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Security Risks and Their Mitigation Strategies: Cloud Computing Perspective 10

Security Risks and Their Mitigation Strategies: Cloud Comput...

10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Pandey, Sakshi Pathak, Prabhash Chandra Tripathi, Shubham Halwai, Sakshi Aggarwal, Shruti Nishant, Neerav Babu Banarasi das University Department of Computer Applications Lucknow India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Punjab India School of Engineering Babu Banarasi das University Lucknow India

ISBN: (纸本)9798350382471

Cloud computing enhances the performance to the highest potential for cloud consumers, service providers, and cloud providers;it makes various services possible. The term 'cloud computing,' which refers to the most current advancement in internet-based computing, is very broad. The exploration of cloud computing has grown among businesses and government organisations. They can profit from the cloud computing model in a number of ways, including less capital and operational costs, with the aid of numerous relatively inexpensive computing entities and cutting-edge reference models, i.e., Platform as a Service, Software as a Service, and Infrastructure as a Service. Even though cloud computing has numerous advantages, its widespread adoption needs to be improved by several security issues, such as client and association vulnerabilities. This paper briefly overviews cloud computing, including its security threats and infrastructure problems. The technology of cloud computing is affected by these security threats. In addition, the influence of threats on security factors affecting cloud computing and cloud services is investigated. Also, it focuses on a few unique security countermeasures that can help people and businesses to avoid security threats in the cloud. © 2023 IEEE.

关键词： cloud computing common weakness enumeration (CWE) preventive measure security security risk

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Federated Learning in the Era of Privacy Preservation: A Comprehensive Review and Future Directions 2

Federated Learning in the Era of Privacy Preservation: A Com...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kulshreshtha, Kushagra Goel, Bhawna Kanyal, Hoshiyar Singh Singhal, Vikas Gla University Department of Computer Engineering & Applications UP Mathura India UP Ghaziabad India Abes Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India Greater Noida Institute of Technology Department of Information Technology UP Greater Noida India

ISBN: (纸本)9798350371055

Federated studying is a disbursed system learning method that lets a set of dispensed users, or clients, collaboratively learn a shared predictive model without sharing any statistics. Through taking part in the model education method, every purchaser contributes to the development of a worldwide, predictive model without sacrificing its very own information privacy. This privateness renovation may be achieved through various techniques, which include differential privateness, cozy multi-celebration computation, and information obfuscation. In this way, model schooling isn't always a task exclusively for centralized facts creditors, but as an alternative, a large-scale collaboration amongst multiple entities with disparate hobbies and facts silos. In the technology of privacy protection, federated studying represents a powerful tool that enables the construction of predictive fashions that might be knowledgeable by means of various information resources at the same time as retaining the integrity and privateness of the individual statistics units. © 2024 IEEE.

关键词： Collaboratively Construction Federated Privateness Technology

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Integrating Cloud-based Data Mining Algorithms for Smart City Infrastructure Management and Decision Support Systems 4

Integrating Cloud-based Data Mining Algorithms for Smart Cit...

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4th International Conference on Soft Computing for Security applications, ICSCSA 2024

作者： Guru Vimal Kumar, M. Jude Moses Anto Devakanth, J. Selvapandian, D. Venkatesan, R. Revathi, J. Aruna, R. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Madanapalle Institute of Technology & Science Department of Computer Applications Chittoor India Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Bannari Amman Institute of Technology Department of Information Technology Erode India Kongu Engineering College Department of Mathematics Erode India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331515720

This study explores cloud-based data mining algorithm integration in elevating smart city infrastructure management and decision support systems. Specifically, the authors focus on optimizing traffic management through the analysis of real-Time traffic sensor data. Timestamped history records of vehicle count, traffic speed, density, weather condition, accidents, and traffic signals were utilized. Machine learning models such as Support Vector Machine, K-Nearest Neighbors, Decision Trees, and Random Forest were trained and tested. The analysis indicated SVM to be the most effective instrument, resulting in an accuracy of 98.98%. The model was also instrumental in reducing vehicle count by 16.7%, traffic density by 25%, and traffic speed was increased by 50%. KNN's overall accuracy experience was 94.5%, and the model was shown to be highly optimized for various forever traffic conditions. However, the overall impact on traffic speed and density was not as significant compared to the previously-discussed model. DT and RF demonstrated respective accuracies of 92.3% and 91.2%, offering varying levels of decision interpretation and ensemble learning opportunities. As a result, the study indicates that machine learning can play a critical role in improving urban traffic management via traffic congestion prediction and minimization, resulting in improved traffic flow, overall trip time reduction, and general urban mobility management. T. © 2024 IEEE.

