IoT-based Cloud Service for Secured Android Markets using PDG-based Deep Learning Classification

China has the world's largest Android population with 270 million active users. However, Google Play is only accessible by about 30% of them, and third-party app stores are thus used by 70% of them for daily Android apps (applications) discovery. The trustworthiness of Android app stores in China is still an open question. In this paper, we present a comprehensive study on the trustworthy level of top popular Android app stores in China, by discovering the identicalness and content differences between the APK files hosted in the app stores and the corresponding official APK files. First, we have selected 25 top apps that have the highest installations in China and have the corresponding official ones downloaded from their official websites as oracle, and have collected total 506 APK files across 21 top popular app stores (20 top third party stores as well as Google Play). Afterwards, APK identical checking and APK difference analysis are conducted against the corresponding official versions. Next, assessment is applied to rank the severity of APK files. All the apps are classified into 3 severity levels, ranging from safe (identical and higher level), warning (lower version or modifications on resource related files) to critical (modifications on permission file and/or application codes). Finally, the severity levels contribute to the final trustworthy ranking score of the 21 stores. The study indicates that about only 26.09% of level APK files are safe, 37.74% of them are at warning level, and 36.17% of them are surprisingly at critical level. We have also found out that 10 (about 2%) APK files are modified and resigned by unknown third-parties. In addition, the average trustworthy ranking score (47.37 over 100) has also highlighted that the trustworthy level of the Android app stores in China is relatively low. In conclusion, we suggest Android users to download APK files from its corresponding official websites or use the highest ranked third-party app stores, and we appeal app stores to ensure all hosting APK files are trustworthy enough to provide a "safe-to-download" environment.

Purpose: Although deep learning (DL) models have shown increasing ability to accurately classify diagnostic images in oncology, significantly large amounts of well-curated data are often needed to match human level performance. Given the relative paucity of imaging datasets for less prevalent cancer types, there is an increasing need for methods which can improve the performance of deep learning models trained using limited diagnostic images. Deep metric learning (DML) is a potential method which can improve accuracy in deep learning models trained on limited datasets. Leveraging a triplet-loss function, DML exponentially increases training data compared to a traditional DL model. In this study, we investigated the utility of DML to improve the accuracy of DL models trained to classify cancerous lesions found on screening mammograms. Methods: Using a dataset of 2620 lesions found on routine screening mammogram, we trained both a traditional DL and DML models to classify suspicious lesions as cancerous or benign. The VGG16 architecture was used as the basis for the DL and DML models. Model performance was compared by calculating model accuracy, sensitivity, and specificity on a blinded test set of 378 lesions. In addition to individual model performance, we also measured agreement accuracy when both the DL and DML models were combined. Sub-analyses were conducted to identify phenotypes which were best suited for each model type. Both models underwent hyperparameters optimization to identify ideal batch size, learning rate, and regularization to prevent overfitting. Results: We found that the combination of the traditional DL model with DML model resulted in the highest overall accuracy (78.7%) representing a 7.1% improvement compared to the traditional DL model (p<.001). Alone, the traditional DL model had an improved accuracy compared to the DML model (71.4% vs 66.4%). The traditional DL model had a higher sensitivity (94.8% vs 73.6 %) , but lower specificity (34.7% vs 55.1%) compared the DML model. Sub-analyses suggested the traditional DL model was more accurate on higher density breasts, whereas the DML model was more accurate on lower density breasts. Additionally, the traditional DL model had the highest accuracy on oval shaped lesions, compared to the DML model which was most accurate on irregularly shaped breast lesions. Conclusion: Our study suggests that addition of DML models with traditional DL models can improve diagnostic image classification performance in cancer. Our results suggest DML models may provide increased specificity and help with classification of unique populations often misclassified by traditional DL models. Further studied investigating the utility of DML on other cancer imaging tasks are necessary to successfully build more robust DL models in cancer imaging. Citation Format: Justin Du, Sachin Umrao, Enoch Chang, Marina Joel, Aidan Gilson, Guneet Janda, Rachel Choi, Yongfeng Hui, Sanjay Aneja. The utility of deep metric learning for breast cancer identification on mammographic images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 184.

