examine HFO characteristics and the utility of the CSV features that are extricated from the EEG signal for classifying seizures early. The MCSV feature employed in this work was made up of six characteristics, namely, full band, gamma, beta, alpha, theta, and delta bands. The percentage of the explained variances for the main components was calculated by taking in to account the abovementioned six bands and then applying the PCA. Results of the investigation indicated that a variance of 81.71% was noted in the first component, 80.93% was noted in the second component, in the third component 43.06% was observed, for the fourth 24.31% was noted, 21.69% for the fifth was observed and for the last a variance of 4.37% was noted. This analysis of variance was undertaken to note how much information is lost with the application of dimensional reduction. The six characteristics were projected in two dimensions. Once the reduction was accomplished, it was noted that there was no specific meaning for each of the components. It was also noted that when fitting the original data to two dimensions, the reduction from six to two dimensions did not permit separation of the classes from each other in an ideal way. Taking into account these observations, it was found that it is not suitable to undertake the reduction for more than two dimensions for classification of the seizure classes.To assess the seizure detection functioning of the proposed MCSV approach, multichannel classification was performed against single channel classification using the SVM classifier. The results of MCSV-SVM method for single and multichannel EEG data are summarized in Based on Various methods for epileptic seizure detection are introduced in literature. A brief exploration of a few of the techniques and their connected investigations with the suggested MCSV-based approach is presented in this section. In addition, refs. ,45,46 haHowever, it is a challenge to compare the proposed approach with the present techniques, since all the discussed methods have their own method of channel-segment selection, their own type and number of features selected, and type of classifier used for analysis. Nevertheless, it was observed from the literature summary that the MCSV approach reported maximum possible average accuracy by considering classification of the seizure signals for all the EEG channels segment-wise. Even though a few methods report greater accuracy than the presented one, it is worthwhile to note that they have reported the results only for a single channel. In addition to this, few approaches have shown superiority for multichannels by considering only a few channels from the entire EEG channel set. Also, the MCSV approach used all the channels without adapting to any channel selection algorithm. In addition, the MCSV approach enhanced the classifier performance by extracting the high frequency seizure component, which a majority of the existing methods fail to do.The authors of  showed tA novel MCSV approach for multichannel epileptic seizure classification was presented in this paper. The proposed approach will help in the real-time monitoring of the EEG signal by utilizing an IoT-based system utilizing a smart phone and the cloud. In the data processing unit, the input EEG signal is pre-processed using the MRAF method for the elimination of physiological artifacts. Choice of the segment size greatly affects the performance of the segment-wise seizure classification. Segment-wise analysis was carried out for EEG segments with and without overlap. From this analysis, we observed that a 4 s segment without overlap performs better for seizure classification. The classification performance of the proposed MCSV approach was tested for three standard classifiers, namely, K-Means clustering, k-NN and SVM. It was observed that the classification performance of the MCSV approach with SVM was superior to the K-Means clustering algorithm and the k-NN classifiers. The MCSV-SVM classifier achieved 97.38% accuracy and a 95.89% average detection rate. This result was further analyzed by employing a topographic map in which the gamma band showed stronger activity related to the origination of the seizure from the left and right ear lobes. We also tested the MCSV approach for single and multichannel EEG signals. It was noted from the analysis that multichannel seizure classification carried more information in comparison to a single channel thereby increasing the average detection rate of the epileptic seizure classification. Also, the proposed MCSV approach outperforms other multichannel-based seizure techniques presented in the literature.


Ransomware is a malicious class of software that utilises encryption to implement an attack on system availability. The target’s data remains encrypted and is held captive by the attacker until a ransom demand is met. A common approach used by many crypto-ransomware detection techniques is to monitor file system activity and attempt to identify encrypted files being written to disk, often using a file’s entropy as an indicator of encryption. However, often in the description of these techniques, little or no discussion is made as to why a particular entropy calculation technique is selected or any justification given as to why one technique is selected over the alternatives. The Shannon method of entropy calculation is the most commonly-used technique when it comes to file encryption identification in crypto-ransomware detection techniques. Overall, correctly encrypted data should be indistinguishable from random data, so apart from the standard mathematical entropy calculations such as Chi-Square ( Ransomware infection remains a current and significant threat to both individuals and organisations , reinforAlthough there are countless strains of ransomware, they mainly fall into two main types. These are crypto-ransomware and locker ransomware. Crypto-ransomware encrypts valuable files on a computer so that they become unusable. Cyber Criminals that leverage crypto-ransomware attacks generate income by restricting access to these files until the victim pays a ransom. Unlike crypto-ransomware, Locker ransomware does not encrypt files. Instead goes one step further, and it locks the victim out of their device. In these types of attacks, cybercriminals will demand a ransom to unlock the device. In both types of attack, users can be left without any option other than payment to recover their files. Over time ransomware attacks have become more sophisticated, performing tasks such as ex-filtrating data and attempting lateral movement to infect other machines. In this paper, we will focus solely on the encryption actions undertaken by crypto-ransomware.A reoccurring theme within many crypto-ransomware detection techniques is the concept of randomness and file entropy. Researchers assert that a good indicator ,5,6,7 ofWhile there exist many techniques available—both purely mathematical, as well as statistical—to determine the entropy or randomness of a file’s contents; in the majority of cases, there is often no discussion in the research into why a specific entropy technique was used over its alternatives. The most common technique used in crypto-ransomware detection systems is to identify files with random content using the Shannon entropy calculation. The closer a file’s overall entropy value is to eight bits, the