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Optimized Bit Matrix based Power Aware Load Distribution Policy for Federated Cloud

Procedia computer science 2020年 167卷 1771-1790页

作者： B S Rajeshwari M Dakshayini Assistant Professor Department of Computer Science and Engineering B M S College of Engineering Bangalore 560019 India Professor Department of Information Science and Engineering B M S College of Engineering Bangalore 560019 India

Federated Cloud is a next generation cloud computing model which works towards a new dynamic scenario in smart ecosystems. It consolidates individual clouds together to form single large cloud to satisfy all on-demand requirements of the users. Federated cloud is currently facing many challenges including: monitoring of individual cloud services, maintaining service level agreement, fair load distribution, power usage etc. Hence, to address these challenges this paper presents a brokerage model “ Optimized Bit Matrix based Power Aware Load Distribution Policy for Federated Cloud (OBMPLP) ”. Presented model includes two novel concepts: Bit Matrix and Load Distribution Factor. Bit Matrix constructed by individual cloud members representing resource availability status and environment in executing the user’s request. Load Distribution Factor representing load distribution level at individual cloud. OBMPLP policy dispenses the incoming requests among multiple clouds by analyzing bits pattern and load distribution factor that not only performs fair load distribution among multiple clouds, but also improves response time, alleviate power consumption at each cloud and achieve better quality service. The performance of the proposed policy is evaluated and results demonstrate reduction in response time, optimized power consumption and fair distribution of load compared to existing approaches.

关键词： Cloud Computing Load Distribution Quality Service Power Consumption Federated Cloud

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Green dream: A deep learning based real-time model for automatic waste segregation from video streams

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AIP Conference Proceedings 2023年第1期2773卷

作者： Prince Puthussery Nikhita Mariam Cherian T. K. Gokul Kiran M. U. Sreeja Abin Oommen Philip Department of Computer Science & Engineering Toc H Institute of Science & Technology Ernakulam Kerala India Assistant Professor Department of Computer Science & Engineering Toc H Institute of Science & Technology Ernakulam Kerala India Assistant Professor Department of CSE-Cyber Security Indian Institute of Information Technology Kottayam Kerala India

In this fast-pacing world one of the substantial problems faced is the drastic increase in waste generation and ensuring efficient and rational management of waste. Recycling tasks reduce waste production, mitigate the environment and improve the whole nation’s prosperity in the future. Inappropriate handling and discarding of useless materials have led to the contamination of groundwater and defiled the land resources. Therefore, it is vital that the methods and processes involved in waste collection and segregation is examined as the present-day disposal system is inefficient, time consuming, cumbersome and not completely viable due to their large amount. The proposed model provides a way of solving the above stated problems by detecting, identifying and segregating the waste materials. The proposed system is an automated waste classifier which uses a deep learning-based object detection model to classify objects into different categories. Using YOLOv5, an object detection algorithm the waste can be identified successfully from the image taken with the help of a camera and classify them into categories. This result is then sent to a segregation unit which categorizes the waste into the designated bin by rotating the conveyor belt using a motor. The proposed model operated at a Mean Average Precision(mAP) of 0.726 and an F1-Score of 0.83. This method of segregation of waste will enable faster ways to recycle the waste and save time and resources.

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RETRACTED ARTICLE: ALBAE feature extraction based lung pneumonia and cancer classification

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Soft Computing 2023年第2期28卷 589-589页

作者： M Braveen S Nachiyappan R Seetha K Anusha A Ahilan A Prasanth A Jeyam Assistant professor senior School of Computer Science and Engineering Vellore institute of technology Chennai Tamil Nadu India Associate Professor School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India Associate Professor School of Information Technology and Engineering Vellore Institute of Technology Vellore Tamil Nadu India Associate Professor Department of Electronics and Communication Engineering PSN College of Engineering and Technology Tirunelveli Tamil Nadu India Assistant Professor Department of Electronics and Communication Engineering Sri Venkateswara College of Engineering Sriperumbudur India Assistant Professor Computer Science and Engineering Lord Jegannath College of Engineering and Technology Kanyakumari Tamil Nadu 629402 India

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Survey on Impact and Learning’s of the Online Courses on the Present Era

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Procedia computer science 2020年 172卷 82-91页

作者： T. Anitha Kumari CH. Hemalatha M Subhani Ali R Naresh Assistant Professor Department of Computer Science Engineering HITAM Hyderabad Telangana 501401 India Associate Professor Department of Mechanical Engineering HITAM Hyderabad Telangana 501401 India Assistant Professor Department of Electrical and Electronics Engineering HITAM Hyderabad Telangana 501401 India

