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International Conference on Communication and Electronics Systems (ICCES)

作者： Banagani Prashanth Suddapalli Gangadhar Vivek Bhardwaj Kovvuru Diwakar Reddy Puli Shiva Reddy Manik Mehra Department of Computer Science and Engineering Lovely Professional University Phagwara India Department of Computer Science and Engineering Manipal University Jaipur Jaipur India School of Computer Application Lovely Professional University Phagwara India

Detecting credit card fraud is an extremely important issue in the financial sector, as it results in significant financial losses and negatively impacts customer trust. The main motive of CCFD is to develop methods to identify fraudulent transactions accurately and efficiently. This research typically involves the use of statistical and machine-learning algorithms to analyze large amounts of transaction data. Utilizing machine learning is a potential solution for addressing credit card fraud. Studies suggest that machine learning techniques can help overcome the challenges of identifying fraudulent transactions with high detection rates, both directly and indirectly. While supervised ML algorithm and unsupervised ML algorithms have been proposed, the limitations of each approach underscore the need for hybrid methods. From the results, it is evident that Logistic Regression has the highest accuracy of 94.86%, highest recall value of 98.97% and highest F1-score of 95.09%.

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VitalisAI: Revolutionizing Healthcare with Intelligent Chatbot Solutions

VitalisAI: Revolutionizing Healthcare with Intelligent Chatb...

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International Conference on Advances in Information Technology (ICAIT)

作者： Mukesh Kumar Ashutosh Painuly Vivek Kumar Teekam Singh Sanjay Sharma Chandradeep Bhatt CSE Department Graphic Era Hill University Dehradun India Computer Science Department Graphic Era Hill University Dehradun India Computer Science and Engineering Quantum University Roorkee India Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Computer Application Department SGRRITS Dehradun India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Promoting good health is essential for a quality life, but accessing timely consultations with healthcare professionals for every health issue can be challenging and Expensive. This idea involves the creation of a Healthcare chatbot System using Artificial Intelligence to diagnose illnesses and deliver fundamental details, Home Remedies and generic medicine for illness. As well as consulting a doctor and Booking Appointment for user. This initiative is designed for healthcare costs reduction, enhance access to medical resources, and ultimately contribute to an improved state of overall well-being. The proposed solution outlines a multilingual healthcare voice operated chatbot system designed to analyseuser Symptoms and predict the Disease on the Symptoms basis.

关键词： Machine learning algorithms Costs Medical services Speech recognition Chatbots Prediction algorithms Information technology Artificial intelligence Medical diagnostic imaging Diseases

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Groundwater Evaluation with Classification Techniques

Groundwater Evaluation with Classification Techniques

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Artificial Intelligence and Smart Communication (AISC), International Conference on

作者： Yakesh Dogra Mizan Ali Khan Department of Master of Computer Application Chandigarh School of Business Department of Computer Science Engineering Sanskriti University Mathura Uttar Pradesh India

We has researched that your earth area contains % comprised of water and the remaining 28 part also with soil. From the 28 per cent, % water is seawater namely it is excessively salted for consuming, for agricultural production and more critical items. That's because the freshwater which seems utilised is extremely small in bulk and its condition is every day growing worse. The condition of the water must be kept since it is a crucial life factor not just the individuals as well as other creatures and flora. Humans demand a suitable level sustained liquid to consume and employ in varied daily tasks Assessments of groundwater recharge and assessment are thus essential for the development of economy and society. Photogrammetry is now a crucial technique for ground component assessment as a result of advances. So to assure if the groundwater we get is excellent or wouldn't we have gathered a set and used multiple Ml techniques to assess the auc. So, the research intends to determine the prognosis of water whether it is of good standard or not by utilizing two algorithms, rbfnn and perceptron and to demonstrate which outperforms.

关键词： Liquids Sociology Soil Classification algorithms Rivers Reliability Task analysis

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An Analysis of Edge Computing with Multi Access in 5-G Technology

An Analysis of Edge Computing with Multi Access in 5-G Techn...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Sitaram Gupta Sonia Department of Computer Science Engineering Vivekananda Global University Jaipur India Department of Master of Computer Application Chandigarh School of Business Jhanjeri

The demands of the rapidly expanding internet. However, since most end users have confined storage space and limiting computing power, it lately evolved into a natural issue how to execute computationally complex apps on resource-bound consumers. The emerging fifth generation (5G) corporation can benefit from Mobile Edge Computing (MEC) innovation upgrade portable assets by facilitating figure concentrated applications, handling a lot of information prior to sending them to the cloud, finding distributed computing assets inside the radio access organization (RAN) close to versatile clients, and giving setting mindful administrations utilizing RAN information. In reality, the paradigm shift from 4G to 5G is accompanied by the introduction of new technical concepts. We first give a comprehensive introduction of MEC technology and some of its possible applications. The most recent research on MEC integration with upcoming technologies that will be employed in 5G and beyond is then is summarized.

