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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Beedhi Reddy, Hemanth Kumar Vijay Anand, M. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350309140

Customer churn is a pressing problem faced by banks, affecting their revenues and customer satisfaction. To solve this problem, this study explores the use of machine learning, specifically Naive Bayes, Decision Trees, and AdaBoost classifiers, to predict and reduce customer churn. By leveraging these complex algorithms, banks can gain insight into customer behaviour and take proactive steps to retain valuable products as customers. The process starts with pre- processed data, model design and coding. This optimization can solve many problems such as excessive priority, missing data, categorical features, etc. Additionally, Data Analytics (EDA) and visualizations to gain a deeper understanding of customer segmentation;This allows to uncover important trends and good standards. When evaluating performance in the model, Focused on simple metrics such as recall and ROC- AUC scores. These metrics provide insight into the model's ability to accurately identify past customers while minimizing false positives. To improve the performance of model, hyperparameter tuning was performed to ensure that we achieved the highest accuracy. The Model adopt ROC-AUC curve to see the balance between true positive and negative value. This analysis provides a better understanding of the performance model, allowing to make informed decisions and strategies to manage the customer's quality. Actual and estimated loss status is displayed as an estimate of Customer's final model. This valuable information provides banks with the strategies necessary to identify potential credit losers and take the necessary steps to maintain their trust. © 2024 IEEE.

关键词： Sales

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Enhancing Peer-to-Peer Network Security using 3D Convolutional Autoencoder based Anomaly Detection 1

Enhancing Peer-to-Peer Network Security using 3D Convolution...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Rana Veer Samara Sihman Bharattej, R. Ajznblasm, Zaid Satish, E.G. Adlin Sylvia, A. Devi, M. Saint Leo University Department of Computer Science FL United States The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798331529246

In this era, Peer-to-Peer (P2P) networks have developed as a revolutionized network for data distribution and alternate to traditional client-server network. The features of P2P networks allow a distributed group of users to stake resources in the form of digital content or information. However, P2P networks lack in controlling access and it has limited security. In this research, Three Dimensional-Convolutional Autoencoder (3D-CA) is proposed to improve the security in P2P network. Initially, the data is obtained from University of New South Wales Network Behavior 2015 (UNSW-NB15) dataset and then preprocessed with data cleaning to ensure data quality. After that, the P2P autoencoder architecture is employed to learn the normal patterns which employs the detection in P2P network. Finally, the proposed 3D-CA is trained to detect the anomalies based on reconstruction error between input node and reconstructed pattern. From the results, the proposed 3D-CA model attained better outcomes compared to existing K-means algorithm in terms of accuracy, precision as well as F1-score by achieving 0.764, 0.90 and 0.80 respectively. © 2025 IEEE.

关键词： Peer to peer networks

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Heart disease prediction using machine learning

Heart disease prediction using machine learning

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Julian, Anitha Deepika, R. Geetha, B. Jeslin Sweety, V. Department of Computer Science and Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9781032669663

Anticipating and diagnosing coronary illness is the greatest test in the clinical business and depends on variables like the actual assessment, side effects and indications of the patient. Factors that impact coronary illness are body cholesterol levels, smoking propensity and corpulence, family background of diseases, pulse, and workplace. Coronary illness can be anticipated in light of different side effects, for example, age, orientation, pulse, and so on and lessens the demise pace of heart patients. In this task, we will be intently working with the coronary illness expectation. The coronary illness dataset from that dataset we will determine different experiences that assist us with knowing the weightage of each element and how they are interrelated to one another. Our only point is to recognize the likelihood of individual that will be impacted by a heart issue or not. We are utilizing K-Closest Neighbor (KNN) to foresee the exactness. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Machine learning

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On-Demand Coordinated Spectrum and Resource Provisioning under an Open C-RAN Architecture for Dense Small Cell Networks

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IEEE Transactions on Mobile Computing 2024年第1期23卷 673-688页

作者： Wu, Sau-Hsuan Ko, Chun-Hsien Chao, Hsi-Lu National Yang Ming Chiao Tung University Institute of Communications Engineering College of Electrical and Computer Engineering Hsinchu30010 Taiwan National Yang Ming Chiao Tung University Institute of Information Engineering Department of Computer Science Hsinchu30010 Taiwan

