The oral route is the most common and practical means of drug administration, particularly from a patient’s perspective. However, the pharmacokinetic profile of oral drugs depends on the rate of drug absorption through the intestinal wall before entering the systemic circulation. However, the enteric epithelium represents one of the major limiting steps for drug absorption, due to the presence of efflux transporters on the intestinal membrane, mucous layer, enzymatic degradation, and the existence of tight junctions along the intestinal linings. These challenges are more noticeable for hydrophilic drugs, high molecular weight drugs, and drugs that are substrates of the efflux transporters. Another challenge faced by oral drug delivery is the presence of first-pass hepatic metabolism that can result in reduced drug bioavailability. Over the years, a wide range of compounds have been investigated for their permeation-enhancing effect in order to circumvent these challenges. There is also a growing interest in developing nanocarrier-based formulation strategies to enhance the drug absorption. Therefore, this review aims to provide an overview of the challenges faced by oral drug delivery and selected strategies to enhance the oral drug absorption, including the application of absorption enhancers and nanocarrier-based formulations based on in vitro, in vivo, and in situ studies. Many studies have been conducted over the past few decades to identify the best permeation enhancers for poorly absorbed drugs that are administered orally. It is important to highlight this issue as patients have shown their preference for oral formulations over other dosage forms, as reported by a survey where 71.91% of the interviewed patients favored the daily intake of an oral formulation relative to those that preferred a daily subcutaneous injection, thus highlighting the need for improved oral drug delivery -mannitol in the presence of sucrose laureate in a concentration-dependent manner in both in vitro and ex vivo studies  mannitol, and dextran [The studies on the role of chitosan as a permeation enhancer are summarized in  dextran ,85. max and AUC0–36h of berberine [The role of chitosan as an absorption enhancer in its salt form was also investigated. Chitosan hydrochloride did not exhibit the same permeability-enhancing effect as the non-salt variant of chitosan. Interestingly, the salt form of chitosan resulted in a decreased Cerberine . This reerberine . Indeed,Solid lipid nanoparticles (SLN) have garnered great interest in their ability to improve the oral absorption of poorly soluble drugs. They are a colloidal system derived from a matrix of lipids that retains their solid state at a temperature below 140 °C with sizes ranging from 50 to 1000 nm and able to disperse in an aqueous medium with the aid of surfactants ,89. w/v Na CMC suspension by oral gavage. The SLN used consisted of a binary lipid mixture of stearic acid and caprylic acid stabilized with the non-ionic surfactant, D-alpha tocopheryl polyethylene glycol 1000 succinate (TPGS) and formulated Poloxamer 188 [Some of the advantages of employing SLN as a drug delivery system for oral administration include mitigating the degradation of entrapped drugs, as well as providing some control over the rate of drug release. Another advantage of utilizing this formulation approach is the ability to avoid hepatic first-pass metabolism through the intestinal lymphatic uptake. This is because the lipophilic nature of the SLN causes the nanocarrier to drain into the thoracic lymph before entering the systemic circulation near the left subclavian vein, thus circumventing the hepatic first-pass metabolism ,91. One amer 188 . Anotheramer 188 . Valdes et al. reported that the bioavailability of 4-(N)-docosahexaenoyl 2′, 2′-difluorodeoxycytidine (DHA-dFdC) improved from 113.55 μg•h/mL to 143.44 μg•h/mL when delivered as a SLN formulation. A conclusion on the exact absorption-enhancing mechanism was not fully elucidated, yet the researchers have proposed a few hypotheses for the absorption-enhancing effect observed. Firstly, the SLN may be absorbed through the microfold cells in the Peyer’s patches, leading to the migration of the nanocarrier into the lymphatic system thus obviating the first-pass metabolism and leading to enhanced bioavailability. Secondly, the authors also proposed that the SLN release lipids upon digestion that alter the gastrointestinal fluid media, that then lead to the enhanced solubility and dissolution of the DHA-dFdC, leading to improved absorption. In addition, it was postulated that the SLN provided a barrier that prevented the drug from being susceptible to enzymatic degradation along the gastrointestinal tract compared to the DHA-dFdC alone, thus enabling the absorption of the intact drug across the intestinal walls . The enhmax) than the drug dispersion alone. Such an observation may be attributed to the time it takes for the formation of lipoproteins at the rough endoplasmic reticulum followed by core expansion at the smooth endoplasmic reticulum. This process then culminates in the formation of a large lipoprotein called chylomicron which then associates with the drug administered before transiting into the intestinal lymphatics. All of the literature reviewed is summarized in Other than that, SLN can improve the uptake of drugs via the intestinal lymphatic systems. Examples of drugs that displayed improved intestinal lymphatic uptake via the use of SLN include curcumin and asenapine maleate ,94. N-caA dendrimer is a synthetic polymer with a highly branched amidoamine structure and an ethylenediamine core . The repSeveral generations of poly amido amine (PAMAM) dendrimers have been investigated in order to understand their absorption-enhancing effect on the drugs with poor permeability across the intestinal layer. The summary of these studies is shown in 2). The increase in the absorption of simvastatin when delivered as a complex with PAMAM (G5-NH2) is attributed to the interruption of the occludin-1, which further enlarged the tight junctions to allow a greater entry of the simvastatin-PAMAM complexes across the gut epithelial. This enhancement in absorption is further promoted by the enhanced solubilization of simvastatin coupled with the inhibition of P-gp by the dendrimer [A study by Qi et al. (2015) supported tight junction modulation as one of the absorption-enhancing mechanisms of dendrimers. The investigators discovered that, under their experimental conditions, simvastatin showed an improved water solubility as well as increased oral bioavailability when the drug was delivered as a molecular complex with PAMAM , than with its anionic counterpart, PAMAM G3.5 (20–30%). This was attributed to the drug being negatively charged, at the pH at which the formulation was prepared, leading to strong electrostatic interaction with the cationic surface of the G4.0 PAMAM dendrimer . Besides that, the charge of the PAMAM dendrimers may play a pivotal role in the adsorptive capacity of the dendrimer with the gastrointestinal lining, due to the interaction of its positive charge of the G4.0 dendrimer surface with negative-charged components of the intestine membrane. This may lead to the adhesion of the gastrointestinal wall, thus promoting the residence time of the drug along the gastrointestinal lining leading to enhanced intestinal permeability of anionic drugs. Another study by D’Emanuele et al. emphasized propranolol-lauroyl-G3 dendrimer conjugates’ capacity to enhance the propranolol permeation by approximately 3.5 times that of propranolol alone, through the inhibition of the P-gp efflux and endocytosis-mediated transepithelial transport . In addition, the PAMAM dendrimers have a size-dependent solubilization-enhancing effect, where the higher generation PAMAM (G4 PAMAM dendrimer) exhibits a greater drug solubility than those with a lower generation (G2 and G3 PAMAM dendrimers) at the same concentration, as shown in the study by Yiyun and Tongwen. This is related to the increasing number of primary and tertiary amines within the dendrimer, with the larger generation size permitting a higher entrapment of more hydrophobic molecules . Indeed, from the studies highlighted, it is apparent that the PAMAM dendrimer has a huge potential in enhancing the solubilization and absorption of poorly soluble drugs. Such an enhancement is attributed to several mechanisms, as highlighted above. As newer generations of PAMAM dendrimers emerge, which are less toxic yet still as effective as the current generations of the dendrimer, the utilization of the PAMAM dendrimer will surely gain popularity as a nanocarrier for the delivery of drugs through the oral route.max, ease of fabrication, as well as the facile scale-up of the manufacturing process [Nanoemulsions are translucent or transparent water-in-oil (w/o) or oil-in-water (o/w) droplets  that are process ,112,113. process .max of candesartan upon oral administration of the CCN compared to the free candesartan–cilexetil suspension at 0.59 h. The underlying mechanism for the reported 27-fold increase in the Cmax of the candesartan was due to the ability of the nanoemulsion to be readily internalized by the enterocytes via clathrin-mediated endocytosis relative to the candesartan–cilexetil suspension. This then leads to the enhanced formation of intestinal chylomicron within the enterocytes for subsequent lymphatic transport into the systemic circulation. In addition, the nanosize droplets of the emulsion also provide a high surface area to volume ratio for the droplets to interact with the apical membrane of the enterocytes, thus increasing the uptake of the nanodroplets by the enterocytes [A number of studies have investigated the viability of utilizing nanoemulsion in facilitating the transport of various drugs across the gut epithelium. The summary of these studies is shown in erocytes .−5 cm/s), albeit not significant, as compared to the complex (7.02 × 10−5 cm/s). Li et al. (2015) investigated the effectiveness of the nanoemulsion as a nanocarrier for the delivery of curcumin. The group formulated curcumin into a nanoemulsion that was stabilized by whey protein isolate as an emulsifier. The nanoemulsion displayed an average particle size of 208 nm and enhanced the solubility and stability of curcumin against pepsin-induced proteolytic degradation while resulting in enhanced permeation across the Caco-2 cell monolayers by two-fold relative to the unformulated curcumin. The authors attributed the enhanced permeation across the cell monolayers as being due to two possible mechanisms . The firapp and Ka of paeonol nanoemulsion increased significantly by 0.65- and 1.64-fold in the colon, respectively, relative to the paeonol suspension [Recent studies have established that the inhibition of P-gp-mediated efflux transportation as the latest mechanism responsible for the absorption enhancement effect of nanoemulsions. Berberine hydrochloride is a P-gp substrate and suffers poor oral absorption, due to the efflux of the drug upon entering the enterocytes. The incorporation of the drug into a nanoemulsion has been an effective and successful strategy to reduce the efflux of berberine by the transporter, thereby enhancing the drug absorption, as shown by a 4.4-fold higher relative oral bioavailability than the delivery of the drug using a conventional drug suspension . This waspension . This st®, Cleviprex, Limethasone, Vitalipid®, and Ropion), it can be deduced that nanoemulsions have a strong clinical translational value and may be a commercially viable and effective strategy to enhance the oral bioavailability of poorly absorbed drugs [The greater surface area of nanoemulsions relative to microemulsions and the ability to modify the tight junctions are other reported advantages of nanoemulsions. Recently, Anuar and co-workers