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Application of machine learning in Customer Churn Prediction 3

Application of Machine Learning in Customer Churn Prediction

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3rd IEEE international Virtual conference on Innovations in Power and Advanced Computing Technologies, i-PACT 2021

作者： De, Soumi Prabu, P Paulose, Joy Department of Computer Science Bangalore India

ISBN: (纸本)9781665426916

Retaining customers is the central component of a company's growth strategy. It is evident that several industries are experiencing a surge in customer churn due to the global pandemic. As a result, customer retention that lies at the core of customer relationship management, has become the foundation for every industry to plan for future growth. By reducing customer churn, a company can maximize its profit. Studies suggest that significant advancements are made in the field of customer churn prediction in domains like telecom, banking, e-commerce and energy sector. The focus of the paper is to present a detailed review of the various machine learning techniques applied to address churn. Fifty-five papers related to churn classification published between 2004 and 2020 are collected and analyzed. The reviewed papers are categorized into five main themes. These themes are feature selection techniques, methods to handle class imbalance, experimentation with machine learning algorithms, hybrid models and ensemble models respectively. Finally, few suggestions are presented as direction for future research. © 2021 IEEE.

关键词： data mining

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Chronic Kidney Disease Analysis using Various Biological Parameters under machine learning Techniques

Chronic Kidney Disease Analysis using Various Biological Par...

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Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： M. Suresh E. Nirmala M. Maragatharajan Manish Yadav School of Computing Science and Engineering VIT Bhopal University Kotri Kalan Madhya Pradesh School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Kotri Kalan Madhya Pradesh

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Kidney chronic disease (KCD) has become one of the most critical illnesses which should be diagnosed properly within a certain limit of time for better treatment. Nowadays different algorithms being used for treatment of patients. By using the best predictory model, doctor can easily predict the disease on time. For this verdict, Kidney Chronic Disease Prediction has been discussed in this article. In this article various datasets have been used by the different article authors to predict the disease which are pre-processed with different methods like Univariate Feature Selection (UFS) and Recursive Feature Elimination (RFE), data mining techniques, etc. which are further used to train and test various ML models get the accuracy by predictory models. The scores gained by the predictory models is then validated to check the precision of the output accuracy of by the model using methods like performance matrices, various cross validation techniques, Mathew Correlation Coefficient (MCC). This whole article is all around the analyses of different articles based on the machine learning and validation techniques with respect to the accuracy. Most of the models attained the accuracy of 99 percent and above. In this article, along with machine learning models it also discussed about two neural networks, which attained the accuracy between 93 to 98% with a good precision value.

关键词： Analytical models Renewable energy sources Accuracy Biological system modeling machine learning Predictive models Feature extraction data models Tuning Diseases

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3rd international Workshop on Deep learning Practice for High-Dimensional Sparse data with KDD 2021 21

3rd International Workshop on Deep Learning Practice for Hig...

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27th ACM SIGKDD international conference on Knowledge Discovery and data mining (KDD)

作者： Zhu, Xiaoqiang Lee, Kuang-chih Zhou, Guorui Jiang, Biye Wang, Zhe Tang, Ruiming Ren, Kan Ai, Qingyao Zhang, Weinan Alibaba Grp Hangzhou Peoples R China Roku Inc San Jose CA USA Huawei Noahs Ark Lab Hong Kong Peoples R China Microsoft Res Redmond WA USA Univ Utah Salt Lake City UT 84112 USA Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9781450383325

Recently, we have witnessed that deep learning-based approaches has been widely applied to empower many internet-scale applications. However, the data in these internet-scale applications are high dimensional and extremely sparse, which makes it different from those applications with dense data processing, such as image classification and speech recognition, where deep learning-based approaches have been extensively studied. One of the main applications is the user-centric platform that consists of great deal of users, items and user generated tabular data which are quite high-dimensional. The characteristics of such data pose unique challenges to the adoption of deep learning in these applications, including modeling, training, and online serving, etc. More and more communities from both academia and industry have initiated the endeavors to solve these challenges. This workshop will provide a venue for both the research and engineering communities to discuss and formulate the challenges, utilize opportunities, and propose new ideas in the practice of deep learning on high-dimensional sparse data.

