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SSrN 2024年

作者： Hayakawa, Kenta Miyashita, Shunsuke Fujiwara, Nagahiro Takamido, ryota Ota, Jun Department of Precision Engineering School of Engineering The University of Tokyo Japan Technology Innovation R&D Dept. II Research & Development Head-quarters KUBOTA Corporation Japan School of Engineering The University of Tokyo Japan

The increasing global demand for food has highlighted the importance of enhancing agricultural productivity. This necessity has spurred interest in smart agriculture, which incorporates advanced technology. A significant challenge within this domain involves optimizing task assignment and route planning for multiple agricultural machines to maximize their efficiency. Although numerous methods have been proposed in prior research, a comprehensive task allocation system that can accommodate diverse constraints and objective functions has yet to be developed. This study introduces a framework for such a system and demonstrates its effectiveness through computer simulations. These simulations highlight the most suitable optimization methods and hyperparameters, taking into account the scale of farmland and computational time constraints. Specifically, environments such as small-scale farms or those with sufficient computational time are found to benefit from simulated annealing. In contrast, large-scale farms or scenarios requiring rapid computations are more apt for local search techniques. Furthermore, genetic algorithms (GA) prove advantageous in large-scale farming contexts, whereas ant colony optimization (ACO) is less effective when compared to other algorithms. © 2024, The Authors. All rights reserved.

关键词： Ant colony optimization

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Lithology-controlled stress variations of Longmaxi shale–Example of an appraisal wellbore in the Changning area

Rock Mechanics Bulletin

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rock Mechanics Bulletin 2022年第1期1卷 53-68页

作者： Xiaodong Ma Shihuai Zhang Xiaowen Zhang Jiandong Liu Juan Jin Wei Cheng Weidong Jiang Guangming Zhang Zhaowei Chen Mark d.Zoback ETH Zürich Department of Earth SciencesSwitzerland Research Institute of Petroleum Exploration&Development PetroChinaChina Key Laboratory of Oil&Gas Production CNPCChina CNPC Engineering Technology R&D Company Limited China Stanford University Department of GeophysicsUnited States

The Longmaxi shale is an extensive,prolific unconventional play in southwestern *** development in the Changning area is affected by ineffective hydraulic fracturing(HF)stimulation,fault reactivation and casing *** is suspected that the stress contrast within and between the shale reservoirs and the formations above and below matters to hydraulic fracture propagation and reservoir *** this end,the Longmaxi shale in the Changning area deserves a dedicated quantification of the in situ stress state and its *** this study,we re-visit the available data from one of the play’s first appraisal wellbores(X01)for an integrated geomechanics study,focusing on profiling the stress across the Longmaxi and its adjacent *** geophysical logs and other stress indicators,we re-interpret its stress profile in the context of lithological *** resulting stress variations are modeled primarily through a viscoplastic stress relaxation framework,compared with the results via the frictional equilibrium and an elastic theory(the Extended Eaton model).We offer some discussions on the differences and similarities of these stress profiling methods,and examine their applicability to Longmaxi shale in the Changning *** objective is to connect the lithology-controlled stress variations to the first-order complexities(HF ineffectiveness and fault reactivation)that have been observed in the area to date.

关键词： China shale formations

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Blockchain technology for Enhanced Transparency, Efficiency, and Security in Supply Chain Management 3

Blockchain Technology for Enhanced Transparency, Efficiency,...

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3rd International Conference on Automation, Computing and renewable Systems, ICACrS 2024

作者： Krishna, Santhosh B.V. Lakshmi, M. Vanitha Sathyanarayanan, S. Balaji, L. Harshith, C.M. Kaliappan, S. New Horizon College of Engineering Bangalore India Department of ECE School of Engineering Presidency University Bengaluru India Sri Sathya Sai University for Human Excellence Karnataka Gulbarga India Dept of ECE School of Electrical and Communication Vel Tech Rangarajan Dr. Sagunthala R&D institute of Science and Technology Avadi Chennai600062 India Division of Research and Development Lovely Professional University Punjab India

ISBN: (纸本)9798331532420

Though innovation in barcodes, GPS, and IoT sensors has enhanced the scope of modern supply chains to track assets to a larger extent, it raises a critical challenge for centralized systems in terms of data breaches, unauthorized access, and manipulation. Blockchain technology serves as an optimum decentralized and tamper-proof solution for further improvement of data integrity, security, and transparency. This paper discusses the integration of blockchain into supply chains its benefits and challenges and its potential to change global supply chain operations at its very core. In a nutshell, the system proposed outlines a blockchain-based framework using IoT, GPS, and smart contracts that guarantee traceability, security, and efficiency throughout the supply chain in real time. The results are showing high improvement in transparency, security, and operational efficiency, which will certainly lead to its usage cases in varied sectors. © 2024 IEEE.

