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8th International Conference on Algorithms, Computing and Systems, ICACS 2024

作者： Wang, Zhuo Li, Zhixiong Tu, Jinxing Huang, Jianqiang Department of Computer Technology and Applications Qinghai University Xining China Intelligent Computing and Application Laboratory of Qinghai Province Qinghai University Xining China

ISBN: (纸本)9798400718304

Triangle counting is a critical problem in graph theory and network analysis, with widespread applications in social network analysis, network science, and bioinformatics. Dynamic graphs are extensively utilized in various domains and are prevalent in real-world scenarios. Over time, dynamic graphs necessitate frequent updates, where vertices and edges are inserted, deleted, or modified. However, existing dynamic graph analysis methods often focus solely on the data structure of dynamic graphs, neglecting the algorithms for dynamic graph processing. Current triangle counting methods are typically designed for static graphs and are not suited for continuously updated dynamic graphs. In this paper, we propose an incremental triangle counting algorithm tailored for dynamic graphs, which only requires recomputing the affected subgraph caused by updated edges, rather than the entire graph. Consequently, in comparison to the previous algorithm, our algorithm is capable of attaining a maximum speedup of . © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Graph algorithms

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Advanced Attendance Management System with Independent Yet Unified Face Recognition Pipeline 3rd

Advanced Attendance Management System with Independent Yet U...

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3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Shantilal, Sankhala Bhavik Singh, Amaresh Chandra Gupta, Nidhi Research and Development Isnartech PVT. LTD. Saharanpur India Department of Computer Applications National Institute of Technology Kurukshetra Kurukshetra India

ISBN: (纸本)9789819743582

An attendance management system allows us to track the attendance of students, teachers, employees, and other staff. It assists us in saving time and effort. Digital attendance systems have been used by some schools and companies in the past, but their accuracy and chances of locating an unidentified face aren’t all that significant. This attendance system is particularly effective for preventing errors while doing manual data entry and it comprises three main stages named Face Detection, Recognition, and Verification. Behavior analysis is combined with the attendance system. Although face detection and recognition have several applications in security, biometrics, and medicine, their best implementation in companies or schools is still in the early stages of research. The proposed approach is the most efficient alternative after comparing its accuracy and computational cost to those of preceding algorithms. This paper describes how an attendance management system integrates various facial recognition algorithms and object recognition techniques. The proposed model’s experimental results, which integrate detection, two-stage recognition, and analysis, are likely to be effective in modern schools and businesses. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Attendance management system Behavior analysis Face detection Face recognition Face recognition pipeline FaceNet model

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Forensic handwriting analysis: a hybrid classification framework for writer identification in Devanagari script

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Multimedia Tools and applications 2025年 1-12页

作者： Sethi, Monika Kumar, Munish Jindal, M.K. Department of Computer Science and Application Panjab University Chandigarh India P.G. Department of Information Technology Goswami Ganesh Dutta Sanatan Dharma College Chandigarh India Department of Computational Sciences Maharaja Ranjit Singh Punjab Technical University Punjab Bathinda India Department of Computer Science and Applications Panjab University Regional Centre Muktsar India

In a period marked by swift technological progress, the task of identifying writers through handwriting holds significant importance across various domains, including forensic investigation, plagiarism detection, and historical research. The importance of this endeavor has been further magnified by the widespread influence of digitalization. This study employs a hybrid approach that integrates cutting-edge deep learning techniques, specifically Convolutional Neural Networks (CNNs), with traditional machine learning methodologies, such as Support Vector Machines (SVM) and Random Forest (RF). Deep learning techniques are particularly valuable for analyzing extensive handwriting samples because they can automatically discern complex features and patterns from large datasets. In this research, we synthesize predictions from deep learning models and employ machine learning classification for writer identification based on handwriting analysis. Central to our study is a comprehensive dataset of Devanagari characters, collected from 220 unique writers. This dataset has been thoughtfully curated to validate the methodology we have employed. Our hybrid classification approach, combining CNN with SVM and CNN with RF, achieves remarkable accuracy rates of 91.49% and 88.78%, respectively—significantly surpassing the performance of standalone SVM and RF models, which achieved accuracies of 59.52% and 59%, respectively. Through a series of comprehensive experiments, we demonstrate the enhanced effectiveness and precision of our proposed methodology in the domain of Devanagari script writer identification. The findings of this study not only underscore the potential of our methodology but also represent a substantial advancement in the fields of pattern recognition and machine learning technology. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Enhancing the Compressed Video Quality through a Neural Network Approach 4

