Interoperability constraints in health information systems pose significant challenges to the seamless exchange and utilization of health data, hindering effective healthcare delivery. This paper aims to evaluate and ...Interoperability constraints in health information systems pose significant challenges to the seamless exchange and utilization of health data, hindering effective healthcare delivery. This paper aims to evaluate and address these constraints to enhance healthcare delivery. The study examines the current state of interoperability in health information systems, identifies the key constraints, and explores their impact on healthcare outcomes. Various approaches and strategies for addressing interoperability constraints are discussed, including the adoption of standardized data formats, implementation of interoperability frameworks, and establishment of robust data governance mechanisms. Furthermore, the study highlights the importance of stakeholder collaboration, policy development, and technical advancements in achieving enhanced interoperability. The findings emphasize the need for a comprehensive evaluation of interoperability constraints and the implementation of targeted interventions to promote seamless data exchange, improve care coordination, and enhance patient outcomes in healthcare settings.展开更多
The main objective of this research is to provide a solution for online exam systems by using face recognition to authenticate learners for attending an online exam. More importantly, the system continuously (with sho...The main objective of this research is to provide a solution for online exam systems by using face recognition to authenticate learners for attending an online exam. More importantly, the system continuously (with short time intervals), checks for learner identity during the whole exam period to ensure that the learner who started the exam is the same one who continued until the end and prevent the possibility of cheating by looking at adjacent PC or reading from an external paper. The system will issue an early warning to the learners if suspicious behavior has been noticed by the system. The proposed system has been presented to eight e-learning instructors and experts in addition to 32 students to gather feedback and to study the impact and the benefit of such system in e-learning environment.展开更多
Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM...Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance.Since,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by SM.This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic outbreaks.DL has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation results.In recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM analysis.This paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM analysis.Finally,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.展开更多
The emergence of Generative Adversarial Network(GAN)techniques has garnered significant attention from the research community for the development of Intrusion Detection Systems(IDS).However,conventional GAN-based IDS ...The emergence of Generative Adversarial Network(GAN)techniques has garnered significant attention from the research community for the development of Intrusion Detection Systems(IDS).However,conventional GAN-based IDS models face several challenges,including training instability,high computational costs,and system failures.To address these limitations,we propose a Hybrid Wasserstein GAN and Autoencoder Model(WGAN-AE)for intrusion detection.The proposed framework leverages the stability of WGAN and the feature extraction capabilities of the Autoencoder Model.The model was trained and evaluated using two recent benchmark datasets,5GNIDD and IDSIoT2024.When trained on the 5GNIDD dataset,the model achieved an average area under the precisionrecall curve is 99.8%using five-fold cross-validation and demonstrated a high detection accuracy of 97.35%when tested on independent test data.Additionally,the model is well-suited for deployment on resource-limited Internetof-Things(IoT)devices due to its ability to detect attacks within microseconds and its small memory footprint of 60.24 kB.Similarly,when trained on the IDSIoT2024 dataset,the model achieved an average PR-AUC of 94.09%and an attack detection accuracy of 97.35%on independent test data,with a memory requirement of 61.84 kB.Extensive simulation results demonstrate that the proposed hybrid model effectively addresses the shortcomings of traditional GAN-based IDS approaches in terms of detection accuracy,computational efficiency,and applicability to real-world IoT environments.展开更多
Breast cancer remains one of the most pressing global health concerns,and early detection plays a crucial role in improving survival rates.Integrating digital mammography with computational techniques and advanced ima...Breast cancer remains one of the most pressing global health concerns,and early detection plays a crucial role in improving survival rates.Integrating digital mammography with computational techniques and advanced image processing has significantly enhanced the ability to identify abnormalities.However,existing methodologies face persistent challenges,including low image contrast,noise interference,and inaccuracies in segmenting regions of interest.To address these limitations,this study introduces a novel computational framework for analyzing mammographic images,evaluated using the Mammographic Image Analysis Society(MIAS)dataset comprising 322 samples.The proposed methodology follows a structured three-stage approach.Initially,mammographic scans are classified using the Breast Imaging Reporting and Data System(BI-RADS),ensuring systematic and standardized image analysis.Next,the pectoral muscle,which can interfere with accurate segmentation,is effectively removed to refine the region of interest(ROI).The final stage involves an advanced image pre-processing module utilizing Independent Component Analysis(ICA)to enhance contrast,suppress noise,and improve image clarity.Following these enhancements,a robust segmentation technique is employed to delineated abnormal regions.Experimental results validate the efficiency of the proposed framework,demonstrating a significant improvement in the Effective Measure of Enhancement(EME)and a 3 dB increase in Peak Signal-to-Noise Ratio(PSNR),indicating superior image quality.The model also achieves an accuracy of approximately 97%,surpassing contemporary techniques evaluated on the MIAS dataset.Furthermore,its ability to process mammograms across all BI-RADS categories highlights its adaptability and reliability for clinical applications.This study presents an advanced and dependable computational framework for mammographic image analysis,effectively addressing critical challenges in noise reduction,contrast enhancement,and segmentation precision.The proposed approach lays the groundwork for seamless integration into computer-aided diagnostic(CAD)systems,with the potential to significantly enhance early breast cancer detection and contribute to improved patient outcomes.展开更多
The problem of automatic and accurate forecasting of time-series data has always been an interesting challenge for the machine learning and forecasting community.A majority of the real-world time-series problems have ...The problem of automatic and accurate forecasting of time-series data has always been an interesting challenge for the machine learning and forecasting community.A majority of the real-world time-series problems have non-stationary characteristics that make the understanding of trend and seasonality difficult.The applicability of the popular deep neural networks(DNNs)as function approximators for non-stationary TSF is studied.The following DNN models are evaluated:Multi-layer Perceptron(MLP),Convolutional Neural Network(CNN),and RNN with Long Short-Term Memory(LSTM-RNN)and RNN with Gated-Recurrent Unit(GRU-RNN).These DNN methods have been evaluated over 10 popular Indian financial stocks data.Further,the performance evaluation of these DNNs has been carried out in multiple independent runs for two settings of forecasting:(1)single-step forecasting,and(2)multi-step forecasting.These DNN methods show convincing performance for single-step forecasting(one-day ahead forecast).For the multi-step forecasting(multiple days ahead forecast),the methods for different forecast periods are evaluated.The performance of these methods demonstrates that long forecast periods have an adverse effect on performance.展开更多
