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A machine learning Model for Customer Churn Prediction using CatBoost Classifier 7

A Machine Learning Model for Customer Churn Prediction using...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Jane Rubel Angelina, J. Subhashini, S.J. Harish Baba, S. Dheeraj Kumar Reddy, P. Sudheer Kumar Reddy, P.V. Sameer Khan, K. Kalasalingam Academy of Research and Education Krishnankovil Department of Computer Science and Engineering Virudhnagar India

ISBN: (纸本)9798350397253

Quality of Service (QoS) is now attracting an increasing number of clients towards a company. Meanwhile, the ability to provide clients with technologically better QoS is highly demanding in recent times. ON the other hand, effective customer interactions may assist the organization to gain new clients, retain existing ones, and improve customer retention by bringing in more income. this research study has used Cat Boost Classifier to predict the customer behavior, retain the customers and increase the company's benefit. the proposed model provides a prediction accuracy of 95%, when compared with the existing methods. © 2023 IEEE.

关键词： Support vector machines

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Personality Analysis Based on the Handwriting Shape using Integration of Image Processing and machine learning Algorithm 7

Personality Analysis Based on The Handwriting Shape using In...

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7th international conference on New Media Studies, CONMEDIA 2023

作者： Rosalina Sahuri, Genta Fahmi, Hasanul Moerland, Joe Wesley Mandala, Rila President University Informatics Study Program Indonesia President University Information System Study Program Indonesia President University Magister of Informatics Study Program Indonesia President University Faculty of Computing Indonesia

ISBN: (纸本)9798350307504

In Indonesia, mental health awareness is not as prominent as physical health, and many adolescents have experienced mental health problems. Some people hide their mental and emotional problems by appearing cheerful and peaceful, but research shows that body language and voice tone can reveal a person's inner psychological state. Similarly, handwriting can be analyzed through graphology to reveal personality traits such as honesty, emotional stability, and fears. Graphology is a time-consuming and expensive process, using image processing and machine learning, graphology can be done quickly without human bias. this research aims to predict the personality traits by implement image processing combined with machine learning to analyze handwriting features. Using lazy learner algorithm of K-Nearest Neighbor algorithm, SVM and Naïve Bayes, the application can predict the MBTI personality from handwriting image with an accuracy of 94%, 78%, and 93% respectively. From the experiment, it is shown that the application able to do the analysis of one's handwriting and then predict the personality traits, fully automated using a computer © 2023 IEEE.

关键词： Forecasting

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Exploring Aromatase Inhibitors to Breast Cancer with machine learning Approach Based Quantitative Structure-Activity Relationships Model Technique 7

Exploring Aromatase Inhibitors to Breast Cancer with Machine...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Pawar, Sahebrao B. Deshmukh, N.K. School of Computational Sciences Swami Ramanand Teerth Marathwada University Nanded India

ISBN: (纸本)9798350304268

the conversion of androgen depends in large part on the overexpression of aromatase in many females. Many therapeutic and pharmaceutical drugs, as well as inhibitors, have been developed, but some of them either don't have the strength to control diseases caused by the aromatase enzyme or they encourage unwelcome mammary epithelial cells inside the breast. the prospect and potential for creating medicines with unique pharmacological and therapeutic capabilities using the Multi-machine learning Classifier Based-Quantitative Structure-Activity Relationship (MMLCB-QSAR) process model were looked into. In order to forecast the chemical classes with the highest potential for Aromatase, the model uses chemical descriptors and supervised machine learning to inhibit utilizing real multidimensional Aromatase inhibitors that were collected from a curated database. the multi-dimensional dataset is mainly explored and visualized using the t-SNE algorithm. Despite having excellent predictive abilities, the Bootstrap aggregation or Bagging Classifier (RandomForest Classifier) algorithm excels. More specifically, regularization and hyperparameter tuning are responsible for outstanding model results in statistics and more accurate prediction using 10-fold cross-validation. Model parameters like the Matthews correlation coefficient score (0.9397), the accuracy Score (0.973%), and the log loss probability (0.1208). © 2023 IEEE.

关键词： Aromatase Enzyme Dimension Reduction Algorithm MMLCB-Quantitative Structure-Activity Relationships PaDEL-Descriptor.jar software PubChem Fingerprint QSAR

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Extraction of Water and Riverine Sand using Deep learning on Multispectral Remote Sensing Images 7

Extraction of Water and Riverine Sand using Deep Learning on...

