Enhancing Energy Efficiency of Sensors and Communication Devices in Opportunistic Networks Through Human Mobility Interaction Prediction by Ambreen Memon, Sardar M. N. Islam, Muhammad Nadeem Ali, Byung-Seo Kim

“…The contribution of this research is that it highlights the limitations of existing mobility prediction models and develops new resource optimization and energy-efficient opportunistic networks that overcome these limitations.…”
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Machine-Learning-Driven Approaches for Assessment, Delegation, and Optimization of Multi-Floor Building by Abtin Baghdadi, Harald Kloft

“…The results were compared against reference calculations and ETABS simulations to validate its effectiveness, demonstrating deviations of less than 3%. …”
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Design and trial of precision spraying system for weeds in winter wheat field at tillering stage by Bo Li, Peijie Guo, Yu Chen, Jun Chen, Haiying Wang, Jing Zhang, Zhixing Zhang

“…The improved models P, R, mAP and F1 were 88 %, 84.6 %, 92.2 % and 86.3 %, which were 3 %, 3.1 %, 2.7 % and 3.1 % higher compared to the original model. The precision spraying algorithms and systems were integrated in a test bed and sprayer to carry out the tests. …”
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TARREAN: A Novel Transformer with a Gate Recurrent Unit for Stylized Music Generation by Yumei Zhang, Yulin Zhou, Xiaojiao Lv, Jinshan Li, Heng Lu, Yuping Su, Honghong Yang

“…Music generation by AI algorithms like Transformer is currently a research hotspot. …”
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Prospective Validation and Usability Evaluation of a Mobile Diagnostic App for Obstructive Sleep Apnea by Pedro Amorim, Daniela Ferreira-Santos, Marta Drummond, Pedro Pereira Rodrigues

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Smoke and Fire-You Only Look Once: A Lightweight Deep Learning Model for Video Smoke and Flame Detection in Natural Scenes by Chenmeng Zhao, Like Zhao, Ka Zhang, Yinghua Ren, Hui Chen, Yehua Sheng

“…The experimental results demonstrate, compared with deep learning models such as YOLOv8, Gold-YOLO, and Faster-RCNN, the SF-YOLO model proposed in this paper is more lightweight and exhibits higher detection accuracy and robustness. …”
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Enhancing placental pathology detection with GAMatrix-YOLOv8 model by Weirui Wu, Zhifa Jiang, Jingwen Liu, Jiahui Ji, Xiaoyan Wei, Xiangyun Ye, Zhen Zhang

“…This provides a theoretical and technical foundation for further research into artificial intelligence-based pathological auxiliary diagnostic systems.…”
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Symmetrical Components Digital Filters for Microprocessor-Based Protection Input Signals by F. A. Romaniuk, V. Yu. Rumiantsev, Yu. V. Rumiantsev

“…The study of the functioning of these filters was carried out using two types of test actions, viz. a three-phase system of sinusoidal signals and a three-phase system of signals close to real secondary currents in short circuits. The results of the research have demonstrated that the developed digital filters of the negative and zero sequences have 1.1–1.4 times higher performance as compared with similar Fourier filters accepted as reference and that they are distinguished by improved magnitudes of dynamic indicators.…”
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Machine learning applications for early detection of esophageal cancer: a systematic review by Farhang Hosseini, Farkhondeh Asadi, Hassan Emami, Rayan Ebnali Harari

“…Given the significance of early detection of EC, this systematic review aims to summarize and discuss the current state of research on ML-based methods for the early detection of EC. …”
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RICE AlgebraBot: Lessons learned from designing and developing responsible conversational AI using induction, concretization, and exemplification to support algebra learning by Chenglu Li, Wanli Xing, Yukyeong Song, Bailing Lyu

“…Specifically, we first examined computational strategies to mitigate the ethical concerns of ConvAI in educational setting with educational big data (npretraining = 2,097,139) and found that researchers could effectively enhance ConvAI responsibility through the investigated algorithmic strategies. …”
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A machine learning-based model for predicting survival in patients with Rectosigmoid Cancer. by Yifei Wang, Bingbing Chen, Jinhai Yu