Day to day technology is changing to upgrade or adopt technology faculty as well as students preferring online courses like NPTEL. With the fast development of internet techniques, data and communication technology is being more and more employed in curricula, and learning portfolios may be mechanically retrieved and maintained as learners act through e-learning platforms. NPTEL provided open access to hundreds and thousands of learners to take the preferred courses and helped to become experts in a particular area. This paper describes the impact of the online courses (NPTEL) on the learners and challenges and learning’s from the online courses. The participants comprised 114 faculty, who were surveyed regarding their learning experiences, motivation, challenges and intentions to use NPTEL. And what can be improved from the Faculty and student point of view?

关键词： Online courses Learning’s Challenges Motivation

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IoT-based shrewd crop field MPA system

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AIP Conference Proceedings 2025年第1期3224卷

作者： Himanshu Agarwal Shweta Agarwal Utkarsh Mishra Vanshika Chaudhary Vanshita Singh Shaurya Chauhan Associate Professor Department of Computer Science & Engineering Moradabad Institute of Technology Moradabad (U.P.)-India Assistant Professor Department of Mathematics Ramabai Ambedkar Government Degree College Gajraula Amroha (U.P.)-India UG Scholars Department of Computer Science & Engineering Moradabad Institute of Technology Moradabad (U.P.)-India

Context: Agriculture stands as a pivotal driver of economic progress within a nation, yet the realm of technical advancements within this sector remains distressingly neglected by a multitude of governments. While farmers contribute untiring efforts to tend to their fields, substantial time is squandered on tasks like irrigation and safeguarding crops from birds and animal threats [1, 2]. This unwavering dedication often exacts a toll on farmers’ health, leading to ailments and respiratory issues stemming from exposure to noxious gases emitted by certain crops. Extensive research endeavours [3-9] have been undertaken to alleviate the burdens faced by farmers. These efforts, however, frequently culminate in singular applications such as automated irrigation systems or electric perimeters for crop protection. A subset of researchers has also delved into probing the prevalence of harmful gases across agricultural fields. This paper proposes an innovative approach to address these challenges through the utilization of Internet of Things (IoT) technology. Objective: The proposed solution canters on a NodeMCU powered intelligent crop field Monitoring-Protection-Alert (MPA) system, which serves as a technical path to revolutionize farming practices. The core objective underpinning the proposed system is the acquisition of real-time insights emanating from the crop field. This critical initiative empowers farmers with timely and accurate data, enabling them to make informed and precise decisions pertaining to their agricultural domain. Methods: By seamlessly integrating various sensors, the system detects the presence of birds, animals, and noxious gases in real-time. Furthermore, it enhances crop productivity by continuously monitoring soil parameters, including temperature and moisture levels, thereby optimizing irrigation processes. For seamless communication, the system is fortified with a GSM module that promptly alerts farmers about potential threats to their crops.

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Abnormal event detection using unsupervised deep one class learning

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AIP Conference Proceedings 2024年第1期3075卷

作者： G. Balamurugan K. Kishore Anthuvan Saharaj V. Arulalan H. Aswini S. Mahalakshmi Assistant Professor Department of Computing Technologies Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur 603203 Chengalpattu Tamil Nadu India Department of Computer Science and Engineering IFET College of Engineering Villupuram Tamil Nadu India Department of Information Science and Engineering BMS Institute of Technology and Management Bengaluru Karnataka India

Intelligent video surveillance and transportation systems have become more important in the field of security in recent years for identifying abnormal events that happen along the side of the road. Recent developments in surveillance systems include the automatic detection of abnormal events in video surveillance. However, only one normal event is accessible for the learning process in the context of abnormal event detection. In this scenario, the idea addresses a novel unsupervised deep one class learning architecture. It can produce optical flow pictures from original movies in addition to generating concise spatio-temporal characteristics for abnormal event detection resolutions. To guarantee that the "deep-one" class learning is classified correctly, it is built using a customized loss function which is the combination of 3 terms: Reconstruction Loss (RL), Generation Loss (GL), and Compactness Loss (CL). On a relatively challenging dataset known as the UCSD anomalous detection dataset, the suggested technique achieved better results than the existing methods, the Satellite Smoke Scene Detection dataset, and the Cross View Geo localization dataset. The experimental results assure that the proposed work provides higher accuracy then the state of art techniques.

