Geographic Information Systems (GIS) have become a fact of our life as they are being used by more people and organizations for more complex decision problems than ever before. The use of GIS can achieve valuable bene...Geographic Information Systems (GIS) have become a fact of our life as they are being used by more people and organizations for more complex decision problems than ever before. The use of GIS can achieve valuable benefits for individuals, organizations and society;however, the achievement of these benefits depends on the success of GIS. While information systems (IS) success models have received much attention among researchers, there is a general scarcity of research conducted to measure the GIS success. This paper proposes a success model for measuring GIS success by extending and modifying previous IS success models. The developed success model consists of two main levels: GIS project diffusion success, and GIS post-implementation success. The first level identifies the critical success factors (CSFs) that influence the success of GIS adoption at each stage of the diffusion process. The second level of the proposed model identifies and organizes the success dimensions (outcome measures) of GIS in temporal and causal relationships. In order to assess the relationships among the success dimensions, 11 hypotheses were tested. Data were collected through a questionnaire that was distributed to 252 GIS users/managers in Egypt and abroad. The empirical results support 6 hypotheses and reject 5 hypotheses.展开更多
This paper describes the implementation of an Information Systems （IS） capstone project management course that is a requirement for graduating seniors in an undergraduate Computer Information Systems （CIS） program...This paper describes the implementation of an Information Systems （IS） capstone project management course that is a requirement for graduating seniors in an undergraduate Computer Information Systems （CIS） program at a regional university. The description provides a model which includes the culmination of students＇ academic training in an IS curriculum which is part of a Bachelor of Business Administration （BBA） program in an accredited college of business. The course requires an application of technical and business skills, as well as systems development and project management skills--while students are working on an actual IS project for an external sponsoring organization. Rationale for implementing this type of course includes the benefits it provides to the students, the project sponsors, and the IS department providing the course. Feedback from the course is used as integral part of the C1S curriculum assessment process used for accreditation purposes.展开更多
The ever-growing network traffic threat landscape necessitates adopting accurate and robust intrusion detection systems(IDSs).IDSs have become a research hotspot and have seen remarkable performance improvements.Gener...The ever-growing network traffic threat landscape necessitates adopting accurate and robust intrusion detection systems(IDSs).IDSs have become a research hotspot and have seen remarkable performance improvements.Generative adversarial networks(GANs)have also garnered increasing research interest recently due to their remarkable ability to generate data.This paper investigates the application of(GANs)in(IDS)and explores their current use within this research field.We delve into the adoption of GANs within signature-based,anomaly-based,and hybrid IDSs,focusing on their objectives,methodologies,and advantages.Overall,GANs have been widely employed,mainly focused on solving the class imbalance issue by generating realistic attack samples.While GANs have shown significant potential in addressing the class imbalance issue,there are still open opportunities and challenges to be addressed.Little attention has been paid to their applicability in distributed and decentralized domains,such as IoT networks.Efficiency and scalability have been mostly overlooked,and thus,future works must aim at addressing these gaps.展开更多
This paper aims to design and implement an automatic heart disease diagnosis system using?MATLAB. The Cleveland data set for heart diseases was used as the main database for training and testing the developed system. ...This paper aims to design and implement an automatic heart disease diagnosis system using?MATLAB. The Cleveland data set for heart diseases was used as the main database for training and testing the developed system. In order to train and test the Cleveland data set, two systems were developed. The first system is based on the Multilayer Perceptron (MLP) structure on the Artificial Neural Network (ANN), whereas the second system is based on the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) approach. Each system has two main modules, namely, training and testing,?where 80% and 20% of the Cleveland data set were randomly selected for training and testing?purposes respectively. Each system also has an additional module known as case-based module,?where the user has to input values for 13 required attributes as specified by the Cleveland data set,?in order to test the status of the patient whether heart disease is present or absent from that particular patient. In addition, the effects of different values for important parameters were investigated in the ANN-based and Neuro-Fuzzy-based systems in order to select the best parameters that obtain the highest performance. Based on the experimental work, it is clear that the Neuro-Fuzzy system outperforms the ANN system using the training data set, where the accuracy for each system was 100% and 90.74%, respectively. However, using the testing data set, it is clear that the ANN system outperforms the Neuro-Fuzzy system, where the best accuracy for each system was 87.04% and 75.93%, respectively.展开更多