关键词： Deep learning High-dimensional Sparse data

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Big data and Deep learning in Smart Cities: A Comprehensive dataset for AI-Driven Traffic Accident Detection and Computer Vision Systems

Big Data and Deep Learning in Smart Cities: A Comprehensive ...

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Computing and machine Intelligence (ICMI), international conference on

作者： Victor Adewopo Nelly Elsayed Zag Elsayed Murat Ozer Constantinos L. Zekios Ahmed Abdelgawad Magdy Bayoumi School of IT University of Cincinnati Cincinnati USA Dept. of Elec. & Comp. Eng. Florida International University Florida United States School of Eng. & Tech. Central Michigan University Michigan USA Dept. of Elec. & Comp. Eng. University of Louisiana Lafayette Louisiana

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

In the dynamic urban landscape, where interplay of vehicles and pedestrians defines the rhythm of life, integrating advanced technology for safety and efficiency is increasingly crucial. This study delves into the application of cutting-edge technological methods in smart cities, focusing on enhancing public safety through improved traffic accident detection. Action recognition plays a pivotal role in interpreting visual data and tracking object motion such as human pose estimation in video sequences. The challenges of action recognition include variability in rapid actions, limited dataset, and environmental factors such as (Weather, Illumination, and Occlusions). In this paper, we present a novel comprehensive dataset for traffic accident detection. This dataset is specifically designed to bolster computer vision and action recognition systems in predicting and detecting road traffic accidents. We integrated datasets from wide variety of data sources, road networks, weather conditions, and regions across the globe. This approach is underpinned by empirical studies, aiming to contribute to the discourse on how technology can enhance the quality of life in densely populated areas. This research aims to bridge existing research gaps by introducing benchmark datasets that leverage state-of-the-art algorithms tailored for traffic accident detection in smart cities. These dataset is expected to advance academic research and also enhance real-time accident detection applications, contributing significantly to the evolution of smart urban environments. Our study marks a pivotal step towards safer, more efficient smart cities, harnessing the power of AI and machine learning to transform urban living.

关键词： Computer vision Visualization Smart cities Tracking Video sequences Transportation Transforms

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Advancements in Disease Diagnosis and Prediction Leveraging Insights Extraction and Computational learning Techniques

Advancements in Disease Diagnosis and Prediction Leveraging ...

引用

Artificial Intelligence For Internet of Things (AIIoT), international conference on

作者： Niveditha Konda Koprada Pydi Durga Lakshmi Sri Vidya Varshith Chavalam Karthikeya Vadde K B Venkata Brahma Rao Department Of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vijayawada Andhra Pradesh India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Technology has revolutionized the health domain largely. Accurate and on-time analysis of any health-related problem It is crucial for the prevention and treatment of the ailment. The traditional way of diagnosis may in the event of a severe illness, it may not prove to be satisfactory. This project aims to design a diagnostic framework for different diseases based on their symptoms. This system utilizes data mining methodologies including classification techniquesin order to achieve such a mode and also ML algorithms for prediction of any diseases can help in a more accurate diagnosis than the conventional method. We have introduced a system for disease prediction that employs several machine learning algorithms. The dataset contains information on symptoms, age, blood group, and gender of individuals, which is used for processing by the diagnosis system. The output of the system is the disease. Here, the SympoDx algorithm, which incorporates Random Forest, Decision Tree, KNN, and Support Vector Classifier, is employed for classification tasks and computes the probabilities of diseases. Following the results, patients can contact the doctor accordingly for further treatment.

关键词： Accuracy machine learning algorithms Medical services Prediction algorithms Vectors Classification algorithms Decision trees

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3rd international conference on Intelligent Technologies and Applications, INTAP 2020

3rd International Conference on Intelligent Technologies and...

