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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Chandramowleeswaran, G. Sindhu, M. Alabdeli, Haider Mishra, Anshu Vinod, Priya Kaur, Harmeet Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Commerce and Business Administration Chennai600062 India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Christ University School Of Business And Management India Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India

ISBN: (纸本)9798350390759

This paper focuses on the improvement of the intelligent time management system which employ Bayesian optimization for suggesting time management plans for each particular person. In this sense, through historical data of input-output patterns and users' preferences, the system aims at increasing productivity and user satisfaction. In the study, Gaussian Processes are used as the surrogate model in the Bayesian optimization so that the required evaluations by the algorithm to realize optimal scheduling methodologies are kept to a minimum. Implementation is done as a web application where users submit their tasks and get the recommended schedule instantly. Indicators like, the degree of task accomplishment, time, and scheduling compliance, and probably the users' satisfaction suggest that system helped enhance time management results. lack of feedback from the users is removed through questionnaire that reveals the simplicity of the system and the quality of its recommended times, thereby supporting the idea of Bayesian optimization as a game changer in the management of time. This research significance points to the need for maintaining efficient and individualized approaches to time management strategies and agrees with others' findings, which suggest that this is an area ample fiction research needs to acknowledge and pursue. © 2024 IEEE.

关键词： Scheduling algorithms

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Comparative Analysis of differential Privacy Implementations on Synthetic data 15

Comparative Analysis of Differential Privacy Implementations...

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15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Said, Huwida E. Mahmoud, Qusay H. Goyal, Mandeep Hashim, Faiza College of Technological Innovation Zayed University Department of Computing and Applied Technolgy Dubai United Arab Emirates Ontario Tech University Department of Electrical Computer and Software Engineering OshawaONL1G 0C6 Canada College of Information Technology Uae University Department of Computer Science and Software Engineering AI-Ain United Arab Emirates

ISBN: (纸本)9798331507695

differential privacy offers a promising solution to balance data utility and user privacy. This paper compares two prominent differential privacy tools-PydP and IBM's diffprivlib-that are applied to a synthetic dataset with medical attributes. We evaluate these tools based on their effectiveness in maintaining data privacy while preserving the statistical integrity of the data. Our results reveal that PydP provides synthetic data that closely matches real-world data, making it appropriate for tasks requiring accuracy while striking a better balance between data utility and privacy. However, IBM's diffprivlib is more suitable for privacy-critical applications due to its stronger privacy guarantees, but at the cost of increased noise and decreased data utility. This paper contributes to the practical understanding of implementing differential privacy in machine learning and software applications and enhances the tools available for developers in sensitive data environments. © 2025 IEEE.

关键词： data integrity

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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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Machine learning in Recent Technology: Shaping the Future of Intelligent Applications and data Analysis 5

Machine Learning in Recent Technology: Shaping the Future of...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Karpagam, P. Uma Maheswari, S. Kumar, A. Pandi, V.Samuthira Jose, P. Virushabadoss, S. Dhanalakshmi College of Engineering Department of Computer Science Engineering India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biosciences Chennai India Dhanalakshmi College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331530013

In the present day, machine learning (Ml), which is a subset of artificial intelligence (AI), has emerged as a crucial technology that has revolutionized the way in which we interact with data and construct intelligent applications. The purpose of this study is to investigate the recent developments in machine learning and the profound impact that these breakthroughs have had on a variety of fields, such as the medical field, the financial sector, the transportation industry, and environmental science. Through an examination of cutting-edge machine learning techniques such as deep learning, reinforcement learning, and transfer learning, the study sheds light on the ways in which these methodologies are leading to the development of intelligent systems in the future. The purpose of this study is to investigate the function of machine learning in the process of data analysis, with a particular emphasis on its capacity to derive insights from intricate and extensive datasets. Additionally, it studies the difficulties that are linked with machine learning, such as the problem of data privacy, the interpretability of models, and the ethical implications of automated decision-making. The objective of this study is to identify major trends and future directions in machine learning by doing an exhaustive examination of the relevant literature, conducting case studies, and conducting experimental analysis. Based on the findings, it appears that the combination of machine learning with other technologies, such as edge computing and the Internet of Things (IoT), will result in intelligent systems that are more advanced and capable of adapting to their environment. The study highlights the significance of a multidisciplinary approach to machine learning, highlighting the necessity of collaboration between computer scientists, domain specialists, and ethicists in order to guarantee the development and deployment of machine learning technologies in a responsible manner. As machin