In the Smart Grid(SG)residential environment,consumers change their power consumption routine according to the price and incentives announced by the utility,which causes the prices to deviate from the initial pattern....In the Smart Grid(SG)residential environment,consumers change their power consumption routine according to the price and incentives announced by the utility,which causes the prices to deviate from the initial pattern.Thereby,electricity demand and price forecasting play a significant role and can help in terms of reliability and sustainability.Due to the massive amount of data,big data analytics for forecasting becomes a hot topic in the SG domain.In this paper,the changing and non-linearity of consumer consumption pattern complex data is taken as input.To minimize the computational cost and complexity of the data,the average of the feature engineering approaches includes:Recursive Feature Eliminator(RFE),Extreme Gradient Boosting(XGboost),Random Forest(RF),and are upgraded to extract the most relevant and significant features.To this end,we have proposed the DensetNet-121 network and Support Vector Machine(SVM)ensemble with Aquila Optimizer(AO)to ensure adaptability and handle the complexity of data in the classification.Further,the AO method helps to tune the parameters of DensNet(121 layers)and SVM,which achieves less training loss,computational time,minimized overfitting problems and more training/test accuracy.Performance evaluation metrics and statistical analysis validate the proposed model results are better than the benchmark schemes.Our proposed method has achieved a minimal value of the Mean Average Percentage Error(MAPE)rate i.e.,8%by DenseNet-AO and 6%by SVM-AO and the maximum accurateness rate of 92%and 95%,respectively.展开更多
IPv6 over Low PowerWireless Personal Area Network(6LoWPAN)provides IP connectivity to the highly constrained nodes in the Internet of Things(IoTs).6LoWPANallows nodeswith limited battery power and storage capacity to ...IPv6 over Low PowerWireless Personal Area Network(6LoWPAN)provides IP connectivity to the highly constrained nodes in the Internet of Things(IoTs).6LoWPANallows nodeswith limited battery power and storage capacity to carry IPv6 datagrams over the lossy and error-prone radio links offered by the IEEE 802.15.4 standard,thus acting as an adoption layer between the IPv6 protocol and IEEE 802.15.4 network.The data link layer of IEEE 802.15.4 in 6LoWPAN is based on AES(Advanced Encryption Standard),but the 6LoWPANstandard lacks and has omitted the security and privacy requirements at higher layers.The sensor nodes in 6LoWPANcan join the network without requiring the authentication procedure.Therefore,from security perspectives,6LoWPAN is vulnerable to many attacks such as replay attack,Man-in-the-Middle attack,Impersonation attack,and Modification attack.This paper proposes a secure and efficient cluster-based authentication scheme(CBAS)for highly constrained sensor nodes in 6LoWPAN.In this approach,sensor nodes are organized into a cluster and communicate with the central network through a dedicated sensor node.The main objective of CBAS is to provide efficient and authentic communication among the 6LoWPAN nodes.To ensure the low signaling overhead during the registration,authentication,and handover procedures,we also introduce lightweight and efficient registration,de-registration,initial authentication,and handover procedures,when a sensor node or group of sensor nodes join or leave a cluster.Our security analysis shows that the proposed CBAS approach protects against various security attacks,including Identity Confidentiality attack,Modification attack,Replay attack,Man-in-the-middle attack,and Impersonation attack.Our simulation experiments show that CBAS has reduced the registration delay by 11%,handoff authentication delay by 32%,and signaling cost by 37%compared to the SGMS(Secure GroupMobility Scheme)and LAMS(Light-Wight Authentication&Mobility Scheme).展开更多
With the rise of the Internet of Vehicles(IoV)and the number of connected vehicles increasing on the roads,Cooperative Intelligent Transportation Systems(C-ITSs)have become an important area of research.As the number ...With the rise of the Internet of Vehicles(IoV)and the number of connected vehicles increasing on the roads,Cooperative Intelligent Transportation Systems(C-ITSs)have become an important area of research.As the number of Vehicle to Vehicle(V2V)and Vehicle to Interface(V2I)communication links increases,the amount of data received and processed in the network also increases.In addition,networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be sufficient.Thus,there is a need to augment them with intelligent network intrusion detection techniques.Some machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent times.However,given the expected large network size,there is a necessity for extensive data processing for use in such anomaly detection methods.Deep learning solutions are lucrative options as they remove the necessity for feature selection.Therefore,with the amount of vehicular network traffic increasing at an unprecedented rate in the C-ITS scenario,the need for deep learning-based techniques is all the more heightened.This work presents three deep learning-based misbehavior classification schemes for intrusion detection in IoV networks using Long Short Term Memory(LSTM)and Convolutional Neural Networks(CNNs).The proposed Deep Learning Classification Engines(DCLE)comprise of single or multi-step classification done by deep learning models that are deployed on the vehicular edge servers.Vehicular data received by the Road Side Units(RSUs)is pre-processed and forwarded to the edge server for classifications following the three classification schemes proposed in this paper.The proposed classifiers identify 18 different vehicular behavior types,the F1-scores ranging from 95.58%to 96.75%,much higher than the existing works.By running the classifiers on testbeds emulating edge servers,the prediction performance and prediction time comparison of the proposed scheme is compared with those of the existing studies.展开更多
On the transmission line,the invasion of foreign objects such as kites,plastic bags,and balloons and the damage to electronic components are common transmission line faults.Detecting these faults is of great significa...On the transmission line,the invasion of foreign objects such as kites,plastic bags,and balloons and the damage to electronic components are common transmission line faults.Detecting these faults is of great significance for the safe operation of power systems.Therefore,a YOLOv5 target detection method based on a deep convolution neural network is proposed.In this paper,Mobilenetv2 is used to replace Cross Stage Partial(CSP)-Darknet53 as the backbone.The structure uses depth-wise separable convolution to reduce the amount of calculation and parameters;improve the detection rate.At the same time,to compensate for the detection accuracy,the Squeeze-and-Excitation Networks(SENet)attention model is fused into the algorithm framework and a new detection scale suitable for small targets is added to improve the significance of the fault target area in the image.Collect pictures of foreign matters such as kites,plastic bags,balloons,and insulator defects of transmission lines,and sort theminto a data set.The experimental results on datasets show that themean Accuracy Precision(mAP)and recall rate of the algorithm can reach 92.1%and 92.4%,respectively.At the same time,by comparison,the detection accuracy of the proposed algorithm is higher than that of other methods.展开更多
Recent advancements in the Internet of Things IoT and cloud computing have paved the way for mobile Healthcare(mHealthcare)services.A patient within the hospital is monitored by several devices.Moreover,upon leaving t...Recent advancements in the Internet of Things IoT and cloud computing have paved the way for mobile Healthcare(mHealthcare)services.A patient within the hospital is monitored by several devices.Moreover,upon leaving the hospital,the patient can be remotely monitored whether directly using body wearable sensors or using a smartphone equipped with sensors to monitor different user-health parameters.This raises potential challenges for intelligent monitoring of patient's health.In this paper,an improved architecture for smart mHealthcare is proposed that is supported by HCI design principles.The HCI also provides the support for the User-Centric Design(UCD)for smart mHealthcare models.Furthermore,the HCI along with IoT's(Internet of Things)5-layered architecture has the potential of improving User Experience(UX)in mHealthcare design and help saving lives.The intelligent mHealthcare system is supported by the IoT sensing and communication layers and health care providers are supported by the application layer for the medical,behavioral,and health-related information.Health care providers and users are further supported by an intelligent layer performing critical situation assessment and performing a multi-modal communication using an intelligent assistant.The HCI design focuses on the ease-of-use,including user experience and safety,alarms,and error-resistant displays of the end-user,and improves user's experience and user satisfaction.展开更多
