Intrusion detection system based on machine learning using least square support vector machine by Pratik Waghmode, Manideep Kanumuri, Hosam El-Ocla, Tanner Boyle

“…Unauthorized access or attack can be avoided in critical systems using a comprehensive approach via an effective intrusion detection system (IDS). Traditional intrusion detection techniques are no longer accurate and effective enough to handle the demands of the big data age. …”
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Integrated pixel-level crack detection and quantification using an ensemble of advanced U-Net architectures by Rakshitha R, Srinath S, N Vinay Kumar, Rashmi S, Poornima B V

“…Automated pavement crack detection faces significant challenges due to the complex shapes of crack patterns, their similarity to non-crack textures, and varying environmental conditions such as lighting and noise. …”
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Smart Fault Detection, Classification, and Localization in Distribution Networks: AI-Driven Approaches and Emerging Technologies by Jianxian Wang, Hazlie Mokhlis, Nurulafiqah Nadzirah Mansor, Hazlee Azil Illias, Agileswari K. Ramasamy, Xingyu Wu, Siqi Wang

“…However, with nations worldwide actively pursuing carbon neutrality and emission peak goals, sustainable energy sources such as solar and wind are increasingly penetrating distribution networks, posing significant challenges to conventional fault detection, classification, and localization techniques due to bidirectional power flows, dynamic fault currents, and rising network complexity. …”
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U-ActionNet: Dual-Pathway Fourier Networks With Region-of-Interest Module for Efficient Action Recognition in UAV Surveillance by Abdul Monaf Chowdhury, Ahsan Imran, Md Mehedi Hasan, Riad Ahmed, Akm Azad, Salem A. Alyami

“…Leveraging a dual-path approach, we propose a novel framework U-ActionNet that integrates FFT-based substance separation and space-time self-attention techniques to improve the accuracy and efficiency of HAR tasks. Catering the server side the first pathway employs a modified C3D model with Fast Fourier Transform (FFT)-based object movement and attention detection mechanisms to effectively extract human actors from complex scenes and capture spatiotemporal dynamics in UAV footage. …”
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Integration of Carbon Nanotubes into Manganese Dioxide Nanorods for Enhanced Enzymeless Electrochemical Glucose Sensing with High Sensitivity and Selectivity by Khawtar Hasan Ahmed, Alonso Moreno Zuria, Mohamed Mohamedi

“…These attributes are essential for accurate glucose detection in complex biological samples. The integration of MnO₂ nanorods with CNTs in freestanding nanostructures opens up exciting opportunities for developing high-performance, robust electrochemical sensors for diverse applications.…”
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DynaOOD-Net: Dynamic Progressive Feature Fusion and Energy Balancing for Robust Out-of-Distribution Detection by Jiting Zhou, Zhihao Zhou, Pu Zhang

“…Experimental results on seven datasets demonstrate that DynaOOD-Net significantly improves the mAP for known categories and consistently outperforms existing state-of-the-art methods in unknown object detection metrics. These findings establish DynaOOD-Net as a robust and efficient solution to the challenges inherent in open-set object detection. …”
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New Methods for Waterfowl and Habitat Survey Using AI and Drone Imagery by Zhenduo Zhai, Zhiguang Liu, Yang Zhang, Andrew Zhao, Yi Shang

“…Existing methods for waterfowl monitoring face critical challenges such as low accuracy in identifying overlapping image regions and limited segmentation accuracy in complex habitats. To address these issues, this paper presents an end-to-end system and several new methods for efficiently and accurately identifying waterfowl populations in their natural habitats using AI and drone imagery. …”
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Enhancing Human Detection in Occlusion-Heavy Disaster Scenarios: A Visibility-Enhanced DINO (VE-DINO) Model with Reassembled Occlusion Dataset by Zi-An Zhao, Shidan Wang, Min-Xin Chen, Ye-Jiao Mao, Andy Chi-Ho Chan, Derek Ka-Hei Lai, Duo Wai-Chi Wong, James Chung-Wai Cheung

“…Natural disasters create complex environments where effective human detection is both critical and challenging, especially when individuals are partially occluded. …”
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Combining chromosome conformation capture and exome sequencing for simultaneous detection of structural and single-nucleotide variants by Maria Gridina, Timofey Lagunov, Polina Belokopytova, Nikita Torgunakov, Miroslav Nuriddinov, Artem Nurislamov, Lyudmila P. Nazarenko, Anna A. Kashevarova, Maria E. Lopatkina, Stanislav Vasilyev, Andrey Zuev, Elena O. Belyaeva, Olga A. Salyukova, Aleksandr D. Cheremnykh, Natalia N. Sukhanova, Marina E. Minzhenkova, Zhanna G. Markova, Nina A. Demina, Yana Stepanchuk, Anna Khabarova, Alexandra Yan, Emil Valeev, Galina Koksharova, Elena V. Grigor’eva, Natalia Kokh, Tatiana Lukjanova, Yulia Maximova, Elizaveta Musatova, Elena Shabanova, Andrey Kechin, Evgeniy Khrapov, Uliana Boyarskih, Oxana Ryzhkova, Maria Suntsova, Alina Matrosova, Mikhail Karoli, Andrey Manakhov, Maxim Filipenko, Evgeny Rogaev, Nadezhda V. Shilova, Igor N. Lebedev, Veniamin Fishman

