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An intelligent Prediction of Customer Churn in Telecom Industry Using Business Analytics Classification Models 12

An intelligent Prediction of Customer Churn in Telecom Indus...

引用

12th IEEE International Conference on Advanced Computing, ICoAC 2023

作者： Gohil, Shradhdha Ameria, Manas Vergin Raja Sarobin, M. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350318210

There are two common challenges faced by any internet service provider: the first being attracting new customers to grow their clientele, the second being retaining the existing customers and prevent them from leaving the company. It is established that client retention is six times cheaper than the cost of acquiring a new one. In order to achieve this, companies are increasingly investing their time and channeling their efforts to keep their clients on board. Understanding client behavior is key to identifying which of them have a high potential of leaving the company, thereby helping businesses develop appropriate retention strategies in response. This client attrition is commonly known as customer churn. Our proposed work focuses on churn prediction on Orange Company Client Dataset using four business data analytics models: Logistic Regression, KNN, Random Forest and Decision Tree. Based on the results, Random Forest boasts with the highest accuracy and F-1 score emerging as the clear winner. © 2023 IEEE.

关键词： Data Analytics

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Review of Task Scheduling based on Different Parameters in Cloud Environments

Review of Task Scheduling based on Different Parameters in C...

引用

2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Pradeep, K. Sharma, Kshitij Edara, Jishnu Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665474511

In IoT applications where connectivity to cloud-based systems is irregular yet low latency is a crucial requirement, fog computing is necessary for success. Fog Computing's dispersed functionality enables storage and execution to take place in totally separate places. Resource management and multiple programming paradigms are required due to the distributed capabilities and numerous user applications taken together. It is important to manage and evaluate failures in network-based systems and services caused by a variety of limitations. The study's main goal is to shed light on various QoS-based scheduling strategies that aim to improve execution in fog computing to evaluate several fault-tolerant methods used in the fog computing environment. © 2023 IEEE.

关键词： Fog computing

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Classification of Traffic Signal Images Using Deep Neural Networks 1

引用

7th International Conference on Smart Trends in Computing and Communications, SmartCom 2023

作者： Kumar, S. Jitesh Santhanam, Naveen Jaisakthi, S.M. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9789819907694

ISBN: (纸本)9789819907687

Traffic sign recognition system plays a crucial role in vehicles that assures the safety of humankind. The system provides feedbacks on-road information to the drivers on time which helps them in driving the car without accidents. Deep Neural Networks (DNN) are gaining more attention in the field of image analysis since it produces a high accuracy rate. Recently many researchers are focusing on the recognition of traffic signs using DNN to get better results. So, in this paper, we have applied the transfer learning technique to classify traffic signs. We choose to work with 2 architectures to classify road signs, namely, fine-tuned Convolutional Neural Network (CNN) and Resnet50. The two architectures were trained using the GTSRB—German Traffic Sign Recognition Benchmark downloaded from Kaggle. With these models, we have obtained good results of 98% and 75% respectively. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Roads and streets

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Blockchain and ML-Based Framework for Diabetes Assessment of Patients in Telesurgery System 3

Blockchain and ML-Based Framework for Diabetes Assessment of...

引用

3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Patel, Yogi Tank, Denisha Gupta, Rajesh Kakkar, Riya Jadav, Nilesh Kumar Gohil, Lata Tanwar, Sudeep Institute of Technology Nirma University Department of Computer Science and Engineering India

ISBN: (纸本)9798350319125

With the advent of innovative technologies, smart healthcare services such as telemedicine and telesurgery adopted across the globe due to their prevalent benefits which helps to perform the surgery in an efficient and reliable manner. However, before the surgeries can be performed on the patient, the patient's health is to be monitored to declare the patient fit for the surgery. Therefore, we have proposed a blockchain-based diabetes detection scheme using machine learning. Our proposed framework leverages blockchain technology to safeguard sensitive data against potential malware threats. We have employed the Interplanetary File System (IPFS) protocol that improves the cost-efficiency of the proposed framework for reliable patient health monitoring. Moreover, we have implemented a random forest (RF) model to predict diabetes in a patient with an improved accuracy when compared to the other machine learning models. Finally, to assess the performance of the proposed framework, we have compared the performance of the proposed framework using RF based on various factors like accuracy, precision, recall, log-loss, and F1-score. © 2023 IEEE.