Rapid technological advancement has enabled modern healthcare systems to provide more sophisticated and real-time services on the Internet of Medical Things(IoMT).The existing cloud-based,centralized IoMT architecture...Rapid technological advancement has enabled modern healthcare systems to provide more sophisticated and real-time services on the Internet of Medical Things(IoMT).The existing cloud-based,centralized IoMT architectures are vulnerable to multiple security and privacy problems.The blockchain-enabled IoMT is an emerging paradigm that can ensure the security and trustworthiness of medical data sharing in the IoMT networks.This article presents a private and easily expandable blockchain-based framework for the IoMT.The proposed framework contains several participants,including private blockchain,hospitalmanagement systems,cloud service providers,doctors,and patients.Data security is ensured by incorporating an attributebased encryption scheme.Furthermore,an IoT-friendly consensus algorithm is deployed to ensure fast block validation and high scalability in the IoMT network.The proposed framework can perform multiple healthcare-related services in a secure and trustworthy manner.The performance of blockchain read/write operations is evaluated in terms of transaction throughput and latency.Experimental outcomes indicate that the proposed scheme achieved an average throughput of 857 TPS and 151 TPS for read and write operations.The average latency is 61 ms and 16 ms for read and write operations,respectively.展开更多
One of the biggest dangers to society today is terrorism, where attacks have become one of the most significantrisks to international peace and national security. Big data, information analysis, and artificial intelli...One of the biggest dangers to society today is terrorism, where attacks have become one of the most significantrisks to international peace and national security. Big data, information analysis, and artificial intelligence (AI) havebecome the basis for making strategic decisions in many sensitive areas, such as fraud detection, risk management,medical diagnosis, and counter-terrorism. However, there is still a need to assess how terrorist attacks are related,initiated, and detected. For this purpose, we propose a novel framework for classifying and predicting terroristattacks. The proposed framework posits that neglected text attributes included in the Global Terrorism Database(GTD) can influence the accuracy of the model’s classification of terrorist attacks, where each part of the datacan provide vital information to enrich the ability of classifier learning. Each data point in a multiclass taxonomyhas one or more tags attached to it, referred as “related tags.” We applied machine learning classifiers to classifyterrorist attack incidents obtained from the GTD. A transformer-based technique called DistilBERT extracts andlearns contextual features from text attributes to acquiremore information from text data. The extracted contextualfeatures are combined with the “key features” of the dataset and used to perform the final classification. Thestudy explored different experimental setups with various classifiers to evaluate the model’s performance. Theexperimental results show that the proposed framework outperforms the latest techniques for classifying terroristattacks with an accuracy of 98.7% using a combined feature set and extreme gradient boosting classifier.展开更多
The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant interest.Accurate and timely diagnos...The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant interest.Accurate and timely diagnosis increases the patient’s chances of recovery.However,issues like overfitting and inconsistent accuracy across datasets remain challenges.In a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib detection.The aim was to create a robust detection mechanism that consistently performs well.Metrics such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for evaluation.The findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated categories.It demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib detection.These insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG signals.The comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib detection.Moreover,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.展开更多
This study describes improving network security by implementing and assessing an intrusion detection system(IDS)based on deep neural networks(DNNs).The paper investigates contemporary technical ways for enhancing intr...This study describes improving network security by implementing and assessing an intrusion detection system(IDS)based on deep neural networks(DNNs).The paper investigates contemporary technical ways for enhancing intrusion detection performance,given the vital relevance of safeguarding computer networks against harmful activity.The DNN-based IDS is trained and validated by the model using the NSL-KDD dataset,a popular benchmark for IDS research.The model performs well in both the training and validation stages,with 91.30%training accuracy and 94.38%validation accuracy.Thus,the model shows good learning and generalization capabilities with minor losses of 0.22 in training and 0.1553 in validation.Furthermore,for both macro and micro averages across class 0(normal)and class 1(anomalous)data,the study evaluates the model using a variety of assessment measures,such as accuracy scores,precision,recall,and F1 scores.The macro-average recall is 0.9422,the macro-average precision is 0.9482,and the accuracy scores are 0.942.Furthermore,macro-averaged F1 scores of 0.9245 for class 1 and 0.9434 for class 0 demonstrate the model’s ability to precisely identify anomalies precisely.The research also highlights how real-time threat monitoring and enhanced resistance against new online attacks may be achieved byDNN-based intrusion detection systems,which can significantly improve network security.The study underscores the critical function ofDNN-based IDS in contemporary cybersecurity procedures by setting the foundation for further developments in this field.Upcoming research aims to enhance intrusion detection systems by examining cooperative learning techniques and integrating up-to-date threat knowledge.展开更多
Intrusion detection systems(IDS)are essential in the field of cybersecurity because they protect networks from a wide range of online threats.The goal of this research is to meet the urgent need for small-footprint,hi...Intrusion detection systems(IDS)are essential in the field of cybersecurity because they protect networks from a wide range of online threats.The goal of this research is to meet the urgent need for small-footprint,highly-adaptable Network Intrusion Detection Systems(NIDS)that can identify anomalies.The NSL-KDD dataset is used in the study;it is a sizable collection comprising 43 variables with the label’s“attack”and“level.”It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks(CNN).Furthermore,this dataset makes it easier to conduct a thorough assessment of the suggested intrusion detection strategy.Furthermore,maintaining operating efficiency while improving detection accuracy is the primary goal of this work.Moreover,typical NIDS examines both risky and typical behavior using a variety of techniques.On the NSL-KDD dataset,our CNN-based approach achieves an astounding 99.728%accuracy rate when paired with channel attention.Compared to previous approaches such as ensemble learning,CNN,RBM(Boltzmann machine),ANN,hybrid auto-encoders with CNN,MCNN,and ANN,and adaptive algorithms,our solution significantly improves intrusion detection performance.Moreover,the results highlight the effectiveness of our suggested method in improving intrusion detection precision,signifying a noteworthy advancement in this field.Subsequent efforts will focus on strengthening and expanding our approach in order to counteract growing cyberthreats and adjust to changing network circumstances.展开更多