Damage of the blood vessels in retina due to diabetes is called diabetic retinopathy(DR).Hemorrhages is thefirst clinically visible symptoms of DR.This paper presents a new technique to extract and classify the hemorrh...Damage of the blood vessels in retina due to diabetes is called diabetic retinopathy(DR).Hemorrhages is thefirst clinically visible symptoms of DR.This paper presents a new technique to extract and classify the hemorrhages in fundus images.The normal objects such as blood vessels,fovea and optic disc inside retinal images are masked to distinguish them from hemorrhages.For masking blood vessels,thresholding that separates blood vessels and background intensity followed by a newfilter to extract the border of vessels based on orienta-tions of vessels are used.For masking optic disc,the image is divided into sub-images then the brightest window with maximum variance in intensity is selected.Then the candidate dark regions are extracted based on adaptive thresholding and top-hat morphological techniques.Features are extracted from each candidate region based on ophthalmologist selection such as color and size and pattern recognition techniques such as texture and wavelet features.Three different types of Support Vector Machine(SVM),Linear SVM,Quadratic SVM and Cubic SVM classifier are applied to classify the candidate dark regions as either hemor-rhages or healthy.The efficacy of the proposed method is demonstrated using the standard benchmark DIARETDB1 database and by comparing the results with methods in silico.The performance of the method is measured based on average sensitivity,specificity,F-score and accuracy.Experimental results show the Linear SVM classifier gives better results than Cubic SVM and Quadratic SVM with respect to sensitivity and accuracy and with respect to specificity Quadratic SVM gives better result as compared to other SVMs.展开更多
In recent years,there has been a rapid growth in Underwater Wireless Sensor Networks(UWSNs).The focus of research in this area is now on solving the problems associated with large-scale UWSN.One of the major issues in...In recent years,there has been a rapid growth in Underwater Wireless Sensor Networks(UWSNs).The focus of research in this area is now on solving the problems associated with large-scale UWSN.One of the major issues in such a network is the localization of underwater nodes.Localization is required for tracking objects and detecting the target.It is also considered tagging of data where sensed contents are not found of any use without localization.This is useless for application until the position of sensed content is confirmed.This article’s major goal is to review and analyze underwater node localization to solve the localization issues in UWSN.The present paper describes various existing localization schemes and broadly categorizes these schemes as Centralized and Distributed localization schemes underwater.Also,a detailed subdivision of these localization schemes is given.Further,these localization schemes are compared from different perspectives.The detailed analysis of these schemes in terms of certain performance metrics has been discussed in this paper.At the end,the paper addresses several future directions for potential research in improving localization problems of UWSN.展开更多
Every developing country relies on transportation,and there has been an exponential expansion in the development of various sorts of vehicles with various configurations,which is a major component strengthening the au...Every developing country relies on transportation,and there has been an exponential expansion in the development of various sorts of vehicles with various configurations,which is a major component strengthening the automobile sector.India is a developing country with increasing road traffic,which has resulted in challenges such as increased road accidents and traffic oversight issues.In the lack of a parametric technique for accurate vehicle recognition,which is a major worry in terms of reliability,high traffic density also leads to mayhem at checkpoints and toll plazas.A system that combines an intelligent domain approach with more sustainability indices is a better way to handle traffic density and transparency issues.The Automatic Licence Plate Recognition(ALPR)system is one of the components of the intelligent transportation system for traffic monitoring.This study is based on a comprehensive and detailed literature evaluation in the field of ALPR.The major goal of this study is to create an automatic pattern recognition system with various combinations and higher accuracy in order to increase the reliability and accuracy of identifying digits and alphabets on a car plate.The research is founded on the idea that image processing opens up a diverse environment with allied fields when employing distinct soft techniques for recognition.The properties of characters are employed to recognise the Indian licence plate in this study.For licence plate recognition,more than 200 images were analysed with various parameters and soft computing techniques were applied.In comparison to neural networks,a hybrid technique using a Convolution Neural Network(CNN)and a Support Vector Machine(SVM)classifier has a 98.45%efficiency.展开更多
Clinical methods are used for diagnosing COVID-19 infected patients,but reports posit that,several people who were initially tested positive of COVID-19,and who had some underlying diseases,turned out having negative ...Clinical methods are used for diagnosing COVID-19 infected patients,but reports posit that,several people who were initially tested positive of COVID-19,and who had some underlying diseases,turned out having negative results after further tests.Therefore,the performance of clinical methods is not always guaranteed.Moreover,chest X-ray image data of COVID-19 infected patients are mostly used in the computational models for COVID-19 diagnosis,while the use of common symptoms,such as fever,cough,fatigue,muscle aches,headache,etc.in computational models is not yet reported.In this study,we employed seven classification algorithms to empirically test and verify their efficacy when applied to diagnose COVID-19 using the aforementioned symptoms.We experimented with Logistic Regression(LR),Support Vector Machine(SVM),Naïve Byes(NB),Decision Tree(DT),Multilayer Perceptron(MLP),Fuzzy Cognitive Map(FCM)and Deep Neural Network(DNN)algorithms.The techniques were subjected to random undersampling and oversampling.Our results showed that with class imbalance,MLP and DNN outperform others.However,without class imbalance,MLP,FCM and DNN outperform others with the use of random undersampling,but DNN has the best performance by utilizing random oversampling.This study identified MLP,FCM and DNN as better classifiers over LR,NB,DT and SVM,so that healthcare software system developers can adopt them to develop intelligence-based expert systems which both medical personnel and patients can use for differential diagnosis of COVID-19 based on the aforementioned symptoms.However,the test of performance must not be limited to the traditional performance metrics.展开更多
Most of the international accreditation bodies in engineering education(e.g.,ABET)and outcome-based educational systems have based their assess-ments on learning outcomes and program educational objectives.However,map...Most of the international accreditation bodies in engineering education(e.g.,ABET)and outcome-based educational systems have based their assess-ments on learning outcomes and program educational objectives.However,map-ping program educational objectives(PEOs)to student outcomes(SOs)is a challenging and time-consuming task,especially for a new program which is applying for ABET-EAC(American Board for Engineering and Technology the American Board for Engineering and Technology—Engineering Accreditation Commission)accreditation.In addition,ABET needs to automatically ensure that the mapping(classification)is reasonable and correct.The classification also plays a vital role in the assessment of students’learning.Since the PEOs are expressed as short text,they do not contain enough semantic meaning and information,and consequently they suffer from high sparseness,multidimensionality and the curse of dimensionality.In this work,a novel associative short text classification tech-nique is proposed to map PEOs to SOs.The datasets are extracted from 152 self-study reports(SSRs)that were produced in operational settings in an engineering program accredited by ABET-EAC.The datasets are processed and transformed into a representational form appropriate for association rule mining.The extracted rules are utilized as delegate classifiers to map PEOs to SOs.The proposed asso-ciative classification of the mapping of PEOs to SOs has shown promising results,which can simplify the classification of short text and avoid many problems caused by enriching short text based on external resources that are not related or relevant to the dataset.展开更多