The absence of Google Play has created a booming area for Android app distribution through third-party app stores in China. Since the study showed that the trustworthy level of app stores was fairly low in 2014, much attention should be paid on the changes of the trustworthiness of Android app stores. In this paper, we present a method to analyze the changes of trustworthiness of the top popular Android app stores in China. In this method, we evaluate the target app stores by analyzing the sampled apps hosted in them. Further more, we have used this method to track the changes of trustworthy level of Android app stores in China about two years. The results indicate that the trustworthy level of the top popular Android app stores in China has been improved 24% on average. It can be seen that the positive changes may be related to the development of the China's mobile market, the improvement of Android system, and the introduced policies. Although the trustworthy level of top popular Android app stores in China is still low, it is predicted to be improving in the future.

The Apple App Store is the only channel through which app developers may distribute their apps on iOS. First launched in 2008, the App Store has evolved into a highly profitable marketplace, with overall consumer spend exceeding $ 50 billion in 2019. However, concerns are increasingly expressed on both sides of the Atlantic that various practices of Apple with regard to the App Store may breach competition law. The purpose of this paper is to examine whether this is indeed the case and, if so, how these concerns can be addressed. With these aims in mind, the paper first introduces the reader to the app ecosystem and the Apple App Store, with a focus on the controversial 30% commission charged for in-app purchases. After engaging critically with various public statements of Apple discussing the services that the 30% commission aims to cover, the paper concludes that the 30% commission is charged for payment processing and related services and not, as Apple asserts, for distribution, since in that case it would be charged on all apps distributed on the App Store and not only on apps delivering “digital goods and services”. The paper then critically reviews several practices of Apple that appear to be at odds with competition law and in particular Article 102 TFEU. We first discuss the issue of market definition and dominance with regard to the App Store. We find that Apple is a monopolist in the market for app distribution on iOS, as it is not subject to any meaningful competitive constraint from alternative distribution channels, such as Android app stores or the web. The result is that Apple is the gateway through which app developers have to go in order to reach the valuable audience of iOS users. This bottleneck position affords Apple the power to engage in several prima facie anti-competitive practices. First, Apple exploits app developers by charging excessive fees for the services it provides, applying its guidelines in a capricious and discriminatory manner, and depriving them of the user data they need to improve the quality of their services and user experience. Second, based on four case studies, the paper illustrates how Apple may use its control of the App Store or iOS to engage in exclusionary behaviour to the detriment of rival apps. Third, the paper shows that Apple may have also engaged in discriminatory practices by treating some app developers more favourably. These practices should be investigated by competition authorities, as they are likely to result in considerable consumer harm, be it in the form of higher app prices, worse user experience or reduced consumer choice. The paper finally proposes a combination of concrete remedies that would address the competition concerns identified.

