Advanced holographic convolutional dense networks and Tangent runner optimization for enhanced polycystic ovarian disease classification by Prathibanandhi Jeyashanker, Annie Grace Vimala Georgewilliam Sundaram, Padmakala Sadagopan, Abid Yahya, Ravi Samikannu, Irfan Anjum Badruddin, Sarfaraz Kamangar, Mohamed Ghouse Shukur

“…However, most of the existing approaches suffer from lots of problems, including low accuracy and capability in feature extraction, and may also be resilient to noise; it can further delay or lead to a wrong diagnosis. …”
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Deep learning and transfer learning for device-free human activity recognition: A survey by Jianfei Yang, Yuecong Xu, Haozhi Cao, Han Zou, Lihua Xie

“…Traditional machine learning has made significant progress by heuristic hand-crafted features and statistical models, but it suffers from the limitation of manual feature design. …”
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Intelligent analysis algorithm for power engineering data based on improved BiLSTM by Yuanyuan Xu, Jiapeng Yang, Xin Cai

“…Abstract Traditional BiLSTM (Bidirectional Long Short-Term Memory) has limited processing capabilities for long-time series, insufficient adaptability to the characteristics of power engineering data, and inadequate accuracy in predicting problems in specific application scenarios. This paper introduces an attention mechanism to improve BiLSTM’s ability to capture long time series, integrate the time series of power engineering data, and design an intelligent analysis algorithm that adapts to multi-dimensional features. …”
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Enhanced futures price-spread forecasting based on an attention-driven optimized LSTM network: integrating an improved grey wolf optimizer algorithm for enhanced accuracy by Yongli Tang, Zhenlun Gao, Zhongqi Cai, Jinxia Yu, Panke Qin

“…Benchmark tests on optimization problems validate IGWO’s superior convergence efficiency. …”
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Furniture design based on image color extraction algorithm by Binglu Chen, Guanyu Chen, Qianqian Hu

“…The results revealed that in the foreground part, the correct rate of color match based on the design method was 95.7%, and in the background part, the correct rate of color match based on the design method was 94.81 %, which proved its effectiveness. The average feature point extraction time and the average feature point matching time of the design-based algorithm were 5.45 ms and 9.83 ms, respectively, which proved its high computational efficiency. …”
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Hyperspectral Anomaly Detection Based on Intrinsic Image Decomposition and Background Subtraction by Jiao Jiao, Longlong Xiao, Chonglei Wang

“…In the anomaly detection of hyperspectral images, the salient feature map and spatial-spectral features are not effectively used, which greatly limits the detection performance. …”
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A Capsule Decision Neural Network Based on Transfer Learning for EEG Signal Classification by Wei Zhang, Xianlun Tang, Xiaoyuan Dang, Mengzhou Wang

“…In order to solve the problem of feature distortion caused by EEG feature extraction algorithm, a deep capsule decision network was constructed. …”
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A Multisource Data Fusion Modeling Prediction Method for Operation Environment of High-Speed Train by Decang Li, Juhui Zhang, Ruxun Xu, Jianjun Meng, Jianming Wang, Xiaoqiang Chen, Xin Jia, Junhui Ma

“…Providing accurate and reliable railway regional environmental data is a key consideration in operation control and dynamic dispatching of high-speed train. However, there are problems of low reliability and high uncertainty in the single data processing of high-speed train operating area environment. …”
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SVMWPE-HLLE Based Fault Diagnosis Approach for Rolling Bearing by Chunhui Li, Youfu Tang, Guolin Xu

“…The high-dimensional matrix obtained by MPE feature extraction to pattern recognition was solved by introducing Hessian local linear embedding (HLLE) dimension reduction method, and the feature extraction method based on SVMWPE-HLLE was proposed. …”
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Center-Guided Network with Dynamic Attention for Transmission Tower Detection by Xiaobin Li, Zhuwei Liang, Jingbin Yang, Chuanlong Lyu, Yuge Xu