demonstrated that the absorption-enhancement effect of an ibuprofen-loaded nanoemulsion relative to conventional ibuprofen oil-in-water emulsion (control), and microemulsion. In this study, sucrose esters oleate was used as a surfactant to form an ibuprofen microemulsion and nanoemulsion. The author also showed that the solubility of ibuprofen was enhanced when the drug was formulated as a nanoemulsion. Such enhancement in solubility enabled the drug to diffuse across the unstirred water layer in the intestine and into the enterocytes, leading to enhanced oral bioavailability in vivo . It can ed drugs .Chitosan, surfactants, and bile salts are among the absorption enhancers that have been recognized for their absorption-enhancing effect. In addition, nano carrier-based drug delivery systems, such as solid lipid nanocarriers, nanoemulsions, and dendrimers, have been proven to have noteworthy contributions to the increased intestinal permeability. More significant progress in the nanocarrier-based drug delivery systems is expected to be discovered and developed in the future. Nonetheless, the effectiveness of this drug delivery system in humans requires more extensive clinical study, as the in vitro and in vivo studies on animals do not reflect the exact state of the human intestinal tract, due to interspecies variations. Nevertheless, these pre-clinical studies have given insight into the value, the potential mechanisms as well as some insight on the safety of these permeation enhancers. It is expected that through judicious choice of permeation enhancers, coupled with the meticulous evaluation of these formulations, such a drug delivery strategy would soon be translated and become more commonplace in a clinical setting. This is of great therapeutic value, as such formulation strategies are capable of enhancing the oral bioavailability for the poorly soluble and poorly permeable drugs, without the need of administrating such therapeutics through a more invasive manner, such as hypodermic injection or intravenous administration.


Conclusions: The two techniques were in agreement in measuring event frequency, duration and intensity in the studied group, suggesting that force-sensing oral appliances have the potential to be easy-to-use tools for home monitoring of bruxism, alone or as complements to portable EMGs.Background: Oral appliances embedding sensors can be interesting tools for monitoring tooth contact bruxism in a home environment, as they address some of the usability limitations of portable electromyography (EMG) systems. In this study, an oral appliance for sleep bruxism monitoring was compared to an electromyograph. Methods: Simulated bruxism events with tooth contact, specifically clenching and grinding, and other occlusal activities unrelated to bruxism, were measured in 23 subjects with the two instruments simultaneously. The recordings were analyzed automatically by a computer program in order to compare the two techniques. Results: The two instruments were found to be strongly correlated in terms of detecting events ( Sleep bruxism is a common condition defined as a masticatory muscle activity during sleep that is characterized as rhythmic (phasic) or non-rhythmic (tonic) . PatientIn daily practice, however, the presence and severity of bruxism in patients is generally assessed from self-reports  and clinical signs (tooth wear) , rather A multiple-night polysomnographic (PSG) recording in a sleep clinic—a complex examination which generally includes an electroencephalogram, an electromyogram of the masticatory muscles, an electrocardiogram, respiratory air flow monitoring and audio/video recordings—has been historically considered the standard of reference for the diagnosis of sleep bruxism, with the number of rhythmic masticatory muscles activation (RMMA) events per hour of sleep being the main metric ,12. LimiA wide application  of PSG is moreover not practical because of the examination cost and duration. To address this problem, several portable electromyographic (EMG) recording devices have been commercialized which allow monitoring sleep bruxism in a home environment, relying on the activity of one of the masseters ,15, of oPortable EMG devices are less complex and thus less expensive with respect to PSG and represent an important advancement in enabling the monitoring of sleep bruxism at home . They doThe use of modified oral appliances, resembling traditional resin splints but embedding sensors that monitor the occlusal pressure, has been proposed for monitoring bruxism ,23,24,25The purpose of this study was to compare a force-sensing oral appliance with an EMG device in detecting simulated bruxism events involving tooth contact. The null hypothesis was that the two instruments are not correlated in terms of detecting (i) the presence, (ii) the duration and (iii) the intensity (exerted force) of the events.Twenty-three subjects, 13 females and 10 males, average age 39.0 ± 15.2 years, participated in the study. Inclusion criteria were diagnosis of bruxism according to the American Academy of Sleep Medicine criteria , age betThe force-sensing oral appliance, AesyBite Discover , intended for monitoring the sleep bruxism activity characterized by tooth contact by sensing the occlusal pressure exerted during bruxism events, was used in this study. The ABD oral appliance is a nightguard that is customized by low-temperature thermoforming directly on the upper dental arch of the user a, which Each participant completed the study in a single session. All the appliance and EMG electrode fitting operations were performed by the same operator.In the first part of the session, the ABD appliance was customized to the dental arch of the subject with the procedure specified by the manufacturer .Four electrodes of the TMJOINT system  were placed in related to the left and right temporalis and masseter muscles of the subject. The same EMG device was used for all the subjects; only the disposable adhesive electrodes were changed.The subject, wearing the ABD oral appliance and the EMG electrodes at the same time, was made to sit upright in front of a computer monitor. Custom software showed a sequence of instructions to be performed for a certain time, and a countdown timer to help the subject in the execution. The actions to be executed in response to the instructions were explained to the subject prior to letting them execute the entire sequence.The actions of the sequence included 20 bruxism-related events (10 teeth clenching and 10 teeth grinding events) and 20 bruxism-unrelated events that can occur during sleep . Every action was separated by 10 s of inactivity.Signals from the EMG system were recorded during the sequence execution with the FreeEMG software  using a sampling frequency of 1 kHz. Data recorded by the ABD appliance were downloaded after the sequence execution by reading the appliance’s memory with an NFC-enabled mobile phone .The DC offset from the EMG signals was eliminated by subtracting the mean from each signal. The signals were bandpass filtered  between 10 and 400 Hz and rectified by taking the absolute value. A final smoothing pass was performed by applying a moving average with a 2.0 s wide window. Four signals were thus obtained: In order to identify categorical events , a binarized signal For each instruction given to the subject, an event detection window—starting 5 s before and ending 5 s after the instruction—was considered . In ordeThe signal from the ABD appliance and the EMG binarized signal were checked for events in each detection window, compiling a 2 × 2 contingency table for each subject .r = 1), the value r = 0.99 was used instead, to allow computing the Fisher z-transformation. The combined correlation for event detection For each subject, the Pearson correlation coefficientn method ,29—i.e.,For the sake of the comparison with the EMG, the following figures of merit referring to event detection were calculated for the ABD appliance: sensitivity = TD/(TD + FU); specificity = TU/(TU + FD); accuracy = (TD + TU)/(TD + TU + FD + FU).Only events correctly detected by both the EMG and ABD were considered for the event duration and intensity analysis. The uptimes The event intensities The combined correlation coefficient found for event detection, The event duration estimates by the two instruments r < 0.4), due to the presence of several falsely detected events.A summary of the correlations calculated is provided in The purpose of this study was to compare a force-sensing oral appliance with an EMG device in detecting simulated bruxism events involving tooth contact. The two instruments were found to be strongly correlated in terms of detecting the presence of the events . An exact correspondence was not expected because the duration of a detected EMG event is known to depend on the detection threshold considered , and thei et al. , which wThe correlation between the event intensities measured by the two instruments is a surprising finding, given the relative simplicity of the intensity measures considered  and the non-linear response expected from capacitive sensors.The fact that the falsely detected events  are more numerous than the falsely undetected ones  is likely due to the pressure sensors of the ABD oral appliance occasionally remaining in a compressed state for some time after an intense event, resulting in a false detection in the immediately following window. Due to the presence of several falsely detected events, three subjects presented poor or no correlation. The reason for this is unclear. It might have been due to the capacitance threshold of the appliance being too low for the forces exerted by these subjects, possibly leading the electronics to detect the sensors as still pressed after a previous intense event has occurred.Similar issues with detecting intense clenching events were observed in other sensorized oral appliances ,31, althA limitation of this study is that it involved simulated events performed by awake subjects, rather than genuine events recorded in sleeping subjects. Additional studies, involving recordings of sleeping bruxers and non-bruxers and comparisons with validated diagnostic instruments, are necessary to investigate whether a diagnostic application of the ABD appliance can be justified. Moreover, the circumstances under which the appliance appears to be overly sensitive in detecting events should be investigated in order to improve the understanding of the lack of correlation in certain subjects.The correlations observed in this study might not be evident in a multi-day monitoring period where the EMG levels variability is expected to increase , thereby affecting the event duration and intensity measurements. The ABD device is expected to suffer less from this variability, as its detection principle is based on the occlusal pressure exceeding a fixed threshold, though further testing is necessary for this assessment.Force-sensing oral appliances, such as the one studied in this work, could be useful tools in the instrumental assessment of sleep bruxism in the home environment. They could be easier-to-use or more comfortable options for patients not compliant with portable EMG monitors, or constitute one of the data sources in multidimensional assessments such as the STAB ,6.The null hypothesis that the EMG and ABD instruments are not correlated in terms of detecting the presence, the duration and the intensity (exerted force) of simulated bruxism events was rejected. The correlation between EMG and ABD observed in the studied group suggests that the two techniques are generally concordant in assessing the characteristics of teeth clenching and grinding events. This correlation suggests that oral appliances such as ABD have the potential to be easy-to-use tools for home monitoring of bruxism, alone or complementary to portable EMGs.


