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AI for Smart Farming: Machine Learning Models for Precision Crop Yield Prediction

AI for Smart Farming: Machine Learning Models for Precision ...

引用

Computational, Communication and Information Technology (ICCCIT), international conference on

作者： RVS Praveen Meenakshi Maindola Munugapati Bhavana Ginni Nijhawan Hemanth Raju Saloni Bansal Digital Engineering and Assurance LTIMindtree Limited Hyderabad Telangana India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana Dept. of Comp. Sc. & Engg. Lovely Professional University Phagwara India Department of Mechanical Engineering New Horizon College of Engineering Bangalore Department of Computer Engineering and Applications GLA University Mathura

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

AI and other advanced technologies are gradually beginning to invade the agriculture sector to serve the increasing demand of the consumers for better methods of food production. A key component of precision agriculture, this work focuses on how to use machine learning models to predict crop yields with high accuracy. Specifically, the nature and volatility of the environmental context – such as the nature and quality of soils, as well as the climatic and water conditions – present a major challenge to the application of the conventional modeling approaches aimed at forecasting productive capacity in agriculture. Still, when comparing, the AI-driven models will allow sorting through much information gathered from different sources, including sensors, satellite imagery, and yield history. This work examines the differences of several machine learning models. Such models are the Random Forests, SVMs, and CNNs and other Deep Learning methodologies respectively. A quality model is evaluated based on scalability, computational performance, and prediction accuracy. Also in scope of the study, there is an opportunity to employ IoT together with data collected with remote sensing to make and provide realtime forecasts and evaluations. By the utilization of the results attained, it is established that the yields expected by the machine learning models are much more precise, which aids in enhancing agricultural decision-making concerning resource utilizing, crop handling, and risk minimization. The purpose of this study is focused on the promotion of the agricultural sustainability and production through the development of the innovative data driven farming practices.

关键词： Smart agriculture Computational modeling Time series analysis Machine learning Predictive models Soil Random forests Crop yield Long short term memory Remote sensing

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Integrating Fuzzy Logic and Morphological Edge Detection for Enhanced UAV Orthomosaic Mapping

Integrating Fuzzy Logic and Morphological Edge Detection for...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE conference on

作者： Pallavi Singh Nagesh K N Neerav Sharma Department of Electronics and Communication Engineering Nagarjuna College of Engineering and Technology Bengaluru India IIT Roorkee Development Foundation Indian Institute of Technology Roorkee Roorkee India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The field of engineering is undergoing continuous advancements aimed at optimizing efficiency and precision. Surveying and mapping, fundamental components of Geomatics engineering, have evolved significantly with the adoption of advanced technologies. Among these, ‘Unmanned Aerial Vehicles (UAVs)’ have been a spotlighted tool for conducting precise and efficient surveying and mapping. This study leverages UAV-acquired data to generate high-resolution point clouds and three-dimensional textured mesh models, forming the basis for Orthomosaic imagery creation. The research highlights critical challenges encountered during the generation and analysis of Orthomosaic images, emphasizing the need for image correction and enhancement to achieve application-level precision in accurate mapping and monitoring. To address these challenges, the study integrates Fuzzy Logic and advanced Edge Detection techniques in image processing to enhance the quality and accuracy of Orthomosaic imagery. The findings provide valuable insights into the methodologies and outcomes of the proposed approach, demonstrating its potential to significantly improve the precision and efficiency of Orthomosaic applications. Furthermore, this research underscores the broader implications for engineering and environmental studies, paving the way for more effective and innovative solutions in these domains.

关键词： Fuzzy logic Point cloud compression Technological innovation Solid modeling Geomagnetism Accuracy Image edge detection Autonomous aerial vehicles Monitoring Image enhancement

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A Novel Technique for Well-Optimized Content Replication in Fog Computing

A Novel Technique for Well-Optimized Content Replication in ...

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Disruptive technologies (ICDT), international conference on

作者： Khushi Joshi Rohini Sharma Department Of Information Technology JSS Academy of Technical Education Noida

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

A fog computing architecture processes huge amount of data by using edge devices. The cloud computing paradigm's fog layer helps to improve quality of service (QoS) in time-sensitive applications. Because devices at the edge of the cloud are very diverse and dynamic, content replication is vital. Replication is essential for consistent service delivery in fog computing. Time-sensitive requests received scant consideration in many of the early approaches. To improve fog computing replication, this research suggests a novel content replication mechanism using KNN and Naïve Bayes classification models. The recommended approach consists of two steps: The first assigns a failure risk classification to the fog servers that are available for replication. In the second phase, we suggest size-based content replication, in which the file or content is replicated on the less likely-to-fail fog server. We have evaluated the performance using metrics like utilization, throughput, and latency. The usefulness of the proposed strategy is validated by simulation results comparing it with the Replica-3 method. Comparing this to the Replica-3 technique, better resource usage, higher throughput, and lower latency are therefore attained. By allowing the system to handle more queries in parallel across multiple computers or processors, KNN's parallel processing capabilities of CPUs and GPUs ensures better resource utilization and greatly increasing throughput as compared to Naïve Bayes model. Additionally, KNN classification offers a useful data structure that significantly reduces latency by minimizing the time required to find the nearest neighbours.