higher the confidence that its contents are encrypted and subsequently—possibly—the consequence of a crypto-ransomware infection. Other techniques that have been used apart from Shannon entropy are Chi-square, Kullback-Liebler distance, serial byte correlation and Monte Carlo .This paper describes the work performed by the authors in firstly identifying alternative file randomness measurement techniques and then subsequently compares their effectiveness. The work is specifically aimed at measuring the effectiveness of these techniques in being able to correctly distinguish between crypto-ransomware encrypted files and other files that generally have a high entropy value, such as with archived or compressed files. When performing this type of research, achieving convincing and reproducible results , is highThe overall research is broken down into two phases. During the first phase, all of the 53 identified randomness tests were executed against a subset of the NapierOne data set. Techniques that exhibited a reasonably-high accuracy were then selected for inclusion in the second phase. During the second phase of tests, the candidates identified in phase one were re-executed, but against the full NapierOne dataset. This resulted in each randomness test being executed on 5000 examples of each of the 54 data distinct types contained within the dataset. It results in performing nearly three million separate entropy calculations on the NapierOne dataset in an attempt to determine which, if any, of the identified randomness calculations is best suited for crypto-ransomware detection. A subsequent investigation was also performed to determine if a hybrid solution of combining separate entropy calculation results could be used to improve the detection accuracy.The hypothesis of this paper is that there is a fundamental difference between different entropy methods and that the best methods can be used to better detect ransomware encrypted files.The main contribution of this paper is in initially assessing the commonly used entropy measurement methods, and use these to create a short-list of contenders that are assessed on a commonly used data set, for a range of success rate measurements against ransomware/encryption detection.Analyse a large and varied selection of entropy calculation methods in order to determine if any of the techniques are able to accurately distinguish between files generated by a crypto-ransomware and other common high entropy files such as compressed or archive file types.No other published research covers this extensive scope of entropy calculation analysis, using a standard data set, and thus provides a core contribution to crypto-ransomware detection research.The contributions of this paper being:The remainder of the paper is structured as follows. In Many crypto-ransomware detection methods use a metric calculated from the file being written to disk as an indicator as to whether the contents of the file are encrypted or not. This calculation is normally used in combination with other identified indicators ,7 to detSince its discovery in 1948 , ShannonWhile Shannon’s entropy has been the technique of choice in the majority of reviewed research, some researchers have used alternative techniques to identify the randomness of a file’s contents. These alternative techniques include: chi-square calculation ,33,34,35pure mathematical techniques where the randomness of the data is determined from a single calculation, there also exist alternative approaches, such as in the test suites that have been developed to validate pseudo-random number generation (PRNG) programs. The aim of random number generators is to produce a series of numbers that do not display any distinguishable patterns in their appearance or generation. The output of these random number generators should thus have very high entropy, which would then lend the suites used to test the outputs of these generators suitable to also test the contents of encrypted files. There exist many test suites currently available to test the output of random number generators, the most significant being: NIST SP 800-22 [Apart from these P 800-22 , the ChiP 800-22 ,39, FIPSP 800-22 ,40, DieHP 800-22 ,42 and tP 800-22 ,44 libraThe main focus of this paper is to compare multiple entropy evaluation techniques using a large and varied dataset, in an attempt to determine which tests, if any, exhibit the highest accuracy in differentiating between crypto-ransomware encrypted files from other common file types. The NapierOne data set ,11 was sThe following tests and test suites were considered as part of the first phase of testing. Tests that performed well with regards to accuracy and performance were later executed in Phase 2 of the tests.The NIST SP800-22 specification ,40,45 frSince the tests only consider the output values and not the implemented methods for generation, some researchers argue that the tests contained within this suite are not useful , arguingThe specific NIST tests used during the first phase of testing are: Frequency (Monobit) Test. Determine whether the number of ones and zeros in an entire sequence is approximately the same as would be expected for a truly random sequence.Frequency Test within a Block. Determine whether the frequency of ones in an M-bit block is approximately Runs Test. Test the total number of runs in the sequence, where a run is an uninterrupted sequence of identical bits. The purpose of the Runs test is to determine whether the number of runs of ones and zeros of various lengths is as-expected for a random sequence. In particular, this test determines whether the oscillation between such zeros and ones is too fast or too slow.Test for the Longest Run of Ones in a Block. Determine whether the length of the longest run of ones within the tested sequence is consistent with the length of the longest run of ones that would be expected in a random sequence.Binary Matrix Rank Test. Check for linear dependence among fixed-length sub-strings of the original sequence. Note that this test also appears in the DIEHARD suite of tests [of tests ,48.Discrete Fourier Transform  Test. Detect periodic features  in the tested sequence that would indicate a deviation from the assumption of randomness. The intention is to detect whether the number of peaks exceeding the 95% threshold is significantly different than 5%.Non-overlapping Template Matching Test. Count the number of occurrences of pre-specified target strings and identifying too many occurrences of a given non-periodic (aperiodic) pattern. An example of an 68-bit aperiodic pattern being 0 1 1 1 1 1 1 1.Overlapping Template Matching Test. Similar to the previous test, this test also looks for occurrences of pre-specified target strings. When a match is found, this test moves the test window by one byte, whereas the previous test moves the test widow to the end of the matching sequence.Universal Statistical Test.Maurer’s  This is the number of bits between matching patterns (a measure that is related to the length of a compressed sequence). The purpose of the test is to detect whether or not the sequence can be significantly compressed without loss of information. A significantly compressible sequence is considered to be non-random.Linear Complexity Test. This tests the length of a linear feedback shift register (LFSR). The linear complexity of a sequence equals to the length