Cyberbullying,a critical concern for digital safety,necessitates effective linguistic analysis tools that can navigate the complexities of language use in online spaces.To tackle this challenge,our study introduces a ...Cyberbullying,a critical concern for digital safety,necessitates effective linguistic analysis tools that can navigate the complexities of language use in online spaces.To tackle this challenge,our study introduces a new approach employing Bidirectional Encoder Representations from the Transformers(BERT)base model(cased),originally pretrained in English.This model is uniquely adapted to recognize the intricate nuances of Arabic online communication,a key aspect often overlooked in conventional cyberbullying detection methods.Our model is an end-to-end solution that has been fine-tuned on a diverse dataset of Arabic social media(SM)tweets showing a notable increase in detection accuracy and sensitivity compared to existing methods.Experimental results on a diverse Arabic dataset collected from the‘X platform’demonstrate a notable increase in detection accuracy and sensitivity compared to existing methods.E-BERT shows a substantial improvement in performance,evidenced by an accuracy of 98.45%,precision of 99.17%,recall of 99.10%,and an F1 score of 99.14%.The proposed E-BERT not only addresses a critical gap in cyberbullying detection in Arabic online forums but also sets a precedent for applying cross-lingual pretrained models in regional language applications,offering a scalable and effective framework for enhancing online safety across Arabic-speaking communities.展开更多
Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive behaviors.Timely and precise ASD detection is crucial,particula...Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive behaviors.Timely and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like Pakistan.This study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local context.The research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate schedulers.In addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the tool.The findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation approach.Specifically,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test images.To validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD detection.This research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.展开更多
Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diamet...Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as lines, arcs, extended arcs, and ellipses are used to detect oval shapes. Thirdly, Histogram Oriented Surface Normal Vector (HOSNV) feature descriptors can be used to identify nodules of different sizes and shapes by using a scaled and rotation-invariant texture description. Smart nodule classification was performed with the XGBoost classifier. The results are tested and validated using the Lung Image Consortium Database (LICD). The proposed method has a sensitivity of 98.49% for nodules sized 3–30 mm.展开更多
Big data and information and communication technologies can be important to the effectiveness of smart cities.Based on the maximal attention on smart city sustainability,developing data-driven smart cities is newly ob...Big data and information and communication technologies can be important to the effectiveness of smart cities.Based on the maximal attention on smart city sustainability,developing data-driven smart cities is newly obtained attention as a vital technology for addressing sustainability problems.Real-time monitoring of pollution allows local authorities to analyze the present traffic condition of cities and make decisions.Relating to air pollution occurs a main environmental problem in smart city environments.The effect of the deep learning(DL)approach quickly increased and penetrated almost every domain,comprising air pollution forecast.Therefore,this article develops a new Coot Optimization Algorithm with an Ensemble Deep Learning based Air Pollution Prediction(COAEDL-APP)system for Sustainable Smart Cities.The projected COAEDL-APP algorithm accurately forecasts the presence of air quality in the sustainable smart city environment.To achieve this,the COAEDL-APP technique initially performs a linear scaling normalization(LSN)approach to pre-process the input data.For air quality prediction,an ensemble of three DL models has been involved,namely autoencoder(AE),long short-term memory(LSTM),and deep belief network(DBN).Furthermore,the COA-based hyperparameter tuning procedure can be designed to adjust the hyperparameter values of the DL models.The simulation outcome of the COAEDL-APP algorithm was tested on the air quality database,and the outcomes stated the improved performance of the COAEDL-APP algorithm over other existing systems with maximum accuracy of 98.34%.展开更多
The generalized travelling salesman problem(GTSP),a generalization of the well-known travelling salesman problem(TSP),is considered for our study.Since the GTSP is NP-hard and very complex,finding exact solutions is h...The generalized travelling salesman problem(GTSP),a generalization of the well-known travelling salesman problem(TSP),is considered for our study.Since the GTSP is NP-hard and very complex,finding exact solutions is highly expensive,we will develop genetic algorithms(GAs)to obtain heuristic solutions to the problem.In GAs,as the crossover is a very important process,the crossovermethods proposed for the traditional TSP could be adapted for the GTSP.The sequential constructive crossover(SCX)and three other operators are adapted to use in GAs to solve the GTSP.The effectiveness of GA using SCX is verified on some GTSP Library(GTSPLIB)instances first and then compared against GAs using the other crossover methods.The computational results show the success of the GA using SCX for this problem.Our proposed GA using SCX,and swap mutation could find average solutions whose average percentage of excesses fromthe best-known solutions is between 0.00 and 14.07 for our investigated instances.展开更多