Detection and identification of misinformation and fake news is a complex problem that intersects several disciplines, ranging from sociology to computer science and mathematics. In this work, we focus on social media analyzing characteristics that are independent of the text language (language-independent) and social context (location-independent) and common to most social media, not only Twitter as mostly analyzed in the literature. Specifically, we analyze temporal and structural characteristics of information flow in the social networks and we evaluate the importance and effect of two different types of features in the detection process of fake rumors. Specifically, we extract epidemiological features exploiting epidemiological models for spreading false rumors; furthermore, we extract graph-based features from the graph structure of the information cascade of the social graph. Using these features, we evaluate them for fake rumor detection with 3 configurations: (i) using only epidemiological features, (ii) using only graph-based features, and (iii) using the combination of epidemiological and graph-based features. Evaluation is performed with a Gradient Boosting classifier on two benchmark fake rumor detection datasets. Our results demonstrate that epidemiological models fit rumor propagation well, while graph-based features lead to more effective classification of rumors; the combination of epidemiological and graph-based features leads to improved performance. Information diffusion describes the way in which information is disseminated through a network  advances in the last decade, it manages to offer contextual characteristics to the information diffusion problem. An essential aspect of these techniques is their ability to generalize for claims, i.e. whether the extracted results are appropriate to make a generalized claim. Content analysis provides some clear comparative advantages over the other two techniques, but it is location-specific and dependent on the Significant research effort has been spent studying and modeling information diffusion on social media  using only epidemiological features, (ii) using only graph-based features, and (iii) using a combination of epidemiological and graph-based features. More specifically, the contributions of our work are the following:The paper is organized as follows. Section Related Work presents prior work. In section A Location-Independent and Language-Independent Approach, we present the epidemiological models and the graph modeling method as well as the employed classification schemes; for the epidemiological models we describe how they are employed in fitting the rumor propagation data and for the graph models how feature extraction is accomplished. Section Experiments and Evaluation presents our evaluation datasets and the results of rumor classification performance using epidemiological features, graph-based features and the combination of both. Section Conclusions presents our conclusions and directions for future work.Epidemiological models have been used extensively to model information diffusion in complex networks. Such models classify the human population into different compartments and define different transitions between them to simulate the spread of information. The SI (Susceptible-Infected) model was originally proposed in 2001 , one may recognize specific individuals as critically important to the information diffusion Valente, . Such usBaseline rumor detection approaches are categorized based on their different feature engineering methods. The categories are (i) user characteristics, (ii) social context and content, and (iii) propagation structure characteristics. Handcrafted features based on user characteristics include such features as the number of followers, number of posts, and relevant information referring to the user of the social media platform  approach using word frequency, message, user, topic, and propagation characteristics to extract features has shown that such combinations allow for promising results  between the susceptible and infected populations per given unit of time dt is β.The SI model classifies the total population of users (N) in two groups, namely the Susceptible (S) and Infected (I) compartments. A user is considered N(t) = S(t) + I(t)The SI model is described formally by the following ordinary differential equations system:Susceptible (S), Exposed (E), Infected (I) and Skeptics (Z) and is shown in The SEIZ model, as adopted by Jin et al. , is compThe SEIZ model is defined by a set of different parameters  for each rumor. For both the SI and the SEIZ models we perform a least squares fitting on the Twitter data. As described earlier, we consider everyone who has retweeted or replied to an original tweet as Infected (I).To fit the epidemiological models on the datasets we first pre-process the raw data to construct sequences that give the cumulative volume of retweets per given time unit . In our approach we use 1 min time intervals. We also observe that most of the retweets happen early on in the diffusion tree and thus we restrict the fittings of the models only to the first 240 h (or 10 days) of diffusion. More specifically, we perform several fittings using a limit of 48, 72, 120 and 240 h. Finally we fit the models using a time limit of just 4 h to evaluate if the models are able to fit the early diffusion of rumors. For each fitting we measure the root mean square error (RMSE) between the fitted and the observed cumulative retweets time sequences. We then average the RMSE for each dataset to evaluate and compare the total fitting processes for every dataset and time limit.Graph modeling constitutes our second approach; we describe the extraction of graph-based features. Considering that we use Twitter datasets for the evaluation of our methods, the description takes into account the features of the specific datasets, although the method can be applied to graphs that are extracted from datasets originating from alternative social platforms as well. In the case of the Twitter datasets, it is important to note that the datasets are labeled. Each rumor cascade is labeled as True, False, Unverified, or Non-Rumor.t = 0.Considering the dataset characteristics, the directed graph structure is composed of nodes that represent the Twitter users involved in the cascade, and edges that represent the retweeting action performed by the destination user on the message appearing on the source user's timeline. The edges' weights are equal to the absolute time of diffusion. The root user is considered to send the message at time For the graph modeling of every cascade in the datasets, we note that the graph edge weights, which represent the time of the retweet, are absolute in value. In case of a negative weight appearing in the data (due to dataset inconsistencies), we re-calibrate the whole cascade by adding an offset value to all diffusion times, following Eq. 7. Furthermore, we consider that diffusion takes place only when a user has retweeted the message. Thus, each destination user appears only once, since a user can retweet a message at most once. We also note that, since we only consider the retweeting action as the edge connection of the graph, a source user cannot be a destination user (retweeting is only one-way). Following these considerations, the resulting rumor propagation graph is acyclic.Average degree: The average number of outgoing edges for every user.Root degree: The total outgoing edges of the root user.Structural virality: A graph theoretic metric for measuring the structural diversity of information diffusion. Describes the manner of diffusion; single broadcast or through several cascading individual nodes.Closeness centrality: A measure of the nodes' capability to spread information efficiently through the graph. It measures the inverse distance of a node to all other nodes.Number of users: The number of unique Twitter users involved in a cascade.Max hops: The maximum number of hops (node to node via edge) possible in a cascade.Nodes of levels 0 and 1: The number of users at one and two degrees of separation from the root user respectively.Baseline average diffusion time for levels 0 and 1: The average diffusion time for users at one and two degrees of separation away from the root user respectively .Average diffusion time  for levels 0 and 1: The average diffusion time for users at one and two degrees of separation away from the root user respectively .Given the constructed graph, we extract the relevant features. For each cascade, the graph-based feature set contains the following:The last two features are both closely related to the levels of the diffusion. We note that for these features, the calculations are performed with no upper time threshold. However, we also calculate both of them using an upper time threshold, so as to simulate early detection and be relevant to the epidemiological model results. The calculations involve upper limits of 4, 24, 48, and 72 h.We use the collected features for the calculation of the average diffusion times per level of diffusion for each type of cascade . We calculate the average diffusion time per level as follows:After identifying the appropriate epidemiological models and extracting the relevant features, we perform tests to find a potential correlation between the epidemiological and the graph-based features. It has not been feasible to identify such correlation. Thus, we merge the two feature sets, in order to arrive at higher classification accuracy. The motivation to merge the two feature sets originates from the fact that the epidemiological features represent temporal characteristics of the rumor diffusion, whereas the graph-based features represent structural characteristics of the diffusion cascade of each rumor. So, combining them, enables a holistic approach to the analysis of the rumor