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6th International Conference on Recent Trends on Electronics, Information, Communication and Technology, RTEICT 2021

作者： saichandana, s. sooda, Kavitha B.M.S. College of Engineering Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781665435598

In Recently, social media networks are popularly emerging through world. This has been a great platform for information sharing through network among people. being ubiquitous in nature, social media are accessible anywhere and at any point of time. To provide Quality of Experience support, a learning-based model for social media is been proposed. This is mainly used to improve user's usage satisfaction and to reduce access delay in Online social Networks (OsN). For this, an analysis of Twitter traces for over fourteen months is conducted. Over 2,800 users Twitter data is collected using Twitter API for the analysis of social media friendship. And a cluster-based analysis for these set of user friends is made for the prefetch prediction. A mechanism using XGboost algorithm and Decision Tree Regressor algorithm, the performance of this framework is been determined. The performance of this framework using trace-drive emulations on the social media network has been evaluated. Evaluation results could do superior performance with XG boost algorithm of around 85.1% accuracy of access delay reduction. © 2021 IEEE.

关键词： social networking (online)

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Teaching style Recommender using Machine Learning 1

Teaching Style Recommender using Machine Learning

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1st IEEE International Conference on Advances in Information Technology, ICAIT 2019

作者： Hariharan, M. sooda, Kavitha vineeth, Nandhini Rekha, G.s. B.M.S College of Engineering Department of Computer Science and Engineering Bengaluru Karnataka India

ISBN: (纸本)9781728132419

this paper aims to bridge the gap between teaching and learning in engineering education. Different students learn using different techniques or styles. There can be different kinds of learners within a single class. The teacher needs to understand this difference in order to deliver their contents in the classroom effectively. The proposed system aids the faculty in understanding the classroom composition in terms of the learning styles of students and suggests specific teaching methods that can be helpful for the entire class. The system uses a survey called the Index of Learning styles (ILs) to understand the learning style of the class. A machine learning model is used to suggest the possible teaching methods based on the learning styles to ensure effective classroom delivery by the faculty. The model is trained on a custom made dataset to understand the styles. In future, this model can be extended for subjects other than engineering and this could be used by all faculties regardless of their experience in the field. © 2019 IEEE.

关键词： students

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Integrating OAuth and aadhaar with e-health care system 3

Integrating OAuth and aadhaar with e-health care system

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3rd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2018

作者： Khatoon, Abida Umadevi, v. B.M.S. College of Engineering Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781538624401

smartphone based E-health applications have the potential to change the way in which information is being exchanged between various stakeholders present in the medical industry by providing quick access to processed medical data on the go. E-health applications have gained wide acceptance among the doctor community because of their ability to help doctors in understanding the various pattern of symptoms that show up in the initial stages of a disease or during a relapse which in turn saves a lot of time and lives. These applications have also been used by a lot of patients to get instant access to their medical history which otherwise would have not have been possible with the traditional way of maintaining printed medical records in a file. Health records of a patient usually contain sensitive information and digitalization of these records will significantly increase the chances of private data falling into the wrong hands so it is very critical to provide secured access mechanisms to all the stakeholders present in this system and ensure users privacy. Medical data breach can be avoided by the implementation of various authentication and authorization techniques wherever necessary. Health care sector doesn't have a centralized way of identifying a patient and this decreases the feasibility of large scale storage and maintenance of the medical records provided by various doctors. Ever since the introduction of Aadhaar in India, all the major organizations have put in immense amount of effort to link various users accounts to their Aadhaar number and this has turned out to be very beneficial because it has reduced the processing time, reduced the leakage in the system and also eradicate the duplicity of account in sectors like LPG subsidy, Academic scholarship, Crop Insurance etc. Through this work, approaches involved in design and implementation of an Aadhaar based E Health application with OAuth is discussed. © 2018 IEEE.

关键词： smartphones

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'vulnerabilities in smart Contracts: A Detailed survey of Detection and Mitigation Methodologies'

'Vulnerabilities in Smart Contracts: A Detailed Survey of De...

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2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCs 2024

作者： Kumar, Nayantara K Honnungar, Niranjan v sharwari Prakash, M. Lohith, J.J. B.M.S. College of Engineering Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350372502

In the ever-changing world of blockchain technology, the emergence of smart contracts has completely transformed the way agreements are executed, offering the potential for automation and trust in decentralized systems. Despite their built-in security features, smart contracts still face persistent vulnerabilities, resulting in significant financial losses. While existing studies often approach smart contract security from specific angles, such as development cycles or vulnerability detection tools, this paper adopts a comprehensive, multidimensional perspective. It delves into the intricacies of smart contract security by examining vulnerability detection mechanisms and defense strategies. The exploration begins by conducting a detailed analysis of the current security challenges and issues surrounding smart contracts. It then delves into established frameworks for classifying vulnerabilities and common security flaws. The paper examines existing methods for detecting, and repairing contract vulnerabilities, evaluating their effectiveness. Additionally, it provides a comprehensive overview of the existing body of knowledge in smart contract security-related research. Through this systematic examination, the paper aims to serve as a valuable reference and provide a comprehensive understanding of the multifaceted landscape of smart contract security. © 2024 IEEE.

