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information-Centric IoT-Based Smart Farming with Dynamic Data Optimization

Computers, Materials & Continua 2023年第2期74卷 3865-3880页

作者： Souvik Pal Hannah VijayKumar D.Akila N.Z.Jhanjhi Omar A.Darwish Fathi Amsaad Department of Computer Science and Engineering Sister Nivedita UniversityKolkataIndia Post Doctoral Researcher Sambalpur UniversitySambalpurIndia Department of Computer Science Anna Adarsh College for WomenChennaiIndia Department of Information Technology Vels Institute of ScienceTechnology and Advanced StudiesChennaiIndia School of Computer Science and Engineering SCETaylor’s UniversitySubang Jaya47500SelangorMalaysia FITTaylor’s UniversitySubang Jaya47500SelangorMalaysia Department of Information Security and Applied Computing Eastern Michigan UniversityUSA School of Information Security and Applied Computing Eastern Michigan UniversityUSA

Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies,including big data,the cloud,and the Internet of Things(IoT).Many researchers try to integrate IoT-based smart farming on cloud platforms *** define various frameworks on smart farming and monitoring system and still lacks to define effective data management *** IoT-cloud systems involve massive structured and unstructured data,data optimization comes into the ***,this research designs an information-Centric IoT-based Smart Farming with Dynamic Data Optimization(ICISF-DDO),which enhances the performance of the smart farming infrastructure with minimal energy consumption and improved ***,a conceptual framework of the proposed scheme and statistical design model has beenwell *** information storage and management with DDO has been expanded individually to show the effective use of membership parameters in data *** simulation outcomes state that the proposed ICISF-DDO can surpass existing smart farming systems with a data optimization ratio of 97.71%,reliability ratio of 98.63%,a coverage ratio of 99.67%,least sensor error rate of 8.96%,and efficient energy consumption ratio of 4.84%.

关键词： Smart farming data optimization information management internet of things cloud

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A novel U-net model for brain tumor segmentation from MRI images

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Alexandria Engineering Journal 2025年 126卷 220-230页

作者： Obayya, Marwa Alshuhail, Asma Mahmood, Khalid Alanazi, Meshari H. Alqahtani, Mohammed Aljehane, Nojood O. Almansour, Hamad Al-Hagery, Mohammed Abdullah Department of Biomedical Engineering College of Engineering Princess Nourah bint Abdulrahman University PO Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal university Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science College of Sciences Northern Border University Arar Saudi Arabia Department of Information System and Cyber Security College of Computing and Information Technology University of Bisha Bisha61922 Saudi Arabia Department of Computer Science Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Applied Collage Najran University Najran Saudi Arabia Department of Computer Science College of Computer Qassim University Saudi Arabia

Segmentation of brain tumors aids in diagnosing the disease early, planning treatment, and monitoring its progression in medical image analysis. Automation is necessary to eliminate the time and variability associated with traditional segmentation methods. Convolutional neural networks (CNNs) and U-Net architectures have demonstrated their efficiency and effectiveness in segmenting brain tumors from MRI images using deep learning techniques. The paper presents an improved U-Net-based segmentation algorithm that integrates nested skip paths to improve encoder-decoder feature fusion. The performance of segmentation was optimized by utilizing a variety of activation functions and loss functions, including Dice Loss and Intersection over Union (IoU). A high level of accuracy was demonstrated in the proposed model when it was evaluated using the LGG Segmentation Dataset. The proposed approach for segmenting medical images has been shown to be both robust and efficient in a comparative analysis. © 2025 The Authors

关键词： Convolutional neural networks

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Deep Reinforcement Learning-Based Framework for Enhancing Cybersecurity

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第3期19卷 170-190页

作者： Al-Nawashi, Malek M. Al-Hazaimeh, Obaida M. Tahat, Nedal M. Gharaibeh, Nasr Abu-Ain, Waleed A. Abu-Ain, Tarik Al-Huson University College Al-Balqa Applied University Irbid Jordan Faculty of Information Technology Department of Information Security and Cybersecurity Philadelphia University Amman Jordan Faculty of Science The Hashemite University Zarqa Jordan College of Science and Computer Engineering Taibah University Yanbu Saudi Arabia College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