关键词： data integrity

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A decision-Making Framework for Financial Trading Using Linear Tree-Based Kernel Support Vector Machine Classifier 7th

A Decision-Making Framework for Financial Trading Using Line...

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7th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： das, Gurumukh Singh, Gurdeep Zindani, divya Shanmugasundar, G. Chohan, Jasgurpreet Singh Kalita, Kanak Uttar Pradesh Agra282 005 India Punjab Ludhiana141013 India College of Engineering Kalavakkam India Department of Mechanical Engineering Sri Sai Ram Institute of Technology Chennai600044 India Department of Mechanical Engineering University Centre for Research and Development Chandigarh University Mohali140413 India Department of Mechanical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi600062 India Jadara University Research Center Jadara University Irbid Jordan

ISBN: (纸本)9783031733239

In today's rapidly evolving financial landscape, numerous investors and hedge funds have gravitated towards stock markets, captivated by their extensive range of instruments and lucrative investment opportunities. despite their allure, navigating the stock market landscape is intricate. It demands financial traders to remain vigilant, identifying thriving firms and diligently tracking stock price oscillations to devise potent trading strategies. In recent times, cutting-edge technological advancements like artificial intelligence and machine learning have revolutionized stock price predictions. Machine learning (ML) algorithms, especially Support Vector Machines (SVM), have become instrumental in predicting stock movements. However, an often-underacknowledged limitation of SVM is its susceptibility to overfit in the presence of noisy and intricate datasets. Addressing this gap, our research introduces the innovative Linear Tree-based Kernel Support Vector Machine (LT-KSVM) for enhanced stock market price forecasts. We empirically validated our approach by creating a portfolio of stocks from the Saudi Stock Exchange (Tadawul). Simulated trades over a span of 3.7 years were deployed to assess the portfolio's predicted performance. We meticulously executed three preprocessing phases to enhance stock price movement forecasts. remarkably, our proposed framework demonstrated astounding potential, boasting over 90% returns. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Forestry

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A review of the Literature on the Effects of Big data and Machine Learning on digital Transformation in Marketing 4

A Review of the Literature on the Effects of Big Data and Ma...

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4th International Conference on Innovative Practices in technology and Management, ICIPTM 2024

作者： Thangamayan, S. Vijai, C. Singh, Navdeep durga, Vijaya Aravinda, K. Sabah, Hawraa Ali Saveetha Institute of Medical and Technical Sciences Saveetha School of Law Department of Research and Development Chennai India Administration Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Commerce and Business Chennai62 India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad India New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350307757

The purpose of this in-depth investigation is to investigate the considerable influence that machine learning (ML) and big data have had on the digital transformation that has occurred in the marketplace. Conventional, intuition-based marketing is being replaced by data-driven strategies that make use of real-time analytics and predictive modelling to enhance customer experience and operational efficiency. This paper reviews a variety of studies to demonstrate how these methods are replacing traditional marketing. Personalized marketing, dynamic strategy adjustments, and deeper customer behaviors insights have all become available as a result of the integration of these technologies, despite the fact that technology has also brought forth severe ethical and privacy concerns. It is discussed in the assessment that it is necessary to find a middle ground between taking into consideration these ethical concerns and making use of big data in order to achieve a competitive advantage. When everything is taken into consideration, the findings shed light on the revolutionary potential of Big data and machine learning in marketing. They also advocate for responsible data governance and a paradigm shift towards methodologies that are more flexible, effective, and customer-focused. © 2024 IEEE.

