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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BDSER-InceptionNet: A Novel Method for Near-Infrared Spectroscopy Model Transfer Based on Deep Learning and Balanced Distribution Adaptation by Jianghai Chen, Jie Ling, Nana Lei, Lingqiao Li

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AD-VAE: Adversarial Disentangling Variational Autoencoder by Adson Silva, Ricardo Farias

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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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Dense-Fusion2Net a more efficient and lightweight short speech speaker recognition system with time-frequency channel attention by Fei Deng, Rui Huang, Peifan Jiang, Lin Yu, Lihong Deng

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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 mutual exclusion key pairing scheme for preserving the privacy of channel shared 5G users by Meshari D. Alanazi, Mohammed Albekairi, Turki M. Alanazi, Salwa Othmen, Chahira Lhioui, Ayman Alfahid, Ghulam Abbas, Ahmed Emara

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Robust zero-watermarking for color images using hybrid deep learning models and encryption by Hager A. Gharib, Noha M. M. Abdelnapi, Khalid M. Hosny

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Knowledge Graph Representation Fusion Framework for Fine-Grained Object Recognition in Smart Cities by Yang He, Ling Tian, Lizong Zhang, Xi Zeng

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An Efficient Random Forest Classifier for Detecting Malicious Docker Images in Docker Hub Repository by Maram Aldiabat, Qussai M. Yaseen, Qusai Abu Ein

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Detecting command injection attacks in web applications based on novel deep learning methods by Xinyu Wang, Jiqiang Zhai, Hailu Yang

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Hybrid deep learning model for accurate and efficient android malware detection using DBN-GRU. by Heena Kauser Sk, Maria Anu V

“…The model extracts static features (permissions, API calls, intent filters) and dynamic features (system calls, network activity, inter-process communication) from Android APKs, enabling a comprehensive analysis of application behavior.The proposed model was trained and tested on the Drebin dataset, which includes 129,013 applications (5,560 malware and 123,453 benign).Performance evaluation against NMLA-AMDCEF, MalVulDroid, and LinRegDroid demonstrated that DBN-GRU achieved 98.7% accuracy, 98.5% precision, 98.9% recall, and an AUC of 0.99, outperforming conventional models.In addition, it exhibits faster preprocessing, feature extraction, and malware classification times, making it suitable for real-time deployment.By bridging static and dynamic detection methodologies, the DBN-GRU enhances malware detection capabilities while reducing false positives and computational overhead.These findings confirm the applicability of the proposed model in real-world Android security applications, offering a scalable and high-performance malware detection solution.…”
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A Modified MobileNetv3 Coupled With Inverted Residual and Channel Attention Mechanisms for Detection of Tomato Leaf Diseases by Rubina Rashid, Waqar Aslam, Romana Aziz, Ghadah Aldehim

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AppAuth: Authorship Attribution for Android App Clones by Guoai Xu, Chengpeng Zhang, Bowen Sun, Xinyu Yang, Yanhui Guo, Chengze Li, Haoyu Wang

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scAMZI: attention-based deep autoencoder with zero-inflated layer for clustering scRNA-seq data by Lin Yuan, Zhijie Xu, Boyuan Meng, Lan Ye

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Intrusion Detection Using Hybrid Pearson Correlation and GS-PSO Optimized Random Forest Technique for RPL-Based IoT by Wei Yang, Xinlong Wang, Zhiming Zhang, Shaolong Chen, Chengqi Hou, Siwei Luo

“…The Pearson correlation can effectively extract key data features for different routing attacks. And the hybrid GS-PSO algorithm can optimize the hyperparameters of IDS model and enhance the accuracy of the detection mechanism while significantly reducing computational overhead. …”
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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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User authentication of industrial internet based on HHT transform of mouse behavior by Yigong ZHANG, Qian YI, Jian LI, Congbo LI, Aijun YIN, Shuping YI

“…The rapid development of the industrial internet had caused widespread concern about the network security, and the end-user authentication technology was considered a research hotspot.According to the characteristics of human-computer interaction in industrial internet, an experimental website was designed.24 users' mouse behavior data in an uncontrolled environment were collected within 2.5 years to conduct case studies.Hilbert-Huang transform (HHT) was used to extract frequency domain features of mouse behavior signals, combined with time domain features to form a time-frequency joint domain feature matrix of 163-dimensional to characterize user mouse behavior patterns.Bagged tree, support vector machine (SVM), Boost tree and K-nearest neighbor (KNN) were used to build a user authentication model， and the comparison result showed that the Bagged tree had the best internal detection effect in this case, with an average false acceptance rate (FAR) of 0.12% and an average false rejection rate (FRR) of 0.28%.In external detection, the FAR was 1.47%.Compared with the traditional mouse dynamics method, the frequency domain information of mouse behavior extracted by HHT can better realize the user authentication, and provide technical support the security of the industrial internet.…”
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Peculiarities of evidence in the course of investigation of criminal offences under parts 1, 2 of Article 111-1 of the Criminal Code of Ukraine by S. Ye. Ablamskyi, O. V. Kovtun, V. V. Ablamska

“…It is substantiated that in modern conditions, evidence in the course of investigation of criminal offences under Parts 1, 2 of Art. 111-1 of the Criminal Code of Ukraine is inseparable from taking into account the features of modern equipment which can be used for information transmission, analysis of computer information and information from correspondence, channels and groups in social networks containing valuable information, samples of signatures, seals and other details of documents which reflect information about the collaboration activities of individuals and groups. …”
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Exploration of machine learning approaches for automated crop disease detection by Annu Singla, Ashima Nehra, Kamaldeep Joshi, Ajit Kumar, Narendra Tuteja, Rajeev K. Varshney, Sarvajeet Singh Gill, Ritu Gill

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