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Harvesting Innovation: Revolutionizing Crop Yields using Machine learning 15

Harvesting Innovation: Revolutionizing Crop Yields using Mac...

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15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kashyap, Dolly Gangwar, Piyush Goel, Mudit Dixit, Kanchan ABES Engineering College Ghaziabad India

ISBN: (纸本)9798331300579

India's agricultural sector has been grappling with the adverse effects of climate change over the last two decades, resulting in the diminished performance of various crops. Predicting crop yields well in advance of harvest holds immense significance, aiding policymakers and farmers in making informed decisions regarding marketing and storage. This project endeavors to provide farmers with a tool that offers predictive insights into crop yields before they embark on the cultivation process. The proposed solution involves thedevelopment of a credit card firm's need to be on the lookout for a fraudulent prototype for an interactive prediction system, featuring a user-friendly web-based graphical interface and employing machine learning algorithms. The prediction results are readily accessible to farmers, enabling them to plan and strategize effectively. In the face of challenges posed by climatic variables such as weather, temperature, humidity, rainfall, and moisture, this project harnesses various data analytics techniques and algorithms to enhance crop yield predictions. The Random Forest algorithm, a robust and widely-used supervised machine learning method capable of both classification and regression tasks, is leveraged for this purpose. © Grenze Scientific Society, 2024.

关键词： Agriculture Crop yield crop-prediction Data Mining Decision Tree Machine learning Supervised Algorithms

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Application of Machine learning Techniques to Characterize Floating Point Benchmarks using Hardware Events 20

Application of Machine Learning Techniques to Characterize F...

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20th international conference on Electrical engineering, computing Science and Automatic Control, CCE 2023

作者： Victor, Rodriguez-Bahena Luis, Pizano-Escalante Omar, Longoria-Gandara Electronics Systems and It Iteso Mexico

ISBN: (纸本)9798350306767

The pursuit of improved computing architecture drives continuous innovation to enhance user application performance. One such advancement involves the implementation of floating-point accelerators. However, identifying applications heavily reliant on floating-point operations proves complex due to application intricacies. Selecting optimal workloads for execution on floating-point accelerators can yield significant benefits, including up to a 30% reduction in execution time. In this article, the authors propose a novel approach that leverages machine learning algorithms and meticulous analysis to identify the minimum hardware performance counters necessary for detecting applications with significant utilization of floating-point operations. Our methodology achieves an accuracy of 69% using unsupervised machine learning and 100% using supervised machine learning. By focusing on workloads that stress new CPU designs, this approach streamlines the development and validation of novel computing architectures. It effectively pinpoints ideal candidates for benchmarking new floating-point co-processors, optimizes test execution time, and minimizes power consumption during the validation phase. This research promises to drive advancements in computing architecture, leading to improved application performance and efficiency. © 2023 IEEE.

关键词： Benchmarking

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Revolutionising Industries and Empowering Human Potential with Artificial Intelligence Tools and Applications 2

Revolutionising Industries and Empowering Human Potential wi...

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2nd IEEE international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Ramachandran, K.K. Takhar, Sarika Jha, Manikesh Kumar Patel, Jaksan D Randhawa, Neetu Lourens, Melanie Dr G R D College of Science Management/Commerce/International Business India Fmmt Sgt University Gurugram India Ece Iimt College of Engineering Uttar Pradesh Greater Noida India Merchant Engineering College Basna Mechanical Engineering Gujarat Mehsana India Indira School of Business Studies Pgdm Pune India Durban University of Technology Deputy Dean Faculty of Management Sciences South Africa

