End-of-Line Quality Control Based on Mel-Frequency Spectrogram Analysis and Deep Learning by Jernej Mlinarič, Boštjan Pregelj, Gregor Dolanc

“…The proposed approach addresses these issues by learning discriminative patterns directly from raw sensor data and automating the classification process. …”
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Enhanced Diagnosis of Thyroid Diseases Through Advanced Machine Learning Methodologies by Osasere Oture, Muhammad Zahid Iqbal, Xining (Ning) Wang

“…Resampling techniques, such as SMOTE oversampling and Random undersampling, are utilised to correct the issue of class imbalance in the dataset. Finally, a web-based application is developed utilising the most effective model, GBC, which facilitates easy classification of thyroid diseases. …”
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A Framework for Constructing Large-Scale Dynamic Datasets for Water Conservancy Image Recognition Using Multi-Role Collaboration and Intelligent Annotation by Xueying Song, Xiaofeng Wang, Ganggang Zuo, Jiancang Xie

“…This paper proposes a method that integrates multi-role collaboration with automated annotation to address these issues. The framework introduces two new roles, data augmentation specialists and automatic annotation operators, to establish a closed-loop process that includes dynamic classification adjustment, data augmentation, and intelligent annotation. …”
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Obesity prevalence rate, depending on various criteria in the average age population of urban and rural residents of the Siberian region by D. P. Tsygankova, K. E. Krivoshapova, S. A. Maksimov, E. V. Indukaeva, E. B. Shapovalova, G. V. Artamonova, O. L. Barbarash

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Accurate earthquake and mining tremor identification via a CEEMDAN-LSTM framework by Chao Wang, Xiufeng Zhang, Weiming Gao, Fangfang Wang, Jianqi Lu, Jianqi Lu, Zhaoyang Yan, Zhaoyang Yan

“…The results demonstrate that CEEMDAN-LSTM effectively addresses the noise and short-period surface wave aliasing issues encountered within the modes, yielding significantly enhanced classification accuracy. …”
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Survey of transmission scheduling methods in wireless sensor networks by Xiao-ling ZHANG, Wei LIANG, Hai-bin YU, Xi-sheng FENG

“…According to the features of wireless sensor networks,the definition and general constraints for transmission scheduling were given,and design principles and classification methods were summarized.Meanwhile,the fundamental mechanism of each recent representative transmission scheduling algorithms was analyzed in detail.The characteristics,performance,and application areas of various transmission scheduling algorithms were adequately compared.Finally,the status of current research development was concluded and the challenging and open research issues were pointed out.…”
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The Problem of Defining the Objects of Forensic Software Analysis and Its Solution via R.S. Belkin's Reflection Theory by A. I. Semikalenova

“…The article deals with the issues of identifying the objects of forensic software analysis for use in court proceedings. …”
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Survey of transmission scheduling methods in wireless sensor networks by Xiao-ling ZHANG, Wei LIANG, Hai-bin YU, Xi-sheng FENG

“…According to the features of wireless sensor networks,the definition and general constraints for transmission scheduling were given,and design principles and classification methods were summarized.Meanwhile,the fundamental mechanism of each recent representative transmission scheduling algorithms was analyzed in detail.The characteristics,performance,and application areas of various transmission scheduling algorithms were adequately compared.Finally,the status of current research development was concluded and the challenging and open research issues were pointed out.…”
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CUSTOMER BEHAVIOR TRANSFORMATION: HISTORY AND TODAY by N. V. Barinova

“…The article points out that as informational-communications technologies develop, information begins to play a more and more important role in the process of customer decision-making. Apart from that issues of market segmentation were considered, classification of key groups of today’s customers was put forward and groups of customers using ICT in the process of decision-making were identified.…”
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Prevalence of Deviated Nasal Septum and Concha Bullosa in Northeast Indian Population: A Retrospective CBCT Study by Saumya Verma, Sri Krishna Koppula, Srishti, Kumari Shalini, Shubhajit Hazra, Aprajita

