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Hybrid CNN-LSTM Approach for Predicting and Analyzing Customer Churn in the Banking Sector 5

Hybrid CNN-LSTM Approach for Predicting and Analyzing Custom...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Kaushik, Amit Gupta, Rahul Singh, Ajay Ahmed, Hassan Safi Rao, Vuda Sreenivasa Vijayalakshmi, B. Anitha Amity University Amity School of Insurance Banking & Actuarial Science Noida India Amity University Noida India Abes Business School Ghaziabad India Al-Farahidi University Department of Biomedical Engineering Baghdad Iraq Koneru Lakshmaiah Education Foundation Department of Cse A.P. Vaddeswaram India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Cse Chennai India

ISBN: (纸本)9798350356236

Customer attrition therefore becomes a major issue in the context of highly competitively, provider specific analysis in banking sector that affects the organizational profitability and its sustainable growth. Identifying those customers most likely to churn is strategic important for the establishment of timely customer retention strategies among banks. This research seeks to reduce customer attrition in the banking framework using a CNN-LSTM composite model. It uses CNN which, for its part, helps extract or learn features while using the LSTM networks for learning the temporal patterns because it also incorporates time and behavioral patterns in the customer data. Further, customer segmentation is applied in order to increase the precision of churn predictions made based on the different levels of tension found in specific customer segments. This hybrid architecture allows for a more comprehensive analytical approach to the customer data with the ability to identify various types of behavioral patterns and temporal changes that may impact churn. For churn prediction, the proposed framework incorporates a CNN-LSTM model. CNN layers work on temporal customer transaction data extracting crucial features;LSTM layers maintain long-term dependencies. The framework also applies segmentation analysis based on cluster analysis to categorize customers with regard to behaviour, age, and product consumption patterns. The model is developed in Python and tested using a real-word data involving customer data. Using churn-forecasting metric, the hybrid CNN-LSTM model achieved accuracy and robustness with an accuracy of 98.5%, precision of 97%, recall score of 95% and an F1-score of 97%. This performance enhanced the capability to capture spatial patterns and temporal dependencies of customer behaviour beyond the traditional models. © 2025 IEEE.

关键词： Sales

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AI-Enabled Muscle Oxygen Sensors for Personalized Exercise Intensity Monitoring and Recovery Analysis 5

AI-Enabled Muscle Oxygen Sensors for Personalized Exercise I...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Balachander, K. Balasubadra, K. Tidke, Bharat Sree Southry, S. Meenakshi, B. Murugan, S. R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Puduvoyal Tamil Nadu Chennai India R.M.D. Engineering College Department of Information Technology Kavaraipettai Tamil Nadu Chennai India University Nagpur Campus Pune India Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India Sri Sairam Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350356236

Through the integration of artificial intelligence (AI) with muscle oxygen sensors, this work presents a unique way to exercise intensity monitoring and recovery analysis. Subjective assessments or heavy equipment are the backbone of the traditional approaches to evaluating exercise intensity and recovery. Using the latest developments in AI and wearable sensor technology, our system offers real-time personalized feedback to athletes and fitness enthusiasts. Our Muscle oxygen sensors were vigorously tested to ensure their precision and performance. Wearing the sensors, participants varied their workout regimen while the sensors sent data on the participants' muscle oxygen levels to an AI program. According to the results, the algorithm could correctly determine workout intensity levels and establish individual recuperation timeframes. Moreover, the AI system learns the user's physiological traits and can personalize recommendations and training programs for everyone. Our approach may help improve athletic performance, avoid overtraining injuries, and maximize fitness results by giving practical advice on recovering from exercises at the right intensity. © 2025 IEEE.

关键词： Sensory perception

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Wearable Assistive Device for Enhanced Navigation in Visually Impaired Individuals Using YOLO-Based Object Detection 5

Wearable Assistive Device for Enhanced Navigation in Visuall...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Rao, Abhishek S. Poojary, Ramaprasad Karthik Pai, B.H. Rohan Arya, A.R. Utkarsh Kumar, Harsh Darshan, S. Shenoy, H. Nagesh Nmam Institute of Technology Department of Information Science and Engineering India School of Engineering and It Manipal Academy of Higher Education Dubai Campus PO Box no 345050 Dubai United Arab Emirates Canara Engineering College Department of Computer Science and Engineering Benjanapadavu India