The need for intubation in methanol-poisoned patients, if not predicted in time, can lead to irreparable complications and even death. Artificial intelligence (AI) techniques like machine learning (ML) and deep learning (DL) greatly aid in accurately predicting intubation needs for methanol-poisoned patients. So, our study aims to assess Explainable Artificial Intelligence (XAI) for predicting intubation necessity in methanol-poisoned patients, comparing deep learning and machine learning models. This study analyzed a dataset of 897 patient records from Loghman Hakim Hospital in Tehran, Iran, encompassing cases of methanol poisoning, including those requiring intubation (202 cases) and those not requiring it (695 cases). Eight established ML (SVM, XGB, DT, RF) and DL (DNN, FNN, LSTM, CNN) models were used. Techniques such as tenfold cross-validation and hyperparameter tuning were applied to prevent overfitting. The study also focused on interpretability through SHAP and LIME methods. Model performance was evaluated based on accuracy, specificity, sensitivity, F1-score, and ROC curve metrics. Among DL models, LSTM showed superior performance in accuracy (94.0%), sensitivity (99.0%), specificity (94.0%), and F1-score (97.0%). CNN led in ROC with 78.0%. For ML models, RF excelled in accuracy (97.0%) and specificity (100%), followed by XGB with sensitivity (99.37%), F1-score (98.27%), and ROC (96.08%). Overall, RF and XGB outperformed other models, with accuracy (97.0%) and specificity (100%) for RF, and sensitivity (99.37%), F1-score (98.27%), and ROC (96.08%) for XGB. ML models surpassed DL models across all metrics, with accuracies from 93.0% to 97.0% for DL and 93.0% to 99.0% for ML. Sensitivities ranged from 98.0% to 99.37% for DL and 93.0% to 99.0% for ML. DL models achieved specificities from 78.0% to 94.0%, while ML models ranged from 93.0% to 100%. F1-scores for DL were between 93.0% and 97.0%, and for ML between 96.0% and 98.27%. DL models scored ROC between 68.0% and 78.0%, while ML models ranged from 84.0% to 96.08%. Key features for predicting intubation necessity include GCS at admission, ICU admission, age, longer folic acid therapy duration, elevated BUN and AST levels, VBG_HCO3 at initial record, and hemodialysis presence. This study as the showcases XAI's effectiveness in predicting intubation necessity in methanol-poisoned patients. ML models, particularly RF and XGB, outperform DL counterparts, underscoring their potential for clinical decision-making.

The Apple App Store is the only channel through which app developers may distribute their apps on iOS. First launched in 2008, the App Store has evolved into a highly profitable marketplace, with overall consumer spend exceeding $ 50 billion in 2019. However, concerns are being increasingly expressed on both sides of the Atlantic that various practices of Apple with regard to the App Store may breach competition law. The purpose of this paper is to examine whether this is indeed the case and, if so, how these concerns can be addressed. With these aims in mind, the paper first introduces the reader to the app ecosystem and the Apple App Store, with a focus on Apple’s in-app payment policies and the 30% commission charged for in-app purchases. After engaging critically with the distinction between apps selling “digital” and apps selling “physical” goods or services, we consider such distinction is unclear, artificial, and unprincipled. The paper then critically reviews several practices of Apple that appear to be at odds with competition law and in particular Article 102 TFEU. We first discuss the issue of market definition and dominance with regard to the App Store. We find that Apple is a monopolist in the market for app distribution on iOS, as it is not subject to any meaningful competitive constraint from alternative distribution channels, such as Android app stores. The result is that Apple is the gateway through which app developers have to go in order to reach the valuable audience of iOS users. This bottleneck position affords Apple the power to engage in several prima facie anti-competitive practices. A first concern is that Apple may exploit app developers by charging excessive fees for the services it provides and by imposing unfair trading conditions. Second, based on four case studies, the paper illustrates how Apple may use its control of the App Store or iOS to engage in exclusionary behaviour to the detriment of rival apps. These practices should be investigated by competition authorities, as they are likely to result in considerable consumer harm, be it in the form of higher app prices, worse user experience or reduced consumer choice. The paper finally proposes a combination of concrete remedies that would address the competition concerns identified.

