EXPLORING A NOVEL HEXACO PERSONALITY TRAITS ON TWITTER: AN ENSEMBLE-BASED NLP METHODOLOGY by Tanvi Desai, Divyakant Meva

“…Our approach integrates advanced NLP techniques across key phases: preprocessing, feature extraction, feature selection, and final detection. …”
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Deep Learning in Visual Computing and Signal Processing by Danfeng Xie, Lei Zhang, Li Bai

“…In this study, we not only review typical deep learning algorithms in computer vision and signal processing but also provide detailed information on how to apply deep learning to specific areas such as road crack detection, fault diagnosis, and human activity detection. …”
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Chest X-Ray Images to Differentiate COVID-19 from Pneumonia with Artificial Intelligence Techniques by Rumana Islam, Mohammed Tarique

“…The other algorithm relies on the inbuilt feature extractor network to extract features from the X-ray images and classifies them with a pretrained deep neural network VGG16. …”
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Smart Grid Security: Proactive Prediction of Advanced Persistent Threats by Motahareh Dehghan, Erfan Khosravain

“…The feature importance analysis reveals that traffic-related features such as packet size variance and connection duration are crucial in identifying Advanced Persistent Threats. …”
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Development of machine learning models for the prediction of the skin sensitization potential of cosmetic compounds by Wu Qiao, Tong Xie, Jing Lu, Tinghan Jia

“…Using two data preprocessing methods and three feature extraction techniques, we constructed and validated models with eight machine learning algorithms. …”
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Aircraft Sensor Fault Diagnosis Based on GraphSage and Attention Mechanism by Zhongzhi Li, Jinyi Ma, Rong Fan, Yunmei Zhao, Jianliang Ai, Yiqun Dong

“…Experiments demonstrate that the proposed method outperforms baseline approaches, achieving better detection performance and faster computational speed. …”
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Graph-Based Semi-Supervised Learning with Bipartite Graph for Large-Scale Data and Prediction of Unseen Data by Mohammad Alemi, Alireza Bosaghzadeh, Fadi Dornaika

“…In this paper, we propose a novel GSSL algorithm tailored for large-scale databases, leveraging anchor points to mitigate the challenges posed by large affinity matrices. …”
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Real-Time Deep Intelligence Analysis and Visualization of COVID-19 Using FCNN Mechanism by Cherukuri Triveni, K. Suvarna Vani, M. Likhitha

“…Database management with respect to virus deep analysis is a critical task to the researcher through conventional algorithms. The RNA, DNA, and biological data are helping to the bio-inspired algorithm but its implementation can be complex by software tools. …”
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Providing contexts for classification of transients in a wide-area sky survey: An application of noise-induced cluster ensemble by Tossapon Boongoen, Natthakan Iam-On, James Mullaney

“…Initial works based on the feature-based approach design this detection as imbalance classification, where a data-level method can be used to resolve the difference in cardinality between classes. …”
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A multi-modality ground-to-air cross-view pose estimation dataset for field robots by Xia Yuan, Kaiyang Wang, Riyu Qin, Jiachen Xu

“…The dataset uniquely integrates field environments and high-resolution LiDAR-aerial-ground data triplets, enabling rigorous evaluation of 3-DoF pose estimation algorithms for orientation alignment and coordinate transformation between perspectives.This resource supports development of robust localization systems for field robots in GNSS-denied conditions, emphasizing cross-view feature matching and multisensor fusion. …”
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RRBM-YOLO: Research on Efficient and Lightweight Convolutional Neural Networks for Underground Coal Gangue Identification by Yutong Wang, Ziming Kou, Cong Han, Yuchen Qin

“…Coal gangue identification is the primary step in coal flow initial screening, which mainly faces problems such as low identification efficiency, complex algorithms, and high hardware requirements. In response to the above, this article proposes a new “hardware friendly” coal gangue image recognition algorithm, RRBM-YOLO, which is combined with dark light enhancement. …”
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Machine learning analysis of cardiovascular risk factors and their associations with hearing loss by Ali Nabavi, Farimah Safari, Ali Faramarzi, Mohammad Kashkooli, Meskerem Aleka Kebede, Tesfamariam Aklilu, Leo Anthony Celi

