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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Samprith, S. Rajesh, T.M. Babu, Tina Nair, Rekha R Kishore, S. Dayananda Sagar Univerity School of Engineering Department of Computer Science & Engineering Bengaluru India Dayananda Sagar University School of Engineering Department of Computer Science & Engineering Bengaluru India Alliance Univerity Department of Computer Science and Engineering Bengaluru India Nitte University Nitte Institute of Professional Education Department of Computer Applications Karnataka India

ISBN: (纸本)9798350313413

Effective credit risk assessment holds significant importance for financial institutions and businesses extending credit to customers. It is crucial to evaluate customers' creditworthiness to minimize default risks and manage financial exposure. This study focuses on enhancing credit risk customer classification through optimized feature selection and classification techniques. The dataset is preprocessed using Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance and enhance performance. Relevant features are then extracted utilizing Cfs Subset evaluation along with various search algorithms. The optimized features are subsequently employed for classification using the Random Forest (RF) classifier. Experimental results reveal that, in credit customer classification, Evolutionary, Tabu, and Scatter search algorithms combined with RF classifier demonstrate a commendable accuracy of 90.31%. The proposed framework also showcases comparable outcomes in terms of other performance metrics. This automated credit customer classification method proves to be highly effective in identifying fraudulent users. © 2023 IEEE.

关键词： classifier Credit risk Detection methods Search Techniques

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Efficient practical Byzantine Consensus-based reputation method for IoT based electronic waste tracking and tracing system using blockchain

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Multimedia Tools and applications 2024年第37期83卷 84639-84672页

作者： Sharma, Lokesh Gupta, Ravi Kant Lamba, Chattar Singh Kumar, Ajay Lathar, Pankaj Department of IT Manipal University Jaipur Rajasthan303007 India Department of Mechanical Engineering Manipal University Jaipur Rajasthan303007 India Department of Computer Science & amp Engineering Manipal University Jaipur Rajasthan303007 India Department of Mechatronics Engineering Manipal University Jaipur Rajasthan303007 India Department of Computer Science and Applications Delhi Skill and Entrepreneurship University Dwarka New Delhi Delhi110077 India

The utilization of smart contracts enables the integration of blockchain technology to provide incentives for the gathering of electronic trash. Traditional blockchain-based Internet of Things (IoT)-E-Waste Tracking systems have challenges such as data privacy issues, significant delays, and low transaction per second (TPS) rates. In order to tackle these difficulties, the proposed approach utilizes a blockchain-based technology called IoT-enabled Efficient Practical Byzantine Consensus-based Reputation (EPBCR). This approach effectively monitors the post-production business processes of electronic devices by using hybrid consensus and reputation optimization algorithms. As a result, it enhances transparency, traceability, and privacy. The system efficiently regulates and oversees electronic gadgets throughout its supply chain by employing smart contracts on an unchangeable distributed ledger. The methodology is supported by analyzing the gas usage of Smart contracts on the Ethereum network. This results in the creation of, efficient, and high-throughput system that assists participants in conducting economic transactions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Smart contract

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Comprehensive Survey on Internet of Medical Things (IoMT) - applications and Challenges 15

Comprehensive Survey on Internet of Medical Things (IoMT) - ...

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

作者： Anusha, V. Sai Kumar, R. Kiran Kumar, G. Charan Mabjan, Palagiri Madanapalle Institute of Technology & Science Department of Electronics and Communication Engineering Madanapalle517325 India Annamacharya University Department of Computer Applications Rajampet India

ISBN: (纸本)9798350370249

The Internet of Medical Things (IoMT) is changing healthcare through the use of the Internet to connect medical devices, apps, and systems. This helps improve patient care, makes healthcare more efficient, and improves health outcomes. This paper deals with the edge-fog-cloud computing architectures for Internet of Health applications. Remote patient monitoring is done by collecting the data from wearable health devices, processing at different communication levels, applying intelligent algorithms and providing insights to the patient and healthcare professionals. This paper reviews the IoMT architecture and how the entire workflow is managed at device, edge, fog, and cloud levels. The paper also discusses IoMT challenges, such as data security and privacy concerns due to the interconnectedness and interoperability of devices at each layer and hurdles for seamless data integration within healthcare networks. © 2024 IEEE.