ISBN: (纸本)9798350356236

The proposed study addresses the pressing challenge of enabling visually impaired individuals to navigate their surroundings with enhanced safety and autonomy. To this end, we propose a wearable assistive device designed to improve spatial awareness and facilitate environmental interaction. The primary objectives include developing an accurate object detection system, integrating real-time distance measurement capabilities, and incorporating situational awareness through weather data integration. The study methodology employs cutting-edge computer vision and machine learning techniques, with a YOLO-based object detection model at its core. This model ensures robust identification of objects, achieving an impressive 97% precision and recall. To support safe navigation, real-time distance measurement functionality was implemented. Additionally, the device incorporates real-time weather data via the Open Weather API, delivering comprehensive updates on temperature, precipitation, and wind conditions to assist in user decision-making. Design considerations focused on user-centric aspects such as battery life, portability, and cost-effectiveness to ensure practicality and accessibility. Evaluation results demonstrated an average mAP of 98.34% at IoU 0.5 across all object classes, with some reaching up to 99.50%. User testing validated the system's efficacy, achieving a 95% success rate in object identification and spatial guidance. These findings underscore the device's potential to enhance independence, safety, and quality of life for visually impaired users, promoting inclusivity through advanced assistive technology. © 2025 IEEE.

关键词： Distance measurement

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Developing Critical Thinking Skills in Undergraduate Students: A Mapping Study in computing Education 17

Developing Critical Thinking Skills in Undergraduate Student...

引用

17th international conference on Computer Supported Education, CSEDU 2025

作者： de Andrade Mota, Davy dos Santos, Simone C. Informatic Center Federal University of Pernambuco Pernambuco Recife Brazil

ISBN: (纸本)9789897587467

In the dynamic context of the digital age, academic training in information technology (IT) transcends the mere assimilation of theoretical knowledge, equally requiring the promotion of critical thinking among students. From this perspective, this research proposes a literary approach dedicated to investigating and analysing effective strategies for developing and stimulating critical thinking in IT students. In an environment where technology advances rapidly, it is crucial to equip future professionals in this field not only with solid technical knowledge but also with the intrinsic ability to critically analyse information and conceive innovative solutions in the face of emerging challenges. Thus, this study is motivated by the following research question: "How to develop critical thinking skills in IT undergraduate students?". To conduct this investigation, Kitchenham's systematic mapping studies method and the ChatGPT 4.0 version tool were used as instruments for formatting the extracted data, and visually, generating the charts and graphs. From 72 studies, the main highlighted results were observed, which reference the primary teaching methodologies and frameworks used, the benefits and limitations of these methods, as well as relevant information for understanding the courses in which these methods were applied, class sizes, course durations, and levels of education. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Students

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A Twin Visualization Driving Method of Elevator Based on Inspection data 1

A Twin Visualization Driving Method of Elevator Based on Ins...

引用

1st international conference on Intelligent Manufacturing and Cloud computing, ICIMCC 2024

作者： Zhang, Donghong Zhou, Pengfei Hou, Xuan Zhao, Dongping Kou, Yanfei Inner Mongolia Institute of Special Equipment Inspection and Research Inner Mongolia China School of Aerocraft XI’AN Aeronautical Institute Shaanxi China School of mechanical engineering North University of China Shaanxi China

ISBN: (纸本)9781643685779

Aiming at the poor matching between elevator inspection data and twin body model, which leads to the problem of data redundancy and delay in elevator twin model operation, based on the analysis of elevator inspection data, we study the driving data configuration based on the correlation analysis to study the running state of elevator twin model, and the driving of elevator twin gross oh operation based on the coordinate transformation, and form the driving method of elevator twin visualization based on the inspection data to realize the inspection data and elevator twin model running automatic matching and driving. Finally, the effectiveness of the method is illustrated by examples. © 2025 The Authors.

关键词： Elevators

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Enhancing Loan Default Prediction with Human-in-the-Loop and XGBoost Ensemble Learning 5

Enhancing Loan Default Prediction with Human-in-the-Loop and...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Khan, Shamim Ahmad Salman, Rahama Majid, Al-Hussein Maysir Mythili, M. Alazzam, Malik Bader Ponni Valavan, M. Glocal School of Science & Technology Glocal University Department of Ece U.P India College of Engineering and Computer Science Jazan University Department of Computer Science Jazan Saudi Arabia Al-Farahidi University Department of Biomedical Engineering Baghdad Iraq Department of English Erode India Jadara University Faculty of Information Technology Irbid Jordan K. Ramakrishnan College of Engineering Department of Ai & Ds Tamil Nadu Trichy India

