Cyber threats in mobile healthcare applications: systematic review of enabling technologies, threat models, detection approaches, and future directions by Anayo Chukwu Ikegwu, Uzoma Rita Alo, Henry Friday Nweke

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Spatial correlation guided cross scale feature fusion for age and gender estimation by Shiyi Jiang, Qing Ji, Hukui Shi, Che Chen, Yang Xu

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Enhancing MANET Security Through Long Short-Term Memory-Based Trust Prediction in Location-Aided Routing Protocols by Saad Mohsen Hassan, Mohd Murtadha Mohamad, Farkhana Binti Muchtar

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A systematic review of features and content quality of Arabic mental mHealth apps by Noorah Ibrahim S. Alnaghaimshi, Noorah Ibrahim S. Alnaghaimshi, Mona S. Awadalla, Scott R. Clark, Mathias Baumert

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Key Technology of Communication Equipment Fingerprint Recognition Based on Intelligent Feature Extraction Algorithm by Meizhen Gao, Yunquan Li, Yetong Gao

“…The autoencoder feature and four kinds of integral bispectrum feature are analyzed and visualized. …”
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A Hybrid Deep Learning Framework for Deepfake Detection Using Temporal and Spatial Features by Fazeel Zafar, Talha Ahmed Khan, Salas Akbar, Muhammad Talha Ubaid, Sameena Javaid, Kushsairy Abdul Kadir

“…The backbone of EfficientNet B0 utilizes Mobile Inverted Bottleneck Convolutions (MBConv) and Squeeze and Excitation (SE) blocks to enhance feature extraction by adjusting channels to highlight details effectively. …”
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Dual-Attention-Enhanced MobileViT Network: A Lightweight Model for Rice Disease Identification in Field-Captured Images by Meng Zhang, Zichao Lin, Shuqi Tang, Chenjie Lin, Liping Zhang, Wei Dong, Nan Zhong

“…Compared to traditional lightweight models, MobileViT-DAP showed significant improvements in model complexity, computational efficiency, and classification performance, effectively balancing lightweight design with high accuracy. …”
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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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Empowering Patients and Caregivers to Use Artificial Intelligence and Computer Vision for Wound Monitoring: Nonrandomized, Single-Arm Feasibility Study by Rose Raizman, José Luis Ramírez-GarciaLuna, Tanmoy Newaz, Sheila C Wang, Gregory K Berry, Ling Yuan Kong, Heba Tallah Mohammed, Robert D J Fraser

“…Feedback from patients and clinicians highlighted during the feasibility testing support insight into the app’s usability, data security features, and ability to enhance remote monitoring that need to be explored in further qualitative research. …”
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BAHGRF3: Human gait recognition in the indoor environment using deep learning features fusion assisted framework and posterior probability moth flame optimisation by Muhammad Abrar Ahmad Khan, Muhammad Attique Khan, Ateeq Ur Rehman, Ahmed Ibrahim Alzahrani, Nasser Alalwan, Deepak Gupta, Saima Ahmed Rahin, Yudong Zhang

“…Abstract Biometric characteristics are playing a vital role in security for the last few years. Human gait classification in video sequences is an important biometrics attribute and is used for security purposes. …”
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A multi-model feature fusion based transfer learning with heuristic search for copy-move video forgery detection by Hessa Alfraihi, Muhammad Swaileh A. Alzaidi, Hamed Alqahtani, Abdulbasit A. Darem, Ali M. Al-Sharafi, Ahmad A. Alzahrani, Menwa Alshammeri, Abdulwhab Alkharashi

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Deep learning based ensemble model for accurate tomato leaf disease classification by leveraging ResNet50 and MobileNetV2 architectures by Jatin Sharma, Asma A. Al-Huqail, Ahmad Almogren, Hardik Doshi, B. Jayaprakash, B. Bharathi, Ateeq Ur Rehman, Seada Hussen

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LiSA-MobileNetV2: an extremely lightweight deep learning model with Swish activation and attention mechanism for accurate rice disease classification by Yongqi Xu, Dongcheng Li, Changcheng Li, Zheming Yuan, Zhijun Dai

“…In the context of intelligent agriculture in China, rapid and accurate identification of crop diseases is essential for ensuring food security and improving crop yield. Although lightweight convolutional neural networks (CNNs) are widely adopted for plant disease recognition due to their computational efficiency, they often suffer from limited feature representation and classification accuracy. …”
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A compact deep learning approach integrating depthwise convolutions and spatial attention for plant disease classification by Amreen Batool, Jisoo Kim, Yung-Cheol Byun

“…We propose a novel Lightweight Deep Learning model, Depthwise Separable Convolution with Spatial Attention (LWDSC-SA), designed to address limitations and enhance feature extraction while maintaining computational efficiency. …”
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An efficient privacy-preserving multilevel fusion-based feature engineering framework for UAV-enabled land cover classification using remote sensing images by S. Nagadevi, G. Abirami, R. Brindha, T. Prabhakara Rao, Gyanendra Prasad Joshi, Woong Cho

“…Initially, the image pre-processing stage applies a joint bilateral filter (JBF) model to enhance image quality by removing noise. Furthermore, the feature extraction process uses the fusion models comprising NASNetMobile, ResNet50, and VGG19. …”
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Med Box: Smart Medication Reminding & Monitoring by Zahid ALI KHAN, Khuzama Nadeem, Ali Hassan

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THE IMPACT OF ECONOMIC COMPANY DATA THEFT by Ramona MARGE, Ştefan IOVAN

“…In terms of preventing internal threats, solutions have powerful encryption algorithms that protect important business information, application control tools, device control, web control and mobile device management features to implement IT security policies. …”
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Vyoma commerce: a blockchain-based decentralized architecture to combat fraud and enhance security and trust in Bangladesh’s e-commerce ecosystem leveraging smart contracts, supply... by Avijit Chowdhury

“…The existing Web 2.0 framework, which employs inadequate security measures, exhibits vulnerabilities when compared to the robust security features of blockchain technology. …”
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Design and implementation of an IoT-based monitoring system for early detection of lumpy skin disease in cattle by Hammad Shahab, Muhammad Iqbal, Ahmed Sohaib, Atiq ur Rehman, Amine Bermak, Kashif Munir

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FloodNet-Lite: A Lightweight Deep Learning for Flood Mapping Using Remote Sensing Data With Optimized UNet and Edge Deployment Approach in 6G by Puviyarasi Thirugnanasammandamoorthi, Debabrata Ghosh, Ram Kishan Dewangan, Mohammad Kamrul Hasan, Khairul Akram Zainol Ariffin, Huda Saleh Abbas, Hashim Elshafie, Rashid A. Saeed, Ala Eldin Awouda

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