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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Gayathri, M. Kavitha, V. SRM Institute of Science and Technology Department of Computer Science and Engineering Kattankulathur Chennai India SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India

ISBN: (纸本)9798350365269

Air pollution is a significant environmental hazard in modern society because of its serious impact on human health and the environment. In point of fact, there has been a substantial rise in the levels of pollution in the environment over the course of the past few years. Data analyst is commonly encountering the issue of data missing. It is crucial to handle them efficiently to get more accurate and dependable data analysis findings. This study aims to improve understanding of data missing mechanisms, data imputation techniques, and examine the efficacy of frequently utilized data imputation techniques on quantitative datasets. Analysts and data scientists frequently face the challenge of missing data in their investigations. Therefore, it is essential to handle them in an efficient manner so as to gain improved and more trustworthy results through the analysis of the data. The objective of this research is to enhance comprehension of data missing processes and data imputed methods, while also evaluating the effectiveness of data imputed approaches often employed in quantitative datasets. A complete comparison of 5 different data imputation methods is presented in this article. These approaches include mean imputation, median imputation, mode imputation, kNN imputation and Linear Regression. For the purpose of analysing and contrasting the efficacy of the various data imputation approaches, we have utilized five distinct numeric datasets that were gathered from several air pollution repositories. Standardized Root Mean Square Error (RMSE) is the technique for assessing the efficacy of data imputation techniques. According on the findings of the analysis, the kNN imputation approach offers superior performance than the other methods. There is no correlation between the length of the dataset or the number of values that are missing in the dataset and the accuracy of the data imputed approach. © 2024 IEEE.

关键词： Air pollution

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Crafting Customer Segmentation Strategies for Advanced Recommendation System 3

Crafting Customer Segmentation Strategies for Advanced Recom...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Seethalakshmi, K. Bhagyalakshmi, A. Anitha, G. Sheela, E. Sandeep, Varada Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Tami Nadu Chennai India S.A Engineering College Department of Computer Science and Business Systems Tamil Nadu Chennai India St Peter's Institute of Higher Education and Research Department of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350369083

With the static nature of recommender systems, it is crucial to take into account the intricate details of customer behaviour to better improve operating efficiencies. This paper is about original methods to augment recommendation frameworks by employing spectral clustering methods, to be more specific. Limited segmentation techniques now cannot catch up with the growing complexity and variety of customer conduct. To address this need, we are proposing a new way in which the spectral clustering technique, a supervised learning technique, can be applied. Revealing in-depth relationships inside customer data will enable an upgrade in the segmentation approach beyond the existing norms. Spectral clustering stands out as a really amazing tool for accomplishing extraordinarily precise customer segments, which help understand the tastes of users on a deeper level. Through discovering separate customer groups, we are aiming for a meaningful contribution in the field of recommendation systems. This project combines the effectiveness of spectral clustering with the convenience of a recommendation system that aims to provide a holistic, meaningful, and user-oriented approach to enhancing recommendation systems in the area of personalised recommendation. Our aim is to create recommendation systems that are not only attuned to the heterogeneity of customer preferences but also adopt new trends in user experience design beforehand. The segmentation is done with K-Means along with the proposed algorithm and the clusters are hence compared and Implementation results shows that spectral clustering is much more efficient than the existing K-Means algorithm © 2024 IEEE.

关键词： Sales

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Implications of Solution Patterns on Adversarial Robustness

Implications of Solution Patterns on Adversarial Robustness

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Liang, Hengyue Liang, Buyun Sun, Ju Cui, Ying Mitchell, Tim University of Minnesota Department of Electrical & Computer Engineering United States University of Minnesota Department of Computer Science & Engineering United States University of Minnesota Department of Industrial & Systems Engineering United States City University of New York Queens College Department of Computer Science United States

ISBN: (纸本)9798350302493

Empirical robustness evaluation (RE) of deep learning models against adversarial perturbations involves solving non-trivial constrained optimization problems. Recent work has shown that these RE problems can be reliably solved by a general-purpose constrained-optimization solver, PyGRANSO with Constraint-Folding (PWCF). In this paper, we take advantage of PWCF and other existing numerical RE algorithms to explore distinct solution patterns in solving RE problems with various combinations of losses, perturbation models, and optimization algorithms. We then provide extensive discussions on the implications of these patterns on current robustness evaluation and adversarial training. A comprehensive version of this work can be found in [19]. © 2023 IEEE.