One of the most rapidly growing areas in the last few years is the Internet of Things(IoT),which has been used in widespread fields such as healthcare,smart homes,and industries.Android is one of the most popular oper...One of the most rapidly growing areas in the last few years is the Internet of Things(IoT),which has been used in widespread fields such as healthcare,smart homes,and industries.Android is one of the most popular operating systems(OS)used by IoT devices for communication and data exchange.Android OS captured more than 70 percent of the market share in 2021.Because of the popularity of the Android OS,it has been targeted by cybercriminals who have introduced a number of issues,such as stealing private information.As reported by one of the recent studies Androidmalware are developed almost every 10 s.Therefore,due to this huge exploitation an accurate and secure detection system is needed to secure the communication and data exchange in Android IoT devices.This paper introduces Droid-IoT,a collaborative framework to detect Android IoT malicious applications by using the blockchain technology.Droid-IoT consists of four main engines:(i)collaborative reporting engine,(ii)static analysis engine,(iii)detection engine,and(iv)blockchain engine.Each engine contributes to the detection and minimization of the risk of malicious applications and the reporting of any malicious activities.All features are extracted automatically fromthe inspected applications to be classified by the machine learning model and store the results into the blockchain.The performance of Droid-IoT was evaluated by analyzing more than 6000 Android applications and comparing the detection rate of Droid-IoT with the state-of-the-art tools.Droid-IoT achieved a detection rate of 97.74%with a low false positive rate by using an extreme gradient boosting(XGBoost)classifier.展开更多
Automated segmentation of blood vessels in retinal fundus images is essential for medical image analysis.The segmentation of retinal vessels is assumed to be essential to the progress of the decision support system fo...Automated segmentation of blood vessels in retinal fundus images is essential for medical image analysis.The segmentation of retinal vessels is assumed to be essential to the progress of the decision support system for initial analysis and treatment of retinal disease.This article develops a new Grasshopper Optimization with Fuzzy Edge Detection based Retinal Blood Vessel Segmentation and Classification(GOFED-RBVSC)model.The proposed GOFED-RBVSC model initially employs contrast enhancement process.Besides,GOAFED approach is employed to detect the edges in the retinal fundus images in which the use of GOA adjusts the membership functions.The ORB(Oriented FAST and Rotated BRIEF)feature extractor is exploited to generate feature vectors.Finally,Improved Conditional Variational Auto Encoder(ICAVE)is utilized for retinal image classification,shows the novelty of the work.The performance validation of the GOFEDRBVSC model is tested using benchmark dataset,and the comparative study highlighted the betterment of the GOFED-RBVSC model over the recent approaches.展开更多
Electroencephalogram(EEG)is a medical imaging technology that can measure the electrical activity of the scalp produced by the brain,measured and recorded chronologically the surface of the scalp from the brain.The re...Electroencephalogram(EEG)is a medical imaging technology that can measure the electrical activity of the scalp produced by the brain,measured and recorded chronologically the surface of the scalp from the brain.The recorded signals from the brain are rich with useful information.The inference of this useful information is a challenging task.This paper aims to process the EEG signals for the recognition of human emotions specifically happiness,anger,fear,sadness,and surprise in response to audiovisual stimuli.The EEG signals are recorded by placing neurosky mindwave headset on the subject’s scalp,in response to audiovisual stimuli for the mentioned emotions.Using a bandpass filter with a bandwidth of 1-100 Hz,recorded raw EEG signals are preprocessed.The preprocessed signals then further analyzed and twelve selected features in different domains are extracted.The Random forest(RF)and multilayer perceptron(MLP)algorithms are then used for the classification of the emotions through extracted features.The proposed audiovisual stimuli based EEG emotion classification system shows an average classification accuracy of 80%and 88%usingMLP and RF classifiers respectively on hybrid features for experimental signals of different subjects.The proposed model outperforms in terms of cost and accuracy.展开更多
A pathological complete response to neoadjuvant chemoradiotherapy offers patients with rectal cancer that has advanced locally the highest chance of survival.However,there is not yet a valid prediction model available...A pathological complete response to neoadjuvant chemoradiotherapy offers patients with rectal cancer that has advanced locally the highest chance of survival.However,there is not yet a valid prediction model available.An efficient feature extraction technique is also required to increase a prediction model’s precision.CDAS(cancer data access system)program is a great place to look for cancer along with images or biospecimens.In this study,we look at data from the CDAS system,specifically bowel cancer(colorectal cancer)datasets.This study suggested a survival prediction method for rectal cancer.In addition,this determines which deep learning algorithm works best by comparing their performance in terms of prediction accuracy.The initial job that leads to correct findings is corpus cleansing.Moving forward,the data preprocessing activity will be performed,which will comprise“exploratory data analysis and pruning and normalization or experimental study of data,which is required to obtain data features to design the model for cancer detection at an early stage.”Aside from that,the data corpus is separated into two sub-corpora:training data and test data,which will be utilized to assess the correctness of the constructed model.This study will compare our autoencoder accuracy to that of other deep learning algorithms,such as artificial neural network,convolutional neural network,and restricted Boltzmann machine,before implementing the suggested methodology and displaying the model’s accuracy graphically after the suggested new methodology or algorithm for patients with rectal cancer.Various criteria,including true positive rate,receiver operating characteristic(ROC)curve,and accuracy scores,are used in the experiments to determine the model’s high accuracy.In the end,we determine the accuracy score for each model.The outcomes of the simulation demonstrated that rectal cancer patients may be estimated using prediction models.It is shown that variational deep encoders have excellent accuracy of 94%in this cancer prediction and 95%for ROC curve regions.The findings demonstrate that automated prediction algorithms are capable of properly estimating rectal cancer patients’chances of survival.The best results,with 95%accuracy,were generated by deep autoencoders.展开更多
文摘Interoperability constraints in health information systems pose significant challenges to the seamless exchange and utilization of health data, hindering effective healthcare delivery. This paper aims to evaluate and address these constraints to enhance healthcare delivery. The study examines the current state of interoperability in health information systems, identifies the key constraints, and explores their impact on healthcare outcomes. Various approaches and strategies for addressing interoperability constraints are discussed, including the adoption of standardized data formats, implementation of interoperability frameworks, and establishment of robust data governance mechanisms. Furthermore, the study highlights the importance of stakeholder collaboration, policy development, and technical advancements in achieving enhanced interoperability. The findings emphasize the need for a comprehensive evaluation of interoperability constraints and the implementation of targeted interventions to promote seamless data exchange, improve care coordination, and enhance patient outcomes in healthcare settings.
