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Intrusion Detection in 5G Cellular Network Using Machine Learning

computer systems Science & engineering 2023年第11期47卷 2439-2453页

作者： Ishtiaque Mahmood Tahir Alyas Sagheer Abbas Tariq Shahzad Qaiser Abbas Khmaies Ouahada Knowledge Unit of Systems and Technology UMT Sialkot CampusSialkot51040Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Faculty of Computer Science National College of Business Administration and EconomicsLahore54660Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351Saudi Arabia Department of Computer Science&IT University of SargodhaSargodha40100Pakistan Department of Electrical and Electronic Engineering Science University of JohannesburgP.O Box 524Auckland ParkJohannesburg2006South Africa

Attacks on fully integrated servers,apps,and communication networks via the Internet of Things(IoT)are growing *** devices’effectiveness harms end users,increases cyber threats and identity theft,raises costs,and negatively impacts income as problems brought on by the Internet of Things network go unnoticed for extended *** on Internet of Things interfaces must be closely monitored in real time for effective safety and *** the 1,2,3,and 4G cellular networks,the 5th generation wireless 5G network is indeed the great invasion of mankind and is known as the global advancement of cellular *** to this day,experts are working on the evolution’s sixth generation(6G).It offers amazing capabilities for connecting everything,including gadgets and machines,with wavelengths ranging from 1 to 10 mm and frequencies ranging from 300 MHz to 3 *** gives you the most recent *** countries have already established this technology within their *** is the most crucial aspect of using a 5G *** of the absence of study and network deployment,new technology first introduces new gaps for attackers and *** Protocol(IP)attacks and intrusion will become more prevalent in this *** efficient approach to detect intrusion in the 5G network using a Machine Learning algorithm will be provided in this *** research will highlight the high accuracy rate by validating it for unidentified and suspicious circumstances in the 5G network,such as intruder hackers/*** applying different machine learning algorithms,obtained the best result on Linear Regression Algorithm’s implementation on the dataset results in 92.12%on test data and 92.13%on train data with 92%precision.

关键词： Intrusion detection system machine learning confidentiality integrity availability

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Analysis and Assessment of Wind Energy Potential of Al-Hodeidah in Yemen

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computers, Materials & Continua 2021年第11期69卷 1995-2011页

作者： Fahd N.Al-Wesabi Murad A.Almekhlafi Mohammed Abdullah Al-Hagery Mohammad Alamgeer Khalid Mahmood Majdy M.Eltahir Ali M.Al-Sharafi Amin M.El-Kustaban Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Electrical Engineering–Communication Engineering Sana’a UniversityYemen Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Information Systems King Khalid UniversityMayahel AseerKSA Department of Computer Science College of Computers and Information TechnologyUniversity of BishaKSA Department of Electronic Faculty of EngineeringUSTSana’aYemen

Renewable energy is one of the essential elements of the social and economic development in any civilized *** use of fossil fuels and the non-renewable form of energy has many adverse effects on the most of *** the high potential of renewable energy sources in Yemen and the absence of similar studies in the region,this study aimed to examine the wind energy potential of Hodeidah-Yemen Republic by analyzing wind characteristics and assessment,determining the available power density,and calculate the wind energy extracted at different *** average wind speed of Hodeidah was obtained only for the data currently available for the five years 2005–2009(due to the current economic and the political situation in Yemen).The results show that the average wind speed in the five years is(25.2 W/m2 at 10 m,93.9 W/m2 at 30 m,and 173.5 W/m2 at 50 m).The average yearly wind power density(25.2 W/m2 at 10 m,93.9 W/m2 at 30 m and 173.5 W/m2 at 50m),and the average yearly energy density(220.8 KWh/m2/year at 10 m,822.6 KWh/m2/year at 30 m and 1519.9 KWh/m2/year at 50 m).This research is a preliminary assessment of the potential of wind energy in Hodeidah,which provides useful information for developing wind energy and an efficient wind *** to the International Wind Energy Rating criteria,the region of Hodeidah falls under‘Class 2’and is classified as‘Marginal’for most of the year.

关键词： Energy potential wind speed weibull distribution wind power density

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Physician Scheduling using Constraint Satisfaction Programming

Physician Scheduling using Constraint Satisfaction Programmi...

