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Artificial Intelligence and its Road to Altering the World Dynamics 3

Artificial Intelligence and its Road to Altering the World D...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Sehaj Batra, Devyansh Sharma, Ashish Chandigarh University Computer Science and Engineering Mohali India Chandigarh University Department of Electrical Engineering Mohali India

ISBN: (纸本)9798350381931

Humans have longed for computers to take over and make the monotonous and tedious tasks obsolete. Artificial Intelligence(AI) is just the right answers to this problem. There are not one but many sectors that are being manipulated by this ever- growing technology. It is safe to say that each day machines are getting better at understanding humans which is due to the evolution and innovation in this field. In this paper, we have uncovered various sectors in which artificial intelligence along with its various branches like Natural Language Processing (NLP), machine learning(ML) and Computer Vision have helped the world curate a better place by personalizing every daily use item. This has enhanced the way we live. However, there is a lot of data that is being used in order to bring these technologies to life. We have highlighted the constraints as well as the pleasures that this technology can bring us. © 2024 IEEE.

关键词： machine learning

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ML Model For Detection of Depression in Menopause Women 3

ML Model For Detection of Depression in Menopause Women

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3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Britto, Kasey Ann Varnikaa, D.T. Sathishkumar, R. Anita Dolorosa, E. St. Joseph's College of Engineering Department of Artificial Intelligence and Data Science Chennai India Jerusalem College of Engineering Department of Science and Humanities Chennai India

ISBN: (纸本)9798350372120

Menopause, an inevitable milestone in a woman's journey, signifies not only the onset of physical transformations but also the potential emergence of mental health complexities. This study investigates the potential of machine learning to predict depression in menopausal women, aiming to improve their well-being. Depression is a prevalent concern during menopause, yet limited specialist access and knowledge gaps hinder effective intervention. We have developed a supervised multi-classifier machine learning system to predict depression and its severity, evaluating various classification algorithms. Our findings demonstrate high accuracy (97%) using a Random Forest model, suggesting promise as an AI-powered tool in this domain. © 2024 IEEE.

关键词： Forecasting

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Mathematics for machine learning 3

Mathematics for Machine Learning

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Tiwari, Seemant SouthernTaiwan University of Science and Technology Department of Electrical Engineering No. 1 Nantai Street Yongkang District Tainan City71005 Taiwan

ISBN: (纸本)9798350381931

machine learning (ML) refers to the process of developing a program that provides modeling that can study given data and make forecasts. ML is a mathematical need. It is employed to understand how or why it functions and also why a particular concept is superior to someone else. Statistics plus probabilities are used to drive machine learning. Professionals examine data and employ a computer to evaluate the possibility inside the dataset, however, the huge amount of information and complexity make it challenging to accomplish this task simply. An approach is to educate computers on how and when to study and find out how they can improve via extensive experience;this is known as machine learning. The effective utilization of elevated system architectures, unrivaled predictive accuracy, and effective utilization statistics methods for data retrieval are the characteristics that make ML the compute-intensive group's preferred option. Assumptions founded on mathematical models incorporating statistics, probabilities, correlation, and regression may be employed by ML techniques. ML has the potential of gathering insights from any data set. ML is a set of techniques and technology that can be employed to address any concerns about data. The purpose of developing a mathematical design of a program is to determine and characterize the numerous cause and consequence linkages among system components, decision factors, and model parameters, as well as to specify and assess system stability. © 2024 IEEE.

关键词： machine learning

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Construction and Validation of Big data Analytics in Air Traffic Control Flow Prediction Models 24

Construction and Validation of Big Data Analytics in Air Tra...

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3rd international conference on Algorithms, data Mining, and Information Technology, ADMIT 2024

作者： Wang, Minghua Qiao, Jiantong Air Force Engineering University ATC&GCL College Shaanxi Xi'an China

ISBN: (纸本)9798400718120

Air traffic control flow prediction is one of the key issues in aviation transportation management, crucial for optimizing resource allocation, enhancing control efficiency, and ensuring flight operation safety. This paper proposes a solution for air traffic control flow prediction based on big data analysis and machine learning techniques. Through extensive feature engineering and model construction using large-scale historical data, multiple prediction models including Support Vector Regression, Random Forest, Gradient Boosting Decision Tree, and an ensemble model were trained. Comprehensive performance evaluations using cross-validation and multiple metrics were conducted. Experimental results demonstrate that the proposed ensemble model effectively combines the strengths of multiple base models, significantly outperforming individual models in terms of prediction accuracy, stability, and generalization ability. This provides an efficient and reliable technical path for air traffic control flow prediction, with broad application prospects. © 2024 Copyright held by the owner/author(s).