关键词： Consumer behavior

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A Sustainable renewable Energy Assessment Framework Using Novel Machine Learning Algorithm 1

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7th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： das, Gurumukh Singh, Gurdeep Zindani, divya Shanmugasundar, G. Chohan, Jasgurpreet Singh Kalita, Kanak Uttar Pradesh Agra282 005 India Punjab Ludhiana141013 India College of Engineering Kalavakkam India Department of Mechanical Engineering Sri Sairam Institute of Technology Chennai600044 India Department of Mechanical Engineering University Centre for Research and Development Chandigarh University Mohali140413 India Department of Mechanical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi600062 India Jadara University Research Center Jadara University Irbid Jordan

ISBN: (数字)9783031733215

ISBN: (纸本)9783031733208

In the quest for sustainable energy solutions, accurate prediction of renewable energy outputs is essential for the seamless integration of these resources into the energy grid. This paper presents a Squirrel Search-Optimized Gradient Boosted decision Tree (SS-GBdT) model, designed to enhance the precision of renewable energy forecasts. The SS-GBdT model leverages the search capabilities of the squirrel search algorithm to optimize the parameters of gradient boosted decision trees, thereby improving their predictive performance. Comparative analyses against established forecasting methods such as dNN, BiLSTM, CNN-BiLSTM, and AB-Net are conducted, with the SS-GBdT model consistently demonstrating superior accuracy, lower error metrics, and an overall robust performance. The findings suggest that the SS-GBdT model could be an invaluable asset for energy sector stakeholders, aiding in the advancement of reliable and efficient renewable energy systems. This study contributes to the growing body of knowledge in renewable energy forecasting and underscores the potential of advanced machine learning techniques in fostering sustainable energy transitions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Clean energy

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Metabolite Profiling of Herbal Medicines using an Artificial Intelligence Approach-A Concise review 4

Metabolite Profiling of Herbal Medicines using an Artificial...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Shet, Ajay S. Somasundaram, r. Sravan Kumar, G. Kaliappan, S. ramprasad, S.r. Srinivas, Maloth Canara Engineering College Department of Cse Karnataka Mangalore India Kongu Engineering College Management Studies Department TN Erode India Telangana Hyderabad India Lovely Professional University Division of Research and Development Punjab Jalandhar India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Dept. of Cse TN Chennai India Telangana Secunderabad India

ISBN: (纸本)9798331518981

Artificial intelligence (AI) is being developed extremely swiftly in medicine. A structured review should be conducted to comprehend the current status of AI models since they have been introduced in Traditional medicine to Investigate and classify the present applications of AI in TM. Following the technique suggested by the Joanna Briggs Institute, a thorough scoping review was conducted. To find papers on AI and CAM, three databases were searched: PubMed, Embase, and Cochrane Library. Studies published only in English since 2000 are taken into account. Those without mention of traditional medicine or AI methods were also disqualified, as were studies that were not peer-reviewed. All pertinent studies were located using a comprehensive search technique. This review demonstrated the potential use of AI models in TM;nevertheless, to develop AI in TM in the era of digital health, future research should concentrate on validating and verifying the models by carrying out a sizable clinical investigation. © 2024 IEEE.

关键词： Medicinal chemistry

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Automated Brain Image Classification Using Nature-Inspired Optimization-Based Machine Learning Algorithm 1

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7th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： das, Gurumukh Singh, Gurdeep Zindani, divya Shanmugasundar, G. Chohan, Jasgurpreet Singh Kalita, Kanak Agra282 005 India Phullanwal Ludhiana Punjab141013 India College of Engineering Kalavakkam India Department of Mechanical Engineering Sri Sai Ram Institute of Technology Chennai600044 India Department of Mechanical Engineering and University Center for Researdh & Development Chandigarh University Mohali140413 India Department of Mechanical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi600062 India Jadara University Research Center Jadara University Irbid21110 Jordan

