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

作者： Vijay, Sandip Gobi, N. Sharma, Ambrish Kumar Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Bangalore India Computer Applications Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350357769

It's an interesting idea to investigate the energy market as a potential means of reducing the power costs related to charging electric cars at public charging stations. Through intraday energy markets., several nations allow electricity trading until just before the delivery time. This research utilizes the market as an illustration to investigate the possibility of lowering power prices via intraday market trading. After conducting extensive simulations., the results show that charging schedule optimization combined with intraday trading in the markets may result in cost savings of around 9% when compared to obtaining all needed energy from the energy provider. A further 2% or more of cost savings may be realized by giving the charging station manager the ability to resell energy to the everyday electricity market. The research explores how the intraday electricity market's lead time and trading unit affect total expenses in addition to any possible cost savings. Lead time is shown to have a major impact on feasible power prices., whereas each trading unit has a negligible impact on the total cost dynamics. © 2023 IEEE.

关键词： charging schedule Electric vehicles (EV's) electricity costs mixed-integer linear programming trading

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5G in Smart Cities: Enhancing Urban Futures for Sustainable Growth

5G in Smart Cities: Enhancing Urban Futures for Sustainable ...

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

作者： Rajvanshi, Anita Mahajan, Prerna Singh, Alka Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Bangalore India Noida Institute of Engineering and Technology Computer Applications Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350357769

This essay delves into the concept of 'Smart Cities,' outlining its numerous benefits. It elucidates the diverse applications and services essential for transforming a city into a Smart City, underscoring the pivotal role of Information and Communication Technology (ICT) in their realization. Furthermore, it scrutinizes the challenges posed by the deployment of Smart Cities for contemporary ICT systems. The study investigates the promised attributes of 5G technology and its potential as a linchpin for the efficient implementation of urban smart cities. Additionally, it provides a catalog of 5G-enabled Smart City use cases, emphasizing how 5G will serve as the bedrock for the Internet of Things (IoT), fostering the evolution of smart cities. © 2023 IEEE.

关键词： 5G technology ICT infrastructure Information and Communication Technology Internet of Things Quality of Life Smart Cities Smart ICT infrastructure Sustainable City Urban Transit and City Governance

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Neural Networks for Financial Fraud Detection: Enhancing Security with Scalable Machine Learning and Deep learning Solutions

Neural Networks for Financial Fraud Detection: Enhancing Sec...

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2025 International Conference on Artificial Intelligence and Data engineering, AIDE 2025

作者： Nuthan, M.C. Raviprakash, B. Rama Moorthy, H. Praveen, N. Krishnaraj Rao, N.S. Nitte Institute of Professional Education Nitte Deemed to be University Department of Computer Applications Karnataka Mangalore India Information Science and Engineering Karnataka Nitte India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527518

As financial fraud evolves, conventional methods struggle to counter increasingly sophisticated schemes. This paper presents an AI-powered system focused on fraud detection, utilizing neural networks for enhanced adaptability and computational efficiency. The proposed system processes extensive financial datasets in real-time, employing a variety of machine learning models such as Random Forest, Gradient Boosting Machines, Support Vector Machines, and deep learning approaches like Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. These techniques excel in identifying intricate fraud patterns by analysing both structured and unstructured data sources, including transaction records, financial databases, and customer information. Neural networks enable the system to dynamically adjust and optimize its detection capabilities while minimizing reliance on traditional data storage systems by efficiently compressing large volumes of financial data. The adaptability of the system ensures it can quickly respond to new fraud trends and regulatory requirements, making it a forward-looking solution for combating *** paper outlines the architecture, implementation, and methodology behind the proposed neural network-driven AI system. A detailed evaluation shows significant improvements in accuracy, processing speed, and system scalability when compared to traditional fraud detection methods. Neural networks, through ensemble learning, enhance the system's ability to integrate various models, resulting in a more effective approach to detecting fraud in real time. This new framework offers a robust alternative to conventional methods, combining machine learning, deep learning, and neural networks for an advanced, scalable, and secure fraud detection system. © 2025 IEEE.