文摘The main objective of this research is to provide a solution for online exam systems by using face recognition to authenticate learners for attending an online exam. More importantly, the system continuously (with short time intervals), checks for learner identity during the whole exam period to ensure that the learner who started the exam is the same one who continued until the end and prevent the possibility of cheating by looking at adjacent PC or reading from an external paper. The system will issue an early warning to the learners if suspicious behavior has been noticed by the system. The proposed system has been presented to eight e-learning instructors and experts in addition to 32 students to gather feedback and to study the impact and the benefit of such system in e-learning environment.
基金authors are thankful to the Deanship of Scientific Research at Najran University for funding this work,under the Research Groups Funding Program Grant Code(NU/RG/SERC/12/27).
文摘Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance.Since,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by SM.This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic outbreaks.DL has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation results.In recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM analysis.This paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM analysis.Finally,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.
基金the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Group Project under grant number(RGP.2/245/46)funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R760)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The research team thanks the Deanship of Graduate Studies and Scientific Research at Najran University for supporting the research project through the Nama’a program,with the project code NU/GP/SERC/13/352-1。
文摘The emergence of Generative Adversarial Network(GAN)techniques has garnered significant attention from the research community for the development of Intrusion Detection Systems(IDS).However,conventional GAN-based IDS models face several challenges,including training instability,high computational costs,and system failures.To address these limitations,we propose a Hybrid Wasserstein GAN and Autoencoder Model(WGAN-AE)for intrusion detection.The proposed framework leverages the stability of WGAN and the feature extraction capabilities of the Autoencoder Model.The model was trained and evaluated using two recent benchmark datasets,5GNIDD and IDSIoT2024.When trained on the 5GNIDD dataset,the model achieved an average area under the precisionrecall curve is 99.8%using five-fold cross-validation and demonstrated a high detection accuracy of 97.35%when tested on independent test data.Additionally,the model is well-suited for deployment on resource-limited Internetof-Things(IoT)devices due to its ability to detect attacks within microseconds and its small memory footprint of 60.24 kB.Similarly,when trained on the IDSIoT2024 dataset,the model achieved an average PR-AUC of 94.09%and an attack detection accuracy of 97.35%on independent test data,with a memory requirement of 61.84 kB.Extensive simulation results demonstrate that the proposed hybrid model effectively addresses the shortcomings of traditional GAN-based IDS approaches in terms of detection accuracy,computational efficiency,and applicability to real-world IoT environments.
基金funded by Deanship of Graduate Studies and Scientific Research at Najran University for supporting the research project through the Nama’a program,with the project code NU/GP/MRC/13/771-4.
文摘Breast cancer remains one of the most pressing global health concerns,and early detection plays a crucial role in improving survival rates.Integrating digital mammography with computational techniques and advanced image processing has significantly enhanced the ability to identify abnormalities.However,existing methodologies face persistent challenges,including low image contrast,noise interference,and inaccuracies in segmenting regions of interest.To address these limitations,this study introduces a novel computational framework for analyzing mammographic images,evaluated using the Mammographic Image Analysis Society(MIAS)dataset comprising 322 samples.The proposed methodology follows a structured three-stage approach.Initially,mammographic scans are classified using the Breast Imaging Reporting and Data System(BI-RADS),ensuring systematic and standardized image analysis.Next,the pectoral muscle,which can interfere with accurate segmentation,is effectively removed to refine the region of interest(ROI).The final stage involves an advanced image pre-processing module utilizing Independent Component Analysis(ICA)to enhance contrast,suppress noise,and improve image clarity.Following these enhancements,a robust segmentation technique is employed to delineated abnormal regions.Experimental results validate the efficiency of the proposed framework,demonstrating a significant improvement in the Effective Measure of Enhancement(EME)and a 3 dB increase in Peak Signal-to-Noise Ratio(PSNR),indicating superior image quality.The model also achieves an accuracy of approximately 97%,surpassing contemporary techniques evaluated on the MIAS dataset.Furthermore,its ability to process mammograms across all BI-RADS categories highlights its adaptability and reliability for clinical applications.This study presents an advanced and dependable computational framework for mammographic image analysis,effectively addressing critical challenges in noise reduction,contrast enhancement,and segmentation precision.The proposed approach lays the groundwork for seamless integration into computer-aided diagnostic(CAD)systems,with the potential to significantly enhance early breast cancer detection and contribute to improved patient outcomes.
文摘The problem of automatic and accurate forecasting of time-series data has always been an interesting challenge for the machine learning and forecasting community.A majority of the real-world time-series problems have non-stationary characteristics that make the understanding of trend and seasonality difficult.The applicability of the popular deep neural networks(DNNs)as function approximators for non-stationary TSF is studied.The following DNN models are evaluated:Multi-layer Perceptron(MLP),Convolutional Neural Network(CNN),and RNN with Long Short-Term Memory(LSTM-RNN)and RNN with Gated-Recurrent Unit(GRU-RNN).These DNN methods have been evaluated over 10 popular Indian financial stocks data.Further,the performance evaluation of these DNNs has been carried out in multiple independent runs for two settings of forecasting:(1)single-step forecasting,and(2)multi-step forecasting.These DNN methods show convincing performance for single-step forecasting(one-day ahead forecast).For the multi-step forecasting(multiple days ahead forecast),the methods for different forecast periods are evaluated.The performance of these methods demonstrates that long forecast periods have an adverse effect on performance.
基金The authors acknowledge the support from the Ministry of Education and the Deanship of Scientific Research,Najran University,Saudi Arabia,under code number NU/-/SERC/10/616.
