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5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Singh, Jagendra Shaik, Nazeer Sahu, Dinesh Prasad Tiwari, Mohit Alam, Mohammad Shabbir Upreti, Kamal School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology—Autonomous Anantapur India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi India Department of Computer Science College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia Ghaziabad India

ISBN: (纸本)9789819788606

This article investigates machine learning techniques’ effectiveness at using computed tomography (CT) images to forecast breast cancer, hoping to expedite early identification and plan treatment. Drawing on many different machine learning models, such as CNN, SVM, VGG16, RNN and RF, we did extensive work to measure their performance distinguishing between malignant and benign breast tissue regions. The dataset includes 2,430 CT pictures, with 70% for training and 30% for testing. It has been carefully selected and prepared in order to guarantee robustness and consistency. The precision, and in-sensitivity measure the accuracy, sensitivity, specificity is used as analytic indicators to measure the models’ ability to predict regions of breast cancer accurately. Our findings show that the proposed CNN model achieved an accuracy of 98.75%, superior performance. Other machine learning models are also highlighted in this study, demonstrating how breast cancer can be predicted using various methods. This research will determine the forms and technologies suitable for breast cancer forecasting. Medical imaging and clinical decision-making can move forward because of this research, offering a glimpse into how integrated machine-learning systems can bring greater precision to diagnosis and prognosis. By careful experimentation and analysis, we hope to prepare people for early intervention and personalized treatment methods. This will make for improved patient outcomes in fighting breast cancer. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Prediction of Insurance Claims for Health Sector using Machine Learning Techniques 3

Prediction of Insurance Claims for Health Sector using Machi...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Pragatheeswari, E. Kavitha, M.N. Saranya, S.S. Dhanushree, D. Jasmitha, S. Abiradhie, P.V. Department of Computer Technology Kongu Engineering College Perundurai Erode India Department of Computer Science and Design Kongu Engineering College Perundurai Erode India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Nowadays, health issues play a tremendous role in day-to-day life and the medical expenditure to get treatment becomes more difficult for the ordinary people. Health insurance has become a vital aspect of people's lives. In this massive community, to access healthcare services such as insurance policies, LIC, ICICI, HDFC ERGO, Star Healthcare are benefits for claiming an amount for their medical expenses. The dataset encompasses three categories, such as generalized data, hospitalized data and claim data. Each record in the dataset represents an individual’s health insurance charges along with corresponding demographic and health-related characteristics. In this paper, Machine Learning techniques are used to group the claim amount that has been adopted by the individuals based on the hospitalized expenditure and insurance charges paid by the individuals. In the health sector, machine learning techniques are used to forecast insurance claims by building predictive models from historical data on claims, patient demographics, treatment types, and results. By predicting future claims based on trends seen in the data, these models help insurance firms better manage risk and allocate resources. In case of a fresher to this concept, it feels challenging to understand whereas generating as a pattern can be more flexible to acquire knowledge about how much amount has been claimed. © 2025 IEEE.

关键词： Electronic health record

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Timestamp Based OTP and Enhanced RSA Key Exchange Scheme with SIT Encryption to Secure IoT Devices

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Journal of Cyber Security and Mobility 2023年第1期12卷 77-102页

作者： Kollipara, V.N. Hemanth Kalakota, Sai Koushik Chamarthi, Sujith Ramani, S. Malik, Preeti Karuppiah, Marimuthu School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632 014 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering & Information Science Presidency University Karnataka Bengaluru560064 India

The Internet of Things (IoT) has become an emerging technology and is expected to connect billions of more devices to the internet in the near future. With time, more and more devices like wearables, intelligent home systems, and industrial automation devices are getting connected to the internet. IoT devices primarily transfer data using wireless communication networks, introducing more vulnerabilities like man-in-the-middle-attacks and eavesdropping. These security concerns are customary for any device communicating over the internet because of its intrinsic open nature. These problems are usually subdued by conventional cryptographic algorithms used in typical systems that are power-hungry and computationally intensive, making them infeasible to be used in IoT devices since they run on lowpowered chips, limiting performance, memory, and bandwidth. Hence, there is a requirement to adopt lightweight cryptographic algorithms that can abate the security issues while using low computational resources, which is the constraint in the given scenario. Hence, we propose an end-to-end secured IoT system that ensures the system's integrity is never compromised using lightweight cryptographic algorithms. We propose a three-module system, where the first module handles user authentication using a time-based one-time password, the second secures communication using lightweight enhanced RSA, and the third performs data encryption using Feistel-based enhanced SIT. This kind of system is designed to deal with security challenges in IoT devices, ensuring adequate data security while reducing the computational footprint using lightweight cryptography. © 2023 River Publishers.