“…Enhanced with targeted long-read sequencing, Exo-C evolves into a cost-efficient solution capable of resolving complex SVs at base-pair accuracy. …”
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Machine learning-based model for acute asthma exacerbation detection using routine blood parameters by Youpeng Chen, Junquan Sun, Yabang Chen, Enzhong Li, Jiancai Lu, Huanhua Tang, Yifei Xie, Jiana Zhang, Lesi Peng, Haojie Wu, Zhangkai J. Cheng, Baoqing Sun

“…Conclusions: This machine learning model provides an efficient and practical tool for detecting AAE using routine blood parameters, offering potential value in clinical practice, especially in resource-limited settings. …”
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A Hybrid Approach for Reachability Analysis of Complex Software Systems Using Fuzzy Adaptive Particle Swarm Optimization Algorithm and Rule Composition by Nahid Salimi, Seyfollah Soleimani, Vahid Rafe, Davood Khodadad

“…Experimental results demonstrate that proposed hybrid algorithm reduced verification time by up to 49.86% compared to Particle Swarm Optimization and 65.17% compared to Genetic Algorithms in reachability analysis of complex models. Furthermore, it explored 32.7% fewer states on average than the hybrid method based on Particle Swarm Optimization and Gravitational Search Algorithms, and 57.4% fewer states compared to Genetic Algorithms, indicating improved search efficiency. …”
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Enhancing Harmony Search Metaheuristic Algorithm for Coverage Efficiency, Test Suite Reduction, and Running Time in Combinatorial Interaction Testing by Aminu Aminu Muazu, Ahmad Sobri Hashim

“…Optimization has developed powerful algorithms for solving complex problems efficiently. The effectiveness of these algorithms, also called metaheuristics, largely depends on the capabilities of their search techniques. …”
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Lightweight deep learning system for automated bone age assessment in Chinese children: enhancing clinical efficiency and diagnostic accuracy by Pang Hai, Zhang Bin, Liu Kesheng, Li Cong, Xu Fei

“…In the first stage, the YOLOv8 algorithm precisely localizes 13 key epiphyses in hand radiographs, achieving a mean Average Precision (mAP) of 99.5% at Intersection over Union (IoU) = 0.5 and 94.0% within IoU 0.5–0.95, demonstrating robust detection performance. The second stage employs a modified EfficientNetB3 architecture for fine-grained epiphyseal grade classification, enhanced by the Rectified Adam (RAdam) optimizer and a composite loss function combining center loss and weighted cross-entropy to mitigate class imbalance. …”
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A comparative study of ultra-massive MIMO intelligent receivers with adversarial robustness and energy efficiency for 6G applications by Pushkar Nidagundi, Advesh Darvekar, Malik Amber, Ramesh R

“…Ultra-Massive Multiple Input Multiple Output (UM-MIMO) systems are pivotal for 6G wireless networks, enabling unprecedented data rates and spectral efficiency. However, optimizing these systems requires a careful balance between energy efficiency, spectral efficiency, and Bit Error Rate (BER) performance. …”
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Performance Analysis of Maximum Likelihood Detection in Cooperative DF MIMO Systems with One-Bit ADCs by Tae-Kyoung Kim

“…Although joint maximum likelihood detection can efficiently mitigate error propagation by leveraging probabilistic information from a source-to-relay link, its computational complexity is impractical. …”
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Visual Detection on Aircraft Wing Icing Process Using a Lightweight Deep Learning Model by Yang Yan, Chao Tang, Jirong Huang, Zhixiong Cen, Zonghong Xie

“…High transparency and blurred edges of icing areas in wing images pose challenges to wing icing detection by machine vision. To address these challenges, this study proposes a detection model, Wing Icing Detection DeeplabV3+ (WID-DeeplabV3+), for efficient and precise aircraft wing leading edge icing detection under natural lighting conditions. …”
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Surface Damage Detection in Hydraulic Structures from UAV Images Using Lightweight Neural Networks by Feng Han, Chongshi Gu

“…However, existing deep learning (DL) models often lack architectural adaptations for the visual complexities of UAV imagery, including low-texture contrast, noise interference, and irregular crack patterns. …”
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Lightweight multidimensional feature enhancement algorithm LPS-YOLO for UAV remote sensing target detection by Yong Lu, Minghao Sun

“…LPS-YOLO offers an effective solution for multi-target detection in UAVs.…”
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Advancing Software Vulnerability Detection with Reasoning LLMs: DeepSeek-R1′s Performance and Insights by Wenting Qin, Lijie Suo, Liangchen Li, Fan Yang

“…The increasing complexity of software systems has heightened the need for efficient and accurate vulnerability detection. …”
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Prioritizing image processing and big data analytics factors for software development using DEMATEL-choquet method in bipolar complex fuzzy context by Ruimin Ma

“…The causal effects indicate that feature extraction, automation and efficiency, as well as object detection, are critical causal factors, whereas image enhancement, security, and segmentation are dependent effects. …”
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