Fusion ConvLSTM-Net: Using Spatiotemporal Features to Increase Residential Load Forecast Horizon by Abhishu Oza, Dhaval K. Patel, Bryan J. Ranger

“…We evaluated the model against several benchmark neural network models by: 1) testing different forecast window sizes ranging from 1.5 to 24 hours, 2) assessing model performance across multiple households, and 3) performing large-scale forecasting by aggregating predictions from 100 households. …”
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Hybrid Deep Learning Architecture with Adaptive Feature Fusion for Multi-Stage Alzheimer’s Disease Classification by Ahmad Muhammad, Qi Jin, Osman Elwasila, Yonis Gulzar

“…Traditional convolutional neural networks (CNNs) and deep learning models often fail to effectively integrate localized brain changes with global connectivity patterns, limiting their efficacy in Alzheimer’s disease (AD) classification. …”
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An enhanced denoising system for mammogram images using deep transformer model with fusion of local and global features by A. Robert Singh, Suganya Athisayamani, Faten Khalid Karim, Ahmed Zohair Ibrahim, Sameer Alshetewi, Samih M. Mostafa

“…The design of these groups ensures that the model can utilize both local features for fine details and global features for larger context, leading to more accurate denoising. …”
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Enhanced safety assessment on tunnel excavation via refined rock mass parameter identification by Hongwei Huang, Tongjun Yang, Jiayao Chen, Zhongkai Huang, Chen Wu, Jianhong Man

“…The study provides significant insights into the intelligent evaluation of safety for continuous tunnel excavation.…”
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Real-Time Bus Departure Prediction Using Neural Networks for Smart IoT Public Bus Transit by Narges Rashvand, Sanaz Sadat Hosseini, Mona Azarbayjani, Hamed Tabkhi

“…We leverage AI-driven models to enhance the accuracy of bus schedules by preprocessing data, engineering relevant features, and implementing a fully connected neural network that utilizes historical departure data to predict departure times at subsequent stops. …”
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A Lightweight Neural Network for Denoising Wrapped-Phase Images Generated with Full-Field Optical Interferometry by Muhammad Awais, Younggue Kim, Taeil Yoon, Wonshik Choi, Byeongha Lee

“…The network architecture integrates a shallow feature extraction module, a series of Residual Dense Attention Blocks (RDABs), and a dense feature fusion module. …”
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A machine learning-based efficient anomaly detection system for enhanced security in compromised and maligned IoT Networks by Anita Punia, Manish Tiwari, Sourabh Singh Verma

“…Malicious actors exploit these vulnerabilities, posing a grave threat to IoT networks and their users. Traditional machine learning approaches cannot detect these threats because IoT data is complex. …”
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A Dynamic and Timely Point-of-Interest Recommendation Based on Spatio-Temporal Influences, Timeliness Feature and Social Relationships by Jun Zhu, Haifeng Lin, Zhinan Gou, Yiqing Xu, Hongying Liu, Ming Tang, Li Wang, Shu Li, Bing Hu

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A Dual-Perspective Self-Supervised IoT Intrusion Detection Method Based on Topology Reconstruction and Feature Perturbation by Ruisheng Li, Huimin Shen, Qilong Zhang

“…As a critical technology for securing IoT, intrusion detection systems aim to identify potential threats by analyzing network traffic features. Yet, traditional models struggle to capture the complex topological structures in IoT environments, and their training often relies heavily on large amounts of labeled data, making them unsuitable for IoT settings where massive data is continually generated. …”
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Multi sensor based monitoring of paralyzed using Emperor Penguin Optimizer and Deep Maxout Network by Vijaya Gunturu, J. Kavitha, Swapna Thouti, N. K. Senthil Kumar, Kamal Poon, Ayman A. Alharbi, Amar Y. Jaffar, V. Saravanan

“…A Deep Maxout Network (DMN) was used to predict the posture of a wheelchair-using patient following the feature selection phase. …”
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MCT-CNN-LSTM: A Driver Behavior Wireless Perception Method Based on an Improved Multi-Scale Domain-Adversarial Neural Network by Kaiyu Chen, Yue Diao, Yucheng Wang, Xiafeng Zhang, Yannian Zhou, Minming Gu, Bo Zhang, Bin Hu, Meng Li, Wei Li, Shaoxi Wang