The Apple App Store is the only channel through which app developers may distribute their apps on iOS. First launched in 2008, the App Store has evolved into a highly profitable marketplace, with overall consumer spend exceeding $50 billion in 2019. However, concerns are being increasingly expressed on both sides of the Atlantic that various practices of Apple with regard to the App Store may breach competition law. The purpose of this paper is to examine whether this is indeed the case and, if so, how these concerns can be addressed. With these aims in mind, the paper first introduces the reader to the app ecosystem and the Apple App Store, with a focus on Apple’s in-app payment policies and the 30 percent commission charged for in-app purchases. After engaging critically with the distinction between apps selling “digital” and apps selling “physical” goods or services, we consider such distinction is unclear, artificial, and unprincipled. The paper then critically reviews several practices of Apple that appear to be at odds with competition law and in particular Article 102 TFEU. We first analyze the issue of market definition and dominance with regard to the App Store. We find that Apple is a monopolist in the market for app distribution on iOS, as it is not subject to any meaningful competitive constraint from alternative distribution channels, such as Android app stores. The result is that Apple is the gateway through which app developers have to go to reach the valuable audience of iOS users. This bottleneck position affords Apple the power to engage in several prima facie anticompetitive practices. A first concern is that Apple may exploit app developers by charging excessive fees for the services it provides and by imposing unfair trading conditions. Second, based on four case studies, the paper illustrates how Apple may use its control of the App Store or iOS to engage in exclusionary behavior to the detriment of rival apps. These practices should be investigated by competition authorities, as they are likely to result in considerable consumer harm, be it in the form of higher app prices, worse user experience or reduced consumer choice. The paper finally proposes a combination of concrete remedies that would address the competition concerns identified.

BackgroundFor many mental health conditions, mobile health apps offer the ability to deliver information, support, and intervention outside the clinical setting. However, there are difficulties with the use of a commercial app store to distribute health care resources, including turnover of apps, irrelevance of apps, and discordance with evidence-based practice.ObjectiveThe primary aim of this study was to quantify the longevity and rate of turnover of mental health apps within the official Android and iOS app stores. The secondary aim was to quantify the proportion of apps that were clinically relevant and assess whether the longevity of these apps differed from clinically nonrelevant apps. The tertiary aim was to establish the proportion of clinically relevant apps that included claims of clinical effectiveness. We performed additional subgroup analyses using additional data from the app stores, including search result ranking, user ratings, and number of downloads.MethodsWe searched iTunes (iOS) and the Google Play (Android) app stores each day over a 9-month period for apps related to depression, bipolar disorder, and suicide. We performed additional app-specific searches if an app no longer appeared within the main searchResultsOn the Android platform, 50% of the search results changed after 130 days (depression), 195 days (bipolar disorder), and 115 days (suicide). Search results were more stable on the iOS platform, with 50% of the search results remaining at the end of the study period. Approximately 75% of Android and 90% of iOS apps were still available to download at the end of the study. We identified only 35.3% (347/982) of apps as being clinically relevant for depression, of which 9 (2.6%) claimed clinical effectiveness. Only 3 included a full citation to a published study.ConclusionsThe mental health app environment is volatile, with a clinically relevant app for depression becoming unavailable to download every 2.9 days. This poses challenges for consumers and clinicians seeking relevant and long-term apps, as well as for researchers seeking to evaluate the evidence base for publicly available apps.