文摘In the Smart Grid(SG)residential environment,consumers change their power consumption routine according to the price and incentives announced by the utility,which causes the prices to deviate from the initial pattern.Thereby,electricity demand and price forecasting play a significant role and can help in terms of reliability and sustainability.Due to the massive amount of data,big data analytics for forecasting becomes a hot topic in the SG domain.In this paper,the changing and non-linearity of consumer consumption pattern complex data is taken as input.To minimize the computational cost and complexity of the data,the average of the feature engineering approaches includes:Recursive Feature Eliminator(RFE),Extreme Gradient Boosting(XGboost),Random Forest(RF),and are upgraded to extract the most relevant and significant features.To this end,we have proposed the DensetNet-121 network and Support Vector Machine(SVM)ensemble with Aquila Optimizer(AO)to ensure adaptability and handle the complexity of data in the classification.Further,the AO method helps to tune the parameters of DensNet(121 layers)and SVM,which achieves less training loss,computational time,minimized overfitting problems and more training/test accuracy.Performance evaluation metrics and statistical analysis validate the proposed model results are better than the benchmark schemes.Our proposed method has achieved a minimal value of the Mean Average Percentage Error(MAPE)rate i.e.,8%by DenseNet-AO and 6%by SVM-AO and the maximum accurateness rate of 92%and 95%,respectively.
基金The authors would like to acknowledge the support of the Deputy for Research and Innovation,Ministry of Education,Kingdom of Saudi Arabia for this research through a Grant(NU/IFC/INT/01/008)under the institutional Funding Committee at Najran University,Kingdom of Saudi Arabia.
文摘IPv6 over Low PowerWireless Personal Area Network(6LoWPAN)provides IP connectivity to the highly constrained nodes in the Internet of Things(IoTs).6LoWPANallows nodeswith limited battery power and storage capacity to carry IPv6 datagrams over the lossy and error-prone radio links offered by the IEEE 802.15.4 standard,thus acting as an adoption layer between the IPv6 protocol and IEEE 802.15.4 network.The data link layer of IEEE 802.15.4 in 6LoWPAN is based on AES(Advanced Encryption Standard),but the 6LoWPANstandard lacks and has omitted the security and privacy requirements at higher layers.The sensor nodes in 6LoWPANcan join the network without requiring the authentication procedure.Therefore,from security perspectives,6LoWPAN is vulnerable to many attacks such as replay attack,Man-in-the-Middle attack,Impersonation attack,and Modification attack.This paper proposes a secure and efficient cluster-based authentication scheme(CBAS)for highly constrained sensor nodes in 6LoWPAN.In this approach,sensor nodes are organized into a cluster and communicate with the central network through a dedicated sensor node.The main objective of CBAS is to provide efficient and authentic communication among the 6LoWPAN nodes.To ensure the low signaling overhead during the registration,authentication,and handover procedures,we also introduce lightweight and efficient registration,de-registration,initial authentication,and handover procedures,when a sensor node or group of sensor nodes join or leave a cluster.Our security analysis shows that the proposed CBAS approach protects against various security attacks,including Identity Confidentiality attack,Modification attack,Replay attack,Man-in-the-middle attack,and Impersonation attack.Our simulation experiments show that CBAS has reduced the registration delay by 11%,handoff authentication delay by 32%,and signaling cost by 37%compared to the SGMS(Secure GroupMobility Scheme)and LAMS(Light-Wight Authentication&Mobility Scheme).
基金The work of Vinay Chamola and F.Richard Yu was supported in part by the SICI SICRG Grant through the Project Artificial Intelligence Enabled Security Provisioning and Vehicular Vision Innovations for Autonomous Vehicles,and in part by the Government of Canada's National Crime Prevention Strategy and Natural Sciences and Engineering Research Council of Canada(NSERC)CREATE Program for Building Trust in Connected and Autonomous Vehicles(TrustCAV).
文摘With the rise of the Internet of Vehicles(IoV)and the number of connected vehicles increasing on the roads,Cooperative Intelligent Transportation Systems(C-ITSs)have become an important area of research.As the number of Vehicle to Vehicle(V2V)and Vehicle to Interface(V2I)communication links increases,the amount of data received and processed in the network also increases.In addition,networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be sufficient.Thus,there is a need to augment them with intelligent network intrusion detection techniques.Some machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent times.However,given the expected large network size,there is a necessity for extensive data processing for use in such anomaly detection methods.Deep learning solutions are lucrative options as they remove the necessity for feature selection.Therefore,with the amount of vehicular network traffic increasing at an unprecedented rate in the C-ITS scenario,the need for deep learning-based techniques is all the more heightened.This work presents three deep learning-based misbehavior classification schemes for intrusion detection in IoV networks using Long Short Term Memory(LSTM)and Convolutional Neural Networks(CNNs).The proposed Deep Learning Classification Engines(DCLE)comprise of single or multi-step classification done by deep learning models that are deployed on the vehicular edge servers.Vehicular data received by the Road Side Units(RSUs)is pre-processed and forwarded to the edge server for classifications following the three classification schemes proposed in this paper.The proposed classifiers identify 18 different vehicular behavior types,the F1-scores ranging from 95.58%to 96.75%,much higher than the existing works.By running the classifiers on testbeds emulating edge servers,the prediction performance and prediction time comparison of the proposed scheme is compared with those of the existing studies.
基金Funding project:Key Project of Science and Technology Research in Colleges andUniversities of Hebei Province.Project name:MillimeterWave Radar-Based Anti-Omission Early Warning System for School Bus Personnel.Grant Number:ZD2020318,funded to author Tang XL.Sponser:Hebei Provincial Department of Education,URL:http://jyt.hebei.gov.cn/Science and Technology Research Youth Fund Project of Hebei Province Universities.Project name:Research on Defect Detection and Engineering Vehicle Tracking System for Transmission Line Scenario.Grant Number:QN2023185,funded toW.JC,member of the mentor team.Sponser:Hebei Provincial Department of Education,URL:http://jyt.hebei.gov.cn/.
文摘On the transmission line,the invasion of foreign objects such as kites,plastic bags,and balloons and the damage to electronic components are common transmission line faults.Detecting these faults is of great significance for the safe operation of power systems.Therefore,a YOLOv5 target detection method based on a deep convolution neural network is proposed.In this paper,Mobilenetv2 is used to replace Cross Stage Partial(CSP)-Darknet53 as the backbone.The structure uses depth-wise separable convolution to reduce the amount of calculation and parameters;improve the detection rate.At the same time,to compensate for the detection accuracy,the Squeeze-and-Excitation Networks(SENet)attention model is fused into the algorithm framework and a new detection scale suitable for small targets is added to improve the significance of the fault target area in the image.Collect pictures of foreign matters such as kites,plastic bags,balloons,and insulator defects of transmission lines,and sort theminto a data set.The experimental results on datasets show that themean Accuracy Precision(mAP)and recall rate of the algorithm can reach 92.1%and 92.4%,respectively.At the same time,by comparison,the detection accuracy of the proposed algorithm is higher than that of other methods.
