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27th International Conference on Pattern Recognition, ICPR 2024

作者： Rather, Sajad Ahmad Roy, Partha Pratim Das, Sujit Department of Computer Science and Engineering Indian Institute of Technology Roorkee247667 India Department of Computer Science and Engineering National Institute of Technology Telangana506004 India

ISBN: (纸本)9783031781032

Multilevel thresholding plays a crucial role in image processing, with extensive applications in object detection, machine vision, medical imaging, and traffic control systems. It entails the partitioning of an image into distinct regions based on optimal pixel values. However, as the number of threshold levels increases, so does the computational cost for segmentation. To address this challenge, a novel method is proposed namely Chaos theory based Gravitational Search Algorithm (CGSA) for multilevel thresholding. CGSA combines the standard Gravitational Search Algorithm (GSA) for exploration with chaotic maps for exploitation of the complex pixel problem space. In this study, Kapur’s entropy method is utilized to segment sample images into various partitions based on optimal pixel values. The effectiveness of CGSA in real-world scenarios is evaluated using COVID-19 chest CT scan imaging datasets from Kaggle database. The quality, symmetry, and consistency of the segmented output are assessed using metrics like Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Feature Similarity Index Measure (FSIM). Qualitative analysis includes convergence curves, segmented graphs, colormap images, and box plots. Statistical validation is conducted using the signed Wilcoxon rank sum test. Additionally, a comparison is made between CGSA’s performance and that of eight state-of-the-art heuristic algorithms. The findings demonstrate the superior performance of CGSA, evidenced by its reduced computational time and enhanced image quality metrics values. Specifically, CGSA achieved SSIM of 0.81, FSIM of 0.82, and PSNR of 24.27, surpassing the performance of other competitive algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Signal to noise ratio

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A novel dominating set and centrality based graph convolutional network for node classification

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Multimedia Tools and Applications 2025年第14期84卷 12979-13004页

作者： Garg, Neeraj Garg, Sneha Jadeja, Mahipal Meena, Yogesh Kumar Gopalani, Dinesh Chauhan, Ganpat Singh Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur Rajasthan Jaipur302017 India Department of Computer Science and Engineering Swami Keshvanand Institute of Technology Jaipur Rajasthan Jaipur302017 India Department of Information Technology School of IT Manipal University Jaipur Rajasthan Jaipur302034 India

To learn and analyze graph-structured data, Graph Neural Networks (GNNs) have emerged as a powerful framework over traditional neural networks, which work well on grid-like or sequential structure data. GNNs are particularly designed to record dependencies and relations in large and complicated networks such as social networks, molecular structures, citation networks and recommendation systems. The main idea behind GNN is the message-passing mechanism that allows sharing of information between nodes and edges in a graph network, enabling the model to learn about graph structures and perform well even with limited training data. GNNs have three different architectures: Graph Convolutional Network (GCN), GraphSAGE, Graph Attention Model (GAT) and other hybrid models. In this paper, we have proposed novel GCN models namely dominating Set with Degree Centrality based GCN (DS_DC_GCN) and dominating Set with Betweenness Centrality based GCN (DS_BWC_GCN) model, for node classification task. In the traditional GNN models, training nodes are selected randomly. In our proposed models, we first identify a subset of the nodes with the help of a dominating set (D) of the underlying network. Then we consider final training nodes (say D′) from set D based on the centrality scores (degree or betweenness). We give preference to nodes having high centrality scores for training. To evaluate the performance of the proposed models, we have used three standard citation network datasets - Cora, Citeseer and Pubmed. Our models obtain improved node classification performance as compared to other state-of-the-art-methods. The consideration of high centrality nodes of dominating set in training helps to improve the feature availability in the underlying computational graphs, which in turn improves learning and hence, model performance. These models also have the potential to offer better results for other network tasks, including link prediction and community detection. © The Author(s), under

关键词： Graph neural networks

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Multi-class Classification of Class Imbalanced Skin Lesion Dataset Using a Modified SMOTE-ENN Gabor-Enhanced VGG-19 Architecture

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SN computer science 2025年第3期6卷 1-17页

作者： Sahoo, Madhusmita Priyadarshini Sridhar, Rajeswari Computer Science and Engineering National Institute Technology Tamilnadu Tiruchirappalli 620015 India

