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检索条件"机构=Dept of Computer Science and Computer Engineering"

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Optimizing Telecom Operations with Segmentation and Churn Prediction 3

Optimizing Telecom Operations with Segmentation and Churn Pr...

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

作者： Neha, Dandu Ameesha, Mohammed Dheeraj, Mukka Sangeeta, K. Dept.Computer Science and Engineering Institute of Aeronautical Engineering Telangana Hyderabad India Institute of Aeronautical Engineering Telangana Hyderabad India

ISBN: (纸本)9798331527549

Client turnover is a significant issue for large organizations, particularly within the telecom sector, where it has a direct impact on profitability. To address this, telecom providers are increasingly focusing on developing predictive models to forecast customer churn and reduce its adverse effects. Identifying the key factors influencing churn is critical for implementing effective retention strategies. This research presents the development of a churn prediction model designed to help telecom companies identify customers at a high risk of leaving. The study involves exploring various data analysis techniques and presenting the results through graphical visualizations. Using machine learning methods, the model leverages large-scale datasets and introduces an innovative approach to feature design and selection. Historical customer data from previous months was utilized for training, evaluation, and validation of the model. Four machine learning algorithms were tested: Logistic Regression, XGBoost, Gradient-Boosted Machines (GBM), Random Forest, and Decision Trees. To further improve model accuracy, parameter tuning was applied during the training phase. The primary objective of this work is to enhance telecom operations by optimizing customer segmentation and churn prediction, ultimately improving both operational efficiency and customer retention. © 2025 IEEE.

关键词： Sales

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Personal AI Trainer Using Two-Way Mirror and Raspberry Pi

Personal AI Trainer Using Two-Way Mirror and Raspberry Pi

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Singh, Ratan Tejnarayan Wasnik, Aarush Mistry, Krishna Modi, Jayen Koshti, Dipali Fr. Conceicao Rodrigues College of Engineering Dept. of Electronics and Computer Science Mumbai India

ISBN: (纸本)9798331512965

A novel smart mirror system is proposed to enhance users' daily routines by integrating advanced technologies. Utilizing a Raspberry Pi, an electronic display, a two-way mirror, a camera, and control mechanisms powered by OpenCV and MediaPipe, the system provides personalized interactions through facial recognition. This feature allows the smart mirror to identify users and tailor its responses accordingly. Beyond its aesthetic appeal, the system functions as an interactive fitness coach, guiding users through workouts, correcting their form, and delivering real-time feedback. This paper explores the potential of smart mirrors to revolutionize personal fitness and care, offering an innovative approach to interactive user engagement. © 2025 IEEE.

关键词： Face recognition

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Comparative Advances in Financial Sentiment Analysis:A Review of BERT,FinBert, and Large Language Models 3

Comparative Advances in Financial Sentiment Analysis:A Revie...

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

作者： Mahendran, Manish Barath Gokul, Aswin Kumar Lakshmi, Poornima Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai600069 India

ISBN: (纸本)9798331527549

Capturing market sentiments and supporting well-informed financial decision-making depend on the developing field of Financial Sentiment Analysis (FSA). Natural language processing (NLP) has made significant strides in comprehending and categorizing sentiment in intricate financial texts, especially with the use of Large Language Models (LLMs). The application of various LLMs, such as Bidirectional Encoder Representations form Transformers(BERT) and Financial BERT (FinBERT), as well as distilled models, such as DistilBERT and DistilRoBERTa, on a variety of financial datasets, including Financial Phrase Bank and LexisNexis news articles, was the main focus of this review article. highlighting different approaches such as model fine-tuning, zero-shot and few-shot learning, and prompt engineering. The study focuses on practical model predictions through a case study of sentimental analysis of cryptocurrencies. While FinBERT, a financial variant of BERT, exhibits high accuracy and robustness, other LLMs exhibit varying degrees of success based on the dataset and domain requirements. The analysis concentrates on the difficulties, compromises, and potential paths for improving LLMs for financial sentiment analysis. © 2025 IEEE.

关键词： Zero-shot learning

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Advantages of MIMO Radar for Contactless Pulse Waveform Monitoring using End-to-End Learning

Advantages of MIMO Radar for Contactless Pulse Waveform Moni...

