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MCHeRA: An Efficient Multi Cloud Heuristic Resource Allocation using Grey Wolf Optimization 5

MCHeRA: An Efficient Multi Cloud Heuristic Resource Allocati...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Gandhimathi, P. Kiruthiga, S. BIndu, K.V. Shanthi, H.J. Velmurugan, V. Sangeethaa, S.N. Srm Madurai College for Engineering and Technology Pottapalayam Sivagangai District630612 India School of Computing Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science &engineering Chennai India Puduvoyal India Department: Computer Applications Hindustan Institute of Technology and Science Deemed to be University Padur India Vel Tech Rangarajan Dr Sagunlatha R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Chennai India Bannari Amman Institute of Technology India

ISBN: (纸本)9798350389609

Cloud computing is a cutting-edge technology that has gained immense popularity in the present era. The burgeoning demand for services has necessitated the development of a framework for efficiently allocating resources to incoming requests. This approach not only enhances the network's efficiency but also reduces costs significantly. The convergence of cloud and edge computing has emerged as a burgeoning field of study in the computing industry over the past few years. The exponential rise in the number of customers and requests for cloud data centers (CDCs) has highlighted the critical need for robust servers and energy-efficient mechanisms. As CDCs expand to meet growing demands, they face significant challenges related to energy consumption, environmental impact, and operational costs. Employing suitable algorithms for resource allocation in CDCs is crucial in order to minimize energy consumption. The objective of this research endeavor was to devise an ingenious approach for dynamically allocating resources in cloud networks by leveraging the power of the Gray Wolf Optimization algorithm. The proposed work performs Multi-Cloud Heuristic Resource Allocation (MCHeRA) using Grey Wolf Optimization (GWO) algorithm and produces 61% of computational resource utilization, 79% of CPU utilization, 64% memory utilization, 74% bandwidth utilization. The algorithm that has been put forth is meticulously assessed and contrasted with its counterparts. The outcome of the simulation manifests the remarkable efficacy of the novel MCHeRA in comparison to alternative algorithms. © 2024 IEEE.

关键词： Cloud platforms

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Predictive Analysis of Diabetes Mellitus Using Decision Tree Approach 2

Predictive Analysis of Diabetes Mellitus Using Decision Tree...

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2nd Asian Conference on Innovation in Technology, ASIANCON 2022

作者： Thotad, Puneeth Bharamagoudar, Geeta R. Anami, Basavaraj S. Kle Institute of Technology Dept. of Master of Computer Applications Hubballi India Kle Institute of Technology Dept. of Computer Science & Engineering Hubballi India

ISBN: (数字)9781665468510

ISBN: (纸本)9781665468510

Diabetes Mellitus is one of the Non-Communicable Disease. It is characterized by hyperglycemia. It may be reason for numerous health difficulties. According to the statistics in coming years, in 2030, worldwide it reaches up to 643 million counts of diabetic patients. The various factors such as Bad Diet, High Blood Pressure, Obesity, Age, Lack of Exercise etc. can led to Diabetes Mellitus. The various diseases like nerve damage, heart problem, eye problem, kidney disease, stroke in many people caused by high risk of Diabetes Mellitus. Machine Learning plays vital role in healthcare sectors. Using various Machine Learning techniques, a study can be done on large datasets and discover unseen facts, unseen patterns to realize awareness from the data and predict results consequently. In this work, the PIMA Indian Diabetes dataset is considered for Predictive Analysis. The Decision Tree algorithm, namely ID3 (Iterative Dichotomize 3) is used for a predictive analysis. The major phases include preprocessing, feature extraction, classification, and prediction. The classification accuracies of 79.76% and 76.3% are obtained for balanced and unbalanced datasets respectively. The maximum specificity and sensitivity obtained are 79.75% and 79.78% respectively. The ROC curve gives 79.57% overall classification accuracy. The work assists physicians as a second opinion. It can help various health industries to predict the diabetes disease in a better way. © 2022 IEEE.