Enhancing the Compressed Video Quality through a Neural Netw...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Panneerselvam, Karthick Rajalakshmi, K. Sornalatha, P. Kpr Institute of Engineering and Technology Department of Computer Science & Engineering Tamil Nadu Uthupalayam641407 India Cmr Institute of Technology Department of Mca Karnataka Bengaluru560037 India Alagappa University Department of Computer Applications Tamil Nadu Karaikudi630003 India

ISBN: (纸本)9798331523923

Video compression mainly focuses on reducing the file size of videos with little loss of quality, but most video compression algorithms on the market do not focus on clarity. Data compression is increasingly being considered as a necessary issue in larger engineering communities. Data compression using neural networks has been the main topic of some related research. The main objective of this study would be to apply the video in an amazing way with the help of an Enhanced Generative Adversarial Network (EGAN). EGAN was better to manage and produced better consistent results with fewer artifacts than other Neural Networks (NN). Deep neural networks (DNNs), recurrent neural networks (RNNs), generational adversarial networks (GANs), and various autoencoder (AE) variants have all been used for various compression methods. It would be a potential option for incredible movies. Based on significantly reduced sample input films, the proposed neural network was able to maintain high-frequency information and generate pleasurable sensory videos. A Residual Dense Block (RDB) is the sort of block utilized in the design. In comparison to previous state-of-the-art approaches, the comprehensive Mean-Opinion-Score (MOS) test, and the Video Multi-Method Assessment Fusion (VMMAF) test, reveal extremely substantial increases in terms of human experiential accuracy. The researchers are also proposing a new data pre-processing method based on the Deep Recurrent Neural Network (DRNN) that has been combined with two leading-edge coding standards, and the Adaptable Video Coding (AVC). The results show consistent encoding benefits across all sequences examined at multiple spatial frequencies, with an average bit rate savings of 4.0 percent relative to AVC. © 2025 IEEE.

关键词： Image coding

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Hyperspectral Images for Crop Prediction using Machine and Deep Learning Models 3

Hyperspectral Images for Crop Prediction using Machine and D...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Mahalakshmi, R. Kumutha, K. Sakthivanitha, M. Lakshmi, R. Bagavathi Sirajudeen, Mohamed Padmanabhan, Sankar Vels Institute of Science Technology & Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Computer Application Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Information Technology Tamil Nadu Chennai India Nilgiri College of Arts and Science Pg and Research Department of Computer Science Tami Nadu The Nilgiris India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

Crop classification is essential for local and national governments to make informed agricultural decisions. Remote sensing technology has made it possible to employ high-resolution hyperspectral images (HSIs) for land cover classification for decades. Machine Learning (ML) and Deep Learning (DL) are becoming more popular for HSI categorization due to advances in processing capacity and technology. HSIs can accurately identify crop types due to their narrow and continuous spectral band reflection. This study compares traditional machine learning (ML) models like Support Vector Machines (SVM) and Random Forest (RF) to deep learning (DL) models like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for hyperspectral image classification (HSI). The HSI categorization was evaluated using performance criteria such as accuracy, precision, recall, and the F1-Score. CNN achieved the highest accuracy of 96%, demonstrating that it is the most reliable solution for complex HSI classification tasks because it successfully captures spatial and spectral data. © 2025 IEEE.

关键词： Classification Deep Learning(DL) Hyperspectral Images(HSI) Machine Learning(ML)

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Evaluation and Enhancement of Standard Classifier Performance by Resolving Class Imbalance Issue Using Smote-Variants Over Multiple Medical Datasets

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SN computer Science 2025年第3期6卷 1-30页

作者： Kumar, Vinod Kumar, Ravi Kant Singh, Sunil Kumar School of Computer Applications and Information Technology Galgotias University Uttar Pradesh Greater Noida India Department of Computer Science and Engineering SRM University AP Andhra Pradesh Guntur India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India karnataka Bengaluru India

In the era of machine learning we are solving the classification problems by training the labeled classes. But sometimes due to insufficient data in some of the training classes, the system training is inadequate for these minority classes. In this case the output for the classes obtained from the less amount of trained data are miserably inappropriate and biased towards the classes having more data. This problem is known as a class imbalance problem. In such cases, standard classifiers tend to be overpowered by the expansive classes and disregard the little ones. As a result, the performance of machine learning and the deep learning algorithms are also reducing and sometimes highly unacceptable too, mainly if it is related to crucial data like medical and health related. Though various researchers provided some methods to solve this problem but mostly they are problem specific and suitable with the specific classifier only. To find a generalized and effective solution to this problem, we have applied various smote variants for solving the imbalanced factors in dataset and finally improved the performance of the various machine learning and deep learning algorithms. We have experimented and analyzed the effects of SMOTE variants on various machine learning techniques over six standard medical datasets. We have found that SMOTE variants are very effective, and they improve the standard performance measures (Accuracy, Precision, Recall and F1-Score). Additionally, based on our research, it is feasible to determine which smote variation works best with machine learning methods and datasets. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Class imbalanced Classifiers Machine learning Medical datasets Smote variants

