BlendNet: a blending-based convolutional neural network for effective deep learning of electrocardiogram signals by S. Premanand, Sathiya Narayanan

“…However, this traditional approach captures only a limited set of features of ECG and thereby limits the effectiveness of DL architectures in disease detection.MethodsThis work proposes “BlendNet,” a DL architecture that effectively extracts the features of an ECG signal using a blending approach termed “alpha blending.” …”
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Recent Progress in Ocean Intelligent Perception and Image Processing and the Impacts of Nonlinear Noise by Huayu Liu, Ying Li, Tao Qian, Ye Tang

“…It also reviewed adaptive image processing processes and their critical support for ocean image recognition and detection, such as image annotation, feature enhancement, and image segmentation. …”
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Acoustic Identification Method of Partial Discharge in GIS Based on Improved MFCC and DBO-RF by Xueqiong Zhu, Chengbo Hu, Jinggang Yang, Ziquan Liu, Zhen Wang, Zheng Liu, Yiming Zang

“…Secondly, wavelet denoising was used to weaken the influence of noise on ultrasonic signals, and conventional, first-order, and second-order differential MFCC feature parameters were extracted, followed by principal component analysis for dimensionality reduction optimization. …”
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Temporal and spatial self supervised learning methods for electrocardiograms by Wenping Chen, Huibin Wang, Lili Zhang, Min Zhang

“…Abstract The limited availability of labeled ECG data restricts the application of supervised deep learning methods in ECG detection. Although existing self-supervised learning approaches have been applied to ECG analysis, they are predominantly image-based, which limits their effectiveness. …”
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Neural-Network-Based Synchronization Acquisition with Hankelization Preprocessing by Gyung-Eun Kim, Jung-Hwan Kim, Jong-Ho Lee, Woong-Hee Lee

“…Conventional synchronization signal detection methods rely on linear correlation function analysis with fixed thresholds, which are insufficient for handling the nonlinear characteristics of practical wireless communication systems. …”
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Minimising Security Deviations in Software-Defined Networks Using Deep Learning by mohammed alghaloom

“…This study aims to enhance the security of Software-Defined Networks (SDN) byemploying deep learning techniques to detect cyber threats and mitigate attacks. A comprehensive data analysis was conducted, beginning with feature identification and dimensionality reduction using the Gain Information method to filter out redundant features, thereby improving model performance. …”
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Latest Trend and Challenges in Machine Learning– and Deep Learning–Based Computational Techniques in Poultry Health and Disease Management: A Review by Shwetha V., Maddodi B. S., Vijaya Laxmi, Abhinav Kumar, Sakshi Shrivastava

“…We have considered the research article published from 2010 to 2023 in this study, which uses ML- and DL-based computation techniques in poultry welfare metrics such as gender identification, tracking of poultry, analysis of broiler chicken behavior, detection of poultry diseases, lameness and broiler weight, and stress monitoring. …”
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A high precision method of segmenting complex postures in Caenorhabditis elegans and deep phenotyping to analyze lifespan by Bingyue Dong, Weiyang Chen

“…Abstract In-depth exploration of the effects of genes on the development, physiology, and behavior of organisms requires high-precision phenotypic analysis. However, the overlap of body postures in group behavior and the similarity of movement patterns between strains pose challenges to accuracy analysis. …”
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Identification of partial discharges in cable terminals of high-speed EMUs based on fuzzy C-means clustering by YANG Yanhua, CEHN Zhenbao, CAO Han, ZHANG Yanlin, LIU Kai, CHEN Kui, GAO Guoqiang

“…By performing envelope analysis on individual pulses, three parameters of the pulses were extracted as the feature vectors. …”
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Advanced Cancer Classification Using AI and Pattern Recognition Techniques by Haddou Bouazza Sara, Haddou Bouazza Jihad