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3rd international conference on Intelligent Technologies and Applications, INTAP 2020

ISBN: (纸本)9783030717100

The proceedings contain 34 papers. The special focus in this conference is on Intelligent Technologies and Applications. The topics include: Interpretable Option Discovery Using Deep Q-learning and Variational Autoencoders;evaluating Predictive Deep learning Models;Pre-trained CNN Based Deep Features with Hand-Crafted Features and Patient data for Skin Lesion Classification;data-Driven machine learning Approach for Human Action recognition Using Skeleton and Optical Flow;multilingual Voice Impersonation dataset and Evaluation;a Survey on Unknown Presentation Attack Detection for Fingerprint;hierarchical Interpolation of Imagenet Features for Cross-dataset Presentation Attack Detection;cross-lingual Speaker Verification: Evaluation on X-Vector Method;fusion of Texture and Optical Flow Using Convolutional Neural Networks for Gender Classification in Videos;exploring Circular Hough Transforms for Detecting Hand Feature Points in Noisy Images from Ghost-Circle patterns;A PM 2.5 Forecasting Model Based on Air Pollution and Meteorological Conditions in Neighboring Areas;the Aquatic Surface Robot (AnSweR), a Lightweight, Low Cost, Multipurpose Unmanned Research Vessel;empirical Analysis of data mining Techniques in Network Intrusion Detection Systems;deep Neural Network Based Malicious Network Activity Detection Under Adversarial machine learning Attacks;towards Low Cost and Smart Load Testing as a Service Using Containers;securityGuard: An Automated Secure Coding Framework;the Multi-objective Feature Selection in Android Malware Detection System;border Management Systems: How Can They Help Against Pandemics;translating Ethical Theory into Ethical Action: An Ethic of Responsibility Approach to Value-Oriented Design;An Example of Privacy and data Protection Best Practices for Biometrics data Processing in Border Control: Lesson Learned from SMILE;protecting IoT Devices with Software-Defined Networks.

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A Study on Various machine learning Algorithms Used for Prediction of Diabetes Mellitus 3rd

A Study on Various Machine Learning Algorithms Used for Pred...

引用

3rd international conference on Computing and Communication, IC3 2020

作者： Pradhan, Gaurav Pradhan, Ratika Khandelwal, Bidita Department of Computer Applications Sikkim Manipal Institute of Technology SMU Majitar Rangpo East Sikkim India Department of Medicine Sikkim Manipal Institute of Medical Sciences SMU Tadong Gangtok East Sikkim India

ISBN: (纸本)9789811573934

Diabetes mellitus (DM) has become one of the common disease in middle- and low-income countries. In the recent past, machine learning (ML) and data mining approach has been applied for prediction of diabetes with acceptable accuracy rate. A reliable diagnostic prediction system is thus desired by medical professionals. This paper highlights various machine learning algorithms for prediction of diabetes that include: Decision tree, random forest, Naïve Bayes, artificial neural network, support vector machine, and logistic regression, etc. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data mining

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Enhancing Aquaculture Sustainability with Deep learning Techniques: Fish Species Detection and Disease Diagnosis

Enhancing Aquaculture Sustainability with Deep Learning Tech...

引用

Artificial Intelligence and machine learning Applications Theme: Healthcare and Internet of Things (AIMLA), international conference on

作者： Swati Sah Logeswaran K Savitha S Kruthika M Latha R. S Rajadevi R Vasugi M Sujit R B Akilesh T Sharda School of Computing Science and Engineering Sharda University Greater Noida Uttar Pradesh India Department of AI Center of Excellence in Real-time Analytic Kongu Engineering College Erode TamilNadu India Department of CSE K S R College Of Engineering Tiruchengode TamilNadu India Department of AI Kongu Engineering College Erode TamilNadu India