of the smallest linear feedback shift register (LFSR) that generates the given sequence. Random sequences are characterised by longer LFSR’s. An LFSR that is too short implies non-randomness.Serial Test. Test the frequency of all possible overlapping m-bit patterns across the entire sequence.Approximate Entropy Test. Similar to the previous test, this tests the frequency of all possible overlapping m-bit patterns across the entire sequence. The purpose of the test is to compare the frequency of overlapping blocks of two consecutive/adjacent lengths (m and m + 1) against the expected result for a random sequence.Cumulative Sums (Cusum) Test. The focus of this test is the maximal excursion (from zero) of the random walk defined by the cumulative sum of adjusted  digits in the sequence. For a random sequence, the excursions of the random walk should be near zero.Random Excursions Test. Test the number of cycles having exactly K visits in a cumulative sum random walk. This test is to determine if the number of visits to a particular state within a cycle deviates from what one would expect for a random sequence.Random Excursions Variant Test. Test the total number of times that a particular state is visited in a cumulative sum random walk. The purpose of this test is to detect deviations from the expected number of visits to various states in the random walk.Dieharder2 tests have been placed in the This test suite from 2006 is a public licensed implementation ,42 of thIs a C++ library for convenient random number generation based on a variety of high-quality algorithms; and also a C++ library of statistical tests for RNGs ,50,51,52Shannon Entropy File entropy refers to a specific measure of randomness. One such measure is called Shannon Entropy [randomness of the data in a file—measured in a scale of zero to eight (eight bits in a byte). Typical text files that contain only alphanumeric characters and no formatting will have a lower value, whereas encrypted or compressed files will have a value closer to 8 [ Entropy  and is u Entropy . It is bser to 8 .Another way to consider entropy is that it is a measure of the predictability or randomness of data. A file with a highly predictable structure or a bit pattern that repeats frequently has low entropy. Such files would be considered to have low information content (or low information density). Files in which the next byte value is relatively independent of the previous byte value would be considered to have high entropy. Such files would be thought to have high information content ,56.H) [The maximum possible entropy per byte is eight bits of entropy per byte signifying that it is completely random. The generally accepted formula for entropy (H) , is showH)  program:H is the entropy (measured in bits)n number of bytes in the samplei appearing in the stream of bytes.The negative sign ensures that the result is always positive or zero.Chi-square. The chi-square x2Monte Carlo. This is a technique of approximating the value of a function where calculating the actual value is difficult or impossible. It uses random sampling to define constraints on the value and then makes a sort of best guess [st guess . The undst guess  program.E = Result of approximationArithmetic Mean. The mean is the average or the most common value in a collection of numbers and is calculated by adding all the numbers in a given data set and then dividing it by the total number of members of that data set. The formula used to calculate this value is presented in Equation . KL divergence, which is closely related to relative entropy, information divergence, and information for discrimination, is a non-symmetric measure of the difference between two probability distributions and has been suggested as a good test [ed files . For raned files . For a sEquation  and was ood test , especian is the number of bytes in the sampleTESTU01 A popular [ popular  software popular . A signiFIPS-2-140 [PS-2-140 ,40 Is rePS-2-140  but no aPS-2-140 .GM/T 0005-2012 [005-2012 ,39 The c005-2012 .The main focus of this paper is aimed at testing techniques that can successfully differentiate between encrypted files and other common high entropy files. For each test performed, a specific threshold will be defined. If the outcome of the test performed is within the defined threshold then the tested file will be classified as encrypted, otherwise, if the results fall outside the threshold, the file will then be classified as unencrypted. No further classification attempts will be made. A basic outline of the experimental steps performed is illustrated in When classifying the file types, there are four possible outcomes, which are presented in Accuracy ACC=Recall Shown in Equation . Just relying on the calculated Shannon entropy value to distinguish between encrypted and non-encrypted files would thus be a difficult task generating a lot of false positives and negatives. The entropy distribution for the file types in this dataset is shown in The resulting value calculated from the execution of the test must be within the threshold, for the file to be classified as encrypted and outside the threshold for the file to be classified as unencrypted. The thresholds used for each of the tests are shown in During the first round of testing, 53 distinct tests were performed against a dataset of 320 files, providing nearly 17,000 results. The accuracy of each test performed was then calculated and recorded. The results for each test against each data type are shown in The performance of the tests was also recorded and these results are presented in The accuracy of the tests performed was then reviewed and the overall time taken for the completion of the test was also taken into consideration. Using these criteria, 42 of the original tests were rejected, resulting in 11 tests being selected for deeper examination in Phase 2 of the testing. Despite some of the tests exhibiting poor accuracy results, it was decided to also include several of the purely mathematical calculation tests, in the second phase of testing anyway. This was due to the fact that they have been used, or mentioned frequently, in other research and the authors thought that a robust evaluation of these calculations would provide a useful reference. A full list of the tests performed and if they qualified for the second phase of testing is presented in The tests identified from the first phase of testing will then be run against 5000 example files for each of the file types present in the NapierOne data set. The file type contained within this data set is shown in As well as running the selected tests against the standard common file types present in the NapierOne dataset, the tests will also be executed against 5000 example files that have been encrypted by each of the following 12 crypto-ransomware strains: BlackMatter, Conti, DarkSide, Dharma, GandCrab, HelloKitty, Maze, Netwalker, NotPetya, Phobos, Ryuk and Sodinokibi (see While both Dharma, HelloKitty and Ryuk use AES, Dharma uses CFB mode ,64 and RDuring this phase the entire NapierOne dataset was used, resulting in 5000 example files for each of the 42 main data types being tested . AdditioThe results of these calculations were then recorded and the classification accuracy for each test, broken down by file type, was calculated. The test’s precision, recall and F1 values were calculated on a per-test basis