diffusion process in space and time.We test our models for rumor detection and classification. First, we use the fitted epidemiological model parameters, to classify the rumors using the labels provided in the dataset. We consider two distinct classification tasks. One where we perform a multi-class classification predicting one of the four labels: True rumor, False rumor, Unverified rumor and Non-rumor. We then perform a binary classification, where we only consider the True and False rumors. To train our classifiers we use a Gradient Boosting Trees algorithm using the fitted model parameters as features and performing a very light hyper parameter tuning. Regarding both the graph-based features and the combined feature set containing graph and epidemiological features, we again employ Gradient Boosting Trees, because both graph and epidemiological features are easily tabulated and the algorithm works well for numerical and categorical values, as in our case. The Gradient Boosting Trees algorithm allows for the sequential connection of individual decision trees and requires very little data pre-processing; thus, it fits very well the particular data of the rumor diffusion problem.We split the dataset 75/25 as a training and test set; this is typical in the prior work that uses these datasets. To evaluate the performance of the classifiers, we measure their accuracy, precision and recall on both classification tasks.Next, we test whether the SEIZ model can detect false rumors by using a single ratio RSI of the fitted parameters as suggested by Jin et al. . Their wOne can easily observe that it is difficult to find a specific threshold value of RSI that would adequately classify the rumors. Based on the results we consider a more complex detection technique where we use a K-nearest neighbors algorithm, classifying a rumor based on its “closest neighbors.” We test two different values for the number of neighbors we consider for each rumor, five and 20 neighbors.In this section we present our experiments, including the necessary pre-processing, and our evaluation results. We present the results of rumor modeling and classification using epidemiological features, graph-based features as well as using their combination. Finally, we compare our method with other similar existing approaches to misinformation diffusion.t = 0.We work with two well-known publicly available datasets, Twitter15 and Twitter16  that can be used to reconstruct the actual propagation tree using the Twitter API. This makes it very difficult to reproduce the original propagation sequences as significant numbers of users delete their own tweets and some even their accounts.The datasets consist of 1,490 and 818 rumor propagation trees, respectively. The rumors themselves are labeled as True rumors, False rumors, Unverified rumors or Non-rumor events  epidemiological models fail to account for the structural components of the diffusion tree and (ii) machine learning models are trained on fitted parameters -instead of the real data- and the models ultimately produce underperforming detection results despite the robust fitting.Graph-based modeling is exploited to capture the structural components of the diffusion mechanism, in order to overcome the limitations of the epidemiological models in rumor classification.Using the graph-based features, we perform binary and 4-class classification. For these two classification schemes, we employ a Gradient Boosting algorithm (as described in section classification) and we present the results for the binary and 4-class classifications in Since no correlation was found between the epidemiological and the graph-based features , we merge the two feature sets and we present the classification results for this combined feature set. The merging the two feature sets enables us to capture both temporal  and structural (graph) components of the rumor diffusion process. We employ a Gradient Boosting algorithm and we present the results for the binary and 4-class classification in Clearly, in both binary and 4-class classification, the results are better in absolute values when we employ the merged feature set, in contrast to the epidemiological or the graph based ones independently. Furthermore, the best accuracy is achieved at a lower time threshold when employing the merged features. Given these two results, we conclude that the combination of the epidemiological  and the graph  features enable higher accuracy for early detection as well as higher detection accuracy in absolute value.It is important to note that, our approach is the only approach that is location-independent and language-independent, since it is based on spreading  and network (graph) characteristics and does not take into account any characteristics of the content. In contrast, all existing methods consider content characteristics to some extent. Thus, the effectiveness of our approach cannot be directly compared with the results in the literature; the comparison with existing results is biased due to the consideration of content characteristics by all the methods we compare to. All the baseline approaches from the literature which are included in The experimental results of the baseline models that appear in Importantly, another common characteristic of existing approaches is the employment of user characteristics extracted from the Twitter API, which allows for a plethora of features. However, such an approach renders the solution platform-specific. The use of the Twitter API introduces issues, such as the deletion of accounts and the loss of the associated features over time. This can be defined as dataset aging, where the data collected at a given point in time become gradually irrelevant and/or non-reproducible. In the case of Twitter15 and Twitter16, several users and tweets are no longer available on Twitter itself, due to account suspensions and/or thread deletions. This, in turn, leads to considerable difficulty in the reproduction of results and to transferability studies. Clearly, reliance on the particular characteristics of Twitter users or specific tweets is severely impeded by dataset aging. Our approach is oblivious to and unaffected by dataset aging, because it examines only the behavioral aspects of diffusion in space and time and does not consider any other external parameters.We propose a readily deployable solution for rumor detection on social media. The proposed framework is based on the diffusion cascade of an input rumor and does not require any additional user characteristics. This enables the use of the framework for any online social media platform, in contrast to existing literature that focuses only on Twitter. Furthermore, the framework does not require complex pre-trained language models or other high complexity content-based solutions and, thus, is applicable to real life systems, with no architectural adjustments or additional overhead.Our method extracts temporal and structural features, enabling classification based on those features. The temporal-based features are extracted by fitting an epidemiological model on the original information diffusion data, and the structural features are gathered from a graph modeling of the same propagation data. We used the publicly available rumor propagation datasets Twitter15 and Twitter16 to evaluate the method. Experiments for binary  and 4-class  classification lead to the result that graph-based features perform better than epidemiological ones, while the most accurate classification is achieved through the combination of both feature sets.Our method is location-independent and language-independent, leading to an approach that can be applied without consideration of the rumor content, in contrast to existing approaches that take into account rumor content. Our method is scalable and easily adaptable to existing real life systems with no modifications or additional overhead. However, it requires further investigation, due to dataset limitations. For example, our evaluations are for rumor cascades that begin from individual users, whereas in real life, multiple users may post the same rumor independently. Our method can accommodate multiple user sources, but no real dataset contains such information. Importantly, our method may provide improved results in that case, since machine learning techniques provide higher precision for large datasets.The limitations of existing datasets are leading our future work, which focuses on collection of additional rumor propagation datasets from different online social networks and collection of data about rumors spreading from multiple root users, creating parallel diffusion trees. These datasets will enable us to evaluate the effectiveness of epidemiological models further and to evaluate graph-based features in rumor modeling and detection as well as overcoming some of the limitations of the Twitter15 and Twitter16 datasets.https://www.dropbox.com/s/7ewzdrbelpmrnxu/rumdetect2017.zip?dl=0.Publicly available datasets were analyzed in this study. This data can be found here: All authors contributed to manuscript revision, read, and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Periodic inspection, commonly performed by a technician, of weld seam quality is important for assessing equipment reliability. To save labor costs and improve efficiency, an autonomous navigation and inspection robot is developed. The development process involves the design of chassis damping, target detection mechanism, control system, and algorithms. For performing weld inspection in complex, outdoor, environments, an algorithm is developed for the robot to avoid any obstacles. This algorithm for planning the inspection route is based on an improved timed-elastic-band (TEB) algorithm. The developed robot is capable of conducting inspection tasks in complex and dangerous environments efficiently and