Leaky ReLU-ResNet for Plant Leaf Disease Detection: A Deep Learning Approach by Smitha Padshetty, Ambika

“…To tackle these challenges, this research introduces a novel approach called the Leaky Rectilinear Residual Network (LRRN) for plant leaf disease detection. The LRRN model comprises three key modules—data pre-processing, feature extraction, and classification. …”
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Sub-milliwatt threshold power and tunable-bias all-optical nonlinear activation function using vanadium dioxide for wavelength-division multiplexing photonic neural networks by Jorge Parra, Juan Navarro-Arenas, Pablo Sanchis

“…On the other hand, performance evaluations using the CIFAR-10 dataset confirmed the device’s potential for convolutional neural networks (CNN). …”
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Hierarchical Adaptive Wavelet-Guided Adversarial Network with Physics-Informed Regularization for Generating Multiscale Vibration Signals for Deep Learning-Based Fault Diagnosis of... by Fasikaw Kibrete, Dereje Engida Woldemichael, Hailu Shimels Gebremedhen

“…Finally, a one-dimensional convolutional neural network (1-D CNN) is employed for extracting features and classifying faults. …”
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Sarcopenia diagnosis using skeleton-based gait sequence and foot-pressure image datasets by Muhammad Tahir Naseem, Na-Hyun Kim, Haneol Seo, JaeMok Lee, Chul-Min Chung, Sunghoon Shin, Chan-Su Lee

“…The skeleton dataset was constructed by extracting 3D skeleton information comprising 25 feature points from the image, whereas the foot-pressure dataset was constructed by exerting pressure on the foot-pressure plates.ResultsAs a baseline evaluation, the accuracies of sarcopenia classification performance from foot-pressure image using Resnet-18 and skeleton sequences using ST-GCN were identified as 77.16 and 78.63%, respectively.DiscussionThe experimental results demonstrated the potential applications of sarcopenia and non-sarcopenia classifications based on foot-pressure images and skeleton sequences.…”
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Assessment model of ozone pollution based on SHAP-IPSO-CNN and its application by Xiaolei Zhou, Xingyue Wang, Ruifeng Guo

“…Then three mainstream machine learning models are compared for SHAP analysis to obtain the significance results of relevant features. Finally, the IPSO algorithm is combined with SHAP analysis to dynamically adjust the training features to optimize the performance of the CNN model. …”
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Making a Real-Time IoT Network Intrusion-Detection System (INIDS) Using a Realistic BoT–IoT Dataset with Multiple Machine-Learning Classifiers by Jawad Ashraf, Ghulam Musa Raza, Byung-Seo Kim, Abdul Wahid, Hye-Young Kim

“…Our trained model, INIDS, is not only up to date and real-time but also capable of accurately identifying multiple categories of attacks specifically related to IoT networks. To achieve maximum accuracy, instead of selecting only one classifier, we evaluated seven advanced machine-learning algorithms and provided a comprehensive comparison of their performance and efficiency in the context of IoT networks. …”
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SPCB-Net: A Multi-Scale Skin Cancer Image Identification Network Using Self-Interactive Attention Pyramid and Cross-Layer Bilinear-Trilinear Pooling by Xin Qian, Tengfei Weng, Qi Han, Chen Wu, Hongxiang Xu, Mingyang Hou, Zicheng Qiu, Baoping Zhou, Xianqiang Gao

“…Deep convolutional neural networks have made some progress in skin lesion classification and cancer diagnosis, but there are still some problems to be solved, such as the challenge of small inter-class feature differences and large intra-class feature differences, which might limit the classification performance of the model as high-level and low-level features are not properly utilized. …”
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Image super-resolution reconstruction based on multi-scale dual-attention by Hong-an Li, Diao Wang, Jing Zhang, Zhanli Li, Tian Ma

“…To solve the above problems, this paper proposes a Multi-scale Dual-Attention based Residual Dense Generative Adversarial Network (MARDGAN), which uses multi-branch paths to extract image features and obtain multi-scale feature information. …”
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A Semi-Supervised Diffusion-Based Framework for Weed Detection in Precision Agricultural Scenarios Using a Generative Attention Mechanism by Ruiheng Li, Xuaner Wang, Yuzhuo Cui, Yifei Xu, Yuhao Zhou, Xuechun Tang, Chenlu Jiang, Yihong Song, Hegan Dong, Shuo Yan

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Advanced Multi-Level Ensemble Learning Approaches for Comprehensive Sperm Morphology Assessment by Abdulsamet Aktas, Taha Cap, Gorkem Serbes, Hamza Osman Ilhan, Hakkı Uzun

