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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Alwash, Riyam Faisal Kadhum Idrees, Ali Al-Obaidi, Salah University of Babylon College of Science for Women Department of Computer Science Babylon Iraq Technology University of Babylon College of Information Department of Information Networks Babylon Iraq

ISBN: (纸本)9798350335095

There is a greater demand to enhance intelligent healthcare systems in order to keep track of the medical condition of patients. This demand leads to increase the interest in remote healthcare systems that make use of biosensors. These biosensors generate a significant amount of vital sensed data, which is then sent to the edge of the Internet of Medical Things (IoMT) for processing before being sent on to the data centers of the cloud. In this paper, we propose an EEG data reduction and epileptic seizures detection in IoMT networks. The suggested method operates on 2 levels within the network: edge and fog levels. At the edge gateway, The EEG data is transformed into time-frequency representations using Short Time Fourier Transformation (STFT). Then, a set of statistical texture feature based on the gray level co-occurrence matrix (GLCM) is extracted. These EEG based features are then compressed before being sent to the fog gateway using a binary-encoded representative method. In Fog Gateway, the received encoded features are decoded and passed into a classifier to determine the patient's situation. A set of classifiers are used to evaluate the proposed method using the Bonn University dataset. The proposed technique demonstrates the outperformance of the proposed method by achieving an accuracy of (100%) in multiple cases and an acceptable accuracy for other cases while considering fewer features compared to existing work. © 2023 IEEE.

关键词： Classification (of information)

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Deep Learning Enabled Intelligent Healthcare Management system in Smart Cities Environment

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computers, Materials & Continua 2023年第2期74卷 4483-4500页

作者： Hanan Abdullah Mengash Lubna A.Alharbi Saud S.Alotaibi Sarab AlMuhaideb Nadhem Nemri Mrim M.Alnfiai Radwa Marzouk Ahmed S.Salama Mesfer Al Duhayyim Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversityTabuk71491Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Science and Arts at MuhayelKing Khalid UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

In recent times,cities are getting smart and can be managed effectively through diverse architectures and *** cities have the ability to support smart medical systems that can infiltrate distinct events(i.e.,smart hospitals,smart homes,and community health centres)and scenarios(e.g.,rehabilitation,abnormal behavior monitoring,clinical decision-making,disease prevention and diagnosis postmarking surveillance and prescription recommendation).The integration of Artificial Intelligence(AI)with recent technologies,for instance medical screening gadgets,are significant enough to deliver maximum performance and improved management services to handle chronic *** latest developments in digital data collection,AI techniques can be employed for clinical decision making *** the other hand,Cardiovascular Disease(CVD)is one of the major illnesses that increase the mortality rate across the ***,wearables can be employed in healthcare systems that instigate the development of CVD detection and *** this motivation,the current study develops an Artificial Intelligence Enabled Decision Support system for CVD Disease Detection and Classification in e-healthcare environment,abbreviated as AIDSS-CDDC *** proposed AIDSS-CDDC model enables the Internet of Things(IoT)devices for healthcare data ***,the collected data is saved in cloud server for *** by,training 4484 CMC,2023,vol.74,no.2 and testing processes are executed to determine the patient’s health *** accomplish this,the presented AIDSS-CDDC model employs data preprocessing and Improved Sine Cosine Optimization based Feature Selection(ISCO-FS)*** addition,Adam optimizer with Autoencoder Gated RecurrentUnit(AE-GRU)model is employed for detection and classification of *** experimental results highlight that the proposed AIDSS-CDDC model is a promising performer compared to other existing models.

关键词： Smart cities e-healthcare decision support system cardiovascular disease deep learning feature selection

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Sailfish Optimization with Deep Learning Based Oral Cancer Classification Model

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computer systems science & Engineering 2023年第4期45卷 753-767页

作者： Mesfer Al Duhayyim Areej A.Malibari Sami Dhahbi Mohamed K.Nour Isra Al-Turaiki Marwa Obayya Abdullah Mohamed Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh4545Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt

Recently,computer aided diagnosis(CAD)model becomes an effective tool for decision making in healthcare *** advances in computer vision and artificial intelligence(AI)techniques have resulted in the effective design of CAD models,which enables to detection of the existence of diseases using various imaging *** cancer(OC)has commonly occurred in head and neck *** identification of OC enables to improve survival rate and reduce mortality ***,the design of CAD model for OC detection and classification becomes ***,this study introduces a novel computer Aided Diagnosis for OC using Sailfish Optimization with Fusion based Classification(CADOC-SFOFC)*** proposed CADOC-SFOFC model determines the existence of OC on the medical *** accomplish this,a fusion based feature extraction process is carried out by the use of VGGNet-16 and Residual Network(ResNet)***,feature vectors are fused and passed into the extreme learning machine(ELM)model for classification ***,SFO algorithm is utilized for effective parameter selection of the ELM model,consequently resulting in enhanced *** experimental analysis of the CADOC-SFOFC model was tested on Kaggle dataset and the results reported the betterment of the CADOC-SFOFC model over the compared methods with maximum accuracy of 98.11%.Therefore,the CADOC-SFOFC model has maximum potential as an inexpensive and non-invasive tool which supports screening process and enhances the detection efficiency.

关键词： Oral cancer computer aided diagnosis deep learning fusion model seagull optimization classification

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Advancing Smartphone Sensor-Based Keystroke Dynamics for Implicit and Active Authentication: Addressing Challenges and Enhancing Usability Control 11th

Advancing Smartphone Sensor-Based Keystroke Dynamics for Imp...

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11th International Symposium on Applied Computing for Software and Smart systems, ACSS 2024

作者： Roy, Soumen Roy, Utpal Sinha, Devadatta Pal, Rajat Kumar Department of Computer Science Bagnan College Khalore Bagnan Howrah711303 India Department of Computer and System Sciences VisvaBharati Santiniketan731235 India Department of Computer Science and Engineering University of Calcutta Acharya Prafulla Chandra Roy Siksha Prangan JD-2 Sector-III Kolkata Saltlake City700106 India

ISBN: (纸本)9789819797615

Smartphone sensors play a pivotal role in delineating user behaviour, particularly in the context of keystroke dynamics-based implicit and active/continuous authentication, as well as liveness detection. The acquisition of multifaceted sensory data during typing serves as a fundamental source of behavioural attributes, yet the field faces several intricate challenges requiring careful consideration to recalibrate the delicate balance between security and usability. Notable challenges encompass the capricious nature of anomaly detectors when confronted with unavoidable influencing factors, the substantial battery drainage associated with prolonged sensor operation, the computational inefficiency intrinsic to smartphones, and the imperative need for usability control in frequently switching applications and balancing security in both device and application levels, especially in scenarios with varying security requirements. This study endeavours to mitigate these challenges through the implementation of cutting-edge anomaly detection techniques, incorporating four distinct schemes—namely, column medians template formation, synthetic samples, soft biometrics and score-level fusion. The study further leverages advanced sensory patterns, including gyroscope, accelerometer and rotational information, captured during abbreviated typing intervals at a low sampling rate to mitigate battery consumption. In the pursuit of enhanced usability control, a Watchman score is assigned to each running application, dictating security adjustments based on genuineness evaluations of micro-behaviours within predefined temporal windows. A novel rule-based mechanism is introduced to govern device and application security within a continuous authentication mode. This empirical investigation, supported by robust statistical evidence, aspires to yield more precise, energy-efficient, and user-friendly implicit and active user identity verification mechanisms. The findings present an avenue for f

关键词： Smartphones

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Machine learning classifiers with pre-processing techniques for rumour detection on social media: an empirical study

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International Journal of Cloud Computing 2022年第4期11卷 330-344页

作者： Al-Sarem, Mohammed Al-Harby, Muna Saeed, Faisal Hezzam, Essa Abdullah College of Computer Science and Engineering Taibah University Medina Saudi Arabia Saba'a Region University Mareb Yemen Information System Department College of Computer Science and Engineering Taibah University Medina Saudi Arabia

The rapid increase in popularity of social media helped the users to easily post and share information with others. However, due to uncontrolled nature of social media platforms, such as Twitter and Facebook, it becomes easy to post fake news and misleading information. The task of detecting such problem is known as rumour detection. This task requires data analytics tools due to the massive amount of shared content and the rapid speed at which it is generated. In this work, the authors aimed to study the impact of different text pre-processing techniques on the performance of classifiers when performing rumour detection. The experiments were performed on a dataset of tweets on emerging breaking news stories which cover several events of Saudi political context (EBNS-SPC). The results have shown that pre-processing techniques have a significant impact on increasing the performance of machine learning methods such as support vector machine (SVM), multinomial naïve Bayes (MNB), and K-nearest neighbour (KNN) classifiers. However, the classifiers react differently when different combinations of pre-processing techniques were used. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Support vector machines