关键词： Authentication

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Exploring the possibilities of integration of cyber-psychology for human behaviour in a smart city

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International Journal of Information and Communication technology 2024年第2期25卷 150-167页

作者： Wen, Liping Ting, Zhou Zheng, Huang Alfred Daniel, J. Antonidoss, A. Department of Psychology University of Chinese Academy of Sciences Beijing100101 China Institute of Psychology Chinese Academy of Sciences Beijing100101 China Department of Medical Psychology School of Health Humanities Peking University Beijing100191 China Department of Computer Science & Engineering SNS College of Technology India Department of Computer Science and Engineering Hindustan Institute of Technology and Science India

The smart city idea differs between cities and nations. In all meanings and characteristics of a smart city, public involvement is the only thing that remains common. Therefore, it is a very significant field to study human behaviour and development in smart cities. This paper presents a framework for identifying qualities necessary for people to be classified as intelligent persons and to integrate these human behavioural characteristics in cyber technology. Human behaviour in a smart city has been analysed using the machine learning algorithm and big data analytics. The integrated machine learning and big data analytics framework (iML-BD) classifies the cyber behaviour of intelligent persons in a smart city by observing the cyber activities performed by the individuals. Furthermore, this paper handles the risk factors for cyber-acquired and cyber-dependant crime violence and abuse that vulnerable internet and public access devices using blockchain technology. Blockchain is a method of storing data that takes too long to alter, modify, or manipulate. A blockchain is an electronic accounting system that is reproduced and spread through the Bitcoin protocol’s entire communication network. The case study performed on iML-BD has resulted in the highest performance in terms of prediction accuracy of 94.98%. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Smart city

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Maximizing Utility and Quality in Smart Healthcare with Incentive Driven Hierarchical Federated Learning 17

Maximizing Utility and Quality in Smart Healthcare with Ince...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Singh, Himanshu Patidar, Sourabh Pratap, Ajay Yasumoto, Keiichi Department of Computer Science and Engineering Varanasi India Nara Institute of Science and Technology Nara Japan

ISBN: (纸本)9798331531195

Recent development of federated learning based smart healthcare techniques facilitates distributed processing of patient data while protecting privacy. However, two significant challenges can notably degrade model learning performance. Firstly, patients often show reluctance to participate in model training due to a lack of incentives. Secondly, the quality of updated models can change significantly due to different aspects such as the size and training data quality. The incorporation of low-quality updates can subsequently degrade global model quality in FL. Considering the above scenarios, this paper formulates a utility maximization problem keeping model quality function and budget into deliberation. To solve the formulated problem, this paper proposes association algorithm utilizing Utility Maximization and Payment (UMaxP) algorithm based on matching approach to select Wireless Body Area Networks (WBANs) and hospitals in a way that increases the utility and improves model quality using Hierarchical FL (HFL). In UMaxP, the allocation of patients to hospitals is optimized to maximize both the sum of local model qualities and the overall utility of the patients. The effectiveness of the proposed algorithmic model is demonstrated through theoretical analysis, experimental validation, and comparative evaluation with various methods using real-world data. © 2025 IEEE.

