Malware continues to pose a significant threat to cybersecurity,with new advanced infections that go beyond traditional detection.Limitations in existing systems include high false-positive rates,slow system response ...Malware continues to pose a significant threat to cybersecurity,with new advanced infections that go beyond traditional detection.Limitations in existing systems include high false-positive rates,slow system response times,and inability to respond quickly to new malware forms.To overcome these challenges,this paper proposes OMD-RAS:Implementing Malware Detection in an Optimized Way through Real-Time and Adaptive Security as an extensive approach,hoping to get good results towards better malware threat detection and remediation.The significant steps in the model are data collection followed by comprehensive preprocessing consisting of feature engineering and normalization.Static analysis,along with dynamic analysis,is done to capture the whole spectrum of malware behavior for the feature extraction process.The extracted processed features are given with a continuous learning mechanism to the Extreme Learning Machine model of real-time detection.This OMD-RAS trains quickly and has great accuracy,providing elite,advanced real-time detection capabilities.This approach uses continuous learning to adapt to new threats—ensuring the effectiveness of detection even as strategies used by malware may change over time.The experimental results showed that OMD-RAS performs better than the traditional approaches.For instance,the OMD-RAS model has been able to achieve an accuracy of 96.23%and massively reduce the rate of false positives across all datasets while eliciting a consistently high rate of precision and recall.The model’s adaptive learning reflected enhancements on other performance measures-for example,Matthews Correlation Coefficients and Log Loss.展开更多
Digital twin technology is revolutionizing personalized healthcare by creating dynamic virtual replicas of individual patients.This paper presents a novel multi-modal architecture leveraging digital twins to enhance p...Digital twin technology is revolutionizing personalized healthcare by creating dynamic virtual replicas of individual patients.This paper presents a novel multi-modal architecture leveraging digital twins to enhance precision in predictive diagnostics and treatment planning of phoneme labeling.By integrating real-time images,electronic health records,and genomic information,the system enables personalized simulations for disease progression modeling,treatment response prediction,and preventive care strategies.In dysarthric speech,which is characterized by articulation imprecision,temporal misalignments,and phoneme distortions,existing models struggle to capture these irregularities.Traditional approaches,often relying solely on audio features,fail to address the full complexity of phoneme variations,leading to increased phoneme error rates(PER)and word error rates(WER).To overcome these challenges,we propose a novel multi-modal architecture that integrates both audio and articulatory data through a combination of Temporal Convolutional Networks(TCNs),Graph Convolutional Networks(GCNs),Transformer Encoders,and a cross-modal attention mechanism.The audio branch of the model utilizes TCNs and Transformer Encoders to capture both short-and long-term dependencies in the audio signal,while the articulatory branch leverages GCNs to model spatial relationships between articulators,such as the lips,jaw,and tongue,allowing the model to detect subtle articulatory imprecisions.A cross-modal attention mechanism fuses the encoded audio and articulatory features,enabling dynamic adjustment of the model’s focus depending on input quality,which significantly improves phoneme labeling accuracy.The proposed model consistently outperforms existing methods,achieving lower Phoneme Error Rates(PER),Word Error Rates(WER),and Articulatory Feature Misclassification Rates(AFMR).Specifically,across all datasets,the model achieves an average PER of 13.43%,an average WER of 21.67%,and an average AFMR of 12.73%.By capturing both the acoustic and articulatory intricacies of speech,this comprehensive approach not only improves phoneme labeling precision but also marks substantial progress in speech recognition technology for individuals with dysarthria.展开更多
Software defect prediction is a critical component in maintaining software quality,enabling early identification and resolution of issues that could lead to system failures and significant financial losses.With the in...Software defect prediction is a critical component in maintaining software quality,enabling early identification and resolution of issues that could lead to system failures and significant financial losses.With the increasing reliance on user-generated content,social media reviews have emerged as a valuable source of real-time feedback,offering insights into potential software defects that traditional testing methods may overlook.However,existing models face challenges like handling imbalanced data,high computational complexity,and insufficient inte-gration of contextual information from these reviews.To overcome these limitations,this paper introduces the SESDP(Sentiment Analysis-Based Early Software Defect Prediction)model.SESDP employs a Transformer-Based Multi-Task Learning approach using Robustly Optimized Bidirectional Encoder Representations from Transformers Approach(RoBERTa)to simultaneously perform sentiment analysis and defect prediction.By integrating text embedding extraction,sentiment score computation,and feature fusion,the model effectively captures both the contextual nuances and sentiment expressed in user reviews.Experimental results show that SESDP achieves superior performance with an accuracy of 96.37%,precision of 94.7%,and recall of 95.4%,particularly excelling in handling imbalanced datasets compared to baseline models.This approach offers a scalable and efficient solution for early software defect detection,enhancing proactive software quality assurance.展开更多