“…Recently, deep learning has gained significant attention in radar signal processing due to its ability to eliminate the need for intricate signal preprocessing and its automatic feature extraction capabilities. In this article, we present a network that incorporates multi-scale and channel-time attention modules, referred to as MCT-CNN-LSTM. …”
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Hybrid Big Bang-Big crunch with cuckoo search for feature selection in credit card fraud detection by Mohd Shukri Ab Yajid, Nilesh Bhosle, Gadug Sudhamsu, Ali Khatibi, Sahil Sharma, Rubal Jeet, R. Sivaranjani, A. Bhowmik, A. Johnson Santhosh

“…Here, the CS algorithm uses the Levy flight attribute to help the BB-BC agents escape from stagnation and premature convergence. After feature selection, classification is performed using Deep Convolutional Neural Networks (DCNN) and Enhanced DCNN (EDCNN) to improve detection accuracy. …”
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A Comprehensive Benchmarking Framework for Sentinel-2 Sharpening: Methods, Dataset, and Evaluation Metrics by Matteo Ciotola, Giuseppe Guarino, Antonio Mazza, Giovanni Poggi, Giuseppe Scarpa

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A Highly Robust Encoder–Decoder Network with Multi-Scale Feature Enhancement and Attention Gate for the Reduction of Mixed Gaussian and Salt-and-Pepper Noise in Digital Images by Milan Tripathi, Waree Kongprawechnon, Toshiaki Kondo

“…HREDN integrates a multi-scale feature enhancement block in the encoder, allowing the network to capture features at various scales and handle complex noise patterns more effectively. …”
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Learning a cross-scale cross-view decoupled denoising network by mining Omni-channel information by Song Qian, Yan Xue, Youbao Chang

“…Our approach relies on an encoder-decoder architecture, which facilitates cross-view and cross-scale feature interactions. The network is trained with a composite loss function that includes both spatial and perceptual domain constraints, ensuring a comprehensive optimization of the denoising process. …”
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Generalized Extraction of Bolts, Mesh, and Rock in Tunnel Point Clouds: A Critical Comparison of Geometric Feature-Based Methods Using Random Forest and Neural Networks by Luke Weidner, Gabriel Walton

“…We used two random forest (RF) implementations and one neural network (NN), as proposed in recent studies, on four datasets collected in different mines and tunnels in the US and Canada. …”
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K-Means Clustering and Classification of Breast Cancer Images Using Histogram of Oriented Gradients Features and Convolutional Neural Network Models: Diagnostic Image Analysis Stud... by Said Salloum

“…ObjectiveThis study aimed to develop an innovative hybrid technique for the classification of breast cancer images involving unsupervised analysis by K-means clustering, feature extraction using Histogram of Oriented Gradients (HOG), and classification of images through a convolutional neural network (CNN). …”
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A lightweight multi-path convolutional neural network architecture using optimal features selection for multiclass classification of brain tumor using magnetic resonance images by Amreen Batool, Yung-Cheol Byun

“…In addition, we evaluate the performance of the proposed M-CNN with the Convolutional Neural Network (CNN), Deep Convolutional Neural Network (D-CNN), and other state-of-the-art deep learning architectures using all and selected features for brain tumor detection. …”
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AirQuaNet: A Convolutional Neural Network Model With Multi-Scale Feature Learning and Attention Mechanisms for Air Quality-Based Health Impact Prediction by Sreeni Chadalavada, Suleyman Yaman, Abdulkadir Sengur, Ravinesh C. Deo, Abdul Hafeez-Baig, Tracy Kolbe-Alexander, Niranjana Sampathila, U. Rajendra Acharya

“…Residual connections are employed to prevent vanishing gradient issues during training, and the self-attention mechanism dynamically weights informative inputs, improving the model’s attention towards informative pollutant features. AirQuaNet was evaluated on two public datasets, the Air Quality and Health Impact Dataset and the Comprehensive Health Data for Asthma. …”
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The key design features and effectiveness of social network interventions for HIV testing and linkage services in low‐ and middle‐income countries: a systematic review and meta‐ana... by Madalo Mukoka, Takondwa C. Msosa, Hussein H. Twabi, Robina Semphere, Marriott Nliwasa, Guy Harling, Alison Price, Katherine Fielding, Augustine T. Choko

“…This review evaluates the key design features and effectiveness of SNIs for HIV testing and linkage in low‐ and middle‐income countries (LMICs). …”
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