关键词： Federated learning

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Machine Learning Model for Sentimental Analysis of Amazon Reviews

Machine Learning Model for Sentimental Analysis of Amazon Re...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Umamageswari, A. Pratishwaran, R.J. Poojitha Reddy, M. Yuvan Sankar Raj, R. SRM Institute of Science and Technology Department of Computer Science and Engineering Ramapuram Chennai India SRM Institute of Science and Technology Department of Computer Science and Engineering With Specialization in Big Data Analytics Ramapuram Chennai India

ISBN: (纸本)9798331522988

The goal of this project is to draw a deeper understanding of the subjective nature behind online product reviews, largely by examining a large dataset received from Amazon that contains numerous star ratings and comments. Since there is such a massive amount of data on the Amazon platform, it was perfect for the analysis. Our methodology is platform-agnostic and can be easily extended to other comparable datasets from other web platforms with comparable guarantees in achieving wide applicability. Our primary motivation is improving the dependability of online shopping. Since product reviews are currently widely used by many consumers as guides in making decisions to buy products, such reviews ought to be fair representations of the actual dependability and quality of the products. The goal of our effort is to identify patterns in the language and ratings of such reviews that would directly reflect the real customer sentiment and also the reliability of the product. Our approach has yielded already a rate of about 92%, which shows it has the potential greatly to raise the legitimacy of online reviews. We'd like to make our customers feel more confident and better equipped with information to make better and safer purchases on a website like Amazon and others. In the final analysis, we strive to make online shopping for our customers worldwide safer and more trustworthy. © 2024 IEEE.

关键词： Data reliability

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Intelligent Deep Learning Based Cybersecurity Phishing Email Detection and Classification

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computers, Materials & Continua 2023年第3期74卷 5901-5914页

作者： R.Brindha S.Nandagopal H.Azath V.Sathana Gyanendra Prasad Joshi Sung Won Kim Department of Computing Technologies SRM Institute of Science and TechnologyKattankulathur603203India Department of Computing Science and Engineering Nandha College of TechnologyErode638052India School of Computing Science and Engineering VIT Bhopal UniversityBhopal466114India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-si38541Gyeongbuk-doKorea

Phishing is a type of cybercrime in which cyber-attackers pose themselves as authorized persons or entities and hack the victims’sensitive data.E-mails,instant messages and phone calls are some of the common modes used in *** the security models are continuously upgraded to prevent cyberattacks,hackers find innovative ways to target the *** this background,there is a drastic increase observed in the number of phishing emails sent to potential *** scenario necessitates the importance of designing an effective classification *** numerous conventional models are available in the literature for proficient classification of phishing emails,the Machine Learning(ML)techniques and the Deep Learning(DL)models have been employed in the *** current study presents an Intelligent Cuckoo Search(CS)Optimization Algorithm with a Deep Learning-based Phishing Email Detection and Classification(ICSOA-DLPEC)*** aim of the proposed ICSOA-DLPEC model is to effectually distinguish the emails as either legitimate or phishing *** the initial stage,the pre-processing is performed through three stages such as email cleaning,tokenization and stop-word ***,the N-gram approach is;moreover,the CS algorithm is applied to extract the useful feature ***,the CS algorithm is employed with the Gated Recurrent Unit(GRU)model to detect and classify phishing ***,the CS algorithm is used to fine-tune the parameters involved in the GRU *** performance of the proposed ICSOA-DLPEC model was experimentally validated using a benchmark dataset,and the results were assessed under several *** comparative studies were conducted,and the results confirmed the superior performance of the proposed ICSOA-DLPEC model over other existing *** proposed model achieved a maximum accuracy of 99.72%.

关键词： Phishing email data classification natural language processing deep learning cybersecurity

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Data-Driven Insights: Leveraging Analytics for Predictive Modeling in Finance 4

Data-Driven Insights: Leveraging Analytics for Predictive Mo...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Purwar, Mitali Deka, Upasana Raj, Himadri Ritu Upes Department of Computer Science & Engineering Dehradun India Noida Institute of Engineering & Technology Department of Computer Science & Engineering Greater Noida India Chandigarh College of Engineering Chandigarh Group of Colleges Department of Computer Science & Engineering Jhanjeri140307 India

