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

共 811 条记录，以下是141-150 订阅

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HMHDTML: Human Mental Health Detection Using Text and machine learning Model 7th

HMHDTML: Human Mental Health Detection Using Text and Machin...

引用

7th international conference on soft computing: theories and Applications, SoCTA 2023

作者： Kshirsagar, Vanita G. Yadav, Sunil Karande, Nikhil Amity University Rajasthan Jaipur India Dr. D. Y. Patil Institute of Technology Pimpri Pune India SISA Information Security Pvt. Ltd. Bangalore India

ISBN: (纸本)9789819720309

Human mental health (HMH) is a serious chronic condition that affects how a person thinks, feels, and behaves negatively. It is a common but serious mood disorder. About 20% of women will experience at least one episode of depression across their lifetime. Scientists examining many potential causes for and contributing factors to women’s increased risk for depression. To diagnose depression, the symptoms must be present at least 2 weeks. A research study has been conducted by detection of depression using sentiment analysis on Twitter, Facebook, Instagram, etc., to understand their behavioral symptoms like depressed mood, loss of interest, change in sleeping, difficulty in thinking and/or concentrating, taking decisions. this paper describes the features and the implementation of the depression using Twitter and sarcastic text with the help of machine learning algorithm to improve the overall results. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sentiment analysis

来源：评论

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A Systematic Review of machine learning Approaches for Trash Classification 7

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

引用

7th international conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Vidhya, S. School of Computing Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350397284

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. © 2023 IEEE.

关键词： Deep learning

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Hybrid Deep learning Technique for Leaf Disease Detection System 7th

Hybrid Deep Learning Technique for Leaf Disease Detection Sy...

引用

7th international conference on soft computing: theories and Applications, SoCTA 2023

作者： Saxena, Khushboo Gupta, Priyanka Gupta, Pranjal Dubey, Shivam Kasana, Poornima ABES Institute of Technology Ghaziabad India

ISBN: (纸本)9789819720880

Plants play a vital part in providing nourishment in all inclusive. Different natural components cause plant illnesses, resulting in significant generation misfortunes. However, manually identifying the plant diseases may be an expensive and time-consuming process. Embracing improved innovations in machine learning (ML) and deep learning (DL) can help to resolve these challenges by allowing early detection of plant illness. therefore, the present work focuses on the study of employment of ML and DL techniques for plant leaf disease detection system from 2016 to 2023. the analysis reveals that the employment of these techniques significantly improves the accuracy and efficiency of disease detection models. Further, this article also addresses the challenges and constraints associated with the employment of ML and DL approaches, such as information accessibility, imaging quality, and efficient classification capability between healthy and sick leaves. Based on the analysis, it has been observed that although DL models dominate over ML algorithms, however, they struggle due to limited availability of data, significant class imbalance, noise, limited availability of resources, etc. therefore, these limitations may motivate researchers to explore other approaches for more efficient plant leaf disease detection at an early stage. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Efficient Time Series Photovoltaic Production Forecasting using FLAML with Traditional machine learning Algorithms 7

Efficient Time Series Photovoltaic Production Forecasting us...

引用

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

作者： Kumar, Dora Praveen Kaur, Navjot Kushana, Shyam Sundar Lovely Professional University Computer Science and Engineering Punjab India Lovely Professional University Aerospace Engineering Punjab India

ISBN: (纸本)9798350397253

this study explores the use of automatic photovoltaic production forecasting. the library is applied with five traditional machine learning algorithms, including Xgb-Limitdepth, Random-Forest, Extra-Tree, XGBoost, and lightGBM. the dataset used in this study is obtained from a photovoltaic power plant located in a sunny region in Europe, Greece, and it includes hourly power production data for a period of four years. the results show that FLAML can automatically optimize hyperparameters and select the best algorithm for the given data, achieving accurate forecasts for photovoltaic power production. the experiment shows that the combination of FLAML with traditional machine learning algorithms performs better than using each algorithm individually. In addition, the study compares the performance of the different algorithms and demonstrates that XGBoost and LGBM outperform the other algorithms. Overall, this study demonstrates the usefulness of FLAML in time series forecasting and its potential to optimize the forecasting process by automating the selection of the best algorithm and hyperparameters. this study's empirical evidence can be useful for researchers and practitioners in the field of renewable energy, particularly in photovoltaic power production, and similar can be used for wind power production and tidal power production. © 2023 IEEE.