关键词： Cloud computing Computational modeling Quality of service Nearest neighbor methods Throughput Data models Bayes methods Servers Resource management Edge computing

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Deep learning for sleep disorder diagnosis: Enhancing EEG Analysis with Machine Learning Techniques

Deep learning for sleep disorder diagnosis: Enhancing EEG An...

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Learning and Technology conference (L&T)

作者： Baher Mohamed Enfel Barkat Dept. of Electrical & Computer Engineering Effat University Jeddah Saudi Arabia

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

The rising prevalence of sleep disorders highlights their disastrous effects on health, well-being, cognitive performance, memory, and learning outcomes. Sleep disorders such as insomnia and sleep apnea normally require extensive instrumentation and expertise for diagnosis, which is not always possible or even available in most cases. This work, therefore, proposes a state-of-the-art use of Machine Learning (ML) to develop a model for the classification and detection of sleep patterns from electroencephalogram (EEG) signals. The model is trained on a synthetic dataset, developed using mathematical simulations of real-life EEG data. This is a novelty in this area, which allows this model to be precise yet robust without relying too much on real-world *** application bridges advanced AI techniques with real-world applications and smooths out the experience for healthcare professionals and researchers alike. This research highlights how ML might transform sleep medicine through efficient, extended solutions for EEG data analysis and sleep disorder detection. We provide a realistic EEG simulation combined with an intuitive application, a powerful platform that makes advanced sleep diagnostics more accessible and impactful, which supports the development of educational strategies or technologies.

关键词： Deep learning Training Sleep Transforms Brain modeling Electroencephalography Mathematical models Data models Medical diagnostic imaging Synthetic data

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modeling of Portable and Wearable Extremely Low-Frequency Magnetic Field Generators for Glioblastoma Treatment: A simulation Study 24

Modeling of Portable and Wearable Extremely Low-Frequency Ma...

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Proceedings of the 2024 5th international conference on Intelligent Medicine and Health

作者： Zichuan Wang Yuheng Xiao Hanxiao Liu Zhige Zou School of Integrated Circuits Huazhong University of Science and Technology Wuhan Hubei China School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan Hubei China

ISBN: (纸本)9798400709616

The use of electromagnetic fields in tumor therapy has gained increasing attention, with notable examples such as Tumor Treating Fields (TTFields) and Transcranial Magnetic Stimulation (TMS). In recent years, research into the use of extremely low-frequency (ELF-MFs) and super-low frequency magnetic fields (SLF-MFs) for cancer treatment, particularly for breast cancer and glioblastoma, has significantly increased. However, unlike the aforementioned technologies, which have FDA- approved commercial products, ELF-MFs and SLF-MFs are predominantly at the stage of cellular experimentation. This research aims to bridge the gap between fundamental cellular studies and clinical applications by modeling and simulating therapeutic devices based on coil-generated extremely low frequency magnetic fields (ELF-MFs). Coils and head tissues were modeled in COMSOL Multiphysics 5.6 to calculate the magnetic flux intensity generated by the coils simulated by a constant current. The feasibility of coil-based therapies was assessed based on the computed results and previous research. Results indicated that the devices can produce magnetic flux density (B) within the range considered therapeutically beneficial for tumor inhibition, offering possibilities for portable and wearable devices. Additionally, the performance of different coils was compared in terms of energy conversion efficiency. Furthermore, this research discusses the feasibility of developing non-portable, larger-scale therapeutic devices that could be applied in clinical environments. These devices can be designed to leverage the unique properties of ELF-MFs, potentially providing an alternative approach to electromagnetic field cancer treatments.

关键词： Coils

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simulation of Solar Phase Change Thermal Storage Distributed Energy System Based on Offshore Oil Platform

Simulation of Solar Phase Change Thermal Storage Distributed...

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Smart Grid and Artificial Intelligence (SGAI), international conference on

作者： Qingfan Zhang Heqi Yang Wei An Jianping Zhao Yidan Wu Bo Wu Key Laboratory for Low Carbon & Environmental Protection of Offshore Oil Field CNOOC Energy Technology & Services Limited Tianjin China China University of Petroleum (East China) College of New Energy Qingdao China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

This study explores the design of a distributed energy system integrated with solar phase change thermal storage. Using MATLAB and Simulink, a mathematical model of the system was developed. Load demand was simulated using DeST software for a North Sea Oil Platform. A convolutional neural network (CNN) was employed for load forecasting, leveraging its effectiveness in analyzing time series data patterns. Suitable unit equipment was selected based on load simulations, and operations were conducted to evaluate load outputs and energy consumption changes across different seasons. The CNN demonstrated success in predicting cooling, heating, and power loads, aiding the platform in anticipating load fluctuations. Additionally, the solar phase change thermal storage system stores thermal energy during transitional seasons and poor weather, providing backup energy output. It also alleviates winter heat load pressure on the distributed energy system. Optimization strategies, including energy utilization rate evaluation and genetic algorithm applications, were employed to enhance system efficiency and maintain stable operation. The results improved energy management during typical daily operations across all seasons.

