Improved YOLO for long range detection of small drones by Sicheng Zhou, Lei Yang, Huiting Liu, Chongqin Zhou, Jiacheng Liu, Yang Wang, Shuai Zhao, Keyi Wang

“…The neck structure is enhanced with a collaborative attention mechanism and multi-scale fusion, improving feature representation. An optimized loss function refines bounding box matching for small targets, while a pruning strategy removes redundant filters, boosting computational efficiency. …”
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IoT-edge anomaly detection for covariate shifted and point time series health data by Partha Pratim Ray, Dinesh Dash

“…We implement the ”IRKernel” to investigate the efficiency of exponentially weighted moving average (EWMA)-based light-weight statistical learning by using three variants e.g. probabilistic (P), shift-detect (SD), and two-stage shift-detect (TS-SD) over the EWMA algorithms. We perform evaluation of execution time of the algorithms in classical, incremental, and optimized modes while following the Kolmogorov–Smirnov hypothesis testing at the two-stage anomaly detection. …”
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Deep Q-Networks for Minimizing Total Tardiness on a Single Machine by Kuan Wei Huang, Bertrand M. T. Lin

“…Due to its computational intractability, exact approaches such as dynamic programming algorithms and branch-and-bound algorithms struggle to produce optimal solutions for large-scale instances in a reasonable time. …”
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Sentiment Analysis Using Stacking Ensemble After the 2024 Indonesian Election Results by Andy Victor Pakpahan, Fahmi Reza Ferdiansyah, Robby Gustian, Muhammad Nur Faiz, Sukma Aji

“…Machine learning is commonly applied in this area, with algorithms such as Naïve Bayes, Support Vector Machine (SVM), and Random Forest being frequently used. …”
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Leveraging Multilingual Transformer for Multiclass Sentiment Analysis in Code-Mixed Data of Low-Resource Languages by Muhammad Kashif Nazir, Cm Nadeem Faisal, Muhammad Asif Habib, Haseeb Ahmad

“…The evaluation results showed a significant improvement in accuracy (+22.55%), precision (+21.06%), recall (+22.55%), and F-measure (+25.50%) compared to benchmark algorithms. Additionally, the proposed model outperformed other transformer-based models, as well as deep learning and machine learning algorithms in sentiment extraction from code-mixed data. …”
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Quality prediction of semi-solid die casting of aluminum alloy in terms of machine learning by Zhiyuan Wang, Xiaogang Hu, Gan Li, Zhen Xu, Hongxing Lu, Qiang Zhu

“…In this study, a machine learning (ML) model has been developed to identify defective products through the detection of injection pressure, thereby providing a foundation for monitoring and further optimizing the manufacturing process. Among various ML algorithms, the Multilayer Perceptron (MLP) is the most effective for overall quality prediction. …”
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AGW-YOLO-Based UAV Remote Sensing Approach for Monitoring Levee Cracks by HU Weibo, ZHOU Shaoliang, ZHAO Erfeng, ZHAO Xueqiang

“…Furthermore, the integration of crack detection results with coordinate transformation algorithms enabled the precise geospatial localization of cracks. …”
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Order picking dataset from a warehouse of a footwear manufacturing companyMendeley Data by Rodrigo Furlan de Assis, William de Paula Ferreira, Mustapha Ouhimmou

“…This dataset is highly versatile and suitable for developing optimization algorithms for picker routing, order batching, wave generation, and intralogistics, as well as for advancing automation and robotics research through navigation-specific data for autonomous guided vehicles (AGVs) and robotic systems. …”
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Butterfly magnetoreception based neighbour awareness strategy protocol for autonomous aerial vehicles by Janjhyam Venkata Naga Ramesh, C. Dastagiraiah, Suraya Mubeen, W. Deva Priya, M. Kameswara Rao, B. H. K. Bhagat Kumar

“…ML algorithms ensure optimal energy consumption and risk mitigation by dynamically selecting the shortest path, even under abrupt environmental changes. …”
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A new approach for diabetes risk detection using quadratic interpolation flower pollination neural network by Yulianto Triwahyuadi POLLY, Adriana FANGGIDAE, Juan Rizky Mannuel LEDOH, Clarissa Elfira AMOS PAH, Bertha S. DJAHI, Kisan Emiliano Rape TUPEN