Depression is a prevalent mental health issue affecting individuals of all age groups globally.Similar to other mental health disorders,diagnosing depression presents significant challenges for medical practitioners a...Depression is a prevalent mental health issue affecting individuals of all age groups globally.Similar to other mental health disorders,diagnosing depression presents significant challenges for medical practitioners and clinical experts,primarily due to societal stigma and a lack of awareness and acceptance.Although medical interventions such as therapies,medications,and brain stimulation therapy provide hope for treatment,there is still a gap in the efficient detection of depression.Traditional methods,like in-person therapies,are both time-consuming and labor-intensive,emphasizing the necessity for technological assistance,especially through Artificial Intelligence.Alternative to this,in most cases it has been diagnosed through questionnaire-based mental status assessments.However,this method often produces inconsistent and inaccurate results.Additionally,there is currently a lack of a comprehensive diagnostic framework that could be effective achieving accurate and robust diagnostic outcomes.For a considerable time,researchers have sought methods to identify symptoms of depression through individuals’speech and responses,leveraging automation systems and computer technology.This research proposed MDD which composed of multimodal data collection,preprocessing,and feature extraction(utilizing the T5 model for text features and the WaveNet model for speech features).Canonical Correlation Analysis(CCA)is then used to create correlated projections of text and audio features,followed by feature fusion through concatenation.Finally,depression detection is performed using a neural network with a sigmoid output layer.The proposed model achieved remarkable performance,on the Distress Analysis Interview Corpus-Wizard(DAIC-WOZ)dataset,it attained an accuracy of 92.75%,precision of 92.05%,and recall of 92.22%.For the E-DAIC dataset,it achieved an accuracy of 91.74%,precision of 90.35%,and recall of 90.95%.Whereas,on CD-III dataset(Custom Dataset for Depression),the model demonstrated an accuracy of 93.05%,precision of 92.12%,and recall of 92.85%.These results underscore the model’s robust capability in accurately diagnosing depressive disorder,demonstrating the efficacy of advanced feature extraction methods and improved classification algorithm.展开更多
Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity users.It hinders the economic growth of utility companies,poses electrical risks,and impacts the hig...Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity users.It hinders the economic growth of utility companies,poses electrical risks,and impacts the high energy costs borne by consumers.The development of smart grids is crucial for the identification of power theft since these systems create enormous amounts of data,including information on client consumption,which may be used to identify electricity theft using machine learning and deep learning techniques.Moreover,there also exist different solutions such as hardware-based solutions to detect electricity theft that may require human resources and expensive hardware.Computer-based solutions are presented in the literature to identify electricity theft but due to the dimensionality curse,class imbalance issue and improper hyper-parameter tuning of such models lead to poor performance.In this research,a hybrid deep learning model abbreviated as RoGRUT is proposed to detect electricity theft as amalicious and non-malicious activity.The key steps of the RoGRUT are data preprocessing that covers the problem of class imbalance,feature extraction and final theft detection.Different advanced-level models like RoBERTa is used to address the curse of dimensionality issue,the near miss for class imbalance,and transfer learning for classification.The effectiveness of the RoGRUTis evaluated using the dataset fromactual smartmeters.A significant number of simulations demonstrate that,when compared to its competitors,the RoGRUT achieves the best classification results.The performance evaluation of the proposed model revealed exemplary results across variousmetrics.The accuracy achieved was 88%,with precision at an impressive 86%and recall reaching 84%.The F1-Score,a measure of overall performance,stood at 85%.Furthermore,themodel exhibited a noteworthyMatthew correlation coefficient of 78%and excelled with an area under the curve of 91%.展开更多