关键词： Decision trees

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Automatic Detection and Epidemiologic Trends in Head and Neck Cancer Using Fast-R-CNN and SVM Methods 7

Automatic Detection and Epidemiologic Trends in Head and Nec...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Sakthivel, M. Patil, Sonal Kamarunisha, M. Pokhariyal, Rajesh Pal, Subharun Tiwari, Mohit Malla Reddy College of Engineering Department of CSE-AIML Hyderabad India G H Raisoni Institute of Engineering and Business Management Jalgaon Department of CSE-IT Maharashtra India Department of Computer Applications Tamilnadu Perambalur India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Indian Institute of Technology Jammu Department of Computer Science and Engineering Jammu & Kashmir India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350340600

H ead and neck squamous cell carcinoma (HNSCC) is the sixth most frequent cancer in the world. Despite improvements in diagnosis and therapy, the five-year survival rate for patients with advanced HNSCC has risen only slowly. This is mostly due to the high number of patients who present with an advanced disease stage at diagnosis, as well as the high frequency of relapse and secondary tumors. Protein biomarkers that aid in the early diagnosis of primary HNSCC or relapse hold promise for improving clinical outcomes. Finding early changes in the mucosal linings previous to the development of invasive tumors and properly assessing the likelihood of malignant transformation, both currently tough activities, may be important. The proposed procedure makes use of preprocessing, feature extraction, segmentation, and model training. Anisotropic Diffusion Filter and Gaussian Filter are used in preprocessing. Extraction of the techniques follows preprocessing. The Grey Level Run Length matrix is used for feature extraction. Segmenting the nucleus is a highly recommended step in the segmentation process. Training is performed using an RCNN- SVM model. When compared to two other models, RCNN and SVM, the proposed method excels. © 2023 IEEE.

关键词： Support vector machines

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Evaluating Multi-Platform IoT Solution for Agricultural Monitoring 2

Evaluating Multi-Platform IoT Solution for Agricultural Moni...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Sharma, Sumit Kumar Saraswat, Birendra Kumar Sharma, Kamal Saraswat, Manish Sharma, Tarun Kumar Ajay Kumar Garg Engineering College Department of Information Technology UP Ghaziabad India Raj Kumar Goel Institute of Technology Computer Science & Engineering UP Ghaziabad India Gla University Department of Mechanical Engineering UP Mathura India Lloyd Institute of Engineering & Technology Department of Mechanical Engineering UP Greater Noida India Abes Engineering College Department of Computer Applications UP Ghaziabad India

ISBN: (纸本)9798350371055

This paper offers a multi-platform Iota answer designed for agricultural tracking. The proposed architecture makes use of three core technologies: low-electricity sensors to acquire environmental data, cloud-based analytics to offer insights into the statistics and a software layer for customers to view and interact with the amassed records. The gadget evaluated the usage of a dataset gathered in real-time from soil moisture and photosynthetic mild distribution sensors. The amassed facts were processed in an actual-time analytics engine, which generated histograms, indicators, and standing messages indicating the situations of the monitored surroundings. The results show that the proposed multi-platform answer finished at a first-class level of accuracy in monitoring the agricultural environment. Additionally, the results reveal that the gadget is able to efficaciously detect early modifications in environmental conditions even using minimum assets. The proposed solution offers an effective and reliable platform for agricultural tracking that can be without difficulty custom-designed and scaled to any size of operation. © 2024 IEEE.

关键词： Accuracy Architecture Conditions Distribution Environmental

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An Analysis of Signature-Based Components in Hybrid Intrusion Detection Systems

An Analysis of Signature-Based Components in Hybrid Intrusio...

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

作者： Agoramoorthy, Moorthy Ali, Ahamed Sujatha, D. Michael Raj, T.F. Ramesh, G. Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Chennai India Computer Science and Engineering Easwari Engineering College Chennai Ramapuram India St.Peters College of Engineering and Technology Department of Computer Science and Engineering Chennai Avadi India Simats Department of Computer Applications Sclas Tamilnadu Chennai India K.L.N.College of Engineering Department of Information Technology Sivagangai - Tamilnadu Pottapalayam India

ISBN: (纸本)9798350394580

In cybersecurity, Intrusion Detection Systems (IDS) protect against emerging cyber threats. Combining signature-based and anomaly-based detection methods may improve IDS accuracy and reduce false positives. This research analyzes hybrid intrusion detection systems' signature-based components' performance and limitations. The paper begins with a detailed history of signature-based detection methods responding to changing threat situations. This research analyzes signature databases to determine their capacity to identify and guard against current threats and cover known vulnerabilities. The paper also examines the intricate relationship between signature-based detection and anomaly-based techniques in hybrid IDS systems. This investigation examines how these two methodologies work together to uncover old and new attack strategies, focusing on zero-day vulnerabilities and polymorphic malware. A diverse dataset of network traffic and attack scenarios is used to test. Detection, false positives, and response times assess signature-based components. Comparative examinations investigate how signature-based detection affects system accuracy and efficiency. This research illuminates the role of signature-based aspects in hybrid intrusion detection systems. This study recommends integrating signature-based detection techniques with anomaly-based methods to improve hybrid intrusion detection systems (IDS) at recognizing and mitigating various cyber threats. © 2023 IEEE.