文摘Recent advancements in the Internet of Things IoT and cloud computing have paved the way for mobile Healthcare(mHealthcare)services.A patient within the hospital is monitored by several devices.Moreover,upon leaving the hospital,the patient can be remotely monitored whether directly using body wearable sensors or using a smartphone equipped with sensors to monitor different user-health parameters.This raises potential challenges for intelligent monitoring of patient's health.In this paper,an improved architecture for smart mHealthcare is proposed that is supported by HCI design principles.The HCI also provides the support for the User-Centric Design(UCD)for smart mHealthcare models.Furthermore,the HCI along with IoT's(Internet of Things)5-layered architecture has the potential of improving User Experience(UX)in mHealthcare design and help saving lives.The intelligent mHealthcare system is supported by the IoT sensing and communication layers and health care providers are supported by the application layer for the medical,behavioral,and health-related information.Health care providers and users are further supported by an intelligent layer performing critical situation assessment and performing a multi-modal communication using an intelligent assistant.The HCI design focuses on the ease-of-use,including user experience and safety,alarms,and error-resistant displays of the end-user,and improves user's experience and user satisfaction.
基金supported by the ministry of education and the deanship of scientific research-Najran University-Kingdom of Saudi Arabia for their financial and technical support under code number NU/-/SERC/10/640.
文摘Damage of the blood vessels in retina due to diabetes is called diabetic retinopathy(DR).Hemorrhages is thefirst clinically visible symptoms of DR.This paper presents a new technique to extract and classify the hemorrhages in fundus images.The normal objects such as blood vessels,fovea and optic disc inside retinal images are masked to distinguish them from hemorrhages.For masking blood vessels,thresholding that separates blood vessels and background intensity followed by a newfilter to extract the border of vessels based on orienta-tions of vessels are used.For masking optic disc,the image is divided into sub-images then the brightest window with maximum variance in intensity is selected.Then the candidate dark regions are extracted based on adaptive thresholding and top-hat morphological techniques.Features are extracted from each candidate region based on ophthalmologist selection such as color and size and pattern recognition techniques such as texture and wavelet features.Three different types of Support Vector Machine(SVM),Linear SVM,Quadratic SVM and Cubic SVM classifier are applied to classify the candidate dark regions as either hemor-rhages or healthy.The efficacy of the proposed method is demonstrated using the standard benchmark DIARETDB1 database and by comparing the results with methods in silico.The performance of the method is measured based on average sensitivity,specificity,F-score and accuracy.Experimental results show the Linear SVM classifier gives better results than Cubic SVM and Quadratic SVM with respect to sensitivity and accuracy and with respect to specificity Quadratic SVM gives better result as compared to other SVMs.
文摘In recent years,there has been a rapid growth in Underwater Wireless Sensor Networks(UWSNs).The focus of research in this area is now on solving the problems associated with large-scale UWSN.One of the major issues in such a network is the localization of underwater nodes.Localization is required for tracking objects and detecting the target.It is also considered tagging of data where sensed contents are not found of any use without localization.This is useless for application until the position of sensed content is confirmed.This article’s major goal is to review and analyze underwater node localization to solve the localization issues in UWSN.The present paper describes various existing localization schemes and broadly categorizes these schemes as Centralized and Distributed localization schemes underwater.Also,a detailed subdivision of these localization schemes is given.Further,these localization schemes are compared from different perspectives.The detailed analysis of these schemes in terms of certain performance metrics has been discussed in this paper.At the end,the paper addresses several future directions for potential research in improving localization problems of UWSN.
基金supported by Researchers Supporting Program(TUMAProject-2021-14)AlMaarefa University,Riyadh,Saudi Arabia.Mohd Anul Haq would like to thank Deanship of Scientific Research at Majmaah University for supporting this work under Project No.R-2022-173.
文摘Every developing country relies on transportation,and there has been an exponential expansion in the development of various sorts of vehicles with various configurations,which is a major component strengthening the automobile sector.India is a developing country with increasing road traffic,which has resulted in challenges such as increased road accidents and traffic oversight issues.In the lack of a parametric technique for accurate vehicle recognition,which is a major worry in terms of reliability,high traffic density also leads to mayhem at checkpoints and toll plazas.A system that combines an intelligent domain approach with more sustainability indices is a better way to handle traffic density and transparency issues.The Automatic Licence Plate Recognition(ALPR)system is one of the components of the intelligent transportation system for traffic monitoring.This study is based on a comprehensive and detailed literature evaluation in the field of ALPR.The major goal of this study is to create an automatic pattern recognition system with various combinations and higher accuracy in order to increase the reliability and accuracy of identifying digits and alphabets on a car plate.The research is founded on the idea that image processing opens up a diverse environment with allied fields when employing distinct soft techniques for recognition.The properties of characters are employed to recognise the Indian licence plate in this study.For licence plate recognition,more than 200 images were analysed with various parameters and soft computing techniques were applied.In comparison to neural networks,a hybrid technique using a Convolution Neural Network(CNN)and a Support Vector Machine(SVM)classifier has a 98.45%efficiency.
文摘Clinical methods are used for diagnosing COVID-19 infected patients,but reports posit that,several people who were initially tested positive of COVID-19,and who had some underlying diseases,turned out having negative results after further tests.Therefore,the performance of clinical methods is not always guaranteed.Moreover,chest X-ray image data of COVID-19 infected patients are mostly used in the computational models for COVID-19 diagnosis,while the use of common symptoms,such as fever,cough,fatigue,muscle aches,headache,etc.in computational models is not yet reported.In this study,we employed seven classification algorithms to empirically test and verify their efficacy when applied to diagnose COVID-19 using the aforementioned symptoms.We experimented with Logistic Regression(LR),Support Vector Machine(SVM),Naïve Byes(NB),Decision Tree(DT),Multilayer Perceptron(MLP),Fuzzy Cognitive Map(FCM)and Deep Neural Network(DNN)algorithms.The techniques were subjected to random undersampling and oversampling.Our results showed that with class imbalance,MLP and DNN outperform others.However,without class imbalance,MLP,FCM and DNN outperform others with the use of random undersampling,but DNN has the best performance by utilizing random oversampling.This study identified MLP,FCM and DNN as better classifiers over LR,NB,DT and SVM,so that healthcare software system developers can adopt them to develop intelligence-based expert systems which both medical personnel and patients can use for differential diagnosis of COVID-19 based on the aforementioned symptoms.However,the test of performance must not be limited to the traditional performance metrics.
