Channel estimation for reconfigurable intelligent surface-aided millimeter-wave massive multiple-input multiple-output system with deep residual attention network by Xuhui Zheng, Ziyan Liu, Shitong Cheng, Yingyu Wu, Yunlei Chen, Qian Zhang

“…Furthermore, to transfer the RIS channel feature maps extracted from the residual attention blocks (RABs) to the end of the estimator for accurate reconstruction, we propose a novel and effective feature fusion approach. …”
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Research on performance optimization for large-scale sparse computation over many-core heterogenous supercomputer by Zhengding HU, Wei XUE

“…With development of supercomputer technique,it is possible to solve extra-scale sparse problems in big data applications.However,irregular feature in computation and memory access of sparse problems brings challenges to implementation and optimization of applications.Many-core heterogenous architecture is popular in supercomputer design,which advances a higher requirement for application developers.How to utilize its extraordinary computing ability becomes a very difficult problem.Challenges in optimizing sparse computing problems were analyzed,and three cases of implementation and optimization based on typical many-core heterogenous computer system were introduced,which of all achieve very high performance.Experiences in those successful cases were summed up,to better solve extra-scale sparse computing problems on many-core heterogenous system of new generation.…”
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Verifiable secure image retrieval for cloud-assisted IoT environments by GUO Jiaqi, MA Zhi, WANG Wensheng, TIAN Cong, DUAN Zhenhua

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Fusion of multi-scale and context for small target detection algorithm of unmanned aerial vehicle rescue by LIU Yuan, ZHAO Jing, JIANG Guoping, XU Fengyu, LU Ningyun

“…Aiming at the problem of insufficient feature information contained in small targets under unmanned aerial vehicle (UAV) images that led to insufficient detection accuracy of the model, a small target detection algorithm for UAV sea rescue images that integrated multi-scale and contextual information was proposed. …”
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Key frame extraction based abnormal vehicle identification technique using statistical distribution analysis by M. A. Y. Peer Mohamed Appa, V. Vanitha, Priti Rishi, Shrddha Sagar, M. Asha Paul

“…The statistical feature extraction technique is used to extract the key frames in a statistical way by using beta distribution estimation. …”
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A prior-knowledge-guided dynamic attention mechanism to predict nocturnal hypoglycemic events in type 1 diabetes by Xia Yu, Zi Yang, Xinzhuo Wang, Xiaoyu Sun, Ruiting Shen, Hongru Li, Mingchen Zhang

“…Abstract Nocturnal hypoglycemia is a critical problem faced by diabetic patients. Failure to intervene in time can be dangerous for patients. …”
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Identification of Tool-Wear State Using Information Fusion and SSA–BP Neural Network by Zishuo Wang, Hongwei Cui, Shuning Liang, Tao Ding, Xingquan Gao

“…This method uses a principal component analysis (PCA) to fuse multi-domain features extracted from three-way vibration signals, power signals, and temperature signals. …”
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Research on Improved YOLO11 for Detecting Small Targets in Sonar Images Based on Data Enhancement by Xiaochuan Wang, Zhiqiang Zhang, Xiaodong Shang

“…Lastly, we employ a hybrid training strategy that combines pre-training with ADA-StyleGAN3-generated data and transfer learning with real data to alleviate the problem of insufficient training samples. The experiments show that, compared to the baseline YOLOv11n, the improved model’s precision and recall increase to 92% and 90.3%, respectively, and mAP50 rises by 12.7 percentage points, highlighting the effectiveness of the SFE-YOLO network and its transfer learning strategy in tackling the challenges of sparse small target features and strong noise interference in sonar images.…”
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Computer-Aided Diagnosis of Acute Lymphoblastic Leukemiaby Using a Novel CAE-CNN Framework by Mohammed Mansoor Alhammadi

“…Acute lymphoblastic leukemia (ALL) is a main health problem throughout the world. Therefore, fast and exact diagnosis is the most crucial factor for providing efficient management and treatment methods. …”
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Automatic detection of cognitive events using machine learning and understanding models’ interpretations of human cognition by Quang Dang, Murat Kucukosmanoglu, Michael Anoruo, Golshan Kargosha, Sarah Conklin, Justin Brooks