Social media forums have emerged as the most popular form of communication in the modern technology era,allowing people to discuss and express their opinions.This increases the amount of material being shared on socia...Social media forums have emerged as the most popular form of communication in the modern technology era,allowing people to discuss and express their opinions.This increases the amount of material being shared on social media sites.There is a wealth of information about the threat that may be found in such open data sources.The security of already-deployed software and systems relies heavily on the timely detection of newly-emerging threats to their safety that can be gleaned from such information.Despite the fact that several models for detecting cybersecurity events have been presented,it remains challenging to extract security events from the vast amounts of unstructured text present in public data sources.The majority of the currently available methods concentrate on detecting events that have a high number of dimensions.This is because the unstructured text in open data sources typically contains a large number of dimensions.However,to react to attacks quicker than they can be launched,security analysts and information technology operators need to be aware of critical security events as soon as possible,regardless of how often they are reported.This research provides a unique event detection method that can swiftly identify significant security events from open forums such as Twitter.The proposed work identified new threats and the revival of an attack or related event,independent of the volume of mentions relating to those events on Twitter.In this research work,deep learning has been used to extract predictive features from open-source text.The proposed model is composed of data collection,data transformation,feature extraction using deep learning,Latent Dirichlet Allocation(LDA)based medium-level cyber-event detection and final Google Trends-based high-level cyber-event detection.The proposed technique has been evaluated on numerous datasets.Experiment results show that the proposed method outperforms existing methods in detecting cyber events by giving 95.96% accuracy.展开更多
There exists various neurological disorder based diseases like tumor,sleep disorder,headache,dementia and Epilepsy.Among these,epilepsy is the most common neurological illness in humans,comparable to stroke.Epilepsy i...There exists various neurological disorder based diseases like tumor,sleep disorder,headache,dementia and Epilepsy.Among these,epilepsy is the most common neurological illness in humans,comparable to stroke.Epilepsy is a severe chronic neurological illness that can be discovered through analysis of the signals generated by brain neurons and brain Magnetic resonance imaging(MRI).Neurons are intricately coupled in order to communicate and generate signals from human organs.Due to the complex nature of electroencephalogram(EEG)signals and MRI’s the epileptic seizures detection and brain related problems diagnosis becomes a challenging task.Computer based techniques and machine learning models are continuously giving their contributions to diagnose all such diseases in a better way than the normal process of diagnosis.Their performancemay sometime degrade due to missing information,selection of poor classification model and unavailability of quality data that are used to train the models for better prediction.This research work is an attempt to epileptic seizures detection by using amulti focus dataset based on EEG signals and brainMRI.The key steps of this work are:feature extraction having two different streams i.e.,EEGusingwavelet transformation along with SVD-Entropy,and MRI using convolutional neural network(CNN),after extracting features fromboth streams,feature fusion is applied to generate feature vector used by support vector machine(SVM)to diagnose the epileptic seizures.From the experimental evaluation and results comparison with the current state-of-the-art techniques,it has been concluded that the performance of the proposed scheme is better than the existing models.展开更多
基金supported by a grant from the Center of Excellence in Information Assurance(CoEIA),King Saud University(KSU).
文摘Malware continues to pose a significant threat to cybersecurity,with new advanced infections that go beyond traditional detection.Limitations in existing systems include high false-positive rates,slow system response times,and inability to respond quickly to new malware forms.To overcome these challenges,this paper proposes OMD-RAS:Implementing Malware Detection in an Optimized Way through Real-Time and Adaptive Security as an extensive approach,hoping to get good results towards better malware threat detection and remediation.The significant steps in the model are data collection followed by comprehensive preprocessing consisting of feature engineering and normalization.Static analysis,along with dynamic analysis,is done to capture the whole spectrum of malware behavior for the feature extraction process.The extracted processed features are given with a continuous learning mechanism to the Extreme Learning Machine model of real-time detection.This OMD-RAS trains quickly and has great accuracy,providing elite,advanced real-time detection capabilities.This approach uses continuous learning to adapt to new threats—ensuring the effectiveness of detection even as strategies used by malware may change over time.The experimental results showed that OMD-RAS performs better than the traditional approaches.For instance,the OMD-RAS model has been able to achieve an accuracy of 96.23%and massively reduce the rate of false positives across all datasets while eliciting a consistently high rate of precision and recall.The model’s adaptive learning reflected enhancements on other performance measures-for example,Matthews Correlation Coefficients and Log Loss.
基金funded by the Ongoing Research Funding program(ORF-2025-867),King Saud University,Riyadh,Saudi Arabia.