关键词： Forecasting

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Optimization with Label Weighting Extreme learning machine for Human Activity Recognition 7

Optimization with Label Weighting Extreme Learning Machine f...

引用

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

作者： Kumar, L. Maria Anthony Murugan, S. Annamalai University Department of Computer and Information Science Tamil Nadu Annamalai Nagar India Dr. M.G.R. Government Arts and Science College for Women Tamil Nadu Villupuram India

ISBN: (纸本)9798350397253

Human Activity Recognition (HAR), is a field of study related to the spontaneous detection of day-to-day activities performed by people based on time series data using sensors. Over the past few years, there have been numerous progressions in interconnected sensing technology namely edge computing, cloud, sensors, and IoT. In recent times, deep learning (DL) based method has gained popularity for HAR because they utilize representation learning technique that could identify hidden patterns in data and could automatically generate optimum features from raw input datasets produced from sensors without human interference. this study introduces a new Fire Hawks optimization with Label Weighting Extreme learning machine (FHO-LWELM) algorithm for HAR the presented FHO-LWELM technique aims to identify and classify different kinds of human activities. To do so, the presented FHO-LWELM technique exploits the LWELM model for recognition purposes. At the same time, the parameters involved in the LWELM model can be optimally chosen by the use of FHO algorithm and it led to improved recognition rate. the performance validation of the FHO-LWELM technique is examined on HAR dataset and the experimental outcomes stated the improved outcomes of the FHO-LWELM technique. © 2023 IEEE.

关键词： Deep learning

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Secured Framework for Banking Chatbots using AI, ML and NLP 7

Secured Framework for Banking Chatbots using AI, ML and NLP

引用

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

作者： Chanda, Rajat Prabhu, Sandeep Symbiosis Institute of Digital and Telecom Management Aff. Symbiosis InternationalDeemed University Pune India

ISBN: (纸本)9798350397253

Artificial intelligence (AI) machine learning (ML), and Natural Language Processing (NLP)are being employed in a growing number of sectors and is evolving as the technology of the future. It has been widely used to improve the quality of decision-making and problem-solving. this research study proposes a secured framework for banking chatbots using AI-ML and NLP. the framework based on qualitative in-depth experts interviews can be used for developing efficient chartbots for managing customer interactions of various data forms. AI enabled chatbots involves use of machine learning algorithms that can communicate with consumers in a conversational manner. AI enabled chatbots can speak common man's language language and utilize voice as a user interface. Chatbots are used in the public and private sectors to provide virtual help in areas such as telecommunications, media, tourism, retail, stock markets, and banking. In the banking business, chatbot can act as a communication tool between customers and banks. It has a special advantage in financial industry as it can be automated and does not necessitate human interaction;and can enhance accessibility and provide a lot of features to customers. © 2023 IEEE.

关键词： Decision making

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machine learning Approach for Predicting the Placement Status of Students 7

Machine Learning Approach for Predicting the Placement Statu...

引用

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

作者： Dhumane, Amol Tamboli, Mubin Ambala, Srinivas Game, Pravin Meshram, Vishal Patil, Rahul Pimpri Chinchwad College of Engineering Department of Computer Engg. Pune India

ISBN: (纸本)9798350304268

Ensuring the successful placement of students is a crucial objective for educational institutions as it directly impacts their reputation and annual admissions. To achieve this, institutions put in significant effort to strengthen their placement department. Any support in this area can have a positive impact on both the institution and the students. this study aims to forecast the placement chances of current students by analysing data from the previous year. the proposed model utilizes an algorithm to achieve this. However, the challenge was to find a suitable classification algorithm that could provide maximum accuracy for our dataset. To address this, we equated our model by using existing classification algorithms such as Decision Tree (DT), KNN, SVM, Logistic Regression (LR), and Random Forest based on accuracy, precision, and recall. We obtained the True Positive, False Positive, True Negative and False Negative values for each algorithm to determine accuracy of the model. Our results show that the proposed algorithm outpaces the other algorithms significantly. © 2023 IEEE.