Security is a serious concern, whether it may be the security of assets, data and human life. Providing humans with security and safety for their belongings and items is an important need. A smart lock door project/ w...Security is a serious concern, whether it may be the security of assets, data and human life. Providing humans with security and safety for their belongings and items is an important need. A smart lock door project/ with different types of methods for entry, like fingerprint and authentication PIN code is suggested with an unnoticeable face tracking camera capturing a photo in case of error data entry. It is to be controlled via the user’s smartphone using Blynk with the implementation of IoT. This technology is made with two microcontrollers. ESP32 is used to control the solenoid lock, fingerprint sensor and keypad. ESP32-CAM is used to capture a photo and send it to the owner’s smartphone to be viewed on Telegram application. Many conclusions are extracted from system results, as well as suggested ideas for future work.展开更多
In this study, a hybrid machine learning (HML)-based approach, incorporating Genetic data analysis (GDA), is proposed to accurately identify the presence of adenomatous colorectal polyps (ACRP) which is a crucial earl...In this study, a hybrid machine learning (HML)-based approach, incorporating Genetic data analysis (GDA), is proposed to accurately identify the presence of adenomatous colorectal polyps (ACRP) which is a crucial early detector of colorectal cancer (CRC). The present study develops a classification ensemble model based on tuned hyperparameters. Surpassing accuracy percentages of early detection approaches used in previous studies, the current method exhibits exceptional performance in identifying ACRP and diagnosing CRC, overcoming limitations of CRC traditional methods that are based on error-prone manual examination. Particularly, the method demonstrates the following CRP identification accuracy data: 97.7 ± 1.1, precision: 94.3 ± 5, recall: 96.0 ± 3, F1-score: 95.7 ± 4, specificity: 97.3 ± 1.2, average AUC: 0.97.3 ± 0.02, and average p-value: 0.0425 ± 0.07. The findings underscore the potential of this method for early detection of ACRP as well as clinical use in the development of CRC treatment planning strategies. The advantages of this approach are highly expected to contribute to the prevention and reduction of CRC mortality.展开更多
Anomaly classification based on network traffic features is an important task to monitor and detect network intrusion attacks.Network-based intrusion detection systems(NIDSs)using machine learning(ML)methods are effec...Anomaly classification based on network traffic features is an important task to monitor and detect network intrusion attacks.Network-based intrusion detection systems(NIDSs)using machine learning(ML)methods are effective tools for protecting network infrastructures and services from unpredictable and unseen attacks.Among several ML methods,random forest(RF)is a robust method that can be used in ML-based network intrusion detection solutions.However,the minimum number of instances for each split and the number of trees in the forest are two key parameters of RF that can affect classification accuracy.Therefore,optimal parameter selection is a real problem in RF-based anomaly classification of intrusion detection systems.In this paper,we propose to use the genetic algorithm(GA)for selecting the appropriate values of these two parameters,optimizing the RF classifier and improving the classification accuracy of normal and abnormal network traffics.To validate the proposed GA-based RF model,a number of experiments is conducted on two public datasets and evaluated using a set of performance evaluation measures.In these experiments,the accuracy result is compared with the accuracies of baseline ML classifiers in the recent works.Experimental results reveal that the proposed model can avert the uncertainty in selection the values of RF’s parameters,improving the accuracy of anomaly classification in NIDSs without incurring excessive time.展开更多
This study investigates the challenges and opportunities pertaining to transportation policies that may arise as a result of emerging autonomous vehicle （AV） technologies. AV technologies can decrease the transporta...This study investigates the challenges and opportunities pertaining to transportation policies that may arise as a result of emerging autonomous vehicle （AV） technologies. AV technologies can decrease the transportation cost and increase accessibility to low-income households and persons with mobility issues. This emerging technology also has far-reaching applications and implications beyond all current expectations. This paper provides a comprehensive review of the relevant literature and explores a broad spectrum of issues from safety to machine ethics. An indispensable part of a prospective AV development is communication over cars and infrastructure （connected vehicles）. A major knowledge gap exists in AV technology with respect to routing behaviors. Connected- vehicle technology provides a great opportunity to imple- ment an efficient and intelligent routing system. To this end, we propose a conceptual navigation model based on a fleet of AVs that are centrally dispatched over a network seeking system optimization literature on two fronts： （i） This study contributes to the it attempts to shed light on future opportunities as well as possible hurdles associated with AV technology; and （ii） it conceptualizes a navigation model for the AV which leads to highly efficient traffic circulations.展开更多
In this work, a hardware intrusion detection system （IDS） model and its implementation are introduced to perform online real-time traffic monitoring and analysis. The introduced system gathers some advantages of man...In this work, a hardware intrusion detection system （IDS） model and its implementation are introduced to perform online real-time traffic monitoring and analysis. The introduced system gathers some advantages of many IDSs： hardware based from implementation point of view, network based from system type point of view, and anomaly detection from detection approach point of view. In addition, it can detect most of network attacks, such as denial of services （DOS）, leakage, etc. from detection behavior point of view and can detect both internal and external intruders from intruder type point of view. Gathering these features in one IDS system gives lots of strengths and advantages of the work. The system is implemented by using field programmable gate array （FPGA）, giving a more advantages to the system. A C5.0 decision tree classifier is used as inference engine to the system and gives a high detection ratio of 99.93%.展开更多
