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检索条件"任意字段=7th International Conference on Machine Learning and Soft Computing, ICMLSC 2023"

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Comparative Analysis of Different machine learning Algorithms on Different Datasets

Comparative Analysis of Different Machine Learning Algorithm...

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international conference on ent computing and Control Systems (ICICCS)

作者： G. S Bapi Raju Chintala Manasa Nandikatti Durga Bhavani Jadi Amulya Dangatla Shirisha Computer Science and Engineering Department Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

machine learning is used to train models and machines without the help of any human interventions and guides. Here the models and machines are trained using algorithms. As it is difficult to train the algorithms one by one and get the analysis of the best algorithm. this research study intends to compare different algorithms used in different datasets to know the best algorithm according to different domains and the overall best algorithm. In this study, most popular supervised algorithms like SVM, Decision Tree, Random Forest, KNN, Logistic Regression, XGBoost, Adaboost, Voting Classifier (Decision Tree + Random Forest), Bagging Classifier (KNN + SVM) and Naïve Bayes. this research study has chosen three different domains for datasets. Different datasets were used to check the efficiency of algorithms. Comparative analysis of the classifiers shows that all algorithms outperform the existing methods with a high accuracy.

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Weapon Detection from Surveillance Footage in Real-Time Using Deep learning

Weapon Detection from Surveillance Footage in Real-Time Usin...

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international conference on computing Communication Control and Automation (ICCUBEA)

作者： Vedantika Jadhav Rutuja Deshmukh Palak Gupta Sharvi Ghogale Mahalakshmi Bodireddy Computer Department Pimpri Chinchwad College of Engineering Pune India

Weapon violence is a serious threat nowadays due to the rise in technology and criminal intelligence. Manual surveillance is a very tedious task because these activities are atypical in comparison with everyday activities. By introducing machine learning techniques to detect such activities, we can minimise the risk of human errors and prevent details from going unnoticed. Current systems fail when it comes to efficiency of the system and ease of use. We have implemented various models like CNN, YOLOV7, YOLOV8 and VGG for weapon detection and identified their strengths and limitations. A complete system for detection of weapons using these models and raising an alarm has been implemented here to overcome the problems of manual surveillance. Results are presented in a table focusing on metrics like precision and recall as these metrics prove to be more insightful and reliable as compared to accuracy for object detection.

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Enhancing Disease Prediction through Symptoms Based machine learning

Enhancing Disease Prediction Through Symptoms Based Machine ...

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international conference on computing Communication Control and Automation (ICCUBEA)

作者： Santosh Kumar Saikumar Padmawar MD Faiz Saiyad Himanshu Ramteke Shrushti Kadam Vishwakarma Institute of Information Technology Pune India

Traditional approaches to diagnosing and treating illnesses may have limitations, particularly when it comes to serious diseases. therefore, it is essential to have precise and timely analysis of health issues for effective prevention and treatment. machine learning (ML) algorithms offer a valuable tool in the development of a medical diagnosis system that can provide more accurate disease predictions compared to conventional methods. Our team has successfully developed a disease prediction system using a variety of ML algorithms, including Decision trees, Random Forest, Naïve Bayes, and Logistic Regression. this system leverages a dataset containing 4962 entries, encompassing 132 symptoms and over 300 diseases. By employing a rule-based approach, we have created a framework that enables the development and application of predictive models. the system's accuracy is remarkable, empowering clinicians to anticipate and analyze ailments at an early stage, thereby addressing health-related challenges more effectively

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Prediction of Bushfire Area Using machine learning Techniques

Prediction of Bushfire Area Using Machine Learning Technique...

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international conference on computing Communication Control and Automation (ICCUBEA)

作者： Debabrata Swain Manish Kumar Nikhil Jain Chirag Devnani Computer Science and Engineering Department Pandit Deendayal Energy University Gujarat India Electronics and Communication Engineering Department Pandit Deendayal Energy University Gujarat India

Wildfires are a widespread natural disaster that affect animal and human lives around the world, and can also cause property damage. It is important for fire-prone areas to have effective measures for curbing the damage caused by fires. Predicting the spread of fires is a useful tool for this. this paper explores the use of state-of-the-art machine learning models, tools, and techniques for predicting the estimated fire area using environmental factors like weather and vegetation index as input parameters. Wildfire data of Australia from 2005 to 2020 is used in this study. the importance of different features is studied and the performance of various models is compared for different feature sets calculated using feature selection and extraction techniques. the accuracy and effectiveness of these techniques are analyzed through comparison of the results in terms of different error measures. Out of all the implemented methods, the Random Forest model has shown the most promising result with an MAE of 78.6 km 2 .

