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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： Y m Blessy Raveendra N Amarnath D. Antony Joseph Rajan R Dillibai. S.T Aarthy. T. R. GaneshBabu Department of Electornics and Communication Engineering R.M.K. Engineering College Chennai Tamil Nadu India Mphasis Campbell Dr Melissa Texas USA Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

In this research, we provide a new real-time method for managing vehicle utilization patterns using cloud-enabled Bayesian networks (BN). Traditional solutions often employ static models and batch processing to account for the ever-changing nature of vehicle utilization. The objective is to use cloud-enabled BN to improve real-time vehicle utilisation monitoring, optimise fleet management, and make better decisions. To improve the precision and responsiveness of consumption predictions, businesses are increasingly turning to cloud computing, enabling continuous, real-time data stream processing. To weather, traffic patterns, and historical data, among other elements that impact vehicle utilization, BN provide a strong foundation for modelling complicated connections. To increase the accuracy of its adaptive use pattern forecasts, the system updates probabilistic associations in real time using incoming data. Fleet managers may optimize resource allocation, improve service delivery, and achieve overall operational efficiency via incorporating cloud computing, enabling scalable and efficient processing. In metropolitan areas, where use patterns might change quickly, this method helps manage vehicle fleets more adaptively and responsively. It showcases the practicality and advantages of our method in simulations and case studies, showing how it can change the game for fleet management techniques. It highlights the revolutionary power of BN offered by the cloud in improving the efficiency and responsiveness of fleet operations.

关键词： Cloud computing Adaptation models Adaptive systems Computational modeling Biological system modeling Transportation Real-time systems Bayes methods Resource management Vehicle dynamics

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A Critical Study on Hybrid Computing and Its Current Influences and Future Challenges

A Critical Study on Hybrid Computing and Its Current Influen...

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Intelligent and Innovative technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Praveen K V Joe Prathap P. m L Sherin Beevi W Vinil Dani Department of Information Technology St. Peter’s College of Engg. and Tech. Chennai India Department of Computer Science and Engineering R.M.D. Engineering College India Department of EEE St. Peter’s College of Engg. and Tech. Chennai India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

This research focuses on the evolving field of hybrid computing, which combines traditional computing architectures with advanced computational paradigms like quantum computing, neuromorphic computing, or cloud-edge computing. Hybrid computing involves integrating multiple computing models or systems to leverage their complementary strengths. For example, combining classical computing with quantum or neuromorphic approaches to solve complex problems more efficiently. The goal is to enhance computational power, increase efficiency, and tackle problems that are difficult for traditional computing models alone. Emerging technologies, including quantum computing, machine learning, and AI, are driving the need for hybrid systems. These technologies enable the handling of more complex computations that classical systems struggle with, such as optimization, simulation, and cryptography. The integration of cloud and edge computing is also central to hybrid computing, enabling decentralized computation and processing closer to data sources. Combining different computing paradigms requires seamless communication and integration between systems, which is a significant technical challenge. main challenging areas are scalability, software and hardware compatibility, energy efficiency and security. There is a need for standard protocols and frameworks to enable smoother integration of various computing systems. New algorithms that can take advantage of hybrid architectures need to be developed. Depend on applications significant advancements in hardware, including quantum processors, neuromorphic chips, and integrated hybrid processors required. The rise of hybrid computing will also bring ethical concerns, such as the impact on jobs, privacy, and inequality in access to advanced technologies. The study concludes by emphasizing the transformative potential of hybrid computing while acknowledging the technical, economic, and societal hurdles that need to be overcome. It highlights

关键词： Quantum computing Program processors Scalability Computational modeling Biological system modeling Standards organizations machine learning Organizations Hardware Optimization

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Implementing Data science and Big Data Analytics to Their Full Potential: Protocols for Gaining Useful Understanding and Promoting Data-Driven Innovation

Implementing Data Science and Big Data Analytics to Their Fu...