ISBN: (纸本)9798350384277

This empirical study examines the manner in which artificial intelligence (AI) is transforming several sectors and the way it might help realize the full potential of people. The study employs an extensive technique that combines a quantitative approach with a detailed examination of the literature. The evolution of artificial intelligence, machine learning, as well as deep learning is followed from historical viewpoints to current developments. This investigation explores how artificial intelligence (AI) is transforming higher education, banking, healthcare, the arts, in addition to manufacturing. It presents quantitative results that demonstrate increased efficiency and better results. The study highlights the necessity of preventative actions, such as reskilling programs, while addressing the difficulties associated with job displacement and ethical issues. The study also explores how AI has developed in order to enhance inventiveness and decision-making, highlighting its beneficial effects on businesses that are undergoing radical change. Understanding AI developments including their revolutionary effects on businesses, as well as clarifying the ways in which AI enhances human potential, are just some of the goals. In general, the study advances a sophisticated comprehension of artificial intelligence's complex influence on the way business and human pursuits will develop in the future. © 2024 IEEE.

关键词： Adversarial machine learning

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Cardiovascular Health in AI: A Comprehensive Overview to Acute Myocardial Infarction Prediction 16th

Cardiovascular Health in AI: A Comprehensive Overview to Acu...

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Mediterranean conference on Medical and Biological engineering and computing (MEDICON) and international conference on Medical and Biological engineering (CMBEBIH)

作者： Muharemovic, Asja Kevric, Jasmin Int Burch Univ Sarajevo Bosnia & Herceg

ISBN: (纸本)9783031490613;9783031490620

Considering cardiovascular difficulties as the primary cause of worldwide mortality, authors examined learning models for predicting acute myocardial infarction (AMI) with the highest possible rate of accuracy, precision and other performance metrics. Prediction of the acute myocardial infarction is developed using Machine learning (ML) algorithms such as: Multilayer perceptron (MLP), Support Vector Machine (SVM), XGBoost, and Naive Bayes (NB). Accuracy higher than 80% is expected for correct predictions of infarction. The models of examined studies are mostly tested in the Python programming language. The importance of this research lies in its commitment to achieving scientific and experimental validation by considering all the essential factors necessary for a rigorous and credible scientific paper. By employing exploratory data analysis (EDA) on input parameters, various models are constructed, and the best feasible prediction models for AMI are presented, which may be further enhanced by integrating more specific risk variables.

关键词： Acute myocardial infarction Machine learning Neural network Support vector machine XGBoost Naive Bayes Prediction mode Optimization

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FPGA-Based Automated EEG Analysis for Efficient Emotion Detection 3

FPGA-Based Automated EEG Analysis for Efficient Emotion Dete...

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

作者： Vinayagam, P. Kishore Kumar, P. Kandhan, M. Saveetha Engineering College Department Of Electronics And Communication Engineering Chennai India

ISBN: (纸本)9798350318609

The recognition of emotions has significant importance in the domains of human-computer interaction and affective computing. The integration of facial expressions, voice analysis, and EEG data in multimodal techniques has shown potential in improving the accuracy of emotion identification. In this study, we provide a novel approach for emotion identification by incorporating facial expression analysis, voice processing, and EEG data analysis into a multimodal system. Machine learning methods are used by the system to classify emotions. The findings of our study indicate that the suggested multimodal technique yields a notable level of classification accuracy across many emotion categories, namely Happy (85%), Sad (75%), Angry (80%), and Neutral (70%). In conclusion, the incorporation of many modalities has been shown to improve the accuracy of emotion identification in comparison to unimodal methodologies. This study highlights the efficacy of multimodal emotion detection systems in capturing a wide range of emotional states, hence facilitating advancements in human-computer interaction and affective computing applications. © 2024 IEEE.

关键词： Analog to digital conversion

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teaching BDD in Active learning Environments: A Multi-study Analysis 14

Teaching BDD in Active Learning Environments: A Multi-study ...