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A Systematic Review of machine learning Approaches for Trash Classification

A Systematic Review of Machine Learning Approaches for Trash...

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international conference on Trends in Electronics and Informatics (ICEI)

作者： S. Vidhya Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai

In the fast-moving environment everyone handles different types of trash. A common man to large industries produces trash and it is accumulating everywhere. Recycling the trashes to reduce the source product requirement necessities huge manual work which reduces the recycle conversion efficiency. Since the manual worker needs to pick up the trash and categorize the trash into the respective category will take more time and also it is a tedious process. Instead of manual sorting for recycling, automatic trash classification systems are used these systems classify the trashes based on their texture, material, and natural characteristics. Various hardware and software modules are incorporated into the trash classification process. this study presents a systematic literature review of different trash classification systems and discusses the features of existing systems. Based on the analysis, a detailed summary is presented in the last part of the research work which will help the research community to develop efficient trash classification systems.

关键词： Deep learning Training Systematics machine learning algorithms Bibliographies Computational modeling Manuals

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An Automated Combined System for Crop Prediction and Yield Prediction Using Deep Hybrid learning Technique

An Automated Combined System for Crop Prediction and Yield P...

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international conference on computing Communication Control and Automation (ICCUBEA)

作者： Pratiksha Pawar Vishwajeet Shinde Aniket Raut Saloni Suke Kavita Kolpe Asmita Manna Pimpri Chinchwad College of Engineering Pune India PCCOE Pune India

Crop yield prediction and recommendation systems are important for farmers to decide the correct crop for their land. this paper proposed a yield prediction and crop recommendation system that leverages deep learning and machine learning methods. the system aims to assist farmers in the decision of crop selection, planting, and harvesting. the system takes into account various factors, such as conditions of weather, soil quality, and historical yield data, to predict the yield of a particular crop. the proposed system was evaluated on a dataset of crop yield data from several regions of Maharashtra state, and the results show that it can accurately predict crop yields and provide useful recommendations for farmers. the dataset is trained using a combined machine learning and deep learning methods, in which initially it predicts the crop for the current season. then the predicted crop is used as input to a deep learning model, which recommends its respective yield for that season. the proposed system has been tested on historical crop yield data and weather data for two seasons, and the results show its own accuracy and efficiency for traditional crop management methods. the future scope is to assist farmers by recommending fertilizer and irrigation requirements and pest control measures.

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Development and Validation of Unsupervised machine learning Clustering Techniques 7th

Development and Validation of Unsupervised Machine Learning ...

引用

7th international conference on Smart Trends in computing and Communications, SmartCom 2023

作者： Kolipaka, Venkata Naga Sri Sai Pranavi Ghanta, Naga Durga Jai Rathan Koppaka, Hemanth Durga Prasad Pellakuri, Vidyullatha Haran Babu, P. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur India

ISBN: (纸本)9789819907687

Nowadays a greater number of users participates is used to create new issues and discussion on social media that form into different kinds of groups such as Positive and negative comments. this paper is focused on a group of user’s discussions and specifies from which category they belong to. the user messages are parsed in the social media data then they identified network relationships and applied the data mining techniques to a group of different types of communities. the collection of objects is considered as similar or non-similar Clustering structures in unsupervised approaches. the aim of this paper is to develop clusters depending on features and their characteristics that are included in the proposed model. this work helps the system to categorize people into groups which also helps to identify people groups that are participated in discussions. this paper shows the clustering algorithms like K-Means, DBSCAN, and Agglomerative to cluster data and to find large streams of clustering community messages in social media data. this paper throws light on a novel use-case of communities and a proposed algorithm that shows the best clustering results. this application tells us which group of people saw the post and who gave their opinions on the post;by this, we can categorize the users. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： K-means clustering

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Constructive Analysis on Prediction and Detection of Diabetic Retinopathy (DR) using machine learning Algorithms - A Generic Survey

Constructive Analysis on Prediction and Detection of Diabeti...