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Multi-class Cancer Classification in Microarray Datasets Using MI-Based Feature Selection and Artificial Neural Network 1

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4th International Conference on Nanoelectronics, Circuits and Communication Systems, NCCS 2018

作者： Jansi Rani, M. Devaraj, D. Assistant Professor Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Virudhunagar India Dean School of Electronics & Electrical Technology Kalasalingam Academy of Research and Education Krishnankoil Virudhunagar India

ISBN: (数字)9789811528545

ISBN: (纸本)9789811528538

Recent developments in microarray data analysis and improvements in soft computing techniques have played a major role in the field of cancer classification. Classification and identification of cancer are very important as early cancer identification is highly useful for its treatment. Cancer diagnosis is challenging as microarray data has large number of genes. Many gene selection algorithms have been applied to effectively select the most promising gene subsets that can accurately identify cancer samples. In this paper, Mutual Information is used to select the informative genes and Feed Forward Neural Network is used to classify the genes. The proposed MI-ANN based gene selection and ANN classification are applied to classify multi-class microarray datasets. The obtained results show that the proposed method provides higher classification accuracy compared to existing methods. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Soft computing

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Exploratory data analysis of Indian Premier League (IPL)

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AIP Conference Proceedings 2023年第1期2773卷

作者： M. K. Manju Abin Oommen Philip M. U. Sreeja Department of Computer Science & Engineering Toc H Institute of Science & Technology Ernakulam Kerala India Assistant Professor Department of CSE-Cyber Security Indian Institute of Information Technology KottayamKerala India Assistant Professor Department of Information Technology Rajagiri School of Engineering & Technology Ernakulam Kerala India

Cricket is one of the games having the most number of followers and spectators in India. Among them, T20 League cricket is getting more attention. This paper have focused on performing an exploratory data analysis on Indian Premier League or IPL dataset utilizing the previous match details to draw hidden insights and patterns in data and further using it for the prediction of match outcomes. Indian Premier League or IPL results are always unpredictable, a large number of factors like venue, toss decision, player’s performance etc., determines the result of matches. Hence, on performing the analysis have drawn many important insights like whether home ground favors the winning of match, whether toss decision plays any role, who is the player of match of the season etc., were identified. Then, machine learning algorithms are applied on the relevant features for the prediction of matches. Finally, the accuracies of these algorithms are plotted to compare the performances to figure out the most accurate among them. The results illustrates that XGB Classifier showed the highest accuracy rate of 69.12%.

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Survey paper on big data tool analysis

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AIP Conference Proceedings 2023年第1期2800卷

作者： Vaishali B. Bhagat V. M. Thakare Sagar Pande Research Scholar PG Department of Computer Science and Engineering Sant Gadge Baba Amravati University Amravati Maharashtra India Professor and Head PG Department of Computer Science and Engineering Sant Gadge Baba Amravati University Amravati Maharashtra India Assistant Professor Intelligent System School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India

In this era of technology, online data is increasing enormously in various forms be it images, videos, texts, sound, etc. Now with increasing data, there arises the issue of storage and that’s the time when Bigdata and its tools come into the picture. To analyze the big data and process it with the available traditional methods, becomes an extremely strenuous task. And to overcome this drawback, there are various big data tools and techniques available. Some of the techniques that are used to analyze big data are the techniques of data mining such as clustering, classification, division, and prediction. The tools that are used in analyzing the big data are MongoDB, NoSQL, HPCC, Apache Storm, Spark, Apache Hadoop, also these tools are used in handling big data. In this paper, analysis of big data tools and techniques has been done with different examples and instances. To understand the tools better, their summaries along with examples are presented.

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Sales upsurge system

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AIP Conference Proceedings 2022年第1期2464卷

作者： R. A. Kalpana R. Sharmikha Sree , S. Meera N. Ramya R. Deepika A. Ganga M. Kiruthiga Devi 1)Department of Computer Science and Engineering Assistant Professor Sri Sairam Engineering College West Tambaram Chennai India

The growth in the number of sellers in both the offline and online markets has necessitated the development of analytic tools that may assist assess whether a company is reaching its sales targets. Our proposal Sales Upsurge System explains the requirement for a system to evaluate the product offered utilising Machine learning, data mining approaches, and algorithms such as Affinity analysis, Association rule learning- Apriori. Our proposed system idea for this project is to create a system (website) that takes the input of sold products, categories the data obtained, analyses the data, and extracts the sales trend, and then optimizes the data based on market requirements, thereby maximize the value of sales and merchandise planning and increasing the organization's overall sales and profits.

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