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14th international conference on Computer Supported Education (CSEDU)

作者： Nascimento, Nicolas Santos, Alan Sales, Afonso Chanin, Rafael Pontificia Univ Catolica Rio Grande do Sul Polytech Sch Ave Ipiranga 6681 Porto Alegre RS Brazil

ISBN: (纸本)9789897585623

Software development practices to enhance software quality and help teams better develop collaboratively have received attention by the academic community. Among these techniques is Behavior-Driven Development (BDD), a development approach which proposes software to be developed focusing primarily on its expected behavior. teaching-wise, introducing BDD on software engineering classes and/or training courses for software developers has become important. In this context, this study presents a body of knowledge on the impacts of teaching BDD in active learning environments (ALE). To achieve this, we have triangulated data from four data sources: (i) a systematic literature review;(ii) an expert panel with active-learning experts, (iii) a survey with participants in a software development course which teaches through active learning, and (iv) a case study on the effects of teaching and using BDD in an ALE. This study results are (i) the-state-of-the-art literature on this topic, (ii) an assessment of benefits and challenges of BDD in ALEs, and (iii) a set of best practices when teaching BDD in ALEs. We concluded that BDD has more positive than negative outcomes and we present a body of knowledge regarding BDD in ALEs.

关键词： Software engineering Education Behavior-Driven Development Agile Development Active learning

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Exploring Factors Influencing Energy Consumption Behavior Among Higher Education Students Using Deep learning 1

Exploring Factors Influencing Energy Consumption Behavior Am...

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1st international conference on computing, Sciences and Communications, ICCSC 2024

作者： Mishra, Amit Kumar Singh, Jagendra Yadav, Alok Kumar Diwakar, Manoj Singh, Prabhishek Lamba, Sumita Graphic Era Hill University Department of CSE Dehradun India School of Computer Science Engineering & Technology Bennett University Greater Noida India Naraina Vidya Peeth Engineering and Management Institute Department of Computer Science and Engineering Kanpur India Graphic Era Deemed to be University Department of CSE Dehradun India School of Computing DIT University Mussoorie India

ISBN: (纸本)9798350353648

This research examines the dynamics of university students' energy usage behaviour using Internet of Things (IoT) technologies. The assignment meticulously positions a range of sensors in labs and lecture rooms and combines them with gadget learning algorithms to be able to maximize energy consumption. For a month, the Internet of Things sends actual-time notifications to students approximately the need for careful strength management. Before the implementation observations provide a comparative evaluation of student behavior and draw interest to the subtle behavioural adjustments as a result of psychological shifts. Machine studying fashions are built to forecast destiny energy use by way of using the information generated by way of those processes. These fashions include Decision Trees, Random Forests, Support Vector Machines, and Artificial Neural Networks. The consequences display a noteworthy decrease in strength use with the IoT machine in region, highlighting the significance of technological interventions in encouraging environmentally pleasant behaviour. From an academic angle, this advances our expertise of strength behaviour and gives useful information to educational establishments trying to find creative techniques to implementing responsible electricity control. © 2024 IEEE.

关键词： Students

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A Novel Phishing Attack Detection System Based on Web Platforms by Using Deep learning Methodology

A Novel Phishing Attack Detection System Based on Web Platfo...

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2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Gandhi, Tella Porkodi, M. Sheela Shantha Kumari, P.K. Vijaya Vardan Reddy, S.P. Giri, Jayant Samhan, Ahmad Abdelhafiz Ali Mlr Institute of Technology Dept of Csit Dundigal Hyderabad India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Cse Chennai India R.M.K. Engineering College Department of EC-ACT Kavaraipettai Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Zarqa University Faculty of Information Technology Department of Software Engineering Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Global economic expansion facilitated by technology has led to an evolution in phishing attacks in recent years. An uptick in phishing and other sophisticated online attacks, together with an uptick in impersonation frauds, has contributed to a general uptick in fraudulent activity losses this year. To safeguard users' online activities, a more effective phishing detection mechanism is necessary, since the worldwide impact of these attacks will only grow. Using the URL and website content such photos, text, and frames, this study aimed to build a deep learning-based phishing detection system to fill this demand. When it comes to image and natural language processing, deep learning methods work wonders. Binary learning with Phishing Detection Strategy (BLPDS) is a novel approach to efficiently detect phishing attacks introduced in this paper. To assess its efficacy, it is cross-validated with the conventional Long Short-Term Memory (LSTM) method. Applying the BLPDS to big datasets allowed for a thorough experimental evaluation and comparison of its efficacy in detecting phishing web pages and attacks. The precise proof demonstrating the efficacy of the suggested system is shown in the result section. © 2024 IEEE.