关键词： Anomaly-Based Detection Cybersecurity False Positives Hybrid IDS Intrusion Detection Systems Network Security Polymorphic Malware Signature-Based Detection Threat Detection Zero-Day Exploits

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An Efficient Integrated approach of Fuzzy C-Means Map Reduce for Weather Forecasting Data Collection 6

An Efficient Integrated approach of Fuzzy C-Means Map Reduce...

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Sathyamoorthy, Malathy Kumar, Rakesh Vanitha, C.N. Sharma, Bhisham Syamraj, V. Chowdhury, Subrata Kongu Engineering College Tamil Nadu Erode638060 India GLA University Department of Computer Engineering and Applications Mathura India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Sreenivasa Institute of Technology and Management Studies Department of Computer Science and Engineering Andra Pradesh Chittoor India

ISBN: (纸本)9798350346961

Networks of specialized, geographically scattered wireless sensors that track and record environmental physical conditions and transmit the gathered information to a central point are known as wireless sensor networks. Simply Wireless sensor networks are networks of sensors that collect data about the environment. Wireless sensor networks are ideal for real-time projects that require the collection of data. These networks can be deployed for various tasks such as monitoring temperature and humidity this system will collect data about the areas affected. By the earthquake to give warning in advance to these people living those areas. Through the use of sensors networks, device will be able to predict the level of flooding in an area and provide helpful forecasts to the people living in the affected areas. A machine learning-based fuzzy c-means algorithm is used as the proposed algorithm and input in the form of a dataset.. © 2023 IEEE.

关键词： Wireless sensor networks

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Optimizing Image Recognition Model through Transfer Learning and Robust Feature Extraction in Neural Networks 2

Optimizing Image Recognition Model through Transfer Learning...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Ramesh, G. Agoramoorthy, Moorthy Ahamed Ali, S. Sujatha, D. Michael Raj, T.F. K.L.N.College of Engineering Department of Information Technology Sivagangai Tamilnadu Pottapalayam India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Easwari Engineering College Computer Science and Engineering Chennai Ramapuram India St. Peters College of Engineering and Technology Department of Computer Science and Engineering Chennai Avadi India Sclas Simats Department of Computer Applications Tamilnadu Chennai India

ISBN: (纸本)9798350369960

These advances in image identification's technology have created massive changes, mainly in the health care facilities plus self-directed systems. Transfer learning strategies coupled with robust feature extraction techniques are used in the model to promote efficiency in the imaging system. A technique of fine-tuning the weights used for recognition tasks on pre-trained convolutional neural network architectures is proposed. State of art feature extraction pipeline composed with edge detection, texture analysis and spatial frequency analysis to enrich network capacity of distinguishing intricate visual textures and patterns. This combination of techniques helps in faster model training with top class prediction results on mixed image datasets. After rigorous testing on various benchmark datasets, including CIFAR-10 and ImageNet, considerable enhancement in recognition performance beyond baselines was observed. Additionally, the proposed approach is resilient to changes in image quality, illumination, and occlusions. The proposed framework enhances the existing model efficiency, flexibility, and robustness for application in real-time image identification. This approach opens new capabilities for the medical imagery, self-navigation cars, and object recognize mechanisms whose image classification must be exact. © 2023 IEEE.

关键词： Feature Extraction Image Recognition Neural Network Transfer Learning

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Autonomous Vehicle Communication with Parrot-Optimized Orientation-Aware Graph Neural Network for Next-Generation IoT Connectivity 4

Autonomous Vehicle Communication with Parrot-Optimized Orien...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Mercy, Immaculate Nainwal, Ankita Balasubramani, R. Nandankar, Praful V. Parikh, Swapnil Taqui, Syed Noeman Department of Computer Applications Vallam Tamil Nadu Thanjavur613403 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Department of Information Science and Engineering Karnataka Nitte574110 India Government College of Nagpur Department of Electrical Engineering Maharashtra 441108 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Gujarat Waghodia India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331540364