文摘Digital twin technology is revolutionizing personalized healthcare by creating dynamic virtual replicas of individual patients.This paper presents a novel multi-modal architecture leveraging digital twins to enhance precision in predictive diagnostics and treatment planning of phoneme labeling.By integrating real-time images,electronic health records,and genomic information,the system enables personalized simulations for disease progression modeling,treatment response prediction,and preventive care strategies.In dysarthric speech,which is characterized by articulation imprecision,temporal misalignments,and phoneme distortions,existing models struggle to capture these irregularities.Traditional approaches,often relying solely on audio features,fail to address the full complexity of phoneme variations,leading to increased phoneme error rates(PER)and word error rates(WER).To overcome these challenges,we propose a novel multi-modal architecture that integrates both audio and articulatory data through a combination of Temporal Convolutional Networks(TCNs),Graph Convolutional Networks(GCNs),Transformer Encoders,and a cross-modal attention mechanism.The audio branch of the model utilizes TCNs and Transformer Encoders to capture both short-and long-term dependencies in the audio signal,while the articulatory branch leverages GCNs to model spatial relationships between articulators,such as the lips,jaw,and tongue,allowing the model to detect subtle articulatory imprecisions.A cross-modal attention mechanism fuses the encoded audio and articulatory features,enabling dynamic adjustment of the model’s focus depending on input quality,which significantly improves phoneme labeling accuracy.The proposed model consistently outperforms existing methods,achieving lower Phoneme Error Rates(PER),Word Error Rates(WER),and Articulatory Feature Misclassification Rates(AFMR).Specifically,across all datasets,the model achieves an average PER of 13.43%,an average WER of 21.67%,and an average AFMR of 12.73%.By capturing both the acoustic and articulatory intricacies of speech,this comprehensive approach not only improves phoneme labeling precision but also marks substantial progress in speech recognition technology for individuals with dysarthria.
基金funded by a grant from the Center of Excellence in Information Assurance(CoEIA),King Saud University(KSU).
文摘Software defect prediction is a critical component in maintaining software quality,enabling early identification and resolution of issues that could lead to system failures and significant financial losses.With the increasing reliance on user-generated content,social media reviews have emerged as a valuable source of real-time feedback,offering insights into potential software defects that traditional testing methods may overlook.However,existing models face challenges like handling imbalanced data,high computational complexity,and insufficient inte-gration of contextual information from these reviews.To overcome these limitations,this paper introduces the SESDP(Sentiment Analysis-Based Early Software Defect Prediction)model.SESDP employs a Transformer-Based Multi-Task Learning approach using Robustly Optimized Bidirectional Encoder Representations from Transformers Approach(RoBERTa)to simultaneously perform sentiment analysis and defect prediction.By integrating text embedding extraction,sentiment score computation,and feature fusion,the model effectively captures both the contextual nuances and sentiment expressed in user reviews.Experimental results show that SESDP achieves superior performance with an accuracy of 96.37%,precision of 94.7%,and recall of 95.4%,particularly excelling in handling imbalanced datasets compared to baseline models.This approach offers a scalable and efficient solution for early software defect detection,enhancing proactive software quality assurance.
文摘Depression is a prevalent mental health issue affecting individuals of all age groups globally.Similar to other mental health disorders,diagnosing depression presents significant challenges for medical practitioners and clinical experts,primarily due to societal stigma and a lack of awareness and acceptance.Although medical interventions such as therapies,medications,and brain stimulation therapy provide hope for treatment,there is still a gap in the efficient detection of depression.Traditional methods,like in-person therapies,are both time-consuming and labor-intensive,emphasizing the necessity for technological assistance,especially through Artificial Intelligence.Alternative to this,in most cases it has been diagnosed through questionnaire-based mental status assessments.However,this method often produces inconsistent and inaccurate results.Additionally,there is currently a lack of a comprehensive diagnostic framework that could be effective achieving accurate and robust diagnostic outcomes.For a considerable time,researchers have sought methods to identify symptoms of depression through individuals’speech and responses,leveraging automation systems and computer technology.This research proposed MDD which composed of multimodal data collection,preprocessing,and feature extraction(utilizing the T5 model for text features and the WaveNet model for speech features).Canonical Correlation Analysis(CCA)is then used to create correlated projections of text and audio features,followed by feature fusion through concatenation.Finally,depression detection is performed using a neural network with a sigmoid output layer.The proposed model achieved remarkable performance,on the Distress Analysis Interview Corpus-Wizard(DAIC-WOZ)dataset,it attained an accuracy of 92.75%,precision of 92.05%,and recall of 92.22%.For the E-DAIC dataset,it achieved an accuracy of 91.74%,precision of 90.35%,and recall of 90.95%.Whereas,on CD-III dataset(Custom Dataset for Depression),the model demonstrated an accuracy of 93.05%,precision of 92.12%,and recall of 92.85%.These results underscore the model’s robust capability in accurately diagnosing depressive disorder,demonstrating the efficacy of advanced feature extraction methods and improved classification algorithm.