关键词： Random forests

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Utilizing Neural Networks for Image and Sound Signal Recognition in Autonomous Driving 2

Utilizing Neural Networks for Image and Sound Signal Recogni...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Chaudhary, Akshita Chaudhary, Gaurav Chattopadhyay, Saumitra Verma, Akhilesh Kumar Sagar, Pramod Kumar Srm Institute of Science and Technology Department of Computer Applications UP Modinagar India Ajay Kumar Garg Engineering College Department of Information Technology UP Ghaziabad India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Mount Litera Zee School Department of Science & Technology UP Greater Noida India Raj Kumar Goel Institute of Technology Computer Science & Engineering UP Ghaziabad India

ISBN: (纸本)9798350371055

This research focuses on exploring the use of neural networks for photograph popularity in autonomous driving. Mainly, the research is investigating how semantic segmentation may be progressed with convolution neural networks (CNNs) to discover avenue objects with higher accuracy and quicker reaction. CNNs are skilled on a large-scale dataset of road scenes, employing complex architectures designed to perceive meaningful photo functions from the information. A contrast of outcomes is then made to standard techniques, which include k-nearest associates and random forest, to assess the performance of the CNNs for street scene segmentation. The goal is to offer a progressed expertise of the capabilities of neural networks for self sustaining driving photo popularity and to expand a framework for deploying such structures in the actual world. © 2024 IEEE.

关键词： architectures performance progressed research world

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Responsive Portfolio Website Using React 2

Responsive Portfolio Website Using React

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Siva Rama Lingham, N. Aruna, R. Dhandayuthabani, Gowreeswaran Sindhia, M. Maria Jeraldin Krishnasaivignesh, M. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India T. John College Gottigere Computer Applications Department Karnataka Bangalore56008 India Jeppiaar Engineering College Chennai Department of Computer Science and Engineering India

ISBN: (纸本)9798350308846

The way people use the Internet has changed significantly with the introduction of third-generation cell phones. Nowadays, websites can be read on a variety of devices of different types and sizes. Since the website should be similarly helpful regardless of the type of device used to view it, this place demands on website design. Responsive web design makes it possible to adjust the visual presentation of web pages depending on the device they are accessed with. Although it is a commonly used tool in web development, it has not been fully explored in technical, aesthetic, and practical terms. To this end, standard definitions for this technique were compiled, and a responsive product was implemented in a paper. A fully responsive portfolio website was created by implementing meta tags and scalable components. The process of creating the product made it possible to understand the possibilities and limitations of flexible web design. © 2023 IEEE.

关键词： CSS Developer Html Pages Portfolio React JS Responsive Technologies Webpage's

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Amalgamation of Transfer Learning and Explainable AI for Internet of Medical Things

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Recent Advances in computer science and Communications 2024年第4期17卷 40-53页

作者： Murugan, Ramalingam Paliwal, Manish Patibandla, Rama Seetha Maha Lakshmi Shah, Pooja Balaga, Tarakeswara Rao Gurrammagari, Deepti Raj Singaravelu, Parvathavarthini Yenduri, Gokul Jhaveri, Rutvij School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar India AP India Kallam Haranadhareddy Institute of Technology Andra Pradesh Guntur India Department of Computer Applications Kongu Engineering College Erode Perundurai India

The Internet of Medical Things (IoMT), a growing field, involves the interconnection of medical devices and data sources. It connects smart devices with data and optimizes patient data with real time insights and personalized solutions. It is mandatory to hold the development of IoMT and join the evolution of healthcare. This integration of Transfer Learning and Explainable AI for IoMT is considered to be an essential advancement in healthcare. By making use of knowledge transfer between medical domains, Transfer Learning enhances diagnostic accuracy while reducing data necessities. This makes IoMT applications more efficient which is considered to be a mandate in today’s healthcare. In addition, explainable AI techniques offer transparency and interpretability to AI driven medical decisions. This can foster trust among healthcare professionals and patients. This integration empowers personalized medicine, supports clinical decision making, and confirms the responsible handling of sensitive patient data. Therefore, this integration promises to revolutionize healthcare by merging the strengths of AI driven insights with the requirement for understandable, trustworthy, and adaptable systems in the IoMT ecosystem. © 2024 Bentham science Publishers.

关键词： Knowledge management

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Intelligent Energy Management for Electric Vehicles: Fuzzy Logic and Neural Network Integration 10

Intelligent Energy Management for Electric Vehicles: Fuzzy L...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Thirumalaikumari, T. Princy, D. Lita Uma, S. Sadaieswaran, R. Malathi, P. Legapriyadharshini, N. Saveetha College of Liberal Arts and Sciences Simats Deemed to be University Department of Computer Science Tamil Nadu Chennai India Meenakshi College of Engineering Department of Master of Computer Applications Tamil Nadu Chennai India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science Engineering Tamil Nadu Mahabalipuram India Tagore College of arts and science Department of Computer Applications Chennai Chromepet India Saveetha College of Liberal Arts and Sciences Simats Deemed to be University Department of Data Analytics Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Simats Deemed to be University Department of Computer Application Tamil Nadu Chennai India

ISBN: (纸本)9798350384369

This study suggests a novel methodology for intelligent energy management in electric vehicles (EVs) through the integration of neural networks and fuzzy logic. Achieving enhanced energy efficiency for electric vehicles is a critical concern that is tackled in this research through the integration of neural networks' decision-making and pattern recognition functionalities with fuzzy logic's adaptability and learning capabilities. The proposed method seeks to optimize energy consumption, enhance overall performance, and extend driving range. The integrated system is subjected to rigorous testing using a meticulously planned experimental setup and simulation environment, which enables it to surpass the performance of more conventional approaches to energy management. The results emphasize the potential of the novel methodology to improve environmentally sustainable transportation options and propel the development of electric vehicle technology. This study not only offers valuable insights into efficient energy management but also establishes the groundwork for subsequent developments in the domain. © 2024 IEEE.

