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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Shetty, Keerthi Tyagi, Swati Jha, Anupama Rao, D N Murali Krishna Arunkumar, J.R. Lakshmikanth, R. Nmam Institute of Technology Department of Mca Udupi India University of Delaware DE United States Vivekananda Institute of Professional Studies Guru Gobind Singh Ip University Vsit Delhi India Jntuh Electrical and Electronics Engineering India Modern Institute of Technology and Research Centre Department of Computer Science and Engineering Rajasthan Alwar India Adithya Institute of Technology Department of Management Studies Coimbatore India

ISBN: (纸本)9798350364699

Natural Language Processing (NLP) has emerged as a game-changing technique in the field of strategic planning analysis. This development represents a significant step forward. Improved methods for obtaining meaningful insights from massive amounts of textual data are made available by natural language processing (NLP). According to the findings of this research project, the application of natural language processing (NLP) technology to enhance the strategic planning processes that are carried out within enterprises is being investigated. Natural language processing (NLP) approaches, such as semantic analysis, sentiment identification, and topic modeling, can be utilized to acquire a more in-depth grasp of market tendencies, consumer feedback, and internal communications. This can be accomplished by utilizing these techniques. The study demonstrates how natural language processing (NLP) can facilitate the discovery of emergent patterns, simplify the understanding of data, and encourage decision-making that is driven by data. There are a great number of case studies that demonstrate the incorporation of natural language processing (NLP) into strategic frameworks. These case studies show improvements in the accuracy of forecasting, risk management, and competitive analysis processes. The findings of this study demonstrate the potential for natural language processing (NLP) to revolutionize strategic planning by providing a more nuanced and comprehensive evaluation of qualitative data. This, in turn, will ultimately result in strategic decisions that are more informed and agile. © 2024 IEEE.

关键词： Risk management

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Development of A Wireless Sensor Node for Storage Bin Volume Measurement 15

Development of A Wireless Sensor Node for Storage Bin Volume...

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15th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Omar, Jaafar J. Aleluya, Earl Ryan M. Clar, Steve E. Pao, Jeanette C. Salaan, Carl John O. Bahinting, Maria Fe P. Miano, Joel I. College of Computer Studies Mindanao State University-Iligan Institute of Technology Department of Computer Applications Iligan City9200 Philippines College of Engineering Mindanao State University-Iligan Institute of Technology Department of Computer Engineering and Mechatronics Iligan City9200 Philippines College of Engineering Mindanao State University-Iligan Institute of Technology Department of Electrical Engineering Iligan City9200 Philippines Graduate School of Engineering Mindanao State University-Iligan Institute of Technology Iligan City9200 Philippines

ISBN: (纸本)9798350381177

One of the crucial processes in the food manufacturing industry is monitoring and measuring the raw products stored in large storage structures, typically called Silos. These procedures are pivotal in dictating the industry's production, quality control, and efficiency. In this study, a wireless sensor node was developed to automatically measure the volume of the product stored in the created mock-up storage bin and a 3D surface reconstruction for visualization. The sensor node utilized a single-broad computer for its computing and wireless functionality. The sensing device comprised LiDAR sensor and a servo motor for data acquisition. The system used an open-source ROS framework to simplify the software along with its data processing pipline and hardware interface integration. The single- board and the end user devices were connected to the same network and used Remote Desktop Protocol (RDP) for remote functionality. Results from three different testing setups revealed an average error of 1.309% between the actual and predicted volume of the flour materials within the mock-up storage bin. This deviation from the actual content volume implies that the developed system offered an alternative method for efficient volume measuring of the storage bin through remote data collection and 3D visualization. © 2023 IEEE.

关键词： Volume measurement

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Information-Centric IoT-Based Smart Farming with Dynamic Data Optimization

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computers, Materials & Continua 2023年第2期74卷 3865-3880页

作者： Souvik Pal Hannah VijayKumar D.Akila N.Z.Jhanjhi Omar A.Darwish Fathi Amsaad Department of Computer Science and Engineering Sister Nivedita UniversityKolkataIndia Post Doctoral Researcher Sambalpur UniversitySambalpurIndia Department of Computer Science Anna Adarsh College for WomenChennaiIndia Department of Information Technology Vels Institute of ScienceTechnology and Advanced StudiesChennaiIndia School of Computer Science and Engineering SCETaylor’s UniversitySubang Jaya47500SelangorMalaysia FITTaylor’s UniversitySubang Jaya47500SelangorMalaysia Department of Information Security and Applied Computing Eastern Michigan UniversityUSA School of Information Security and Applied Computing Eastern Michigan UniversityUSA

Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies,including big data,the cloud,and the Internet of Things(IoT).Many researchers try to integrate IoT-based smart farming on cloud platforms *** define various frameworks on smart farming and monitoring system and still lacks to define effective data management *** IoT-cloud systems involve massive structured and unstructured data,data optimization comes into the ***,this research designs an Information-Centric IoT-based Smart Farming with Dynamic Data Optimization(ICISF-DDO),which enhances the performance of the smart farming infrastructure with minimal energy consumption and improved ***,a conceptual framework of the proposed scheme and statistical design model has beenwell *** information storage and management with DDO has been expanded individually to show the effective use of membership parameters in data *** simulation outcomes state that the proposed ICISF-DDO can surpass existing smart farming systems with a data optimization ratio of 97.71%,reliability ratio of 98.63%,a coverage ratio of 99.67%,least sensor error rate of 8.96%,and efficient energy consumption ratio of 4.84%.

