Temporal-Spatial Feature Extraction in IoT-Based SCADA System Security: Hybrid CNN-LSTM and Attention-Based Architectures for Malware Classification and Attack Detection by Onur Polat, Ali Ayid Ahmad, Saadin Oyucu, Enes Algul, Ferdi Dogan, Ahmet Aksoz

“…The developed model identifies complex attacks in the network by taking advantage of the strengths of CNNs that reveal spatial features and LSTMs that detect temporal dependency. …”
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SDA-Net: A Spatially Optimized Dual-Stream Network with Adaptive Global Attention for Building Extraction in Multi-Modal Remote Sensing Images by Xuran Pan, Kexing Xu, Shuhao Yang, Yukun Liu, Rui Zhang, Ping He

“…To address these challenges, a novel building extraction network based on multi-modal remote sensing data called SDA-les (AGAFMs) was designed in the decoding stage to fuse multi-modal features at various scales, which dynamically adjust the importance of features from a global perspective to better balance the semantic information. …”
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ENHANCING NETWORK INTRUSION DETECTION USING MACHINE LEARNING AND META-MODELLING FOR IMPROVED CYBER SECURITY PERFORMANCE by Sunita, Pankaj Verma, Nitika, Jaspreet Kaur, Vijay Rana

“…This study is based on the analysis of network intrusion detection and the improvement of various machine learning methods that produce high accuracy and guarantee secure network traffic from malicious activities. …”
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Multi-scale feature fusion and feature calibration with edge information enhancement for remote sensing object detection by Lihua Yang, Yi Gu, Hao Feng

“…EMF-DETR introduces a multi-scale edge-aware feature extraction network named MEFE-Net. The network improves object recognition and localization capabilities by extracting multi-scale features and enhancing edge information for targets at each scale, demonstrating exceptional performance in small object detection. …”
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Explainable brain age prediction: a comparative evaluation of morphometric and deep learning pipelines by Maria Luigia Natalia De Bonis, Giuseppe Fasano, Angela Lombardi, Carmelo Ardito, Antonio Ferrara, Eugenio Di Sciascio, Tommaso Di Noia

“…In this study, we present a comparative evaluation of two pipelines: one using morphometric features from FreeSurfer and the other employing 3D convolutional neural networks (CNNs). …”
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ANALYSIS AND EVALUATION OF ROMANIAN VOICE COMMANDS FOR THE CONTROL OF MECHATRONIC SYSTEMS by BOROZAN, Olesea

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Hybrid Optimized Feature Selection and Deep Learning Method for Emotion Recognition That Uses EEG Data by asmaa Bashar Hmaza, Rajaa K. Hasoun

“…First, particle swarm optimization (PSO) identifies and optimizes critical functions and reduces feature dimensionality. Thereafter, long short-term memory (LSTM), gated recurrent unit (GRU), and simple recurrent neural network (RNN) architectures are used in emotion identification. …”
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Multi-Scenario Simulation Evaluation and Strategic Zoning of Habitat Services Based on Habitat Quality and Ecological Network: A Case Study of Lanzhou City by Jin Shi, Xianglong Tang

“…The findings indicate that under four development scenarios, the ecological network generally shows a three-segment distribution. …”
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BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification by Anderson P. Avila Santos, Breno L. S. de Almeida, Robson P. Bonidia, Peter F. Stadler, Polonca Stefanic, Ines Mandic-Mulec, Ulisses Rocha, Danilo S. Sanches, André C.P.L.F. de Carvalho

“…Extracted features, when embedded into the deep network, enable optimal utilization of spatial and sequential nuances of ncRNA sequences. …”
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Convolution neural network–based Alzheimer's disease classification using hybrid enhanced independent component analysis based segmented gray matter of T2 weighted magnetic resonan... by Shaik Basheera, M Satya Sai Ram

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Evaluating machine learning models comprehensively for predicting maximum power from photovoltaic systems by Samir A. Hamad, Mohamed A. Ghalib, Amr Munshi, Majid Alotaibi, Mostafa A. Ebied