“…Abstract Hearing loss poses immense burden worldwide and early detection is crucial. The accurate models identify high-risk groups, enabling timely intervention to improve quality of life. …”
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End-to-end multiple object tracking in high-resolution optical sensors of drones with transformer models by Yubin Yuan, Yiquan Wu, Langyue Zhao, Yuqi Liu, Yaxuan Pang

“…Conventional approaches rely heavily on manual feature engineering and intricate algorithms, which can further limit efficiency. …”
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Design and trial of precision spraying system for weeds in winter wheat field at tillering stage by Bo Li, Peijie Guo, Yu Chen, Jun Chen, Haiying Wang, Jing Zhang, Zhixing Zhang

“…To improve the accuracy of weed detection, GCGS-YOLO is proposed as a weed target detection model, and we integrate the Global Context (GC) attention mechanism with the traditional C3 module to optimize the backbone feature extraction network, and introduce the GSConv module to improve the neck network. …”
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Deep Learning Approaches for Retinal Disease Identification in Fundus Imaging: A Comprehensive Overview by Ismael Abdulkareem Ali, Sozan Abdullah Mahmood

“…While in the medical field, manually detecting ocular pathology has significant difficulty. …”
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Electronic Nose System Based on Metal Oxide Semiconductor Sensors for the Analysis of Volatile Organic Compounds in Exhaled Breath for the Discrimination of Liver Cirrhosis Patient... by Makhtar War, Benachir Bouchikhi, Omar Zaim, Naoual Lagdali, Fatima Zohra Ajana, Nezha El Bari

“…This study demonstrates the significant potential of e-nose technology towards VOCs analysis in exhaled breath, as a valuable tool for LC diagnosis. It also explores feature extraction methods and suitable algorithms for effectively distinguishing between LC patients and controls. …”
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Ultrasound-based machine learning model to predict the risk of endometrial cancer among postmenopausal women by Yi-Xin Li, Yu Lu, Zhe-Ming Song, Yu-Ting Shen, Wen Lu, Min Ren

“…Three models were developed: (1) R model: radiomics-based machine learning (ML) algorithms; (2) CNN model: image-based CNN algorithms; (3) DLR model: a hybrid model combining radiomics and deep learning features with ML algorithms. …”
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Adaptive Vision-Based Gait Environment Classification for Soft Ankle Exoskeleton by Gayoung Yang, Jeong Heo, Brian Byunghyun Kang

“…This paper presents a vision-based intelligent gait environment detection algorithm for a lightweight ankle exosuit designed to enhance gait stability and safety for stroke patients, particularly during stair negotiation. …”
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ST-YOLO: a deep learning based intelligent identification model for salt tolerance of wild rice seedlings by Qiong Yao, Qiong Yao, Pan Pan, Pan Pan, Xiaoming Zheng, Xiaoming Zheng, Guomin Zhou, Guomin Zhou, Guomin Zhou, Jianhua Zhang, Jianhua Zhang

“…Diversified feature extraction paths are introduced to enhance the ability of feature extraction; Introducing CAFM (Context Aware Feature Modulation) convolution and attention fusion modules into the backbone network to enhance feature representation capabilities while improving the fusion of features at various scales; Design a more flexible and effective spatial pyramid pooling layer using deformable convolution and spatial information enhancement modules to improve the model’s ability to represent target features and detection accuracy.ResultsThe experimental results show that the improved algorithm improves the average precision by 2.7% compared with the original network; the accuracy rate improves by 3.5%; and the recall rate improves by 4.9%.ConclusionThe experimental results show that the improved model significantly improves in precision compared with the current mainstream model, and the model evaluates the salt tolerance level of wild rice varieties, and screens out a total of 2 varieties that are extremely salt tolerant and 7 varieties that are salt tolerant, which meets the real-time requirements, and has a certain reference value for the practical application.…”
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Discovering Human Presence Activities with Smartphones Using Nonintrusive Wi-Fi Sniffer Sensors: The Big Data Prospective by Weijun Qin, Jiadi Zhang, Bo Li, Limin Sun

“…By deploying in the real-world office environment, we found that the performance of Wi-Fi messages aggregation of CAOCA and CACFA algorithms is over 3.8 times higher than the worst channel of FCA algorithms and about 76% of the best channel of FCA algorithms, and the human presence detection rate reached 87.4%.…”
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