“…<b>Methods:</b> We propose a novel ensemble-based classification approach that combines convolutional neural network (CNN)-derived features using both feature-level and decision-level fusion techniques. …”
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Masked and Noise-Masked Multimodal Brain Tumor Image Segmentation Using SegFormer and Shared Encoder Framework by K. Hemalatha, P. R. Vishnu Vardhan, Alfred Dharmaraj Aravindraj, S. Hari Hara Sudhan

“…Additionally, noise-masking introduces a controlled Gaussian noise into the MRI images creating random variations in the pixel intensities and thereby encouraging the model to learn the invariant and essential patterns in MRI images. Evaluation on Brain Tumor Segmentation (BraTS2020) challenge dataset demonstrates that MNMS outperforms conventional convolutional neural network (CNN) based methods, achieving superior accuracy and Dice Similarity Coefficient (DSC) scores. …”
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Integrated single-cell analysis reveals the regulatory network of disulfidptosis-related lncRNAs in bladder cancer: constructing a prognostic model and predicting treatment respons... by Jiafu Xiao, Jiafu Xiao, Jiafu Xiao, Wuhao Liu, Wuhao Liu, Wuhao Liu, Jianxin Gong, Jianxin Gong, Jianxin Gong, Weifeng Lai, Weifeng Lai, Weifeng Lai, Neng Luo, Neng Luo, Neng Luo, Yingfan He, Junrong Zou, Junrong Zou, Zhihua He, Zhihua He, Zhihua He

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Deep learning-driven drug response prediction and mechanistic insights in cancer genomics by Guili Yu, Qiangqiang Fan

“…Recent advancements in large-scale in vitro drug screening assays have generated extensive drug testing and genomic data, providing valuable resources to explore the relationship between genomic features and drug responses. In this study, we developed a deep neural network model, DrugS (Drug Response prediction Utilizing Genomic features Screening), utilizing gene expression and drug testing data from human-derived cancer cell lines to predict cellular responses to drugs. …”
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FFT-RDNet: A Time–Frequency-Domain-Based Intrusion Detection Model for IoT Security by Bingjie Xiang, Renguang Zheng, Kunsan Zhang, Chaopeng Li, Jiachun Zheng

“…These time–frequency domain features are fused to construct a two-dimensional feature space, which is then processed by a streamlined residual network using depthwise separable convolution. …”
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A Comparative Analysis of Time Series Forecasting Models and the Novel ELSTMD Approach for Renewable Energy Generation by Yousef Alkhanafseh, Tahir Cetin Akinci, Alfredo A. Martinez-Morales

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Spatial Analysis of Carbon Metabolism in Different Economic Divisions Based on Land Use and Cover Change (LUCC) in China by Cui Yuan, Yaju Liu, Jingzhao Lu, Chengyi Guo, Tingting Quan, Wei Su

“…The standard deviation ellipse analytic techniques were also employed to research the spatiotemporal evolution features of carbon flow in various economic zones. …”
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Competitive pattern of global tin products trade from the perspective of industry chain by Yuning Jin, Yuning Jin, Yuning Jin, Tao Dai, Tao Dai

“…This paper selects key competitive countries and relationships, examines the characteristics of specific communities, and analyzes the network’s structural features and evolution patterns over time. …”
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Optimizing Group Activity Recognition With Actor Relation Graphs and GCN-LSTM Architectures by M. R. Tejonidhi, C. V. Aravinda, S. V. Aruna Kumar, C. K. Madhu, A. M. Vinod

“…Our architecture employs a Convolutional Neural Network (CNN) with Inception-V3 as the foundational model for initial feature extraction. …”
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Hyperspectral Imaging for Non-Destructive Moisture Prediction in Oat Seeds by Peng Zhang, Jiangping Liu

“…Seven spectral preprocessing techniques—standard normal variate (SNV), multiplicative scatter correction (MSC), first derivative (FD), second derivative (SD), and combinations such as SNV + FD, SNV + SD, and SNV + MSC—were systematically evaluated. Among them, SNV combined with FD was identified as the optimal preprocessing scheme, effectively enhancing spectral feature expression. …”
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A Distributed Detection Method for Quality-related Faults in Complex Non-stationary Industrial Processes by Jie DONG, Daye LI, Yanmei WEI, Kaixiang PENG, Hui YANG

“…The static and dynamic features of stationary and non-stationary variables within the sub-blocks are then extracted using partial least squares (PLS) and long short-term memory (LSTM) network methods, respectively, and the features are fused. …”
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