Transformer attention fusion for fine grained medical image classification by Danyal Badar, Junaid Abbas, Raed Alsini, Tahir Abbas, Wang ChengLiang, Ali Daud

“…It extracts features at three different resolutions (12 × 12, 24 × 24, 48 × 48), allowing it to detect subtle local features and global elements. …”
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Construction of a predictive model for cognitive impairment among older adults in Northwest China by Yu Wang, Ni Wang, Yanjie Zhao, Xiaoyan Wang, Yuqin Nie, Liping Ding

“…We used random forest algorithms to select important features from potential predictors. …”
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Transfer learning for securing electric vehicle charging infrastructure from cyber-physical attacks by Ahmad Almadhor, Shtwai Alsubai, Imen Bouazzi, Vincent Karovic, Monika Davidekova, Abdullah Al Hejaili, Gabriel Avelino Sampedro

“…Existing EV charging station security systems are unable to identify many known and undiscovered threats since they primarily rely on feature selection and categorization accuracy. It is common for these systems to be constructed using conventional machine learning algorithms. …”
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Perception-Based H.264/AVC Video Coding for Resource-Constrained and Low-Bit-Rate Applications by Lih-Jen Kau, Chin-Kun Tseng, Ming-Xian Lee

“…To this end, macroblocks (MBs) containing human faces are detected using the Viola–Jones algorithm, which leverages AdaBoost for feature selection and a cascade of classifiers for fast and accurate detection. …”
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An intelligent attention based deep convoluted learning (IADCL) model for smart healthcare security by J. Maruthupandi, S. Sivakumar, B. Lakshmi Dhevi, S. Prasanna, R. Karpaga Priya, Shitharth Selvarajan

“…The proposed system employs publicly available datasets such as CIC-IDS 2017, CIC-IDS 2018, CIC-Bell DNS 2021, and NSL-KDD to present a robust detection framework. IRKO selects features, reducing the feature dimensions and hence isolating the most relevant attributes. …”
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A Network Traffic Anomaly Classification Model Based on Self-Attention Mechanism and Convolutional Gated Recurrent Unit by Yulian Li, Yang Su

“…However, traditional methods struggle to effectively identify minority-class traffic under conditions of data imbalance, which affects detection accuracy. To address this issue, this paper proposes a Custom Synthetic Minority Over-Sampling Technique (Custom SMOTE) algorithm, which precisely handles minority-class samples while significantly reducing memory consumption. …”
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Multi-information fusion welding defect identification combining neighborhood rough set and optimized SVM by Zhiqiang FENG, Xianping ZENG, Naiwen FANG, Daidi ZHAO, Quan LI, Jiutian LUO, Xin LI

“…A multi-information fusion welding defect recognition method is proposed by combining NRS with optimized SVM to address the “big data” generated during the multi-sensor information fusion welding process. A fast reduction algorithm based on NRS was constructed using feature importance as inspiration information, and the DOA was used to select the key parameters of SVM. …”
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Multimodal deep learning for art behavior analysis and personalized teaching path generation by Yikun Li, Jie Shi

“…The research constructs a Multimodal Feature Fusion Network (MFFN) and an Evolutionary Path Generation Algorithm (EPG), utilizing Kinect sensors, digital tablets, and other devices to collect multi-modal data including visual, tactile, behavioral, and environmental information, establishing a large-scale art learning dataset. …”
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A hybrid CFD and machine learning study of energy performance of photovoltaic systems with a porous collector: Model development and validation by Yinling Wang, Lei Yu, Mazhar Ali, Imran Ali Khan, Tahir Maqsood, Haining Gao, Qi Wang, Xiaolei Guo

“…A systematic preprocessing pipeline was developed to enhance model performance, including outlier detection and feature normalization. Hyperparameter optimization process in this study uses the Water Cycle Algorithm (WCA), a metaheuristic method inspired by natural processes. …”
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Enhancing weld quality of novel robotic-arm arc welding: Vision-based monitoring, real-time control seam tracking by Aman Sharma, Rishabh Chaturvedi, Kamal Sharma, Saad Abrahim Binhowimal, Jayant Giri, T. Sathish

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Enhanced Blind Separation of Rail Defect Signals with Time–Frequency Separation Neural Network and Smoothed Pseudo Wigner–Ville Distribution by Mingxiang Zhang, Kangwei Wang, Yule Yang, Yaojia Cao, Yong You