文摘Most of the international accreditation bodies in engineering education(e.g.,ABET)and outcome-based educational systems have based their assess-ments on learning outcomes and program educational objectives.However,map-ping program educational objectives(PEOs)to student outcomes(SOs)is a challenging and time-consuming task,especially for a new program which is applying for ABET-EAC(American Board for Engineering and Technology the American Board for Engineering and Technology—Engineering Accreditation Commission)accreditation.In addition,ABET needs to automatically ensure that the mapping(classification)is reasonable and correct.The classification also plays a vital role in the assessment of students’learning.Since the PEOs are expressed as short text,they do not contain enough semantic meaning and information,and consequently they suffer from high sparseness,multidimensionality and the curse of dimensionality.In this work,a novel associative short text classification tech-nique is proposed to map PEOs to SOs.The datasets are extracted from 152 self-study reports(SSRs)that were produced in operational settings in an engineering program accredited by ABET-EAC.The datasets are processed and transformed into a representational form appropriate for association rule mining.The extracted rules are utilized as delegate classifiers to map PEOs to SOs.The proposed asso-ciative classification of the mapping of PEOs to SOs has shown promising results,which can simplify the classification of short text and avoid many problems caused by enriching short text based on external resources that are not related or relevant to the dataset.
文摘One of the most rapidly growing areas in the last few years is the Internet of Things(IoT),which has been used in widespread fields such as healthcare,smart homes,and industries.Android is one of the most popular operating systems(OS)used by IoT devices for communication and data exchange.Android OS captured more than 70 percent of the market share in 2021.Because of the popularity of the Android OS,it has been targeted by cybercriminals who have introduced a number of issues,such as stealing private information.As reported by one of the recent studies Androidmalware are developed almost every 10 s.Therefore,due to this huge exploitation an accurate and secure detection system is needed to secure the communication and data exchange in Android IoT devices.This paper introduces Droid-IoT,a collaborative framework to detect Android IoT malicious applications by using the blockchain technology.Droid-IoT consists of four main engines:(i)collaborative reporting engine,(ii)static analysis engine,(iii)detection engine,and(iv)blockchain engine.Each engine contributes to the detection and minimization of the risk of malicious applications and the reporting of any malicious activities.All features are extracted automatically fromthe inspected applications to be classified by the machine learning model and store the results into the blockchain.The performance of Droid-IoT was evaluated by analyzing more than 6000 Android applications and comparing the detection rate of Droid-IoT with the state-of-the-art tools.Droid-IoT achieved a detection rate of 97.74%with a low false positive rate by using an extreme gradient boosting(XGBoost)classifier.
文摘Automated segmentation of blood vessels in retinal fundus images is essential for medical image analysis.The segmentation of retinal vessels is assumed to be essential to the progress of the decision support system for initial analysis and treatment of retinal disease.This article develops a new Grasshopper Optimization with Fuzzy Edge Detection based Retinal Blood Vessel Segmentation and Classification(GOFED-RBVSC)model.The proposed GOFED-RBVSC model initially employs contrast enhancement process.Besides,GOAFED approach is employed to detect the edges in the retinal fundus images in which the use of GOA adjusts the membership functions.The ORB(Oriented FAST and Rotated BRIEF)feature extractor is exploited to generate feature vectors.Finally,Improved Conditional Variational Auto Encoder(ICAVE)is utilized for retinal image classification,shows the novelty of the work.The performance validation of the GOFEDRBVSC model is tested using benchmark dataset,and the comparative study highlighted the betterment of the GOFED-RBVSC model over the recent approaches.
基金Authors would like to acknowledge the support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia for this research through a Grant(NU/IFC/ENT/01/014)under the institutional Funding Committee at Najran University,Kingdom of Saudi Arabia.
文摘Electroencephalogram(EEG)is a medical imaging technology that can measure the electrical activity of the scalp produced by the brain,measured and recorded chronologically the surface of the scalp from the brain.The recorded signals from the brain are rich with useful information.The inference of this useful information is a challenging task.This paper aims to process the EEG signals for the recognition of human emotions specifically happiness,anger,fear,sadness,and surprise in response to audiovisual stimuli.The EEG signals are recorded by placing neurosky mindwave headset on the subject’s scalp,in response to audiovisual stimuli for the mentioned emotions.Using a bandpass filter with a bandwidth of 1-100 Hz,recorded raw EEG signals are preprocessed.The preprocessed signals then further analyzed and twelve selected features in different domains are extracted.The Random forest(RF)and multilayer perceptron(MLP)algorithms are then used for the classification of the emotions through extracted features.The proposed audiovisual stimuli based EEG emotion classification system shows an average classification accuracy of 80%and 88%usingMLP and RF classifiers respectively on hybrid features for experimental signals of different subjects.The proposed model outperforms in terms of cost and accuracy.
文摘A pathological complete response to neoadjuvant chemoradiotherapy offers patients with rectal cancer that has advanced locally the highest chance of survival.However,there is not yet a valid prediction model available.An efficient feature extraction technique is also required to increase a prediction model’s precision.CDAS(cancer data access system)program is a great place to look for cancer along with images or biospecimens.In this study,we look at data from the CDAS system,specifically bowel cancer(colorectal cancer)datasets.This study suggested a survival prediction method for rectal cancer.In addition,this determines which deep learning algorithm works best by comparing their performance in terms of prediction accuracy.The initial job that leads to correct findings is corpus cleansing.Moving forward,the data preprocessing activity will be performed,which will comprise“exploratory data analysis and pruning and normalization or experimental study of data,which is required to obtain data features to design the model for cancer detection at an early stage.”Aside from that,the data corpus is separated into two sub-corpora:training data and test data,which will be utilized to assess the correctness of the constructed model.This study will compare our autoencoder accuracy to that of other deep learning algorithms,such as artificial neural network,convolutional neural network,and restricted Boltzmann machine,before implementing the suggested methodology and displaying the model’s accuracy graphically after the suggested new methodology or algorithm for patients with rectal cancer.Various criteria,including true positive rate,receiver operating characteristic(ROC)curve,and accuracy scores,are used in the experiments to determine the model’s high accuracy.In the end,we determine the accuracy score for each model.The outcomes of the simulation demonstrated that rectal cancer patients may be estimated using prediction models.It is shown that variational deep encoders have excellent accuracy of 94%in this cancer prediction and 95%for ROC curve regions.The findings demonstrate that automated prediction algorithms are capable of properly estimating rectal cancer patients’chances of survival.The best results,with 95%accuracy,were generated by deep autoencoders.
