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An Effective and Comprehensive Crop Recommendation System Using Machine Learning

An Effective and Comprehensive Crop Recommendation System Us...

引用

2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCS 2024

作者： Mohideen Badhusha, S. Athmashree, H.A. Bhoomika, E.R. Anusha Bhuvan, M. AIET Dept.Computer Science and Engineering Moodbidri India

ISBN: (纸本)9798350372502

Agriculture plays a crucial role in Indian economy. However, the agriculture sector is suffering from low productivity and yielding due to various factors. One of the factors is the failure to select suitable crops for a particular type of soil in an agricultural region. Most of the farmers highly depend on the direction of agricultural experts and experienced farmers in the task of crop selection. The shortage of technical knowledge and awareness of the latest technology hinders the progress of the farmers resulting in low yielding. Therefore, a machine learning model is developed to predict a set of best suitable crops that are intended to produce high-yielding for a particular agricultural region based on environmental and soil conditions. In this regard, comprehensive data sets which are intended to produce high yielding for different crop types, are gathered from various regions of the country. Both environmental parameters (viz., temperature, humidity, and moisture) and soil parameters (viz., pH, Nitrogen, Potassium, Phosphorus values) are considered as a feature set for training the proposed model. The comprehensive datasets from different parts are trained and tested based on performance metrics like precision, accuracy, recall, confusion matrix, and F1-score. The proposed model achieves 75% to 80% accuracy in predicting suitable crops for a soil type in a certain region. © 2024 IEEE.

关键词： Crops

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Advancing Healthcare Accessibility: Development of an AI-Driven Multimodal Chatbot 4

Advancing Healthcare Accessibility: Development of an AI-Dri...

引用

4th International Conference on Intelligent Technologies, CONIT 2024

作者： Garimella, Bala Subrahmanyam Garlapati, Hari Sharan Choul, Sriharini Cherukuri, Rajesh Lanke, Pallavi B v Raju Institute of Technology Dept.Computer Science and Engineering Narsapur India

ISBN: (纸本)9798350349900

In today's rapidly evolving healthcare sector, the need for efficient and accessible medical assistance is more crucial than ever. With the advancements in Artificial intelligence (AI), healthcare chatbots have emerged as a promising solution to address this need. These chatbots are capable of providing personalized medical information, scheduling appointments, and offering support to patients, thereby enhancing healthcare accessibility and patient engagement. The purpose of this project is to develop an advanced AI-powered healthcare chatbot that builds upon existing technologies to bring significant improvements in patient interaction and healthcare delivery. By leveraging the capabilities of Natural Language Processing (NLP) algorithms and deep learning techniques, the project aims to enhance the chatbot's functionality by integrating multimodal interaction features, enabling users to interact via text, voice, etc and provide more accurate and relevant responses to user queries. Additionally, the project seeks to create a comprehensive patient portal where users can securely access their medical records, reports, appointments, and engage with the chatbot for personalized assistance. © 2024 IEEE.

关键词： Deep learning

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Optimizing Telecom Operations with Segmentation and Churn Prediction 3

Optimizing Telecom Operations with Segmentation and Churn Pr...

引用

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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Mood -Enhancing Music Recommendation System based on Audio Signals and Emotions 6

Mood -Enhancing Music Recommendation System based on Audio S...

引用

6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Mounika, V. Charitha, Y. Dept.Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798350398496

The identity of emotional speech is a significant topic within the discipline of interactions between humans and computers. Many strategies of figuring out emotions in human speech had been introduced and installed through diverse researchers. To identify noises in audio documents is the purpose of one of these versions. Together with gender recognition and YouTube video will be played depending on mood, this suggested computer also features speech emotion detection, which listens for sentiments like happiness, rage, and sadness in audio cues. This output is sent as input to YouTube, which plays song within the user's mind, resulting in the person's temper to stabilize fast. Using the CNN characteristic extraction approach, the function sizes vector become processed with NumPy, and the audio class became carried out in MFCC. This research study mainly uses two programs: RAVDESS and SAVEE. Using the acquired datasets, a new version of the look was produced in-depth. The device area is the platform where the Google Colab is used to perform code execution. © 2023 IEEE.

关键词： Emotion Recognition

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Career Prediction Using Machine Learning 8

Career Prediction Using Machine Learning

引用

8th International Conference on Inventive Systems and Control, ICISC 202

作者： Jha, Kundan Likhitha, D. Chandana, M.Siri Reddy, M.Ram Prakash Bhargavi, Maridu Vignan's Foundation for Science Technology and Research Dept.Computer Science and Engineering Guntur AP Vadlamudi India

ISBN: (纸本)9798350386578

This study uses advanced machine learning techniques to predict students' career paths more accurately. By analyzing and preprocessing various data, including academic performance, extracurricular activities, internships, and personality traits, the study identifies patterns. Ethical considerations, privacy protection, and bias prevention are part of the model development process. The practical applications include personalized career counseling, improving curricula, and optimizing alumni networks. Despite challenges like model interpret ability and adapting to changing job markets, interdisciplinary collaboration is crucial. Overall, educational institutions can use these techniques to provide customized support, helping students make informed career decisions in today's ever-changing workforce. © 2024 IEEE.

关键词： Adversarial machine learning

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Helmet Detection and Number Plate Recognition using YOLOv3 in Real-Time 3

Helmet Detection and Number Plate Recognition using YOLOv3 i...