关键词： Adversarial machine learning

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Cloud Top Management Role in Reducing Mobile Broadband Transmission Hazards and Offering Safety 3

Cloud Top Management Role in Reducing Mobile Broadband Trans...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Manjunath, C.R. Sharma, Sachin Sharma, Aditi Sunil Kumar, M. Kelkar, Sandeep Vishal deshmukh, Sheetal Department of Computer Science and Engineering Bangalore India Dev Bhoomi Uttarakhand University Department of Computer Science and Engineering Uttarakhand Dehradun India Parul University PIT Department of Computer Science and Engineering Gujarat Vadodara India Mohan Babu University Erstwhile SreeVidyanikethan Engineering College Department of Computer Science and Engineering AP Tirupathi India Prin L.N. Welingkar Institute of Management Development and Research India Yashwantrao Mohite Institute of Management Department of Computer Applications Karad India

ISBN: (纸本)9798350399264

In both the academic and industrial worlds, cloud technology is becoming a widely used research area. Both providers of cloud services and consumers profit from cloud computation. The literature has mostly focused on the security issues related to the cloud. This thorough literature study's objective is to assess the state of knowledge on dangers, difficulties, and privacy for public cloud. This studies from different countries at the areas of study that some well rising institutions are focusing between on 2010. and 2020. We chose 81 papers after carefully examining several attempts to solve the stated research issues. The study's results revealed seven serious security threats to internet data centres. The results revealed that a few of the extensively covered subjects with in chosen works included data modification and leaking. Network discontinuities, storing, and information inside the realm of public cloud were also recognised potential threats. The findings of this Study also shown that CSPs and internet users alike still have difficulties when reassessing client data. The cryptocurrency was identified in our research report as an innovation activitythat would help to allay security worries. The SlR findings reveal a few things that should be done in efforts to provide categorization, legitimacy, and information availability in the present. © 2023 IEEE.

关键词： Industrial research

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Service Migration for delay-Sensitive IoT Applications in Edge Networks

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IEEE Transactions on Services Computing 2025年第3期18卷 1782-1797页

作者： li, Xiaocui Zhou, Zhangbing Wang, Yasha deng, Shuiguang Hung, Patrick C. K. School of Information Engineering Beijing100083 China Peking University National Research & Engineering Center of Software Engineering Beijing100871 China Peking University New Generation Information Technology Research Institute Tianjin300380 China School of Information Engineering Frontiers Science Center for Deep-Time Digital Earth Beijing100083 China Institut Polytechnique de Paris Télécom SudParis Palaiseau91120 France Zhejiang University College of Computer Science and Technology Hangzhou310012 China Ontario Tech University Faculty of Business and Information Technology Oshawa Canada

