A 3D lightweight network with Roberts edge enhancement model (LR-Net) for brain tumor segmentation by Qingxu Meng, Weijiang Wang, Hang Qi, Hua Dang, Minli Jia, Xiaohua Wang

“…We propose a 3D Spatial Shift Convolution and Pixel Shuffle (SSCPS) module, the SSCPS module present a low-parameter, low-computational-cost spatial shift convolution that overcomes the limitation of receptive field and improves the ability to extract global contextual information, Pixel Shuffle (PS) module extracts spatial information from feature dimensions, efficiently replacing traditional upsampling module. …”
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TSD-Net: A Traffic Sign Detection Network Addressing Insufficient Perception Resolution and Complex Background by Chengcheng Ma, Chang Liu, Litao Deng, Pengfei Xu

“…Existing methods face limitations including high computational cost, inconsistent feature alignment, and insufficient resolution in detection heads. …”
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DEMNet: A Small Object Detection Method for Tea Leaf Blight in Slightly Blurry UAV Remote Sensing Images by Yating Gu, Yuxin Jing, Hao-Dong Li, Juntao Shi, Haifeng Lin

“…An efficient EMAFPN neck structure further facilitates deep–shallow feature interaction while reducing the computational cost. Additionally, a novel CMLAB module replaces the traditional C2f structure, employing multi-scale convolutions and local attention mechanisms to recover semantic information in blurry regions and better detect densely distributed small targets. …”
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Improved YOLOv8-Based Algorithm for Citrus Leaf Disease Detection by Zhengbing Zheng, Yibang Zhang, Luchao Sun

“…This modification increases the model’s parameter count without adding to the computational cost in terms of floating-point operations. Next, a fast convolution layer is implemented to replace the original C2f module, improving both detection accuracy and computational efficiency. …”
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SLiTRANet: An EEG-Based Automated Diagnosis Framework for Major Depressive Disorder Monitoring Using a Novel LGCN and Transformer-Based Hybrid Deep Learning Approach by Sagnik de, Anurag Singh, Vivek Tiwari, Harshita Patel, G. N. Vivekananda, Dharmendra Singh Rajput

“…In this study, we proposed a novel Linear Graph Convolution Network-Transformer-based deep learning approach for categorizing MDD through a time-frequency analysis of EEG signals. …”
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BSO-CNN: A BSO Pressure Optimized CNN Model for Water Distribution Networks by Waghmare Shwetambari Pandurang, Renu Praveen Pathak, Imtiyaz Ahmad Wani

“…To overcome these difficulties, a brand-new Convolutional Neural Network (CNN) Pressure Optimization Model is proposed. …”
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Automatic detection and classification process for concrete defects in deteriorating buildings based on image data by Suhyun Kang, Seungho Kim, Sangyong Kim

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Object Recognition and Positioning with Neural Networks: Single Ultrasonic Sensor Scanning Approach by Ahmet Karagoz, Gokhan Dindis

“…Evaluating this dataset from a single sensor scanning can be a perfect application for convolutional neural networks (CNNs). This study proposes an imaging technique based on a scanned dataset obtained by a single low-cost ultrasonic sensor. …”
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Machine Learning Model for Road Anomaly Detection Using Smartphone Accelerometer Data by Mahdi Zareei, Carlos Alonzo Lopez Castaneda, Faisal Alanazi, Fausto Granda, Jesus Arturo Perez-Diaz

“…With Mexico’s road network experiencing significant deterioration and potholes ranking as citizens’ top concern, we propose a convolutional neural network (CNN) model that analyzes accelerometer and gyroscope data from Android smartphones to detect road anomalies. …”
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Multi-species Fish Identification using Hybrid DeepCNN with Refined Squeeze and Excitation Architecture by Jansi Rani Sella Veluswami, Nivetha Panneerselvam