“…Simulation and experiments showed that compared with traditional algorithms like independent component analysis, shallow neural networks, and time–frequency blind source separation, the proposed algorithm can provide better separation performance and higher stability in rail crack detection.…”
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OR-FCOS: an enhanced fully convolutional one-stage approach for growth stage identification of Oudemansiella raphanipes by Runze Fang, Huamao Huang, Nuoyan Guo, Haichuan Wei, Shiyi Wang, Haiying Hu, Ming Liu

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Passive forensic based on spatio-temporal localization of video object removal tampering by Linqiang CHEN, Quanxin YANG, Lifeng YUAN, Ye YAO, Zhen ZHANG, Guohua WU

“…To address the problem of identification of authenticity and integrity of video content and the location of video tampering area,a deep learning detection algorithm based on video noise flow was proposed.Firstly,based on SRM (spatial rich model) and C3D (3D convolution) neural network,a feature extractor,a frame discriminator and a RPN (region proposal network) based spatial locator were constructed.Secondly,the feature extractor was combined with the frame discriminator and the spatial locator respectively,and then two neural networks were built.Finally,two kinds of deep learning models were trained by the enhanced data,which were used to locate the tampered area in temporal domain and spatial domain respectively.The test results show that the accuracy of temporal-domain location is increased to 98.5%,and the average intersection over union of spatial localization and tamper area labeling is 49%,which can effectively locate the tamper area in temporal domain and spatial domain.…”
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FragMangro: A cross-domain zero-shot model for monitoring fragmented mangrove ecosystems by Ruoxin Zhang, Angela An, Mingcan Cen, Chong Zhang, Aimee Huangfu, Sunny Vinnakota, Shuxiang Song

“…FragMangro integrates parallel multi-scale convolution for progressive dimensional upscaling, improving spectral feature extraction. Additionally, it employs a dynamic learning rate and iterative control algorithm, which adaptively adjusts learning rates and training epochs based on loss monitoring to prevent suboptimal convergence. …”
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Validation of Taylor’s Frozen Hypothesis for DAS-Based Flow by Shu Dai, Lei Liang, Ke Jiang, Hui Wang, Chengyi Zhong

“…It proposes a dispersion feature enhancement algorithm based on cross-correlation, which combines a rotatable elliptical template with normalized cross-correlation coefficients to suppress interference from non-target directions. …”
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Empirical Evaluation and Analysis of YOLO Models in Smart Transportation by Lan Anh Nguyen, Manh Dat Tran, Yongseok Son

“…You Only Look Once (YOLO) and its variants have emerged as the most popular real-time object detection algorithms. They have been widely used in real-time smart transportation applications due to their low-latency detection and high accuracy. …”
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Extraction of typical oyster pile columns in the Maowei Sea, Beibu Gulf, based on unmanned aerial vehicle laser point cloud orthophotos by Jinze Du, Jinze Du, Meiqin Huang, Zhenjun Kang, Zhenjun Kang, Zhenjun Kang, Yichao Tian, Yichao Tian, Yichao Tian, Jin Tao, Jin Tao, Qiang Zhang, Qiang Zhang, Yutong Xie, Yutong Xie, Yutong Xie, Jinying Mo, Jinying Mo, LiYan Huang, LiYan Huang, Yusheng Feng, Yusheng Feng

“…It utilizes multi-spectral image data from unmanned aerial vehicles (UAVs), light detection and ranging (LiDAR) point cloud technology, and deep learning algorithms to extract representative oyster pile columns in Maowei Sea within Beibu Gulf. …”
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Infrared Thermography-Based Insulator Fault Classification via Unsupervised Clustering and Semi-Supervised Learning by Usman Shafique, Syed Muhammad Alam, Umar Rashid, Wahab Javed, Haris Anwaar, Malik Shah Zeb, Talha Ahmad, Uzair Imtiaz, Frederic Nzanywayingoma

“…Then, in the supervised learning phase, a Gaussian kernel support vector machine (SVM) algorithm classifies various insulator defects using extracted features. …”
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Enhancing personalized learning: AI-driven identification of learning styles and content modification strategies by Md. Kabin Hasan Kanchon, Mahir Sadman, Kaniz Fatema Nabila, Ramisa Tarannum, Riasat Khan

“…This research endeavors to devise an effective method for detecting a learner’s preferred learning style and subsequently adapting the learning content to align with that style, utilizing artificial intelligence AI techniques. …”
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Automated Eddy Identification and Tracking in the Northwest Pacific Based on Conventional Altimeter and SWOT Data by Lan Zhang, Cheinway Hwang, Han-Yang Liu, Emmy T. Y. Chang, Daocheng Yu

“…Future work should refine the PET algorithm for SWOT’s swath altimetry, addressing data gaps and unclosed contours. …”
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