ISBN: (数字)9798331538965

ISBN: (纸本)9798331538972

In order to improve fish illness detection and fish species recognition two crucial facets of aquatic ecosystem management this study focusses on utilising deep learning techniques. This study investigates how deep learning may dramatically increase the precision and effectiveness of fish population monitoring by using cutting-edge neural network designs. The study's first section explores the categorisation of fish species, showing how convolutional neural networks (CNNs) can successfully differentiate between several species using visual characteristics like colour, texture, and form. Through the analysis of bulk data containing illness symptoms and fish health markers based on both deep learning and conventional machine learning models, the second module tackles the early diagnosis of fish diseases. The models are taught to identify microscopic patterns and anomalies that may signal the presence of illnesses in aquaculture, hence enabling prompt intervention and preventative actions. The results demonstrate how deep learning-based methods have the potential to completely transform fish population management, guaranteeing sustainability, disease prevention, and improved decision-making in aquatic resource conservation.

关键词： Deep learning Accuracy Biological system modeling Prevention and mitigation Predictive models Fish Sustainable development Aquaculture Diseases Residual neural networks

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Technique for Forecasting Future Market Movement Using machine learning and Deep learning Algorithms

Technique for Forecasting Future Market Movement Using Machi...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Rahul Bhatt Alka Kumari Rajesh A Suresh Babu Rajasekaran Vishal Pandurang Deshmukh Anil Srivastava Department of CSE Dev Bhoomi Uttarakhand University Dehradun Uttarakhand India Department of Computer Science ARKA JAIN University Jamshedpur Jharkhand India Department of CSE JAIN (Deemed-to-be University) Bangalore India Group Product Manager NVIDIA Corporation US Department of Business Administration Yashwantrao Mohite Institute of Management Bharati Vidyapeeth (Deemed to be University) Karad India College of Commerce SPPU Nande Pune

Because of the advent of vast realities, the practise of deep reading to foresee financial charges and patterns has gained even more traction. We structured limited stock market data sets, provided fully customizable capacity designing, and thorough dominating-based recreation to estimate the value change of stock company sectors. The suggested method integrates a customised deep learning-based device for stock commercial centre rate pattern anticipation with several functions designing approaches and pre-processing the dataset of the stock market marketplace. We performed indepth evolutions with widely used models based on machine learning, and we proved that the offered solution is superior because of the meticulous function-design work we put into it. For predicting trends in the stock market, the gadget achieves a very high level of accuracy. By carefully preparing for and evaluating projected time, developing features, and re-processing methods of data, the proposed work adds to stock inspection and focuses on local region within the financial and specialist industries.

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Sparrow Mapping using Hyperspectral Remote Sensing and Deep learning 3

Sparrow Mapping using Hyperspectral Remote Sensing and Deep ...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), international conference on

作者： Thilagavathi K Kaavya K Muvin M Harei Sharan S H Department of Electronics and Communication Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Biodiversity loss, driven by habitat destruction, poses a significant threat to ecological balance and the survival of endangered species. Accurate identification and monitoring of critical habitats are essential for effective conservation efforts. However, traditional field-based habitat mapping methods are often time-intensive, expensive, and spatially constrained. This work aim to address these limitations by integrating hyperspectral remote sensing with advanced deep-learning methodologies for precise habitat identification and mapping. This work integrates hyperspectral remote sensing with deep learning methodologies, specifically Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANNs), to analyse high-dimensional hyperspectral data. CNNs are utilized for extracting spatial features, effectively capturing both spectral and spatial variations in the imagery. ANNs are applied for spectral pattern recognition, enabling accurate classification of habitats based on their unique spectral signatures. The pre-processing pipeline includes resizing and grayscale conversion of hyperspectral images to optimize them for deep learning analysis. Validation using field data ensures the reliability of the model outputs. The methodology demonstrated promising results, with ANN achieving a superior accuracy of 99% and CNN achieving an accuracy of 84%. These results are intended to support conservation planning, resource allocation, and evidence-based decision-making. This work contributes to advancing biodiversity conservation and underscores the potential of deep learning in ecological monitoring and management.

关键词： Deep learning Accuracy Biological system modeling Habitats Vegetation mapping Predictive models Feature extraction Convolutional neural networks Monitoring Hyperspectral imaging

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