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Investigation of Momordica charantia seed biodiesel with cerium oxide nanoparticle on CI engine

International Journal of Ambient Energy 2021年第14期42卷 1615-1619页

作者： Shaisundaram, V.S. Chandrasekaran, M. Shanmugam, Mohanraj Padmanabhan, S. Muraliraja, r. Karikalan, L. Chennai India Chennai India Department of Materials Engineering and Nanotechnology Politecnico di Milano Milano Italy Department of Automobile Engineering Vel Tech Rangarajan Dr. Sangunthala R&D Institute of Science and Technology Chennai India

The main fuel for transport is diesel. It adds to the success of overall economy, as it is widely used due to its high efficiency in combustion, versatility, unwavering quality and cost-effectiveness. One of the major sources of air pollution on the Earth is emission from diesel engines. Over the years, the need for biofuels has enabled Momordica charantia seed oil to become biodiesels. Momordica charantia seed oil having low calorific value than other biodiesels made it an alternative for diesel which shows lower emission and improvement in performance characteristics. In this exploration work, to study the emission and performance characteristics of biodiesel, four blends of Momordica charantia seed oil with cerium oxide added substance are investigated. The results showed that B30 (69% diesel, 30% biodiesel and 1% cerium oxide) performs better than different blends. © 2019 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Biodiesel

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Prediction of the Tensile Strength, and Nanoindentation Hardness of directionally Solidified Tial Alloy by Machine Learning Based on Changed Training-Test Size ratio

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SSrN 2023年

作者： Kwak, Seungmi Kim, Jaehwang ding, Hongsheng Liang, He Chen, ruirun Guo, Jingjie Fu, Hengzhi National Key Laboratory for Precision Hot Processing of Metals School of Materials Science and Engineering Harbin Institute of Technology Harbin150001 China Carbon Materials Application R&D Group Korea Institute of Industrial Technology Jeonju54853 Korea Republic of

Methods for predicting the mechanical properties of alloys using machine learning have been widely used recently. Also, efforts to increase the prediction of mechanical properties through machine learning are continuing. In machine learning, the training-test size ratio significantly contributes to the predicted result. When using machine learning to predict the mechanical properties of directionally solidified TiAl alloy, experiments were conducted by varying the training-test data size ratio to 9:1, 8:2, 7:3, and 6:4. Mechanical properties were predicted using the random Forest regression (rFr) algorithm, one of the representative algorithms in machine learning, and r2 values and feature importance values with different training-test data size ratios were obtained. The variation in the prediction results when changing the training-test size ratio from the traditional 8:2 ratio allowed for the observation of how these changes affected the obtained values. This, in turn, enabled a quantitative connection to be established between mechanical properties and microstructure. Using feature importance, it was possible to identify the most influential factors for each mechanical property, and these factors were consistently the same across all training-test data size ratios. For tensile strength, Ti, for elongation, Al, for nanoindentation, pulling velocity, and for interlamellar space, Cr were identified as the factors with the most significant influence. These results allowed for the identification of the most suitable training-test data size ratio for predicting mechanical properties of directionally solidified TiAl alloy, as well as the ability to connect the appropriate training-test data size ratio to each specific mechanical property © 2023, The Authors. All rights reserved.

关键词： Tensile strength

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Oxide Channel Ferroelectric NANd device with Source-Tied Covering Metal Structure: Wide Memory Window (14.3 V), reliable retention (> 10 Years) and disturbance Immunity $(\delta \mathrm{V}_{\text{th}}\leq 0.1\mathrm{V})$ for QLC Operation

Oxide Channel Ferroelectric NAND Device with Source-Tied Cov...

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International Electron devices Meeting (IEdM)

作者： Hongrae Joh Giuk Kim Jihye Ock Seungyeob Kim Sangmok Lee Sangho Lee Kwangsoo Kim Suhwan Lim Jongho Woo Wanki Kim daewon Ha Jinho Ahn Sanghun Jeon School of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) Korea Semiconductor R&D Center Samsung Electronics Co. Ltd Korea Division of Materials Science and Engineering Hanyang University Korea

ISBN: (数字)9798350365429

ISBN: (纸本)9798350365436

We show an oxide-channel (Ox. Ch.) based gate-injection type ferroelectric NANd (FeNANd) device with source-tied covering metal (SCM) and control dielectric (***). The SCM and *** play crucial roles in the performance enhancement of FeNANd cells. (i) The proposed structure addresses the channel depletion issue of ***. by utilizing accumulated holes in the grounded SCM during ErS. This increases the electric field across the gate interlayer and FE layer, thereby boosting ErS efficiency. (ii) The read operation of erase states distributes a significant portion of the read voltage to ***, intensifying the $\mathrm{V}_{\text{th},\text{ErS}}$ . These effects result in a wide memory window (MW~14.3V). Furthermore, the introduction of a $\text{Si}_r\mathrm{N}_r$ deep trap-level layer and careful gate stack design led to superior retention (MW:11.5V after 10 years) and disturbance immunity ( $\delta \mathrm{V}_{\text{th}} < 0.1\mathrm{V}$ after $10^r$ disturb cycles). We also verify the roles of SCM and *** through analytical modeling. Lastly, the proposed structure is compatible with current 3d-NANd fabrication, offering equivalent channel hole pitch and density.

关键词： Analytical models Three-dimensional displays distributed databases Switches Logic gates Immunity testing Iron Time measurement dielectrics Time factors

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Novel Temporary Bonding Film Adapted to Xenon Flash Lamp de-bonding System for FOWLP Application

Novel Temporary Bonding Film Adapted to Xenon Flash Lamp De-...

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作者： Matsubara, Hiroaki Shibata, Tomoaki Akasu, Yuta Ogawa, Saeko Kuroda, Ayaka Sobue, Shogo Packaging Solution Center AIRBIC 3F 7-7 Shin-Kawasaki Saiwai-ku Kawasaki-shi Kanagawa212-0032 Japan Advanced Technology Research & Development Center 48 Wadai Ibaraki Tsukuba-shi300-4247 Japan Packaging Materials R&D Department 14 Goiminamikaigan Ichihara-shi Chiba290-8567 Japan

***@*** bonding film (TBF) is one of the key materials for assembly process of fan out wafer level package (FOWLP). In this paper, we have proposed a novel TBF and xenon flash lamp de-bonding system. The TBF was mainly composed of cross-linkable and low-polarity polymers to meet the various requirement for not only easy peeling-off after assembly but also stability against thermal and chemical environment exposed in FOWLP process. In order to evaluate applicability of the developed material, die-first/face up test wafer was assembled. As a result, it was found that the TBF has high stability and can be de-bonded by xenon light irradiation. In addition, the die shift through assembly process was within the estimated range. These results offer that the developed TBF is practically applicable for FOWLP assembly process. © 2022 IMAPS-International Microelectronics and Packaging Society. All rights reserved.

关键词： Chemical stability

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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 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 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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