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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Iswarya, V. Govindasamy, V. Akila, V. Puducherry Technological University Department of Information Technology Puducherry India Puducherry Technological University Department of Computer Science and Engineering Puducherry India

ISBN: (纸本)9798350387933

Complex networks are useful for modeling many real-life complicated phenomena, including social networks. The social network has taken on a variety of identities, providing a wide range of services: virtual networking sites (e.g., Facebook, LinkedIn), microblogs, community media sites, etc. In complex networks, detecting the most influential spreaders is a crucial task. The traditional centrality methods are degree centrality, betweenness centrality, closeness centrality, page rank, and Katz centrality measures. In top-k ranking mode, these measures can determine the individuals most responsible for spreading an infection within a complex network, but there are still some issues. Our study examines a variety of existing global centrality measures, including k-shell decomposition and k-core, as well as their merits and demerits. © 2024 IEEE.

关键词： Complex networks

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Optimizing Lane Configuration and ETC Adoption to Minimize Congestion and Costs at Toll Plazas 17th

Optimizing Lane Configuration and ETC Adoption to Minimize C...

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17th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2024

作者： Klaykul, Pattarapon Lee, Wilaiporn Prayote, Akara Department of Computer and Information Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Department of Electrical and Computer Engineering King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand

ISBN: (纸本)9789819606917

At toll plazas, congestion frequently occurs during peak hours due to inadequate lane management and misalignment of toll payment lanes with traffic volume and payment patterns. This study proposes a model to determine the optimal number of Manual Toll Collection (MTC) lanes to open or close and to optimize the proportion of Electronic Toll Collection (ETC) lanes. The objective is to minimize average queue lengths while striving to meet secondary-level service requirements. The current system often exceeds these standards, and this study aims to simulate the effects of increasing ETC usage and ETC lanes to improve queue management. The analysis shows that ETC lanes are more efficient and can handle a higher traffic volume than MTC lanes, leading to shorter average queue lengths based on queueing theory. It was observed that lane adjustments are only feasible during off-peak periods, optimizing queue lengths to meet standards. During peak hours, simulations show significant reductions in average queue lengths for various ETC usage scenarios, with reductions of up to 91.96% depending on lane configuration. Additionally, converting one MTC lane to ETC reduces hourly operating costs per lane from 73.17 Baht to 5.42 Baht, a cost reduction of approximately 92.59%. These results emphasize the importance of strategic planning and scheduling for dynamic lane adjustments and advocate for increased ETC adoption through targeted policies and incentives to enhance overall toll plaza efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Queueing theory

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The Challenges and Opportunities of Cloud Security as a Service for Enhancing IoT Security

The Challenges and Opportunities of Cloud Security as a Serv...

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2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Mandal, Sanjeev Kumar Hemelatha, S. Sasirekha, R. Chavan, Gurunath T. Kaneria, Ojasvee Jaffar, A Mohamed Department of Computer Science and Information Technology Karnataka Bangalore India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Atlas SkillTech University Department of Isme Maharastra Mumbai India

ISBN: (纸本)9798350329773

The internet of things (Iota) has had a remarkable impact on modern lifestyles, revolutionizing the way we display, communicate, and have interaction with the sector round us. In conjunction with this new generation, but, come new challenges for organizations in terms of safety. Iota devices frequently lack the security features wished for defensive towards cyber attacks and statistics robbery, leaving them prone to malicious actors. Cloud safety as a carrier (Cases) might also provide the solution to those challenges, via providing a more suitable layer of protection for Iota networks. This new sort of protection answer utilizes cloud-based technologies to provide comfortable get admission to, multi-thing authentication, safety analytics, and strong management abilities for Iota networks. With the aid of imparting this comfy environment, Cases can offer organizations with the warranty that their Iota information can remain comfortable, even within the face of ever-evolving cyber threats. At the identical time, however, heavily investing in Cases increases a number of questions. Factoring in implementation fees, compliance requirements, and commercial enterprise goals may be a daunting task for corporations, and Cases might not always offer a greatest solution. It's vital for businesses to correctly determine their safety necessities when thinking about Cases, and to understand the benefits. © 2023 IEEE.

关键词： Cybersecurity

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Enhanced Click Fraud Detection in Digital Advertising Through Ensemble Deep Learning 14th

Enhanced Click Fraud Detection in Digital Advertising Throug...