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Deep Learning-Based Trend Analysis on Indian Stock Market in COVID-19 Pandemic Scenario and Forecasting Future Financial Drift

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Journal of The Institution of Engineers (India): Series B 2022年第5期103卷 1459-1478页

作者： Nayak, Janmenjoy Dash, Pandit Byomakesha Naik, Bighnaraj Mohapatra, Subhashree Routray, A.R. Department of Computer Science Maharaja Sriram Chandra BhanjaDeo University Odisha Baripada Mayurbhanj757003 India Tekkali532201 India Department of Computer Application Veer Surendra Sai University of Technology Odisha Burla768018 India Department of Computer Science & Engineering Institute of Technical Education and Research Siksha ‘O’ Anusandhan University Bhubaneswar751030 India Department of Information & Communication Technology Fakir Mohan University Odisha Balasore India

This present study has used the long-short-term memory (LSTM) network-based deep learning architecture to analyze the influence of the current widespread COVID-19 on the Indian stock market. The major contribution of this work is as follows: (1) Designing LSTM-based deep neural network is used to study the effect of the COVID-19 outbreak and Lockdown on the Indian stock exchange (Nifty 50), and (2) designing a prediction model to capture the effect of various COVID-19 waves in India on Indian Stock exchange. The outcomes of the analysis show that the increase in daily new confirmed cases, recovered cases, and death cases have a significant adverse impact on the trend of the stock market. Moreover, the results of the work have also analyzed the impact of government policy such as ‘lockdown city’ with a reaction to increased Pandemic cases. This work is briefly summarized as follow: (1) LSTM-based deep neural network is used for this study to analyze the effect of the COVID-19 outbreak on the Indian stock exchange. (2) The Indian Stock exchange affected by the COVID-19 pandemic has been studied. Here, the analysis is based on the impact of COVID-19 including the effect of lockdown. (3) A prediction model has been proposed for the study of the behavior of the Indian stock index (Nifty 50) during the COVID-19 pandemic. (4) Comparison of the efficacy of the suggested approach with other existing baseline regression models. © 2022, The Institution of Engineers (India).

关键词： COVID-19

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Enhanced face recognition using rank-level fusion with multi-feature vectors and interval type-3 fuzzy set

Enhanced face recognition using rank-level fusion with multi...

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IEEE Applied Signal Processing Conference (ASPCON)

作者： Manas Ghosh Jamuna Kanta Sing Department of Computer Application RCC Institute of Information Technology Kolkata India Department of Computer Science & Engineering Jadavpur University Kolkata India

In the near past, interval type-3 fuzzy set theory has been introduced as a more effective means of addressing uncertainty in decision-making than its fuzzy set counterparts. This paper suggests an approach to face recognition which integrates multiple feature vectors and interval type-3 fuzzy sets for fuzzy rank-level fusion. Use of multiple feature vectors resolves inter-class resemblances while incorporating interval type-3 fuzzy membership function in generating fuzzy rank reduces the uncertainty due to intra-class divergences. The output of the classifier is regarded as confidence factor. For each feature vector, the fuzzy ranks are generated by formulating an interval type-3 fuzzy membership function for each of top-ordered H classes based on confidence factors utilizing all training feature vectors and those of the training samples of the respective class. Multiple fuzzy ranks, built upon interval type-3 fuzzy set, are generated by employing of multiple feature vectors. To determine the final score of a class, summation of the fuzzy rank complements is combined with the complement of arithmetic mean of respective classifier’s output. Final scores are evaluated to conclude test image's class. Two publicly accessible face databases are used to evaluate the method and it has shown that the solution, advocated here, exhibits an improved effectiveness over its existing related counterparts.

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Adaptive Index Modulated OFDM Spread Spectrum for Underwater Acoustic Integrated Sensing and Communication Networks

Adaptive Index Modulated OFDM Spread Spectrum for Underwater...

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2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023

作者： Wang, Junfeng Cui, Yue Zhou, Mingzhang Sun, Haixin Liu, Lanjun Zhang, Ailing School of Integrated Circuit Science and Engineering Tianjin University of Technology Tianjin300384 China Key Laboratory of Southeast Coast Marine Information Intelligent Perception and Application MNR China College of Computer and Information Engineering Tianjin Normal University Tianjin300387 China School of Information Science and Engineering Xiamen University Xiamen361005 China College of Engineering Ocean University of China Qingdao266100 China

