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machine learning & Internet of things in Plant Disease Detection: A comprehensive Review 7

Machine Learning & Internet of Things in Plant Disease Detec...

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

作者： Pareek, Shalinee Kumar, Awanit Degadwala, Sheshang Computer Science and Engineering Sangam University Bhilwara India HOD Computer Engineering Sigma Institute of Engineering Gujarat Vadodara India

ISBN: (纸本)9781665464086

Agriculture is crucial to the growth of every country's economy. Due to population growth, unpredictable weather, and a lack of available resources, meeting the world's food needs has become an increasingly challenging undertaking in recent years. there are a number of issues that threaten agriculture's long-term viability, but new technologies are providing farmers with a way to combat them. To effectively control agricultural productivity, it is essential to accurately diagnose plant diseases. the use of machine learning (ML) and the Internet of things (IoT) is a relatively new development in the agricultural industry with the potential to improve crop management. Improved agricultural yields are possible with the integration of machine learning and other Internet of thing's technologies. this study provides a comprehensive analysis of the ways in which ML and IoT have been used in the agricultural sector. Here, this study reviews IoT and ML-powered smart farming and agricultural strategies that have been tried and tested. this study suggests a new strategy for integrating ML and IoT in real-time environment. © 2023 IEEE.

关键词： Support vector machines

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Study of California Earthquake Prediction Using machine learning Approach 7

Study of California Earthquake Prediction Using Machine Lear...

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

作者： Swain, Debabrata Shah, Vyom Shah, Dhruvin Bhilare, Amol Pandit Deendayal Energy University Computer Science and Engineering Department Gandhinagar India

ISBN: (纸本)9798350304268

Earthquake prediction is a highly challenging and complex task that has been the subject of scientific inquiry for many years. Even though we have learned a lot about earthquakes, it is still hard to predict them accurately and reliably. this is because earthquakes are complicated and hard to predict, and because our scientific knowledge and technology are not as advanced as they could be. there have been many ideas about how to predict earthquakes, such as using seismological and geophysical data,. But a lot of work still needs to be done to make a reliable and effective earthquake prediction system that can warn people in time and lessen the damage caused by earthquakes. In this work, an intelligent earthquake prediction model is developed using various machine learning algorithms. the seismological data of USGS is used for the model training and validation. the system has shown an prediction accuracy of 92.7% © 2023 IEEE.

关键词： California Decision Tree(DT) Earthquake Logistic Regression(LR) Random Forest (RF) USGS

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An Integrated Framework for Smart Monitoring of Orange Orchard 7

An Integrated Framework for Smart Monitoring of Orange Orcha...

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7th international conference on Electronics, Materials Engineering and Nano-Technology, IEMENTech 2023

作者： Chakraborty, Shuvam Jana, Susovan Institute of Engineering & Management Department of Information Technology Kolkata India Kolkata India

ISBN: (纸本)9798350305517

Oranges hold significant sway in the global food market, making the monitoring of their production a matter of utmost importance. Keeping track of the production procedure is the most important part of having a great yield of oranges. through the integration of cloud computing, IoT, machine learning, and advanced image analysis techniques, we aim to streamline orange orchard management. machine learning and computer vision have brought revolutionary changes to the agricultural sector, particularly in orchard monitoring. this paper introduces an integrated framework for orchard monitoring, focusing on real-time imaging of oranges, cloud-based storage, and the application of machine learning algorithms for automated monitoring and yield notifications to farmers. Additionally, we have conducted a comparative analysis of various machine learning models to evaluate their capacity to detect and count overlapped oranges within the dataset. Our proposed framework encompasses image capture, cloud storage, and machine learning analysis, providing farmers with invaluable insights into orange production. the comparative analysis delves into the merits and limitations of different machine learning models, including the K-means and Distance Least Square, Hough Transform, and YOLOv3, addressing the challenge of detecting overlapped oranges. © 2023 IEEE.

关键词： learning algorithms

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An Automated Combined System for Crop Prediction and Yield Prediction Using Deep Hybrid learning Technique 7

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

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

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

ISBN: (纸本)9798350304268

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. © 2023 IEEE.

关键词： Crop Recommendation Crop Yield Deep Hybrid learning Long Short Term Memory (LSTM) machine learning (ML) Soil Recommendation

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Personality Prediction Using ML and NLP 7

Personality Prediction Using ML and NLP

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

作者： Mahandule, Adinath Fiske, Gaurav Malusare, Vedang Bhambure, Omkar Shedge, Rajashree Ramrao Adik Institute of Technology Dept. of Computer Engineering Navi Mumbai400706 India

ISBN: (纸本)9798350304268

Estimating a person's personality is essential in understanding how they manage stress, lead, communicate, collaborate, and influence others. One of the critical factors that affect how people interact with their environment is their personality. this project aims to utilize machine learning and also natural language processing techniques to predict the personality of students based on a questionnaire. the system analyzes the user's personality in the database, which already records personality traits. the system then identifies the user's personality based on the MBTI model, a widely used personality model. this project's significance lies in the capability of the system to aid students, teachers, and businesses in identifying students' personalities to plan their actions better. the ML and NLP techniques implemented in this system can categorize or anticipate students' personalities, thus assisting businesses in their hiring process. © 2023 IEEE.