and the results are shown in For the lower entropy file type, the purely mathematical calculations appear to perform better than the test suite tests, while for the higher entropy file types it can be seen that no technique was ideally suited to differentiating between crypto-ransomware encrypted files and compressed files, it was noticed that some tests performed better than others. For this testing it can be seen that the most consistently accurate tests were: block frequency, sums and SP-800 Serial correlation from the test suite and Chi-square as a purely mathematical calculation. These findings tend to agree with [It can also be noted that the Serial Byte Correlation Coefficient tests had an extremely high accuracy in identifying compressed files with almost 100% TN rate on all the compressed file formats. After observing this characteristic some further testing was performed. An evaluation was made to determine if the use of a combination of a purely mathematical calculation together with the results of the Serial Byte Correlation Coefficient calculation, could be used to improve the overall classification accuracy. Using these two techniques in a collaborative approach. In essence, the Serial Byte Correlation Coefficient calculation would be used to determine if the file was an archive and thus improve the TN rate and reduce the FP rate of the purely mathematical calculation.Firstly, the primary entropy value was calculated, using one of the pure mathematical techniques such as Shannon, Chi-Square or Monte Carlo. Then the Serial Byte Correlation Coefficient was calculated on the same file to determine if the file was an archive. If the Serial Byte Correlation Coefficient test confirmed that the file was actually an archive then any decision made by the first calculation, that the file is encrypted, is reversed and the conclusion of the combined test is that the file is not encrypted. However, if the Serial Byte Correlation Coefficient calculation confirms that the file is not an archive then any decision made by the pure mathematical test, that the file is encrypted, is upheld. This should have the effect of reducing the FP results for the primary calculation on archive files, thus raising the overall accuracy. The final technique evaluated was to combine the five pure mathematical calculations: Shannon, Chi-Square, Mean, Monte Carlo and Serial Byte Correlation Coefficient. The values for each of these techniques was calculated and then a majority voting approach was adopted. If three or more of these calculations concluded that the file was encrypted, then this was the result given, otherwise, the file is deemed to be unencrypted. Unfortunately, this approach had very little impact on detection accuracy.From the research performed and the results achieved, it is clear that the use of pure mathematical techniques such as Shannon entropy, Chi-square and Monte Carlo calculations as an indicator for crypto-ransomware encrypted files is not ideal. Especially when applying these calculations to a broad and modern data set, such as NapierOne, it can be seen that these calculations struggle to differentiate consistently between encrypted files and other high entropy files such as compressed or archived files. However, of the purely mathematical calculations tested, then the results from the Chi-Square calculation appear to produce the highest accuracy.For certain file types, for example, the files created from the execution of the NetWalker crypto-ransomware, all the techniques tested, were unable to successfully identify even 50% of the files as being encrypted. This is interesting as this crypto-ransomware uses the Salsa20 symmetric encryption algorithm, which is the same encryption algorithm used by both the NotPetya and Sodinokibi crypto-ransomware strains.In certain circumstances, a higher detection accuracy of crypto-ransomware encrypted files was achieved, by using a combination of pure mathematical calculations together with the results produced by the serial byte correlation coefficient calculation. Another interesting discovery was that no tests from the DieHarder2 test suite were accurate enough for the identification of crypto-ransomware encrypted files and no test from this suite qualified the phase 2 test round. Even though the documentation for the Dieharder2 test suite state that some of the tests were similar to the test found in the SP 800-22 test suite, the results did not reflect this.To the best of the authors’ knowledge, a comprehensive test incorporating both pure mathematical calculations as well as statistical test suite analyses when applied to crypto-ransomware encryption identification has not been performed, and it is hoped that the results from this investigation would prove useful to crypto-ransomware researchers when designing their detection techniques and systems.Using an entropy value as an indication of crypto-ransomware infection has been a common feature in ransomware detection systems for many years, however, some previous research , and morThe authors of these entropy reduction technique ,74 stateThere exist techniques that can be used to reduce the overall entropy of a file generated by crypto-ransomware ,74 so onSome interesting outcomes from this research which could be investigated further are why all the techniques struggled in identifying files generated by the NetWalker crypto-ransomware despite it using the same symmetric encryption algorithm as other tested crypto-ransomware. Furthermore, why were the tests from the DieHarder2 test suite not effective in performing this crypto-ransomware encryption classification? Finally, further investigation into the definition of the threshold for Chi-Square calculation may be performed to determine if modification of this could improve the overall accuracy of the crypto-ransomware encrypted file identification calculation.


Numerous total knee prosthetic implants are currently available on the orthopedic market, and this variety covers a set of different levels of constraint: among the various models available, a significant role is covered by mobile bearing cruciate-retaining design with an ultra-congruent insert, mobile bearing cruciate-retaining design, fixed-bearing posterior stabilized prosthesis and fixed-bearing constrained condylar knee. A biomechanical comparative study among them could therefore be helpful for the clinical decision-making process. This study aimed to compare the effect of these different levels of constraint in the knee biomechanics of a patient, in three different configurations representing the typical boundary conditions experienced by the knee joint during daily activities.The investigation was performed via finite element analysis with a knee model based on an already published and validated one. Four different types of prosthesis designs were analyzed: two mobile-bearing models and two fixed-bearing models, each one having a different level of constraint. The different designs were incorporated in to the 3D finite element model of the lower leg and analyzed in three different configurations reproducing the landing and the taking-off phases occurring during the gait cycle and chair-rising. Implant kinetics (in terms of polyethylene contact areas and contact pressure), polyethylene and tibial bone stresses were calculated under three different loading conditions for each design.i.e. medial