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

“…The system’s low-cost implementation and high accuracy indicate that it may be well suited for large-scale road condition monitoring using mobile crowd-sensing paradigms.…”
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Multi-species Fish Identification using Hybrid DeepCNN with Refined Squeeze and Excitation Architecture by Jansi Rani Sella Veluswami, Nivetha Panneerselvam

“…This problem can be solved using traditional manual annotation on the images. To reduce manpower, cost, and tremendous time, deep learning approaches are used which always require large datasets. …”
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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

“…A literature review indicates the urgency of improving diagnostic methods and identifies thermography as a promising, cost-effective, non-invasive, adjunctive, and complementary detection method. …”
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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

“…While various technologies, such as the Internet of Things (IoT), machine learning (ML), and artificial intelligence (AI), have been used, portable, cost-effective, and energy-efficient solutions suitable for resource-constrained environments such as edge applications in agriculture are needed. …”
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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

“…Its multi-scale feature extraction pathways were pivotal in effectively addressing overlapping red reflex patterns and subtle variations between classes.ConclusionThis study establishes the feasibility of smartphone-based photorefractive assessment integrated with artificial intelligence for scalable and cost-effective vision screening. By training the CNN model with a real-world dataset representative of Southeast Asian populations, this system offers a reliable solution for early refractive error detection with significant implications for improving accessibility to eye care services in resource-limited settings.…”
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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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Method for Automatic Determination of a 3D Trajectory of Vehicles in a Video Image by I. G. Zubov, N. A. Obukhova

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Gesture-controlled omnidirectional autonomous vehicle: A web-based approach for gesture recognition by Huma Zia, Bara Fteiha, Maha Abdulnasser, Tafleh Saleh, Fatima Suliemn, Kawther Alagha, Jawad Yousaf, Mohammed Ghazal

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3D-CNN detection of systemic symptoms induced by different Potexvirus infections in four Nicotiana benthamiana genotypes using leaf hyperspectral imaging by Rizos-Theodoros Chadoulis, Ioannis Livieratos, Ioannis Manakos, Theodore Spanos, Zeinab Marouni, Christos Kalogeropoulos, Constantine Kotropoulos

“…Abstract Purpose Hyperspectral imaging combined with machine learning offers a promising, cost-effective alternative to invasive chemical analysis for early plant disease detection. …”
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Half-hourly electricity price prediction model with explainable-decomposition hybrid deep learning approach by Sujan Ghimire, Ravinesh C. Deo, Konstantin Hopf, Hangyue Liu, David Casillas-Pérez, Andreas Helwig, Salvin S. Prasad, Jorge Pérez-Aracil, Prabal Datta Barua, Sancho Salcedo-Sanz

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Enhancing natural disaster image classification: an ensemble learning approach with inception and CNN models by Kashvi Ankitbhai Sheth, Rujuta Prajakt Kulkarni, G. K. Revathi

“…Existing detection methods are often time-consuming and costly. The purpose of this research is to introduce an innovative approach to the multi-class classification of natural disasters using image data from a Kaggle dataset encompassing Cyclone, Wildfire, Flood, and Earthquake incidents. …”
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Cumulative Failure Rate Prediction of EDCU in Subway Vehicles Based on RF–CNN–LSTM Model by Tian Hang, Jing Wen, Shubin Zheng

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WIVIDOSA-Net: Wigner–Ville distribution based obstructive sleep apnea detection using single lead ECG signal by Amit Bhongade, Tapan Kumar Gandhi

“…Currently, it is diagnosed with polysomnography (PSG), which is costly and sometimes uncomfortable. Researchers are now exploring the use of electrocardiogram (ECG) signals as a potential alternative for diagnosing OSA. …”
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