autonomously. Robots also play a significant role in welding, such as spot welding for automotives, arc welding for bridge girders, and welding of polymer-matrix composites. Welding is widely used for metallic materials, from vessels and pipelines to bridges and railways2. Good weld quality can ensure the strength and toughness of the joints. During service, welds often will deteriorate, by corrosion and fatigue cracking, and result in structural failures. The most serious failures often involve welds in critical locations, where damage caused by corrosion leads to cracking, leakage, or bursting of vessels. In large-scale infrastructures where the welds deteriorate over a large area, it is impractical and cumbersome to inspect the welds using human labor. The use of robots becomes necessary and feasible for these cases3. The design of weld inspection robots revolves around two questions: how can the robot accurately reach the place where the weld is located, and how can the robot accurately inspect the weld.With the rapid developments, robots can perform simple or complex tasks in dangerous environments that are beyond people's reach. Today, the use of robots as work aids has become increasingly common in both industrial and consumer spaces. Robots can reduce labor costs, save time, improve safety, and improve the quality of work4. The propulsion system consisted of three omnidirectional drive wheels with a great maneuverability. Combined with a vacuum system comprising seven controllable vacuum chambers and a large fluid reservoir operating system, the robot had pressure sensors and valves integrated for controls. Shang et al. introduced a method that utilized neodymium permanent magnets for bonding, giving the robot a payload-carrying capacity5. The arrangement of the magnets improved the ground clearance, enabling the robot to overcome obstacles. To be able to work on curved surfaces, a wheeled robot with two articulated segments was designed, which had the advantages of high speed and good maneuverability.Different solutions have been proposed by researchers to solve these essential weld-inspection robotic questions. A robotic climber with multiple breathing chambers for inspection was designed for the inspection of concrete walls7. Adsorption or magnet suction affixed to the location of detection had been the most common for welding inspecting robots. The sensors would detect the weld seam and then extracts the weld seam geometry, and correct the robot's position in real time9, but the degree of automation was low and the requirements for sensor accuracy were high.A robot with magnetic wheels and vision sensors for defect detection was designed, and the drive mechanisms of inspection robots had included three forms: a track, a wheel, and a leg10 have studied a semi-autonomous tracked inspection robot to detect defects in building ceilings. The developed inspection robots were equipped with wireless cameras and data processing functions, which could provide valuable information for the repair of the damaged structures. The inspection robot was able to assess the damage without the need for an engineer on site. Krenich11 designed a six-axis robot that could move autonomously for inspection, while a human could also inspect around the weld seam with a camera carried by the robot. Bruzzone12 introduced a mobile robot with a hybrid wheel and leg design, which featured wheels that could roll on flat grounds at a high speed, while the legs enabled the robot to avoid obstacles and to climb hills13.Inspection robots have recently evolved to become autonomous and semi-autonomous, for saving inspection time and reducing labor costs. Nita and other researchers14 It could classify the identify the weld defects with a high accuracy and in real-time. Li et al. proposed a deep learning-based algorithm for weld seam image recognition15, which accelerated the neural training on several thousand images of the welds. The disadvantage of this algorithm was that it was too computationally intensive and hardware demanding. Yang et al. proposed a method to improve the defect localization of U-net mesh to improve the automatic localization accuracy for weld detection16. For low-cost robot development using low- to mid-cost main control boards, less computationally intensive algorithms need to be developed to achieve the identification and localization of weld defects.Detection algorithms were developed for corrosion and cracking in aged welds. The use of advanced vision algorithms based on deep learning and machine learning made it possible to detect and recognize the defects. An algorithm based on an improved Gaussian mixture model for weld seam detection and classification was introduced by Sun et al.Based on the above survey of literature, the objectives of the present research include: (1) Design of an autonomous weld inspection robot and its control system. The robot can achieve autonomous weld inspection, and can pass smoothly between narrow spaces with less time while autonomously avoiding obstacles. (2) Design of a yolo5-based target algorithm for weld seam detection and identification in complex environments.Figure The design of the autonomous inspection robot is divided into four different components: mechanical structure design, chassis motion control design, vision detection system design, and control system architecture design.An aluminum chassis is used as the main frame of the robot, and each motor is individually attached to the chassis through a motor mounting plate. Shock absorbers are placed between the motor mounting plate and the chassis to reduce the vibration of the robot and to help it pass through obstacles as shown in Fig. Popular control models for chassis drive include the Wheel Differential model, Ackerman model, and Omnidirectional model. The turning radius of the Ackerman model cannot be 0, which does not allow the robot to turn in narrow space. The Omnidirectional model needs to use McNamee wheels instead of the common wheels. In addition, the gaps between each small wheel of the McNamee wheel are liable to be struck by foreign objects and affect the robot's movement. Therefore, the Wheel Differential model is selected, and optimized for turning around tight corners and for reducing sliding friction.x, y, z), is located at the center of gravity (COG) of the model. The motion of the robot is on the horizontal plane, formed by the Xw and Yw axes of the world coordinate system, shown in Fig. For the kinematics of the robot, a local coordinate frame, denoted as , and radius of motion of the differential robot is as follows:The decomposition of the velocity of the left wheel and the right wheel can be found as:From this, the relationship between the overall linear velocity of the robot, the angular velocity, and the left and right wheels can be solved as follows:As shown in Fig. 17. In order to investigate the slippage problem of the above motion model, the simulation is carried out in gazebo. The dynamic tag is added to the tire link tag and the ground link tag in the XACRO file of the robot model. A dynamic tag is to add to measure the friction coefficient between the tire and the ground. The parameters are shown in Table The performance of key parts of the robot needs to be analyzed using specialized software before it is actually applied in order to avoid unnecessary waste of time and moneyThe visual inspection function is for identifying and locating the weld seam. The images are captured at the robot side and the weld seam pictures are identified at the computer side using YOLOv5 inspection algorithm. The YOLOv5 based target detection algorithm, which has the advantages of high detection speed and lightweight deployment model, is used for the robot's inspection system. The YOLOv5 algorithm has four structures  to represent different depths and widths of the network. It uses indicators to deepen and widen the network, but as the detection accuracy increases, the response velocity becomes progressively slower. The device design in this study requires model lightweight, real-time responses, and any-size image input, so YOLOv5 is chosen as the benchmark model, and its structure is shown in Fig. 18. Among them, the batch normalization (BN) layer solves the problems of gradient disappearance and gradient explosion through data normalization. The sigmoid weighted linear unit (SiLU) activation function is a smooth and non-monotonic function, and it prevents the gradient from gradually decreasing to 0 during slow training.The YOLOv5 network structure is consisted of the input, backbone, neck, and detection end. The input includes the data enhancement of the mosaic, adaptive calculations of the anchor frame and adaptive scaling of the image. The back-bone is mainly composed of the CBS , SPPF , and C3 (concentrated-comprehensive convolution) modules19 and PANet20. The deep-feature map contains stronger semantic features and weaker localization information, while the shallow-feature map contains stronger location information and weaker semantic features. FPN transfers the semantic features from the deep-layer to the shallow-layer to enhance the semantic representation at multiple scales, while PANet transfers the location information from the shallow-layer to the deep-layer to enhance localization at multiple scales. PANet adds a bottom-up direction enhancement on top of FPN.The neck is the combination of FPNLloss) is as follows21:Lcls indicates the classification error; Lbox indicates the bounding box regression error; Lconf indicates the confidence error.For network training, the loss function plays an important role in the weld detection model, which marks the difference between the predicted and actual values of the model. In YOLOv5s, a joint loss function is used to train bounding box regression, classification, and confidence. The used loss function (The weld seam is inspected and the acquisition photos are of the completed weld seam. The image acquisition device is a camera (China Vistra Q16 2k usb camera) with an f-value of 1.8. Photographs of the weld seam are collected in different environments . A total of 300 images of the weld seam are collected at a distance of about 20–30 cm. To speed up the model training, the images are compressed to 512 × 341 pixels, and saved in jpg format. Example images are shown in Table The training model for weld detection is simulated on an HP Shadow Wizard computer with the configurations shown in Table The hyperparameters optimized for training using the above hardware are: epoch value is 150, learning rate is 0.01, momentum is 0.937, weight decay is 0.0005, batch size is 16, workers are 8, optimizer is stochastic gradient descent (SGD), and a single graphic processing unit (GPU) is used to speed up the training. All hyperparameters are performed for the pre-training on the Validation set. The change in the loss values of the pre-training process is shown in Fig. The control architecture of the robot is shown in Fig. The motors do not use stm32 or the Arduino control method, but directly connect to the motor driver board through the IO port on the Raspberry Pi, which improves the convenience of operation and the sensitivity of control. The core controller of the robot is the Raspberry Pi 4B. The data processed on this robot is moderate, so the Raspberry Pi 4B is sufficient, which has Ubuntu 18.04 Linux and Robot Operating System (ROS) installed. The data collected by various sensors are transferred to the ROS system for processing. The various sensors, main control board, GUI, and PC are integrated through the ROS framework. To reduce the pressure on the Raspberry Pi for processing the data, the collected data are transferred through WIFI from Raspberry Pi to the PC, for processing on the distributed framework of ROS.When they are connected to the same network, both the robot and PC can be remotely monitored and controlled through the distributed ROS. Communication between the robot and control device is carried out through ROS' Message Queue Telemetry Transfer (MQTT). When the navigation control node is started on the mobile side, a message is received on the PC, and a map building command can be executed on the PC at the same time, which controls the robot to build a map and issues a point-to-point cruise operation. This operation reduces the computational pressure on the Raspberry Pi 4B onboard the robot. A graphical user interface for monitoring and control is developed for the PC using the qt software. The robot can be viewed through the GUI when it is working in an unknown environment, and is assigned with control buttons and video outputs.22, Genetic Algorithm23, Dynamic Window method (DWA) 24, Neural Network Algorithm, and other intelligent algorithms. The ATF method tends to fall into local minima and fails to reach the focus position25, and the Neural Network algorithm is too demanding on the performance of the main control board26. All the above algorithms have a lower convergence speed, and none of them have the ability to avoid local extremes.Path planning requires the cooperation of global path planning and local path planning. The weight of global path planning in the obstacle avoidance process is less than that of local path planning. Therefore, in this study, the local path planning is emphasized. The most critical issue is that the robot must safely avoid static obstacles and dynamic obstacles during an inspection. The local path planning methods in complex environments include the Artificial Potential Field method (APF)27 and was based on the classical elastic band algorithm, which was an obstacle avoidance method by optimizing multi-objective trajectory optimization. Compared with the local path planning algorithms described above, the TEB algorithm can set multiple constraints as needed to ensure the applicability of the algorithm. The multi-objective optimization of the TEB algorithm relies on only a few continuous states, thus optimizing for a sparse matrix model. Rösmann et al. proposed that the sparsity problem of the hypergraph-based TEB algorithm can be solved quickly and efficiently using the G2o framework to improve the computational speed. However, mobile robots equipped with the TEB algorithm can appear to be trapped in local minima and unable to cross obstacles in complex environment navigation. To solve this problem, Rösmann et al. proposed an extension of the TEB technique by using parallel trajectory planning in a spatially unique topology29. However, these approaches only considered the location of obstacles and did not consider potential collisions between the robot and surrounding obstacles. Lan et al. 30 proposed an active timed elastic band (PTEB) technique for autonomous mobile robot navigation systems in dynamic environments. Previous work to improve the effectiveness of TEB algorithms operating in complex environments has focused on obstacle avoidance. However, most of the related research only pursued avoiding local minima and smoothing the planned paths in complex environments. They did not consider the shortest local path, and the planned local path might not be the optimal path31. Therefore, the improved TEB algorithm still suffered from the robot backing up during turns, local detours, and unable to enter narrow areas.An improved TEB algorithm is, therefore, selected to implement the local path planning. The TEB algorithm was proposed by Rösmann The improved TEB algorithm proposed in this study optimizes the behavior of local bypassing and reversing, adds the constraint of angular acceleration to the constraints of the multi-objective optimization, and considers the time consumption brought by excessive turning. The proposed TEB algorithm is proved by experiments to achieve fast turning and reduce the behavior of reversing, improve the detection range of the inspection robot, and reduce the time cost of the inspection.Xi is the poses at the time I, and Q is the sequence of the poses; ΔTi is the time interval between adjacent poses, and τ is the time interval sequence; the pose sequence and the time interval sequence are combined into a trajectory sequence B.The proposed TEB algorithm is based on the elastic-band-algorithm with the addition of temporal information between bit-pose sequences, as shown in Eq. , which cxr is the critical value, S is the scaling factor, and n is the polynomial coefficient, which usually takes the value of 2; ε is a small section of displacement near the critical value.Because the objective function of the TEB algorithm depends on only a few continuous pose states, this leads to a sparse system matrix that represents these constraints as objectives according to a segmented continuous, differentiable cost function. The function penalizes the violation of the constraints that represent the boundaries, as in Eq. .7\documefk(B) is a constraint function in Fig. k is the weight corresponding to the constraint function.The multi-objective optimization function is shown in Eq. .8\documeThe trajectory constraints of the TEB algorithm are divided into two parts. The first part is constrained by global path planning; the second part is constrained by velocity, acceleration, and its own kinematic model. In this study, the focus is on optimizing the velocity, acceleration, and obstacle constraints.dmin, and the distance between the robot and the obstacle is set to D. The position information of the obstacle on the map is obtained by sensors such as LIDAR. To ensure the safety of the planned trajectory, each bit posed on the TEB trajectory is related to the obstacles appearing on the map, and the penalty function is triggered when the distance D between the robot and the obstacle is lower than dmin. The penalty function is expressed as Eq. . The adjacent path points are , the angle between the line connecting the two points and the robot's initial test pose is θi. A minimum threshold angle θimin is set. When θi is greater than the minimum threshold, the angular velocity is set to the maximum. The robot will accelerate its turning to avoid reversing, and as the θi becomes smaller, the angular velocity also decreases to achieve a smooth transition of the turn. The penalty function can be expressed as Eq.  with autonomous navigation and obstacle avoidance as a carrier to inspect and locate weld seams using vision detection algorithms. The inspector can set the inspection location as needed, and the robot can independently reach the inspection location, identify and locate the weld seam, and provide inspection information to the inspector. The vast majority of current inspection weld robotsThis paper presents the design of a novel flexible inspection robot. The inspection robot is equipped with a four-wheel independent suspension adapted to undulating sections of the ground, a flexible inspection head that can detect all around, and a control algorithm that can detect in narrow passages, all of which improve the efficiency of the robot's inspection. Detection route planning is simulated by an improved Timed-Elastic-Band (TEB) algorithm. Experiments on the path planning algorithm, a key problem for the robot, show that the improved planning algorithm can effectively control the robot in a narrow space, ensuring that the robot does not encounter other obstacles and that the running time is shortened by 12%; the target detection algorithm of yolov5s is also used to train the weld seam detection model with a detection accuracy of better than 90%, based on the robot-provided photo information to identify and locate the weld seam, and provide information to the weld inspector. There is a shortcoming in this study, that the robot recognizes only a single type of weld, and does not detect and classify weld defects.


