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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kumar, Anup Inzimam Patel, Srinivasan Arka Jain University Department of Mechnical Engineering Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India School of Management - Pg Department of Management Karnataka Bangalore India

ISBN: (纸本)9798350370249

Exploring Dimensionality discount strategies for Analytical Processing (OLAP) is an area of research that makes a specialty of the use of diverse techniques to reduce the variety of dimensions needed to investigate a records set. That is high-quality because it reduces processing time, garage area requirements, and simplifies the statistics and outcomes acquired. Diverse strategies are to be had for reducing the wide variety of dimensions, and those are based totally upon the application and the associated information set. The strategies include central factor assessment (PCA), characteristic desire, weighted length reduction, and Locality Sensitive Hashing (LSH). This paper provides a comparative evaluation of those strategies and discusses their strengths and obstacles. The outcomes of this observation will help researchers and practitioners understand each method's talents and limitations while using it in OLAP packages. © 2024 IEEE.

关键词： Analytical Processing application Dimensionality Locality Observation Researchers Technique

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The Way to Check the Reliability Test in Smart Grids for the Smart Protection System

The Way to Check the Reliability Test in Smart Grids for the...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Hannah Jessie Rani, R. Goyal, Manish Kumar Mishra, Awakash Faculty of Engineering and Technology Department of Electrical and Elceltronics Engineering Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Maharishi University of Information Technology Engineering Department Uttar Pradesh India

ISBN: (纸本)9798350357769

The complex operation of contemporary power systems is closely linked to the widespread integration of computer networks, or cyber networks. These applications fall into two general categories: direct and indirect interdependencies, which are the result of how they communicate with power networks. Past studies explored the area of direct interdependencies in cyber-power networks and presented a state mapping-based model that was carefully designed to assess its impact on power system dependability. The investigation of indirect relationships between cyber and electricity networks is the new emphasis of this article. A wide range of applications involving indirect interdependencies in modern power systems are examined, laying the foundation for the release of a model that is based on state updating. This model is carefully crafted to provide a numerical evaluation of cyber-power networks' dependability in the face of indirect interdependencies. Two separate models are used to maximize data communication inside the cyber network and minimize load limitation in the power network, respectively, in order to maximize the functioning of these integrated networks. In order to demonstrate the efficacy of the suggested solution model, the research focuses on a high-voltage converter equipped with both protection and monitoring devices. By taking a comprehensive approach, the research makes a substantial contribution to a deeper understanding of the complex interactions between cyber and electricity networks, providing priceless insights for enhancing the dependability of these interconnected systems. © 2023 IEEE.

关键词： Cyber-Power Interdependencies Dependability Indices and Optimization Framework Indirect Relationships Monitoring Systems P- Table Protection Systems Reliability Assessment Smart Grid State Updating

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A Deep Learning Model for Classification of COVID-19 Related Fake News 2nd

A Deep Learning Model for Classification of COVID-19 Related...

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2nd International Conference on Electronic Systems and Intelligent Computing, ESIC 2021

作者： Dutta, Rakesh Das Adhikary, Dibya Ranjan Majumder, Mukta Department of Computer Science & Application Hijli College Kharagpur India Department of Computer Science & Engineering Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar India Department of Computer Science & Application University of North Bengal Siliguri India

ISBN: (纸本)9789811694875

The health crisis caused by COVID-19 throws the whole world into the biggest emergency of the century. Moreover, the pandemic has become awful because of the spread of inadequate and fake news or information among common people. Fake news, gossip and misleading information are on the rise due to the popularity of web-based information sources among people, such as social media, news feeds, online blogs and e-news articles. Monitoring and identifying such fake stories is a prerequisite to cease unwanted panic in this pandemic. But carrying out this task manually is challenging and labour intensive. computer-assisted pattern recognition can now be used to replace human contact thanks to developments in machine learning, deep learning models and natural language processing. This is also essential for accurately distinguishing between true and false information automatically. A hybrid deep learning classification model has been proposed here to identify and classify the fake news and misleading information on the ‘COVID-19 Fake News Dataset’ (taken from Mendeley) which is a collection of news or web article related to COVID-19. The proposed classification model has achieved an accuracy of 75.34% and outperforms the existing LSTM and BiLSTM techniques. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Effective Intrusion Detection Model Using Raptor Optimized Deep Convolutional Neural Network

Effective Intrusion Detection Model Using Raptor Optimized D...