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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CleverFox: Integrating Visual Mnemonics with AI for Enhanced Language Learning 31st

CleverFox: Integrating Visual Mnemonics with AI for Enhanced...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Chiang, Yung-Chu Tang, Zi-Xian Luo, Yi-Ching Chang, Jason S. Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Technology National Taiwan University of Science and Technology Taipei Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan

ISBN: (纸本)9789819620739

We introduce a method for generating explanatory or mnemonic illustrations of words for a given reader to enhance vocabulary learning. Our method involves generating textual mnemonic and explanatory feedback and providing accompanying illustrations for target words. We present a prototype multimedia English learning platform, CleverFox, that integrates these reference and visual aids into language learning, reduces cognitive load, increases learner engagement, and ultimately leads to more effective learning. Preliminary evaluation of a set of real English Learners’ articles shows that the method significantly enriches the learning experiences of reading through illustrative images and mnemonic tips for memorizing difficult words. CleverFox platform is available at http://***:8080/ and our demonstration video is available in the link: https://***/3ji062k7O_I. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Smart Intelligence-Driven Deep Learning Framework for Reliable Detection and Accurate Classification of Skin Cancer Diseases 3

Smart Intelligence-Driven Deep Learning Framework for Reliab...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Rathish Babu, T.K.S. Chandramohan, S. Be, Hajira Niroshini Infantia, H. Srm Institute of Science and Technology Department of Computer Science Engineering Ramapuram Chennai89 India Sri Chandrasekarendra Saraswathi Viswa Maha Vidyalaya Department of Computer Science Engineering Chennai India Karpaga Vinayaga College of Engineering and Technology Department of Computer Applications Maduranthakam Taluk India St. Joseph's Institute of Technology Department of Information Technology OMR Chennai600119 India

ISBN: (纸本)9798331509675

The interest in skin cancer detection is growing remarkably because of the prevalence of this health condition worldwide. It improves the chances of survival and enhances general treatment outcomes when diagnosis has been done early. Most the traditional methods for skin lesion detection involve visual inspection performed by dermatologists;hence, it is time-consuming and, to some extent, subjective. Therefore, with the rise of deep learning-Artificial Intelligence, auto-detection of skin cancer has gained a foothold in improving diagnostic accuracy, hence treatment outcomes. The presented paper proposes a GAN-based approach for skin cancer detection by deep learning analysis of dermoscopic images, classifying them as malignant and benign. The proposed approach uses the generator and discriminator parts of GAN to obtain an effective discrimination between cancerous and non-cancerous lesions with high precision. The proposed method incorporates the native abilities of GANs for boosting diagnostic precision. GANs consist of two major components: a generator and a discriminator. These two adversaries collaborate with each other in a way to encourage the classification performance of the framework. The generator synthesizes the realistic data representations by augmenting the dataset with various variations of dermoscopic images. This helps not only to compensate for the insufficiency of annotated data but also to make the model generalize more robustly to diverse skin lesion patterns. At the same time, the discriminator learns to distinguish malignant from benign lesions with high accuracy by using the adversarial nature in GAN training. These results clearly indicate the effectiveness of the proposed GAN-based approach over state-of-the-art traditional deep learning models. GAN makes not only the detection system more robust but also reduces its dependency on large annotated datasets. © 2025 IEEE.

关键词： Adversarial machine learning

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Bibliometric Analysis of Digital Twin: Scientometrics & Future Research Trends

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IEEE Internet of Things Journal 2025年

作者： Bhatia, Munish Kumar, Vijay National Institute of Technology Department of Computer Applications Kurukshetra136119 India

A Digital Twin (DT) is a precise digital replica of an object or the physical world in which real-time data is input to model, analyze, forecast, and improve key performance indicators. To explore the complex landscape of DT research, the current scientometric review focuses on four pivotal components: Architecture, Application, Middleware, and Tools. For the study, the Citespace visualization tool is employed and articles are sourced from the Scopus database, focusing on publications between 2010 and 2024. The initial search yielded 11,247 publications, which, after applying filters dataset was reduced to a final dataset of 2,214 relevant articles. The view in the current study is centered on the following: keyword co-occurrence network (KCN), country analysis, document co-citation network (DCN), and burst reference analysis. The goal of the current study is to explore the four important components: architecture, applications, middleware, and tools. Limited previous study has explored such components. The current work presents a statistical summary of the development of research on DTs, examining existing patterns and highlighting important scientific discoveries. Finding the most popular trends and patterns of DT technology around the world will assist future researchers gain important knowledge about new sectors and indicate areas that could benefit from more research and development. © 2025 IEEE.

关键词： Middleware

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