ISBN: (纸本)9798350316728

It is predicted that underwater acoustic integrated sensing and communication networks (UAISACNs) would be one of the main enablers for supporting a variety of emerging applications due to benefits of communication-Assisted sensing, sensing-Assisted communication and their integration. Mean-while, link adaptation and index modulated orthogonal frequency division multiplexing spread spectrum (IM-OFDM-SS) are a kind of powerful transmission technique for underwater acoustic communications networks, which can exploit the time-varying behavior of underwater communication channels to transmit much higher speeds. Thus, this paper aims to propose the adaptive IM-OFDM-SS for the UAISACNs, where the adaptive technique is integrated with the IM-OFDM-SS. The presented scheme is capable of adaptive transmitting the downlink information to the communication node in the UAISACNs by employing the estimating sensing parameters of its echo, which can reduce large amount of the signaling overhead and the long latency resulted from the conventional feedback-based protocol. Our suggested framework can choose the best possible transmission mode to maximize the achievable throughput, which can be verified through computer simulations, where we show the benefits of our proposed adaptive transmission scheme. © 2023 IEEE.

关键词： Orthogonal frequency division multiplexing

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Analysis of Energy Quality Issues in the Solar Energy System

Analysis of Energy Quality Issues in the Solar Energy System

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International Conference on Electrical Power and Energy Systems (ICEPES)

作者： Ishita Uniyal Shweta Tiwari Vishal Shiv Ashish Dhondiyal Department of Electrical Engineering Graphic Era Hill University Dehradun Department of Computer Science & Engineering University Institute of Engineering Chandigarh University Mohali Punjab School of Computer Application Lovely Professional University Jalandhar Punjab Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798350390728

ISBN: (纸本)9798350390735

Photovoltaic systems are being used more and more in the production of electricity because of the high cost of fossil fuel-based energy. The main issue that happens between grid and end-user transmission lines is power quality. DSTATCOM is a power quality compensating device that corrects voltage sag and swell are examples of power quality concerns. In high voltage line for power transmission this can be happened. The study was carried out with MATLAB or Simulink software to resolve the voltage sag and swell, and the model was created. The simulation results show the problem of voltage sag and swell is minimize to very large extent.

关键词： Power transmission lines Voltage fluctuations Software packages Simulation Power quality Energy resolution Power transmission

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Resources in IoT Through Bctechnology

Resources in IoT Through Bctechnology

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Manish Kumar Goyal Karishma Sharma Department of Computer Science Engineering Vivekananda Global University Jaipur India Department of Master of Computer Application Chandigarh School of Business Jhanjeri

With the exponential rise in IoTapplicationit difficult to keep up with the demand for services with lower latency, higher bandwidth, easier accessibility, and advanced intelligence. To meet such demand the distributed and decentralized computing architectureisproposed. Edge-centric computing resources don't need to be managed by a distant but centralized third party can managed locally and individually. However, because to a lack of trust between IoT devices and , obstacles (ECNs). The trust-absence issue can be effectively resolved by a blockchain, which is a decentralized, trustless, and immutable public ledger. discuss the concerns associated with we outline the main features of blockchains that make them ideal for edge-centric IoT applications. In addition, we offer a broad architecture that outlines the sequential steps required to complete for scenarios based on blockchain and edge computing that enable IoT. Additionally, to demonstrate how artificial intelligence (AI) and blockchains may function together, we create a We also go through the advantages of blockchains and AI coming together. The simulation's findings are then displayed.

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ASLPP-RR Routing Based on Improved IDAF-FIT Clustering with Secure Data Aggregation in Wireless Sensor Network

ASLPP-RR Routing Based on Improved IDAF-FIT Clustering with ...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Megha Garg Yakesh Dogra Department of Computer Science Engineering Vivekananda Global University Jaipur India Department of Master of Computer Application Chandigarh School of Business Jhanjeri

Wireless Sensor Network (WSN) technology has recently emerged as a critical component for monitoring and tracking tasks across a variety of application domains. The most difficult problem now is developing a power-efficient data collection technique. This is due to the fact that each sensor node in the network only has a certain amount of power resources. Clustering is used to improve power efficiency, network connectivity, and delay minimization. Therefore, because of data aggregation and cluster head selection, approaches base on clustering for information collection assume an essential part as far as power-saving and expanding the lifetime of the network. The IDAF- FIT (Fuzzy and Distributed Autonomous Fashion Integration The algorithm (If-then Rules) is suggested for clustering in this suggested approach, and in the interim, the Cluster kind of Head is also chosen. The routing concept is then started in order to transfer the packet from the source to the target node by selecting the best route. ASLPP-RR, Routing is suggested using an adaptive source location privacy preservation method. in this context. Additionally, Using Secure Data Aggregation based on Principle Component Analysis and integrity are preserved (SDA-PCA) technique. Finally, a thorough analysis of the security of sensitive data yields results that are superior to those of the current methods. The total performance of the suggested approach is described as a 20% increase in packet delivery ratio, a 15% decrease in packet dropping ratio, an 18% increase in residual power, a 22% increase in network lifespan, and a 16% decrease in power consumption when compared to existing.

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