Demystifying DFT-Based Harmonic Phase Estimation, Transformation, and Synthesis by Marco Oliveira, Vasco Santos, André Saraiva, Aníbal Ferreira

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Empowering sentiment analysis in social media: a comprehensive approach to enhance the classification of abusive Tamil comments using transformer models by Malliga Subramanian, Sathishkumar Veerappampalayam Easwaramoorthy, Nandhini Subbarayan, Usha Moorthy

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RRFNet: A free-anchor brain tumor detection and classification network based on reparameterization technology. by Wei Liu, Xingxin Guo

“…This study, introduces a backbone network built using RepConv and RepC3, along with FGConcat feature map splicing module to optimize the brain tumor detection model. …”
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Morphometry of upper Gilgel Abay watershed in southern Tana Basin, Ethiopia, 2023: analysis and implication for land and water resource management by Simachew Bantigegn Wassie, Melkamu Alebachew Anleye

“…This study tried to delineate the UGAW and its sub-watersheds, compute basic morphometric parameters, and link each value with its drainage characteristics. …”
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Improved Algorithm for Vehicle Bottom Safety Detection Based on YOLOv8n: PSP-YOLO by Di Zhao, Yulin Cheng, Sizhe Mao

“…Finally, the Partial Convolution (PConv) lightweight module was also added to the feature fusion network, effectively reducing the model’s parameters and computational load, conserving computing resources, and improving detection speed and accuracy. …”
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YOLO11-ARAF: An Accurate and Lightweight Method for Apple Detection in Real-World Complex Orchard Environments by Yangtian Lin, Yujun Xia, Pengcheng Xia, Zhengyang Liu, Haodi Wang, Chengjin Qin, Liang Gong, Chengliang Liu

“…Furthermore, the distilled model significantly reduces parameters and doubles the inference speed (FPS), enabling rapid and precise apple detection in challenging orchard settings with limited computational resources.…”
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Bone scintigraphy based on deep learning model and modified growth optimizer by Omnia Magdy, Mohamed Abd Elaziz, Abdelghani Dahou, Ahmed A. Ewees, Ahmed Elgarayhi, Mohammed Sallah

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A comprehensive analysis of deep learning and transfer learning techniques for skin cancer classification by Manishi Shakya, Ravindra Patel, Sunil Joshi

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MECHANICAL STATE ANALYSIS USING QUANTUM THEORY AND MATHEMATICAL MORPHOLOGICAL FILTER by SHI Ying

“…Aiming at the erosion operator for the mathematical morphological filter（ MMF）,the quantum-inspired weighting structuring element（ QWSE） is proposed based on the quantum theory to extract fault information of mechanical vibration signal.Firstly,after analyzing the system with multiple quantum bits,a method which is employed to map the quantum space to the real number space is presented and the calculation formula of the QWSE is obtained. Then in order to compute all the parameters of the QWSE,the stochastic characteristics of mechanical vibration signal is utilized to acquire the quantum-inspired weighting while the local feature of signal is utilized to generate the dynamic height of the QWSE. …”
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K-Nearest Neighbors for Anomaly Detection and Predictive Maintenance in Water Pumping Systems by João Pablo Santos da Silva, André Laurindo Maitelli

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Advanced artificial intelligence with federated learning framework for privacy-preserving cyberthreat detection in IoT-assisted sustainable smart cities by Mahmoud Ragab, Ehab Bahaudien Ashary, Bandar M. Alghamdi, Rania Aboalela, Naif Alsaadi, Louai A. Maghrabi, Khalid H. Allehaibi

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Using lightweight method to detect landslide from satellite imagery by Jinchi Dai, Xiaoai Dai, Renyuan Zhang, JiaXin Ma, Wenyu Li, Heng Lu, Weile Li, Shuneng Liang, Tangrui Dai, Yunfeng Shan, Donghui Zhang, Lei Zhao

“…Increasingly general-purpose detection models are being deployed for these complex and dynamic tasks involving features that are difficult to characterize. However, these models are computationally expensive and memory-hungry, while the accuracy and detection efficiency remain wanting. …”
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Research on a Multi-Dimensional Information Fusion Mechanical Wear Fault-Diagnosis Algorithm Based on Data Regeneration by Qifan Zhou, Bosong Chai, Kunwen Ran, Yingqing Guo, Shan Zhou, Wangyu Wu, Kun Wang, Yao Ni

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Red Raspberry Maturity Detection Based on Multi-Module Optimized YOLOv11n and Its Application in Field and Greenhouse Environments by Rongxiang Luo, Xue Ding, Jinliang Wang

“…Secondly, dilation-wise residual (DWR) is fused with the C3k2 module of the network and applied to the entire network structure to enhance feature extraction, multi-scale perception, and computational efficiency in red raspberry detection. …”
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Integrating hybrid bald eagle crow search algorithm and deep learning for enhanced malicious node detection in secure distributed systems by Feras Mohammed Al-Matarneh

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Interfacial adhesion between dissimilar thermoplastics fabricated via material extrusion-based multi-material additive manufacturing by Felix Richter, Dazhong Wu

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Age- and Post-Based Complex Analyses of Heart Rate Variability in Young Male Handball Players for Potential Prevention of Overload-induced Injuries by Zita Petrovszki, Olivér Czimbalmos, Vera Gal, Gabor Korosi, Edit Nagy, Rita Mikulan, Gyongyi Horvath

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Mamba-YOLO-ML: A State-Space Model-Based Approach for Mulberry Leaf Disease Detection by Chang Yuan, Shicheng Li, Ke Wang, Qinghua Liu, Wentao Li, Weiguo Zhao, Guangyou Guo, Lai Wei

“…Traditional detection methods relying on chemical pesticides and manual observation prove inefficient and unsustainable. Although computer vision and deep learning technologies offer new solutions, existing models exhibit limitations in natural environments, including low recognition rates for small targets, insufficient computational efficiency, poor adaptability to occlusions, and inability to accurately identify structural features such as leaf veins. …”
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Drone Detection Using YOLOv5 With GhostNet Backbone and Adaptive Fourier Neural Operator (AFNO 2D) by K. Kavin Akash, Uma Kuppusamy

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GPPK4PCM: pest classification model integrating growth period prior knowledge by Jianhua Zheng, Junde Lu, Yusha Fu, Ruolin Zhao, Jinfang Liu, Zhaoxi Luo, Zhijie Luo

“…The model is composed of three sub-modules where: i) A deep learning network first identifies the growth periods of pests, and this prior knowledge is then used to guide the text encoder of the CLIP pre-trained model in generating period-specific textual features. ii) A parallel deep learning network extracts visual features from pest images. iii) An efficient low-rank multimodal fusion module integrates textual and visual features through parameter-optimized tensor decomposition, significantly improving classification accuracy across pest developmental phases. …”
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