pivot designs).The tibial stress distribution in the different regions of interest of the tibia remains relatively homogeneous regardless of the type of design used. The main relevant difference was observed between the mobile and fixed-bearing models, as the contact areas were significantly different between these models in the different loading conditions. As a consequence, significant changes in the stress distribution were observed at the interface between the prosthetic components, but no significant changes were noted on the tibial bone. Moreover, the different models exhibited a symmetrical medial and lateral distribution of the contact areas, which was not always common among all the currently available prostheses (The changes of the prosthetic implant did not induce a big variation of the stress distribution in the different regions of the tibial bone, while they significantly changed the distribution of stress at the interface between the prosthetic components.The online version contains supplementary material available at 10.1186/s42836-022-00157-0. When the patient requires a total knee arthroplasty (TKA) but his posterior cruciate ligament (PCL) is healthy enough to ensure the stability of the knee joint, the surgeon could opt for the implant of a cruciate-retaining (CR) knee prosthesis. Those prostheses are categorized as non-constrained implants as the femoral and tibial components are not linked together and rely on the patient's native ligaments to guarantee the stability of the knee . The CR The posterior stabilized (PS) designs, instead, involve PCL resection and incorporate a post-cam system to act as a substitute for the cruciate ligaments in anterior-posterior stability . Those pTogether with the different types of inserts listed above, a choice, in terms of insert-tibial tray constraint, is also available: These two components can be rigidly fixed or have a degree of freedom allowing for relative rotation. In mobile-bearing (MB) implants, indeed, the tibial insert can rotate upon the tibial component while in fixed-bearing (FB) implants the tibial insert is fixed firmly to the metal tibial tray. MB TKA is thus designed with a rotating platform, providing higher conformity of the different components' articular surfaces . Thanks With all the different options provided, the surgeon has then to choose the appropriate combination from the wide range of existing implant components, taking into consideration all their relative implications on the short- and long-term survival of the implant. In order to provide a clearer overview of the outcomes expected from the different possibilities, and thus decide which could represent the ideal one, this study took into consideration and biomechanically analyzed different prosthesis configurations from the same family of products . The implants analyzed present different levels of constraint and, as a consequence, each implant provides a different stability in the TKA and distinct interactions are observed between the bone and the prosthesis .The purpose of this study was therefore to compare the effect of these four prosthetic implants with their different levels of constraint in the knee biomechanics of a patient, simulating three different configurations representing the typical boundary conditions experienced by the knee joint during daily activities.Finite element analysis is a technique that involves the use of a numerical solver to implement an input model and to perform a virtual simulation, then providing the consequent mechanical outputs: it represents thus an optimal tool to perform this type of analysis. The possibility offered by this approach is to guarantee the application of a same exact set of boundary conditions on different models, and this represents one of the fundamental features that contribute to the wide adoption of this technique in the research and industrial biomechanical fields .The finite element model developed for this study was based on a previously validated and published knee finite element model , and it According to previous studies , 27–29, The LCL and MCL were considered isotropic and a validated pre-strain modeling approach was followed , 27, 28 The femoral component, as well as the tibial component, were given the properties of cobalt-chromium (CoCr), while for the tibial insert ultra-high-molecular-weight polyethylene (UHMWP) was used. All these materials were assumed to be homogeneous and isotropic , 28, 32 Tie contacts were implemented between the femoral bone and the relative prosthetic component, while general contact was adopted to simulate the interface between the tibial bone and the tibial component. A coefficient of friction of 0.04 was considered for the interaction between all the prosthetic components  , 27, 32.Three configurations were defined according to previous studies , 33–37 aConfiguration A reproduced the landing phase occurring during the gait cycle Fig. a and is Configuration B represented the taking-off phase occurring during the gait cycle and is defined with a flexion angle of 30° and a vertical axial load of 2200 N, applied to the reference point coupled with the proximal surface of the femur and with an inclination of 15° with respect to the direction of the tibial longitudinal axis  was allowed except for the flexion-extension, in order to keep each model in the studied configuration with the appropriate flexion angle.In order to simulate the different behaviors of fixed and mobile bearing prostheses, different constraints were applied to the inserts. For both fixed-bearing models, the relative motion between the insert and the tibial tray was rigidly constrained via a tied contact; for both mobile bearing models, instead, the insert was allowed to rotate about the longitudinal axis thanks to the relative degree of freedom left unconstrained.In all the analyzed configurations, the tibia and all the relative collateral ligaments attachment points  were distally fixed –30, 39.All the parts of the models were meshed using linear tetrahedral elements, with the element sizes of each part being between 1 and 5 mm. This was chosen based on a convergence test performed to verify the mesh quality for every region of the model. ABAQUS/ Standard version 2019 (Dassault Systèmes) was used to perform all the finite element simulations.The medial and lateral proximal zones: two regions close to the tibial tray with a thickness of 5 mm Fig. a;The medial and lateral distal zones: two regions 30 mm from the tibial cut surface with a thickness of 20 mm Fig. b.Four regions of interest were defined to study the tibial bone stress:The first two regions were selected to compare the tibial stress induced by the different types of prostheses in the region closest to the cut, while the distal two aimed to provide a more global overview of the variation of the stress distribution  along the tibial bone. This decision was taken in agreement with the literature . For allIn order to conduct a more in-depth analysis of the different outcomes, the tibial prosthesis inserts were divided into three parts: medial, lateral and middle ones Fig. . Medial Figures Figure Figure Figure Tables The MB CR design with a standard insert still induced higher von Mises stresses in the proximal and distal regions compared to the FB models, but only in the configuration A and B as this was not true for configuration