ISBN: (数字)9783031733185

ISBN: (纸本)9783031733178

The field of medicine greatly depends on medical imaging. In modern brain imaging, image classification is used to separate abnormal tissues from healthy tissue. Using various categorization algorithms, the region of the brain tumor as well as the brain tumor size is recognized in the MrI images, allowing the brain tumor to be diagnosed. Brain Magnetic resonance imaging (MrI) can quickly and effectively identify tumors, aiding neurologists in their diagnosis. Cancer, the most prevalent or leading cause of death globally, may be made more likely by tumors. Currently, efficient automation of tumor detection is crucial to finding brain tumors. In this research we suggested a strategy that combines the strength of machine learning algorithms with nature-inspired optimization approaches to classify brain image automatically. The study proposed a new classifier called Self-Adaptive deer Hunting Optimized k-Nearest Neighbors (S-AdHO-k-NN). To start, the input image is first preprocessed to eliminate any noise using the median filtering (MF) method. The image was classified using a suggested S-AdHO-k-NN. The experimental results showed that the S-AdHO-k-NN model significantly outperformed the comparative methods with regard to of effectiveness. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Tumors

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Enhanced rice Leaf disease detection using densenet201 for Improved Classification Accuracy and reduced Misclassification Error 8

Enhanced Rice Leaf Disease Detection using Densenet201 for I...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Tamilselvi, M. Nalini, N. ramkumar, G. Giri, Jayant Kanan, Mohammad Saveetha University Saveetha School of Engineering Department of Ece Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India University of Business and Technology College of Engineering Department of Industrial Engineering Jeddah Saudi Arabia Zarqa University College of Engineering Department of Mechanical Engineering Zarqa Jordan

ISBN: (纸本)9798350367904

rice is a vital staple grain grown in numerous countries. rice leaf diseases can result in substantial harm to crop cultivation, resulting in diminished yields and financial losses. Conventional methods for detecting diseases are typically lengthy, entail a lot of effort, and necessitate specialized knowledge. Automated leaf disease detection methods assist farmers in identifying illnesses with minimal or no human intervention. Prior research about this detection of diseases in rice leaves mostly relied on the utilization of image processing and ML methodologies. The agricultural sector extensively necessitates automated identification and examination of rice ailments to prevent the squandering of financial and other resources, minimize crop yield reduction, enhance processing effectiveness, and achieve bountiful and disease-free harvests. The advancements achieved in deep learning methodologies have had a substantial influence on the field of agricultural disease diagnoses. The occurrence of leaf diseases in rice Plants can significantly reduce crop productivity, making it crucial to accurately diagnose these diseases to prevent their negative impact. The majority of the current approaches are insufficient in detecting leaf images and accurately identifying their condition. This study introduces the denseNet201 technique for automatic leaf detection, which offers improved efficiency compared to existing methods. The new approach guarantees a 98 % accuracy rate and a misclassification error value of 0.0167, highlighting the significance of the suggested method compared to the existing one. © 2024 IEEE.

关键词： denseNet201 Image Processing leaf disease Machine Learning misclassification error

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The effect of auditory feedback on user perception during rotary switch operation 24

The effect of auditory feedback on user perception during ro...

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24th International Congress on Acoustics, ICA 2022

作者： Miyairi, Toru Shimomura, Hisato Shirasaka, Takeshi Otomo, Takashi Toi, Takeshi Graduate School of Science and Engineering Chuo University Japan Research and Development Department Tokyo Metropolitan Industrial Technology Research Institute Japan R&D Department Alps Alpine Co. Ltd. Japan Department of Science and Engineering Chuo University Japan

rotary switches are used in various in-vehicle devices and home appliances. It is well known that operating experience contributes to the overall user perception of product quality. This study examines the effect of sound on user perception during rotary switch operation. To this end, experiments were conducted in which participants were asked to evaluate their experience during switch operation corresponding to various values of click torque and in the presence and absence of sound. We used Japanese onomatopoeic expressions to evaluate the magnitude of tactile impressions-the participants rated the degree of consonance between their operating experience and the onomatopoeic expressions in each case on a 7-point scale. The results revealed that the scores of onomatopoeias conveying smooth impressions (e.g.,/suru-suru/) decreased in the presence of sound. In contrast, the scores of onomatopoeias conveying hard impressions (e.g., kata-kata/) increased in the presence of sound. The difference was particularly obvious corresponding to low values of click torque. These conclusions are expected to contribute to the complete understanding of the effects of auditory feedback on the operating experience of rotary switches. © ICA *** rights reserved

关键词： auditory feedback multi-modal perception rotary switch

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