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2025 IEEE Radio and Wireless Symposium, RWS 2025

作者： Bauder, Chandler J. Fathy, Aly E. University of Tennessee Knoxville Dept. of Electrical Engineering & Computer Science KnoxvilleTN United States

ISBN: (纸本)9798350377743

Radar-based methods for contactless pulse monitoring typically use heart rate estimation accuracy as the primary performance evaluation metric. Recently, more in-depth metrics such as heart rate variability (HRV) have been of interest, requiring precise pulse waveform reconstructions for reliable estimations. Traditional phase variation-based vital signs signal processing techniques have proven to be susceptible to sources of noise, impacting the shape of the extracted pulse waveform. In this work, the advantages of using raw multi-channel radar data and deep learning in an end-to-end manner for precise pulse waveform reconstruction are explored. Preliminary results show electrocardiogram (ECG) waveforms can be reconstructed from raw in-phase and quadrature 60 GHz multiple-input-multiple-output (MIMO) frequency modulated continuous wave (FMCW) radar data using an end-to-end neural network. Additionally, an ablation study is performed, revealing that waveform reconstruction similarity and estimation accuracy of several HRV metrics generally improve as the number of utilized MIMO channels increases. © 2025 IEEE.

关键词： Electrocardiography

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Reinforcement Learning for Adaptive Traffic Signal Control Using Deep Q-Networks 1

Reinforcement Learning for Adaptive Traffic Signal Control U...

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1st International Conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Bohra, Asha Rani Selvi, Thamarai Vishal, V. Sunkara, Pawan Sai Sunku, Balaji Jignesh, Bhavsar Nitya New Horizon College of Engineering Dept. of Computer Science and Engineering Karnataka Bengaluru India New Horizon College of Engineering Dept. of Information Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798331520540

Traffic congestion is a critical issue in urban areas, contributing to increased travel time, fuel consumption, and environmental pollution. Traditional traffic signal control methods, such as fixed-time systems, cannot adapt to real-time changes in traffic conditions. This paper presents a novel approach using Deep Q-Networks (DQN) to control traffic signals based on realtime traffic data dynamically. Our simulation environment models a four-way intersection, where vehicle densities and wait times are continuously monitored. The DQN agent optimizes green light durations by minimizing vehicle wait times and maximizing traffic throughput. Extensive simulation results demonstrate that our system significantly reduces congestion and improves traffic flow compared to traditional fixed-time systems. Furthermore, the approach shows potential for real-world applications in urban traffic control systems, particularly in smart cities equipped with Internet of Things (IoT) devices. © 2025 IEEE.

关键词： Traffic congestion

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Transfer Learning based Deep Learning Model for the Identification of Facial Expression 3

Transfer Learning based Deep Learning Model for the Identifi...

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

作者： Ekambaram, Gothai Rakkiannan, Thamilselvan Natesan, P. Jayakkavin, E. Dept of Computer Technology Kongu Engineering College Tamilnadu Perundurai India Dept Computer Science and Engineering Kongu Engineering College Tamilnadu Perundurai India Dept of Artificial Intelligence Kongu Engineering College Tamilnadu Perundurai India Kongu Engineering College Tamilnadu Perundurai India

ISBN: (纸本)9798331527549

The changes that occur on a person's face to communicate their internal emotional states, intentions, or social cues are known as facial expressions. The human face is a crucial tool for communicating with other sand determining their emotional states from their facial expressions. Because it is essential in human interaction, real-time facial expression analysis has become a research topic. Face reading is used in various areas, such as social interaction, emotional intelligence, computer vision, biometric security, and social intelligence. This work focuses on an overview of the various facial expression recognition methods and their advantages and disadvantages. The body's natural response to internal and external stimuli is emotional. Whether it's a common emotion, stress, anger, etc., it can cause serious harm to health. Both psychological and physiological manifestations of these responses emotions and facial expressions appear. The model is used to analyse real-time snapshots of the user's face and detect the existence of stressful information as well as convolutional neural network capable of recognizing emotions, and facial emotions, a proof of concept for the emotion detector was developed. The classification model was developed via transfer learning and fine-tuning. For deep learning process, two classifier designs were taken into account. Following a numberoftests, it was shown that a classifier built using a Separable Conv2D convolutional layer and LeakyReLU as activation method CNN performed the best in categorising emotions. The results are encouraging, and the proposed emotion detection system is minimallyintrusive as it only needs a webcam to monitor the user's facial expressions. © 2025 IEEE.

关键词： Contrastive Learning

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Edge Node Load Balancing Using Game Theoretic Approaches 5

Edge Node Load Balancing Using Game Theoretic Approaches

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Haldar, Rajdeep Rooj, Saptadip Sharma, Neeraj Das, Ritambhar Mandal, Arnab Bandyopadhyay, Anjan Dept. of Information Technology Bhubaneswar India Lovely Professional University School of Computer Science and Engineering Jalandhar India School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798331522100

This paper explores the application of game theoretic approaches to load balancing across edge nodes within distributed computing systems. Focusing on a non-cooperative game model, we aim to enhance system efficiency by strategically optimizing resource allocation and load distribution among autonomous edge nodes. Initial simulations demonstrate that our approach not only addresses the inherent complexities of edge environments but also outperforms traditional load balancing mechanisms in terms of both operational efficiency and system responsiveness. © 2025 IEEE.

