DaGAM-Trans: Dual graph attention module-based transformer for offline signature forgery detection by Sara Tehsin, Ali Hassan, Farhan Riaz, Inzamam Mashood Nasir

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Deep learning based bio-metric authentication system using a high temporal/frequency resolution transform by Sajjad Maleki Lonbar, Akram Beigi, Nasour Bagheri, Nasour Bagheri, Pedro Peris-Lopez, Carmen Camara

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MalGEA: A malware analysis framework via matrix factorization based node embedding and graph external attention by Ruisheng Li, Qilong Zhang, Huimin Shen

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Online Face Detection and Recognition in a Video Stream by Maha Hasso Al-Ghurery, Manar Zidan Al-Abaji

“…In this work a system is designed for Online Face Detection and Recognition depending on multiple algorithms that are: AdaBoost algorithm for the face detection and the two algorithms Principle Component Analysis (PCA) and Linear Discriminate Analysis (LDA) to extract features and use back propagation neural network in recognition. …”
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Deep CNN based brain tumor detection in intelligent systems by Brij B. Gupta, Akshat Gaurav, Varsha Arya

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An Optimized Transformer–GAN–AE for Intrusion Detection in Edge and IIoT Systems: Experimental Insights from WUSTL-IIoT-2021, EdgeIIoTset, and TON_IoT Datasets by Ahmad Salehiyan, Pardis Sadatian Moghaddam, Masoud Kaveh

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GradCAM-PestDetNet: A deep learning-based hybrid model with explainable AI for pest detection and classification by Ramitha Vimala, Saharsh Mehrotra, Satish Kumar, Pooja Kamat, Arunkumar Bongale, Ketan Kotecha

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Smart Real-time Attendance System for Nigerian Universities by Mikailu Habila, Faseki N. Francisca, Luwani Ishaya, Muhammed Kabir Ahmed, Umar A. Muhammad, Haruna P. Charles

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Sustainable AI for plant disease classification using ResNet18 in few-shot learning by Fareeha Naveed, Adven Masih, Jabar Mahmood, Moeez Ahmed, Aitizaz Ali, Aysha Saddiqa, Mohamed Shabbir Hamza Abdulnabi, Ebenezer Agbozo

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Predicting user's movement path in indoor environments using the stacked deep learning method and the fuzzy soft‐max classifier by Masoumeh Bourjandi, Meisam Yadollahzadeh‐Tabari, Mehdi GolsorkhtabariAmiri

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Malicious Traffic Detection on Tofino Using Graph Attention Model by Xichang Gao, Lizhuang Tan, Shengpeng Chen, Peiying Zhang, Jian Wang

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Performance Evaluation of Various Deep Learning Models in Gait Recognition Using the CASIA-B Dataset by Nakib Aman, Md. Rabiul Islam, Md. Faysal Ahamed, Mominul Ahsan

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Integration of OWL Password-Authenticated Key Exchange Protocol to Enhance IoT Application Protocols by Yair Rivera Julio, Angel Pinto Mangones, Juan Torres Tovio, María Clara Gómez-Álvarez, Dixon Salcedo

“…Its one-round exchange model and resistance to both passive and active attacks make it particularly well-suited for constrained devices and dynamic network topologies. The originality of the proposal lies in embedding OWL directly into protocols like CoAP, enabling secure session establishment as a native feature rather than as an auxiliary security layer. …”
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Enhancing plant disease detection through deep learning: a Depthwise CNN with squeeze and excitation integration and residual skip connections by Asadulla Y. Ashurov, Mehdhar S. A. M. Al-Gaashani, Nagwan A. Samee, Reem Alkanhel, Ghada Atteia, Hanaa A. Abdallah, Mohammed Saleh Ali Muthanna

“…The architectural modifications are specifically designed to enhance feature extraction and classification performance, all while maintaining computational efficiency. …”
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Spiking Residual ShuffleNet-Based Intrusion Detection in IoT Environment by Sneha Leela Jacob, H. Parveen Sultana

“…The Internet of Things (IoT) system has been developed to create a smart environment. Privacy and security are critical issues in IoT systems. Security vulnerabilities in IoT-enabled models generate threats that impact various applications. …”
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A deep learning based intrusion detection system for CAN vehicle based on combination of triple attention mechanism and GGO algorithm by Hongwei Yang, Mehdi Effatparvar

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Research on multi-user identity recognition based on Wi-Fi sensing by Zhongcheng WEI, Wei CHEN, Yanhu DONG, Bin LIAN, Wei WANG, Jijun ZHAO

“…With the advancement of wireless sensing technology, research on Wi-Fi-based identity recognition has garnered significant attention in fields such as human-computer interaction and home security.While identity recognition based on Wi-Fi signals has achieved initial success, it is currently primarily suitable for scenarios involving individual user behavior.Identity recognition for multiple users in concurrent behavior scenarios still faces a series of challenges, including issues related to mutual interference between users and poor model robustness.Therefore, a Wiblack system for recognizing multiple user identities in a concurrent distribution behavior scenario was proposed.The core idea was to train a multi-branch deep neural network (Wiblack-Net) to extract unique features for each individual user.Firstly, the common features among multiple users were extracted using the backbone network.Then, a binary classifier was assigned to each user to determine the presence of the target user within a given group, thereby achieving identity recognition for multiple users based on concurrent behavior.In addition, experiments comparing Wiblack with several independent binary classification models and a single multiclassification model were conducted to analyze operational efficiency.System performance experimental results demonstrate that when simultaneously identifying the identities of three users, Wibalck achieves an average accuracy of 92.97%, an average precision of 93.71%, an average recall of 93.24%, and an average F1 score of 92.43%.…”
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Real-time prediction model of public safety events driven by multi-source heterogeneous data by Quanlong Fan, Gang Xu

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Diabetes: Non-Invasive Blood Glucose Monitoring Using Federated Learning with Biosensor Signals by Narmatha Chellamani, Saleh Ali Albelwi, Manimurugan Shanmuganathan, Palanisamy Amirthalingam, Anand Paul

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Deep Learning-Based Image Watermarking Using Catalan Transform and Non-Negative Matrix Factorization by Md. Fahim Hossain Saikat, Md. Al-Mamun Provath, Kaushik Deb, Pranab Kumar Dhar, Tetsuya Shimamura

“…The main contribution of the proposed method is its new combination of the Catalan transform (CT) and non-negative matrix factorization (NMF) with an artificial neural network (ANN) for enhanced watermark embedding and extraction. …”
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