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14th International Conference on Frontier Computing, FC 2024

作者： Batool, Amreen Kim, Jisoo Byun, Yung-Cheol Department of Electronic Engineering Institute of Information Science and Technology Jeju National University Jeju63243 Korea Republic of Collage of Engineering Jeju National University Jeju63243 Korea Republic of Department of Computer Engineering Major of Electronic Engineering Institute of Information Science and Technology Jeju National University Jeju63243 Korea Republic of

ISBN: (纸本)9789819623945

As digital marketing becomes more and more crucial in today’s business strategies, the issue of click fraud. Which aims to deplete advertising budgets through activities. Has grown, leading to the need, for defenses. This research introduces a blend of Convolutional Neural Networks (CNN) Bidirectional Long Short Term Memory networks (BiLSTM) and Random Forest (RF) classifiers to effectively combat click fraud. This innovative model combines the power of learning for extracting features with RFs classification capabilities. By incorporating CNN and BiLSTM our method improves the identification of spatial features from data, which are then classified using an RF model. This ensemble approach is designed to address shortcomings in existing models that depend mainly on machine learning methods and often demand extensive feature manipulation. We showcase the efficiency of our model through testing on a dataset, achieving an impressive accuracy rate of 99.19% surpassing previous techniques. Additionally, we evaluate metrics such as precision, recall, and F1 score all highlighting the superiority of our approach. This research does not only push forward the tools for detecting click fraud but also offers a scalable framework for potential adaptation and implementation, in real world scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Impact of Data Poisoning Attack on the Performance of Machine Learning Models

Impact of Data Poisoning Attack on the Performance of Machin...

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International Conference on Data Science and Network engineering, ICDSNE 2023

作者： Das, Dipan Roy, Sharmistha Sahoo, Bibhudatta Department of Computer Science & Engineering NIT Rourkela Rourkela India Faculty of Computing and Information Technology Usha Martin University Ranchi India

ISBN: (纸本)9789819967544

The twenty-first century has witnessed widespread adoption of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). These techniques have provided reliable solutions in various areas, including statistics, information theory, and mathematics. Given the prevalence of ML techniques, there exist various adversaries which question the robustness of ML models. Adversaries aim to manipulate models to their advantage, reducing their performance and accuracy. Data poisoning attack is one such adversary in which the attacker manipulates models by introducing specially crafted poisoned data into the training dataset. This paper presents the performance analysis of different machine learning models with and without the influence of data poisoning attack to predict the probability of diabetes and its effect on accuracy and precision. It has been observed that the SVM (RBF) classifier performs best on clean data, while the KNN classifier is highly affected by data poisoning, with a lesser impact on the SVM (Linear kernel) classifier. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reliability analysis

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Multimedia IoT Data Compression using Deep Learning Technique 2

Multimedia IoT Data Compression using Deep Learning Techniqu...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Yathavaraj, V. Karthikeyan, N. Kousalya, A. Sindhuja, P. Dr. N.G.P. Institute of Technology Department of Computer Science and Engineering Coimbatore India Sns College of Technology Department of Computer Science and Engineering Coimbatore India Sri Krishna College of Engineering and Technology Department of Information Technology Coimbatore India United Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665456302

Due to the considerable complexity of the multimedia data acquired, IoT (Internet of Things) and multimedia will encounter a number of energy and communication overload limits. One well-known solution to the problem of large datasets is to use the lossy compression algorithm. However, the lower transmission rate required by current lossy compression techniques to ensure endurable apparent image/picture quality only results in a minor reduction in the amount of communicated data and, as a result, a minor reduction in power and bandwidth use. An effective firmness technique is required to strike a fair equilibrium flanked by information volume and visual degradation. A deep-learning super-resolution technique is utilized in this study to restore high-resolution photographs from damaged photos with a considerable compression ratio as input. The results of the study clearly show how the suggested approach improves the graphical fidelity of compressed and down scaled images. The suggested method reduces largely the communication transparency and energy consumption of constrained multimedia IoT framework components as a result. © 2023 IEEE.

关键词： Internet of things

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Privacy-Aware and AI Techniques for Healthcare Based on K-Anonymity Model in Internet of Things

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IEEE Transactions on engineering Management 2024年 71卷 12448-12462页

作者： Sangaiah, Arun Kumar Javadpour, Amir Ja'fari, Forough Pinto, Pedro Chuang, Huan-Ming National Yunlin University of Science and Technology International Graduate Institute of Artificial Intelligence Douliou64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102 2801 Lebanon Harbin Institute of Technology Department of Computer Science and Technology Shenzhen150001 China Instituto Politécnico de Viana Do Castelo ADiT-Lab Electrotechnics and Telecommunications Department Porto4900-347 Portugal Sharif University of Technology Department of Computer Engineering Tehran11155-9161 Iran National Yunlin University of Science and Technology Department of Information Management Yunlin640 Taiwan

The government and industry have given the recent development of the Internet of Things in the healthcare sector significant respect. Health service providers retain data gathered from many sources and are useful for patient diagnostics and research for pivotal analysis. However, sensitive personal information about a person is contained in healthcare data, which must be protected. Individual privacy protection is a crucial concern for both people and organizations, particularly when those firms must send user data to data centers due to data mining. This article investigated two general states of increasing entropy by changing the entropy of the class set of characteristics based on artificial intelligence and the k-anonymity model in privacy in context, and also three different strategies have been investigated, i.e., the strategy of 'selecting the feature with the lowest number of distinct values,' 'selecting the feature with the lowest entropy,' and 'selecting the feature with the highest entropy.' For future tasks, we can find an optimal strategy that can help us to achieve optimal entropy in the least possible repetition. The results of our work have been compared by lightweight and MH-Internet of Things, FRUIT methods and shown that the proposed method has high efficiency in entropy criteria. © 1988-2012 IEEE.