关键词： Electric Mobility Electric Vehicles Energy Management Intelligent Systems Neural Networks Smart Grid

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Play Store App Analysis & Rating Prediction Using Classical ML Models & Artificial Neural Network 7

Play Store App Analysis & Rating Prediction Using Classical ...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Moharana, Bhimasen Biswal, Bhramara Bar Dey, Snehasis Rath, Manas Kumar Banerjee, Shobhan Lovely Professional University Department of Computer Science and Engineering Punjab India College of Engineering P.G. Department of Computer Science & Applications Bhubaneswar India College of Engineering Electronics & Telecommunication Engineering Bhubaneswar India Maharaja Sriram Chandra Bhanja Deo University Department of Computer Application Baripada India Birla Institute of Technology and Science Department of Engineering Technology Pilani India

ISBN: (纸本)9798350304268

Each app available in the App Store or Play Store has various aspects linked to it such as its version, category, number of installations, genre, etc. which makes it unique, robust & popular. The reviews and ratings given by end users play an important role for both the developers and other users for the performance and survival of the app in the marketplace. Reviews are just a description for justification of the rating given by a user & the rating plays an important role in the first look. In this paper, we have used the Play Store Analysis Dataset to descriptively analyze the various attributes present in it and create models that predict the rating of an app, given its specifications. The models have been compared based on a common accuracy metric based on which their reliability can be judged and used in real-time rating predictions for a new application in the store for which a certain amount of data is available over some period. © 2023 IEEE.

关键词： Descriptive Statistics ML Classification Models Model Comparison Play Store App Analysis Rating Prediction

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Compared to The Threshold Dependent Segmentation Technique, Normal Type Lung Cancer Identification in CT Images Utilizing Structural Flood Segmentation Process to Enhance Efficiency 3

Compared to The Threshold Dependent Segmentation Technique, ...

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

作者： Yadav, Ashok Kumar Kashyap, Sneha Pund, Sachin S. Mahmood Salman, Hayder Ernest Ravindran, R.S. V. Sriramakrishnan, G. Galgotias University Department of Computer Applications India Arka Jain University Department of Computer Science India Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India Al-Turath Universiy College Computer Science Department Iraq Koneru Lakshmaiah Education Foundation Department of Ece India Mohan Babu University Computer Science and Engineering Tirupati India

ISBN: (纸本)9798350399264

Image segments are a method for analyzing digital pictures that divides one piece of the image with sufficient details to match other image data.. The aim of lung tumor detection in the input figure is to identify the affected images as malignant based on the existence of a tumor. The use of a morphological watershed transforms for segmenting benign type lung tumour affected regions in CT images is suggested in this study. The proposed work is compared to threshold dependent segmentation, another image segmentation technique, with a sample size of 30 for each group. Mean Squared Error (MSE) and Universal Index Quality Measure (UIQI) parameters are used to evaluate the output of an image segmentation algorithm. Better segmentation is indicated by high MSE and UIQI values. The image thresholding and the cutoff transformation, in that order, provide the average MSE rates, mean MSE values, and means. The effectiveness of the stream is tracked the decision is made through descriptive statistics employing a picking area T-test and actual results. © 2023 IEEE.

关键词： Image segmentation

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Cement Strength Prediction using Regression Techniques 10

Cement Strength Prediction using Regression Techniques

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Dewan, Ritu Polishetty, Rohith Sharan, Bhagwati Goyal, Mayank Kumar Balde, Amadou Mouctar Greynoon, Ameen Mohamad Ahmad Galgotias College of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Sr. Software Engineer Migo India Limited Hyderabad India Srm University-AP School of Engineering and Sciences Department of Computer Science and Engineering Amaravati India Sharda University School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India School of Engineering and Technology Sharda University Department of Computer Science and Applications Greater Noida India

ISBN: (纸本)9789380544472

One of the primary structural materials in the development business is concrete. Compressive strength is the most frequently utilized metric when assessing the strength and nature of cement. This paper clarifies how to make use of technical regression (RT) methodology to decide the legitimate dose of the concrete mixture. Water, Cement, Fly Ash, Blast Furnace, Superplasticizer, Coarse, and Fine Aggregate, were used as input parameters, on the other hand, compressive strength was used as an output parameter. The produced models were evaluated using statistical parameters: Linear Regression, Decision Tree Regression, and Random Forest Regression. In contrast with other applicable models, it was found that the Gaussian process regression model based on the function of the nucleus of radial bases delivered relatively better results. The recommended models should get a good deal on saving money on materials, labor, and time while also improving accuracy. The proposed concrete should be more durable and accordingly more cost-effective. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Cements

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