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international conference on computing Methodologies and Communication (ICCMC)

作者： Chukka Keerthana Peram Tejasree Mudivarthi. Venkata Subba Rao R. S. Sai Pavan Kumar Prasanth Yalla Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Andhra Pradesh India

Diabetic retinopathy is an eye disorder that affects people with diabetes. When high blood sugar levels harm the retina's blood vessels, the blood vessels may enlarge and leak or they may block the flow of blood. All these alterations may cause visual loss. In the current era, machine learning is very useful and plays a key role in medical applications. In this research, investigate the effectiveness and capability of different types of machine learning (ML) algorithms based diabetic retinopathy detection systems. the authors tested large quantities of retinal fundus images & gray scale pictures from Kaggle, Messidor, IEMRC and multiple available datasets. the application of an ensemble of machine learning classification algorithms to characteristics taken from the results of various retinal image processing algorithms is used to determine the presence of disease. the proposed goal is to anticipate the DR before it results in the worst scenario and prevent the patient from losing their visual acuity. the proposed method uses both supervised algorithms like Support Vector machine (SVM), ResNet and DensNet, Naive Bayes, K-Nearest Neighbors, and Neural Networks (NN), as well as unsupervised algorithms like k-means clustering, hierarchical clustering, and Markov chains to classify a variety of characteristics in DR. the best overall result is displayed by ResNet and DensNet with the accuracy of 96.22%.

关键词： Support vector machines Visualization machine learning algorithms Retinopathy Clustering algorithms Retina Prediction algorithms

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Smart Agriculture: A Compendium of Image-Based learning Algorithms for Plant Leaf Diseases

Smart Agriculture: A Compendium of Image-Based Learning Algo...

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international conference on computing Communication Control and Automation (ICCUBEA)

作者： Jayshree Ghorpade-Aher Prasad Ligade Pravin S. Game SCET Pune Maharashtra PCCOE Pune Maharashtra

In the lives of humans, agriculture is incredibly vital and is a backbone of every national economy. Agriculture in India is crucial because of the nation's speedy population growth and increased food consumption. Plant leaf diseases are a major cause of decreased yield and lower farmer revenue. Experts are currently putting great effort into creating a system that can recognize plant diseases automatically. the existence of bacterial, viral, and fungal infections exerts a substantial influence on reduced crop yields. Early diagnosis of plant leaf diseases is essential since they influence the growth of the particular species. the use of Artificial Intelligence is growing across many sectors to automate tasks and increase productivity. Plant leaf diseases have been identified and categorized using numerous machine learning models, however, recent progress in a subset of machine learning known as Deep learning appear to offer tremendous promise for improved accuracy in this area of research. In this research study, various studies on plant leaf diseases are analyzed based on significant parameters like dataset used, plant names, different machine learning and Deep learning models utilized, different disease types detected and overall accuracy achieved by the model. Even though numerous studies have already produced plant disease detection systems the proposed technique with convolutional neural networks (CNN) performs better.

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A Scientometric Review of machine learning and Deep learning Techniques

A Scientometric Review of Machine Learning and Deep Learning...

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international conference on Circuit, Power and computing Technologies (ICCPCT)

作者： Dimple Kapoor Deepali Gupta Mudita Uppal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

machine learning and deep learning has emerged as technological innovations transforming industries and changing landscape of the society. this review paper offers a comprehensive analysis of the present state of machine learning (ML) and Deep learning (DL) as a burgeoning field, emphasizing its fundamentals, its workings and its types. Beginning with a literature review of ML and DL, this paper explores the core concepts underlying various ML techniques, including supervised, unsupervised, semi-supervised and reinforcement learning. this study also discusses relevant topics including issues such as data privacy, security and ethical considerations inherent in ML and DL development and applications. the scientometric analysis reveals a substantial rise in ML and DL papers during the previous ten years, indicating growing interest in the field with the maximum number of documents published in the year 2023. the findings underscore the rapid growth and dynamic nature of ML and DL fields, driven by advancements in computational power and the increasing availability of big data. By analyzing recent trends and the number of publications done in the past years, this paper provides important perspective into the evolving landscape of machine learning and deep learning and its impact on society.

关键词： Deep learning Industries Technological innovation Ethics Data privacy Reviews Bibliometrics

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