关键词： smart contract

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Power aware and signal strength based routing algorithm for mobile ad hoc networks

Power aware and signal strength based routing algorithm for ...

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International Conference on Communication systems and Network Technologies

作者： varaprasad, G. Dept of Computer Science and Engineering B.M.S. College of Engineering Bangalore 560019 India

ISBN: (纸本)9780769544373

A mobile ad hoc network is collection of mobile nodes. The mobile nodes can move freely from one place to another. In this paper, we propose a new routing algorithm for the mobile ad hoc networks to route the data packets to the destination based on the residual battery capacity and signal strength. The proposed model has simulated with the help of network simulator and has compared with the algorithms namely, MTPR and CMMbCR. The simulation results are shown that the proposed model has reached top position in terms of the network lifetime, network throughput and percentage of path reconstructions as compared to MTPR and CMMbCR models. In MTPR model, 7th node dies around 25.96% earlier than the proposed model and 4.95% before CMMbCR model. At 11th node in MTPR model will die around 96.32% prior to the proposed model and 93% against to CMMbCR model. In fact, the proposed model has reduced the number of route reconstructions by 83.86% as compared to CMMbCR model whereas MTPR model, it takes more number of path reconstructions(81.82%) than CMMbCR model if the node mobility is 10 m/sec. © 2011 IEEE.

关键词： Mobile ad hoc networks

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An Exploratory Analysis of GsDMM and bERTopic on short Text Topic Modelling 4

An Exploratory Analysis of GSDMM and BERTopic on Short Text ...

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4th International Conference on Cognitive Computing and Information Processing, CCIP 2022

作者： Udupa, Abhinandan Adarsh, K.N. Aravinda, Anvitha Godihal, Neelam H Kayarvizhy, N. B M S College of Engineering Department of Computer Science and Engineering Bengaluru Karnataka560019 India

ISBN: (纸本)9781665456487

Topic models may be a useful tool for locating latent subjects in collections of documents. short text clustering has become a more important task as social networking sites like Twitter have gained popularity. short text is characterised by high sparsity, high-dimensionality, and large-volume. These characteristics are challenging to overcome. Two of the most well-known short text modelling algorithms are bERTopic and the Gibbs sampling Dirichlet Multinomial Mixture Model (GsDMM). GsDMM is a topic model which can infer the count of topic clusters automatically with a good compromise between the fullness and uniformity of the clustering results, and is fast to converge. bERTopic is a neural topic model that extracts coherent topic representations based on the semantic similarity of words and phrases in the and clustering with the help of a class-based form of TF-IDF. We compare these two algorithms in this paper to determine which model is more effective in short text topic modelling. © 2022 IEEE.

关键词： semantics

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An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier

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International Journal of Cognitive Computing in engineering 2021年 2卷 40-46页

作者： Kumari, saloni Kumar, Deepika Mittal, Mamta Department of Electronics and Communication Engineering Bharati Vidyapeeth's College of Engineering New Delhi India Department of Computer Science & Engineering Bharati Vidyapeeth's College of Engineering New Delhi India Department of Computer Science & Engineering G B Pant Govt. College of Engineering New Delhi 110020 India

Diabetes is a dreadful disease identified by escalated levels of glucose in the blood. Machine learning algorithms help in identification and prediction of diabetes at an early stage. The main objective of this study is to predict diabetes mellitus with better accuracy using an ensemble of machine learning algorithms. The Pima Indians Diabetes dataset has been considered for experimentation, which gathers details of patients with and without having diabetes. The proposed ensemble soft voting classifier giv.s.binary classification and uses the ensemble of three machine learning algorithms viz. random forest, logistic regression, and Naive bayes for the classification. Empirical evaluation of the proposed methodology has been conducted with state-of-the-art methodologies and base classifiers such as Adaboost, Logistic Regression,support vector machine, Random forest, Naïve bayes, bagging, Gradientboost, XGboost, Catboost. by taking accuracy, precision, recall, F1-score as the evaluation criteria. The proposed ensemble approach gives the highest accuracy, precision, recall, and F1_score value with 79.04%, 73.48%, 71.45% and 80.6% respectively on the PIMA diabetes dataset. Further, the efficiency of the proposed methodology has also been compared and analysed with breast cancer dataset. The proposed ensemble soft voting classifier has given 97.02% accuracy on the breast cancer dataset. © 2021

关键词： classification Diabetes ensemble Machine Learning soft voting

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Heart Disease Prediction using Logistic Regression with PCA-MFsA Feature Extraction Technique and Multidimensional scaling (MDs) Pre-processing Approach 3rd

Heart Disease Prediction using Logistic Regression with PCA-...