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Global Conference for Advancement in technology (GCAT)

作者： Arthy m S. B. Nivethitha SP. Karuppiah T. Vijayanandh Sumalatha A T. Ramesh Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology (SRMIST) Kattankulathur Tamil Nadu India Mechatronics Engineering Rajalakshmi Engineering College Chennai Tamil Nadu India Department of MBA St.Joseph's College of Engineering Department of Computer Science & Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science Kristu Jayanti College Autonomous Bengaluru Department of Computer Science & Engineering R.M.K Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

The proper implementation of data science and big data analytics has become an essential component for gaining valuable insight and driving innovation across a variety of industries in this era, which is characterized by the constant presence of data. The strategies and protocols that are necessary to fully exploit the promise of these transformational technologies are investigated in depth by this research. A complete framework for enterprises to harness their data assets is the goal of the study, which will be accomplished by analyzing the fundamental principles of data science as well as the difficulties connected with big data. A number of advanced analytics methods, including machine learning, predictive modeling, and natural language processing, are being investigated as potential integrations into the decision-making process as part of this research. The relevance of data governance, quality, and the ethical considerations that surround data usage are also discussed in this document. It is demonstrated in this study that data-driven insights may lead to strategic advantages, operational efficiencies, and the development of creative goods and services. This is accomplished through a number of case studies and practical examples. In order for businesses to make the most of the potential that are given by data science and big data analytics, it is vital for them to have a culture of data literacy as well as a robust infrastructure, according to the results made. At the end of the study, a collection of best practices and recommendations for businesses and governments to implement in order to cultivate a data-driven environment that encourages innovation and usage of data in a responsible manner are presented. In light of the findings of this study, it is clear that data science and big data analytics require a more comprehensive approach, one that strikes a balance between technological capabilities, organizational readiness, and the influence on society.

关键词： technological innovation Ethics Protocols Decision making machine learning Data science Big Data Predictive models Natural language processing Data governance

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Data Visualization for Corona Patients Globally using Real-Time APIs

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

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Sustainable Computing and Data Communication Systems (ICSCDS), International Conference on

作者： S. Durai m. mohamed Iqbal S Niresh Kumar Chockalingam Alagappan Dept. of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai School of Computer Science and Engineering VIT-AP University Amaravati Department of Computer Science And Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram Department of Electrical and Electronics Engineering M.Kumarasamy College of Engineering Karur Tamilnadu India

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.

关键词： COVID-19 Personal protective equipment Pandemics computer viruses Soft sensors Data visualization Real-time systems

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A Neural Radiance Field-Based Architecture for Intelligent multilayered View Synthesis

arXiv

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arXiv 2023年

作者： Dhinakaran, D. Udhaya Sankar, S.m. Elumalai, G. Jagadish kumar, N. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India R.M.K College of Engineering and Technology Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Computer Science and Business Systems Sri Sairam Engineering College Chennai India

A mobile ad hoc network is made up of a number of wireless portable nodes that spontaneously come together en route for establish a transitory network with no need for any central management. A mobile ad hoc network (mANET) is made up of a sizable and reasonably dense community of mobile nodes that travel across any terrain and rely solely on wireless interfaces for communication, not on any well before centralized management. Furthermore, routing be supposed to offer a method for instantly delivering data across a network between any two nodes. Finding the best packet routing from across infrastructure is the major issue, though. The proposed protocol's major goal is to identify the least-expensive nominal capacity acquisition that assures the transportation of realistic transport that ensures its durability in the event of any node failure. This study suggests the Optimized Route Selection via Red Imported Fire Ants (RIFA) Strategy as a way to improve on-demand source routing systems. Predicting Route Failure and energy Utilization is used to pick the path during the routing phase. Proposed work assess the results of the comparisons based on performance parameters like as energy usage, packet delivery rate (PDR), and end-to-end (E2E) delay. The outcome demonstrates that the proposed strategy is preferable and increases network lifetime while lowering node energy consumption and typical E2E delay under the majority of network performance measures and factors. © 2023, CC BY.

关键词： Energy utilization

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Prediction of severity of Knee Osteoarthritis on X-ray images using deep learning

Prediction of severity of Knee Osteoarthritis on X-ray image...