ISBN: (纸本)9798350356236

Human-in-the-Loop (HITL) Machine Learning with Ensemble Learning uses the best of both automated algorithms and human input in making predictions and decision making when it comes to Machine Learning (ML). In HITL frameworks they include human inputs in model training and validation, allowing the resultant models to be built based on actual needs rather than white algorithms, which may be insensitive to some details otherwise. It is more relevant in such areas or applications that involve higher risk such as credit risk applications in today's financial world where predictions form the basis for major loans' approval. Although previous studies that employed fully automated ML models have some shortcomings that include being prone to giving biased predictions, being less interpretable and scalability issues and failure to handle edge cases hence reducing reliability and trustworthiness. To overcome these problems, this study introduces a new approach of combining the HITL with the XGBoost, which is one of the most potent ensemble learning algorithms to develop a balanced model of loan default prediction. Through the analysis of loan data from Lending Club, this method has attained an accuracy of 99.4% along with a high precision and a high recall, thus pointing to well-rounded performance of the model. Expert knowledge in the form of the HITL loop improves the regularization and optimization of the XGBoost model leading to a better, efficient, reliable, and more interpretable model for credit scoring. It was in python the model was developed taking advantage of the powerful ml and data analysis libraries which make it easy to develop, scalable and reusable for real life organizations especially in the financial arena where precision and accuracy are paramount. This approach lays some basis for subsequent innovations in the ICT applications in HITL ML systems in different fields. © 2025 IEEE.

关键词： Personnel training

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Optimizing English Lexical databases with BERT and Reinforcement Learning 5

Optimizing English Lexical Databases with BERT and Reinforce...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Saranya, V. Murthy, G.R.K. Alapati, Purnachandra Rao Mythili, M. Swarnamughi, K. Bala, B. Kiran Panimalar Engineering college Department of English Chennai India Narasaraopet India Prasad V Potluri Siddhartha Institute of Technology Department of English Andhra Pradesh India Department of English Erode India Sathyabama Institute of Science And Technology Department of English Chennai India K.Ramakrishnan College of Engineering Department of Ai & Ds Trichy India

ISBN: (纸本)9798350356236

The evolution of natural language processing (NLP) has significantly transformed the way this study analyzes and understand language. This study presents a novel approach to generating lexical databases for English language sources using AI-powered methods, specifically leveraging BERT and reinforcement learning. Traditional methods for lexical database creation often rely on manual compilation or semi-automated techniques, which can be time-consuming and may lack comprehensive coverage of linguistic nuances. These existing approaches struggle with scalability and adaptability, particularly when addressing evolving language use and the inclusion of rare or domain-specific terms. The Open English WordNet dataset serves as a foundational resource, providing rich lexical relationships and contextual information necessary for effective database generation. Our proposed method integrates BERT for context-aware embedding generation and reinforcement learning for continuous optimization of lexical extraction and enrichment processes. This innovative combination not only enhances the accuracy of detecting synonyms, antonyms, hypernyms, and partof-speech tags but also ensures that the system learns from user feedback, leading to ongoing refinement. The advantages of this method include improved precision and recall, rapid processing times, and the ability to adapt to new linguistic data dynamically. Ultimately, this study demonstrates that AI-driven approaches can significantly enhance the quality and usability of lexical databases, paving the way for more sophisticated applications in natural language processing and linguistics. © 2025 IEEE.

关键词： Natural language processing systems

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Adversarial Artifact Removal: A GAN-Based Framework for Enhanced Image Quality and Reliability 5

Adversarial Artifact Removal: A GAN-Based Framework for Enha...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Pande, Pournima Misba, M. Gupta, Deepak Rani, V.Kanpur Ahmed, Hassan Safi Balakumar, A. Yeshwantrao Chavan College of Engineering Department of Applied Chemistry Nagpur India Tamil Nadu Chennai India Graphic Era Hill University Department of Mechanical Engineering Uttarakhand Dehradun India Graphic Era Deemed to be University Department of Mechanical Engineering Uttarakhand Dehradun India Hindustan Institute of Technology and Science Department of Eee Chennai India Al-Farahidi University Department of Biomedical Engineering Baghdad Iraq K.Ramakrishnan College of Engineering Department of Ece Trichy India

ISBN: (纸本)9798350356236

Artifacts in images are significant challenges in many fields and degrade the quality and reliability of data analysis. Existing methods for artifact removal face a problem of complex and diverse artifacts, limiting their effectiveness. Traditional techniques for removing artifacts have a problem with low adaptability to different types of artifacts and can sometimes delete important features of an image. The artifact removal method takes the concept of GANs into use to remove artifacts within the adversarial training model from images. It rectifies the issues stated previously through the proposed approach based on training a GAN. The G learns artifact-free versions of the images and the D tells them clean or corrupted ones while the G continues its progression towards artifact removal guided by D. Once it was trained, G had completely removed artifacts in novel unseen images. Results prove the superiority of this proposed artifact removal compared with existing approaches across a spectrum of datasets including different forms of artifacts: noise compression, and occlusion results of removing such different kinds of artifacts without sacrificing much the image quality along with preserving other critical features that need removal. Adversarial removal artifacts can be taken as one such error value 112.31 solution in making an improved version of enhancing quality as well as reliability of the image while making applications over the range. © 2025 IEEE.