Effective communication between autonomous vehicles is essential for ensuring their safe and efficient operation. This research introduces Parr-OrAGNet, a novel approach that addresses the limitations of traditional communication methods in IoT automotive networks. Parr-OrAGNet integrates Macro-Micro Depth Normalization (MaMiDN) for accurate data normalization, Orientation-Aware Graph Neural Network (OrAGNet) for enhanced neural network modeling, and Parrot Optimizer (ParrOpt) for optimization. This combined approach effectively reduces packet loss, communication overhead, and latency. The evaluation results demonstrate significant improvements in communication efficiency, with a 97% reduction in packet loss and a 95% decrease in communication overhead. Additionally, Parr-OrAGNet achieves exceptional performance metrics in terms of accuracy, precision, recall, and computational efficiency. This research contributes to the advancement of autonomous vehicle communication by establishing a new benchmark for performance and scalability. Parr-OrAGNet offers a promising solution for addressing the challenges of IoT automotive networks and paving the way for the realization of fully autonomous driving. © 2024 IEEE.

关键词： Macroinvertebrates

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Optimization Model for Machine Learning Based Intrusion Detection System (OML-IDS) in Mobile-Edge Computing 4

Optimization Model for Machine Learning Based Intrusion Dete...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Kiruthiga, R. Jeyalaksshmi, S. Vanitha, V. Khan, Abdul Azeem Pal, Souvik Kumar, Rahul SRM Institute of Science & Technology Department of Computer Applications Kattankulathur Chennai India Depart. of IT Vels Institute of Science Technology & Advanced Studies Chennai India Sri Sankara Arts and Science College Department of Computer Applications Kanchipuram India Faculty of Islamic Technology Brunei Sister Nivedita University Department of CS and Engineering Kolkata India Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

The Internet of Things (IoT) and 5G communications now primarily use mobile edge computing (MEC). The fundamental concept behind MEC is the deployment of a hierarchy of edge servers with more computational power to handle the computing duties that are being offloaded by low-end IoT edge devices. Some of the security risks in MEC pertain to the application layer, the network layer, the data, and the node. Out main aim is to protect MEC from different cyber-attacks and continuous monitoring of digital assets. The network layer security for MEC is protected by intrusion detection systems (IDSs). We need an updated IDS system capable of detecting modern cyber-attacks as well as monitoring the devices. Thus, this paper proposes an optimization model for MEC settings that functions in conjunction with an OML-IDS, a machine learning-based intrusion detection system. To begin identifying attack patterns in a MEC environment, this model first develops an optimization-based feature selection strategy for selecting an ideal subset of characteristics. For the purpose of intrusion categorization and detection, a hybrid ML algorithm is also used. Enhancing the offloading latency, synchronization time, and turnaround time is necessary if you want to be able to recognize assaults in real time. © 2024 IEEE.

关键词： Network intrusion

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Continuous Tweet Stream Analysis using Apache Spark Streaming API & Kafka 15

Continuous Tweet Stream Analysis using Apache Spark Streamin...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Nisha, A.G. Shivaprasad, N. Nischitha, C.B. Shashikala, B.M. Sujithra, M. Chaba, Yogesh Electronics & Communications Engineering JSS Science & Technology University Mysuru India Dept.of Electronics & Communications Engineering Sri Jayachamarajendra College of Engineering JSS Science & Technology University Mysuru India Dept.of Computer Science St.Joseph's First Grade College Mysore India Dept.of Computer Applications Sri Jayachamarajendra College of Engineering JSS Science & Technology University Mysuru India Department of Computing-Data Science Coimbatore Institute of Technology Coimbatore India Department of Computer Science and Engineering Guru Jambeshwar University of Science and Technology Haryana Hisar India

ISBN: (纸本)9798331300579

Social media has become a trend nowadays people are spending more time in social media platform like Facebook, Twitter, Instagram to share their opinions, creating new interests, ideas and generate *** provide a major platform for collecting information which helps us to analyze them for our *** such microblogging social network is twitter which allows information collaboration and quick information sharing that provides relief from rising *** has several millions of people tweeting and expressing their perspectives on a due to its distinctively brief and straightforward expressing *** enormous volume of raw data from tweets can be utilised for analysis and processing based on industrial or business *** this technical paper, the application of tweet sentiment analysis by extracting a vast number of tweets using continuous tweet stream provided by Spark and Kafka has been *** system fetches live tweets from Twitter based on the search criteria, process the tweets, by using text mining techniques and classify them based on different labels. © Grenze Scientific Society, 2024.

关键词： Tweets

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