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2023 IEEE Guwahati Subsection Conference, GCON 2023

作者： Mahanta, Kshyamasagar Maringanti, Hima Bindu Bhale, Pradeepkumar Maharaja Sriram Chandra Bhanja Deo University Department of Computer Application Odisha Mayurbhanj757003 India Indian Institute of Technology Department of Computer Science and Engineering Guwahati Guwahati781039 India

ISBN: (纸本)9798350337785

Malicious behavior in the system must be recognized for ensure security because the occurrence of the intrusions can threaten the valuable data and threatens the quality of the network. An important area of research in network security is network intrusion detection. In recent years the intrusion detection model faced several problems due to low accuracy, data imbalance, lack of optimization and so on. To overcome this issue, in this research, a raptor-optimized deep CNN is used to develop the intrusion detection model, where the occurrence of intrusions is effectively detected. The deep CNN classifier effectively learns the features, and the enabling of the raptor optimization optimizes the weights and bias parameters to enhance the performance of the deep CNN classifier. The raptor optimization improves the classifier convergence time and global search capability, and the enabling of Synthetic Minority Oversampling Technique (SMOTE) resolved the issues of data imbalance, which reduced the complexity of the system. By evaluating the parameters of accuracy, sensitivity, and specificity, the research's superiority is demonstrated, and the suggested raptor-optimized deep CNN classifier achieved values of 96.51%, 90.23%, and 97.71% for dataset 1. The proposed raptor optimization also achieved values of 95.39%, 95.83%, and 95.15% for dataset 2, which is highly efficient compared to other existing methods. © 2023 IEEE.

关键词： Network security

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Stage Wise Classification and prediction of Alzheimer disease using Hybrid Appraoch 4

Stage Wise Classification and prediction of Alzheimer diseas...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Prabagaran, A. Dwivedi, Vinay Neelam Rani, Jyoti Angelin Blessy, J. Padmanabhan, P. Gupshup Technology India Pvt Ltd Bengaluru India Galgotias University School of Computer Science and Engineering Uttar Pradesh Greater Noida India Noida Institute of Engineering & Technology Department of Computer Science and Engineering Uttar Pradesh Greater Noida India Galgotias Univeristy School of Computing Science and Engineering Dept of Computer Application India Greater Noida Institute of Technology Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350349900

Alzheimer's, the most prevalent cause of dementia, leads to a continuous decline in cognitive functions, social skills, and behavior, affecting individuals' independent functioning. As per survey 2010 in India around 3.7 million affected by dementia, and this data is expected to twofold by 2030. The associated cost of care is projected to triple, necessitating ongoing support. This issue is a significant societal concern that warrants careful consideration. Prediction and classification of Alzheimer's disease (AD) are crucial for identifying and addressing it at earlier stages. The disease is typically classified into three stages: early-stage dementia, middle-stage, and late-stage. However, several challenges exist, including limited awareness, diagnostic difficulties, insufficient technologies and treatments, unpredictable progression across stages, and a lack of comprehensive stage-by-stage predictions. This work proposes a hybrid model for predicting, classifying, and stage-wise classifying Alzheimer's disease. The hybrid model aims to address the challenges associated with the disease, utilizing different features for prediction and classification. The hybrid model used a Random forest, convolutional neural network (CNN), kernel density algorithm (KDA) with hyperparameter tuning for accurate prediction and classification. The proposed hybrid method used Random forest used for basic classification, CNN and kernel density for stage wise classification. The proposed hybrid method achieved an impressive 95.6% accuracy in stage wise classification and prediction of AD. © 2024 IEEE.