关键词： Phishing

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A Grading Model for Jasmine Flowers Using Deep learning and Quantum Machine learning 4

A Grading Model for Jasmine Flowers Using Deep Learning and ...

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4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Pratiksha Cauvery, K.P. Thrisha, R. Harshitha, T. Panigrahi, Suman Thangavel, Senthil Kumar Somasundaram, K. Boopathy, C.P. Amrita School of Physical Sciences Department of Mathematics Tamil Nadu Coimbatore India Amrita School of Computing Department of Computer Science and Engineering Tamil Nadu Coimbatore India Amrita School of Engineering Department of Elelctical and Electronics Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798331523923

The flowers of Jasmine hold immense economic importance in the agricultural sector and the perfume industry, as the value and satisfaction of the market, are heavily influenced by the quality of these flowers. The already very labor-intensive, subjective, and inefficient manual method of grading plants based on their physical and aromatic characteristics is unsuitable on a large scale. This paper handles these limitations by introducing automation in the grading process using Deep learning and Quantum Machine learning. The conceptual methodology mandates automatic flower placement and high-resolution imagery with a conveyor belt. The deep learning model, exactly a type of Convolutional Neural Network (CNN), would explain freshness in terms of visual features like color, texture, structural strength, and vice versa. Additionally, quantum-inspired algorithms have been employed to enhance the computation and performance of the model. From the analysis, a decision-making module classifies entrant flowers into fresh and otherwise, thereby sending them to their respective places, where all packing or disposal could be achieved. This DL-QML hybrid approach points out the exemplary high accuracy, scalability, and efficiency in jasmine grading and hence minimizes manual intervention in quality control assembly lines. The findings provide the card of the feasibility of joining QML in addressing computational challenges of agro-product quality assessment, opening avenues in application far broader than floriculture. © 2025 IEEE.

关键词： Decision making

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Artificial Intelligence-based Service Chains Scheduling for Medical Emergency in Healthcare 7

Artificial Intelligence-based Service Chains Scheduling for ...

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Soni, Mukesh Rusho, Maher Ali Byeon, Haewon Alghamdi, Azzah Hannan, Shaikh Abdul Dave, Parth Rameshchandra Dr. D. Y. Patil School of Science & Technology Tathawade Dr. D. Y. Patil Vidyapeeth Marashtra Pune India University of Colorado Department of Lockheed Martin Engineering Management BoulderCO80309 United States Inje University Department of Ai and Software & Medical Big Data Research Centre Gimhae50834 Korea Republic of Imam Abdalrhman Bin Faisal University College of Computer Science and Information Technology Computer Information Systems Department Khobar Saudi Arabia AlBaha University Faculty of Computing and Information AlBaha Saudi Arabia L. D. College of Engineering Department of Computer Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798350350067

This study analyses the weekly scheduling challenge of health and medical information network service function chains to meet medical emergency categorization and grading needs. Emergency services are divided into four sorts by urgency and service area, with priorities and weights. The health and medical information network architecture, service model, and operation method are built accordingly. To reduce weighted completion time, a service function chain scheduling model is created. The function chain scheduling scheme is solved by matching game and Q -learning reinforcement learning algorithms for different issue scales. Finally, simulation studies demonstrate that this approach can accomplish graded medical emergency service, build health and medical information networks, and progress smart healthcare. © 2024 IEEE.

关键词： Electronic health record

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