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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Aishwarya, S. Selvamurugan, C. Parthiban, K.G. Nathan, B. Manoj Prabhakar, J. Mohamed Noordeen, A. Sri Eshwar college of Engineering Department of Computer Science and Engineering Coimbatore India Dhaanish Ahmed Institute of Technology Department of Science and Humanities Coimbatore India Dhaanish Ahmed Institute of Technology Department of Biomedical Engineering Coimbatore India Dhaanish Ahmed Institute of Technology Department of Computer Science and Engineering Coimbatore India Dhaanish Ahmed Institute of Technology Department of Artificial Intelligence & Data Science Coimbatore India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

GenAI has revolutionized the generation of realistic and imaginative data in ways that were previously beyond the capabilities of other machine learning algorithms. This area is rapidly gaining traction, with extensive research currently underway to advance it further. The most recent EY Reimagining Industry Futures Study highlights the generative AI (GenAI) to be a pivotal technology. The surge in GenAI's prominence can largely be attributed to advancements in deep neural networks, hardware technology such as GPUs and increased availability of data. This research study reviews the most popular GenAI models, spanning text, image, and audio modalities. This study provides a comparative analysis of various models, serving as a comprehensive resource for understanding the functionality and efficiency of the top GenAI models available today. © 2024 IEEE.

关键词： Generative adversarial networks

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AI-Driven Zero Trust Architecture: Enhancing Cyber-Security Resilience 8

AI-Driven Zero Trust Architecture: Enhancing Cyber-Security ...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Chokkanathan, K. Karpagavalli, S.M. Priyanka, G. Vanitha, K. Anitha, K. Shenbagavalli, P. Madanapalle Institute of Technology & Science Department of Artificial Intelligence India Erode Sengunthar Engineering College Department of M.Tech Computer Science and Engineering Erode India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Csi College of Engineering Department of Information Technology Coimbatore India Department of Artificial Intelligence and Data Science Rathinam Technical Campus Coimbatore India

ISBN: (纸本)9798331505462

In the ever-changing field of cybersecurity, conventional defenses that rely on boundaries are no longer adequate to safeguard against sophisticated and persistent assaults. The Zero Trust Architecture (ZTA) paradigm, powered by AI, provides a strong answer by removing the concept of inherent trust within a network and demanding ongoing verification for each access request. This article examines the combination of artificial intelligence (AI) with Zero Trust principles to improve the resilience of cyber-security. This study demonstrates the enhanced accuracy, sensitivity, and specificity of AI-driven techniques in comparison to conventional security models. The findings highlight the significance of utilizing AI-driven Zero Trust Architecture (ZTA) to protect contemporary digital infrastructures. It provides robust protection against emerging cyber threats as well as addressing challenges related to data privacy and system integration. The paper also explores potential areas for future study and the practical consequences of implementing AI-driven Zero Trust Architecture (ZTA) in different organizational settings. © 2024 IEEE.

关键词： Adversarial machine learning

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Hybrid Model for Lung Cancer Prognosis: Integrating SVM and 3D CNN on Nodule data 2

Hybrid Model for Lung Cancer Prognosis: Integrating SVM and ...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Carmel Mary Belinda, M.J. Kannan, E. Alex David, S. Kannan, Sriram Ravi Kumar, S. Vathani, Anstey Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Fidelity Investments Data Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala RD Institute of Science and Technology Department of Civil Engineering India

ISBN: (纸本)9798331534950

In this research, a huge dataset of lung cancer images is processed using advanced image processing techniques gathered from different medical establishments. Images will be edited and color profiles retouched from original look into Strawberry corrected one. Key part of our research is to preprocess the images by using Gaussian preprocessing at initial front levels followed with Gabor filtering which improves clarity and focus in x-ray image. Findings also contribute to the field of biomedical science in studies related to lung cancer diagnosis, by proving that advanced image processing techniques are useful. These techniques are essential tools in many medical fields ensuring high quality imaging, which allows early detection and treatment planning. Lung cancer, characterized by rapid uncontrolled proliferation of cells in the lungs with a potential for distant spread either via blood or lymphatic system requires precise diagnostic techniques. After preprocessing, we apply segmentation and enhancement techniques on images to detect the salient features separating normal from affected lung tissues. Extensive experiments are conducted to validate our method compared with other methods using pixel level percentage and mask labels for anomaly detection. This work highlights the importance of a robust imaging approach for health care, especially in biomedical research where various diseases could be detected and treated. © 2024 IEEE.