“…Additionally, the study assessed the correlation and feature importance to evaluate model compatibility and the factors impacting the predictive accuracy of the ML models. …”
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Feature-based ensemble modeling for addressing diabetes data imbalance using the SMOTE, RUS, and random forest methods: a prediction study by Younseo Jang

“…Purpose This study developed and evaluated a feature-based ensemble model integrating the synthetic minority oversampling technique (SMOTE) and random undersampling (RUS) methods with a random forest approach to address class imbalance in machine learning for early diabetes detection, aiming to improve predictive performance. …”
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Orbital Behavior Intention Recognition for Space Non-Cooperative Targets Under Multiple Constraints by Yuwen Chen, Xiang Zhang, Wenhe Liao, Guoning Wei, Shuhui Fan

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Polynomial-SHAP as a SMOTE alternative in conglomerate neural networks for realistic data augmentation in cardiovascular and breast cancer diagnosis by Chukwuebuka Joseph Ejiyi, Dongsheng Cai, Francis Ofoma Eze, Makuachukwu Bennedith Ejiyi, Jennifer Ene Idoko, Sarpong Kwadwo Asere, Thomas Ugochukwu Ejiyi

“…To overcome these challenges, we propose two augmentation-free neural network models, Double Conglomerate (D-CongNet) and Triple Conglomerate (T-CongNet), which integrate Polynomial feature transformations and SHAP (Shapley Additive Explanations) for feature analysis, ensuring both high predictive performance and robust interpretability. …”
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FEATURES AND PERSPECTIVES OF THE DEVELOPMENT OF ELECTRONIC COMMERCE by E. Bratischeva, I. Chepurova, A. Gladysheva

“…We can see that all new online stores, unique products and services offered by various users on the Internet open new opportunities, new approaches and new e-commerce solutions. We evaluate access to information technology and the network in the article. …”
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Deep learning model for grading carcinoma with Gini-based feature selection and linear production-inspired feature fusion by Shreyan Kundu, Souradeep Mukhopadhyay, Rahul Talukdar, Dmitrii Kaplun, Alexander Voznesensky, Ram Sarkar

“…Additionally, a Gini-based feature selection method is implemented to prioritize the most discriminative features, and the extracted features from each network are optimally combined using a fusion technique modeled after a linear production function, maximizing each model’s contribution to the final prediction. …”
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Hybrid Integrated Feature Fusion of Handcrafted and Deep Features for Rice Blast Resistance Identification Using UAV Imagery by Peng Zhang, Zibin Zhou, Huasheng Huang, Yuanzhu Yang, Xiaochun Hu, Jiajun Zhuang, Yu Tang

“…To address these issues, this article proposes a hybrid integrated feature fusion (HIFF) method, in which a novel handcrafted-design-guided convolutional neural network module was employed to alleviate the problem of image degradation. …”
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Deep learning for gender estimation using hand radiographs: a comparative evaluation of CNN models by Hilal Er Ulubaba, İpek Atik, Rukiye Çiftçi, Özgür Eken, Monira I. Aldhahi

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Leveraging In-Network Caching in Vehicular Network for Content Distribution by Haiyan Tian, Yusuke Otsuka, Masami Mohri, Yoshiaki Shiraishi, Masakatu Morii

“…In order to solve this issue to deliver proximity marketing files, in this paper we propose in-network caching scheme in vehicular networks in accordance with traffic features, in which every vehicle is treated as either a subscriber to request a file or as a cache node to supply other nodes so as to accelerate file transmission effectively. …”
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Efficient resource management using 5G multi-connectivity for high throughput and reliable low latency communication by Snigdhaswin Kar, Prabodh Mishra, Kuang-Ching Wang

“…In this work, we propose and evaluate 5G deployments with multi-connectivity, which can be used to ensure that these 5G networks are able to meet the demanding requirements of future services with efficient resource management.…”
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