“…We applied feature selection techniques such as the F Test, Signal-to-Noise Ratio (SNR), T-test, ReliefF, Correlation Coefficient, Mutual Information, and minimum redundancy maximum relevance, along with classifiers including K-Nearest Neighbors, Support Vector Machines, Linear Discriminant Analysis, Decision Tree Classifiers, and Naive Bayes. …”
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A Novel Sparse Enhancement Neural Network for Rolling Bearing Fault Diagnosis by Yong Zhang, Junjie Ye, Wenhu Yang, Jinwang Shi, Wangpeng He, Gaigai Cai

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Federated learning-based fault location and identification in hybrid AC/DC distribution systems considering bidirectional power flow by Zhonggen Xu, Lin Jiang

“…The approach introduces a complete solution encompassing fault detection, classification and localization using feature-extracted voltage and current signals. …”
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Maximum Entropy Principle Based on Bank Customer Account Validation Using the Spark Method by Xiaorong Qiu, Ye Xu, Yingzhong Shi, S. Kannadhasan Deepa, S. Balakumar

“…This approach will have a mapping from clustering and feature extraction to classification for accurate detection. …”
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Adaptive machine learning framework: Predicting UHPC performance from data to modelling by Yinzhang He, Shaojie Gao, Yan Li, Yongsheng Guan, Jiupeng Zhang, Dongliang Hu

“…The framework has several key modules: data preprocessing, feature selection, outlier detection, model training, hyperparameter optimization, and model interpretation. …”
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Automated Quantification of Rebar Mesh Inspection in Hidden Engineering Structures via Deep Learning by Yalong Xie, Xianhui Nie, Hongliang Liu, Yifan Shen, Yuming Liu

“…This paper presents an in-depth study of the automated recognition and geometric information quantification of rebar meshes, proposing a deep learning-based method for rebar mesh detection and segmentation. By constructing a diverse rebar mesh image dataset, an improved Unet-based model was developed, incorporating residual modules to enhance the network’s feature extraction capabilities and training efficiency. …”
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Predicting future morphological changes of lesions from radiotracer uptake in 18F-FDG-PET images. by Ulas Bagci, Jianhua Yao, Kirsten Miller-Jaster, Xinjian Chen, Daniel J Mollura

“…Delineated regions were used to extract shape and textural features, with the proposed adaptive feature extraction framework, as well as standardized uptake values (SUV) of uptake regions, to conduct a broad quantitative analysis. …”
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Deep convolutional neural network architecture for facial emotion recognition by Dayananda Pruthviraja, Ujjwal Mohan Kumar, Sunil Parameswaran, Vemulapalli Guna Chowdary, Varun Bharadwaj

“…Facial emotion detection is crucial in affective computing, with applications in human-computer interaction, psychological research, and sentiment analysis. …”
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Explainable Model of Fusion Network With Enhanced Optimization Approach for Tuberculosis Diagnosis by C. R. Dhivyaa, K. Nithya, C. Sathiya Kumar, R. Sudhakar

“…In the first approach, TrioFusionNet integrates three Convolutional models known as ResNet-50, InceptionV3, and EfficientB4 to detect deep features. After deep feature set creation, Principal Component Analysis is employed to decrease the feature dimensionality. …”
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Mathematical model of the process for diagnosing defects in aircraft structure elements made of composite materials by I. A. Davydov

“…The scientific article is the result of a study aimed at creating a mathematical model for diagnosing defects in aircraft structural elements made of composite materials. Its feature is an innovative approach to assessing the probability of defects and their characteristics, based on the analysis of the material properties and technical parameters of the structure. …”
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MSFragger-DDA+ enhances peptide identification sensitivity with full isolation window search by Fengchao Yu, Yamei Deng, Alexey I. Nesvizhskii

“…Utilizing MSFragger’s fragment ion indexing algorithm, MSFragger-DDA+ performs a comprehensive search within the full isolation window for each tandem mass spectrum, followed by robust feature detection, filtering, and rescoring procedures to refine search results. …”
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