The persistent rise in skin cancer cases has resulted in a high mortality rate of the affected patients due to late detection. In this paper, we proposed a sophisticated deep-learning model MSGNet for the automatic multiclass categorization of skin lesions to address the issues in the multiclass dataset. This work is subdivided into three components. First, in the preprocessing layer, a modified version of SMOTE-ENN is proposed, which uses radius neighbor-based grouping of class elements, an improved data augmentation technique for effectively detecting minority classes in a multiclass environment. Second, in the feature extraction layer, we created a modified VGG19 model which contains parallel Gabor-based convolutional layers in the initial layers. The augmented data is then fed to the parallel convolution layers which capture various extract discriminative features from the same input image. In the third step, in the classification layer, the last layer of modified VGG19 is replaced with two more fully connected layers and one softmax layer to classify skin lesion images. We considered the HAM 10000 and ISIC 2019 datasets in our experiment. MSGNet significantly improved the detection in almost all the classes, and the overall accuracy was found to be 89.33% and 90.62% for the HAM 10000 and ISIC 2019 datasets respectively. Also, we observed significant drift in the F1 score of individual classes which demonstrates that our modified approach efficiently distinguishes each class. It has been experimented that MSGNet performed better than some existing works, and offers a creative and workable way to automate and speed up the task of classifying skin lesions while saving effort, time, and human lives. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Class-imbalance handling Gabor convolution Multi-class Parallel convolution layer Skin lesion classification SMOTE-ENN VGG-19

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Evaluation of Hybrid Encryption Method to Secure Healthcare Data 3rd

Evaluation of Hybrid Encryption Method to Secure Healthcare ...

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

作者： Das, Sangjukta Singh, Maheshwari Prasad Namasudra, Suyel Department of Computer Science and Engineering National Institute of Technology Patna Bihar India Department of Computer Science and Engineering National Institute of Technology Agartala Agartala India

ISBN: (纸本)9789819743582

Technological advancements have brought a new era of growth for the healthcare industry. Nowadays, the security of healthcare data and the preservation of user privacy inside smart healthcare systems are being severely compromised by cyber attackers. This work introduces an encryption method that makes use of a hybrid encryption approach by combining both asymmetric and symmetric encryption methods to improve healthcare data security. This scheme solves the key escrow problem and its effectiveness is analyzed through performance evaluations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Triple Stack Deep Variational Autoencoder For Improved Hand Gesture Recognition 15

Triple Stack Deep Variational Autoencoder For Improved Hand ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Bahuguna, Arti Bhaumik, Gopa Govil, Mahesh Chandra National Institute of Technology Sikkim Computer Science and Engineering Sikkim India National Institute of Technology Jamshedpur Computer Science and Engineering Jharkhand India

ISBN: (纸本)9798350370249

This paper proposes a novel approach for hand gesture recognition using a triple-stack deep variational autoencoder. By employing a VAE framework, we facilitate both efficient representation learning and the generation of meaningful latent spaces for gesture recognition and classification. This is an extension of the traditional VAE architecture that incorporates three layers of encoding and decoding with a spatial deep neural network. In a regular VAE, there is typically only one layer for both the encoder and decoder. By stacking three layers in both the encoder and decoder, a triple-stack deep VAE can learn more complex hierarchical representations of the input data. Each layer in the encoder extracts increasingly abstract features from the input, while each layer in the decoder reconstructs the input from these abstract representations. The performance of the proposed model is evaluated in terms of accuracy, precision, recall, and F1-score on six benchmark datasets: ASL Static, Massey University Dataset (MUGD), ASL Digit, NUS-2, Bengali Sign Language (BSL), and Hagrid-14 (HG-14) datasets. The results of the experiment show that the proposed 3S-DVE achieves an accuracy of 76% (MUGD Set 1), 80% (MUGD Set 2), 97% (MUGD Set 3), 96% (MUGD Set 4), 86% (MUGD Set 5), 97% (ASL Digit), 62% (ASL Static), 74% (NUS-II Dataset), 66% (BSL), 76% (MUGD Set 1), 80% (MUGD Set 2), 97% (MUGD Set 3), 96% (MUGD Set 4), 86% (MUGD Set 5), 97% (ASL Digit), 62% (ASL Static), 74% (NUS-II Dataset), 66% (BSL), and 79% (HG-14), respectively, which is better compared to the state-of-the-art methods. © 2024 IEEE.