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7th international conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Sharma, Megha Supriya, M. Kumar, Anil Dhyani, Kartik Chaturvedi, Parth Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Indian Institute of Remote Sensing Dehradun India Cambridge Institute of Technology Bengaluru India

ISBN: (纸本)9798350340600

the study of land-use-land cover (LULC) has become a necessity with the advancement of the urbanization process. Increased erosion of soil, increased silting, and sedimentation of the rivers are key effects that require study and analysis. Deep learning has a significant impact on classification tasks, particularly in the field of remote sensing image analysis. the proposed framework classifies LULC classes by employing the characteristics of deep learning. In this work, we compared the proposed method with the traditional machine learning methods in extracting water and riverine sand from multispectral remote sensing images. Further, we analyse the impact of Stochastic Gradient Descent (SGD) and Adam optimizers. the Adam optimizer implemented in this work gives higher accuracy than other combinations. © 2023 IEEE.

关键词： Remote sensing

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'Sign Language Talk' Using machine learning 7

'Sign Language Talk' Using Machine Learning

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Mahajan, Pushkar Rajani, P.K. Mali, Vaishnavi Khope, Darshan Pimpri Chinchwad College of Engineering Department of Electronics and Telecommunication Engineering Pune India

ISBN: (纸本)9798350304268

People with special needs frequently experience hearing loss, which impacts many parts of their lives and prevents them from interacting socially and communicating with others efficiently. the community of people who are speech-impaired use sign languages as their native tongues and have complete access to communication. Hand gestures are used in sign language to convey the intended meaning of people with specific communication needs to the outside world. this project proposes 'Sign Language Talk,' a machine learning-based sign language-to-speech system that will assist people in improving their communication skills. It helps in the conversion of real-time sign language image input to text and speech which allows communication between speech-impaired people and normal people. the speaker output helps to understand this sign language to visually impaired people too. the project is implemented on Convolutional Neural Network (CNN) using Python tools, which takes input through the camera and compares it with a pre-loaded Indian Sign Language (ISL) dataset and generates text output on the screen as well as speech output through speakers. the parameters such as accuracy, precision, and recall were taken into consideration for evaluating the project. During the training of the model, Accuracy was obtained to be 100%. the Accuracy outcome with the project in runtime was observed to be 87%. the software can also be used for educational purposes in practicing sign language in schools of speech-impaired students. © 2023 IEEE.

关键词： Convolutional Neural Network Indian Sign language machine learning Sign language Talk Speech impaired

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Cancer Detection Using machine learning Algorithms 7

Cancer Detection Using Machine Learning Algorithms

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Tajane, Kapil Patil, Vedant Nimbhorkar, Sarvadnya Raut, Ritesh Giri, Pradnya Pitale, Rahul Pimpri Chinchwad College of Engineering Pune India

ISBN: (纸本)9798350304268

Due to their environment and way of life, people today experience a broad variety of diseases. So, it is critical to identify issues at an early stage. though it is very difficult for a doctor to make an exact diagnosis based only on symptoms. Accurate diagnosis of a problem is the most difficult task to complete. the key to resolving this problem is to foresee the illness using data mining. the amount of data in the medical sciences is expanding quickly every year. Early patient treatment has profited from the proliferation of data in the healthcare and medical industries, which has boosted the accuracy of medical data analysis. Data mining reveals hidden patterns in the vast volume of medical data by using medical data. the detection and diagnosis of cancer are becoming increasingly automated. the prognosis for cancer will be quite straightforward in the future, so we won't even need to go to the hospital. As we can see, a range of technologies are being used and tested by the medical business. this says that cancer diagnosis will probably become less difficult in the future. the approaches for the detection of cancer using Random Forest, Decision tree, KNN, and Naive Bayes are all being evaluated in this paper. We are working on a machine learning-based cancer prediction model that takes into consideration various cancer kinds. Several features are offered by cancer prediction algorithms that ask for user input before delivering findings. © 2023 IEEE.

关键词： cancer prediction model diagnosis Diseases KNN machine learning Naive Bayes Random Forest sickness

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Prediction of Crop Yield with Feature Dimension Reduction Method Using Regression Models 7

Prediction of Crop Yield with Feature Dimension Reduction Me...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Raddekar, Ashwini Chikmurge, Diptee Subhedar, Mansi School of Computer Engineering Mit Academy of Engineering Department of Computer Engineering Maharashtra Alandi India Pillai Hoc College of Engineering and Technology Department of Electronics and Telecommunication Engineering Tal. Khalapur Dit. Raigad Maharashtra Rasayani India

ISBN: (纸本)9798350304268

the important factor in the development of the Indian economy is agriculture. Agricultural production supports more than 70% of rural households. Approximately 17% of the GDP of India is contributed by the agricultural sector, making it a significant economic sector. the main aim of the proposed method is to predict crop yield and analyze the best model to predict the yield of crops among various machine learning algorithms, this model can certainly help farmers to take suitable decisions to grow crops and improve the Indian economy. the dataset employed here includes various parameters such as the amount of fertilizer, the area planted, harvested area, etc. In this work lint crop dataset is considered for crop yield prediction. In the initial step, Principal Component Analysis (PCA) is used for the feature dimension reduction without losing important information/context of the data. It achieves this by reducing the number of dimensions in the data. After reducing the dimension of the dataset using PCA, various machine learning regression algorithms such as Linear rearcssion, Lasso regression, Decision tree, Adaboost, and XGBoost are employed to predict the crop yield. Further, the best model is found by comparing the model's performance metrics which include Mean absolute error (MAE), R2 Score, and Root mean square error (RMSE). © 2023 IEEE.