Study question Can heatmaps generated by occlusion explain the patterns learned by deep learning (DL) models classifying the embryo viability in ART? Summary answer Occlusion experiments generate heatmaps that reveal which regions in frames of time-lapse video (TLV) are more discriminative for classification and prediction by the DL models. What is known already DL has widely been explored in ART for embryo selection. Depending upon input (video or image), different DL models classifying embryo viability are developed. However, whether the prediction is based on actual input features or random guessing is unknown. The embryo selection in ART is subjective. If the intention is using DL models’ prediction to transfer, freeze or discard the embryo, explanations of how they interpret embryonic development features brings transparency and trust. In other areas, heatmaps are used for explaining DL predictions. The heatmaps can be a tool to understand patterns learned by DL models for embryo selection. Study design, size, duration We trained two separate DL models for predicting the presence of fetal heartbeat for the transferred embryos. We further used occlusion generated heatmaps to explain the predictions. For training, retrospective data was used. The input dataset consisted of 136 TLVs and corresponding patient data for 132 participants (128: single embryo transfers and 8: double embryo transfer) from both IVF and ICSI treatment. Each video was assessed by an embryologist. Participants/materials, setting, methods DL models (A as ResNet–18, B as VGG16) are trained for predicting the presence of fetal heartbeat on a single frame extracted from TLV after day three or later. Model A has a better recall (0.7) compared to B (0.5). Heatmaps explain the reason behind models’ recall rate by visually representing patterns learned by them. Using occlusion filter size 30*30 with stride 14 and size 50*50 with stride 25, we generate heatmaps for both models. Main results and the role of chance The heatmaps generated using occlusion can represent visually the patterns discovered by the DL models when predicting the presence of a fetal heartbeat. Using occlusion filter size 30*30 with stride 14, we verified that Model B has lower recall because the heatmaps show that the model finds redundant features present outside the embryo region in many input frames. It could be interpreted that either the model has not learned relevant patterns or is more robust to noise. This representation of DL models equips us in better decision-making, whether to consider or discard the prediction or rather train the model further, preprocess training data or change network architecture. The heatmaps revealed that for frames where significant patterns learned by the models are within the embryo region, more weight was given to specific features like the inner cell mass, trophectoderm and some parts within the zona pellucida. Moreover, the heat maps generated using occlusion are independent of the underlying model’s architecture as the same experiment settings were used for both models. For occlusion filter size 50*50 with stride 25, the expanse of input regions (in or outside the embryo) considered relevant could be visualized for both models A and B. Limitations, reasons for caution Heatmaps generated by occluding input regions give a visual representation of features in individual frames not directly on videos. Explaining DL models by heatmaps besides occlusion, other techniques (Grad-Cam) exist but were not evaluated. Furthermore, there is no quantitative measure for evaluating whether heatmaps are a good explanation or not. Wider implications of the findings: The heatmaps make the patterns discovered by DL models visually recognized and bring forth the prominent portions of embryo regions. This will again improve understanding and trust in DL models’ predictions. Visual representation of DL models using heatmaps enables interpreting a prediction, performing model analysis and determining scope for improvement. Trial registration number Not applicable

BackgroundAccurate and user-friendly assessment tools for quantifying alcohol consumption are a prerequisite for effective interventions to reduce alcohol-related harm. Digital assessment tools (DATs) that allow the description of consumed alcoholic drinks through animation features may facilitate more accurate reporting than conventional approaches.ObjectiveThis review aims to identify and characterize freely available DATs in English or Russian that use animation features to support the quantitative assessment of alcohol consumption (alcohol DATs) and determine the extent to which such tools have been scientifically evaluated in terms of feasibility, acceptability, and validity.MethodsSystematic English and Russian searches were conducted in iOS and Android app stores and via the Google search engine. Information on the background and content of eligible DATs was obtained from app store descriptions, websites, and test completions. A systematic literature review was conducted in Embase, MEDLINE, PsycINFO, and Web of Science to identify English-language studies reporting the feasibility, acceptability, and validity of animation-using alcohol DATs. Where possible, the evaluated DATs were accessed and assessed. Owing to the high heterogeneity of study designs, results were synthesized narratively.ResultsWe identified 22 eligible alcohol DATs in English, 3 (14%) of which were also available in Russian. More than 95% (21/22) of tools allowed the choice of a beverage type from a visually displayed selection. In addition, 36% (8/22) of tools enabled the choice of a drinking vessel. Only 9% (2/22) of tools allowed the simulated interactive pouring of a drink. For none of the tools published evaluation studies were identified in the literature review. The systematic literature review identified 5 exploratory studies evaluating the feasibility, acceptability, and validity of 4 animation-using alcohol DATs, 1 (25%) of which was available in the searched app stores. The evaluated tools reached moderate to high scores on user rating scales and showed fair to high convergent validity when compared with established assessment methods.ConclusionsAnimation-using alcohol DATs are available in app stores and on the web. However, they often use nondynamic features and lack scientific background information. Explorative study data suggest that such tools might enable the user-friendly and valid assessment of alcohol consumption and could thus serve as a building block in the reduction of alcohol-attributable health burden worldwide.Trial RegistrationPROSPERO International Prospective Register of Systematic Reviews CRD42020172825; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020172825