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2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022

作者： Arumugam, Sajeev Ram Balakrishna, R. Rajeshram, V. Gowr, Sheela Karuppasamy, Sankar Ganesh Premnath, S.P. Sri Krishna College of Engineering and Technology Department of AI&DS Coimbatore India Dr. Sakunthala Engineering College Vel Tech High Tech Dr. Rangarajan Department of AI&DS Chennai India M.Kumarasamy College of Engineering Department of Computer Science and Engineering Karur India Department of CSE Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE Chennai India Sri Krishna College of Engineering and Technology Department of ECE Coimbatore India

ISBN: (纸本)9781665453424

The most prevalent kind of arthritis is osteoarthritis (OA). Radiologists use to employ the Kellgren-Lawrence (KL) grading system to identify the aggressiveness of OA based on the information shown on the pair of knee joints. computer-assisted strategies have recently been proposed to enhance the accuracy of OA diagnosis. Previous semiautomatic segmentation approaches, on the other hand, required human interaction, limiting their use on huge datasets. Furthermore, CNN is used to quantify OA rigorousness to investigate the relationships among distinct local regions. SSD reduces human interaction and provides a back-to-back approach to computerized osteoarthritis detection by incorporating the object detection model. The rating is based on X-ray scans from the Osteoarthritis Initiative (OAI) dataset. At the cost of training on a huge dataset with over 8260 knee joint samples, our method accurately segments 96.37% of data. © 2022 IEEE.

关键词： Object detection

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Securing Neural Network in the Cloud: A Systematic Approach of Cloud Security Based on ANN-SVm model

Securing Neural Network in the Cloud: A Systematic Approach ...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Ar Arunarani V. Vijayagopal L. Kartheesan Vijay Kumar Dwivedi Rajesh Kumar A Neerav Nishant Department of Computational Intelligence School of Computing College of Engineering SRM Institute of Science and Technology Chennai Tamilnadu India Faculty of Computer Applications Dr. M.G.R. Educational and Research Institute Chennai Tamilnadu India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Mathematics Vishwavidyalaya Engineering College Surguja Chhattisgarh India Department of Computer Science and Engineering N.S.N. College of Engineering and Technology Karur Tamilnadu India Department of Computer Science and Engineering School of Engineering Babu Banarasi Das University Lucknow India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

many services have made the transition to the cloud because of the increased reliability and efficiency of cloud computing. Due to their capacity to ease service access while also securing communications in the public network, the three-factor mutual Authentication and Key Agreement (mAKA) protocols are seeing increased use in multi-server designs. A lack of formal security proof or high communication and computation costs makes the limited three-factor mAKA protocols that are currently available susceptible to a wide range of attacks. The proposed approach consists of three phases, which are preprocessing, feature selection, and model training. Text scheduling and normalization make up preprocessing. In feature selection, the PSO is an optimization method that uses a population-based approach to find optimal solutions. An ANN-SVm was employed throughout the model's training phase. With an average accuracy of 93.30%, the proposed technique surpasses ANN and SVm.

关键词： Training Support vector machines Protocols Accuracy Systematics Cloud computing security Neural networks Feature extraction Vectors Reliability

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Chaotic Particle Swarm Optimization Empowered Deep Convolutional Neural Network for Prostate Cancer Diagnosis

Chaotic Particle Swarm Optimization Empowered Deep Convoluti...

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International Conference on Advances in Information technology (ICAIT)

作者： Gokulnath C N. Kalaiarasi Yeshaswini R P. Brinda Jeyakarthika K S.T. Gopukumar Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Campus Kattankulathur Chengalpattu Department of ECE R.M.K college of engineering and technology Chennai Department of computer science Rajarajeshwari college of engineering Kumbalgodu Bangalore Department of Computer science and Engineering Veltech High tech Dr rangarajan Dr sakunthala engineering college Avadi Computer science and Engineering Ramco Institute of Technology Virudhunagar District Tamil Nadu Department of Research Activities and Biostatistics Sarada Krishna Homoeopathic Medical College Kanniyakumari District Tamilnadu India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Prostate cancer (PCa) Classification on magnetic resonance imaging (mRI) is an innovative use of medical imaging and machine learning (mL) models that objects to precisely recognize and classify PCa lesions in mRI scans. This advanced technique permits healthcare experts to analyze PCa more efficiently, define its level and severity, and improve modified treatment tactics. By analyzing subtle differences in tissue faces and recognizing cancerous areas, this methodology provides enhanced early recognition which is vital for patient results. It allows oncologists and radiologists to make knowledgeable verdicts, improving accuracy and efficacy of PCa diagnosis and successive treatment plans. This manuscript provides a proposal for Chaotic Particle Swarm Optimization Empowered Deep Convolutional Neural Network (CPSO-DCNN) technique for PCa Diagnosis. The developed CPSO-DCNN architecture incorporates a multi-faceted approach, starting with Wiener filtering (WF) for sound reduction, improving excellence and clarity of medical images. Feature extraction accomplished by employing SqueezeNet which is a great deep learning (DL) design expert at taking complex image patterns. This method further influences CPSO for hyperparameter tuning, enhancing its performance in order to support exact dataset characteristics. For critical task of PCa diagnosis, classification performed via bi-directional gated recurrent unit (BiGRU) networks and well-known for their capability to seize temporal needs within data. The simulation outcomes highlighted that CPSO-DCNN technique characterizes a substantial development in field of medical diagnostics. It provides a strong and exact solution for early recognition and classification of PCa.