关键词： Lung cancer

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Uncertainty propagation in the internet of things

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Discover Internet of Things 2024年第1期4卷 1-20页

作者： Pal, Shantanu Khalifa, Sara Miller, Dimity Dedeoglu, Volkan Dorri, Ali Ramachandran, Gowri Moghadam, Peyman Kusy, Brano Jurdak, Raja The School of Information Technology Faculty of Science Engineering and Built Environment Deakin University MelbourneVIC 3125 Australia The School of Information Systems Faculty of Science Queensland University of Technology BrisbaneQLD4000 Australia The School of Electrical Engineering and Robotics Faculty of Engineering Queensland University of Technology BrisbaneQLD4000 Australia The Distributed Sensing Systems Group Data61 CSIRO PullenvaleQLD 4069 Australia The School of Computer Science Faculty of Science Queensland University of Technology BrisbaneQLD 4000 Australia The CSIRO Robotics Data61 CSIRO PullenvaleQLD 4069 Australia

The Internet of Things (IoT) detects context through sensors capturing data from dynamic physical environments, in order to inform automation decisions within cyber physical systems (CPS). Diverse types of uncertainty in the IoT pipeline can propagate within and across nodes, involving complex interactions with security, privacy, and trust that remain largely unexplored. This paper conducts an in-depth analysis of the types of uncertainties in IoT and how they propagate within IoT nodes and networks. We consider adversarial uncertainty in the context of distributed IoT networks to capture perturbations due to malicious actors in the network that can influence IoT security, privacy and trust. We examine the propagation of adversarial uncertainty and well-known uncertainty types, namely aleatoric and epistemic uncertainty, within the five distinct stages of sensing, communication, storage, processing and decision-making in an IoT pipeline, across network layer boundaries, and across nodes within an IoT network. Using this mapping, we analyse the interactions between the uncertainty types and their propagation, and security, privacy, and trust in IoT. Based on this analysis, we discuss guidelines and considerations for mitigating uncertainty in IoT through a smart grid use case study. © The Author(s) 2024.

关键词： Differential privacy

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A Novel Energy Efficient Sensor data Encryption in Fog Based IoT Networks

A Novel Energy Efficient Sensor Data Encryption in Fog Based...

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2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

作者： Manicka Raja, M. Manoj Kumar, S. Kiruba, B. Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Coimbatore India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350319088

Smart devices equipped with embedded systems, including CPUs, sensors, and communication hardware, utilize web connectivity to gather and relay data from their environment, forming a segment of the Internet of Things network. Sensors in an IoT network are resource-constrained devices, but traditional data security techniques use complicated security mechanisms with long processing and reaction times, which reduce the network's overall lifespan. As a consequence, we proposed an EELWEP (energy efficient light weight encryption process) system to keep the data acquired by each sensor node private. When using this strategy, the secret key is exchanged using the Diffie-Hellman method using the Pretty Good Privacy (PGP) program, and a large portion of the operations is carried out via the use of symmetric cryptography. The energy usage and calculation time on the sensor network are significantly reduced as a result of this method. The suggested system is simulated, and their results are analysed using a variety of parameters in comparison to existing benchmark schemes. Comparing the suggested technique to the current approaches reveals that it outperforms the alternatives in the vast majority of situations studied. © 2023 IEEE.

关键词： Internet of things

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Big data Mining And Clustering Using Distributed Bayesian Matrix Decomposition

Big Data Mining And Clustering Using Distributed Bayesian Ma...