C.In configuration A, the proximal tibial stress for the MB CR with the standard insert was 2–4% higher in the medial region and 26–27% higher in the lateral region when compared to the tibial stresses of the FB designs. In configuration C, on the opposite, it was the proximal tibial stresses for the FB designs which were almost 9–11% higher in the medial region and 5–6% higher in the lateral region compared to the MB CR design with the standard insert.When looking at the medial and lateral tibial stress distribution reported in Tables In this study, four knee prostheses from a single family of products, each one characterized by a different constraint level, were analyzed via the finite element method in order to compare their performances in terms of contact areas as well as von Mises tibial stress in the bone and in the insert.From the results obtained with the simulations, it emerged that the mobile bearing inserts tend to be more compliant in the anterior part, exhibiting a higher contact area in this latter.Innocenti  reportedet al. [Shiramizu et al.  highlighet al. [Hofer et al.  reportedThe results of the present study showed that the distribution of the contact areas was significantly different for the mobile and fixed-bearing models in the different loading conditions, but it remains in an acceptable range. It is finally to be highlighted that the different models analyzed exhibited a symmetrical medial and lateral distribution of the contact areas, which is not always common among all the currently available prostheses  .In addition, the main relevant difference in the average tibial bone stresses was previously observed between the mobile and fixed-bearing models. The proximal and distal tibial bone stresses obtained with the different designs were similar to the ones obtained in other studies , 29. BesSathasivam and Walker  suggesteConsidering the results obtained and the comparison among the different models reported here, the most important finding that can be highlighted concerns the ultra-congruent mobile bearing model: This design indeed appeared to be the one able to provide the highest congruency in terms of the contact area when compared to the other prostheses analyzed, and these higher values guarantee, therefore, an overall more homogeneous stress distribution in the insert. These results, together with the literature , suggestIt is then remarkable to note that, regardless of the studied configuration, the distribution of the stress in the different regions of interest of the tibia  was not remarkably different among all the models. Changing the prosthetic implant would therefore not induce a big variation in the tibial stress distribution. However, it would remarkably change the distribution of stress at the interface between the prosthetic components  and therefore this factor is the main one to take into consideration in the decision-making process.There are various limitations associated with this study, mainly related to assumptions made during the implementation of the FE models.Soft tissues (such as the muscles or some ligaments) were not incorporated into the different models, but their contribution was nonetheless considered as an influence on the loading conditions applied . AnotherOf note, the patello-femoral joint was not included in the model, since it was not the focus of the analysis. This choice was made by taking into account the fact that the forces involved in this joint in the configurations analyzed were negligible compared to the ones involved in the considered regions of interest. Furthermore, this choice was made in agreement with previously validated models addressing similar configurations , 24, 28.Finally, the positioning and the alignment of the prosthetic components were assumed to be ideal during the simulations, ignoring the possible postoperative misalignments occurring during TKA surgeries , 50. AlsThis study investigated the biomechanics of four prostheses with different levels of constraint in total knee arthroplasty  in three different configurations representing the typical boundary conditions taken by the knee joint during daily activities. Results demonstrated that the level of constraint influenced the femoral-insert interface, with the MB CR implant providing the highest polyethylene contact areas compared to the other prosthetic models, consequently leading to benefits on both implant survival and performance. On the contrary, bone stress appeared not to be significantly influenced by the changes in constraint levels.Additional file 1.Additional file 2.Additional file 3.


Display crosstalk defect detection is an important link in the display quality inspection process. We propose a crosstalk defect detection method based on salient color channel frequency domain filtering. Firstly, the salient color channel in RGBY is selected by the maximum relative entropy criterion, and the color quaternion matrix of the displayed image is formed with the Lab color space. Secondly, the image color quaternion matrix is converted into the logarithmic spectrum in the frequency domain through the hyper-complex Fourier transform. Finally, Gaussian threshold band-pass filtering and hyper-complex inverse Fourier transform are used to separate the low-contrast defects and background of the display image. The experimental results show that the accuracy of the proposed algorithm reaches 96% for a variety of crosstalk defect detection. Compared with the current advanced defect detection algorithms, the effectiveness of the proposed method for low-contrast crosstalk defect detection is confirmed. Display quality inspection plays a key role in the display production process. The existing display quality detection process still relies on manual detection, which is affected by the subjective feelings of the inspectors along with the problems of low efficiency and unstable accuracy . ParticuThe existing display defect detection methods are mainly divided into three types: methods based on image registration ,3,4, bacThe crosstalk picture is a specific display pattern during the display quality detection process, which is designed for testing the crosstalk display capability. A crosstalk picture that cannot be displayed properly by a defective display screen is called a crosstalk defect. There are two main situations where the detection of crosstalk defects is especially difficult: (1) when the defect covers a large area of nearby half of the screen—it appears as continuous color lines with variable colors; (2) when the contrast between the defect area and the background is not fixed—low contrast makes it difficult to detect the defect area.The existing methods present some difficulties in the detection of crosstalk defects, so we propose a crosstalk defect detection method based on salient color channel and frequency domain filtering. For the problem of a large crosstalk defect area, we adopted the salient color feature extraction method , used re(1)A new crosstalk defect detection method is proposed, which combines color feature extraction and frequency-domain GTB filtering to achieve efficient and accurate detection of crosstalk defects under low contrast and strong background noise.(2)An adaptive salient color channel selection method is proposed, which can retain salient color features for large defects and solve the problem of difficult feature extraction.