关键词： Blockchain IPFS Machine learning Random forest Telesurgery

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Comparative Study of Popular Word Embeddings and Deep Learning Methods for Tweets Classification 5

Comparative Study of Popular Word Embeddings and Deep Learni...

引用

5th IEEE International Conference on Advances in Electronics, computers and Communications, ICAECC 2023

作者： Sethumadhavan, Praneeth Alluru, Kavya School of Computer Science & Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350305449

This study aims to find the best deep learning models and word embedding methods that can be used for the classification of tweets as positive or negative. Data was preprocessed and lemmatized and then tokenization of tweets was done to be made suitable for training. Four different word embedding methods Word2Vec, GloVe, FastText, and SpaCy were used as embedding layers for deep learning models. Six different neural networks, simple NN, CNN, LSTM, GRU, Bi-LSTM, and BiGRU, were trained and tested. Bi-LSTM along with Word2Vec word embedding layer produced the highest accuracy of 84.28%. © 2023 IEEE.

关键词： Embeddings

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Secure Sentinel Leveraging Machine Learning for Fraud Detection in Blockchain Transactions 5

Secure Sentinel Leveraging Machine Learning for Fraud Detect...

引用

5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Shirley, C.P. Thanga Helina, S. Berin Jeba Jingle, I. Saran, P. Absin, S.J. Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India Kpr College of Arts Science and Research Department of Commerce with Computer Application Coimbatore India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

Blockchain technology's decentralized and transparent transaction platforms have changed a number of sectors. However, harmful activities like fraud and money laundering are also drawn to blockchain due to its distributed and unchangeable nature. The distinctive features of blockchain transactions frequently prove to be too much for conventional fraud detection techniques to handle. In order to identify fraud in blockchain transactions, this work suggests "Secure Sentinel,"a machine learning-based method based on the Isolation Forest concept. By separating anomalies in a binary tree structure, the Isolation Forest algorithm is highly effective in detecting abnormalities inside datasets. By utilizing this approach, our system is able to identify transactions on the blockchain that are suspicious or stray from the norm. The Isolation Forest model will be trained using attributes such transaction amount, frequency, source/destination addresses, and transaction time. The project's goal is to improve blockchain networks' security and integrity by offering a real-time fraud detection system. Users, miners, and regulators are among the blockchain stakeholders who can take proactive steps to reduce risks and preserve system confidence by automatically identifying potentially fraudulent transactions. We will use past blockchain data to test our model's performance and determine how well it detects different kinds of fraudulent activity in terms of accuracy, precision, recall, and efficiency. By using machine learning to protect blockchain ecosystems from new dangers, Secure Sentinel is a step in the right direction toward advancing the acceptance and sustainability of blockchain technology. © 2024 IEEE.

关键词： Adversarial machine learning

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Mitigating Sycophancy in Large Language Models via Direct Preference Optimization

Mitigating Sycophancy in Large Language Models via Direct Pr...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Khan, Azal Ahmad Alam, Sayan Wang, Xinran Khan, Ahmad Faraz Neog, Debanga Raj Anwar, Ali University of Minnesota-Twin Cities Department of Computer Science and Engineering United States Indian Institute of Technology Guwahati India Virginia Tech Department of Computer Science United States

ISBN: (纸本)9798350362480

Large language models (LLMs) have demonstrated remarkable capabilities, yet they occasionally exhibit sycophantic behavior, generating responses that align with or agree with a user's stated opinions or preferences, even when those opinions are incorrect or biased. This sycophantic tendency can undermine the trustworthiness and reliability of LLMs. This work proposes a novel approach to mitigate sycophancy in LLMs by fine-tuning them on a carefully curated dataset comprising prompts paired with sycophantic and non-sycophantic responses 1. Our method leverages Direct Preference Optimization (DPO), which optimizes LLMs to generate responses that align with the preferred (non-sycophantic) outputs without requiring explicit reward modeling. We develop a dataset of 1000 prompts with sycophantic and non-sycophantic responses to fine-tune LLMs. Our approach achieves an average reduction of 85% in persona-based tests and 84% in preference-driven tests, demonstrating significant mitigation of sycophantic behaviors. Our findings pave the way for more trustworthy and reliable language models that can provide objective and unbiased responses, aligning with human preferences while maintaining factual accuracy. © 2024 IEEE.