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

作者： Das, Bappaditya Murari, Thejovathi Das, Chandan Rajeswari, P. Dr. B.C. Roy Engineering College Computer Science and Engineering West Bengal Durgapur India Acharya Nagarjuna University Computer Science and Engineering Andhra Pradesh Guntur India Artificial Intelligence & Data Science Panimalar Engineering College Chennai India

ISBN: (纸本)9798350313413

Technological advancement has enabled us to store and process massive amounts of data in a much shorter period of period. The phrase 'Big data' merely describes the enormous amounts of data that are now regularly utilized in business and academic settings. The emphasis here is not only on data collection, but also on careful data analysis in order to obtain meaningful results. There are several approaches to dealing with massive data streams. data clustering into compact units is one such method. This not only reduces data size but also allows it to be used more efficiently. Because all of these devices are from different manufacturers, the data generated is heterogeneous in nature and contains noise. A fundamental problem is gaining knowledge from heterogeneous, noisy data. Several strategies for mining knowledge from big data have already been proposed. Because big data is frequently acquired from dispersed locations and stored on multiple machines, handling these extremely large data sets in a single machine is either inefficient or impossible. We provide a distributed Bayesian matrix decomposition model (DBMD) for big data mining and clustering. Statistical inference, alternate direction method of multipliers (ADMM), and accelerated gradient descent (AGD) are the three techniques utilized to implement distributed computing. Investigated are the theoretical convergence tendencies of various approaches. We offer a perfect plug-in weighted average that minimizes estimate variance to get over noise heterogeneity. In real-world experiments, our algorithms outperform or are on par with two popular distributed approaches, Scalable- nonnegative matrix factorization (NMF) and Scalable- k-means++, which are confirmed by our theoretical results from synthetic studies. © 2023 IEEE.

关键词： Big data Mining Clustering Distributed Bayesian Matrix Decomposition (DBMD) Scalable-k-means++ Scalable-nonnegative matrix factorization (NMF)

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Advancing horizons in remote sensing: a comprehensive survey of deep learning models and applications in image classification and beyond

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Neural Computing and Applications 2024年第27期36卷 16727-16767页

作者： Paheding, Sidike Saleem, Ashraf Siddiqui, Mohammad Faridul Haque Rawashdeh, Nathir Essa, Almabrok Reyes, Abel A. Department of Computer Science and Engineering Fairfield University 1073 N Benson Rd FairfieldCT06824 United States Department of Applied Computing Michigan Technological University 1400 Townsend Dr HoughtonMI49931 United States Department of Computer Science West Texas AM University 2501 4th Ave CanyonTX79016 United States Department of Mathematics Computer Science and Data Science John Carroll University 1 John Carroll Blvd University HeightsOH44118 United States

In recent years, deep learning has significantly reshaped numerous fields and applications, fundamentally altering how we tackle a variety of challenges. Areas such as natural language processing (NLP), computer vision, healthcare, network security, wide-area surveillance, and precision agriculture have leveraged the merits of the deep learning era. Particularly, deep learning has significantly improved the analysis of remote sensing images, with a continuous increase in the number of researchers and contributions to the field. The high impact of deep learning development is complemented by rapid advancements and the availability of data from a variety of sensors, including high-resolution RGB, thermal, LiDAR, and multi-/hyperspectral cameras, as well as emerging sensing platforms such as satellites and aerial vehicles that can be captured by multi-temporal, multi-sensor, and sensing devices with a wider view. This study aims to present an extensive survey that encapsulates widely used deep learning strategies for tackling image classification challenges in remote sensing. It encompasses an exploration of remote sensing imaging platforms, sensor varieties, practical applications, and prospective developments in the field. © The Author(s) 2024.

关键词： Remote sensing

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Boruta Feature Selection for Prediction of Coronary Artery Disease

Boruta Feature Selection for Prediction of Coronary Artery D...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Yuvalatha, S. Nithyapriya, S. Kuzhali, R. Tamizh Savitha, S. Department of Computer Science and Business Systems Bannari Amman Institute of Technology Sathyamangalam India Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Sathyamangalam India Department of Artificial Intelligence and Data Science C K college of Engineering and Technology Chennai India Department of Computer Science nad Engineering PSR Engineering College Sivakasi India

ISBN: (纸本)9798350305708

Day by day cardio vascular disease death cases increasing. It mainly affects the human heart and blood vessels and it is difficult to diagnosis it. In this paper various machine learning algorithm is used to predict the cardio vascular disease. The data set of 14 attributes is used for prediction. The irrelevant features are handled using Boruta algorithm of 100 iterations. The proposed work uses Adaboosting with different hyper parameter and the multiple decision trees are built for the wrongly classified feature. At finally the Adaboosting algorithm achieve 93.74% accuracy in predicting the disease. © 2023 IEEE.