ISBN: (纸本)9798350387490

A distinct gap in the most recent business intelligence literature is addressed by this research, which discusses the possibility of radical changes in the market based on the application of predictive analytics. The key goal is to evaluate the impact of the prediction models on ethics, expansiveness, precision and reliability. The evaluation is of a qualitative nature, and topic analysis along with case histories is used to acquire specific, contextualised data. Basic findings indicate that employment of market forecasting skills is enhanced through the use of predictive models since decisions are made much more deliberately. The analysis reveals that these models remain prone to change in a variety of contexts and demonstrates their capacity to deal with large data efficiently. Ethical issues is stressed out, which leads to the conclusion that there should be stricter rules for personal data protection and more openness for procedures to gain the compliance and trust. The conclusions underline the major competitive advantages that, if applied line by line, predictive analytics can offer, calling for more adaptability and sensitivity to the market trends. This report which proposes a 50-50 mix of value creation and integrity does, nonetheless;provide notice of potential ethical issues and data privacy. The guidelines for enhancing the accuracy of the prediction, which complies with the ethical standard are strengthening the information management system and developing novel predictive model technologies. Future research must continue to analyze shifts in predictive analytics understanding new technologies and possible changes in the market. © 2024 IEEE.

关键词： Prediction models

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R3N2 : a novel approach for review based custom star rating using recurrent neural network

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9089-9097页

作者： Kumar, Krishan Department of Computer Science and Engineering National Institute of Technology Uttarakhand Garhwal Uttarakhand Srinagar246174 India

Online shopping is growing in popularity as internet services are used more frequently. Many consumers consider online shopping due to the comfort, ease of access, and variety of products offered. However, this wide variety of products makes it hard for consumers to select one product from many available options. It is infeasible for any customer to go through many customer reviews given to the products. Therefore, the customers’ star rating becomes a viable solution for the star rating assigned to the products. However, the star rating-based system takes the mean of star ratings given by all the consumers. The mean value represents the masses’ view but fails to represent personal views. In this work, a novel star ratings-based system is proposed for the products to reflect the consumer’s individual needs and expectations by extracting customer reviews. A recurrent neural network is used as a sequential prediction model to hold aspect words and identify them after tokenization of a detailed review. The proposed R3N2 model enhances 0.9%, 2.5%, 4.9%, and 6.7% accuracy on digital products, electronics products, Kaggle movie reviews, and Apps datasets than the existing state-of-the-art (SOTA) models, respectively. Therefore, the proposed model outperforms SOTA models and may be used for real-time applications as it takes 2.56 ms per review. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Sentiment analysis

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Registration of Lung CT-Images Affected with Covid-19 Using Centroid Opposition-Based Multiverse Optimizer

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SN computer science 2025年第3期6卷 1-18页

作者： Nayak, Somen Sarkar, Achyuth Mondal, Subhodip Department of Computer Science and Engineering National Institute of Technology Arunachal Pradesh Jote Arunachal Pradesh Itanagar 791113 India Department of Computer Science and Engineering Bankura Unnayani Institute of Engineering Subhankar Nagar Pohabagan West Bengal Bankura 722146 India

Medical image registration can be used to address a variety of practical difficulties that are related to the collection of medical pictures from multiple modalities. Multimodal registration uses intensity-based registration approaches, and these strategies will amalgamate supplementary images that possess the same content into a singular representation through image manipulation. It is crucial to optimize the modification of computed tomography (CT) scans. The improvement of the similarity metric between the various scans is really *** optimization methods have been proposed recently, focusing on the foundational elements of optimization. However, from the perspectives of efficiency and quality, there is still a lot that may be improved. For diagnosis, Lung CT image registration is crucial in identifying the spot differences between the various Lung CT images. The registration of lung CT scans using multiverse optimization (MVO) with computing opposition-based learning followed by chaotic-based initialization is suggested in this research. We have proposed the Meta heuristics-based Optimization (MVO_COBL) algorithm to register lung CT images for both multimodal and monomodal medical image registration. We first collected CT scans of the lungs from the data *** are then processed using MVO_COBL, and the registered images are finally extracted. The suggested lung CT image registration method’s outcomes are contrasted with those of the hybrid particle swarm optimization with Grey wolf optimizer (HPSGWO), crow search algorithms (CSA), Grey wolf optimizer (GWO), and particle swarm optimization (PSO) based methods. The experimental results statistically speaking that, the suggested method performs better than HPSGWO, PSO, GWO and CSA methods in registering lung CT images. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Feature-based registration Intensity-based registration Medical image Meta-heuristics algorithm Mono modality image registration Multi modality Mutual information Mutual information Similarity metric

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