The monomer fraction density based analysis of precise thermophysical data for pure fluids is developed to study the molecular structures in supercritical fluids in general and in CO2 in particular. The series expansi...The monomer fraction density based analysis of precise thermophysical data for pure fluids is developed to study the molecular structures in supercritical fluids in general and in CO2 in particular. The series expansion by powers of the monomer fraction density of the potential energy density is used to discover the cluster structure in supercritical fluids and the clusters’ bond energies in CO2. The method of clusters separation between classes of loose and dense clusters in the CO2 supercritical fluid is developed. The method of the energetically averaged number of dense clusters is developed to study the mechanism of the soft structural transition between the gas-like and liquid-like fluids in the supercritical CO2.展开更多
文摘Geographic Information Systems (GIS) have become a fact of our life as they are being used by more people and organizations for more complex decision problems than ever before. The use of GIS can achieve valuable benefits for individuals, organizations and society;however, the achievement of these benefits depends on the success of GIS. While information systems (IS) success models have received much attention among researchers, there is a general scarcity of research conducted to measure the GIS success. This paper proposes a success model for measuring GIS success by extending and modifying previous IS success models. The developed success model consists of two main levels: GIS project diffusion success, and GIS post-implementation success. The first level identifies the critical success factors (CSFs) that influence the success of GIS adoption at each stage of the diffusion process. The second level of the proposed model identifies and organizes the success dimensions (outcome measures) of GIS in temporal and causal relationships. In order to assess the relationships among the success dimensions, 11 hypotheses were tested. Data were collected through a questionnaire that was distributed to 252 GIS users/managers in Egypt and abroad. The empirical results support 6 hypotheses and reject 5 hypotheses.
文摘This paper describes the implementation of an Information Systems （IS） capstone project management course that is a requirement for graduating seniors in an undergraduate Computer Information Systems （CIS） program at a regional university. The description provides a model which includes the culmination of students＇ academic training in an IS curriculum which is part of a Bachelor of Business Administration （BBA） program in an accredited college of business. The course requires an application of technical and business skills, as well as systems development and project management skills--while students are working on an actual IS project for an external sponsoring organization. Rationale for implementing this type of course includes the benefits it provides to the students, the project sponsors, and the IS department providing the course. Feedback from the course is used as integral part of the C1S curriculum assessment process used for accreditation purposes.
文摘The ever-growing network traffic threat landscape necessitates adopting accurate and robust intrusion detection systems(IDSs).IDSs have become a research hotspot and have seen remarkable performance improvements.Generative adversarial networks(GANs)have also garnered increasing research interest recently due to their remarkable ability to generate data.This paper investigates the application of(GANs)in(IDS)and explores their current use within this research field.We delve into the adoption of GANs within signature-based,anomaly-based,and hybrid IDSs,focusing on their objectives,methodologies,and advantages.Overall,GANs have been widely employed,mainly focused on solving the class imbalance issue by generating realistic attack samples.While GANs have shown significant potential in addressing the class imbalance issue,there are still open opportunities and challenges to be addressed.Little attention has been paid to their applicability in distributed and decentralized domains,such as IoT networks.Efficiency and scalability have been mostly overlooked,and thus,future works must aim at addressing these gaps.
文摘This paper aims to design and implement an automatic heart disease diagnosis system using?MATLAB. The Cleveland data set for heart diseases was used as the main database for training and testing the developed system. In order to train and test the Cleveland data set, two systems were developed. The first system is based on the Multilayer Perceptron (MLP) structure on the Artificial Neural Network (ANN), whereas the second system is based on the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) approach. Each system has two main modules, namely, training and testing,?where 80% and 20% of the Cleveland data set were randomly selected for training and testing?purposes respectively. Each system also has an additional module known as case-based module,?where the user has to input values for 13 required attributes as specified by the Cleveland data set,?in order to test the status of the patient whether heart disease is present or absent from that particular patient. In addition, the effects of different values for important parameters were investigated in the ANN-based and Neuro-Fuzzy-based systems in order to select the best parameters that obtain the highest performance. Based on the experimental work, it is clear that the Neuro-Fuzzy system outperforms the ANN system using the training data set, where the accuracy for each system was 100% and 90.74%, respectively. However, using the testing data set, it is clear that the ANN system outperforms the Neuro-Fuzzy system, where the best accuracy for each system was 87.04% and 75.93%, respectively.
基金The Deanship of Scientific Research(DSR)at King Abdulaziz University(KAU),Jeddah,Saudi Arabia has funded this project,under grant no.(RG-91-611-42).
