Mathematical Modeling of Cyberattack Defense Mechanism Using Hybrid Transfer Learning With Snow Ablation Optimization Algorithm in Critical Infrastructures by Mohamad Khairi Ishak

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Structure-Enhanced Prompt Learning for Graph-Based Code Vulnerability Detection by Wei Chang, Chunyang Ye, Hui Zhou

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Abnormal event detection in surveillance videos through LSTM auto-encoding and local minima assistance by Erkan Sengonul, Refik Samet, Qasem Abu Al-Haija, Ali Alqahtani, Rayan A. Alsemmeari, Bandar Alghamdi, Badraddin Alturki, Abdulaziz A. Alsulami

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Botnet Detection Using Support Vector Machines with Artificial Fish Swarm Algorithm by Kuan-Cheng Lin, Sih-Yang Chen, Jason C. Hung

“…The number of mobile devices used globally substantially increases daily; therefore, information security concerns are increasingly vital. The botnet virus is a major threat to both personal computers and mobile devices; therefore, a method of botnet feature characterization is proposed in this study. …”
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Smart Grid System Based on Blockchain Technology for Enhancing Trust and Preventing Counterfeiting Issues by Ala’a Shamaseen, Mohammad Qatawneh, Basima Elshqeirat

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YOLOv8 framework for COVID-19 and pneumonia detection using synthetic image augmentation by Uddin A Hasib, Raihan Md Abu, Jing Yang, Uzair Aslam Bhatti, Chin Soon Ku, Lip Yee Por

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EmotionNet-X: An Optimized CNN Architecture for Robust Facial Emotion Analysis by Syed Muhammad Aqleem Abbas, Qaisar Abbas, Syed Muhammad Naqi

“…Facial emotions are expressions of people’s inner feelings. A computer’s ability to recognize emotions is known as emotion recognition (ER), which involves extracting facial characteristics or expressions from a person’s face in order to enable the computer to communicate emotionally with them. …”
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Review of malware detection and classification visualization techniques by Jinwei WANG, Zhengjia CHEN, Xue XIE, Xiangyang LUO, Bin MA

“…With the rapid advancement of technology, network security faces a significant challenge due to the proliferation of malicious software and its variants.These malicious software use various technical tactics to deceive or bypass traditional detection methods, rendering conventional non-visual detection techniques inadequate.In recent years, data visualization has gained considerable attention in the academic community as a powerful approach for detecting and classifying malicious software.By visually representing the key features of malicious software, these methods greatly enhance the accuracy of malware detection and classification, opening up extensive research opportunities in the field of cyber security.An overview of traditional non-visual detection techniques and visualization-based methods were provided in the realm of malicious software detection.Traditional non-visual approaches for malicious software detection, including static analysis, dynamic analysis, and hybrid techniques, were introduced.Subsequently, a comprehensive survey and evaluation of prominent contemporary visualization-based methods for detecting malicious software were undertaken.This primarily encompasses encompassed the integration of visualization with machine learning and visualization combined with deep learning, each of which exhibits distinct advantages and characteristics within the domain of malware detection and classification.Consequently, the holistic consideration of several factors, such as dataset size, computational resources, time constraints, model accuracy, and implementation complexity, is necessary for the selection of detection and classification methods.In conclusion, the challenges currently faced by detection technologies are summarized, and a forward-looking perspective on future research directions in the field is provided.…”
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ED-Swin Transformer: A Cassava Disease Classification Model Integrated with UAV Images by Jing Zhang, Hao Zhou, Kunyu Liu, Yuguang Xu

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Leveraging self attention driven gated recurrent unit with crocodile optimization algorithm for cyberattack detection using federated learning framework by Manal Abdullah Alohali, Hatim Dafaalla, Mohammed Baihan, Sultan Alahmari, Achraf Ben Miled, Othman Alrusaini, Ali Alqazzaz, Hanadi Alkhudhayr

“…Cybersecurity includes decreasing the risk of mischievous computer, software, and network attacks. Novel techniques have been combined into emerging artificial intelligence (AI) that attains cybersecurity. …”
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DCSLK: Combined large kernel shared convolutional model with dynamic channel Sampling by Zongren Li, Shuping Luo, Hongwei Li, Yanbin Li

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Unveiling Arabic named entity recognition using natural language processing with artificial intelligence approach on Moroccan dialect by Wala Bin Subait, Nazir Ahmad, Muhammad Swaileh A. Alzaidi, Mohammed Alqahtani, Asma A. Alhashmi, Nojood O. Aljehane, Mohammed Alshahrani, Shoayee Dlaim Alotaibi

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Fractional Artificial Protozoa Optimization Enabled Deep Learning for Intrusion Detection and Mitigation in Cyber-Physical Systems by Shaik Abdul Rahim, Arun Manoharan

“…CPSs are gradually growing and utilized in important infrastructure and industries for attaining smart grid, smart transportation, and smart healthcare, which assists governments and citizens. Nevertheless, the network and wireless communication technology creates high complexity, and the intelligence and dynamic of network intrusions make CPS more insecure to network intrusions and provide more critical threats to human life and national security. …”
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A Reinforcement Learning Approach Combined With Scope Loss Function for Crime Prediction on Twitter (X) by Liu Yang, Jiang Guofan, Zhang Yixin, Wei Qianze, Zhang Jian, Roohallah Alizadehsani, Pawel Plawiak

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Interdisciplinary framework for cyber-attacks and anomaly detection in industrial control systems using deep learning by Qawsar Gulzar, Khurram Mustafa

“…In this study, we introduced an interdisciplinary framework that aims to enhance network intrusion detection systems (NIDSs). In this framework, we introduced an IDS via feature selection and feature reduction technique(s) with the attention-driven lightweight deep neural networks: Deep Recurrent Neural Networks (RNN), Deep Long Short-Term Memory (LSTM), and Deep Bi-directional Long Short-Term Memory (Bi-LSTM). …”
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DMN-YOLO: A Robust YOLOv11 Model for Detecting Apple Leaf Diseases in Complex Field Conditions by Lijun Gao, Hongwu Cao, Hua Zou, Huanhuan Wu

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Deepfake Image Forensics for Privacy Protection and Authenticity Using Deep Learning by Saud Sohail, Syed Muhammad Sajjad, Adeel Zafar, Zafar Iqbal, Zia Muhammad, Muhammad Kazim

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A New Hybrid ConvViT Model for Dangerous Farm Insect Detection by Anil Utku, Mahmut Kaya, Yavuz Canbay

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Multi-View Cluster Structure Guided One-Class BLS-Autoencoder for Intrusion Detection by Qifan Yang, Yu-Ang Chen, Yifan Shi

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Advancing Corn Yield Mapping in Kenya Through Transfer Learning by Ahaan Bohra, Sophie Nottmeyer, Chenchen Ren, Shuo Chen, Yuchi Ma

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