关键词： crop prediction machine learning regression algorithms principal component analysis

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Abstractive Text Summarization Models Using machine learning Algorithms 7

Abstractive Text Summarization Models Using Machine Learning...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Narwadkar, Yogita P. Bagade, Anant M. SCTR's Pune Institute of Computer Technology Department of Computer Engineering Pune India SCTR's Pune Institute of Computer Technology Department of Information Technology Pune India

ISBN: (纸本)9798350304268

In today's era of cyberspace and intermedia, the number of e-documents have been enlarged enormously. the speedy growth of large data and documents in the field of Data Mining and emerging domain such as Information Retrieval (IR) and the demanding area of Natural Language Processing (NLP) needs Automated Text Summarization. Text Summarization is a process that decreases size of the source document while keeping its important information with actual meaning. It is burdensome and laborious for Homo sapiens to physically do the summarization of huge documents of text. thus it is necessary to revise old methods and techniques of abstractive text summarization models and modernize them to be efficient and fulfill the requirements of users as per the need by resolving issues of continuously increased data. Now this paper presents a survey of the current futuristic models and the various algorithms and techniques used in Abstractive Text Summarization systems liable to be subjected to an Encoder-Decoder framework. this research can help to gain the overall concepts of recent machine learning algorithms and encoder-decoder architecture resting on abstractive text summarization prototypes with a review of the challenges, issues, and future scope of these models. this research can help solve the research community's problems, newsletters, email overload, and media monitoring. © 2023 IEEE.

关键词： Abstractive Text Summarization Evaluation Parameters machine learning encoder-decoder Natural Language Processing

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Automatic Attendance System based on FaceRecognition using machine learning 7

Automatic Attendance System based on FaceRecognition using M...

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7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Sharmila, Sk. Kavya Nagasai, G. Sowmya, M. Sai Prasanna, A. Navya Sri, S. Meghana, N. Department of Information Technology Vignan's Nirula Institute of Technology and Science for Women Peda Palakaluru Andhra Pradesh Guntur India

ISBN: (纸本)9781665464086

Advancements have been made in the field of face recognition technology. Controlling aperson's attendance in real time via facial recognition technology. Face recognition is the process of recognizing a person by their facial characteristics. Various computer vision algorithms, including those used for face detection, expression recognition, and video surveillance, can make use of a person's unique facial features. A face detection and recognition- based attendance monitoring system might very well rapidly and accurately locate and identify people in photographs or video footage. In addition to being laborious to maintain, the time-honored practice of physically ticking off attendees is inefficient. this research work presents the working of a cascade classifier built with machine learning to improve the face detection results. this has been done by comparing the face images in the current image to a database of previously trained faces. the acquired image contributions are searched for a previously registered face, and once found, the person's attendance is recorded automatically. © 2023 IEEE.

关键词： Face recognition

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Comparison of machine learning Techniques for Prediction of Diabetes 7

Comparison of Machine Learning Techniques for Prediction of ...

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7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Govindraj, M. Asha, V. Prasad, Arpana Kumar, Sharath P. Rakshitha, C. New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9781665464086

Diabetes mellitus is a long-term condition characterized by hyperglycemia. It could lead to a variety of problems. According to current trends, the world's diabetes patients will total 642 million by 2040, implying that one in every ten people will be diabetic. Without a doubt, this calls for an immediate action. machine learning has been applied to many areas of medical health and hence it is also applied to predict diabetes. In this study, Diabetes Mellitus (DM) is predicted by using decision trees, random forests, and neural networks. Physical examination results from a hospital in Luzhou, China, are included in the dataset. It has fourteen characteristics. the models were tested by using fivefold cross validation in this study. Based on the applicability, this study selected several approaches with the best performance to conduct independent test trials. As a training set, 68994 data points were healthy people and 68994 data points were diabetic people. Furthermore, the data considered are almost 5 times at random due to unbalanced data. the result is the average of these five tests. this study has used PCA and mRMR to reduce the dimensionality. When all of the variables are considered, the RF prediction is more accurate: 0.8084. © 2023 IEEE.

关键词： Decision trees

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