“…In this study, we have proposed a new method of hybrid Deep Convolutional Neural Network (CNN) along with a Support Vector Machine (SVM) for classification. …”
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Fully Interpretable Deep Learning Model Using IR Thermal Images for Possible Breast Cancer Cases by Yerken Mirasbekov, Nurduman Aidossov, Aigerim Mashekova, Vasilios Zarikas, Yong Zhao, Eddie Yin Kwee Ng, Anna Midlenko

“…This research explores the potential of using machine learning techniques, specifically Bayesian networks combined with convolutional neural networks, to improve possible breast cancer diagnosis at early stages. …”
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Spatial and Channel Attention Integration with Separable Squeeze-and-Excitation Networks for Image Classifications by Nazmul Shahadat, Shleshma Regmi, Anup Rijal

“… In recent years, convolutional neural networks (CNNs) have performed remarkably well in various computer vision tasks. …”
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Graph Neural Network Classification in EEG-Based Biometric Identification: Evaluation of Functional Connectivity Methods Using Time-Frequency Metric by Roghaieh Ashenaei, Ali Asghar Beheshti Shirazi

“…Despite reduced setup complexity, our GCNN achieves over 98% identification accuracy, comparable to CNN-based studies using 64 channels, with significantly lower computational cost and trainable variables reduced to less than 0.25 of those in a Convolutional Neural Network (CNN). …”
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Sb-PiPLU: A Novel Parametric Activation Function for Deep Learning by Ayan Mondal, Vimal K. Shrivastava, Ayan Chatterjee, Raghavendra Ramachandra

“…We evaluated Sb-PiPLU through a series of image classification experiments across various Convolutional Neural Network (CNN) architectures. Additionally, we assessed its memory usage and computational cost, demonstrating that Sb-PiPLU is both stable and efficient in practical applications. …”
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From Neural Networks to Emotional Networks: A Systematic Review of EEG-Based Emotion Recognition in Cognitive Neuroscience and Real-World Applications by Evgenia Gkintoni, Anthimos Aroutzidis, Hera Antonopoulou, Constantinos Halkiopoulos

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AI and IoT-powered edge device optimized for crop pest and disease detection by Jean Pierre Nyakuri, Celestin Nkundineza, Omar Gatera, Kizito Nkurikiyeyezu, Gervais Mwitende

“…This study presents the development of a portable smart IoT device that integrates a lightweight convolutional neural network (CNN), called Tiny-LiteNet, optimized for edge applications with built-in support of model explainability. …”
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Hybrid TCN-transformer model for predicting sustainable food supply and ensuring resilience by Ibrahim Alrashdi, Rasha M. Abd El-Aziz, Ahmed I. Taloba, Mohammed Farsi

“…These methods often have reduced accuracy, high computational cost, and poor generalization when applied to shifting patterns in the food supply system. …”
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Automated classification of chest X-rays: a deep learning approach with attention mechanisms by Burcu Oltu, Selda Güney, Seniha Esen Yuksel, Berna Dengiz

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Deep learning for vision screening in resource-limited settings: development of multi-branch CNN for refractive error detection based on smartphone image by Muhammad Syauqie, Muhammad Syauqie, Harry Patria, Sutanto Priyo Hastono, Kemal Nazaruddin Siregar, Nila Djuwita Farieda Moeloek

“…IntroductionUncorrected refractive errors are a leading cause of preventable vision impairment globally, particularly affecting individuals in low-resource regions where timely diagnosis and screening access remain significant challenges despite the availability of economical treatments.AimThis study introduces a novel deep learning-based system for automated refractive error classification using photorefractive images acquired via a standard smartphone camera.MethodsA multi-branch convolutional neural network (CNN) was developed and trained on a dataset of 2,139 corneal images collected from an Indonesian public eye hospital. …”
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Yield prediction method for regenerated rice based on hyperspectral image and attention mechanisms by Tian Hu, Zhihua Liu, Rong Hu, Lu Zeng, Kaiwen Deng, Huanglin Dong, Ming Li, Yang-Jun Deng

“…Regenerated rice has the characteristics of dual harvest, labor-saving and cost-saving, which is of great significance for solving the global food problem. …”
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