The detection of cyberattacks has been increasingly emphasized in recent years, focusing on both infrastructure and people. Conventional security measures such as intrusion detection, firewalls, and encryption are insufficient in protecting cyber systems against growing and changing threats. In order to address this problem, scholars have explored reinforcement learning (i.e., RL) as a potential solution for intricate cybersecurity decision-making difficulties. Nevertheless, the use of RL faces several obstacles, including dynamic attack scenarios, insufficient training data, and the challenge of replicating real-world complexities. This study presents a novel framework that uses deep reinforcement learning (i.e., DRL) to simulate harmful cyberattacks and improve cybersecurity. This study presents an agent-based framework that is capable of ongoing learning and adaptation in a dynamic network security environment. The agent determines the optimal course of action by considering the current state of the network and the rewards it receives for its decisions. The CIC-IDS-2018 database, constructed using Python 3.7 programming, was used. The conducted studies yielded outstanding results, with a detection accuracy of 98.82% achieved for the CIC-IDS-2018 database in cyber-attack classification. © 2025 by the authors of this article. Published under CC-BY.

关键词： Cybersecurity

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TNEST: Training Sparse Neural Network for FPGA Based Edge Application 2nd

TNEST: Training Sparse Neural Network for FPGA Based Edge A...

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2nd International Conference on Advances in computing Research, ACR 2024

作者： Das, Rammi Karn, Rupesh Raj Heikkonen, Jukka Kanth, Rajeev Nepal College of Information Technology Pokhara University Kathmandu Nepal Center for Cyber Security New York University Abu Dhabi United Arab Emirates Department of Computing University of Turku Turku Finland Department of Information Technology Savonia University of Applied Sciences Kuopio Finland

ISBN: (纸本)9783031569494

Machine learning (ML) hardware inference has developed ultra-low-power edge devices that accelerate inferential applications performance. An FPGA (Field Programmable Gate Array) is a popular option for such systems. The FPGA has power budget, memory, compute resource, area, etc. constraints but possesses several key advantages, including bandwidth saving, speed, real-time inference, offline activity, etc. Neural networks have been extensively used in Edge systems due to their significant popularity in AI. Requirements of complex neural networks have only been recently realized for edge applications, so the research community has yet to develop a standard model for such applications. The sparse neural architecture reduces the active neurons and connections, making the entire system computationally more efficient and utilizing less memory. Such essential facts are more and more evident in edge-based IoT applications. In this work, we have customized neural network training algorithms to fit precisely for the Edge systems. Rather than a traditional top-down approach where MLs are fully trained and then pruned to fit within edge device resources, we adopted a generative approach where MLs are prepared with the least number of parameters, and further components are added as the need arises to improve inference accuracy. Our generative model shows significant savings in FPGA resource consumption compared to the top-down approach for the same precision. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： High level synthesis

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Optimizing Spatial Pattern Analysis in Serial Remote Sensing Images through Empirical Mode Decomposition and Ant Colony Optimization

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Journal of Harbin Institute of Technology(New Series) 2024年第4期31卷 52-60页

作者： J Srinivasan S Uma Saleem Raja Abdul Samad Jayabrabu Ramakrishnan Department of Computer Applications Madanapalle Institute of Technology&Science(MITS)Madanapalle 517325Andhra PradeshIndia Department of Information Technology Panimalar Engineering CollegeChennai 600123India Information Technology Department College of Computing and Information SciencesUniversity of Technology and Applied Sciences-Shinas 324Sultanate of Oman Department of Information Technology and Security College of Computer Science and Information TechnologyJazan UniversityJazan 45142Kingdom of Saudi Arabia

Serial remote sensing images offer a valuable means of tracking the evolutionary changes and growth of a specific geographical area over *** the original images may provide limited insights,they harbor considerable potential for identifying clusters and *** aggregation of these serial remote sensing images(SRSI)becomes increasingly viable as distinct patterns emerge in diverse scenarios,such as suburbanization,the expansion of native flora,and agricultural *** a novel approach,we propose an innovative method for extracting sequential patterns by combining Ant Colony Optimization(ACD)and Empirical Mode Decomposition(EMD).This integration of the newly developed EMD and ACO techniques proves remarkably effective in identifying the most significant characteristic features within serial remote sensing images,guided by specific *** findings highlight a substantial improvement in the efficiency of sequential pattern mining through the application of this unique hybrid method,seamlessly integrating EMD and ACO for feature *** study exposes the potential of our innovative methodology,particularly in the realms of urbanization,native vegetation expansion,and agricultural activities.