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Electronics, Communications, and Computations (JAC-ECC), International Japan-Africa Conference on

作者： Hend A. Bakr Ahmed M. Salama Ahmed Fares Ahmed B. Zaky Biomedical Engineering Program Faculty of Engineering Cairo University Giza Egypt Computer Systems Engineering Program Faculty of Engineering at Shoubra Benha University Banha Egypt Computer science and information technology Programs Egypt-Japan University of Science and Technology (E-JUST) Alexandria Egypt On leave from Computer Systems Engineering Program Faculty of Engineering at Shoubra Benha University

Physician scheduling is a critical task that impacts the quality of patient care, staff satisfaction, and operational efficiency in healthcare institutions. The traditional approach to physician scheduling is manual and time-consuming, which can result in errors, staff burnout, and suboptimal schedules. To address these challenges, researchers have turned to optimization techniques like CSP, which has shown promise in solving physician scheduling problems. This paper reviews the existing literature on CSP for physician scheduling and highlights the benefits and limitations of this approach. CSP's benefits include generating schedules quickly and efficiently, incorporating complex constraints and preferences, and handling changes and disruptions in real time. However, CSP also has some limitations, such as the need for a formalized model and the fact that it may not always generate the most intuitive schedules. Overall, the findings suggest that CSP is a promising approach to physician scheduling that can produce high-quality schedules while minimizing staff burnout and improving operational efficiency.

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Advancing Early Detection of Colorectal Adenomatous Polyps via Genetic Data Analysis: A Hybrid Machine Learning Approach

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Journal of computer and Communications 2024年第7期12卷 23-38页

作者： Ahmed S. Maklad Mohamed A. Mahdy Amer Malki Noboru Niki Abdallah A. Mohamed Computer Science Department College of Computer Science and Engineering in Yanbu Taibah University Medina Saudi Arabia Information Systems Department Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suif Egypt Computer Science Department Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suif Egypt Institute of Advanced Science and Technology Tokushima University Tokushima Japan Information Systems Department College of Computer Science and Engineering in Yanbu Taibah University Medina Saudi Arabia Mathematics and Computer Science Department Faculty of Science Menoufia University Menoufia Egypt

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.

关键词： Colorectal Adenoma Detection Colorectal Cancer Diagnosis Hybrid Machine Learning Genetics Analysis

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Bit Rate Reduction in Cloud Gaming Using Object Detection Technique

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computers, Materials & Continua 2021年第9期68卷 3653-3669页

作者： Daniyal Baig Tahir Alyas Muhammad Hamid Muhammad Saleem Saadia Malik Nadia Tabassum Natash Ali Mian Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Statistics and Computer Science University of Veterinary and Animal SciencesLahore54000Pakistan Department of Industrial Engineering Faculty of EngineeringRabighKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology-RabighKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Virtual University of PakistanLahore54000Pakistan School of Computer and Information Technology Beaconhouse National University Lahore53700Pakistan

The past two decades witnessed a broad-increase in web technology and on-line *** the broadband confinements is viewed as one of the most significant variables that prompted new gaming *** immense utilization of web applications and games additionally prompted growth in the handled devices and moving the limited gaming experience from user devices to online cloud *** internet capabilities are enhanced new ways of gaming are being used to improve the gaming *** cloud-based video gaming,game engines are hosted in cloud gaming data centers,and compressed gaming scenes are rendered to the players over the internet with updated *** such systems,the task of transferring games and video compression imposes huge computational complexity is required on cloud *** basic problems in cloud gaming in particular are high encoding time,latency,and low frame rates which require a new methodology for a better *** improve the bandwidth issue in cloud games,the compression of video sequences requires an alternative mechanism to improve gaming adaption without input *** this paper,the proposed improved methodology is used for automatic unnecessary scene detection,scene removing and bit rate reduction using an adaptive algorithm for object detection in a game *** a result,simulations showed without much impact on the players’quality experience,the selective object encoding method and object adaption technique decrease the network latency issue,reduce the game streaming bitrate at a remarkable scale on different *** proposed algorithm was evaluated for three video game *** this paper,achieved 14.6%decrease in encoding and 45.6%decrease in bit rate for the first video game scene.

关键词： Video encoding object detection bit rate reduction game video motion estimation computational complexity

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Detection of Multiple Sclerosis Using Convolutional Neural Networks: A Comparative Study

Detection of Multiple Sclerosis Using Convolutional Neural N...