文摘Rapid technological advancement has enabled modern healthcare systems to provide more sophisticated and real-time services on the Internet of Medical Things(IoMT).The existing cloud-based,centralized IoMT architectures are vulnerable to multiple security and privacy problems.The blockchain-enabled IoMT is an emerging paradigm that can ensure the security and trustworthiness of medical data sharing in the IoMT networks.This article presents a private and easily expandable blockchain-based framework for the IoMT.The proposed framework contains several participants,including private blockchain,hospitalmanagement systems,cloud service providers,doctors,and patients.Data security is ensured by incorporating an attributebased encryption scheme.Furthermore,an IoT-friendly consensus algorithm is deployed to ensure fast block validation and high scalability in the IoMT network.The proposed framework can perform multiple healthcare-related services in a secure and trustworthy manner.The performance of blockchain read/write operations is evaluated in terms of transaction throughput and latency.Experimental outcomes indicate that the proposed scheme achieved an average throughput of 857 TPS and 151 TPS for read and write operations.The average latency is 61 ms and 16 ms for read and write operations,respectively.
文摘One of the biggest dangers to society today is terrorism, where attacks have become one of the most significantrisks to international peace and national security. Big data, information analysis, and artificial intelligence (AI) havebecome the basis for making strategic decisions in many sensitive areas, such as fraud detection, risk management,medical diagnosis, and counter-terrorism. However, there is still a need to assess how terrorist attacks are related,initiated, and detected. For this purpose, we propose a novel framework for classifying and predicting terroristattacks. The proposed framework posits that neglected text attributes included in the Global Terrorism Database(GTD) can influence the accuracy of the model’s classification of terrorist attacks, where each part of the datacan provide vital information to enrich the ability of classifier learning. Each data point in a multiclass taxonomyhas one or more tags attached to it, referred as “related tags.” We applied machine learning classifiers to classifyterrorist attack incidents obtained from the GTD. A transformer-based technique called DistilBERT extracts andlearns contextual features from text attributes to acquiremore information from text data. The extracted contextualfeatures are combined with the “key features” of the dataset and used to perform the final classification. Thestudy explored different experimental setups with various classifiers to evaluate the model’s performance. Theexperimental results show that the proposed framework outperforms the latest techniques for classifying terroristattacks with an accuracy of 98.7% using a combined feature set and extreme gradient boosting classifier.
文摘The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant interest.Accurate and timely diagnosis increases the patient’s chances of recovery.However,issues like overfitting and inconsistent accuracy across datasets remain challenges.In a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib detection.The aim was to create a robust detection mechanism that consistently performs well.Metrics such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for evaluation.The findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated categories.It demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib detection.These insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG signals.The comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib detection.Moreover,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.
基金Princess Nourah bint Abdulrahman University for funding this project through the Researchers Supporting Project(PNURSP2024R319)funded by the Prince Sultan University,Riyadh,Saudi Arabia.
文摘This study describes improving network security by implementing and assessing an intrusion detection system(IDS)based on deep neural networks(DNNs).The paper investigates contemporary technical ways for enhancing intrusion detection performance,given the vital relevance of safeguarding computer networks against harmful activity.The DNN-based IDS is trained and validated by the model using the NSL-KDD dataset,a popular benchmark for IDS research.The model performs well in both the training and validation stages,with 91.30%training accuracy and 94.38%validation accuracy.Thus,the model shows good learning and generalization capabilities with minor losses of 0.22 in training and 0.1553 in validation.Furthermore,for both macro and micro averages across class 0(normal)and class 1(anomalous)data,the study evaluates the model using a variety of assessment measures,such as accuracy scores,precision,recall,and F1 scores.The macro-average recall is 0.9422,the macro-average precision is 0.9482,and the accuracy scores are 0.942.Furthermore,macro-averaged F1 scores of 0.9245 for class 1 and 0.9434 for class 0 demonstrate the model’s ability to precisely identify anomalies precisely.The research also highlights how real-time threat monitoring and enhanced resistance against new online attacks may be achieved byDNN-based intrusion detection systems,which can significantly improve network security.The study underscores the critical function ofDNN-based IDS in contemporary cybersecurity procedures by setting the foundation for further developments in this field.Upcoming research aims to enhance intrusion detection systems by examining cooperative learning techniques and integrating up-to-date threat knowledge.
基金The authors would like to thank Princess Nourah bint Abdulrahman University for funding this project through the Researchers Supporting Project(PNURSP2023R319)this research was funded by the Prince Sultan University,Riyadh,Saudi Arabia.
文摘Intrusion detection systems(IDS)are essential in the field of cybersecurity because they protect networks from a wide range of online threats.The goal of this research is to meet the urgent need for small-footprint,highly-adaptable Network Intrusion Detection Systems(NIDS)that can identify anomalies.The NSL-KDD dataset is used in the study;it is a sizable collection comprising 43 variables with the label’s“attack”and“level.”It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks(CNN).Furthermore,this dataset makes it easier to conduct a thorough assessment of the suggested intrusion detection strategy.Furthermore,maintaining operating efficiency while improving detection accuracy is the primary goal of this work.Moreover,typical NIDS examines both risky and typical behavior using a variety of techniques.On the NSL-KDD dataset,our CNN-based approach achieves an astounding 99.728%accuracy rate when paired with channel attention.Compared to previous approaches such as ensemble learning,CNN,RBM(Boltzmann machine),ANN,hybrid auto-encoders with CNN,MCNN,and ANN,and adaptive algorithms,our solution significantly improves intrusion detection performance.Moreover,the results highlight the effectiveness of our suggested method in improving intrusion detection precision,signifying a noteworthy advancement in this field.Subsequent efforts will focus on strengthening and expanding our approach in order to counteract growing cyberthreats and adjust to changing network circumstances.