基金a grant from the Center of Excellence in Information Assurance(CoEIA),KSU.
文摘Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity users.It hinders the economic growth of utility companies,poses electrical risks,and impacts the high energy costs borne by consumers.The development of smart grids is crucial for the identification of power theft since these systems create enormous amounts of data,including information on client consumption,which may be used to identify electricity theft using machine learning and deep learning techniques.Moreover,there also exist different solutions such as hardware-based solutions to detect electricity theft that may require human resources and expensive hardware.Computer-based solutions are presented in the literature to identify electricity theft but due to the dimensionality curse,class imbalance issue and improper hyper-parameter tuning of such models lead to poor performance.In this research,a hybrid deep learning model abbreviated as RoGRUT is proposed to detect electricity theft as amalicious and non-malicious activity.The key steps of the RoGRUT are data preprocessing that covers the problem of class imbalance,feature extraction and final theft detection.Different advanced-level models like RoBERTa is used to address the curse of dimensionality issue,the near miss for class imbalance,and transfer learning for classification.The effectiveness of the RoGRUTis evaluated using the dataset fromactual smartmeters.A significant number of simulations demonstrate that,when compared to its competitors,the RoGRUT achieves the best classification results.The performance evaluation of the proposed model revealed exemplary results across variousmetrics.The accuracy achieved was 88%,with precision at an impressive 86%and recall reaching 84%.The F1-Score,a measure of overall performance,stood at 85%.Furthermore,themodel exhibited a noteworthyMatthew correlation coefficient of 78%and excelled with an area under the curve of 91%.
基金funded by a grant from the Center of Excellence in Information Assurance(CoEIA),KSU.
文摘Social media forums have emerged as the most popular form of communication in the modern technology era,allowing people to discuss and express their opinions.This increases the amount of material being shared on social media sites.There is a wealth of information about the threat that may be found in such open data sources.The security of already-deployed software and systems relies heavily on the timely detection of newly-emerging threats to their safety that can be gleaned from such information.Despite the fact that several models for detecting cybersecurity events have been presented,it remains challenging to extract security events from the vast amounts of unstructured text present in public data sources.The majority of the currently available methods concentrate on detecting events that have a high number of dimensions.This is because the unstructured text in open data sources typically contains a large number of dimensions.However,to react to attacks quicker than they can be launched,security analysts and information technology operators need to be aware of critical security events as soon as possible,regardless of how often they are reported.This research provides a unique event detection method that can swiftly identify significant security events from open forums such as Twitter.The proposed work identified new threats and the revival of an attack or related event,independent of the volume of mentions relating to those events on Twitter.In this research work,deep learning has been used to extract predictive features from open-source text.The proposed model is composed of data collection,data transformation,feature extraction using deep learning,Latent Dirichlet Allocation(LDA)based medium-level cyber-event detection and final Google Trends-based high-level cyber-event detection.The proposed technique has been evaluated on numerous datasets.Experiment results show that the proposed method outperforms existing methods in detecting cyber events by giving 95.96% accuracy.
基金the Deanship of Scientific Research at King Saud University for funding this work through research group No(RG-1439-023).
文摘There exists various neurological disorder based diseases like tumor,sleep disorder,headache,dementia and Epilepsy.Among these,epilepsy is the most common neurological illness in humans,comparable to stroke.Epilepsy is a severe chronic neurological illness that can be discovered through analysis of the signals generated by brain neurons and brain Magnetic resonance imaging(MRI).Neurons are intricately coupled in order to communicate and generate signals from human organs.Due to the complex nature of electroencephalogram(EEG)signals and MRI’s the epileptic seizures detection and brain related problems diagnosis becomes a challenging task.Computer based techniques and machine learning models are continuously giving their contributions to diagnose all such diseases in a better way than the normal process of diagnosis.Their performancemay sometime degrade due to missing information,selection of poor classification model and unavailability of quality data that are used to train the models for better prediction.This research work is an attempt to epileptic seizures detection by using amulti focus dataset based on EEG signals and brainMRI.The key steps of this work are:feature extraction having two different streams i.e.,EEGusingwavelet transformation along with SVD-Entropy,and MRI using convolutional neural network(CNN),after extracting features fromboth streams,feature fusion is applied to generate feature vector used by support vector machine(SVM)to diagnose the epileptic seizures.From the experimental evaluation and results comparison with the current state-of-the-art techniques,it has been concluded that the performance of the proposed scheme is better than the existing models.