“…<h4>Background</h4>The unique anatomical characteristics and blood supply of the rectosigmoid junction confer particular significance to its physiological functions and clinical surgeries. However, research on the prognosis of rectosigmoid junction cancer (RSC) is scarce, and reliable clinical prediction models are lacking.…”
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Improved YOLOv8 Model for Phenotype Detection of Horticultural Seedling Growth Based on Digital Cousin by Yuhao Song, Lin Yang, Shuo Li, Xin Yang, Chi Ma, Yuan Huang, Aamir Hussain

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A Web-Based Interface That Leverages Machine Learning to Assess an Individual&#x2019;s Vulnerability to Brain Stroke by Divyansh Bhandari, Arnav Agarwal, R. Reena Roy, Rajaram Priyatharshini, Rodriguez Rivero Cristian

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Real-Time Polarimetric Imaging and Enhanced Deep Learning Model for Automated Defect Detection of Specular Additive Manufacturing Surfaces by Dingkang Li, Xing Peng, Hongbing Cao, Yuanpeng Xie, Shiqing Li, Xiang Sun, Xinjie Zhao

“…Defect detection technology remains a research focus in AM process monitoring. While machine learning and neural network algorithms have recently achieved significant advancements in innovative applications for AM defect detection, practical implementations still face challenges, including insufficient detection accuracy and poor system robustness. …”
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A supervised machine learning statistical design of experiment approach to modeling the barriers to effective snakebite treatment in Ghana. by Eric Nyarko, Edmund Fosu Agyemang, Ebenezer Kwesi Ameho, Louis Agyekum, José María Gutiérrez, Eduardo Alberto Fernandez

“…However, significant challenges in achieving this goal include a lack of robust research evidence related to snakebite incidence and treatment, particularly in sub-Saharan Africa. …”
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Design of a Conveyer Trough Bolt Signal Acquisition System and Bayesian Ensemble Identification Method for Working State by Yi Lian, Bangzhui Wang, Meiyan Sun, Kexin Que, Sijia Xu, Zhong Tang, Zhilong Huang

“…This system meets the demands for high-frequency, high-precision monitoring of the bolted structure. Finally, after comparing machine learning algorithms, Support Vector Machine was chosen for its superior performance. …”
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A Metric Learning-Based Improved Oriented R-CNN for Wildfire Detection in Power Transmission Corridors by Xiaole Wang, Bo Wang, Peng Luo, Leixiong Wang, Yurou Wu

“…Experimental results demonstrated that the proposed model achieved superior performance in wildfire detection within power transmission corridors, achieving a mAP of 90.4% on the test dataset—an improvement of 6.4% over the baseline model. Compared with other commonly used object detection algorithms, the model developed in this study exhibited improved detection performance on the test dataset, offering research support for wildfire monitoring in power transmission corridors.…”
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Towards Real-Time Spatial Distance Monitoring of Power Transmission Lines Using LiDAR Point Clouds and Visual Imaging by Li Zhendong, Wang Feiran, Han Geng, Guo Xinyang, Shi Zhaoyang

“…CONCLUSION: It uniquely combines LiDAR and image data with advanced algorithms for precise, real-time distance measurement and monitoring. …”
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Deep Learning Approaches for Retinal Disease Identification in Fundus Imaging: A Comprehensive Overview by Ismael Abdulkareem Ali, Sozan Abdullah Mahmood

“…The results show that great accuracy is consistently achieved with DL algorithms compared to traditional Machine learning approaches. …”
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Efficient Identification and Classification of Pear Varieties Based on Leaf Appearance with YOLOv10 Model by Niman Li, Yongqing Wu, Zhengyu Jiang, Yulu Mou, Xiaohao Ji, Hongliang Huo, Xingguang Dong

“…Among them, the precision rate of nine varieties reached 100%. Compared with existing recognition networks and target detection algorithms such as YOLOv7, ResNet50, VGG16, and Swin Transformer, YOLOv10 performs the best in pear leaf recognition in natural scenes. …”
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