关键词： Constrained optimization

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Improving Breast Cancer Diagnosis through Multi-Class Segmentation using Attention UNet Model 2

Improving Breast Cancer Diagnosis through Multi-Class Segmen...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Ghantasala, G.S. Pradeep Unhelkar, Bhuvan Chakrabarti, Prasun Vidyullatha, Pellakuri Pyla, Meghana Department of Information Systems and Decision Sciences Muma College of Business University of South Florida South Florida United States Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Department of Computer Science and Engineering Sir Padampat Singhania University Rajasthan Udaipur India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India

ISBN: (纸本)9798350383867

One of the prominent causes of death for women universally is breast cancer. The number of early deaths is reduced with early detection. Using an ultrasound scan, the data examine the ultrasound images of breast cancer. Three categories, including normal, benign, and malignant images, comprise the Breast Ultrasound Dataset. Incorporating machine learning with breast ultrasound images might yield significant outcomes for the identification, classification, and segmentation of breast cancer. Through a mediocre image size of 500 500 pixels, the collection comprises 750 images. Attention U-Net/UNet is the best-known model for Multi-Class Segmentation to create an attention U-Net Model. The Model will consist of a Decoder Block Encoder Block and an Attention Gate. The attention gate layers have neurons with sizes of 256, 128, and 64, which typically correspond to the number of channels in the feature maps. The output layer in the U-Net model characteristically consists of a single convolutional layer through a kernel size of 1 1. The activation function used in the output layer was sigmoid;whichever is collective for binary classification tasks like image segmentation. The sigmoid activation function outputs values between 0 and 1, representing the possibility of each pixel fitting to the target class. The loss function stated is "binary_crossentropy," which is appropriate in binary classification tasks. The optimizer specified has Adam, which is well-liked for neural network training because of its adaptable learning rate characteristics. In contrast to conventional gradient descent techniques, the Adam optimizer adjusts the learning rate during training, which might result in faster convergence and improved results with 98% accuracy at epoch 17 with a validation loss of 0.05. ©2024 IEEE.

关键词： Image segmentation

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Data Mining with Comprehensive Oppositional Based Learning for Rainfall Prediction

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computers, Materials & Continua 2023年第2期74卷 2725-2738页

作者： Mohammad Alamgeer Amal Al-Rasheed Ahmad Alhindi Manar Ahmed Hamza Abdelwahed Motwakel Mohamed I.Eldesouki Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Computing and Information SystemMecca24382Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia

Data mining process involves a number of steps fromdata collection to visualization to identify useful data from massive data *** same time,the recent advances of machine learning(ML)and deep learning(DL)models can be utilized for effectual rainfall *** this motivation,this article develops a novel comprehensive oppositionalmoth flame optimization with deep learning for rainfall prediction(COMFO-DLRP)*** proposed CMFO-DLRP model mainly intends to predict the rainfall and thereby determine the environmental ***,data pre-processing and correlation matrix(CM)based feature selection processes are carried *** addition,deep belief network(DBN)model is applied for the effective prediction of rainfall ***,COMFO algorithm was derived by integrating the concepts of comprehensive oppositional based learning(COBL)with traditional MFO ***,the COMFO algorithm is employed for the optimal hyperparameter selection of the DBN *** demonstrating the improved outcomes of the COMFO-DLRP approach,a sequence of simulations were carried out and the outcomes are assessed under distinct *** simulation outcome highlighted the enhanced outcomes of the COMFO-DLRP method on the other techniques.

关键词： Data mining rainfall prediction deep learning correlation matrix hyperparameter tuning metaheuristics

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Advancements in Human Activity Recognition: A comprehensive Review of Smartphone and Wearable Sensor Integration 7

Advancements in Human Activity Recognition: A comprehensive ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Dhohada, Cheruku Sumalatha, Konatham Vellore Institute of Technology Computer Science And Engineering Vellore India Vellore Institute of Technology Department of Database Systems Vellore India

ISBN: (纸本)9798350359299

In recent years, deep learning and machine learning methods have been extensively employed in almost every field due to their capability of data processing and analysis. These are the subdomains of Artificial intelligence that gather information in different formats and from different sources. Human Activity Recognition using smartphones and wearable sensors has evolved into a dynamic field with numerous applications and challenges. Ongoing research efforts continue to refine methodologies, address challenges, and expand the scope of Human activity recognition, making it an exciting area with significant potential. Nowadays everyone is using smartphones, so it is easy to find their applications in health monitoring, fitness tracking, context-aware computing, and security. The ability to automatically detect activities such as walking, running, and sleeping opens avenues for personalized services and interventions that help in people's daily lives. While traditional machine learning and deep learning models offer valuable insights individually, ensemble learning presents an opportunity to further enhance accuracy and robustness. This research work aims to give a concise overview of the various deep learning and machine learning approaches, their convergence, and an analysis of the applications of these algorithms in the field of smartphone sensor-based human activity recognition. © 2024 IEEE.