关键词： classification machine learning algorithm placement

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Optimising the Detection of Fake News in Multilingual Exposition using machine learning Techniques 7

Optimising the Detection of Fake News in Multilingual Exposi...

引用

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

作者： Ramesh Reddy, B. Tejaswi, T. Naveen, P. Giresh, J. Vamsi, B. LBRCE Mylavaram JNTUK Dept. of ECE AP Kakinada India

ISBN: (纸本)9798350397253

As social media has gotten more and more popular in recent years, there are many websites and social media platforms that claim to report on forth coming events, but whose veracity has been called into doubt. this has thrown news websites and blogs into the spotlight. As a result of the controversy surrounding these websites and news organisations the concept of 'fake news' is now a crucial component of the news industry. Today's media content is considerably affected by the falsified news i.e;from satirical pieces to manufactured news and government publicity schemes. Fake news and a lack of media trust are growing problems with serious repercussions for the general public. Automated technologies for spotting false news, including machine learning models, are now a must. the work here presents the evaluation of four machine learning models' performance on four real-world datasets. Other languages, like Hindi, which is extensively spoken all over India are the subject of relatively fewer study. But the models which we have built are equally efficient in classifying news proliferated in Hindi also. the method proposed utilises NLP techniques for data preprocessing and TF-IDF is the feature extraction technique considered for this model's development. Using simple machine learning techniques an accuracy of about 0.99 is achieved. © 2023 IEEE.

关键词： machine learning

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Rainfall Induced Landslide Detection in Study Area using machine learning Model 7

Rainfall Induced Landslide Detection in Study Area using Mac...

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7th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Rawat, Pradeep Singh Yadav, Anuj Kumar Barthwal, Varun Gaur, Sachin Dit University Dehradun School of Computing Dehradun India H.N.B Garhwal University Pauri Department of Information Technology Pauri Garhwal India B.T Kumaon Institute of Technology Dwarahat Department of Computer Science and Engineering Dwarahat India

ISBN: (纸本)9798350341485

In the present era of computing, communication and Technology, natural disaster can be controlled in an efficient manner. the natural disaster in hill sate is common. It can be mitigated using machine learning techniques. In this manuscript out objective is proposed a machine learning model which focus on rainfall induced landslide prediction in uttarakhand state districts using benchmark dataset. there is a good correlation between landslide and antecedent rain fall. the antecedent rain fall supports the machine learning model for better accuracy and correctness. the machine learning model with optimal performance metrics provides the prior information about the level rainfall and its impact level on landslide in a study area of the focused state. the results show that Random Forest model outperforms the linear model, SVR model, and neural network model respectively. the key performance indicators i.e. mean absolute error(MAE), and root mean square error(RMSE) are improved by a factor of 79.05%, and 83.34% respectively. the key performance indicators evaluated and analyzed against state of art methodologies i.e. Random Forest model outperforms the linear model, SVR model, and neural network © 2023 IEEE.

关键词： Landslides

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Crop Advancement with machine learning 7

Crop Advancement with Machine Learning

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

作者： Pawar, Rajendra Bisht, Vishnu Pratap Pandey, Ritika Lodhe, Urvik Pawar, Aryan Chouksey, Aaryan Jatale, Sarthak School of Computer Engineering & Technology Mit World Peace University Maharashtra Pune India

ISBN: (纸本)9798350304268

Crop prediction is a crucial task in agriculture that can help farmers and agricultural researchers decide which crops to grow based on environmental variables such as soil type, temperature, rainfall, and humidity. In this project, we propose a data-driven approach for crop prediction using K-means clustering and classification algorithms. We use K-means clustering to group the data into different clusters based on similarities in the environmental variables, and then we use a classification algorithm such as decision trees or support vector machines to predict the best crop to grow in a particular location. this approach can help farmers and agricultural researchers make more informed decisions about crop selection and improve crop yields. We validate our model by comparing historical data on crop yields to our predictions and conducting experiments by growing different crops in different locations. Overall, this project demonstrates the potential of machine learning algorithms for crop prediction in agriculture. © 2023 IEEE.

关键词： Agriculture Classification algorithm Crop prediction Environmental variables K-means clustering

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