“… This study aims to evaluate and compare five algorithms in diabetes detection, namely Flower Pollination Neural Network (FPNN), Particle Swarm Optimization Neural Network (PSONN), Bat Artificial Neural Network (BANN), Stochastic Gradient Descent (SGD), and Quadratic Interpolation Flower Pollination Neural Network (QIFPNN). …”
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Quantitative and Spatially Explicit Clustering of Urban Grocery Shoppers in Montreal: Integrating Loyalty Data with Synthetic Population by Duo Zhang, Laurette Dubé, Antonia Gieschen, Catherine Paquet, Raja Sengupta

“…This study integrates customer loyalty program data with a synthetic population to analyze grocery shopping behaviours in Montreal. Using clustering algorithms, we classify 295,631 loyalty program members into seven distinct consumer segments based on behavioural and sociodemographic attributes. …”
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Prototyping a graph-based approach for efficient vehicle tracking in a cluster controlled CCTV surveillance systems by Apurva NARAYAN, Charumathi PARIVALLAL, Olirva MAHESWARAN, Sarath CHANDRAN KALATHILPARAMBIL RAMACHANDRAN

“…The optical character recognition (OCR) enables a detailed number plate recognition, while the multimodal data fusion enhances the robustness of vehicle tracking in dynamic conditions. …”
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APPLICATION OF ARTIFICIAL NEURON NETWORK AND FUZZY INFERENCE IN THE HOUSEPLANT WATERING INTELLIGENT SYSTEM by Roman Korostenskyi, Igor Olenych

“…The proposed approach ensures an optimal soil moisture level of 50–60%, suitable for plant growth and development, while also reducing the average monthly water consumption by 25–27%. …”
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An Optimised CNN Hardware Accelerator Applicable to IoT End Nodes for Disruptive Healthcare by Arfan Ghani, Akinyemi Aina, Chan Hwang See

“…In the evolving landscape of computer vision, the integration of machine learning algorithms with cutting-edge hardware platforms is increasingly pivotal, especially in the context of disruptive healthcare systems. …”
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Acoustic-Based Machine Main State Monitoring for High-Speed CNC Drilling by Pimolkan Piankitrungreang, Kantawatchr Chaiprabha, Worathris Chungsangsatiporn, Chanat Ratanasumawong, Peemdej Chancharoen, Ratchatin Chancharoen

“…By integrating big data concepts and intelligent algorithms, the system supports continuous monitoring, anomaly detection, and process optimization. …”
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A Comprehensive Framework for Transportation Infrastructure Digitalization: TJYRoad-Net for Enhanced Point Cloud Segmentation by Zhen Yang, Mingxuan Wang, Shikun Xie

“…The proposed method addresses the challenge of accurately capturing the current state of infrastructure while minimizing redundancy and optimizing computational efficiency. …”
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Linguistic Markers of Pain Communication on X (Formerly Twitter) in US States With High and Low Opioid Mortality: Machine Learning and Semantic Network Analysis by ShinYe Kim, Winson Fu Zun Yang, Zishan Jiwani, Emily Hamm, Shreya Singh

“…Semantic network analysis revealed denser discourse in HDS (density=0.28) focused on distress and barriers to care, while LDS discourse emphasized recovery and optimism. …”
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Advanced Technologies for Smart Fertilizer Management in Agriculture: A Review by Jian-Jun Liu, Hao Wu, Imran Riaz

“…Precision fertilization has emerged as a critical component of modern smart agriculture, providing a transformative approach to optimizing nutrient application, enhancing crop productivity, and minimizing environmental harm while improving resource use efficiency. …”
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Enhancing Autonomous Driving Perception: A Practical Approach to Event-Based Object Detection in CARLA and ROS by Jingxiang Feng, Peiran Zhao, Haoran Zheng, Jessada Konpang, Adisorn Sirikham, Phuri Kalnaowakul

“…In contrast, event-based cameras, which operate asynchronously and capture rapid changes with high temporal resolution and expansive dynamic range, offer a promising augmentation. While the previous research on event-based object detection has predominantly focused on algorithmic enhancements via advanced preprocessing and network optimizations to improve detection accuracy, the practical engineering and integration challenges of deploying these sensors in real-world systems remain underexplored. …”
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Quantum annealing feature selection on light-weight medical image datasets by Merlin A. Nau, Luca A. Nutricati, Bruno Camino, Paul A. Warburton, Andreas K. Maier

“…Abstract We investigate the use of quantum computing algorithms on real quantum hardware to tackle the computationally intensive task of feature selection for light-weight medical image datasets. …”
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