关键词： Convolutional neural networks

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Analysis and Evaluation of Bio-Inspired Algorithmic Framework, Potential application in Cloud/Multi-Cloud Environment 5

Analysis and Evaluation of Bio-Inspired Algorithmic Framewor...

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5th IEEE International Conference on Cybernetics, Cognition and Machine Learning applications, ICCCMLA 2023

作者： Kaur, Ramanpreet Anand, Divya Kaur, Upinder Verma, Sahil School of Computer Applications Lovely Professional University Phagwara India Baba Farid College Department of Computer Application Bathinda India School of Computer Science and Engineering Lovely Professional University Phagwara India Akal University Talwandi Sabo Department of Computer Science Bathinda India CGC Department of Computer Science and Engineering Mohali India

ISBN: (纸本)9798350338287

Cloud computing allows web-based services to use a variety of reasonably priced services or resources, removing the requirements of centralized knowledge access. There are many provocations available in cloud computing and multi-cloud, so bio-inspired algorithms frequently used to handle particular problems such as load imbalance, resource equipping, and performance optimization. Bio-inspired algorithms have a proclivity for spontaneously resolving a wide range of challenges by giving optimum solutions. The ability has an impact on how bio-inspired algorithms are used to tackle the crucial issues in cloud computing. This study presents a comprehensive review of bio-inspired methods for resolving and optimizing solutions in multi-cloud. ACO, GA, PSO, FPA, BA and COSMIC bio inspired techniques are considered based on cost, scalability, fault tolerance, security, energy consumption, and throughput. ACO is secure and having capability of fault tolerance, PSO is fast but less secure and costly than others. Thus, FPA has a better capability than others for various parameters. FPA is better in security, cost, fault tolerance and energy consumption © 2023 IEEE.

关键词： Cloud computing

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Implementing RNN and LSTM Models to Electrical Load Predictions 2

Implementing RNN and LSTM Models to Electrical Load Predicti...

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Ramesh, T.R. Kumar, Kirana Asha, V. Kumar, S. Naveen Kumar, Manish Kareem, Ali School of Sciences and Computer Studies Bengaluru India Institute of Aeronautical Engineering Department of Electronics and Communication Engineering Hyderabad India New Horizon College of Engineering Department of Master of Computer Application Bangalore India Mlr Institute of Technology Department of Electronics and Communication Engineering Hyderabad India Lovely Professional University Phagwara India Hilla University College Babylon Iraq

ISBN: (纸本)9798350364699

Electrical utilities attach significant emphasis to power load forecasting to design the generating, transmission, and distribution systems. Additional uses of load forecasting in the power network include demand and supply management, determination of power plant operational resource needs, planning of spinning reserve, and scheduling power unit activities. Additionally, there is the option to use a plethora of other applications. The researcher used two distinct machine learning approaches, including Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM), also known to forecast the expected future demand for electrical power in this study. Historical load information's are used to assess the efficiency of implemented measures. The Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) are analytical metrics used to assess the accuracy of the methods being used. © 2024 IEEE.

关键词： Electric load forecasting

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Exploring Deep Learning Techniques for Automated Cataract Detection in Digital Camera Imagery

Exploring Deep Learning Techniques for Automated Cataract De...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Kamepalli, Sujatha Veeranjaneyulu, N. Bodapati, Jyostna Devi Vignan's Foundation For Science Technology And Research Dept. Department Of Information Technology And Computer Application Guntur India Vignan's Foundation For Science Technology And Research Dept. Department Of Advnced Computer Science And Engineering Guntur India