关键词： Electronic health record

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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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Telecom Churn Classification using Scatter Search and Random Forest Classifier

Telecom Churn Classification using Scatter Search and Random...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Varun, A.S. Babu, Tina Nair, Rekha R Kishore, S. Dayananda Sagar Univerity School of Engineering Department of Computer Science & Engineering Bengaluru India Alliance Univerity Department of Computer Science and Engineering Bengaluru India Nitte Institute of Professional Education Nitte University Department of Computer Applications Karnataka India

ISBN: (纸本)9798350313413

The Telecom Churn Dataset is a valuable resource that contains customer-related information such as demographics, usage patterns, billing data, and customer service interactions. The proposed model is used to explore various factors that might impact churn behavior and apply machine learning techniques to create predictive models for proactive retention efforts. This study have employed different approaches and methods, including random forest, logistic regression, and decision trees, as well as feature selection techniques like Correlation-based Feature Selection (CFS), scatter search etc. The proposed methodology involves preprocessing the data using techniques like SMOTE to handle class imbalance, selecting relevant features through Scatter search, and applying classification algorithms like the Random Forest (RF) classifier. The proposed model using Scatter search and Random Forest classifier has best formed with an accuracy of 91.9%, precision and recall of 0.919. Thus the model is well suited for telephone churn classification with the optimized feature set selection and classification. © 2023 IEEE.

关键词： Classification feature reduction random forest SMOTE Telecom churn

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Assessing the Impact of Migration from SOA to Microservices Architecture

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SN computer science 2023年第5期4卷 577页

作者： Raj, Vinay Bhukya, Hanumanthu Department of Computer Applications National Institute of Technology Tiruchirappalli Tamil Nadu Tiruchirappalli 620015 India Department of Computer Science and Engineering (Networks) Kakatiya Institute of Technology and Science Telangana Warangal 506015 India

Microservices has become a buzzword in IT and large enterprise firms such as Netflix, Twitter, Spotify and others have started to design their applications by adopting this new architectural style. A few organizations have started migrating their traditional monolithic and SOA-based applications to microservices in order to benefit from the features of new style. Software architects, on the other hand, are in chaos whether to adopt this new style or not as they are unaware of the pros and cons of microservices architecture. Also, the impact of the migration on SOA-based applications in terms of performance and complexity is unknown, leading to dilemma on the migration process. In this paper, a study of the migration impact on the existing SOA based applications to microservices is presented. For this study, change propagation probability and architectural stability metrics are used to examine the effect of migrating a SOA-based application to microservices architecture. The proposed approach is illustrated on a case study application designed using SOA and then migrated to microservices. It is observed from the results that though the impact of migration is high, migrating to microservices has significant benefits and it is best suitable for large enterprise applications. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Change propagation Microservices Migration Service oriented architecture Stability

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Colon Cancer Prediction with Transfer Learning and K-Means Clustering 7th

Colon Cancer Prediction with Transfer Learning and K-Means...

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7th International Conference on Emerging applications of Information Technology, EAIT 2022

作者： Babu, Tina Nair, Rekha R. Department of Computer Science and Engineering School of Engineering Dayananda Sagar University Karnataka Bengaluru India Department of Computer Applications School of Engineering Dayananda Sagar University Karnataka Bengaluru India

ISBN: (纸本)9789811951909

The automatic diagnosis of colon cancer is important for patients and their prognosis through the analysis of histopathological images. Traditional feature extraction methods extract low-level image features, and prior knowledge is required to choose meaningful features, which may be modified significantly by humans. Hence, unsupervised and supervised deep convolutions neural networks were utilized to analyze histopathological images of colon cancer. To overcome the impact of the unbalanced histopathology images in sub-classes, the balanced sub-classes are turned right and left, up and down, and rotated counter clockwise by 90∘ and 180∘. The proposed experimental findings for supervised histopathological image classification of colon cancer demonstrate that Inception_V3 and Inception ResNet_V2 outperform current algorithms. These findings suggest that the Inception_ResNet_V2 network is superior deep learning architecture for analyzing histopathological images for diagnosis colon cancers. As a result, in order to perform unsupervised image analysis, Inception_ResNet_V2 is utilized to extract features from colon cancer histopathology images. In addition, a new autoencoder network was created to convert the features collected by Inception_ResNet_V2 to a low-dimensional space for image clustering analysis. The test results demonstrate that the proposed autoencoder network outperforms the Inception_ResNet_V2 network in terms of clustering. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diseases

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A Comprehensive Analysis of Techniques and applications in Multimodal Deep Learning 1