关键词： Decoding

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A novel ensemble ARIMA-LSTM approach for evaluating COVID-19 cases and future outbreak preparedness

Health Care Science

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Health Care science 2024年第6期3卷 409-425页

作者： Somit Jain Shobhit Agrawal Eshaan Mohapatra Kathiravan Srinivasan School of Computer Science and Engineering Vellore Institute of TechnologyVelloreIndia

Background:The global impact of the highly contagious COVID-19 virus has created unprecedented challenges,significantly impacting public health and economies *** research article conducts a time series analysis of COVID-19 data across various countries,including India,Brazil,Russia,and the United States,with a particular emphasis on total confirmed ***:The proposed approach combines auto-regressive integrated moving average(ARIMA)'s ability to capture linear trends and seasonality with long short-term memory(LSTM)networks,which are designed to learn complex nonlinear dependencies in the *** hybrid approach surpasses both individual models and existing ARIMA-artificial neural network(ANN)hybrids,which often struggle with highly nonlinear time series like COVID-19 *** integrating ARIMA and LSTM,the model aims to achieve superior forecasting accuracy compared to baseline models,including ARIMA,Gated Recurrent Unit(GRU),LSTM,and ***:The hybrid ARIMA-LSTM model outperformed the benchmark models,achieving a mean absolute percentage error(MAPE)score of 2.4%.Among the benchmark models,GRU performed the best with a MAPE score of 2.9%,followed by LSTM with a score of 3.6%.Conclusions:The proposed ARIMA-LSTM hybrid model outperforms ARIMA,GRU,LSTM,Prophet,and the ARIMA-ANN hybrid model when evaluating using metrics like MAPE,symmetric mean absolute percentage error,and median absolute percentage error across all countries *** findings have the potential to significantly improve preparedness and response efforts by public health authorities,allowing for more efficient resource allocation and targeted interventions.

关键词： COVID-19 time series ARIMA LSTM GRU prophet ANN proposed model

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Hybrid millimeter wave heterogeneous networks with spatially correlated user equipment

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Digital Communications and Networks 2024年第4期10卷 904-917页

作者： Arif Ullah Ziaul Haq Abbas Ghulam Abbas Fazal Muhammad Jae-Mo Kang Department of Computer Engineering College of IT ConvergenceChosun UniversityGwangju61452South Korea Faculty of Electrical Engineering GIK Institute of Engineering Sciences and TechnologyTopi23640Pakistan Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23640Pakistan Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Artificial Intelligence Kyungpook National UniversityDaegu41566South Korea

In this paper,we analyze a hybrid Heterogeneous Cellular Network(HCNet)framework by deploying millimeter Wave(mmWave)small cells with coexisting traditional sub-6GHz macro cells to achieve improved coverage and high data *** consider randomly-deployed macro base stations throughout the network whereas mmWave Small Base Stations(SBSs)are deployed in the areas with high User Equipment(UE)*** user centric deployment of mmWave SBSs inevitably incurs correlation between UE and *** a realistic scenario where the UEs are distributed according to Poisson cluster process and directional beamforming with line-of-sight and non-line-of-sight transmissions is adopted for mmWave *** using tools from stochastic geometry,we develop an analytical framework to analyze various performance metrics in the downlink hybrid HCNets under biased received power *** UE clustering we considered Thomas cluster process and derive expressions for the association probability,coverage probability,area spectral efficiency,and energy *** also provide Monte Carlo simulation results to validate the accuracy of the derived ***,we analyze the impact of mmWave operating frequency,antenna gain,small cell biasing,and BSs density to get useful engineering insights into the performance of hybrid mmWave *** results show that network performance is significantly improved by deploying millimeter wave SBS instead of microwave BS in hot spots.