The proliferation of Internet of Things (IoT) applications prompts extraordinary demands for the collaboration of large amounts of computational resources provided by IoT devices in edge networks, and these applications are mostly delay-sensitive. Generally, these resources are encapsulated as IoT services. Thereafter, IoT applications can be performed, such that the collaboration of their sub-tasks is achieved through the composition of functionally complementary and geographically contiguous IoT services. The status of computational resources in IoT devices may change continuously along with their occupancy and release by IoT services. Considering the resource-scarceness of IoT devices, when the workload of IoT devices increases due to more services to be processed, certain IoT devices may hardly have enough remaining resources to co-host more instances of certain IoT services prescribed by forthcoming IoT applications with strict constraints. As a result, the delay satisfaction of both on-running and forthcoming IoT applications may be negatively impacted, or even hardly be satisfied any longer. To solve this issue, this paper proposes a rEsource-Efficient service Configuration (E2rC) mechanism, which aims to optimize the configuration of computational resources provided by IoT devices with respect to complex requirements prescribed by IoT applications, through service migration techniques. This service migration problem is formulated as markov multi-phases decisions, which is solved through our enhanced deep Reinforcement learning (dRl) approach with a two-layer Q-network. Extensive experiments have been conducted upon the dataset of our testbed system. Evaluation results show that our E2rC is more efficient than the state-of-art counterparts in satisfying delay constraints of IoT applications, while reducing the energy consumption and improving the resource utilization efficiency of IoT devices. © 2025 IEEE. All rights reserved.

关键词： Markov processes

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MlIdS: Revolutionizing of IoT based digital Security Mechanism with Machine learning Assisted Intrusion detection System

MLIDS: Revolutionizing of IoT based Digital Security Mechani...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Vijaya Vardan Reddy, S.P. Manonmani, S.Priskilla Anitha, C. Jaganathan, d. Reena, R. Suresh, M. R.M.K. Engineering College Department of Ece Tamil Nadu Chennai India Meenakshi Sundararajan Engineering College Department of Information Technology Tamil Nadu Chennai India Saveetha School of Engineering Simats Department of Cse Tamil Nadu Chennai India Madanapalle Institute of Technology & Science Department of Artificial Intelligence Andhra Pradesh India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Mathematics Tamil Nadu Chennai India

ISBN: (纸本)9798350382723

It is becoming more critical to detect intrusions on these devices due to the exponential growth of the Internet of Things (IoT) and the accompanying explosion in the number of IoT devices. In order to create intrusion detection systems that really work, researchers are utilizing machine learning methods. Machine learning Assisted Intrusion detection System (MlIdS) is a new intrusion detection system that we provide in this study. It efficiently identifies network unusual traffic. A cross-validation test using the traditional learning model XGBoost allows for a transparent evaluation of the suggested algorithm's performance. Next, the preprocessed data is categorized using the suggested MlIdS and XGBoost methods. To get the best detection performance, the model's hyperparameters are tuned using optimization logic. The evolution of cybercrime has necessitated massive advancements in intrusion detection system (IdS) technology. In order to gain access to our computers' private data, hackers nowadays deploy a wide variety of techniques. To protect against these threats, there are a plethora of intrusion detection algorithms. There are growing worries over the secure communication and protection of digital information due to the exponential expansion and usage of the internet. To gain useful information, hackers nowadays deploy a wide variety of techniques. Those various assaults may be detected with the use of several intrusion detection algorithms, methods, and approaches. The overarching goal of this paper is to present a comprehensive analysis of intrusion detection systems, including but not limited to: different types of intrusion identification techniques, types of events, a number of approaches, and tools, future research requirements, difficulties and, subsequently the development of an IdS device for research purposes that can detect and prevent intrusions. © 2024 IEEE.

关键词： Internet of things

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Analysis of different Encryption Techniques used in Watermarking Algorithm for the Security of Medical Image 10

Analysis of Different Encryption Techniques used in Watermar...

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10th International Conference on Emerging Trends in engineering and Technology - Signal and Information Processing, ICETET-SIP-22 2022

作者： Pulgam, Namita d. Shinde, Subhash K. Ramrao Adik Institute of Technology D y Patil Deemaed to Be University Computer Engineering Navi Mumbai India Lokmanya Tilak College of Engineering Computer Engineering Navi Mumbai India