关键词： Finetuning Large Language Models

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DeepPose: An Integrated Deep Learning Model for Posture Detection Using Image and Skeletal Data 14

DeepPose: An Integrated Deep Learning Model for Posture Dete...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Singh, Manvendra Alam Ansari, Md. Sarfaraj Chandra Govil, Mahesh National Institute of Technology Sikkim Computer Science and Engineering Sikkim India

ISBN: (纸本)9798350335095

Identifying human actions and postures presents significant challenges for computerized systems. The categorization of these tasks holds particular relevance in the fields of health and robotics. Leveraging artificial intelligence technologies, it becomes feasible to define and classify recurring physical movements and postures accurately. Proper posture is further essential for the rehabilitation of patients because it affects the effectiveness of exercise training. Unfortunately, patients fail to follow the correct sequence when performing the exercises. To pursue the problem, a new method is proposed for posture recognition and pose estimation that does not require wearable devices. The proposed model utilizes 2D coordinates derived from the 2D poses as inputs with 18 joints of the human body as key points, along with an image dataset, to accurately classify various postures. This study involved training a custom CNN named DeepPose using both image and keypoint datasets and conducting a comparative analysis with the performance of two other pre-trained models. The result shows that the proposed model with the keypoint dataset outperforms over image dataset. © 2023 IEEE.

关键词： Patient rehabilitation

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Revolutionizing Kidney Disease Diagnosis: A Comprehensive CNN-Based Framework for Multi-Class CT Classification

Revolutionizing Kidney Disease Diagnosis: A Comprehensive CN...

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Sharma, Gunjan Anand, Vatsala Chauhan, Rahul Pokhariya, Hemant Singh Gupta, Sheifali Sunil, G. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India School of Computer Science & Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350382693

A major global health concern, kidney infections are linked to rising death rates, especially when they worsen and cause the breakdown of the kidneys. Kidney function is seriously threatened by common kidney disorders such as nephrolithiasis, kidney tumors, and cyst development. Kidney failure, which can be brought on by conditions including tumors, stones, and cysts, can be avoided with prompt diagnosis and treatment. computer-aided diagnostics are essential due to the rising incidence of chronic renal illness, the lack of specialists, and the increased need for evaluation and monitoring. Though artificial intelligence (AI) methods, such as machine and deep learning, have been investigated for the identification of renal illness, their effectiveness is still lacking. In order to fill this gap, this study implements a deep learning-based Convolutional Neural Network (CNN) model for kidney illness prognosis and classification using a benchmark kidney dataset from Computed Tomography. CNN uses data reprocessing to extract features from the CT images. The results show how effective the suggested method is in correctly classifying renal illness, with a noteworthy accuracy of 99.88%, precision of 99.8%, recall of 99.7%, and an F1-score of 0.98. This work represents a potential development in the field of computer-assisted renal health diagnostics by supporting the use of the refined CNN model as a trustworthy instrument for kidney illness identification. This research can be applied in the medical field for the diagnosis of kidney diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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Comparative Analysis of ResNet for Offline Signature Verifcation using Various Activation Functions 3

Comparative Analysis of ResNet for Offline Signature Verifca...

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

作者： Pandey, Gaurav Kumar Saxena, Pankaj Singh, Poonam Srivastava, Sumit Computer Science and Engineering Department Birla Institute of Technology Mesra Ranchi India MCA Department Raja Balwant Singh Management and Technical Campus Agra India Computer Science and Engineering Department Hindustan College of Science and Technology Mathura India

ISBN: (纸本)9798331527549

Signature verification is an essential function in biometric authentication, especially for safeguarding document integrity and confirming identity. This research investigates the utilization of ResNet, a deep convolutional neural network, for offline signature verification. We conduct a comparative analysis of ResNet-18 utilizing various activation functions, emphasising ReLU (Rectified Linear Unit) and ELU (Exponential Linear Unit). The selection of an activation function can profoundly affect model performance, influencing both convergence speed and total accuracy. We utilize a publicly accessible signature dataset (CEDAR) to assess the models’ performance, assessing critical metrics such as accuracy. Extensive investigation reveals that selecting activation function is crucial in achieving a balance between learning capacity and generalization. Our findings indicate that ResNet-18 with the SWISH activation function surpasses both ELU and ReLU in specific contexts, especially in managing the subtleties of signature variations. This research provides significant insights into enhancing deep learning models for biometric applications, offering a framework for advancing signature verification systems. © 2025 IEEE.

关键词： Authentication Protocol

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