“…Aim: Oral physicians frequently encounter patients having headache, mouth breathing issues and rarely cases of anosmia too. Deviated Nasal septum (DNS) has been known to be a causative factor for the above mentioned issues. …”
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Harnessing self-supervised learning to boost malicious traffic detection with enhanced attention by SUN Jianwen, ZHANG Bin, LI Hongyu, CHANG Heyu

“…A packet sequence feature fusion strategy based on channel attention was introduced, and a multi-dimensional attention mechanism, along with labeled data, were used to fine-tune the model’s weights to adapt to traffic identification and classification tasks. Before making classification decisions, the integration ability of high-weight features was enhanced to further improve the model's detection precision. …”
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Research on land desertification in Ejin Banner from 1991 to 2021 based on Albedo-MSAVI feature space model by Xiaofang Jiang

“…Desertification is a serious issue of land degradation in the arid areas of northwest China. …”
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Improving the Accuracy of Indoor Object Localization in the Internet of Things Using Recurrent Neural Networks (LSTM) in Challenging Environments by Hussein Aqeel Hussein, Mehdi Hamidkhani, Nasseer K. Bachache, Mohammadreza Soltan Aghaei

“…The approach uses nearly all the standard techniques to find best signal processing concepts and classification methods, successfully merging them together. …”
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Leveraging stacking machine learning models and optimization for improved cyberattack detection by Neha Pramanick, Jimson Mathew, Shitharth Selvarajan, Mayank Agarwal

“…There are high hurdles in managing high-dimensional network traffic since current methodologies are limited in dealing with imbalanced data issues of minority classes versus the majority and high false positive rate in classification accuracy. …”
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Introducing an ensemble method for the early detection of Alzheimer's disease through the analysis of PET scan images by Arezoo Borji, Taha-Hossein Hejazi, Abbas Seifi

“…When a person is in MCI, they still have significant cognitive issues, especially with memory, but they are still able to perform many daily tasks on their own. …”
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A multi-class hybrid variational autoencoder and vision transformer model for enhanced plant disease identification by Folasade Olubusola Isinkaye, Michael Olusoji Olusanya, Ayobami Andronicus Akinyelu

“…Their high computational and memory demands also limit scalability for large datasets. To overcome these issues, we propose a hybrid model with the potential to combine Variational Autoencoders and Vision Transformers for enhanced accuracy and robustness of plant disease classification. …”
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Deep Learning Model for Analyzing EEG Signal Analysis by Varun Gupta, Vivek Kumar, Prince, Saurabh Singh, Young-Seok Lee, In-Ho Ra

“…Furthermore, feature selection optimizes classification accuracy by refining the fitness function of bats in the classification layer. …”
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Scale-Consistent and Temporally Ensembled Unsupervised Domain Adaptation for Object Detection by Lunfeng Guo, Yizhe Zhang, Jiayin Liu, Huajie Liu, Yunwang Li

“…To ensure high-quality pseudo-labels, TEPLS combines temporal localization stability with classification confidence, mitigating the impact of noisy predictions and improving both classification and localization accuracy. …”
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An OGFA+CNN Approach for Multi-Level Disease Identification in Fundus Images by Preethi Kulkarni, K. Srinivasa Reddy

“…Fundus disease classification is critical for the early detection of conditions such as diabetic retinopathy and glaucoma, which can lead to blindness if left untreated. …”
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Hybrid IoT-CAD system: optimized feature selection based gated recurrent residual deep learning for cyber attack detection in IoT networks by Susheela Vishnoi, Sunil Kumar, Saikat Samanta

“…In this work, a novel approach is introduced that combines deep learning (DL) and machine learning (ML) to improve cyber-attack detection in IoT networks, which solves cybersecurity issues. The proposed IoT system includes four steps: pre-processing, augmentation, feature selection, and classification. …”
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