关键词： Data mining

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Localization Improvements in Faster Residual Convolutional Neural Network Model for Temporomandibular Joint – Osteoarthritis Detection 1st

Localization Improvements in Faster Residual Convolutional N...

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1st International Conference on Artificial Intelligence of Things, ICAIoT 2023

作者： Vijaya Kumar, K. Baskaran, Santhi Department of Computer Science and Engineering Puducherry Technological University Puducherry India Department of Information Technology Puducherry Technological University Puducherry India

ISBN: (纸本)9783031487736

To recognize the Osteoarthritis of the Temporomandibular Joint (TMJ-OA) from panoramic dental X-ray images, deep learning algorithms are widely used these days. Among others, an Optimized Generative Adversarial Network with Faster Residual Convolutional Neural Network (OGAN-FRCNN) was recently achieved better FRCNN training by providing more synthetic images for TMJ-OA recognition. However, the localization of a small condyle OA region was ineffective because of the complex background, occlusion and low-resolution images. Hence, this article proposes the OGAN with Progressive Localization-improved FRCNN (PLFRCNN) model for TMJ-OA recognition. First, the OGAN can augment the number of panoramic X-ray scans. Then, ResNet101 can extract the Feature maps (F-maps) at multiple levels, followed by the Feature Pyramid Network (FPN) with a Region-of-Interest (RoI)-grid attention for multiscale F-map extraction. Those F-maps are given to the Modified Region Proposal Network (MRPN), which applies a multiscale convolution feature fusion and an Improved Non-Maximum Suppression (INMS) scheme for creating the Region Proposals (RPs) with more information. To resolve the localizing variance and obtain the proposal F-maps, the improved RoI pooling based on bilinear interpolation merges both F-maps and RPs. Moreover, the fully connected layer is used to classify those F-maps into corresponding classes and localize the target Bounding Box (BB). Additionally, the BB regression in the TMJ-OA localization stage is enhanced by the new Intersection-Over-Union (IOU) loss function. Finally, the test outcomes reveal that the OGAN-PLFRCNN model attains an accuracy of 98.18% on the panoramic dental X-ray corpus, in contrast to the classical CNN models. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Passive on-chip isolators based on the thin-film lithium niobate platform

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Chinese Physics B 2025年第3期34卷 408-413页

作者： Jiacheng Liu Gongyu Xia Qilin Hong Pingyu Zhu Kai-Kai Zhang Keyu Xia Ping Xu Shiqiao Qin Zhihong Zhu College of Advanced Interdisciplinary Studies&Hunan Provincial Key Laboratory of Novel Nano Optoelectronic Information Materials and Devices National University of Defense TechnologyChangsha 410073China Institute for Quantum Information and State Key Laboratory of High-Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China College of Engineering and Applied Sciences National Laboratory of Solid State Microstructuresand Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210023China

Optical isolators,the photonic analogs of electronic diodes,are essential for ensuring the unidirectional flow of light in optical systems,thereby mitigating the destabilizing effects of back ***-film lithium niobate(TFLN),hailed as“the silicon of photonics,”has emerged as a pivotal material in the realm of chip-scale nonlinear optics,propelling the demand for compact optical *** report a breakthrough in the development of a fully passive,integrated optical isolator on the TFLN platform,leveraging the Kerr effect to achieve an impressive 10.3 dB of isolation with a minimal insertion loss of 1.87 *** theoretical simulations have demonstrated that our design,when applied to a microring resonator with a Q factor of 5×10^(6),can achieve 20 dB of isolation with an input power of merely 8 *** advancement underscores the immense potential of lithium niobate-based Kerr-effect isolators in propelling the integration and application of high-performance on-chip lasers,heralding a new era in integrated photonics.

关键词： thin-film lithium niobate Kerr effect optical isolator

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Human Activity Recognition Using Efficientnet-B0 Deep Learning Model

Human Activity Recognition Using Efficientnet-B0 Deep Learni...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Silpa, A.S. Benifa, J.V. Bibal Anu, K.P. Vijayakumar, Aiswarya Department of Computer Science Kerala Irinjalakuda India Indian Institute of Information Technology Department of Computer Science and Engineering Kerala Kottayam India

ISBN: (纸本)9798350394580

Human activity has many varieties of applications. By using HAR one can predict the behaviour of a person, one can predict the wellness of a person, whether he or she is healthy or not. It can be used in surveillance area, by which one can detect the abnormal activities of a person. Also HAR helps to detect the human activities in our daily life. In this study, implementation of efficientNet-Bo provided a model accuracy of 72 percentage for 31 epochs. This model gave a highest accuracy of 74 percentage for the epoch 11. By implementing this model on HAR dataset we can detect the human activities calling, clapping, cycling, dancing, drinking, eating, fighting, hugging, laughing etc. © 2023 IEEE.

关键词： EfficientNet-B0 HAR Human activity

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