ISBN: (纸本)9781665467414

Today, in the computerized age, the development and use of internet technology in healthcare has been increasing rapidly. Quick improvement is going on in the telemedicine field with a clear goal to design integrated applications in diagnostics and treatment. Hence medical images are preserved in databases for some time so that post diagnosis treatment will become easier for the patient and for doctors as well. during the process of the treatment patients tend to take experts' advice and that time images of the reports will be shared from one hospital to another. With the use of sharing and transmission technique post treatment cost gets reduced whereas increases the issue concerning the security of patients medical reports. It is important to keep track of images shared between two medical centers to identify sources of image generation as well as protecting patient's sensitive information from intruders or hackers against unauthorized access and modification. digital watermarking can be used to provide ownership and copyright protection. In the watermarking system image or text can be inserted in the cover image to get a watermarked image. The watermark image or secret image to be inserted can be protected with encryption technique. Hence this paper presents a digital watermarking system designed with encryption techniques to provide security to patients' medical records. The system is evaluated by implementing it with various encryption techniques like Advanced Encryption Standard (AES) with 256 bits, AES-GCM, Triple data Encryption Standard (TdES), Blowfish etc. The performance of the system is measured with PSNR and SSIM and assessment of the evaluation parameters shows that AES encryption is better in comparison with TdES and Blowfish. © 2022 IEEE.

关键词： Telemedicine Watermarking Information processing Copyright protection Market research Robustness Encryption

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data Visualization for Corona Patients Globally using Real-Time APIs 2

Data Visualization for Corona Patients Globally using Real-T...

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2nd International Conference on Sustainable Computing and data Communication Systems, ICSCdS 2023

作者： durai, S. Iqbal, M. Mohamed Kumar, S Niresh Alagappan, Chockalingam Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Avadi Dept. of Cse Chennai India VIT-AP University School of Computer Science and Engineering Amaravati India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Mamallapuram India M.Kumarasamy College of Engineering Department of Electrical and Electronics Engineering Tamilnadu Karur639113 India

ISBN: (纸本)9781665491990

All the nations' administrative units are concerned about the COVId-19 outbreak in different regions of the world. India is also trying to control the spread of the virus with strict measures and has managed to slow down its growth rate. The administration of each country faces the challenge of maintaining records of corona patients. To address this challenge, this work builds a website from scratch using real-time APIs for data visualization. The website provides information on the number of active cases, death cases, recovery cases, and total cases of COVId-19 patients in each country. The data can be visualized using graphs, making it easier to compare the situation in different countries. The website allows for monitoring which country has a higher number of deaths, patients, favorable recovery rates, and a large number of active cases. The COVId-19 status regarding patients can be analyzed through graphical representation using real-time data. © 2023 IEEE.

关键词： COVId-19

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Privacy-Preserving Human Activity Recognition in Smart Homes using deep learning and Edge Computing for Real-Time Processing 5

Privacy-Preserving Human Activity Recognition in Smart Homes...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Mariappan, K. Gopal, Baskar Parthasarathy, J. Sreekanth, G.R. Nanmaran, R. Jegajothi, B. SRM Institute of Science and Technology Department of Computer Science and Applications Chennai India College of Engineering University of Technology & Applied Science Nizwa Oman Computer Science & Engineering Jayalakshmi Institute Of Technology Dharmapuri India Information Technology Nandha Engineering College Erode52 India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India SRS Tech Solutions Chennai India

ISBN: (纸本)9798331530013

The rapid advancement of smart home technologies necessitates efficient human activity recognition (HAR) systems while ensuring user privacy. This research presents a novel architecture that integrates deep learning and edge computing for real-time HAR, emphasizing privacy protection. By employing differential privacy methods and federated learning, the approach minimizes personal data exposure risks. Using the UCI HAR dataset, the model achieves an accuracy of 96.50%, with precision, recall, and Fl-score metrics of 95.75%, 96.20%, and 95.97%, respectively. The results indicate high accuracy and adaptability to varying smart home environments, while edge computing ensures minimal latency. This work highlights the importance of balancing performance and privacy in HAR systems, paving the way for secure smart home applications. Additionally, it points to the potential of deep learning in medical diagnostics, suggesting future research could enhance diagnostic capabilities across various medical imaging modalities, including CT scans and MRIs, to improve healthcare outcomes. © 2024 IEEE.

关键词： Smart homes

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