关键词： Photointerpretation

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Non-Invasive Technique for Fingerprint-Based Blood Group Identification 5

Non-Invasive Technique for Fingerprint-Based Blood Group Ide...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Thakre, Devyani Panpatte, Manswi Khandelwal, Richa Shri Ramdeobaba College of Engineering And Management Electronics and Computer Science Department Nagpur India Ramdeobaba University Department of Electronics Engineering Nagpur India

ISBN: (纸本)9798350356236

This work delves into the development of a novel, non-invasive method for determining blood groups using finger-print analysis. Traditional blood group testing methods, such as blood sampling, are invasive, time-consuming, and require specialized equipment. In contrast, our approach utilizes image processing techniques to analyze the unique patterns present in fingerprints, aiming to accurately predict an individual's blood type without the need for direct blood contact. The methodology begins with the acquisition of high-resolution fingerprint images, which are then processed through a series of image enhancement and normalization steps to ensure consistent quality. Feature extraction techniques are employed to identify distinctive patterns and characteristics in the fingerprints that correlate with specific blood groups. These extracted features are subsequently fed into advanced classification algorithms, such as Convolutional Neural Networks (CNNs), which are trained to recognize and classify the fingerprint data into one of the primary blood groups: A, B, AB, and O, with both positive and negative Rh factors. Through extensive training and validation, the classification model achieved a training accuracy of 99.47% and a validation accuracy of 80%, demonstrating its robustness and reliability. Additionally, the model achieves high F1-scores across most blood groups, with an average F1-score of 0.83, highlighting its precision and recall balance. Compared to traditional blood testing methods, this approach offers a faster, cost-effective, and painless alternative, achieving comparable accuracy while significantly reducing time and costs. This work leverages state-of-the-art machine learning and image processing methods, optimizing accuracy and reliability by utilizing a large dataset of labeled fingerprint images. By eliminating the need for invasive procedures, this work aims to enhance patient comfort, reduce the risk of infection, and streamline the process of

关键词： Noninvasive medical procedures

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Improving Automated Assessment of English Spoken Discourse Using Transfer Learning Models-Wav2Vec 2.0 5

Improving Automated Assessment of English Spoken Discourse U...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Hameed, N. Sheik Vijayakumar, S. Rajaraman, K. Kumar, V. Vinod Tamilarasan, P. Faizal, M. K. Mohamed B. S Abdur Rahman Crescent Institute of Science and Technology Department of English Chennai India Annamalai University Department of English Tamil Nadu Chidambaram India Bharathidasan University Department of English Tiruchirappalli India Srm University Department of English Chennai India K.Ramakrishnan College of Engineering Department of Ai & Ds Tamil Nadu Trichy India

ISBN: (纸本)9798350356236

Automated Assessment of English Spoken Discourse is invaluable tool used in the process of learning foreign languages, teaching and communication as it is capable of delivering a large number of assessments of spoken language without significant human interferences. However, common approaches may yield some issues in terms of accuracy, methods' applicability to a variety of accents and languages, and the scalability of the techniques. Conventional techniques including HMM-GMM and contemporary DL techniques like the CNN-RNN triangulate unsatisfactory results due to the inability to capture more intricate spoken discourse since they are unable to pick tune from the din. In response to these difficulties, this research proposal presents the following framework using Wav2Vec 2.0, a self-supervised learning model involved in speech signals. The proposed method makes use of Wav2Vec 2.0 to capture rich representations directly from the raw input data to minimize dependence on labelled data and improve robustness to different patterns of speech. Applicable in Python, the model is fine-tuned in spoken discourse datasets and is assessed based on primary metrics of performance;test performance: 99.1%. These results are much better than traditional and baseline methods and clearly demonstrate the high stability and potential of the model. This way, the study that addresses prior limitations and offers a stable solution contributes to the development of the automated speech assessment. Further work that could be suggested is the selection of multimodal integration approaches and real time as a way to increase more the scores of the different models and the flexibility of the system that has been proposes in education and work environments. © 2025 IEEE.

关键词： Self-supervised learning

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