关键词： Adaboosting Boruta Algorithm Cardio vascular Hyper parameter

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Self-training through Classifier Disagreement for Cross-Domain Opinion Target Extraction 23

Self-training through Classifier Disagreement for Cross-Doma...

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32nd ACM World Wide Web Conference, WWW 2023

作者： Sun, Kai Zhang, Richong Samuel, Mensah Nikolaos, Aletras Mao, Yongyi Liu, Xudong Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China Department of Computer Science University of Sheffield Sheffield United Kingdom School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

ISBN: (纸本)9781450394161

Opinion target extraction (OTE) or aspect extraction (AE) is a fundamental task in opinion mining that aims to extract the targets (or aspects) on which opinions have been expressed. Recent work focus on cross-domain OTE, which is typically encountered in real-world scenarios, where the testing and training distributions differ. Most methods use domain adversarial neural networks that aim to reduce the domain gap between the labelled source and unlabelled target domains to improve target domain performance. However, this approach only aligns feature distributions and does not account for class-wise feature alignment, leading to suboptimal results. Semi-supervised learning (SSL) has been explored as a solution, but is limited by the quality of pseudo-labels generated by the model. Inspired by the theoretical foundations in domain adaptation [2], we propose a new SSL approach that opts for selecting target samples whose model output from a domain-specific teacher and student network disagree on the unlabelled target data, in an effort to boost the target domain performance. Extensive experiments on benchmark cross-domain OTE datasets show that this approach is effective and performs consistently well in settings with large domain shifts. © 2023 ACM.

关键词： Sentiment analysis

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Ruppert's Delaunay Triangulation Refinement Scheme for Optimal RSUs Placement in Vehicle to Infrastructure Communication Network

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China Communications 2023年第7期20卷 303-320页

作者： Selvakumari P Chinnasamy A Sree Rathna Lakshmi NVS Sheela D Department of Computer Science and Engineering Tagore Engineering CollegeChennai 600048India Department of Data Science and Business Systems School of ComputingSRMIST kattankulathur CampusChennai 603203India Department of Electronics and Communication Engineering Department of ECE at Agni College of TechnologyChennai 603103India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATSChennai 600124India

Road Side Units(RSUs)are the essential component of vehicular communication for the objective of improving safety and mobility in the road *** are generally deployed at the roadside and more specifically at the intersections in order to collect traffic information from the vehicles and disseminate alarms and messages in emergency situations to the neighborhood vehicles cooperating with the ***,the development of a predominant RSUs placement algorithm for ensuring competent communication in VANETs is a challenging issue due to the hindrance of obstacles like water bodies,trees and *** this paper,Ruppert’s Delaunay Triangulation Refinement Scheme(RDTRS)for optimal RSUs placement is proposed for accurately estimating the optimal number of RSUs that has the possibility of enhancing the area of coverage during data *** RDTRS is proposed by considering the maximum number of factors such as global coverage,intersection popularity,vehicle density and obstacles present in the map for optimal RSUs placement,which is considered as the core improvement over the existing RSUs optimal placement *** is contributed for deploying requisite RSUs with essential transmission range for maximal coverage in the convex map such that each position of the map could be effectively covered by at least one RSU in the presence of *** simulation experiments of the proposed RDTRS are conducted with complex road traffic *** results of this proposed RDTRS confirmed its predominance in reducing the end-to-end delay by 21.32%,packet loss by 9.38%with improved packet delivery rate of 10.68%,compared to the benchmarked schemes.

关键词： road side units(RSUs) ruppert’s delau-nay triangulation refinement area of global coverage optimal RSUs placement

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