关键词： Resource allocation

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Federated learning for Smart Agriculture: Challenges and Opportunities 3

Federated Learning for Smart Agriculture: Challenges and Opp...

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3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Kondaveeti, Hari Kishan Sai, Guduru Balamanikanta Athar, Shaik Asneem Vatsavayi, Valli Kumari Mitra, Alakananda Ananthachari, Preethi School Of Computer Science And Engineering VIT-AP University Andhra Pradesh India Andhra University Department Of Cs & Se Andhra Pradesh India University Of Nebraska-Lincoln Nebraska Water Center LincolnNE United States Endicott College Of International Studies Woosong University Ai & BigData Dept Daejeon Korea Republic of

ISBN: (纸本)9798350318609

FL is a machine learning approach that allows knowledge sharing with privacy maintenance and cost reduction. FL has the potential to revolutionize the smart agriculture sector by enabling farmers to train and deploy machine learning models on their own devices, without the need to share their data with a central server. In this article, we review the applications of FL in smart agriculture, such as disease diagnosis and severity assessment, the Internet of Agricultural Things (IoAT), and yield forecasting. We also highlight the challenges and opportunities of using FL for smart agriculture. © 2024 IEEE.

关键词： Cost reduction

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Clustering Method Analysis for Gene Expression data using Fire Fly Optimization and Simple K-means Algorithm with machine learning 3

Clustering Method Analysis for Gene Expression Data using Fi...

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3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Sridharan, S. Satheeshkumar, K. Rajesh, R. Deivasigamani, Sugitha Department of Computer Science and Engineering Nagapattinam India

ISBN: (纸本)9798350375190

data mining is an endeavor to identify patterns, correlations, similarities, and links among massive datasets in order to uncover hidden, intriguing information. The goal of clustering is to arrange data sets that share similar attributes into groups, or clusters. The gene data are enormous, complex, and contain information on gene expression simultaneously, making it difficult to manage the unmanageable amount of data. The massive volume of data makes cluster analysis and interpretation difficult. Self-organizing maps are thought to be a helpful method for evaluating and visualizing high dimensional gene data for machine learning algorithms. An instrument for the best cluster mining in gene samples is the Firefly algorithm. This approach helps to improve visualization and suggests a fresh angle for identifying reliable clusters from the massive amounts of gene expression data. The results of this work yield the most accurate and optimal clusters from the large gene data sets. © 2024 IEEE.

关键词： Cluster analysis

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ML-On-Rails: Safeguarding machine learning Models in Software Systems - A Case Study 24

ML-On-Rails: Safeguarding Machine Learning Models in Softwar...

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IEEE/ACM 3rd international conference on AI engineering - Software engineering for AI (CAIN)

作者： Abdelkader, Hala Abdelrazek, Mohamed Barnett, Scott Schneider, Jean-Guy Rani, Priya Vasa, Rajesh Deakin Univ Geelong Vic Australia Monash Univ Clayton Vic Australia RMIT Univ Melbourne Vic Australia

ISBN: (纸本)9798400705915

machine learning (ML), especially with the emergence of large language models (LLMs), has significantly transformed various industries. However, the transition from ML model prototyping to production use within software systems presents several challenges. These challenges primarily revolve around ensuring safety, security, and transparency, subsequently influencing the overall robustness and trustworthiness of ML models. In this paper, we introduce ML-On-Rails, a protocol designed to safeguard ML models, establish a well-defined endpoint interface for different ML tasks, and clear communication between ML providers and ML consumers (software engineers). ML-On-Rails enhances the robustness of ML models via incorporating detection capabilities to identify unique challenges specific to production ML. We evaluated the ML-On-Rails protocol through a real-world case study of the MoveReminder application. Through this evaluation, we emphasize the importance of safeguarding ML models in production.

关键词： Robustness Trustworthy-AI Protocol

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Reviewing Flood Susceptibility Mapping Utilizing machine learning: Insights and Prospects for Future Research 3

Reviewing Flood Susceptibility Mapping Utilizing Machine Lea...