关键词： Support vector machines

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Quantum Machine Learning Techniques based on Nurturing Agri-Ontology Framework in Agricultural science 3

Quantum Machine Learning Techniques based on Nurturing Agri-...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Deepa, R. Jayalakshmi, V. Thilakavathy, P. Bennet Prabhu, A. Surendran, R. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India Drbccc Hindu College Department of Computer Applications Chennai India Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Kone Elevator India Technology & Engineering Centre Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

The absence of a consistent ontological framework often impedes efficient knowledge organization and sharing within the agricultural community, despite the quantity of data created in agricultural research. The lack of defined vocabulary and conceptual frameworks hinders data integration, agricultural research, and innovation by preventing interoperability between databases and systems. To address these issues, this research work proposes the Nurturing Agri-Ontology Framework (NAOF), which offers a systematic approach to creating and updating agricultural ontologies. Ontologies that faithfully portray agricultural ideas and connections can be more easily created with the help of NAOF, which combines ideas from knowledge management, agricultural science, and ontology engineering. NAOF aims to ensure that the ontologies produced reflect the wide range of viewpoints and terminology used in agricultural research by encouraging cooperation among stakeholders, ontology engineers, and domain specialists. In this study, the improvement of knowledge organization, sharing and integration in the agricultural area is shown with the application of NAOF. Using this research study, scientists can build ontologies that improve agricultural decision-making, data interoperability, and the discovery of new information. Using quantum machine learning techniques, a strong foundation of knowledge can be laid for several agricultural applications, such as precision agriculture, crop management, and the formulation of agricultural policies. Ontology coverage, interoperability, scalability, and usability are established evaluation parameters that are used to assess the efficacy of NAOF in this research work. The analysis shows that NAOF effectively solves the problems and creates a better agricultural knowledge ecosystem by thoroughly evaluating it using these standards. © 2024 IEEE.

关键词： Ontology

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Experimental Analysis on Banking Customer Segmentation using Machine Learning Techniques

Experimental Analysis on Banking Customer Segmentation using...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Pandey, Trilok Nath Navya Kala, S.V. Amrutha, S. Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar Vellore Institute of Technology School of Computer Science and Engineering Chennai India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India

ISBN: (纸本)9798350308167

The intent of customer segmentation is primarily to aid institutions to custom-make their marketing techniques and methods to suit the specific requirements of every consumer group. As this helps to solve the segregating of the groups of customers for banks efficiently and helps provide more custom-made services for customers of each group better, applying the customer segmentation for banking data can provide many insights that can be beneficial to banks as well as their customers. This increases the effectiveness of customer interaction and can maximize selling opportunities. It makes marketers more efficient in terms of time, money, and other resources. © 2023 IEEE.

关键词： Customer segmentation Gaussian mixture model clustering hierarchical agglomerative clustering hyper parameter tuning k-modes

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Observing Different Machine Learning Approaches for Students’ Performance Using Demographic Features 6th

Observing Different Machine Learning Approaches for Students...

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Srivastava, Neeraj Kumar Pandey, Prafull Mishra, Manoj Kumar Mishra, Vikas Department of Computer Science and Engineering United Institute of Technology Prayagraj Naini India Department of Computer Science and Engineering United College of Engineering and Research Prayagraj Naini India Department of Computer Applications United Institute of Management Prayagraj Naini India

ISBN: (纸本)9789819930098

The use and importance of educational data mining (EDM) are growing very fast as the entire world is going digital. The predictions of EDM [1, 2] are playing very important role not only in improving the academic performance of individual students but also are very useful for institutions, policy makers, administrators to plan and design new policies that will certainly help them to achieve high academic performance of their students. On this basis, this paper approaches students’ performance in secondary school of two Portuguese schools. The informational elements comprise demographic, societal, and educational [3] features. Although there are many ML [4, 5] algorithms, yet in this paper, five ML classifiers such as random forest, Naïve Bayes, logistic regression, decision tree, and support vector machine have been used to predict the students’ performance that on the basis of given features, whether a student can be passed in secondary education or not. On the dataset, the Naïve Bayes classifier gives the best performance than other classifiers, when the dataset is partitioned into 80–20 ratio as training and test datasets. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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A holistic securing approach to speech steganography system with message embedding and pause optimization

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Multimedia Tools and applications 2025年 1-27页

作者： Kumar, Surendra Yadav, Arun Singh Dwivedi, Mridula Kumar, Mohit Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura281401 India Department of Computer Science University of Lucknow Uttar Pradesh Lucknow226007 India Lucknow India Department of IT Dr. B.R. Ambedkar National Institute of Technology Jalandhar India

Nowadays, prevention control of message sharing as well as security enhancement in speech transmission which occurs due to users having a passion for novel inventions, communication system benefits. In this paper, the main objective is to design a speech steganography framework with covert message data transmission including speech. The secret message data is encrypted with the help of the Hyperelliptic Curve Cryptography (HECC) algorithm to provide a maximum security environment. The designed speech steganography framework is integrated with the pause removal mechanism respectively. Since, the Stegospeech is sent into the channel, as the Stegospeech as of now obtained the secret Message data its transfer speed will increment. Then at that point, it needs high bandwidth for transmission as well as needs higher energy utilization. In this manner, to decrease the data transfer speed, the best procedure is to eliminate the undesirable pause in the speech, the pause removal strategy carried out utilizing the combinations of Ensemble Empirical Mode Decomposition (EEMD) with Hilbert-Huang Transform (HHT). Then, some techniques are used to embed the message into the speech like 4-level Discrete Wavelet Transformation (DWT), Matrix definition, and Single Value Decomposition (SVD). They are likewise utilized for the secret message extraction from speech signal as well as message information from the Stegospeech signal. The presented strategy is carried out and analyzed towards the different invasions in the output. The Correlation Coefficient (CC), the Peak Signal-To-Noise Ratio (PSNR), the Signal-To-Noise Ratio (SNR), the Number of Pixels Changing Rate (NPCR), and the Unified Average Changing Intensity (UACI) are some of the popular standards that are utilised when comparing two encrypted voice signals. The numbers 33.3% and 100%, respectively, for UACI and NPCR are considered to be optimum states. It is noticed that the presented technique provides better Statistical resul