基金funded by Scientific Research Deanship at University of Ha’il-Saudi Arabia through Project Number RG-23092。
文摘Cyberbullying,a critical concern for digital safety,necessitates effective linguistic analysis tools that can navigate the complexities of language use in online spaces.To tackle this challenge,our study introduces a new approach employing Bidirectional Encoder Representations from the Transformers(BERT)base model(cased),originally pretrained in English.This model is uniquely adapted to recognize the intricate nuances of Arabic online communication,a key aspect often overlooked in conventional cyberbullying detection methods.Our model is an end-to-end solution that has been fine-tuned on a diverse dataset of Arabic social media(SM)tweets showing a notable increase in detection accuracy and sensitivity compared to existing methods.Experimental results on a diverse Arabic dataset collected from the‘X platform’demonstrate a notable increase in detection accuracy and sensitivity compared to existing methods.E-BERT shows a substantial improvement in performance,evidenced by an accuracy of 98.45%,precision of 99.17%,recall of 99.10%,and an F1 score of 99.14%.The proposed E-BERT not only addresses a critical gap in cyberbullying detection in Arabic online forums but also sets a precedent for applying cross-lingual pretrained models in regional language applications,offering a scalable and effective framework for enhancing online safety across Arabic-speaking communities.
文摘Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive behaviors.Timely and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like Pakistan.This study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local context.The research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate schedulers.In addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the tool.The findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation approach.Specifically,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test images.To validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD detection.This research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.
文摘Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as lines, arcs, extended arcs, and ellipses are used to detect oval shapes. Thirdly, Histogram Oriented Surface Normal Vector (HOSNV) feature descriptors can be used to identify nodules of different sizes and shapes by using a scaled and rotation-invariant texture description. Smart nodule classification was performed with the XGBoost classifier. The results are tested and validated using the Lung Image Consortium Database (LICD). The proposed method has a sensitivity of 98.49% for nodules sized 3–30 mm.
基金funded by the Deanship of Scientific Research(DSR),King Abdulaziz University(KAU),Jeddah,Saudi Arabia under Grant No.(IFPIP:631-612-1443).
文摘Big data and information and communication technologies can be important to the effectiveness of smart cities.Based on the maximal attention on smart city sustainability,developing data-driven smart cities is newly obtained attention as a vital technology for addressing sustainability problems.Real-time monitoring of pollution allows local authorities to analyze the present traffic condition of cities and make decisions.Relating to air pollution occurs a main environmental problem in smart city environments.The effect of the deep learning(DL)approach quickly increased and penetrated almost every domain,comprising air pollution forecast.Therefore,this article develops a new Coot Optimization Algorithm with an Ensemble Deep Learning based Air Pollution Prediction(COAEDL-APP)system for Sustainable Smart Cities.The projected COAEDL-APP algorithm accurately forecasts the presence of air quality in the sustainable smart city environment.To achieve this,the COAEDL-APP technique initially performs a linear scaling normalization(LSN)approach to pre-process the input data.For air quality prediction,an ensemble of three DL models has been involved,namely autoencoder(AE),long short-term memory(LSTM),and deep belief network(DBN).Furthermore,the COA-based hyperparameter tuning procedure can be designed to adjust the hyperparameter values of the DL models.The simulation outcome of the COAEDL-APP algorithm was tested on the air quality database,and the outcomes stated the improved performance of the COAEDL-APP algorithm over other existing systems with maximum accuracy of 98.34%.
基金the Deanship of Scientific Research,Imam Mohammad Ibn Saud Islamic University(IMSIU),Saudi Arabia,for funding this research work through Grant No.(221412020).
文摘The generalized travelling salesman problem(GTSP),a generalization of the well-known travelling salesman problem(TSP),is considered for our study.Since the GTSP is NP-hard and very complex,finding exact solutions is highly expensive,we will develop genetic algorithms(GAs)to obtain heuristic solutions to the problem.In GAs,as the crossover is a very important process,the crossovermethods proposed for the traditional TSP could be adapted for the GTSP.The sequential constructive crossover(SCX)and three other operators are adapted to use in GAs to solve the GTSP.The effectiveness of GA using SCX is verified on some GTSP Library(GTSPLIB)instances first and then compared against GAs using the other crossover methods.The computational results show the success of the GA using SCX for this problem.Our proposed GA using SCX,and swap mutation could find average solutions whose average percentage of excesses fromthe best-known solutions is between 0.00 and 14.07 for our investigated instances.
