Machine Learning in Information and Communications Technology: A Survey by Elias Dritsas, Maria Trigka

“…Machine learning (ML) has emerged as a powerful tool, enabling more adaptive, efficient, and scalable systems in this field. This article presents a comprehensive survey on the application of ML techniques in ICT, covering key areas such as network optimization, resource allocation, anomaly detection, and security. …”
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Optical Frequency Comb-Based 256-QAM WDM Coherent System with Digital Signal Processing Algorithm by Babar Ali, Ghulam Murtaza, Hafiz Muhammad Bilal, Tariq Mahmood, Muhammad Rashid, Zaib Ullah

“…Digital signal processing (DSP) techniques, including digital filtering, detection, and impairment compensation, contribute to high transmission and spectral efficiency (SE). …”
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Enhancing the Distinguishability of Minor Fluctuations in Time Series Classification Using Graph Representation: The MFSI-TSC Framework by He Nai, Chunlei Zhang, Xianjun Hu

“…Compared with ten benchmark methods, MFSI-TSC exhibits both high accuracy and computational efficiency, making it more suitable for deployment in sensor systems to complete incipient fault detection tasks.…”
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Spectrum Sensing Using Fast Slepian Transform for Cognitive Radio Networks by Aman Kumar, Indrasen Singh, Dilip Kumar Choudhary

“…Additionally, we introduce a computational efficiency metric based on floating-point operations (FLOPs) to evaluate the energy cost of detection. …”
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Optimization of Sorghum Spike Recognition Algorithm and Yield Estimation by Mengyao Han, Jian Gao, Cuiqing Wu, Qingliang Cui, Xiangyang Yuan, Shujin Qiu

“…By optimizing the full detection–tracking–yield pipeline, this solution overcomes challenges in small object missed detections, ID switches under occlusion, and real-time processing in complex scenarios. …”
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MLK-TR: a Multi-branch Large Kernel TRansformer for UAV-based images by Xun Li, Yuzhen Zhao, Yang Zhao, Zhun Guo, Jianjing Gao, Baoxi Yuan

“…These results confirm the effectiveness of MLK-TR in addressing the complexities of UAV object detection.…”
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AGW-YOLO-Based UAV Remote Sensing Approach for Monitoring Levee Cracks by HU Weibo, ZHOU Shaoliang, ZHAO Erfeng, ZHAO Xueqiang

“…The advanced WIoUv3 loss function further boosted the model's performance, achieving a mAP@0.5 of 84.5% and an F1 score of 83%, marking an approximate 3.4% improvement over the baseline, and showcasing a favorable balance between detection accuracy and model efficiency. Comparative experiments indicated that AGW-YOLO outperformed several mainstream object detection algorithms, including Faster R-CNN, YOLOv8n, YOLOv9-tiny, YOLOv10n, RT-DETR-R50, and TPH-YOLO, across most evaluation metrics, offering high recognition accuracy with lower computational complexity. …”
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YOLOv11-RDTNet: A Lightweight Model for Citrus Pest and Disease Identification Based on an Improved YOLOv11n by Qiufang Dai, Shiyao Liang, Zhen Li, Shilei Lyu, Xiuyun Xue, Shuran Song, Ying Huang, Shaoyu Zhang, Jiaheng Fu

“…However, existing object detection models face limitations in complex backgrounds, target occlusion, and small target recognition, and they struggle to be efficiently deployed on resource-constrained devices. …”
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Attention-based BiLSTM-XGBoost model for reliability assessment and lifetime prediction of digital microfluidic systems by Lifeng He, Qili Yang, Junxi Chen, Wenjing Liu, Zhijie Luo

“…This often leads to evaluation lag and limits their application, especially for complex droplets. To address these issues, this study proposes a novel prediction model for digital microfluidic (DMF) devices. …”
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A Semi-Supervised Object Detector Based on Adaptive Weighted Active Learning and Orthogonal Data Augmentation by Meng Wang, Xiao Xu, Haipeng Liu

“…To efficiently utilize limited resources, this paper proposes a semi-supervised object detection (SSOD) approach based on novel adaptive weighted active learning (AWAL) and orthogonal data augmentation (ODA). …”
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YOLOv8n-WSE-Pest: A Lightweight Deep Learning Model Based on YOLOv8n for Pest Identification in Tea Gardens by Hongxu Li, Wenxia Yuan, Yuxin Xia, Zejun Wang, Junjie He, Qiaomei Wang, Shihao Zhang, Limei Li, Fang Yang, Baijuan Wang

“…The addition of the Spatial and Channel Reconstruction Convolution structure in the Backbone layer reduces redundant spatial and channel features, thereby reducing the model’s complexity. The integration of the Efficient Multi-Scale Attention Module with Cross-Spatial Learning enables the model to have more flexible global attention. …”
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A method of identification and localization of tea buds based on lightweight improved YOLOV5 by Yuanhong Wang, Yuanhong Wang, Jinzhu Lu, Jinzhu Lu, Qi Wang, Qi Wang, Zongmei Gao