关键词： Image recognition magnetic resonance imaging Feature extraction Proposals medical diagnosis Prostate cancer Particle swarm optimization Tuning medical diagnostic imaging Principal component analysis

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K-means and Decision Tree Approach to Predict the Severity of Diabetes Cases 2

K-Means and Decision Tree Approach to Predict the Severity o...

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2nd IEEE mysore Sub Section International Conference, mysuruCon 2022

作者： Adgaonkar, Amarja Ganesan, R. Pavithra, D. Srilakshmi, C.H. mavaluru, Dinesh Latha mary, S Angel K.C. College of Engineering and Management Studies and Research Department of Information Technology Maharashtra India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Dr. N.G.P Institute of Technology Department of Information Technology Coimbatore India R.M.D Engineering College Thiruvallur India Saudi Electronic University Department of Information Technology Riyadh Saudi Arabia Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

The prevalence of type 2 diabetes is fairly high worldwide. Early identification is essential for Diabetic management and prophylaxis. Due to their capacity to classify data, machine Learning (mL) techniques are currently becoming more and more important in the field of diagnostic purposes. To aid in the diagnosis of Type 2 diabetes, a mixed proposed methodology is put out in this work. K-means has been employed in the proposed model-based minimization and a J48 decision tree is used as a classifier to classify. We were using the Pima Indians Diabetic Database from the UCI mL Repository to obtain the results of the experiment. The outcome demonstrates that, in comparison to other earlier research that is described in the literature, the proposed model has achieved more reliability. These findings demonstrate that the proposed methodology might be useful for diagnosing Type 2 diabetes. © 2022 IEEE.

关键词： Decision trees

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Secure Android Location Tracking Application with Privacy Enhanced technique 5

Secure Android Location Tracking Application with Privacy En...

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5th International Conference on Computational Intelligence and Communication technologies, CCICT 2022

作者： Dhinakaran, D. Rajesh Khanna, m. Panimalar, S.P. Anish, P. Pradeep Kumar, S. Sudharson, K. Velammal Institute of Technology Department of Information Technology Chennai India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Information Technology Chennai India Saveetha Engineering College Department of Information Technology Chennai India R.M.K College of Engineering and Technology Department of Cse Chennai India Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai India

ISBN: (数字)9781665472241

ISBN: (纸本)9781665472241

In today's world, tracking the position of an Android device has become a must. There have been a slew of effective retrieval strategies, such as Google's Find my Device, but they fall short in some situations, such as when the phone does not have a Gmail account and does not have GPS or Internet access. To address the limitations of existing solutions, our suggested SALT-Secure Android Location Tracker system gathers location based on mobile device in the meantime without such individual's consent. The location permissions are set in the Android manifest file, which transmits location data to the Firebase cloud per 10 seconds. Even if the participant's android device's sim card is swapped once it has been stolen or lost, or the GPS is turned off, the user may see the exact location of a device. Because it stores geolocation in the Firebase cloud, the suggested solution is secured and much more vulnerable to security assaults. To the Firebase cloud, all data transit is in JSON format. In future work, we'll concentrate on integrating the functionality in the Android OS build so that the program is not erased when the phone is factory reset. © 2022 IEEE.

关键词： Location

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