关键词： Edge computing

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Deep Learning Techniques for Object Detection in Underwater Environments

Deep Learning Techniques for Object Detection in Underwater ...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Praveen, R.V.S. Maindola, Meenakshi Anandhi, R.J. Thandra, Nagarjuna Bansal, Saloni Sharma, Vishal LTIMindtree Limited Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India New Horizon College of Engineering Department of Information Science Engineering Bangalore India MLR Institute of Technology Dept. Computer Science and Engineering Telangana Hyderabad India GLA University Department of Computer Engineering and Applications Mathura India Lovely Professional University Phagwara India

ISBN: (纸本)9798331512965

The degradation of image quality caused by things like light absorption, scattering, and distortion makes object detection in underwater environments a unique challenge. Applications like marine research, environmental monitoring, and autonomous underwater vehicles (AUVs) are restricted in their effectiveness by traditional image processing techniques, which often struggle with these complexities. Recent years have seen the rise of deep learning techniques as a potent weapon in the fight against these obstacles. In order to accurately detect objects underwater, this paper investigates the use of deep learning models, specifically convolutional neural networks (CNNs). We explore cutting-edge models and methods like attention mechanisms, transfer learning, and hybrid deep learning architectures to improve detection performance in challenging underwater environments. Furthermore, we utilize data augmentation techniques and synthetic dataset generation to tackle the critical issue of limited labeled underwater datasets. From a detection accuracy and robustness perspective, the experimental results show that our suggested deep learning method is light years ahead of the competition. Marine exploration, autonomous underwater vehicle navigation, and underwater surveillance systems can all benefit from this study's new insights into how to better detect objects in real time while submerged. © 2025 IEEE.

关键词： Convolutional neural networks

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Cost-balanced Adaptive Replica Server Cache Management under Time-Varying User Request Density in CCDNs 14

Cost-balanced Adaptive Replica Server Cache Management under...

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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Gupta, Rohit Kumar Mazumdar, Arka Prokash Dept. of Computer Science & Engineering Malaviya National Institute of Technology Jaipur Manipal University Jaipur India Dept. of Computer Science & Engineering Malaviya National Institute of Technology Jaipur Jaipur India

ISBN: (纸本)9798331531935

In today’s growing Internet, cost-effective on-demand provisioning of caching resources in Cloud-based Content Delivery Networks (CCDNs) is essential to preserve the cache hit ratio while reducing storage requirements. However, during the high and fluctuating request rates at different locations, the sudden increase in cache to mitigate the miss rate requires a balance between storage cost (from dynamic cache provisioning) and miss cost (from latency in retrieving content from the Origin Server (OS) or other Replica Servers (RSs)). In such an environment, some RS instances remain underutilized while others are overburdened across different locations and time intervals, which requires effective RS management to activate only sufficient RSs by adapting to the user demands. To overcome these challenges, a cost-balanced adaptive cache management approach is proposed for dynamic cache provisioning in CCDN. This method aims to dynamically manage the activation of RS to optimize resource utilization, and cache instance provisioning and content caching operations in these RSs are managed by introducing an Adaptive Cache Manager that retrieves content from the OS or other RSs by effectively balancing costs in different time intervals. The experimental results indicate that the proposed method reduces the number of active replica servers in CCDN across different time intervals while decreasing the average total cost by about 56% relative to the LRU caching policy on fixed cache and about 10% compared to the dynamic selective caching policy on RCW cache. © 2025 IEEE.

关键词： Cache memory

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Email Validator Dashboard: A Comprehensive Tool for Accurate Email Validation

Email Validator Dashboard: A Comprehensive Tool for Accurate...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Chand, Syed Riyyan Ahmed, Uzair Ismail, T Mohammed Kumar, B.P. Pradeep Chand, Syed Bilal Chaitra, P. Design Atria Institute of Technology Dept. of Computer Science Bangalore India Atria Institute of Technology Dept. of Computer Science Engineering Bangalore India HostUp Cloud Technologies Private Limited Bangalore India

ISBN: (纸本)9798331538538

Email authentication is of the utmost importance in maintaining the reliability and quality of email communication, specifically in database management and bulk email marketing. The centerpiece of the project is the development of an Email Validator Dashboard, which is a very user-friendly functionality filled with other useful parts that guarantee the accurate verification of email addresses. The dashboard really presents valuable inputs quickly in terms of deliverability, risk factors, and domain reliability due to its dynamic visualization along with real-time email validation procedures. The project's structure is such that the users get real-time data in the form of interactive charts and graphs, which make them see the transformations occurring as validation is going on, thus distinguishing it from the traditional tools. It comes with cutting-edge back-end data integrity validation processes, such as mailbox verification, domain DNS checks, and syntax verification, alongside powerful analytics. This project is really useful for organizations and developers who work with large email lists and at the same time can ensure high deliverability and compliance with data standards. © 2025 IEEE.

关键词： Marketing

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