文摘Security is a serious concern, whether it may be the security of assets, data and human life. Providing humans with security and safety for their belongings and items is an important need. A smart lock door project/ with different types of methods for entry, like fingerprint and authentication PIN code is suggested with an unnoticeable face tracking camera capturing a photo in case of error data entry. It is to be controlled via the user’s smartphone using Blynk with the implementation of IoT. This technology is made with two microcontrollers. ESP32 is used to control the solenoid lock, fingerprint sensor and keypad. ESP32-CAM is used to capture a photo and send it to the owner’s smartphone to be viewed on Telegram application. Many conclusions are extracted from system results, as well as suggested ideas for future work.
文摘In this study, a hybrid machine learning (HML)-based approach, incorporating Genetic data analysis (GDA), is proposed to accurately identify the presence of adenomatous colorectal polyps (ACRP) which is a crucial early detector of colorectal cancer (CRC). The present study develops a classification ensemble model based on tuned hyperparameters. Surpassing accuracy percentages of early detection approaches used in previous studies, the current method exhibits exceptional performance in identifying ACRP and diagnosing CRC, overcoming limitations of CRC traditional methods that are based on error-prone manual examination. Particularly, the method demonstrates the following CRP identification accuracy data: 97.7 ± 1.1, precision: 94.3 ± 5, recall: 96.0 ± 3, F1-score: 95.7 ± 4, specificity: 97.3 ± 1.2, average AUC: 0.97.3 ± 0.02, and average p-value: 0.0425 ± 0.07. The findings underscore the potential of this method for early detection of ACRP as well as clinical use in the development of CRC treatment planning strategies. The advantages of this approach are highly expected to contribute to the prevention and reduction of CRC mortality.
文摘Anomaly classification based on network traffic features is an important task to monitor and detect network intrusion attacks.Network-based intrusion detection systems(NIDSs)using machine learning(ML)methods are effective tools for protecting network infrastructures and services from unpredictable and unseen attacks.Among several ML methods,random forest(RF)is a robust method that can be used in ML-based network intrusion detection solutions.However,the minimum number of instances for each split and the number of trees in the forest are two key parameters of RF that can affect classification accuracy.Therefore,optimal parameter selection is a real problem in RF-based anomaly classification of intrusion detection systems.In this paper,we propose to use the genetic algorithm(GA)for selecting the appropriate values of these two parameters,optimizing the RF classifier and improving the classification accuracy of normal and abnormal network traffics.To validate the proposed GA-based RF model,a number of experiments is conducted on two public datasets and evaluated using a set of performance evaluation measures.In these experiments,the accuracy result is compared with the accuracies of baseline ML classifiers in the recent works.Experimental results reveal that the proposed model can avert the uncertainty in selection the values of RF’s parameters,improving the accuracy of anomaly classification in NIDSs without incurring excessive time.
文摘This study investigates the challenges and opportunities pertaining to transportation policies that may arise as a result of emerging autonomous vehicle （AV） technologies. AV technologies can decrease the transportation cost and increase accessibility to low-income households and persons with mobility issues. This emerging technology also has far-reaching applications and implications beyond all current expectations. This paper provides a comprehensive review of the relevant literature and explores a broad spectrum of issues from safety to machine ethics. An indispensable part of a prospective AV development is communication over cars and infrastructure （connected vehicles）. A major knowledge gap exists in AV technology with respect to routing behaviors. Connected- vehicle technology provides a great opportunity to imple- ment an efficient and intelligent routing system. To this end, we propose a conceptual navigation model based on a fleet of AVs that are centrally dispatched over a network seeking system optimization literature on two fronts： （i） This study contributes to the it attempts to shed light on future opportunities as well as possible hurdles associated with AV technology; and （ii） it conceptualizes a navigation model for the AV which leads to highly efficient traffic circulations.
文摘In this work, a hardware intrusion detection system （IDS） model and its implementation are introduced to perform online real-time traffic monitoring and analysis. The introduced system gathers some advantages of many IDSs： hardware based from implementation point of view, network based from system type point of view, and anomaly detection from detection approach point of view. In addition, it can detect most of network attacks, such as denial of services （DOS）, leakage, etc. from detection behavior point of view and can detect both internal and external intruders from intruder type point of view. Gathering these features in one IDS system gives lots of strengths and advantages of the work. The system is implemented by using field programmable gate array （FPGA）, giving a more advantages to the system. A C5.0 decision tree classifier is used as inference engine to the system and gives a high detection ratio of 99.93%.
文摘The monomer fraction density based analysis of precise thermophysical data for pure fluids is developed to study the molecular structures in supercritical fluids in general and in CO2 in particular. The series expansion by powers of the monomer fraction density of the potential energy density is used to discover the cluster structure in supercritical fluids and the clusters’ bond energies in CO2. The method of clusters separation between classes of loose and dense clusters in the CO2 supercritical fluid is developed. The method of the energetically averaged number of dense clusters is developed to study the mechanism of the soft structural transition between the gas-like and liquid-like fluids in the supercritical CO2.
