A Novel Metaheuristic-Based Methodology for Attack Detection in Wireless Communication Networks by Walaa N. Ismail

“…The framework used involves developing a lightweight model based on a convolutional neural network with 11 layers, referred to as CSO-2D-CNN, which demonstrates fast learning rates and excellent generalization capabilities. …”
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CNN‐based off‐angle iris segmentation and recognition by Ehsaneddin Jalilian, Mahmut Karakaya, Andreas Uhl

“…While deep learning techniques (i.e. segmentation‐based convolutional neural networks (CNNs)) are increasingly being used to address this problem, there is a significant lack of information about the mechanism of the related distortions affecting the performance of these networks and no comprehensive recognition framework is dedicated, in particular, to off‐angle iris recognition using such modules. …”
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MTGNet: Multi-Agent End-to-End Motion Trajectory Prediction with Multimodal Panoramic Dynamic Graph by Yinfei Dai, Yuantong Zhang, Xiuzhen Zhou, Qi Wang, Xiao Song, Shaoqiang Wang

“…Based on this, we propose the multimodal transformer graph convolution neural network (MTGNet) framework. The MTGNet framework can not only construct a panoramic, fully connected dynamic traffic map for agents but also dynamically adjust the size of traffic scenes. …”
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Automated identification of autism spectrum disorder from facial images using explainable deep learning models by El-Sayed Atlam, Khulood O. Aljuhani, Ibrahim Gad, Elsaid Md. Abdelrahim, Ahmed E. Mansour Atwa, Ali Ahmed

“…This study introduces an effective deep learning framework for automated ASD detection that leverages pre-trained convolutional neural networks (CNNs), including VGG16, VGG19, InceptionV3, VGGFace, and MobileNet. …”
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PVD-GSTPS: design of an efficient parallel vehicle detection based green signal time prediction system by Nikhil Nigam, Dhirendra Pratap Singh, Jaytrilok Choudhary, Surendra Solanki

“…Recent advancements in Convolutional Neural Networks (CNNs) have enabled better capturing of patterns in traffic data. …”
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Learning Feature Fusion in Deep Learning-Based Object Detector by Ehtesham Hassan, Yasser Khalil, Imtiaz Ahmad

“…In this work, we present an object detection framework by learning-based fusion of handcrafted features with deep features. …”
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Human-like Dexterous Grasping Through Reinforcement Learning and Multimodal Perception by Wen Qi, Haoyu Fan, Cankun Zheng, Hang Su, Samer Alfayad

“…The framework’s key innovation lies in its Tactile-Driven DCNN architecture—a lightweight convolutional network achieving 98.5% object recognition accuracy using spatiotemporal pressure patterns—coupled with an RL policy refinement mechanism that dynamically correlates finger kinematics with real-time tactile feedback. …”
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Enconv1d Model Based on Pseudolite System for Long-Tunnel Positioning by Changgeng Li, Yuting Zhang, Changshui Liu

“…Four experimental tests in a 4.6 km long real-world tunnel demonstrate that the proposed framework delivers continuous decimeter-level positioning accuracy.…”
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AI-Driven Drought Monitoring: Advanced Machine Learning Techniques for Early Prediction by Vij Priya, Tiwari Ankita

“…The research culminates in a scalable, adaptable, and institutionalized AI framework that addresses urgent environmental challenges. …”
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Multi-Dimensional Anomaly Detection and Fault Localization in Microservice Architectures: A Dual-Channel Deep Learning Approach with Causal Inference for Intelligent Sensing by Suchuan Xing, Yihan Wang, Wenhe Liu

“…This paper proposes a dual-channel deep learning framework that integrates Temporal Convolutional Networks with Variational Autoencoders to address these challenges. …”
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Occupant-Centric Load Optimization in Smart Green Townhouses Using Machine Learning by Seyed Morteza Moghimi, Thomas Aaron Gulliver, Ilamparithi Thirumarai Chelvan, Hossen Teimoorinia

“…This paper presents an occupant-centric load optimization framework for Smart Green Townhouses (SGTs). A hybrid Long Short-Term Memory and Convolutional Neural Network (LSTM-CNN) model is combined with real-time Internet of Things (IoT) data to predict and optimize energy usage based on occupant behavior and environmental conditions. …”
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Deep learning-based object detection for environmental monitoring using big data by Wenbo Lin, Tingting Li, Xiao Li

“…However, high-dimensionality, spatiotemporal variability, and heterogeneous data sources continue to pose major challenges.MethodsIn this work, we introduce the Environmental Graph-Aware Neural Network (EGAN), a novel framework designed to model and analyze large-scale, multi-modal environmental datasets. …”
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A novel method for distracted driving behaviors recognition with hybrid CNN-BiLSTM-AM model by Dengfeng Zhao, Haojie Li, Zhijun Fu, Bao Ma, Fang Zhou, Chaohui Liu, Wenbin He

“…The proposed framework consists of hybrid convolutional neural network and bidirectional long short term memory network to extract multi-scale spatiotemporal features of high-dimensional distracted behavior data. …”
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A Hybrid MLP and CNN Architecture for Sequential Recommendation by Li Yuan, Xue-Yi Zhao

“…In this context, we propose a novel multi-interest sequential recommendation framework that effectively captures both long-term and short-term user interests. …”
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Improving thermal state preparation of Sachdev–Ye–Kitaev model with reinforcement learning on quantum hardware by Akash Kundu

“…We demonstrate the effectiveness of the RL framework in both noiseless and noisy quantum hardware environments, maintaining high accuracy in thermal state preparation. …”
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A Research Approach to Port Information Security Link Prediction Based on HWA Algorithm by Zhixin Xia, Zhangqi Zheng, Lexin Bai, Xiaolei Yang, Yongshan Liu

“…The algorithm can obtain hypergraphs without knowing the attribute information of hypergraph nodes and combines the graph convolutional network (GCN) framework to capture node feature information for link prediction. …”
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GAT-Enhanced YOLOv8_L with Dilated Encoder for Multi-Scale Space Object Detection by Haifeng Zhang, Han Ai, Donglin Xue, Zeyu He, Haoran Zhu, Delian Liu, Jianzhong Cao, Chao Mei

“…In this paper, a multi-scale feature fusion framework based on an improved version of YOLOv8_L is proposed. …”
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A modified deep neural network enables identification of foliage under complex background by Xiaolong Zhu, Junhao Zuo, Honge Ren

“…For the sake of enhancing the identification ability of current network and meeting the needs of the high accuracy of distinguishing similar small objects (foliage) in the complex scenes, this paper proposes a modified region-based fully convolutional network which adopts Inception V3 accompanying with residual connection as the main framework. …”
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Neural network-based forecasting and uncertainty analysis of new power generation capacity of electric energy by Xingyu Dou, Zehan Cui

“…To solve this, we propose a framework combining an optimized multiscale convolutional neural network (MSCNN) and long - short - term memory network (LSTM). …”
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AutoWindLoc: Precise Localization of Wind Turbines in High-Resolution Orthophotos for Enhanced Registers by J. Middendorf, A. Kelm, S. Frintrop

“…The paper proposes a novel framework for automatically detecting wind turbines in orthophotos, transferring this information to a database, and linking detected turbines to an existing registry to minimize location inaccuracy. …”
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