关键词： Catboost Gradient Boosting machine learning Myers-Briggs Type Indicator (MBTI) Natural Language Processing TF-IDF(Term Frequency-Inverse Document Frequency)

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Insights into Automated Diagnosis of Cervical Cancer 7

Insights into Automated Diagnosis of Cervical Cancer

引用

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

作者： Gaikwad, Aaishwarya Shinde, Swati Bhuta, Nishi Jangale, Param Jadhav, Tejas Pimpri Chinchwad College of Engineering Department of Computer Engineering Pune India

ISBN: (纸本)9798350304268

the cervix is the last part of the uterus where cervical cancer usually develops. Identification of the cervical cells can be very taxing owing to their complexity of morphological changes in the structural parts of the cell. the problem here is that the detection process is tardy and there is not much awareness about it. By means of this research, we have taken a step to attempt to automate this detection process and understood the various methods used like Logistic regression along with Ll Regularization, CNN, SVM and ANN produced excellent accuracy of 100% © 2023 IEEE.

关键词： Bioinformatics Cervical Cancer Computer Vision machine learning Medical Sciences

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A machine learning-Based Approach to Assess and Predict Drought Events: A Case of Rajasthan, India 7th

A Machine Learning-Based Approach to Assess and Predict Drou...

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

作者： Firdaus, Tuba Abimannan, Satheesh Mishra, S. Sangita Gupta, Preeti Amity School of Engineering and Technology Amity University Maharashtra Mumbai India School of Technology Management and Engineering Narsee Monjee Institute of Management Studies Navi Mumbai India

ISBN: (纸本)9789819720309

the work focuses on understanding and analyzing drought events in the region of Rajasthan, India, using drought indices and machine learning models. the Standard Precipitation Index (SPI) and Standardized Precipitation and Evapotranspiration Index (SPEI) are calculated to assess drought severity, duration, and intensity. the study area includes two stations, Jodhpur and Banswara, with different climatic characteristics. Various hydro-climatic factors such as precipitation, temperature, humidity, and solar radiation are considered to analyze their correlation with drought indices. the machine learning models, long short-term memory (LSTM) networks, and random forest (RF) are employed for drought prediction. the results indicate that LSTM performs better than RF in short-term predictions, with lower root mean square error (RMSE) values. the study provides valuable insights into drought monitoring, characteristics, and prediction, contributing to effective drought mitigation strategies in the region. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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A Literature Review on Remaining Useful Life Prediction of Lithium-Ion Batteries 7

A Literature Review on Remaining Useful Life Prediction of L...

引用

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

作者： Khandelwal, Kunal S. Shete, Vriendra V. MIT School of Engineering MIT ADT University Ece Deptt. Pune India

ISBN: (纸本)9798350304268

Lithium-ion batteries have evolved into a critical part of the power supply system, and predicting their remaining usable life is critical to ensuring their reliability. Intricate operational characteristics of lithium-ion batteries with the changes with their model parameter as they age, improving the accurateness in the online remaining usable life forecast is hard yet important for the reliable power supply application. We've analyzed research papers in different aspects like: dataset utilized for evaluations, different techniques utilized for machine learning, Analysis of SOH prediction, classification of RUL prediction, filtering based methods as well as different data-driven algorithms. the paper also summarizes, each of for accuracy, error, and cost. Moreover, the current challenges faced in the RUL prediction will be rendered, which will stand a milestone for the future investigators. © 2023 IEEE.

关键词： Classification Algorithm Research Gaps and Challenges Filtering based Methods Lithim-ion batteries machine learning (ML) Remaining Useful Life (RUL) State of Charge (SOC) State of Health (SOH) prediction

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Prediction of Soil Pollution Using machine learning Methods in Absheron Peninsula 16th

Prediction of Soil Pollution Using Machine Learning Methods ...

引用

16th international conference on Application of Fuzzy Systems, soft computing and Artificial Intelligence Tools, ICAFS 2023

作者： Abdullayeva, Maya Babayeva, Tunzala Dadashova, Shebnem Azerbaijan State Oil and Industry University Azadlig 16/21 Baku Azerbaijan

ISBN: (纸本)9783031725050

this paper presents a comprehensive study contributing to soil pollution research by applying machine learning techniques. the research explores predicting soil heavy metals and pollution indices, identifying, and classifying potentially polluting enterprises, and reviewing machine learning models for soil pollution prediction. the study employs various machine learning algorithms, including linear regression, decision tree regressor, and k nearest neighbor, to predict soil pollution at depths not physically investigated. this approach proves instrumental in reducing the time, staff, and resources required for pollution measurement in the Absheron Peninsula. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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A Scientometric Review of machine learning and Deep learning Techniques 7

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

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7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

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

ISBN: (纸本)9798350372816

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. © 2024 IEEE.

关键词： Deep reinforcement learning

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