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Optimizing Polynomial-Time Solutions to a Network Weighted Vertex Cover Game

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 512-523页

作者： Jie Chen Kaiyi Luo Changbing Tang Zhao Zhang Xiang Li the Adaptive Networks and Control Laboratory Department of Electronic EngineeringFudan UniversityShanghai 200433China the College of Mathematics and Computer Science Zhejiang Normal UniversityJinhua 321004 Yuxi Middle School Taizhou 317399China IEEE the College of Physics and Electronics Information Engineering Zhejiang Normal UniversityJinhua 321004China the College of Mathematics and Computer Science Zhejiang Normal UniversityJinhua 321004China the Adaptive Networks and Control Laboratory Department of Electronic EngineeringFudan UniversityShanghai 200433 the Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous SystemsTongji UniversityShanghai 201210China

Weighted vertex cover(WVC)is one of the most important combinatorial optimization *** this paper,we provide a new game optimization to achieve efficiency and time of solutions for the WVC problem of weighted *** first model the WVC problem as a general game on weighted *** the framework of a game,we newly define several cover states to describe the WVC ***,we reveal the relationship among these cover states of the weighted network and the strict Nash equilibriums(SNEs)of the ***,we propose a game-based asynchronous algorithm(GAA),which can theoretically guarantee that all cover states of vertices converging in an SNE with polynomial ***,we improve the GAA by adding 2-hop and 3-hop adjustment mechanisms,termed the improved game-based asynchronous algorithm(IGAA),in which we prove that it can obtain a better solution to the WVC problem than using a the ***,numerical simulations demonstrate that the proposed IGAA can obtain a better approximate solution in promising computation time compared with the existing representative algorithms.

关键词： Game-based asynchronous algorithm(GAA) game optimization polynomial time strict Nash equilibrium(SNE) weighted vertex cover(WVC)

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Blockchain Driven Metaheuristic Route Planning in Secure Wireless Sensor networks

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computers, Materials & Continua 2023年第1期74卷 933-949页

作者： M.V.Rajesh T.Archana Acharya Hafis Hajiyev E.Laxmi Lydia Haya Mesfer Alshahrani Mohamed K Nour Abdullah Mohamed Mesfer Al Duhayyim Department of Computer Science and Engineering Aditya Engineering CollegeSurampalem533437India Department of Business Administration Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Accounting and Audit Azerbaijan State University of Economics(UNEC)BakuRepublic of Azerbaijan Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Recently,Internet of Things(IoT)has been developed into a field of research and it purposes at linking many sensors enabling devices mostly to data collection and track *** sensor network(WSN)is a vital element of IoT paradigm since its inception and has developed into one of the chosen platforms for deploying many smart city application regions such as disaster management,intelligent transportation,home automation,smart buildings,and other such IoT-based *** routing approaches were extremely-utilized energy efficient approaches with an initial drive that is,for balancing the energy amongst sensor *** clustering and routing procedures assumed that Non-Polynomial(NP)hard problems but bio-simulated approaches are utilized to a recognized time for resolving such *** this motivation,this paper presents a new blockchain with Enhanced Hunger Games Search based Route Planning(BCEHGS-RP)scheme for IoT assisted *** presented BCEHGS-RP model majorly employs BC technology for secure communication in the IoT supportedWSN *** addition,an effective multihop route planning approach was designed by the use of EHGS *** proposed EHGS technique is derived from the concept of Hill Climbing strategy(HCS)and HGS ***,a fitness function with two parameters namely residual energy(RE)and intercluster distance to elect optimal *** performance validation of the BCEHGS-RP model is experimented with under diverse number of *** experimental outcomes highlighted the better performance of the BCEHGS-RP technique on recent approaches.