引用

3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Gomathy, C.K. Dhanush, Manganti Sai Pushkar, Sikharam Geetha, V. SCSVMV Deemed to Be University Dept.Computer Science and Engineering Kancheepuram India

ISBN: (纸本)9798350343632

Motorcycles have traditionally served as the primary mode of transportation in various countries across the globe. However, there has been a rise in motorcycle-related incidents resulting in harm or fatalities over time. Inadequate head protection remains one of the primary factors leading to lethal outcomes resulting from such incidents. This research endeavours to tackle the issue by designing a system capable of identifying motorcyclists who are not wearing helmets instantly. The proposed system aims to facilitate the use of helmets among motorcycle riders in real-time, helping reduce fatalities due to head injuries, which are a major concern in many countries. Additionally, the system also includes a method for real-time number plate detection and recognition, which has numerous good significances in law enforcement and surveillance. This helps the officers to keep on the people who violate traffic rules and regulations, ensuring their safety as well as that of pedestrians. The system utilizes state-of-the-art deep learning algorithms, such as YOLOv3, to achieve real-time helmet-wearing detection with unparalleled speed and accuracy of 99%. © 2023 IEEE.

关键词： Helmets License Plate Surveillance

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An Effective And Comprehensive Crop Recommendation System Using Machine Learning

An Effective And Comprehensive Crop Recommendation System Us...

引用

Emerging Technologies in computer science for Interdisciplinary Applications (ICETCS), International Conference on

作者： S Mohideen Badhusha H A Athmashree E R Bhoomika Anusha M Bhuvan dept.Computer Science and Engineering AIET Moodbidri India

ISBN: (数字)9798350372502

ISBN: (纸本)9798350372519

关键词： Training Computational modeling Biological system modeling Crops Machine learning Soil Predictive models

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The Impact of Optimization Techniques on Cancer Classification with Gene Expression Data 11

The Impact of Optimization Techniques on Cancer Classificati...

引用

11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Varun.b.p, Sai Jonnalagadda, Ishita Ch, Vishnu Vardhan Vasavi, C.S. Sahay, Apurvanand Amrita Vishwa Vidyapeetham Amrita School of Computing Dept.Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Artificial Intelligence Department of Artificial Intelligence Bengaluru India

ISBN: (纸本)9798350379136

Cancer classification using gene expression profiling is a fundamental component of cancer research and development, because it contributes to the identification of cancer subtypes as well as the identification of proper treatments for patients. The purpose of this paper is to assess the efficiency in classification of different optimization algorithms. PCA, GA, FI, SFS, and VT methods are applied for the feature selection on gene expression data. The selected features are then passed into a Gaussian Naive Bayes classifier to classify it into a particular class. In order to compare the efficiency of each presented optimization technique we calculate various evaluation metrices, and CPU time. To overcome the shortcomings of both the high accuracy methods, we proposed a new approach that integrates VT and GA. The outcomes of this work help to contribute to the research of the best combination of optimization tools for accurate classification of cancer, which will help further develop the concept of precision medicine and individualized therapy. © 2024 IEEE.

关键词： Diseases

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Advancing Healthcare Accessibility: Development of an AI-Driven Multimodal Chatbot

Advancing Healthcare Accessibility: Development of an AI-Dri...

引用

Intelligent Technologies (CONIT), International Conference on

作者： Bala Subrahmanyam Garimella Hari Sharan Garlapati Sriharini Choul Rajesh Cherukuri Pallavi Lanke dept.Computer Science and Engineering B V Raju Institute of Technology Narsapur India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

In today’s rapidly evolving healthcare sector, the need for efficient and accessible medical assistance is more crucial than ever. With the advancements in Artificial intelligence (AI), healthcare chatbots have emerged as a promising solution to address this need. These chatbots are capable of providing personalized medical information, scheduling appointments, and offering support to patients, thereby enhancing healthcare accessibility and patient engagement. The purpose of this project is to develop an advanced AI-powered healthcare chatbot that builds upon existing technologies to bring significant improvements in patient interaction and healthcare delivery. By leveraging the capabilities of Natural Language Processing (NLP) algorithms and deep learning techniques, the project aims to enhance the chatbot’s functionality by integrating multimodal interaction features, enabling users to interact via text, voice, etc and provide more accurate and relevant responses to user queries. Additionally, the project seeks to create a comprehensive patient portal where users can securely access their medical records, reports, appointments, and engage with the chatbot for personalized assistance.

关键词： Deep learning Accuracy Medical services Chatbots Artificial intelligence Portals

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Career Prediction Using Machine Learning

Career Prediction Using Machine Learning

引用

International Conference on Inventive Systems and Control (ICISC)

作者： Kundan Jha D. Likhitha M.Siri Chandana M.Ram Prakash Reddy Maridu Bhargavi Dept.Computer Science and Engineering Vignan’s foundation for science technology and research vadlamudi Guntur AP India

ISBN: (数字)9798350386578

ISBN: (纸本)9798350386585

This study uses advanced machine learning techniques to predict students’ career paths more accurately. By analyzing and preprocessing various data, including academic performance, extracurricular activities, internships, and personality traits, the study identifies patterns. Ethical considerations, privacy protection, and bias prevention are part of the model development process. The practical applications include personalized career counseling, improving curricula, and optimizing alumni networks. Despite challenges like model interpret ability and adapting to changing job markets, interdisciplinary collaboration is crucial. Overall, educational institutions can use these techniques to provide customized support, helping students make informed career decisions in today’s ever-changing workforce.

关键词： Employee welfare Adaptation models Privacy Ethics engineering profession Prevention and mitigation Machine learning

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