“…There is an urgent need to investigate the critical technologies of intelligent picking robots for tea. The complexity of the model requires high hardware computing resources, which limits the deployment of the tea bud detection model in tea-picking robots. …”
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Integration of deep learning and railway big data for environmental risk prediction models and analysis of their limitations by Liuhui Quan, Minjie Wang, Lyu Baihang, Zhang Ziwen, Zhang Ziwen, Zhang Ziwen

“…The rapid evolution of railway systems, driven by digitization and the proliferation of Internet-of-Things (IoT) devices, has resulted in an unprecedented volume of diverse and complex data. This railway big data offers immense opportunities for advancing safety, efficiency, and sustainability in transportation but presents significant analytical challenges due to its heterogeneity, high-dimensionality, and temporal dependencies. …”
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Edge-Based Dynamic Spatiotemporal Data Fusion on Smart Buoys for Intelligent Surveillance of Inland Waterways by Ruolan Zhang, Chenhui Zhao, Yu Liang, Jingfeng Hu, Mingyang Pan

“…Additionally, a novel regional division and a Kalman filter-based method for AIS and video data fusion were proposed, effectively resolving the issues of data sparsity and coordinate transformation robustness under complex waterway conditions. This approach substantially advances the precision and efficiency of waterway monitoring systems, providing a robust theoretical and practical framework for the intelligent supervision of inland waterways.…”
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Real Time Vehicle Classification Using Deep Learning—Smart Traffic Management by Tejasva Maurya, Saurabh Kumar, Mritunjay Rai, Abhishek Kumar Saxena, Neha Goel, Gunjan Gupta

“…To optimize performance, the model leverages the YOLOv8 deep learning framework, known for its speed and precision in object detection. By using transfer learning with pre‐trained YOLOv8 weights, the model improves accuracy and efficiency, particularly in low‐resource environments. …”
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The use and integration of airborne laser scanning and satellite time series data in precision forest management for plantations of fast-growing tree species by Gonzalo Gavilan-Acuna, Nicholas C. Coops, Piotr Tompalski, Dominik Roeser, Andrés Varhola

“…ALS-based Enhanced Forest Inventories provide spatially detailed forest metrics, while satellite time series and vegetation indices support continuous monitoring of growth and early detection of drought, fire, and pest stress. ALS individual tree detection models offer insights into competition, stand structure, and spatial variability, informing thinning and fertilization decisions by identifying trees under stress or with high growth potential. …”
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Sustainable Phosphate Remediation via Hierarchical Mg-Fe Layered Double Hydroxides on Magnetic Biochar from Agricultural Waste by Xiuling Li, Lei Xin, Yuhan Peng, Shihao Zhang, Delong Guan, Jing Song

“…Crucially, the material demonstrates exceptional selectivity over competing Cl⁻ and NO₃⁻ ions while maintaining 87.83% efficiency after three regeneration cycles via alkaline treatment. …”
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A dual fluorescence channel RAA-based CRISPR-Cas12a/Cas13a system for highly sensitive detection of Gyrovirus galga1 and Gyrovirus homsa1 by Dan Yu, Zhixun Xie, Yanfang Zhang, Zhiqin Xie, Qing Fan, Sisi Luo, Liji Xie, Meng Li, Tingting Zeng, Minxiu Zhang, Xiaofeng Li, You Wei, Aiqiong Wu, Lijun Wan

“…These findings demonstrate that our RAA-based CRISPR – Cas12a/Cas13a dual-channel detection system can detect two different subtypes of gyrovirus in a sample with good specificity and high sensitivity, improving the detection efficiency and providing a new technique for the study of viral infection dynamics.…”
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Sustainable soil organic carbon prediction using machine learning and the ninja optimization algorithm by Anis Ben Ghorbal, Azedine Grine, Marwa M. Eid, Marwa M. Eid, El-Sayed M. El-kenawy, El-Sayed M. El-kenawy

“…To address these challenges, we propose a novel integration of the Ninja Optimization Algorithm (NiOA) for simultaneous feature selection and hyperparameter optimization, aimed at enhancing both predictive accuracy and computational efficiency. …”
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The direction of the breeding process in a herd of Ayrshire breed cows in the conditions of the Vologda region by N. I. Abramova, O. L. Khromova, N. V. Zenkova, M. O. Selimyan

“…The reduction of the age of the first insemination of heifers to 13.4 months (-2.2 months) and the first fruitful insemination to 14.0 months (-2.1 months) in animals from full-age to the first incomplete lactation was revealed. A targeted selection of cows was carried out in the herd according to the level of milk productivity of mothers after the first lactation - the selection efficiency was 977 kg of milk, according to the highest - 456 kg. …”
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