(3)The GTB frequency-domain filtering method is proposed, which enhances the salient regions of defects and suppresses the interference of background noise, and realizes the effective separation of low-contrast crosstalk defects and background noise.Our contributions are as follows:This article is organized as follows: In the past ten years, with the rapid development of display technology, a large number of methods for detecting weak contrast defects in display screens have appeared. Ngo et al.  used lowThe saliency target detection method is mainly aimed at the target area that the human eye is most interested in natural scenes. Itti et al.  proposedFirstly, the original image is converted between the RGBY color space and the Lab color space, and the relative entropy maximum criterion is used for the opposite color channels in the RGBY space to evaluate the saliency of channel defects. Then the salient color channel and Lab color space are selected to form the image color quaternion matrix. Secondly, the quaternion color matrix is transformed into a hyper-complex frequency domain space using the HFT, while the magnitude spectrum is processed using GTB filtering. Finally, the inverse hyper-complex Fourier transform (IHFT) is employed to obtain the frequency domain saliency map, which then undergoes region segmentation to obtain the defect detection result.The RGBY color space  adopts tThe human visual competition color space RGBY proposed by  is generr, g and b are the three channels of the original image. The RGBY color space decomposes the RGB input image into two parts: the RGBY color feature and the luminance (I). It is pointed out by [It is calculated as:d out by  that humRG and BY channels, the opposite space GR and YB channels are added, which are expressed as:Based on the original RG, GR, and BY, YB between the defect and the background, the opposite color channel is adaptively selected. Since it is difficult to directly calculate the area of the defect, we use the calculation of the area of the background to achieve this. When the background area contained in the opposite space is large, we should discard this channel. The comparison of the area part adopts the relative entropy calculation, and the calculation formula of the relative entropy:By comparing the area difference of x) of the input feature x) of the input feature Q. When the two are the same, I brightness respectively, and retain the opposite feature space with a large value.The relative entropy represents the difference between the grayscale distribution The maximum entropy criterion is described as:The Lab color space conforms to the perceptual properties of the human eye , which pThe image quaternion color matrix is represented as follows:The image quaternion matrix is transformed to the frequency domain space using the hyper-complex Fourier transform, calculated as follows:Inverse transform of the hyper-complex Fourier transform is calculated as follows:The amplitude spectrum, phase spectrum, and Eigen-axis spectrum are calculated as follows:To compute the magnitude spectrum of an image quaternion matrix, it can be converted to a log spectrum as follows:To enhance the saliency of the defect region, the magnitude spectrum is convolved with a Gaussian template. The existing way to obtain saliency results is to perform an information entropy calculation on the obtained saliency map, and the goal of maximum entropy is the required saliency map. Through experimental analysis, the best saliency map of crosstalk defects can be obtained by calculating the minimum entropy of the magnitude spectrum.Define the size of the input image as   to deterWe use different Gaussian functions to perform Gaussian convolution on the original amplitude spectrum, use one-dimensional entropy to calculate the entropy value of the original image, and use NSS to evaluate all the saliency maps obtained after convolution, as shown in The horizontal axes in To enhance the saliency of the defect area and suppress the background noise, threshold screening and band-pass filtering are performed on the magnitude spectrum after Gaussian convolution.Threshold screening is needed to calculate the mean and standard deviation of the original amplitude spectrum. The calculation formula is as follows:The filtering conditions are as follows:The selection of the band-pass requires a comparison of the amplitude spectrum of the defect-free area and the threshold-screened amplitude spectrum of the defect.A suitable band-pass filter needs to be designed to filter the amplitude spectrum filtered by the threshold. The filter is described as:The three main types of crosstalk defect are shown in To quantitatively evaluate the relationship between defects and background in the original image, PSNR   and MSE The image quality evaluation results are shown in The value of MSE in In HFT  and PQFTAs shown in We use both SCRG  and BSF (Background Suppression Factor)  to calcuWe use SCRG and BSF in RGB space as benchmarks for comparison. When the above two parameters are close to the effect of RGB space, this type of feature can’t effectively separate background and defect information.As shown in To illustrate the effectiveness of using the GTB approach, we compare the saliency maps obtained using GTB with only Gaussian template convolution.As shown in We evaluated the detection performance of our algorithm. Two evaluation metrics are used: TDR and FDR. TDR is defined as the sum of correctly detected pixels in the test image divided by the sum of true crosstalk defect pixels, and FDR is defined as the ratio of falsely detected pixels to total detected pixels , as showThe TDR of the three types of defect detection results achieved by the method in this paper is more than 90 percent, and the FDR can be controlled within an acceptable range. This shows that our method can detect crosstalk defects effectively and stably, and can achieve accurate detection of low-contrast defects.We compare the detection effects of the commonly used input feature combinations including Lab , RGBYI  and HRGBIt can be seen that the combination of Lab and RGBY cannot detect crosstalk defects, while the saliency map focuses on the edge parts. The detection results of HRGBYI, which is proposed to detect fabric defects, is also concentrated on the edge parts, keeping only a small amount of actual defect information. Compared with the above input feature combinations, which cannot effectively obtain the saliency map of defects, the combination of input features we proposed can effectively achieve the separation of defects and backgrounds.Our proposed method is compared with the current state-of-the-art defect detection methods to analyze crosstalk defect detection capabilities, including polynomial fitting  and discAs shown in We propose a crosstalk defect detection method based on salient color channel frequency-domain filtering. Firstly, the feature extraction and combination of images are analyzed and verified, and an effective feature extraction method for crosstalk defects is realized. For frequency domain filtering, we propose the GTB filtering method, which realizes the detection of low-contrast defects. We demonstrate the effectiveness of our method with detailed experiments and comparisons, and finally, show that our method can detect display crosstalk defects more accurately than the mainstream detection methods.