Mobile networks have seen more than a thousand times traffic increases in the past decade. Network operators face an ever increasing demand and cost to deploy denser networks in order to maintain the quality of services. In view of this paradigm shift, an open cloud radio access network (C-RAN) service model is studied in this work. Integrating the cross-layer functions of spectrum resource sharing, channel and power allocation, and interference management in a C-RAN architecture, we explore the feasibility of providing a scalable yet efficient broadband wireless service with dense small cell networks (DSN). The proposed methods and architecture can be applied to DSN that support the functions of (further enhanced) inter-cell interference cancelation (feICIC/ICIC) techniques of 3GPP standard, and have the potential to provide a cost-effective solution for high-quality DSN. Simulation results across an area of 100 km2 show that the proposed scheme can offer an aggregate downlink throughput of 21 Gbps over a maximum channel bandwidth of 20 MHz, which is 5 times the throughput with a typical ICIC method. Moreover, the service satisfaction degree can reach 85% under the proposed C-RAN architecture, making it a promising and cost-effective solution for future broadband wireless services. © 2002-2012 IEEE.

关键词： Radio access networks

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MedConnect - A Comprehensive AI-driven Platform for Seamless Patient care and Optimization of Medical Services 8

MedConnect - A Comprehensive AI-driven Platform for Seamless...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Ida Christy, J. Abisha, D. Navedha Evanjalin, R. Sriranjani, K. Dimple Abirami, S. Guru Varshini, B. National Engineering College Department of Computer Science and Engineering Kovilpatti India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

In the rapidly evolving landscape of healthcare, technological advancements play a pivotal role in enhancing patient care and optimizing medical services. New solutions are being developed and implemented, both those catered for treatment of organ-specific diseases, MedConnect's aim is to improve the process of treatment delivery. It helps the user streamline an 'in-matching' selection process with ways of overcoming the challenges posed by manual processes, having to wait over long periods, and more. Modifying a GPT-3 API within chatbot components increased the applications power and responsiveness as well as made use of the turbo version. The implementation offered users a straightforward health information retrieval system when patients could retrieve their medical records, look for relevant diseases and information on their prevention. Results also demonstrated a substantial enhancement in user satisfaction and usability of operations. Such results are of great importance, indicating clearly how transformational technology that incorporates AI can be in healthcare. MedConnect seeks to enhance the use of available medical facilities in as much as it improves the system of medical services provision;it has proved to be a success story in the application of technology in fighting for patients' needs. © 2024 IEEE.

关键词： Electronic health record

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A Privacy-Preserving Quantum Blockchain Technique for Electronic Medical Records

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IEEE engineering Management Review 2023年第4期51卷 137-144页

作者： Venkatesh, Ranjitha Hanumantha, Brahmananda Savadatti Gandhi Institute of Technology and Management Department of Computer Science Engineering Bengaluru561203 India

Nowadays, electronic medical records are increasing rapidly. Electronic medical records consist of sensitive and confidential information. These pieces of information need to be protected from attackers during the exchange with patients. Blockchain technology can protect information from attackers during the exchange of information. There are various classical techniques to protect electronic medical records. These techniques can exchange information correctly but cannot resist collective and coherent attacks and have more communication and computation costs. In this article, we introduced a privacy-preserving quantum blockchain technique to protect electronic medical records. This technique can resist the intercept, intercept-resend, entangle-measure, man-in-the-middle, collective, and coherent attacks with less communication and computation costs. © 1973-2011 IEEE.

关键词： Quantum entanglement

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Indoor Navigation System using Augmented Reality 7

Indoor Navigation System using Augmented Reality

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Parab, Devang Kishor Deshpande, Pranav Prasanna Thakur, Raj Manoj Warke, Vedant Atul Khanvilkar, Shushma Xavier Institute of Engineering Department of Computer Science And Engineering Mumbai India

ISBN: (纸本)9798350359299

This research work presents the design and implementation of an advanced indoor navigation system tailored for college campus (Xavier Institute of engineering), leveraging LiDAR sensors and QR code technology for accurate location mapping. The system integrates LiDAR sensors to create high-resolution 3D maps of indoor environments, providing detailed spatial information. QR codes are strategically placed throughout the campus, acting as reference points for precise localization. Users' smartphones scan these QR codes and collect real-time LiDAR data, allowing the system to properly pinpoint their location. The proposed indoor navigation application offers students, faculty, and visitors an intuitive and efficient way to navigate complex college buildings. The LiDAR-based mapping ensures precise indoor positioning, while QR codes serve as reliable landmarks for seamless orientation. The system's user-friendly interface provides step-by-step directions, optimizing routes based on real-time data, and enhancing the overall user experience. The integration of LiDAR technology and QR codes addresses the limitations of conventional indoor navigation systems, offering a scalable and adaptable solution for educational institutions. The implementation demonstrates the potential of this hybrid approach in transforming indoor navigation experiences, making it an invaluable tool for enhancing campus accessibility, improving way finding efficiency, and ensuring a positive user experience. © 2024 IEEE.