关键词： Downlink cell association Heterogeneous cellular networks Integrated sub-6GHz and mmWave networks Millimeter wave communications Poisson cluster process

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A Novel Deep Learning Approach Toward Efficient and Accurate Recommendation Using Improved Alternating Least Squares in Social Media

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Journal of The Institution of Engineers (India): Series B 2024年第3期105卷 657-675页

作者： Dhawan, Sanjeev Singh, Kulvinder Batra, Amit Choi, Anthony Choi, Ethan Department of Computer Science and Engineering University Institute of Engineering and Technology Kurukshetra University Haryana India Department of Electrical and Computer Engineering Mercer University MaconGA United States

The increasing number of electronic transactions on the Internet has given rise to the design of recommendation systems. The main objective of these systems is to give recommendations to the users about the items (i.e., movies, music, books, etc.) they are likely to purchase in near future. These recommendations are on the foundation of the response provided by the other individuals about these products in the terms of ratings, which are in the form of a five-star rating system. Plenty of algorithms have been proposed in the past for building recommender systems. The most popular of these algorithms comprise content-centered and collaborative filtering recommendation methodologies. Recently, matrix factorization has appeared as a technique in recommender systems. Several algorithms centered on matrix factorization have been suggested in the recent past. In this paper, an improved alternating least squares algorithm has been proposed for building more efficient and accurate recommender systems which improves the performance of matrix factorization. The performance of the model has been evaluated using metrics like root-mean-square-error and execution time. However, in general, the time complexity of both algorithms is O(n^3), where n is the number of users or items. This is because both IALS and ALS involve iteratively solving a system of linear equations, and the time complexity of solving a system of linear equations is O(n^3). However, IALS can achieve significant performance improvements over ALS, particularly for large datasets, due to its incorporation of stochastic gradient descent and parallelization techniques. © The Institution of Engineers (India) 2024.

关键词： Recommender systems

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Message Priority Classification Framework for Autonomous Vehicles in Supply Chain Management 2

Message Priority Classification Framework for Autonomous Veh...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Virani, Mili Kansara, Drashti Pathak, Lakshit Gupta, Rajesh Tanwar, Sudeep Sawhney, Sahil Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Department of Computer Science and Engineering Model Institute of Engineering & Technology J&K India

ISBN: (纸本)9798331530389

In modern supply chain management, efficient communication and timely decision-making are essential to ensuring smooth operations and avoiding descriptions. This is ensured by efficient communication and timely decision-making. Prioritizing essential messages with these systems becomes essential as autonomous technologies become more integrated. This work presents a message priority classification framework formulated to refine communication processes within autonomous vehicles (AVs). This plays a growing role in supply chain logistics. Our framework uses machine learning (ML) models such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), and Logistic Regression (LR) which are implemented to classify message priorities in real-time, assuring efficient and uninterrupted communication. The experimental outcomes show that the RF model outperformed other models and achieved the highest accuracy of 0.9714. Further, the proposed framework enhances the modularity of AV communication systems and supports efficiency in decision making in complex and variable supply chains. This study emphasizes the significance of ML in optimizing communication leading to more dependable and efficient supply chain operations for AVs. © 2025 IEEE.

关键词： Supply chain management

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DSF-GAM: a location inference model in social network Twitter

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International Journal of computers and Applications 2025年第2期47卷 141-161页

作者： Paul, Amit Changder, Narayan Dutta, Animesh Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur India TCG CREST Research and Education in Science and Technology West Bengal Kolkata India

Analyzing the social interactions and texts on Twitter can provide valuable insights into users' behavior, opinions, and even their geographical locations. Location inference of users within a social network finds applications in various domains, such as targeted advertising and public health monitoring. Inference techniques through social interactions, such as co-mentions, remain a favored method for constructing user social networks. Co-mentions, however, introduce a substantial number of noisy relationships that impact the overall network structure and prediction of the location of a user. Furthermore, previous approaches have overlooked the examination of cohesive groups or community structures within an individual's ego network for location prediction. This paper introduces a predictive model called DSF-GAM (DS stand for document similarity, F stands for frequencies and GAM stands for Generalized Additive Model). which redesigns social interactions based on mentions to infer user location on Twitter. DSF-GAM introduces a novel and innovative social graph, termed a user relation graph. DSF-GAM employs a generalized additive model (GAM) for location inference. The fundamental contribution of DSF-GAM lies in the representation of data using a new graph structure with enriched community features and edges. The experimental results on well-known datasets demonstrate that DSF-GAM outperforms other state-of-the-art methods based on both content and network approaches and mixed results in hybrid approaches. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Tweets

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