ISBN: (纸本)9798350371567

Cataracts, a prevalent eye condition impacting vision, demand accurate and prompt identification for effective risk reduction and blindness prevention. Traditional methods of cataract diagnosis rely heavily on manual examination by ophthalmologists, which can be time-consuming and subject to human error. With the advent of digital imaging technology, there is an opportunity to enhance the efficiency of cataract detection. The deep neural network models VGG19 and ResNet-50 are recommended in this paper for automatic cataract identification on digital camera images. To optimize network performance, adjustments to loss and activation functions were made, incorporating minimal kernels, reduced training parameters, and layers. This resulted in a ResNet-50 model with significantly decreased computational cost and average operating time compared to the cutting-edge models. The proposed ResNet 50 model achieved an average accuracy of 93.41%, outperforming the VGG 19 model, which had an accuracy of 91.13%. This research contributes to the evolution of cataract detection methodologies, emphasizing the potential of deep learning models, particularly ResNet-50, in improving diagnostic accuracy and operational efficiency for enhanced eye health outcomes. © 2024 IEEE.

关键词： Digital cameras

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Stock Price Prediction by Using Machine Learning Techniques: A Study of TCS Ltd

Stock Price Prediction by Using Machine Learning Techniques:...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： Yogesh Kumar Jakhar Pawan Sharma Bilal Ahmed Department of Computer Application University of Engineering & Management Jaipur India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Prediction of stock prices is challenging, but now stock price prediction is becoming very popular among researchers. Exact prediction of the stock market is not feasible, but the present study is an attempt to understand stock price prediction with special attention to TCS Ltd. The key objective of the present study is to forecast the high price of TCS stock on a daily basis. To achieve the objective successfully, this study comprises the use of historical time-based data for 15 years, from 2009 to 2023. (12 years of data for training purposes and 3 years of data for testing purposes are significantly categorized). Day-wise high prices have been significantly predicted by using open prices, low prices, and close prices. This study comprises the use of Multi Linear Regression (MLR), Support Vector Machine (SVM) with Linear Kernel, Ploy Kernel, and RBF Kernels, Multilayer Perceptron (MLP 2, 2), and XGBOOST algorithms. To access the accuracy of results from different models, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) also been significantly calculated. Multi Linear Regression (MLR) model showed more accurate results for the prediction of day-wise high prices as compared to other algorithms. Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of MLR found 11.57 and 14.69 respectively, the lowest of all the algorithms. Traditional regression tools are not as effective as these algorithms because these algorithms can provide more accurate and precise results to serve the interests of investors. This study concluded with suggestions to the investors while deciding whether to hold the stocks or sell them on a particular day.

关键词： Support vector machines Training Machine learning algorithms Accuracy Linear regression Machine learning Prediction algorithms

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How do benefits and challenges influence students’ satisfaction with online education? The mediation effect of awareness

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International Social Science Journal 2024年第252期74卷 299-318页

作者： Chawla, Udit Sondhi, Sundeep Singh Jabbar, Meerja Akhil Mishra, Varsha Singh, Harsh Vikram Department of Business Administration University of Engineering & Management Kolkata India Department of CSE(AI & ML) Vardhaman College of Engineering Hyderabad India Digital Marketing Royal Holloway University of London Egham United Kingdom Computer Application Techno India University Kolkata India

Traditional education is shifting towards online instruction, and the temporary closure of educational institutions due to COVID-19 has prompted its dramatic increase in the last couple of years. With additional benefits, such as flexible schedule, low cost and anywhere learning, online education will determine the near future of education. Due to the increase in Internet reach in India, face-to-face instructional methods to which students are habituated to can be substituted. This study aims to analyse students’ satisfaction with the parameters of awareness, challenges and benefits of using new technologies to learn online. With the help of a structured questionnaire and interactions with local inhabitants, the researchers obtained 430 samples from the outskirts of five different cities (Kolkata, Mumbai, Delhi, Chennai and Bengaluru) in India. A conceptual model was built around the constructs of awareness, challenges, benefits and student satisfaction, and their relationship was established and empirically tested. It is found that factors like challenges, benefits and awareness have a significant influence on student satisfaction with online learning. We also find that awareness helps in decreasing the challenges faced and increasing the value of benefits of online education for students. © 2023 John Wiley & Sons Ltd.

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