ObjectiveMicrovascular invasion (MVI) is of great significance for the individualized treatment of hepatocellular carcinoma (HCC) and preoperative noninvasive prediction of MVI is still an urgent clinical problem. To explore the effects of different regions of interest (ROI) and image input dimensions on the performance of deep learning (DL) models, and to select the best result to develop and validate a DL model for preoperative prediction of MVI.Materials and methodsA total of 206 patients with pathologically confirmed HCC from three hospitals were retrospectively enrolled and divided into training, internal validation and external test set. Based on hepatobiliary phase images (HBP) of gadoxetic acid-enhanced MRI, 2D DL, 3D DL and 2.5D deep multi-instance learning (MIL) models were established. The receiver operating characteristic curve (ROC) was used to evaluate the predictive efficacy of the above models. Based on the optimal performance model, the T1WI-FS and T2WI-FS images were preprocessed correspondingly, and a multimodal prediction model including three sequences was constructed. The ROC, and decision curve were used to visualize the predictive ability of the model.ResultsCompared with 2D DL and 3D DL models, the 2.5D DL model based on all axial images of ROI had the highest performance, with the AUC values of 0.802 (95% CI, 0.669–0.936) and 0.759 (95% CI, 0.643–0.875) in the validation and test sets. The AUCs of the multimodal MRI model were 0.954 (95% CI, 0.920–0.989) in the training set, 0.857 (95% CI, 0.736–0.978) in the validation set, and 0.788 (95% CI, 0.681–0.895) in the test set.ConclusionThe DL model that selects all axial slices of intratumor and peritumor as input shows robust capability in predicting MVI, which is expected to help clinical decision-making of individualized treatment for HCC.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12885-025-14971-7.

IoT-based green city architecture using secured and sustainable android services

Failure to predict stroke promptly may lead to delayed treatment, causing severe consequences like permanent neurological damage or death. Early detection using deep learning (DL) and machine learning (ML) models can enhance patient outcomes and mitigate the long-term effects of strokes. The aim of this study is to compare these models, exploring their efficacy in predicting stroke. This study analyzed a dataset comprising 663 records from patients hospitalized at Hazrat Rasool Akram Hospital in Tehran, Iran, including 401 healthy individuals and 262 stroke patients. A total of eight established ML (SVM, XGB, KNN, RF) and DL (DNN, FNN, LSTM, CNN) models were utilized to predict stroke. Techniques such as 10-fold cross-validation and hyperparameter tuning were implemented to prevent overfitting. The study also focused on interpretability through Shapley Additive Explanations (SHAP). The evaluation of model’s performance was based on accuracy, specificity, sensitivity, F1-score, and ROC curve metrics. Among DL models, LSTM showed superior sensitivity at 96.15%, while FNN exhibited better specificity (96.0%), accuracy (96.0%), F1-score (95.0%), and ROC (98.0%) among DL models. For ML models, RF displayed higher sensitivity (99.9%), accuracy (99.0%), specificity (100%), F1-score (99.0%), and ROC (99.9%). Overall, RF outperformed all models, while DL models surpassed ML models in most metrics except for RF. DL models (CNN, LSTM, DNN, FNN) achieved sensitivities from 93.0 to 96.15%, specificities from 80.0 to 96.0%, accuracies from 92.0 to 96.0%, F1-scores from 87.34 to 95.0%, and ROC scores from 95.0 to 98.0%. In contrast, ML models (KNN, XGB, SVM) showed sensitivities between 29.0% and 94.0%, specificities between 89.47% and 96.0%, accuracies between 71.0% and 95.0%, F1-scores between 44.0% and 95.0%, and ROC scores between 64.0% and 95.0%. This study demonstrates the efficacy of DL and ML models in predicting stroke, with the RF models outperforming all others in key metrics. While DL models generally surpassed ML models, RF’s exceptional performance highlights the potential of combining these technologies for early stroke detection, significantly improving patient outcomes by preventing severe consequences like permanent neurological damage or death.