Lymph node metastasis in breast cancer may be accurately predicted using a DenseNet-169 model. However, the current system for identifying metastases in a lymph node is manual and tedious. A pathologist well-versed with the process of detection and characterization of lymph nodes goes through hours investigating histological slides. Furthermore, because of the massive size of most whole-slide images (WSI), it is wise to divide a slide into batches of small image patches and apply methods independently on each patch. The present work introduces a novel method for the automated diagnosis and detection of metastases from whole slide images using the Fast AI framework and the 1-cycle policy. Additionally, it compares this new approach to previous methods. The proposed model has surpassed other state-of-art methods with more than 97.4% accuracy. In addition, a mobile application is developed for prompt and quick response. It collects user information and models to diagnose metastases present in the early stages of cancer. These results indicate that the suggested model may assist general practitioners in accurately analyzing breast cancer situations, hence preventing future complications and mortality. With digital image processing, histopathologic interpretation and diagnostic accuracy have improved considerably. Breast cancer is a severe illness that will impact one in nine women throughout their lifetime. It is observed from the surveys that 1 in 32 women may die from breast cancer. In 2018, breast cancer was expected to account for approximately 1 in 4 cases of cancer identified in women and amounted to the second-biggest category of new cancer cases. Breast cancer is the most significant risk factor for cancer in women and the seventeenth most important cause of mortality worldwide. It is the most prevalent kind of malignancy among women aged 15–49 years and the third most frequent malignancy in women aged 50–59 years [Despite significant breakthroughs in understanding diseases and implementing treatment options, breast cancer remains the most frequently diagnosed cancer globally. Furthermore, it is the second leading reason behind deaths related to cancer in women ,3,4,5. CA lymph node examination is essential for diagnosing cancer and determining suitable therapy choices. Multiple lymph node levels are involved in assessing the prognosis, and appropriate staging requires meticulous examination of lymph node health. On the other hand, manually screening several slides may be tiresome and challenging for the pathologist, and individuals must undergo multiple scans for precise assessment, which is hazardous. As a result, advances in automated tissue categorization utilize machine learning techniques that precisely identify metastases over lymph node tissue . The areMachine intelligence (MI) has transformed oncological research in recent years. Numerous studies have demonstrated that MI can correctly classify tissue samples as benign or malignant, particularly on hematoxylin and eosin (H&E) spattered slides. It has been shown that intelligent models, specifically image interpretation using convolutional neural networks (CNN), can accurately discriminate between malignant and benign from the images in prostate biopsies. Additionally, computer-controlled Gleason grading attained a comparable level of accuracy to that of specialized pathologists performing Gleason grading. Recent research has demonstrated that CNNs can also detect changes in protein expression and genetic mutations on H&E slides of cancer samples from various cancer types, such as prostate cancer, breast cancer, and liver cancer . These fA few studies published in peer-reviewed journals illustrate the remarkable functionality of artificially intelligent (AI) techniques through diagnostic models, like the identification of regions of interest (ROI) or perhaps the characterization and classification of types of cells, traits, or epithelial tissue ,14. For The motivation of the present study is to give a detailed one-stop solution for the early and automatic detection of cancer using whole slide images; early identification always has advantages. Using advanced ML libraries, we can now prevent the aggravated malignant state of cancer with minimal effort. Two terms used throughout this work are FastAI and the 1-cycle policy. FastAI is an open-source DL library  built usTo precisely identify the presence of metastases from 96 × 96 px digital histopathology images.To build a model that can precisely predict metastasis growth in early stages for better treatment.Fine-tuning the DenseNet-169 model by batch normalization and weight optimization strategies for a more precise outcome.By incorporating the 1-cycle policy and FastAI, the training rate of the model would tremendously increase and assist in faster convergence towards the solution.The entire manuscript is further divided into the following sections: The introduction presents generalized information about the scope of the study, motivation, and contribution. The related work presents the past research in With the advancement of technology, scientists and researchers worldwide have put in a lot of effort to develop robust frameworks and methodologies for the early and effective detection of cancer using image processing (IP) and DL techniques. CT Scans, Ultrasound, Nuclear Imaging, and MRI scans have been used extensively for cancer detection. However, none of those techniques have given a highly accurate cancer prediction. Therefore, researchers have shown more interest in histopathological WSI for cancer detection in the past few years. The application of CNNs to detect different ailments in medical images dates back to the mid-1990s . Ever siEven though CNNs have proven effective in medical image classification, it still faces many challenges. A few of these challenges are: (1) due to the excessive deepening of cancer image classification networks, the number of training parameters increases rapidly, which leads to the threat of overfitting the model. In addition, many image samples are required to reduce the risk of overfitting, which is not always possible. Hence, data augmentation methods are used to enhance the size of the dataset and prevent overfitting the model . (2) HypMany recent studies have demonstrated that training a fine-tuned CNN rather than a new one takes substantially less time, yet these fine-tuned models outperform new models . This imIn light of the fact that breast cancer is among the most frequent cancers, most samples evaluated in cancer pathology are obtained from victims of this disease . PatholoThe 1-Cycle policy  has beenOne of the most widely used DL frameworks/libraries, FastAI, is undoubtedly one of the most prominent paradigms in the DL world. It has been used for numerous works in DL research. Multiple DL libraries within FastAI were used to detect malicious URLs . FastAI This section presents the different concepts, models, and algorithms used in this study. Essentially, this section talks about the following: FastAI deep learning framework, DenseNet-169 model architecture, the 1-Cycle Policy, and the gradient-weighted class activation mapping (Grad-CAM).FastAI is a profound DL library  that furThe 1-cycle policy  improvesAn incentive for total steps is mentioned explicitly.steps_per_epoch) are given. As shown in the following Equation, the instances of incremental steps are evaluated in this situation.A few epochs and a few steps for every epoch   uses claOur training method must be looked atWe might have to collect additional dataMaybe the model is not prepared yet for deploymentThis section gives an overview of the architecture of the proposed model, the dataset used, weight assignment and optimization, the hyperparameters used to validate the model’s performance and the implementation environment. The initial stage of the proposed DenseNet-169 encompasses the data pre-processing task. Upon performing the data pre-processing, the images are divided into the train and test instances, where the training data would be sent to the CNN Model. Finally, validation of Fine-tuned DenseNet-169 is performed over the test data. The dataset used in this work is a filtered version of the pcam dataset. The main difference between the pcam dataset and the dataset used in this work is that all the redundant pictures in the pcam dataset have been removed (filtered out). The pcam dataset was extracted from the Camelyon16 Challenge dataset, containing 400 H&E-stained images of sentinel lymph node zones procured and digitized at two distinct centers utilizing a 40× objective. The pcam’s dataset utilizes 10× under-sampling to expand the field of view, with a pixel resolution of 2.43 microns. From the data description, the positive and negative data instances are equally balanced for the training and testing portions of the dataset. The training dataset has an approximate 60–40 negative-positive distribution. A positive label implies at least one pixel of tumorous tissue in the picture’s central portion (32 × 32 Th px). Tumorous tissue in the external area of this patch does not impact the label. This means that a negatively labeled image might have metastases on the outside. As a result, cropping the photographs to the center section might be a suitable option. The pcam dataset is described in full in The sample raw images that are part of the pcam dataset are presented in The image label is influenced only by the center region (32 × 32 px), so it would make sense to crop our data to that region only. However, there is always the loss of valuable information around the image. Data augmentation, data regularization, and simpler model designs might be utilized to prevent overfitting the model. The approaches for picture augmentation were immediately included in the image loader function. Additionally, test time augmentation (TTA) improved the outcomes and average forecasts throughout the testing phase, as seen in Data Augmentation is used in image processing tasks to create new examples from the existing training data. The model can learn from an extended range of examples and generalize well to different possible orientations of the input images. Adding to that, a small dataset size usually leads to the overfitting of the model. Hence, creating more data using data augmentation introduces variety to the input data and helps avoid the overfitting of the model. The data augmentation techniques used in the current work include random rotation, random crop, random flip, and random light, which are elaborated on in the current section.Random rotation encompasses rotating the training images to different angles so that the essential meaning of the image does not change, however, it gives a new point of view to the model being trained. When a model may be utilized in a non-fixed location, rotating the picture  is crucial. Rotating a picture may be problematic since graphical glitches on the image’s edges might be problematic.Cropping is essentially selecting a region of the image and saving it as a new training instance. This cropping region might be chosen randomly or based on some strategy. Cropping also involves making an image square by expanding existing dimensions to suit a square or maintaining the existing aspect ratio and adding additional pixels to fit in the newly produced empty spaces.Randomly flipping a picture around its x- or y-axis (while maintaining the image’s core structure and meaning) drives our model to realize that an item does not always have to be interpreted either left to right or up to down.If a model must perform in various lighting conditions, adjusting image intensity to be arbitrarily brighter and darker is most beneficial. Changing the intensity to match the situations the model will encounter in production, instead of the pictures provided for training, aids with generalization. The random augmentation of WSI is shown in DenseNet-169 is one of the architectures of the DenseNet family with 169 layers and is a widely used architecture for DL classification tasks. It has far less trainable parameters when compared to its fellow DenseNet architectures with fewer layers. DenseNet-169 and the other DenseNet architectures have the ability to overcome the vanishing gradient problem, have a strong feature propagation strategy, minimize the number of trainable parameters, and encourage the reuse of features, thus making them a family of very reliable DL architectures. DenseNet models can be found in Tensorflow (Keras) and PyTorandes. The layered architecture of the DenseNet-169 used in the current study is presented in The architecture involves convolutional layers, maxpool layers, dense layers (fully connected layers), and transition layers. The model uses the ReLU activation function throughout the architecture and uses SoftMax activation for the final layer. The convolutional