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International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Nahla Afifi Amr Talaat Abdel-Hamid Bassem A. Abdullah Computer and Systems Engineering Department Ain Shams University Cairo Egypt Faculty of Information Engineering and Technology German University in Cairo Cairo Egypt

Brain imaging is one of the most essential methods in detecting brain lesions, which can be caused by many diseases. Multiple sclerosis (MS) is an autoimmune disease that attacks the body's nervous system, including the brain and spinal cord. MS diagnosis is carried out normally by differential diagnosis because most neuro diseases exhibit the same symptoms, so it is carried out by eliminating similar diseases, which may cause false diagnosis. In this research, we intend to diagnose MS from magnetic resonance images using machine and deep learning methods by classifying the images into MS or healthy subjects. Many approaches were tested, including convolutional neural network (CNN), random forest, and support vector machine. The best results were obtained from CNN implemented with VGG16 architecture, with the following results (accuracy: 98.44%; specificity: 99.4%; sensitivity: 97.56 % ). This deep learning approach proved to be efficient in MS diagnosis and would help clinicians to reassure their proposed diagnosis.

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Using transformers and Bi-LSTM with sentence embeddings for prediction of openness human personality trait

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PeerJ computer Science 2025年 11卷 1-42页

作者： Naz, Anam Khan, Hikmat Ullah Alsahfi, Tariq Alhajlah, Mousa Alshemaimri, Bader Daud, Ali Department of Information Technology University of Sargodha Punjab Sargodha Pakistan Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Applied of Computer Science College King Saud University Riyadh Saudi Arabia Software Engineering Department College of Computing and Information Sciences King Saud University Riyadh Saudi Arabia Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates

Understanding human personality traits is significant as it helps in decision making related to consumers’ behavior, career counselling, team building and top candidates’ selection for recruitment. Among various traits, openness is essential as it shows both diverse aspects of sensitive nature or intuitive nature. The individuals having a sensing nature tends to be more practical and prefer to focus on concrete information whereas the users having intuitive trait type is characterized by a focus on abstract ideas, creative thinking and future-oriented perspectives. In this research work, we aim to explore diverse natural language processing (NLP) based features and apply state of the art deep learning algorithms for openness trait prediction. Using standard Myers-Briggs Type Indicator (MBTI) dataset, we propose the use of the latest deep features of sentence embeddings which captures contextual semantics of the content to be used with deep learning models. For comparison, we explore textual features of Frequency-Inverse Document (TF-IDF) and parts of speech (POS) tagging with machine learning models and deep features of word2vec and global vectors for word representation (GloVe) with deep learning models. The comprehensive empirical analysis reveals that TF-IDF used with gradient boosting achieves high accuracy of 90% whereas, the deep feature of sentence embeddings when used and with deep model bidirectional long short-term memory (Bi-LSTM) achieves 90.5% accuracy. The best results have been achieved using the latest Transformer-based DistilBERT, which achieves the highest accuracy of 92% outperforming the existing studies in relevant literature. Subjects Human-computer Interaction, Artificial Intelligence, Data Mining and Machine Learning, Natural Language and Speech © 2025 xxxx

关键词： Deep learning

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Iterative layerwise training for the quantum approximate optimization algorithm

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Physical Review A 2024年第5期109卷 052406-052406页

作者： Xinwei Lee Xinjian Yan Ningyi Xie Dongsheng Cai Yoshiyuki Saito Nobuyoshi Asai Graduate School of Systems and Information Engineering University of Tsukuba Ibaraki Prefecture 305-8577 Japan Graduate School of Science and Technology University of Tsukuba Ibaraki Prefecture 305-8577 Japan Faculty of Engineering Information and Systems University of Tsukuba Ibaraki Prefecture 305-8577 Japan Graduate School of Computer Science and Engineering University of Aizu Fukushima Prefecture 965-0006 Japan School of Computer Science and Engineering University of Aizu Fukushima Prefecture 965-0006 Japan

The capability of the quantum approximate optimization algorithm (QAOA) in solving combinatorial optimization problems has been intensively studied in recent years due to its application in the quantum-classical hybrid regime. Despite having difficulties that are innate in the variational quantum algorithms (VQA), such as barren plateaus and the local minima problem, QAOA remains one of the applications that is suitable for the recent noisy intermediate scale quantum (NISQ) devices. Recent works have shown that the performance of QAOA largely depends on the initial parameters, which motivate parameter initialization strategies to obtain good initial points for the optimization of QAOA. On the other hand, optimization strategies focus on the optimization part of QAOA instead of the parameter initialization. Instead of having absolute advantages, these strategies usually impose trade-offs to the performance of the optimization problems. One such example is the layerwise optimization strategy, in which the QAOA parameters are optimized layer by layer instead of the full optimization. The layerwise strategy costs less in total compared to the full optimization, in exchange of lower approximation ratio. In this work, we propose the iterative layerwise optimization strategy and explore the possibility for the reduction of optimization cost in solving problems with QAOA. Using numerical simulations, we found that by combining the iterative layerwise strategy with proper initialization strategies, the optimization cost can be significantly reduced in exchange for a minor reduction in the approximation ratio. We also show that in some cases, the approximation ratio given by the iterative layerwise strategy is even higher than that given by the full optimization.