“…Specifically, we apply ResNet and FPN as the feature extractor to extract high-quality and multi-scale features. …”
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Digital security risk identification and model construction of smart city based on deep learning by Zhilei Zhao

“…In order to deal with these problems, this paper designs a flexible three-layer architecture framework, and introduces a new intrusion detection feature selection method that combines flock optimization (CSO) and genetic algorithm (GA) to reduce the complexity of feature selection and enhance the detection and processing power of security vulnerabilities through deep neural networks (DNN). …”
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MaskDGNets: Masked-attention guided dynamic graph aggregation network for event extraction. by Guangwei Zhang, Fei Xie, Lei Yu

“…On the other hand, the designed masked attention mechanism can effectively balance the word vector features and sequence semantics and refine these features. …”
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Prioritizing image processing and big data analytics factors for software development using DEMATEL-choquet method in bipolar complex fuzzy context by Ruimin Ma

“…Abstract Bipolar complex fuzzy set (BCFS) theory is a newly developed framework for addressing real-world decision-making (DM) problems that involve ambiguous, uncertain, and dual-valued information. …”
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Enhancing Classification Models With Sophisticated Counterfactual Images by Xiang Li, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama

“…This causes deep learning models to learn incorrect relationships between features when using these training data. However, because these models have <italic>black boxes</italic>, these problems cannot be solved effectively. …”
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Cherry defect and classification detection based on improved YOLOX model by LIU Jing-yu, PEI Yue-kun, CHANG Zhi-yuan, CHAI Zhi, CAO Pei-pei

“…Methods: Firstly, the YOLOX network was used to detect the defective fruit, in order to solve some problems where the defect was not obvious. The detection accuracy of the inconspicuous defect was improved by setting the appropriate fusion factor for the feature pyramid network, and in order to solve the problem of imbalance between various types of real samples, Focal Loss was integrated into the loss function. …”
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Fault Signal Recognition Method of Submarine Cable based on Energy Entropy and GWO-ELM by ZHANG Tao, LIU Hao, ZHANG Peilei, LIU Zheheng, SHI Guangrui, FAN Xiping

“…【Objective】Aiming at the problems of poor feature extraction and low fault diagnosis efficiency in the study of submarine cable vibration signals, a fault diagnosis method for submarine cable is presented based on the combination of energy entropy features, Grey Wolf Optimizer (GWO) algorithm and Extreme Learning Machine (ELM).…”
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Machine Learning and Medical Data: Predicting ICU Mortality and Re-admission Risks by Runia Roy, Ulya Bayram

“…Four machine learning algorithms are applied to the finalized feature set to understand the difference between the binary and the multi-class classification problems. …”
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Hybrid Deep Learning Model for Cataract Diagnosis Assistance by Zonghong Feng, Kai Xu, Liangchang Li, Yong Wang

“…Therefore, this paper proposes a hybrid deep learning network for assisting in the diagnosis of cataract fundus images, attempting to solve the above problems and limitations. The network is based on the idea of transfer learning for feature extraction of fundus images, and introduces the Squeeze-and-Excitation (SE) module and prototype network for feature enhancement and classification, improving the model’s generalization ability for new disease samples. …”
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A Novel Improved Local Binary Pattern and Its Application to the Fault Diagnosis of Diesel Engine by Yanping Cai, Guanghua Xu, Aihua Li, Xu Wang

“…Aiming at the feature extraction difficulty of vibration signals, an improved local binary pattern- (ILBP-) based diesel engine fault diagnosis approach is proposed. …”
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Attention-fused residual transformer CNN for robust lower limb movement recognition by A. Anitha, D. Jeraldin Auxillia

“…Data augmentation is applied to create more samples for minority classes, addressing class imbalance problems and improving recognition reliability. The AF-RT-CNN architecture combines residual blocks, attention mechanism and Transformer Encoder aiding robust feature extraction, good generalization capability and pattern recognition. …”
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