关键词： spatial pattern analysis EMD ACO

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Personalized Food Recommendations: A Machine Learning Model for Enhanced Dining Choices 14

Personalized Food Recommendations: A Machine Learning Model ...

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14th IEEE International Conference on Control System, computing and Engineering, ICCSCE 2024

作者： Al-Hubaishi, Mohammed Ali, Musab A. M. Tahir, Nooritawati Md Halic University Department of Computer Engineering Istanbul Turkey University of Technology and Applied Sciences College of Computing and Information Sciences Oman International Information Technology University Department of Computer Engineering and Information Security Kazakhstan Universiti Teknologi MARA Malaysia Universiti Teknologi MARA College of Engineering Malaysia Riyadh Saudi Arabia

ISBN: (纸本)9798350364507

This research introduces an innovative approach to enhance dining experiences through a personalized meal suggestion system. Our method leverages decision tree-based machine learning to predict user preferences by analyzing various attributes. The aim is to assist individuals in making optimal food choices at restaurants, thereby enriching their culinary journey. QR codes are integrated into an Android restaurant application, facilitating convenient access to the food menu. User inquiries are efficiently managed through a MySQL database accessible across multiple platforms, including a Java GUI, an Android app, and a dedicated website catering to both user and administrator roles. © 2024 IEEE.

关键词： Adversarial machine learning

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A Robust Model for Translating Arabic Sign Language into Spoken Arabic Using Deep Learning

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Intelligent Automation & Soft computing 2023年第8期37卷 2037-2057页

作者： Khalid M.O.Nahar Ammar Almomani Nahlah Shatnawi Mohammad Alauthman Department of Computer Sciences Faculty of Information Technology and Computer SciencesYarmouk University-Irbid21163Jordan School of Computing Skyline University CollegeSharjahP.O.Box 1797United Arab Emirates IT-Department-Al-Huson University College Al-Balqa Applied UniversityP.O.Box 50IrbidJordan Department of Information Security Faculty of Information TechnologyUniversity of PetraAmmanJordan

This study presents a novel and innovative approach to auto-matically translating Arabic Sign Language(ATSL)into spoken *** proposed solution utilizes a deep learning-based classification approach and the transfer learning technique to retrain 12 image recognition *** image-based translation method maps sign language gestures to corre-sponding letters or words using distance measures and classification as a machine learning *** results show that the proposed model is more accurate and faster than traditional image-based models in classifying Arabic-language signs,with a translation accuracy of 93.7%.This research makes a significant contribution to the field of *** offers a practical solution for improving communication for individuals with special needs,such as the deaf and mute *** work demonstrates the potential of deep learning techniques in translating sign language into natural language and highlights the importance of ATSL in facilitating communication for individuals with disabilities.

关键词： Sign language deep learning transfer learning machine learning automatic translation of sign language natural language processing Arabic sign language

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A Review of Lightweight security and Privacy for Resource-Constrained IoT Devices

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Computers, Materials & Continua 2024年第1期78卷 31-63页

作者： Sunil Kumar Dilip Kumar Ramraj Dangi Gaurav Choudhary Nicola Dragoni Ilsun You Department of Computer Science and Engineering National Institute of Technology JamshedpurJamshedpurIndia School of Computing Science and Engineering VIT University BhopalBhopal466114India Center for Industrial Software The Maersk Mc-Kinney Moller InstituteUniversity of Southern DenmarkSonderborgOdenseDenmark Department of Applied Mathematics and Computer Science Technical University of DenmarkLyngby2800Denmark Department of Information Security Cryptologyand MathematicsKookmin UniversitySeoulSouth Korea

The widespread and growing interest in the Internet of Things(IoT)may be attributed to its usefulness in many different *** settings are probed for data,which is then transferred via linked *** are several hurdles to overcome when putting IoT into practice,from managing server infrastructure to coordinating the use of tiny *** it comes to deploying IoT,everyone agrees that security is the biggest *** is due to the fact that a large number of IoT devices exist in the physicalworld and thatmany of themhave constrained resources such as electricity,memory,processing power,and square *** research intends to analyse resource-constrained IoT devices,including RFID tags,sensors,and smart cards,and the issues involved with protecting them in such restricted *** lightweight cryptography,the information sent between these gadgets may be *** order to provide a holistic picture,this research evaluates and contrasts well-known algorithms based on their implementation cost,hardware/software efficiency,and attack resistance *** also emphasised how essential lightweight encryption is for striking a good cost-to-performance-to-security ratio.

