Improved YOLOv8 Object Detection Method for Drone Aerial Images by Zhong Shuai, Wang Liping

“…The experimental results on the VisDrone2019 dataset show: compared with the YOLOv8 model, the BDI-YOLO model in accuracy mAP@50 and mAP@50:95 has increased by 3.8% and 2.7% respectively, with a 4% increase in recall, a 9.4% decrease in computational complexity, and a 28.8% decrease in parameter count. …”
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Extracting road maps from high-resolution satellite imagery using refined DSE-LinkNet by Prativa Das, Satish Chand

“…The experiments are performed on a publicly available dataset, DeepGlobe Road Extraction Challenge 2018, to show its efficacy over the D-LinkNet, winner of DeepGlobe Challenge 2018, by achieving IoU of 0.69 with lesser number of parameters and better computational complexity.…”
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Smart crop disease monitoring system in IoT using optimization enabled deep residual network by Ashish Saini, Nasib Singh Gill, Preeti Gulia, Anoop Kumar Tiwari, Priti Maratha, Mohd Asif Shah

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HFC-YOLO11: A Lightweight Model for the Accurate Recognition of Tiny Remote Sensing Targets by Jinyin Bai, Wei Zhu, Zongzhe Nie, Xin Yang, Qinglin Xu, Dong Li

“…Experimental results on the AI-TOD and VisDrone2019 datasets demonstrate that the improved model achieves mAP50 improvements of 3.4% and 2.7%, respectively, compared to the baseline YOLO11s, while reducing its parameters by 27.4%. Ablation studies validate the balanced performance of the hierarchical feature compensation strategy in the preservation of resolution and computational efficiency. …”
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Parameter optimization of 3D convolutional neural network for dry-EEG motor imagery brain-machine interface by Nobuaki Kobayashi, Musashi Ino

“…On the other hand, however, the edge is limited by hardware resources, and the implementation of models with a huge number of parameters and high computational cost, such as deep-learning, on the edge is challenging. …”
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An evolutionary algorithm‐based classification method for high‐dimensional imbalanced mixed data with missing information by Yi Liu, Gengsong Li, Qibin Zheng, Guoli Yang, Kun Liu, Wei Qin

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COMPUTER SCIENCE, COMPUTER ENGINEERING AND MANAGEMENT SYNTHESISING THE MODEL OF THE PREDICTIVE MAINTENANCE OF ATMOSPHERIC PIPE STILL by E. S. Gebel, A. A. Ibatullin, M. S. Peshko, V. N. Gudinov

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A robust and statistical analyzed predictive model for drug toxicity using machine learning by Deepak Rawat, Rohit Bajaj, Rachit Manchanda, Ankush Mehta, Prabhu Paramasivam, Suraj Kumar Bhagat, Abinet Gosaye Ayanie

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Solid Oxide Fuel Cell Voltage Prediction by a Data-Driven Approach by Hristo Ivanov Beloev, Stanislav Radikovich Saitov, Antonina Andreevna Filimonova, Natalia Dmitrievna Chichirova, Egor Sergeevich Mayorov, Oleg Evgenievich Babikov, Iliya Krastev Iliev

“…The training dataset consisted of experimental results from SOFC laboratory experiments, comprising 32,843 records with 47 control parameters. The study evaluated the effectiveness of input matrix dimensionality reduction using the following feature importance evaluation methods: mean decrease in impurity (MDI), permutation importance (PI), principal component analysis (PCA), and Shapley additive explanations (SHAP). …”
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Application of machine learning techniques to predict the compressive strength of steel fiber reinforced concrete by Ala’a R. Al-Shamasneh, Arsalan Mahmoodzadeh, Faten Khalid Karim, Taoufik Saidani, Abdulaziz Alghamdi, Jasim Alnahas, Mohammed Sulaiman

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Multibranch 3D-Dense Attention Network for Hyperspectral Image Classification by Junru Yin, Changsheng Qi, Wei Huang, Qiqiang Chen, Jiantao Qu

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Chinese Word Sense Disambiguation Based on Word translation and Part of speech by ZHANG Chunxiang, ZHAO Lingyun, GAO Xueyao

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A Hybrid Image Registration for Large Global and Non-Linear Local Deformed Images by Issa W. AlHmoud, Balakrishna Gokaraju, Marwan Bikdash

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Decom-UNet3+: A Retinal Vessel Segmentation Method Optimized With Decomposed Convolutions by Qun Li, Juntao Zhang, Licheng Hua, Songyin Fu, Chenjie Gu

“…Specifically, the encoders replace standard convolutional layers with asymmetric convolutions and depthwise separable convolutions, reducing the number of parameters while enhancing capability for feature extraction. …”
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On the generalisation capabilities of Fisher vector‐based face presentation attack detection by Lázaro J. González‐Soler, Marta Gomez‐Barrero, Christoph Busch

“…In contrast, for more realistic scenarios, existing algorithms face difficulties in detecting unknown PAI species which are only included in the test set. A feature space based on Fisher Vectors computed from compact binarised statistical image features histograms, which allows discovering semantic feature subsets from known samples to enhance the detection of unknown attacks is presented. …”
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SWMD-YOLO: A Lightweight Model for Tomato Detection in Greenhouse Environments by Quan Wang, Ye Hua, Qiongdan Lou, Xi Kan

“…The accurate detection of occluded tomatoes in complex greenhouse environments remains challenging due to the limited feature representation ability and high computational costs of existing models. …”
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A Lightweight Forward–Backward Independent Temporal-Aware Causal Network for Speech Emotion Recognition by Sijia Fei, Qiang Feng, Fei Gao

“…Meanwhile, the numerical results show that the proposed method has a good application prospect with a small number of parameters (0.21M) and low computational cost (80.72 MFLOPs).…”
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Detection and Classification of Power Quality Disturbances Based on Improved Adaptive S-Transform and Random Forest by Dongdong Yang, Shixuan Lü, Junming Wei, Lijun Zheng, Yunguang Gao

“…The IAST employs a globally adaptive Gaussian window as its kernel function, which automatically adjusts window length and spectral resolution based on real-time frequency characteristics, thereby enhancing time–frequency localization accuracy while reducing algorithmic complexity. To optimize computational efficiency, window parameters are determined through an energy concentration maximization criterion, enabling rapid extraction of discriminative features from diverse PQ disturbances (e.g., voltage sags and transient interruptions). …”
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DScanNet: Packaging Defect Detection Algorithm Based on Selective State Space Models by Yirong Luo, Yanping Du, Zhaohua Wang, Jingtian Mo, Wenxuan Yu, Shuihai Dou

“…Through experiments on its own dataset, BIGC-LP, DScanNet achieves a high accuracy of 96.8% on the defect detection task compared with the current mainstream detection algorithms, while the number of model parameters and the computational volume are effectively controlled.…”
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MED-AGNeT: An attention-guided network of customized augmentation of samples based on conditional diffusion for textile defect detection by Jun Liu, Haolin Li, Hao Liu, Jiuzhen Liang

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