关键词： Quantum algorithms Quantum benchmarking Quantum circuits

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A Review of Key Elements for Underwater Wireless Sensor Networks

A Review of Key Elements for Underwater Wireless Sensor Netw...

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Decision Aid Sciences and Application (DASA), International Conference on

作者： Amjed Abbas Ahmed Ali Hamza Najim Aymen Mohammed Al-Dulaimi Khattab M. Ali Alheeti Nurhizam Safie Mohd Satar Salah A. Aliesawi Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Imam AlKadhum College (IKC) Baghdad Iraq Department of Computer Technical Engineering Imam Al-Kadhum College (IKC) Al-Diwaniyah Iraq Department of Communication Technical Engineering College of Engineering Techniques Al-Farahidi University Baghdad Iraq Computer Networking Systems Department College of Computer and Information Technology University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Malaysia Department of Computer Networking Systems College of Computer and Information Technology University of Anbar Anbar Iraq

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

Underwater Wireless Sensor Networks (UWSNs) are emerging and have huge prospects since they have various applications in oceanography, environmental monitoring, and underwater communication. These networks contain low-cost submerged sensors that are mostly connected by way of acoustic media. Sensors are relatively new to underwater research, and thanks to recent technological development, they can now function at different depths. In this article, the author explores what UWSNs must do to operate smoothly by exploring the basic features that they must meet for further development. Real-time Underwater communication remains an open problem due to underwater acoustic channels important for undersea data acquisition, undersea navigation, and undersea remote sensing. To mitigate such challenges and in light of the use of new technologies, researchers seek to improve the reliability and reliability of UWSNs. Finally, there is great potential with UWSNs for investigating and exploiting the underwater environment by improving scenarios of different domains in line with subaqueous data and communications.

关键词： Wireless communication Wireless sensor networks Underwater communication Reviews Sensor phenomena and characterization Real-time systems Reliability Security Underwater acoustics Remote sensing

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CSELM-QE: A Composite Semi-supervised Extreme Learning Machine with Unlabeled RSS Quality Estimation for Radio Map Construction

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Chinese Journal of Electronics 2020年第6期29卷 1016-1024页

作者： ZHAO Jianli WANG Wei SUN Qiuxia HUO Huan SUN Guoqiang GAO Xiang ZHU Chendi College of Computer Science and Engineering Shandong University of Science and Technology College of Mathematics and Systems Science Shandong University of Science and Technology Faculty of Engineering and Information Technology University of Technology Sydney

Wireless local area network(WLAN)fingerprint-based localization has become the most attractive and popular approach for indoor ***,the primary concern for its practical implementation is the laborious manual effort of calibrating sufficient location-labeled *** Semi-supervised extreme learning machine(SELM) performs well in reducing calibration *** SELM methods only use Received signal strength(RSS) information to construct the neighbor graph and ignores location information,which helps recognizing prior information for manifold *** propose Composite SELM(CSELM) method by using both RSS signals and location information to construct composite ***,the issue of unlabeled RSS data quality has not been *** propose a novel approach called Composite semisupervised extreme learning machine with unlabeled RSS Quality estimation(CSELM-QE) that takes into account the quality of unlabeled RSS data and combines the composite neighbor graph,which considers location information in the semi-supervised extreme learning *** results show that the CSELM-QE could construct a precise localization model,reduce the calibration effort for radio map construction and improve localization *** quality estimation method can be applied to other methods that need to retain high quality unlabeled Received signal strength data to improve model accuracy.

关键词： calibration graph theory indoor radio learning (artificial intelligence) RSSI wireless LAN signal strength data quality estimation method CSELM-QE Composite Semisupervised extreme learning machine unlabeled RSS Quality estimation radio map construction wireless local area network fingerprint-based localization indoor localization location-labeled fingerprints calibration effort traditional SELM methods received signal strength information location information Composite SELM method RSS signals composite graph unlabeled RSS data quality composite neighbor graph precise localization model localization accuracy Wireless local area network (WLAN) WiFi fingerprints Radio map construction Semisupervised extreme learning machine (SELM) Received signal strength (RSS) data quality estimation Location based services

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