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3rd International Conference on Advancements in smart Computing and Information security, AsCIs 2024

作者： Priscila, s. silvia sakthivanitha, M. sadhana, C. Lavanya, M. Poornima, v. Department of Computer Science Bharath Institute of Higher Education and Research Tamil Nadu Chennai India Department of Information Technology Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India Department of MCA Prathyusha Engineering College Tamil Nadu Chennai India Department of Computer Applications S.D.N.B Vaishnav College for Women Tamil Nadu Chennai India Department of Computer Science Vels Isntitute of Science Technology and Advanced Studies Tamil Nadu Chennai India

ISBN: (纸本)9783031862922

Heart disease prediction is critical because it has the potential to save lives, increase patient outcomes, and save the cost of healthcare. An important task in medical diagnostics is the forecasting of heart disease. In this article, we investigate the efficacy of integrating Multidimensional scaling (MDs) preprocessing with various feature extraction methods for precise LR (Logistic Regression) heart disease prediction. The feature extraction methods under investigation include Independent Component Analysis (ICA), Principal Component Analysis (PCA) and Mean Fisher score-based Algorithm (PCA-MFsA). PCA with MFsA emerges as the most effective method, consistently producing superior outcomes in the forms of accuracy rate, precision, and recall value compared to ICA and PCA. This underscores the significance of combining PCA with the specialized feature selection process offered by PCA-MFsA to enhance the LR model’s discriminatory power. Moreover, The incorporation of MDs preprocessing with PCA-MFsA results in a significant improvement in accuracy rate, precision value, and recall. From the result obtained PCA-MFsA produces accuracy of 91%, precision of 0.90 and recall of 0.86 respectively. © The Author(s), under exclusive license to springer Nature switzerland AG 2025.

关键词： Logistic regression

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Fungal disease detection in maize leaves using haar wavelet features 3rd

Fungal disease detection in maize leaves using haar wavelet ...

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3rd International Conference on Information and Communication Technology for Intelligent systems, ICTIs 2018

作者： Deshapande, Anupama s. Giraddi, shantala G. Karibasappa, K.G. Desai, shrinivas D. Department of Computer Science and Engineering B.V. Bhoomaraddi College of Engineering and Technology Hubli India School of Computer Science and Engineering KLE Technological University Hubli India

ISBN: (纸本)9789811317415

Agriculture is the backbone of Indian economy. Diseases in crops are causing huge loss to the economy. Only early detection can reduce these losses. Manual detection of the diseases is not feasible. Automated detection of plants diseases using image processing techniques would help farmers in earlier detection and thus prevent huge losses. Maize is an important commercial cereal crop of the world. The aim of this study is the detection of common fungal diseases, common rust, and northern leaf blight in maize leaf. The proposed system aims at early detection and further classification of diseases into common rust, northern leaf blight, multiple diseases, or healthy using first-order histogram features and Haar wavelet features based on GLCM features. Two classifiers, namely, k-NN and svM are considered for the study. The highest accuracy of 85% is obtained with k-NN for k = 5 and accuracy obtained with svM-based classification is 88%. © 2019, springer Nature singapore Pte Ltd.

关键词： Classification (of information)

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MPPT Control using Modified sepic Converter in Pv Modules Connected DC Micro Grid systems 7

MPPT Control using Modified Sepic Converter in PV Modules Co...

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： varatharaju, v.M. senthilkumar, b. Manivannan, R. Mahalakshmi, s. Geetha, C. Gomathi, s. University of Technology and Applied Science Ibra Oman V.S.B Engineering College Karur India St.Joseph's Institute of Technology Chennai India R.M.K. Engineering College Chennai India Saveetha School of Engineering Simats Chennai India

ISBN: (纸本)9798350397284

This research study applies the predictive control method to implement a distributed maximum power point tracking algorithm, which enhances the energy-extracting capabilities of a cascaded power optimizer-based system in variable environmental conditions. Two control loops are used in the developed method, one for each converter to implement a model predictive control loop based on tracking the maximum power point and the other for power optimization across all cascaded Pv modules. Closed loop response of FOPID and ANN controllers simulation results are compared and verified with the prototype. The experimental findings support high energy output, rapid dynamic response, and almost distinct oscillations near the MPP. © 2023 IEEE.

关键词： Maximum power point trackers

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