A Comprehensive Analysis of Techniques and Applications in M...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Tomar, Vivek Sharma, Swati Arora, Sangeeta Tyagi, Aditya Dayal University Computer Science & Engineering Department Dehradun India KIET Group of Institutions Delhi-NCR Computer Science & Engineering Department Ghaziabad India KIET Group of Institutions Delhi-NCR Computer Applications Department Ghaziabad India Sharda University CSA Department Greater Noida India

ISBN: (纸本)9798350353648

In recent years, deep learning has gained popularity and been used in many different applications. Models that can process and link data utilizing several modalities are the aim of multimodal deep learning (MMDL). Even with all the advancements made, unimodal learning is still unable to fully capture the entirety of human learning. Multimodal learning facilitates a better understanding and analysis of situations in which information is processed through multiple senses. The various modalities covered in this article include picture, video, text, audio, flow, RGB, stance, depth, face expressions, body motions, and point clouds. There has been a thorough examination of the most current developments in multimodal deep learning applications in the last few years, as well as a detailed investigation of the baseline methodologies. A detailed taxonomy of distinct multimodal deep learning techniques is presented, providing further details on specific applications. Finally, key issues are outlined for each domain separately, along with potential avenues for further research. © 2024 IEEE.

关键词： Artificial Intelligence Deep Learning Machine Learning Multimodal Natural Language Processing (NLP) UniModal

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Anomaly Detection in Medical Images Using Deep Reinforcement Learning 2

Anomaly Detection in Medical Images Using Deep Reinforcement...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rastogi, Divya Sharma, Aparna Yadav, R.K. Varshney, Neeraj Saraswat, Laxmi Computer Science and Application Sharda University UP Greater Noida India Electronics & Communication Engineering Raj Kumar Goel Institute of Technology UP Ghaziabad India Gla University Department of Computer Engineering and Applications UP Mathura India Abes Engineering College Department of Computer Science UP Ghaziabad India

ISBN: (纸本)9798350371055

Anomaly detection in clinical pix is vital to identify and diagnose diseases. Deep reinforcement gaining knowledge is a practical gadget-mastering approach implemented for more than a few medical obligations. This technique applies a synthetic intelligence version to research by revealing an iterative procedure to discover a set of top-of-the-line parameters for ultimate selection-making. The cause of this examination is to use deep reinforcement mastering for anomaly detection in medical pix. This approach is used to identify strange areas in clinical pix, including tumors, mental diseases, and other abnormalities. Deep reinforcement mastering is mixed with Convolutional Neural Networks (CNNs) to extract capabilities from the medical pictures and then classify them consistent with their deviation from normal. The detection accuracy is evaluated using a novel assessment metric, referred to as the precision-recollect (PR) curve, which is designed to degree the version's accuracy. The results show that deep reinforcement learning can gain high-performance anomaly detection accuracy compared with conventional methods. © 2024 IEEE.

关键词： Accuracy Anomalous Evaluated Evaluation Measure

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Mental Health Predictive Analysis using Multiple Machine Learning Techniques 2

Mental Health Predictive Analysis using Multiple Machine Lea...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Tressa, Neethu Asha, V. Prasad, Arpana Prathith Pramodh Kumar, D. Saju, Binju New Horiznon College of Engineering Department of Master of Computer Applications Bengaluru India Adi Shankara Institute of Engineering & Technology Department of Computer Science - Ai Engineering Kerala Kalady India

ISBN: (纸本)9798350369960

The growth in mental health issues and the demand for high-quality medical care have prompted the requirement of machine learning techniques in the psychological research area. For the better prediction of mental health complications, this work presents an up-to-date thorough evaluation of machinelearning algorithms. This article reviews the difficulties, limitations and future directions of using machine learning approaches in psychological health. By searching reliable databases, relevant studies and publications on machine learning methods were collected for predicting mental health related issues. For the completion of this work, 40 research outcomes were analyzed using screening and classification procedures. The analyzed works were categorized according to mental health conditions such as schizophrenia, bipolar disorder, depression or anxiety, PTSD, and mental health conditions affecting children. While discussing the results, the difficulties and limitations that many researchers have encountered when studying mental health related problems were also considered. The paper also encompasses specific directions for future research work that are feasible and the need for utilizing varying approaches in machine learning for psychological health disorder predictions. © 2023 IEEE.

关键词： Decision Tree Classifier Flask Deployment K Nearest Neighbor Classifier Logistic Regression Algorithm Naïve Bayes Random Forest Stacking

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