关键词： Energy consumption Offshore installations Load forecasting Mathematical models Convolutional neural networks Solar heating Optimization Genetic algorithms Meteorology Load modeling

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Artificial Intelligence in Solar Stills: A Bibliometric Analysis of Trends, applications, and Future Prospects

Artificial Intelligence in Solar Stills: A Bibliometric Anal...

引用

Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Ait Baha Asmaa Omar Bouattane Naoual Belouaggadia Abdellah Zamma Laboratory of Modeling and Simulation of Intelligent Industrial Systems (M2S2I) ENSET Hassan II University of Casablanca Morocco Laboratory of Electrical Engineering and Intelligent Systems (IESI) ENSET Hassan II University of Casablanca Morocco Builders Lab Builders École D’ingénieurs COMUE Normandie Université Epron France

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

Solar stills are relatively affordable, renewable energy distillers with low maintenance, which can help solve water problems in remote areas. Artificial intelligence (AI) has enhanced efficiency through adaptive control, the adjustment of operational parameters, and the development of forecasting methods and tools. AI solutions, such as hybrid models and machine learning, enhance design, automate tasks, and reduce costs by optimizing insulation and tilt angle, among other factors. There are also additional complementary technologies, such as nanotechnology and phase change materials, that improve sustainability and performance. In this paper, we review the adoption of artificial intelligence technology in solar still research, utilizing a bibliometric technique to analyze 2216 articles published between 2019 and 2024. The results show that the development of AI-integrated solar still technology experienced significant growth between 2019 and 2024, demonstrating how artificial intelligence enhances the efficiency, design, and performance of solar desalination systems. The findings also reveal that The Journal of Cleaner Production and Desalination are the most prominent sources in this field, driving knowledge dissemination and innovation. Additionally, the results highlight that China, India, and Egypt are the most active contributors to this research domain, reflecting their strong commitment to tackling water scarcity and promoting sustainable desalination.

关键词： Phase change materials Renewable energy sources Technological innovation Bibliometrics Desalination Production Maintenance Artificial intelligence Optimization Nanotechnology

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Average modeling of Fly-buck Converter 9

Average Modeling of Fly-buck Converter

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9th international conference on simulation and modeling methodologies, technologies and applications (SIMULTECH)

作者： Zaikin, Denys, I Mikkelsen, Simon L. Jonasen, Stig Sirenko, Konstantin Serenergy AS Aalborg Denmark EKTOS Ukraine LLC Kharkiv Ukraine

ISBN: (纸本)9789897583810

This document presents an average macro model for the fly-buck converter. The model can be used for both large and small signal modeling. Parasitic and lossy components are included in the model, and it is partially based on a conventional average switch model for a buck stage. For isolated output, the analytic solution of the average current in a secondary winding is proposed. The presented model is implemented in SPICE, and simulation results are compared to switching model simulation and experimental data.

关键词： Average Model Fly-buck Dc/dc Converter

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simulation and modelling methodologies, technologies and applications, volume I

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2014年

作者： Honghuan Tan

ISBN: (数字)9781845649838

ISBN: (纸本)9781784660024;9781845649821

This two-volume set contains the proceedings of the 2014 international conference on simulation and modeling methodologies, technologies and applications (SMTA 2014), held in Sanya, China. The papers presented at the conference focused on recent progress in various aspects of advances in simulation and modeling methodologies, technologies and applications. Topics covered relate to the main subject areas of simulation tools and platforms; Formal methods; Complex systems modelling and simulation; Application domains

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On the Implementation of simulation-based on Representation by Rules Methodology to Plan Port Logistics Operation 11

On the Implementation of Simulation-based on Representation ...

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11th international conference on simulation and modeling methodologies, technologies and applications (SIMULTECH)

作者： de Azevedo, Anibal Tavares Univ Estadual Campinas Appl Sci Sch Rua Pedro Zaccaria 1300 Limeira Brazil

ISBN: (纸本)9789897585289

This paper proposes a fast and agile methodology that enables easy incorporation of different business rules for planning port logistics operations. The rules could be embedded in a simplified discrete-event and multi-agent simulation scheme that clearly shows the impacts of different rules in each part of the port operation. Furthermore, the developed approach enables an analysis of how operational decisions in one port could affect subsequent ports.

关键词： simulation Port Logistic Operation Representation by Rules

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