关键词： IoT a sensor device lightweight cryptography block cipher smart card security and privacy

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Optimizing Phishing URL Detection: A Comparative Analysis of Feature Counts and Training Times 5

Optimizing Phishing URL Detection: A Comparative Analysis of...

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5th International Conference on Intelligent computing, Communication, Networking and Services, ICCNS 2024

作者： Alajlouni, Moayyad Tashtoush, Yahya Dossari, Saud Alhajaj Al. Darwish, Omar Jordan University of Science and Technology Department of Computer Science Irbid Jordan Prince Sattam bin Abdulaziz University Department of Electrical Engineering Wadi Addwasir Saudi Arabia Eastern Michigan University Information Security and Applied Computing Department YpsilantiMI48197 United States

ISBN: (纸本)9798350354690

Phishing attacks are the biggest cybersecurity threats in the digital world. Attackers exploit users by impersonating real, authentic web sites to obtain sensitive information such as passwords and bank statements. One common technique used in these attacks is malicious URLs. These malicious URLs mimic legitimate URLs, misleading users into interacting with malicious websites. This practice, URL phishing, presents a big threat to internet security, emphasizing the need for advanced detection methods. This paper presents a method for detecting malicious URLs using statistical features extracted from n-grams. These n-grams are extracted from the hexadecimal representation of URLs. We used Explainable AI (XAI) to explore the extracted features and evaluate their importance and role in phishing detection. A key advantage of our method is its ability to reduce the required features and the training time by using fewer features after applying XAI techniques. Our technique only uses statistical features extracted from n-grams and the n-gram itself, without requiring any high-level features. We applied XAI techniques, SHapley Additive exPlanations (SHAP), and Local Interpretable Model-agnostic Explanations (LIME). Through the explanation provided by XAI methods, we were able to determine the most important features in our feature set, enabling a reduction in feature count. Using fewer features (4, 7, 10, 13, 15), we got good accuracy compared to the 41 features and 87 features used in the original experiment and reduced the models' training times and complexity. This research aimed to enhance phishing URL detection by optimizing feature selection and analyzing the impact on training times. Our findings show the importance of using minimal statistical features to identify malicious URLs effectively and efficiently. © 2024 IEEE.

关键词： Phishing

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Analyzing darknet traffic through machine learning and neucube spiking neural networks

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Intelligent and Converged Networks 2024年第4期5卷 265-283页

作者： Iman Akour Mohammad Alauthman Khalid M.O.Nahar Ammar Almomani Brij B.Gupta Information Systems Department College of Computing&InformaticsUniversity of SharjahSharjahUnited Arab Emirates Department of Information Security Faculty of Information TechnologyUniversity of PetraAmman 11196Jordan the Faculty of Computer Studies Arab Open UniversityRiyadh 11681Saudi Arabia Department of Information Technology Al-Huson University CollegeAl-Balqa Applied UniversityIrbid 19117Jordan the Department of Computer Information Science Higher Colleges of TechnologySharjahUnited Arab Emirates the Department of Computer Science and Information Engineering Asia UniversityTaichung 413ChinaSymbiosis Centre for Information TechnologySymbiosis International(Deemed University)Pune 412115India Centre of Inter-disciplinary Research&Innovation University of Petroleum and Energy StudiesDehradun 248007India

The rapidly evolving darknet enables a wide range of cybercrimes through anonymous and untraceable communication *** detection of clandestine darknet traffic is therefore critical yet immensely *** research demonstrates how advanced machine learning and specialized deep learning techniques can significantly enhance darknet traffic analysis to strengthen *** diverse classifiers such as random forest and naïve Bayes with a novel spiking neural network architecture provides a robust foundation for identifying concealed *** on the CIC-Darknet2020 dataset establishes state-of-the-art results with 98%accuracy from the random forest model and 84.31%accuracy from the spiking neural *** pioneering application of artificial intelligence advances the frontiers in analyzing the complex characteristics and behaviours of darknet *** proposed techniques lay the groundwork for improved threat intelligence,real-time monitoring,and resilient cyber defense systems against the evolving landscape of cyber threats.

关键词： darknet traffic machine learning deep learning spiking neural network PCA

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