“…In this study, we detect cognitive events for the task-evoked pupillary response across four domains (vigilance, emotion processing, numerical reasoning, and short-term memory). The problem is framed as a binary classification. We train one generalized model and four task-specific models on 1-s pupil diameter and gaze position segments. …”
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Sentiment Analysis using Support Vector Machine and Random Forest by Talha Ahmed Khan, Rehan Sadiq, Zeeshan Shahid, Muhammad Mansoor Alam, Mazliham Bin Mohd Su'ud

“…Additionally, the paper covers preprocessing techniques, feature extraction, model training, evaluation, and challenges encountered in sentiment analysis. …”
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MFFNet: a building change detection method based on fusion of spectral and geometric information by Zhihao Guo, Jianping Pan, Peng Xie, Ling Zhu, Chen Qi, Xunxun Wang, Yihan Yang, Yan Wang, Huijuan Zhang, Zhaohui Ren

“…This mitigates false detections by making the model more aware of areas where the elevation has changed over time. (2) Because building extraction is affected by shadows and vegetation, we designed a multiscale feature shuffle module. It takes multiscale features and establishes relationships between neighbouring pixels using the pixel-shuffle algorithm, then fuses and reorganizes the multiscale features to highlight the relationships between global contexts, thereby mitigating the problem of building occlusion by shadows. …”
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A Methodology to Extract Knowledge from Datasets Using ML by Ricardo Sánchez-de-Madariaga, Mario Pascual Carrasco, Adolfo Muñoz Carrero

“…A new dataset generation and a new ML classification measurement methodology were developed to determine whether the feature subsets (FSs) best classified by a specific ML algorithm corresponded to the most KE-relevant combinations of features. …”
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Improving kidney segmentation in pathological images: a multiscale approach to resolve fragmentation and incomplete boundaries by Muhammad Wajeeh Us Sima, Chengliang Wang, Muhammad Arshad, Jamshed Ali Shaikh, Reem Ibrahim Alkanhel, Dina S. M. Hassan, Ammar Muthanna

“…However, current methods for segmenting pathological images often fail, resulting in incomplete and fragmented representations of kidney structures due to broken boundaries, poor integration of features, and limited scalability. These problems reduce the accuracy and reliability of existing approaches. …”
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Learning optimal image representations through noise injection for fine-grained search by Vidit Kumar, Vikas Tripathi, Bhaskar Pant, Manoj Diwakar, Prabhishek Singh, Anchit Bijalwan

“…Concurrently, noise injection in the features acts as regularization, facilitating the acquisition of generalized features and mitigating model overfitting. …”
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Trajectory-Driven Deep Learning for UAV Location Integrity Checks by Mincheol Shin, Sang-Yoon Chang, Jonghyun Kim, Kyungmin Park, Jinoh Kim

“…Specifically, we define a set of attributes effectively capturing the movement of aerial vehicles over time, resulting in eight features with no signal-dependent information. We then present our deep sequence method, implemented on top of either a recurrent neural network (RNN) or a Transformer with a backend classifier, performing integrity checks with the newly defined feature set. …”
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Multi-Model Attentional Fusion Ensemble for Accurate Skin Cancer Classification by Iftekhar Ahmed, Biggo Bushon Routh, Md. Saidur Rahman Kohinoor, Shadman Sakib, Md Mahfuzur Rahman, Farag Azzedin

“…Artifacts like hair can further obscure important features. This research addresses the problem and introduces a novel deep learning approach for accurate skin cancer classification by combining ResNet50V2, MobileNetV2, and EfficientNetV2 models. …”
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A Lightweight Neural Network for Denoising Wrapped-Phase Images Generated with Full-Field Optical Interferometry by Muhammad Awais, Younggue Kim, Taeil Yoon, Wonshik Choi, Byeongha Lee

“…The network architecture integrates a shallow feature extraction module, a series of Residual Dense Attention Blocks (RDABs), and a dense feature fusion module. …”
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Robust UAV Target Tracking Algorithm Based on Saliency Detection by Hanqing Wu, Weihua Wang, Gao Chen, Xin Li

“…Firstly, this article analyzes the features from both spatial and temporal dimensions, evaluates the representational and discriminative abilities of different features, and achieves adaptive feature fusion. …”
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Improving drug–target affinity prediction by adaptive self-supervised learning by Qing Ye, Yaxin Sun

“…Its goal is to maximize the retention of original feature information, thereby bridging the objective gap between self-supervised learning and drug-target affinity prediction and alleviating the sample mismatch problem. …”
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