关键词： Internet of things wireless sensor networks routing metaheuristics blockchain

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EEG based Mental Stress Detection using Deep Learning Techniques 2

EEG based Mental Stress Detection using Deep Learning Techni...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Tahira, Maryam Vyas, Prerna Teerthanker Mahaveer University College of Computing Science & It Department of Computing Science & It Moradabad India Jaipur National University School of Computer & System Sciences Department of Computer Sciences Jaipur India

ISBN: (纸本)9798350347456

The importance of identifying stress in living in a fast culture cannot be overstated. The electrical bio-signals produced by the brain are read out using an electroencephalography (EEG) method. Yet, owing to their intricacy, EEG signals can only be deciphered by a physician with extensive training in this area. In order to identify human stress, this research offers a DWT-based hybrid deep learning model that utilizes convolution neural networks and Bidirectional Long Short-Term Memory (CNN-BLSTM). Helpful for psychiatrists, psychologists, and other medical professionals who need to assess a patient's stress levels. Mental math stress is detected with the use of the Physionet EEG dataset. Using Discrete Wavelet Transform, noise has been eliminated and split into four levels from multi-channel (19 channels) EEG data (DWT). After decomposition, the signals are subjected to an automated feature selection technique using a CNN. At last, stress levels are categorized using BLSTM. The suggested model's accuracy is evaluated in comparison to that of CNN-based LSTM and prior work. Based on the data, it's clear that the suggested mixed model is superior to others in terms of classification accuracy (99.20 percent was attained). Moreover, the suggested model is validated with a classification results of 98.10% using the stratification tenfold cross-validation method. © 2023 IEEE.

关键词： Electroencephalography

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Doped low-density parity-check codes

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Digital Communications and networks 2024年第1期10卷 217-226页

作者： Yong Li Rui Liu Xianlong Jiao Youqiang Hu Zhen Luo Francis C.M.Lau College of Computer Science Chongqing UniversityChongqing400044China School of Automation Chongqing UniversityChongqing400044China College of Electronic and Information Engineering Southwest UniversityChongqing400715China Future Wireless Networks and IoT Focusing Area Department of Electronic and Information EngineeringHong Kong Polytechnic UniversityHong KongChina

In this paper,we propose a doping approach to lower the error floor of Low-Density Parity-Check(LDPC)*** doping component is a short block code in which the information bits are selected from the coded bits of the dominant trapping sets of the LDPC ***,an algorithm for selecting the information bits of the short code is proposed,and a specific two-stage decoding algorithm is *** results demonstrate that the proposed doped LDPC code achieves up to 2.0 dB gain compared with the original LDPC code at a frame error rate of 10^(-6)Furthermore,the proposed design can lower the error floor of original LDPC Codes.

关键词： LDPC codes Doped LDPC codes Tanner graph Quadratic residue codes Trapping sets

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Security Monitoring and Management for the Network Services in the Orchestration of SDN-NFV Environment Using Machine Learning Techniques

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computer systems science & Engineering 2024年第2期48卷 363-394页

作者： Nasser Alshammari Shumaila Shahzadi Saad Awadh Alanazi Shahid Naseem Muhammad Anwar Madallah Alruwaili Muhammad Rizwan Abid Omar Alruwaili Ahmed Alsayat Fahad Ahmad Department of Computer Science College of Computer and Information SciencesJouf UniversitySakakaAl Jouf72341Saudi Arabia Department of Computer Sciences Kinnaird College forWomenLahorePunjab54700Pakistan Department of Information Sciences Division of Sciences and TechnologyUniversity of EducationLahore54770Pakistan Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72341Saudi Arabia Computer Science Florida Polytechnic UniversityLakelandFlorida33805USA Delta3T LahorePunjab54700Pakistan Department of Basic Sciences Common First YearJouf UniversitySakaka72341Saudi Arabia

Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified network lifecycle,and policies *** vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration(NFV MANO),and malicious attacks in different scenarios disrupt the NFV Orchestrator(NFVO)and Virtualized Infrastructure Manager(VIM)lifecycle management related to network services or individual Virtualized Network Function(VNF).This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end *** anomaly detector investigates these identified risks and provides secure network *** enables virtual network security functions and identifies anomalies in Kubernetes(a cloud-based platform).For training and testing purpose of the proposed approach,an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf,Neptune,Teardrop,Pod,Land,IPsweep,etc.,categorized as Probing(Prob),Denial of Service(DoS),User to Root(U2R),and Remote to User(R2L)*** anomaly detector is anticipated with the capabilities of a Machine Learning(ML)technique,making use of supervised learning techniques like Logistic Regression(LR),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),and Extreme Gradient Boosting(XGBoost).The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation *** classifier has shown better outcomes(99.90%accuracy)than other classifiers in detecting anomalies/intrusions in the containerized environment.

关键词： Software defined network network function virtualization network function virtualization management and orchestration virtual infrastructure manager virtual network function Kubernetes Kubectl artificial intelligence machine learning

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