layers extract the features in the image, and the maxpool layers reduce the dimensionality of their inputs. The fully connected layers follow the flatten layer, which acts as an artificial neural network with a single array input coming from the flatten layer. The details of the layered architecture are depicted in Convolution Layer: A convolutional layer, in basic words, applies a filter to an input, resulting in the activation. When the filter is applied repeatedly to an input, the result is a feature map representing the intensity of the discovered features at different positions in the input. Once a feature map is created using multiple filters, it can be passed through activation functions such as ReLU. The filter used in a convolutional layer is smaller than the input data, and, generally, the operation performed between these two entities is a dot product. Assume a MaxPool Layer: The main purpose of using a maxpool layer in a CNN is to minimize the dimensionality of the feature map. Like a convolutional layer, the maxpool layer also runs a filter over the feature map and summarizes the features within the region covered by the pooling filter. Assume a feature map has dimensions Dense Layer: A dense layer in a neural network is deeply connected with its preceding layer, i.e., each neuron of the dense layer has a connection with each neuron in its preceding layer. The neuron in the dense layer receives inputs from each neuron in its previous layer and performs a matrix-vector multiplication. Following is the standard formula for a matrix-vector multiplication as shown in Equation (5)The convolutional layer applies the assessed non-linearity as shown in Equation (3).From the above Equation, the variable The From the above equations, the variable Transition Layer: A transition layer is used in a CNN to reduce the complexity of the model. A typical transition layer minimizes the sum of channels by using a 1 × 1 convolutional layer and decreases the width and height of the input by half using a filter with stride 2.SoftMax Activation Function: The softmax activation function is a standard non-linear activation used widely for classification problems in deep learning architectures. The general form of a non-linear activation function is defined in Equation (12), with weight identified by the variable The softmax function is engaged with the output layer of a convolutional neural network when predicting the probabilities of each output class. By definition, the softmax function outputs one value for every neuron in the output layer. The output by each such neuron in the output layer is the likelihood (or probability) of that node being the output. The softmax function is defined over the softmax function With softmax as the activation function, the loss function used in this work is the binary cross-entropy loss function. Conventionally, binary cross-entropy is used while dealing with binary classification problems. Equations (14) and (15) depict the binary cross-entropy loss function, for a network of The primary purpose behind optimal weight initialization is to prevent the explosion of layer activation functions or the vanishing gradient problem during forwarding propagation in a feedforward network. If one of these two issues occurs, then the gradient loss will either be too small or too large, and the network will take excessive time to converge even if it can do so. If the network weights are initialized optimally/properly, then the task of loss function optimization will be accomplished in minimal time; otherwise, converging to the minimum using the gradient descent approach will be highly impractical. Since weight initialization is significant for neural network training in DL, different techniques can initialize weights for a neural network. The most widely used technique is the random weight initialization technique.One of the best practices while initializing weights is using leaky ReLU or ReLU as the activation function. They are resistant to the exploding or vanishing gradient problems, and leaky ReLU never has a zero gradient, ensuring continual training. Another good practice is using heuristics to initialize the weights in a network. While using the ReLU activation function, the He et al.  heuristidth feature value of the nearest hit of the instance dth feature value of the ath the nearest miss of the instance Weight optimization involves changing the parameters such that the equation lowers according to the current instance and its nearby hits and misses at each step. It’s possible to alter a given instance’s closest neighbors by computing the closest hits and misses of every selected instance at any given time. For k-nearest neighbors, the method iterates m times, resulting in Equation (18).nstances . Weight The hyperparameters that are associated with the fine-tunes DenseNet-169 model, which include the learning rate and the loss associated with batch processing, are discussed in the current section. It is desirable to choose the optimal parameters for better training and testing performances and avoid underfitting and overfitting of the model. The loss and accuracy of training and testing are discussed in the current study. The ideal learning rate range is reached at the initial point of divergence of the model. Ideally, at this point, the loss must still go on decreasing when the learning rate is chosen. As for the L2 penalty of the optimizer (weight decay), the author  proposesThe above graph in As shown in The maximum learning rate, which is identified using the variable (max_lr), is reached in the middle of the learning process. Then it slows down again towards the conclusion of the image. Because the model cannot settle for narrow and sharp local minima, the larger rate has a regularizing impact, pushing the model toward broader and more stable ones. As we near the midpoint of our cycle, we begin to slow down our pace of learning in the hopes that we have reached a stable state. This signifies that we begin searching for the area’s smallest values.Even before fine-tuning the model, the model performs very well during training as shown in It can be observed from the It is observed that the fine-tuned DenseNet-169 model has exhibited a better performance compared to the other models, and it has proven to exhibit better performance over the conventional DenseNet-169. The feature weight optimizations have assisted in a much better way to identify the metastases in lymph nodes precisely. This experiment is carried forward over the Kaggle’s compiler (online platform) . The FasIn the current section, the efficacy of the F=fine-tuned DenseNet-169 model for metastases in lymph nodes is evaluated across various metrics like the sensitivity, specificity, accuracy, and F1 score. The performances are analyzed against the other state-of-art models like logistic regression (LR), neural network (NN), random forest (RF), support vector machine (SVM), CNN, and DL models like VGG-16 and ConcatNet. The proposed model has outperformed the various approaches with reasonable performance discussed in the current section. The other parameters like the receiver operator characteristic curve and the test time augmentation are discussed for better comprehensibility of the model. The confusion matrix is a table that depicts the instances of true positives (TruP), true negatives (TruN), false positives (FlsP), and false negatives (FlsN) . An outpThe fine-tuned model significantly improves over the original DenseNet-169 model from the above confusion matrices and the tables. The instances of true positives increase by 141, and the instances of true negatives increase by 89. This also implies that the instances of FPs and FNs decrease. The confusion matrix for the fine-tuned model is also used to assess the metrics such as sensitivity, specificity, and the F1-score.For binary classification tasks in medical testing, the diagnostic odds ratio (DOR)  is a parThe DOR for Dense-Net-169 is:Similarly, the DOR for fine-tuned Dense-Net-169 is:The diagnostic odds ratio is greater than one for useful tests, and a higher value of DOR indicates a better performance. A DOR value of less than one indicates that the performance of the test can be improved simply by inverting the result of the test. Given this interpretation of DOR, since the DOR of DenseNet169 is 458.62 whereas the DOR of the fine-tuned model is 877.88, there is a huge jump in the DOR value, indicating a huge improvement in the performance of the model.The test scores associated with each random sample in the testing phase that varies among the random samples and most incorrect samples and the correct samples and their associated probabilities to classify them either as tumors or not are presented in Random samples are predictions made on some random instances from the data.Most incorrectly labeled samples are the models predicted wrongly with a very high probability.The model predicted correctly with a very high probability is the most correctly labeled sample.The performances of the fine-tuned DenseNet-169 are evaluated concerning accuracy, sensitivity, specificity, and F1-Score concerning the various state-of-art models, whose values are presented in The other most predominantly used performance evaluation parameter, receiver operator characteristic (ROC), is a graphical depiction of the diagnostic ability of binary classification models. A ROC curve for a model is constructed by presenting the false positive rate of the model against the true positive rate. The ROC curve essentially depicts the dependencies among the sensitivity, i.e., true positive rate, and specificity, i.e., false positive rate. Binary classifiers which can cover the maximum area under the curve (or are closest to the top-left corner) are the best ones. The test time augmentation (TTA) is considered in testing the model. The input data fed to the model is traversed through a process in which the test data is transformed with random data augmentation techniques during test time. The proposed model predicts the whole tumor image as tumorous with 86.42% probability. The front-end interface of the mobile system may be used to deploy the proposed smart diagnosis technology for identifying the growth of metastasis. Primary in diagnosing metastasis, the mobile framework technology might assist both the patient and the doctor. Name, date of birth, gender, height, diabetes status, weight, and hypertension are among the information the users will produce initially. At the later stages, the user will provide the metastasis whole slide image for predicting the presence of metastasis. The model uses the input image and the training data to predict the abnormality over the input image. The back-end of the architecture relies on the service, like FlaskApi, which is used to integrate the iOS framework into the Kaggle. Authentication and a secure socket layer (SSL) can secure the model. The block diagram for the app integration with DenseNet-169 is presented in In the proposed IntelliJ-Diagnosis mobile application, the security of the patient data is considered to be sensitive and exceptionally important to ensure the privacy of the information. To ensure the privacy of the data, the encryption of mobile data is performed. Data sent is transferred among the mobile app, and data transferred to the server is encrypted using the secure sockets layer (SSL) protocol. While data is encrypted, the asymmetric key techniques use a public key for encrypting the data and a private key that is only known to the receiver of the message ,78. The The figure above depicts the future perspective model’s user interface. This work aimed to facilitate the development of digital diagnosis in MBCs and explore the applicability of a novel CNN architecture on MBCs. In this paper, we proposed a fine-tuned DenseNet-169 CNN architecture to automatically diagnose the presence of cancer cells in the pathological tissue of breast cancers. The fine-tuned DenseNet-169 detects metastases from whole slide images using the FastAI framework and the 1-cycle policy. The results obtained from this research were better than any other approaches proposed earlier. The AUC-ROC of the model is 97%, whereas the accuracy of the baseline model was approximately 92%. The DL model proposed in this work can be enhanced further for other cancer data with different data augmentation techniques. A base model other than DenseNet-169 can also be used to see if a different model gives even better performance. Another area of future research could be adding noise to the region of interest and seeing how that may affect the edge cases. Therefore, it can provide an efficient, reliable, and economical alternative for medical facilities in relevant fields. In the future, the proposed DenseNet-169can be applied to diverse cancer datasets to enhance the clarity and quality of results.