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3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Mishra, Priya Prasad, Sanjeev Kumar Galgotias University Abes Engineering College School of Computer Science and Engineering Greater Noida Ghaziabad India Galgotias University School of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350375190

Over the past decade, the utilization of machine learning for flood forecasting has experienced significant growth, propelled by advancements in high-performance computing, risk-informed decision-making, and collaborative efforts within the domains of weather and climate forecasting. These approaches offer the potential to enhance flood mapping methodologies and address the limitations inherent in numerical models, thus gaining traction in flood risk management. This study aims to conduct a comprehensive review of the literature on machine learning applications in flood estimation and prediction across various flood mapping contexts. The investigation will delve into diverse flood types, influential conditioning factors, and varied data sources. By studying 34 pertinent articles within the Indian context, this research endeavors to provide a comprehensive overview of current research endeavors, pinpoint existing knowledge gaps, and explore promising avenues for future research. Additionally, the paper underscores the imperative to enhance flood forecasting technologies and bridge the divide between academic inquiry, hydro-meteorological model advancement, and pragmatic flood management practices by leveraging the capabilities of probabilistic machine learning and deep learning models. © 2024 IEEE.

关键词： Support vector machines

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Classification of Tweets using a machine learning and Natural Language Processing Algorithm for Disaster Prediction 3

Classification of Tweets using a Machine Learning and Natura...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Gill, Kanwarpartap Singh Anand, Vatsala Upadhyay, Deepak Dangi, Sarishma Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350381931

This research investigates the use of machine learning (ML) and natural language processing (NLP) algorithms for the categorization of tweets to anticipate disasters. This study aims to use the extensive and up-to-date social media data, namely from Twitter, to construct a reliable model for distinguishing tweets that pertain to disasters from those that do not. The technique being offered encompasses many key steps, including the gathering of data, pre-processing of the collected data, extraction of relevant features, and the subsequent deployment of several machine learning models. The primary objective is to develop a highly effective and precise system that can classify tweets in real-time, hence enhancing early warning systems and catastrophe management. The efficacy of the model will be assessed using evaluation criteria such as precision, recall, and accuracy. This will position the model as a helpful tool for boosting catastrophe prediction skills. The primary objective of this research is to forecast if a particular tweet pertains to an actual catastrophe or not. If this is the case, make a prediction of 1. If the condition is not met, the anticipated outcome would be a value of zero. The outcomes are also represented in the form of learning Rate and Confusion Matrices in the proposed research. © 2024 IEEE.

关键词： Disasters

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Harvest Harmony by Leveraging machine learning for Optimal Crop Yield Recommendations 3

Harvest Harmony by Leveraging Machine Learning for Optimal C...

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3rd international conference for Advancement in Technology, ICONAT 2024

作者： Sahu, Premananda Bharany, Salil Kaushik, Keshav Kaur, Upinder Kumar, Anand Choudhury, Amitava Lovely Professional University School of CSE Punjab Jalandhar India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India Amity School of Engineering and Technology Amity University Punjab Mohali India Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara144411 India School of Technology Department of Computer Science and Engineering Pandit Deendayal Energy University Gandhinagar India

ISBN: (纸本)9798350354171

In the past few years, advancement and upgradation in terms of technology has been a massive aspect including the field of data science and machine learning. Taking any one instance out of that aspect is 'CropYieldML systems'. The abstract behind this system is such that it makes up the utilization of complex algorithms to analyze extensive datasets containing information properties of the soil, climatic conditions, and historical crop yields. Based on the data, it's a great assistance for the farmers to make certain decisions on which crop has to be grown upon the desired sort of soil. The focus of this project certainly lies in developing deeply the potential of machine learning techniques in recommending suitable crops based on a variety of environmental and soil parameters. Modeling and Thorough analysis make the project seek to enhance agricultural productivity, profitability, and sustainability. This project can reduce the costs of the inputs and environmental impact by helping the farmers with selective recommendations to optimize their yield by analyzing weather patterns, past crop performances, and soil composition. The data-driven insights can also help the farmers selective decisions for better crops which in the future lead to the sustainability of agriculture. The outcomes of this project are expected to have significant implications for agricultural practices. © 2024 IEEE.

关键词： Adversarial machine learning

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