Messenger RNA (mRNA) technology has already been successfully tested preclinically and there are ongoing clinical trials for protein replacement purposes; however, more effort has been put into the development of prevention strategies against infectious diseases. Apparently, mRNA vaccine approval against coronavirus disease 2019 (COVID-19) is a landmark for opening new opportunities for managing diverse health disorders based on this approach. Indeed, apart from infectious diseases, it has also been widely tested in numerous directions including cancer prevention and the treatment of inherited disorders. Interestingly, self-amplifying RNA (saRNA)-based technology is believed to display more developed RNA therapy compared with conventional mRNA technique in terms of its lower dosage requirements, relatively fewer side effects, and possessing long-lasting effects. Nevertheless, some challenges still exist that need to be overcome in order to achieve saRNA-based drug approval in clinics. Hence, the current review discusses the feasibility of saRNA utility for protein replacement therapy on various health disorders including rare hereditary diseases and also provides a detailed overview of saRNA advantages, its molecular structure, mechanism of action, and relevant delivery platforms. Despite significant progress in science and medicine, until now, there are a number of diseases for which the development and improvement of new strategies are emerging. The potential of messenger RNA (mRNA) application is enormous for the well-being of humans . CompareApparently, the mRNA vaccine approach has multiple benefits compared with the conventional way of vaccine development ,15. IndesaRNA technology indeed represents a promising approach for advancing the field of vaccinology and protein replacement therapy for various diseases. The manufacturing process of the vaccine based on saRNA technology is entirely synthetic. It does not require a viral culture or any complicated process that is usually related to the manufacturing process of conventional licensed vaccines . BasicalWhile plasmid DNA has advantages compared to direct protein therapy for protein substitution, mRNA is superior to the two strategies mentioned ,24. MostThe saRNA vector is a positive-strand RNA (+saRNA) containing the genes coding for the viral replicase and the gene of interest (GOI) downstream of a subgenomic promoter (sgPr). The replicon is based on the alphavirus nsPs. There are three types of alpha-viruses that are used in the saRNA approach: Venezuelan Equine Encephalitis Virus—VEEV (commonly used), Semliki Forest Virus—SFV, and Sindbis Virus. By deleting the viral structural proteins, the RNA cannot produce an infectious virus. After being delivered to the cytoplasm, the nsPs form an RNA-dependent RNA polymerase (RDRP). Each of the nsPs plays a role in the formation of the RDRP: nsP1 is required for 5′ capping of viral RNA. nsP2 has helicase and protease activity. On the one hand, it unwinds the RNA duplex during replication, while, on the other hand, it cleaves the polyprotein into individual nsPs . nsP3 plAfter the delivery to the host cells, saRNA as well as the double-stranded RNA intermediates produced during the self-replication  stimulatIn terms of infectious diseases, the COVID-19 outbreak already showed us how important preparedness is for future pandemics, therefore, along with the conventional mRNA approach, saRNA indeed represents one of the main targets for quick design and development of vaccines for any outbreaks in the future. It is already evidenced that the mRNA vaccine works effectively against the COVID-19 pandemic; hence, research on advancing the mRNA approach has also started. There are a number of clinical trials based on mRNA technology for protein replacement. However, it would be more rational to use the RNA technology for protein replacement, which would result in the production of more proteins with a lower dose while eliciting a long-lasting effect. More importantly, the administration of a lower dose is associated with reduced side effects and the approach itself is cost-effective. However, it is also noteworthy that there is no saRNA-based drug approved in clinics so far, although the promising results are demonstrated by Arcturus Therapeutics and they currently are in clinical trial Phase 3 for saRNA vaccines against COVID-19. Currently, most of the scientific attention is paid to infectious diseases, while there are a number of other diseases that deserve a proper fight and should not be forgotten. Since the start of the current pandemic, fighting infectious diseases became the main target for researchers and more efforts are put in place against SARS-CoV-2 and other infectious diseases , while oTherapeutic in vitro transcribed-saRNA can be used for encoding the downregulated proteins that are responsible for diseases. These genomic defects and other diseases include Fabry disease, which is associated with the deficiency of alpha-galactosidase A, propionic acidemia, which is manifested with the deficiency of an enzyme propionyl-CoA carboxylase, hemophilia B, which is a condition of blood coagulation defects and is caused by the deficit of coagulation factor IX, ornithine transcarbamylase (OTC) deficiency, cystic fibrosis caused by the defect of cystic fibrosis transmembrane conductance regulator, diabetes mellitus (DM) caused by the deficiency of insulin, etc. Moreover, the first preclinical study conducted in 1992 has already demonstrated the positive effect of mRNA application as a protein replacement. In the first study, a temporary reversal of diabetes insipidus (DI) took place in mice that were injected with the mRNA of vasopressin in the hypothalamus . InteresBesides the abovementioned, the saRNA approach might be considered for cosmetic uses such as alopecia treatment. In case of AGA, it has been proposed that miRNAs have the ability to inhibit certain proteins to activate signaling pathways involved in this process . On the All the abovementioned information supports the concept of saRNA application for protein replacement. In order to achieve the desired outcomes, the right strategy is the key. Here, we discuss the steps for saRNA research design in vivo. The first step is to design and synthesize the plasmid vector containing the GOI. The sequence of sgPr varies and depends on the selected alphavirus for the saRNA approach . After tThe schematic illustration of the experimental workflow for the development of the saRNA approach for protein replacement therapeutic use is given in Apart from its application as an immunization approach for infectious diseases, mRNA technology is thought to be a next-generation therapeutic strategy for single-gene disorders that are caused by the DNA change in a single gene ; hence, Recently, the development of cancer vaccines is the main focus of cancer research. Indeed, a number of methods are being developed to elicit a strong anti-tumor immune response. For designing the vaccine for cancer, the identification of tumor-associated antigens is of great importance. In addition, knowing the tumor microenvironment that allows the progression of the tumor and escaping from the host’s immune system is essential for the design of a cancer vaccine ,70. CancThe major challenge in developing saRNA vaccines is a delivery system that can carry saRNA to the target cells . MoreoveThe application of LNPs for the delivery of genetic materials originated from the development of liposomal drug carrier systems for small molecules. The principle of LNPs is to encapsulate the nucleic acid molecule into the system containing lipids that are organized in a bilayer form to deliver it into the target cells ,82. LNPsA number of other approaches for saRNA delivery have also been studied. These delivery systems include chitosan nanogel alginate (Chitosan NGA) , dendrimAs for all of the novel approaches, the saRNA technique is also characterized by certain limitations that need to be overcome. The main obstacles are the lack of studies regarding the immunogenicity of RDRP complex, limited clinical data, the necessity of prime-boost administration , large sThe current review provides insights into the saRNA approach for protein replacement therapy. saRNA indeed has a huge potential to display a better RNA approach compared to a conventional mRNA technique, especially for those disorders which necessitate protein replacement therapy. Till now, all the clinical studies on saRNA have been performed in order to develop prevention strategies against infectious diseases . This isThere are crucial points to be addressed: determining the precise dosage of saRNA is essential. It is also important to evaluate the time period of the saRNA replication into the body. Time alike dosage might be dependent on the certainty of the disorder and the species of mammals. Multiple studies are required for this reason in the future.Taken together, saRNA is gaining momentum worldwide due to the number of advantages that makes it superior to the conventional approaches . On the