BackgroundApproximately 800 million people, representing 11% of the world’s population, are affected by mental health problems. The COVID-19 pandemic exacerbated problems and triggered a decline in well-being, with drastic increase in the incidence of conditions such as anxiety, depression, and stress. Approximately 20,000 mental health apps are listed in mobile app stores. However, no significant evaluation of mental health apps in French, spoken by approximately 300 million people, has been identified in the literature yet.ObjectiveThis study aims to review the mental health mobile apps currently available on the French Apple App Store and Google Play Store and to evaluate their quality using Mobile App Rating Scale–French (MARS-F).MethodsScreening of mental health apps was conducted from June 10, 2022, to June 17, 2022, on the French Apple App Store and Google Play Store. A shortlist of 12 apps was identified using the criteria of selection and assessed using MARS-F by 9 mental health professionals. Intraclass correlation was used to evaluate interrater agreement. Mean (SD) scores and their distributions for each section and item were calculated.ResultsThe highest scores for MARS-F quality were obtained by Soutien psy avec Mon Sherpa (mean 3.85, SD 0.48), Evoluno (mean 3.54, SD 0.72), and Teale (mean 3.53, SD 0.87). Mean engagement scores (section A) ranged from 2.33 (SD 0.69) for Reflexe reussite to 3.80 (SD 0.61) for Soutien psy avec Mon Sherpa. Mean aesthetics scores (section C) ranged from 2.52 (SD 0.62) for Mental Booster to 3.89 (SD 0.69) for Soutien psy avec Mon Sherpa. Mean information scores (section D) ranged from 2.00 (SD 0.75) for Mental Booster to 3.46 (SD 0.77) for Soutien psy avec Mon Sherpa. Mean Mobile App Rating Scale subjective quality (section E) score varied from 1.22 (SD 0.26) for VOS – journal de l’humeur to 2.69 (SD 0.84) for Soutien psy avec Mon Sherpa. Mean app specificity (section F) score varied from 1.56 (SD 0.97) for Mental Booster to 3.31 (SD 1.22) for Evoluno. For all the mental health apps studied, except Soutien psy avec Mon Sherpa (11/12, 92%), the subjective quality score was always lower than the app specificity score, which was always lower than the MARS-F quality score, and that was lower than the rating score from the iPhone Operating System or Android app stores.ConclusionsMental health professionals assessed that, despite the lack of scientific evidence, the mental health mobile apps available on the French Apple App Store and Google Play Store were of good quality. However, they are reluctant to use them in their professional practice. Additional investigations are needed to assess their compliance with recommendations and their long-term impact on users.

The protection and privacy of the 5G-IoT framework is a major challenge due to the vast number of mobile devices. Specialized applications running these 5G-IoT systems may be vulnerable to clone attacks. Cloning applications can be achieved by stealing or distributing commercial Android apps to harm the advanced services of the 5G-IoT framework. Meanwhile, most Android app stores run and manage Android apps that developers have submitted separately without any central verification systems. Android scammers sell pirated versions of commercial software to other app stores under different names. Android applications are typically stored on cloud servers, while API access services may be used to detect and prevent cloned applications from being released. In this paper, we proposed a hybrid approach to the Control Flow Graph (CFG) and a deep learning model to secure the smart services of the 5G-IoT framework. First, the newly submitted APK file is extracted and the JDEX decompiler is used to retrieve Java source files from possibly original and cloned applications. Second, the source files are broken down into various android-based components. After generating Control-Flow Graphs (CFGs), the weighted features are stripped from each component. Finally, the Recurrent Neural Network (RNN) is designed to predict potential cloned applications by training features from different components of android applications. Experimental results have shown that the proposed approach can achieve an average accuracy of 96.24% for cloned applications selected from different android application stores.