关键词： Steganography

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A Study on Swarm-Based Approaches for Intrusion Detection System in Cloud Environment

A Study on Swarm-Based Approaches for Intrusion Detection Sy...

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International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2022

作者： Nishika Solanki, Kamna Dalal, Sandeep University Institute of Engineering and Technology MDU Rohtak India Department of Computer Science and Applications MDU Rohtak India

ISBN: (纸本)9789819909803

Cloud is an advanced Internet of Things enabled network. The layered and distributed nature of this network opens this network to the public environment and varied kind of users. Bulk of data is stored on centralized system, and large amount of information is communicated in the real environment. This public and large amount of shared data and communication opens the network for intruders. Various intrusion detection systems were designed and implemented on different layers of the cloud network to detect and prevent different kinds of internal and external attacks. In the recent years, various swarm-based methods were integrated within intrusion detection system to optimize the performance. In this paper, a detailed study and review of cloud- and Internet of Things-based intrusion detection systems is presented. The analytical and descriptive study of swarm-based intrusion detection system is provided in this paper. This study identifies that the swarm integration within intrusion detection and classification methods improved the performance and reliability of conventional systems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Intrusion detection

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AMBTC-Based High Capacity Data Hiding Scheme Exploiting PVD and BRP 3rd

AMBTC-Based High Capacity Data Hiding Scheme Exploiting PVD ...

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3rd International Conference on Advanced Computing and applications, ICACA 2024

作者： Jana, Manasi Jana, Biswapati Joardar, Subhankar Department of Computer Applications Haldia Institute of Technology West Bengal Haldia India Department of Computer Science Vidyasagar University West Bengal West Midnapore India Department of Computer Science and Engineering Haldia Institute of Technology West Bengal Haldia India

ISBN: (纸本)9789819747986

Secret data hiding in compression domain reduces the transmission cost and also ensures more security than spatial and transform domain. In this paper, we present a data concealing strategy on the compression domain that delivers high embedding capacity (EC) while also improving the quality of transformed images. To compress the original image, a modified AMBTC method is used for its low time complexity. Modified AMBTC is used to transform one bit plane into two bit plane, which enhances the quality of the stego image. Three levels of security are applied on secret data to make the scheme more secure. Data are embedded into two adjacent pixels of each 1×2 block using PVD method. The robustness of the scheme is analysed by applying unknown permutation, unknown shared key and unknown iteration steps. The experimental results reveal that the suggested method outperforms the previous schemes in terms of embedding capacity and imperceptibility. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ambtc BRP Data hiding PSNR PVD

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An Imperial Analysis of Large Language Models for Automated Tweet Sentiment Prediction

An Imperial Analysis of Large Language Models for Automated ...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Akhouri, Shivam Ajbani, Vidhi Lakshminarayanan, Ritika Pandey, Trilok Nath Nigam, Meher Shrishti Patra, Sudhansu Shekhar Vellore Institute of Technology School of Computer Science and Engineering Chennai India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India

ISBN: (纸本)9798350313987

Company' s brand perception majorly depends on customer experience and the reviews which follow that. A customer is capable of influencing many more people just on the basis of reviews given by him/her. Google released Pathways Language Model (PaLM) which is a major advancement in Artificial Intelligence (AI). It has been trained with the Pathways System, which allows it to generalize tasks in various domains. In this work, a trustworthy platform is provided for the examination of millions of people's continually moving and changing perspectives. Twitter data is captured, and effective sentiment and data analysis is used to generate trustworthy and helpful info graphics reflecting public opinion. Product sales, stock returns, election outcomes, and other commercial and social events may all be predicted and explained using the information found in tweets. Brands, product manufacturers, and other companies may utilise the information derived from data analysis to better understand their brand image, expand their market share by targeting the relevant demographics at the right moments, and enhance their offerings in terms of both quality and customer service. © 2023 IEEE.

关键词： Attention Units Data visualization Masks Sentiment Analysis Tokenization

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