关键词： Augmented reality

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Scalable anonymity in file sharing: Leveraging Serverless Technologies for Secure and Private File Distribution 1

Scalable anonymity in file sharing: Leveraging Serverless Te...

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1st International Conference on Technological Innovations and Advance Computing, TIACOMP 2024

作者： Khan, Mustafa Hasan Srivastava, Akhilesh Kumar Abes Engineering College Department of Computer Science & Engineering Ghaziabad India

ISBN: (纸本)9798350392111

This research paper introduces an innovative file-sharing platform, engineered to redefine the standards of scalability, privacy, and security in digital file distribution. By utilizing user-provided remote storage services like Google Drive and OneDrive, the platform enables users to share files without directly hosting them, thereby achieving unparalleled scalability. The core mechanism involves encrypting the file's URL and storing it in a key-value database. When a file download is requested, the platform employs serverless computing technologies, such as AWS Lambda, to initiate a download stream directly from the remote host. This approach ensures that the file's origin remains concealed, upholding user anonymity and file security. By leveraging serverless technology, the platform mitigates traditional file-sharing challenges, offering a scalable, secure, and private method of file distribution. This paper delves into the technical architecture of the platform, evaluates its performance, and discusses its implications in the current digital landscape, where data security and privacy are paramount. © 2024 IEEE.

关键词： Anonymity

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Deep learning model for plant-leaf disease detection in precision agriculture

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International Journal of Intelligent Systems Technologies and Applications 2023年第1期21卷 72-91页

作者： Bajpai, Chandrabhanu Sahu, Ramnarayan Naik, K. Jairam Department of Computer Science and Engineering National Institute of Technology Raipur Chhattisgarh Raipur India

Crop disease in the agricultural is the main factor limiting yield and food quality. It requires timely diagnosis of crop illnesses for the better economy developing country. Manual crop illness assessment is limited due to lesser accuracy and restricted accessibility. It is very difficult to accurately identify and classify plant diseases due to corrupt in the data samples, lesser intensity of foreground and background, and the extreme similarity between unhealthy and healthy leaves in terms of colouring and size of crop leaves. Hence the employments of automated and computerised optimisations are needed. To identify plant leaf diseases, a DNSVM classification strategy fusing DenseNet-201 with support vector machine (SVM) is proposed in this work. Plant-Village dataset that provides good-variations, colour-differences, differences in orientation and size-of-leaves. Sugarcane plants-leaves were used for performance analysis of proposed model and obtained 97.78% of classification accuracy over the existing DenseNet-121-based classifier model (94%). © 2023 Inderscience Enterprises Ltd.

关键词： Crops

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Multiuser Capacity Enhancement in B5G Networks using Hybrid CDMA-NOMA Scheme

Multiuser Capacity Enhancement in B5G Networks using Hybrid ...

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2024 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2024

作者： Sherien, Sabahat Sadia, Haleema Iqbal, Hafsa Ahmed Hablulmateen, Ridha Alaa Ceme Nust Department of Electrical Engineering Islamabad Pakistan School of Electrical Engineering and Computer Science Nust Islamabad Pakistan University of Kufa Department of Electronics and Communication Engineering Najaf Iraq

ISBN: (纸本)9798350357905

The rapid advancement of beyond fifth-generation (B5G) networks has opened a new era of high throughput demands, necessitating novel access schemes to overcome the associated challenges. Non-orthogonal multiple access (NOMA) has emerged as a promising technique, outperforming conventional orthogonal multiple access (OMA) in heterogeneous wireless networks. This paper introduces a new hybrid code division multiple access (CDMA)-NOMA scheme to improve multi-user capacity and power allocation (PA) efficiency in wireless networks. By combining the strengths of CDMA and NOMA, our proposed scheme forms NOMA clusters, each accommodating multiple users with varying channel characteristics. Intra-cluster interference is mitigated through successive interference cancellation (SIC), while inter-cluster interference is eliminated using CDMA spreading codes. We address the challenge of PA, a critical aspect of NOMA networks, by assigning distinct codes to all users within a cell. Through rigorous analysis, we evaluate the bit error rate performance of our hybrid scheme in diverse channel conditions, including the NYUSIM, AWGN, Rician, and Rayleigh channel models. The results presents an innovative solution to enhance B5G network capacity, demonstrating the potential of our hybrid CDMA-NOMA scheme to support more users efficiently while reducing SIC complexity. © 2024 IEEE.

关键词： Code division multiple access

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