关键词： Learning systems

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Modeling and Clustering of Parabolic Granular Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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Securing 3D Point and Mesh Fog Data Using Novel Chaotic Cat Map

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computers, Materials & Continua 2023年第3期74卷 6703-6717页

作者： K.Priyadarsini Arun Kumar Sivaraman Abdul Quadir Md Areej Malibari Department of Data Science and Business Systems School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChennai603203India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

With the rapid evolution of Internet technology,fog computing has taken a major role in managing large amounts of *** major concerns in this domain are security and ***,attaining a reliable level of confidentiality in the fog computing environment is a pivotal *** different types of data stored in the fog,the 3D point and mesh fog data are increasingly popular in recent days,due to the growth of 3D modelling and 3D printing ***,in this research,we propose a novel scheme for preserving the privacy of 3D point and mesh fog *** Cat mapbased data encryption is a recently trending research area due to its unique properties like pseudo-randomness,deterministic nature,sensitivity to initial conditions,ergodicity,*** boost encryption efficiency significantly,in this work,we propose a novel Chaotic Cat *** sequence generated by this map is used to transform the coordinates of the fog *** improved range of the proposed map is depicted using bifurcation *** quality of the proposed Chaotic Cat map is also analyzed using metrics like Lyapunov exponent and approximate *** also demonstrate the performance of the proposed encryption framework using attacks like brute-force attack and statistical *** experimental results clearly depict that the proposed framework produces the best results compared to the previous works in the literature.

关键词： Chaotic cat map fog computing encryption 3D point fog 3D mesh

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An Improved Ant Lion Optimization Algorithm Based Convolutional Neural Network for Big data Classification

An Improved Ant Lion Optimization Algorithm Based Convolutio...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Umaeswari, P. Vellingiri, J. Kumar, Gotte Ranjith Devendhiran, A. Naik, Ashitha V R.M.K. Engineering College Department of Computer Science and Business Systems Kavaraipettai India Institute of Technology School of Computer Science Engineering and Information Systems Vellore India Sr University School of Computer Science & Artificial Intelligence Warangal India Vivekanandha College of Engineering for Women Department of Computer Science and Technology Namakkal India Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India

ISBN: (纸本)9798350313413

The continuous advancement of remote sensor technology is contributing to a daily surge in data production, necessitating improvements in the accuracy of big data classification. This research proposes a unique feature selection method named as Improved Ant Lion Optimization (IALO) algorithm that integrates the behavior of animal migration with Ant Lion Optimization (ALO) algorithm. The proposed feature selection method enhances the performance of the ALO algorithm. The datasets utilized for the research are iris, wine, Cleveland, and Switzerland datasets. Next, the map reduces framework is utilized and then feature extraction is processed by Linear Discriminant Analysis (LDA). The feature selection is performed by the proposed IALO algorithm and the selected features are given to the Convolutional Neural Network (CNN) classifier for the big data classification. The performance of the proposed method is evaluated by parameters such as accuracy, computation time, specificity, and sensitivity. The proposed method attained a high accuracy of 98.05% with less computation time of 4.8 secs in the wine dataset which is comparatively higher than other existing methods such as Grey Wolf Optimization based CNN (GWO-CNN), Particle Swarm Optimization based CNN (PSO-CNN), Cuckoo Search Optimization based CNN (CSO-CNN), Ant Lion Optimization based CNN (ALO-CNN). © 2023 IEEE.

关键词： Ant Lion Optimization Big data Convolutional Neural Network MapReduce and Migration behavior

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Optimizing Secure Multi-User ISAC systems With STAR-RIS: A Deep Reinforcement Learning Approach for 6G Networks

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IEEE Access 2025年 13卷 31472-31484页

作者： Kamal, Mian Muhammad Zain Ul Abideen, Syed Al-Khasawneh, M.A. Alabrah, Amerah Sohail Ahmed Larik, Raja Irfan Marwat, Muhammad Southeast University School of Electronic Science and Engineering Jiangning Jiangsu Nanjing211189 China Qingdao University College of Computer Science and Technology Qingdao266071 China Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19111 Jordan Skyline University College School of Computing University City Sharjah Sharjah United Arab Emirates King Saud University College of Computer and Information Science Department of Information Systems Riyadh11543 Saudi Arabia Ilma University Department of Computer Science Sindh Karachi75190 Pakistan University of Science and Technology Bannu Department of Software Engineering Bannu28100 Pakistan

The rapid evolution of wireless communication technologies and the increasing demand for multi-functional systems have led to the emergence of integrated sensing and communication (ISAC) as a key enabler for future 6G networks. Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have recently garnered significant attention for their ability to enhance signal coverage and improve system efficiency. This paper investigates a STAR-RIS-assisted ISAC system designed to secure communication for multiple legitimate users (LUs) while safeguarding against multiple eavesdroppers (Eves). By jointly optimizing the base station (BS) transmit beamforming, STAR-RIS transmission and reflection coefficients, and receive filters, the proposed framework aims to maximize the long-term average secrecy rate for all LUs. Constraints are imposed to ensure minimum echo signal-to-noise ratios (SNRs) for sensing and meet the achievable rate requirements of LUs. To address the inherent complexity of this non-convex problem, two deep reinforcement learning (DRL) algorithms are proposed. Numerical results demonstrate that the proposed system achieves significant improvements in secrecy rate compared to conventional RIS setups. This work provides a scalable and efficient approach for secure multi-user ISAC systems, making it highly relevant for future 6G networks, smart cities, and IoT applications. © 2025 The Authors.

关键词： Deep reinforcement learning

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