关键词： Smart farming data optimization information management internet of things cloud

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Efficient COVID-19 super pixel segmentation algorithm using MCFO-based SLIC

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9217-9232页

作者： Faragallah, Osama S. El-Hoseny, Heba M. El-Sayed, Hala S. Department of Information Technology College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Computer Science The Higher Future Institute for Specialized Technological Studies El Shorouk Egypt Department of Electrical Engineering Faculty of Engineering Menoufia University Shebin El-Kom32511 Egypt

In computer vision segmentation field, super pixel identity has become an important index in the recently segmentation algorithms especially in medical images. Simple Linear Iterative Clustering (SLIC) algorithm is one of the most popular super pixel methods as it has a great robustness, less sensitive to the image type and benefit to the boundary recall in different kinds of image processing. Recently, COVID-19 severity increased with the lack of an effective treatment or vaccine. As the Corona virus spreads in an unknown manner, th-ere is a strong need for segmenting the lungs infected regions for fast tracking and early detection, no matter how small. This may consider difficult to be achieved with traditional segmentation techniques. From this perspective, this paper presents an efficient modified central force optimization (MCFO)-based SLIC segmentation algorithm to discuss chest CT images for detecting the positive COVID-19 cases. The proposed MCFO-based SLIC segmentation algorithm performance is evaluated and compared with the thresholding segmentation algorithm using different evaluation metrics such as accuracy, boundary recall, F-measure, similarity index, MCC, Dice, and Jaccard. The outcomes demonstrated that the proposed MCFO-based SLIC segmentation algorithm has achieved better detection for the small infected regions in CT lung scans than the thresholding segmentation. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： COVID-19

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Double-Quantitative Feature Selection Approach for Multigranularity Ordered Decision Systems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2385-2396页

作者： Li, Wentao Deng, Chaojun Pedrycz, Witold Castillo, Oscar Zhang, Chao Zhan, Tao Southwest University College of Artificial Intelligence Chongqing400715 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Tijuana Institute of Technology Division of Graduate Studies and Research Tijuana22379 Mexico Shanxi University School of Computer and Information Technology Taiyuan030006 China Southwest University School of Mathematics and Statistics Chongqing400715 China

Double-quantitative-based granular computing implies the systematic perspective, completeness, and accuracy of rough approximation. However, most of the existing research works only focus on the case of single quantification, and there are few research study on the simultaneous computing method of double quantification. In this article, we explore feature selection with double quantification in multigranularity ordered decision systems (MG-ODSs). First, the related concepts of quantitative functions are interpreted from different viewpoints of relative and absolute quantification. Then, the multigranularity double-quantitative rough sets in an ordered decision system (ODS) from optimistic and pessimistic cases, the related properties, and three-way decisions based on the presented quantitative levels are discussed. Furthermore, the greedy algorithm for feature selection is derived. By using 12 datasets from a public repository, evaluations and comparisons are made on the parameter setting and classification accuracy. From these comparative experiments, the advantages and effectiveness of the proposed feature selection algorithm could be demonstrated over the existing approaches. © 2020 IEEE.

关键词： Data mining

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Assessing the Impact of Physical Configuration and Lead Time on WRF Forecasting of an Extreme Wind Event

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Journal of Meteorological Research 2025年第1期39卷 154-171页

作者： Rocío OTERO Matías SUAREZ Edgardo PIEROBON Leandro MATURANO Ignacio MONTAMAT Juan Ezequiel SANCHEZ Lucia SANDALIO Andrés RODRIGUEZ Denis POFFO Hydrometeorology Laboratory Department of ExactPhysical and Natural SciencesNational University of CordobaCórdobaX5016GCAArgentina Institute for Advanced Studies in Engineering and Technology National Scientific and Technical Research CouncilCordobaX5016GCAArgentina Radar and Remote Sensing Laboratory Department of MathematicsAstronomyPhysics and Computer ScienceNational University of CordobaCordobaX5016HUAArgentina

The central region of Argentina is known to be a source of some most extreme weather events in the world,which are partially associated with the passage of cold fronts accompanied often by extreme wind *** may cause severe property damage and even loss of human ***,there is a lack of studies that evaluate the performance of the numerical weather prediction(NWP)models such as weather research and forecasting(WRF)model in anticipating this type of weather in the *** study compares the performance of the operational WRF in Argentina using four combinations of various planetary boundary layer(PBL)and microphysics parameterization schemes under varied lead times in predicting an extreme wind event(gusts>30 m s^(-1))in Central *** results demonstrate that the WRF model is capable of providing an acceptable prediction of wind speed during an extreme *** is found that no single combination outperforms the others,although there is a slight tendency for Combination A,which utilizes the Mellor-Yamada-Janjic(MYJ)parameterization for the PBL and the Eta similarity parameterization for the surface layer,to more accurately capture the extreme wind *** with wind gust observations at five weather stations,the wind gust parameterization predicted the intensity and occurrence time of the peak wind,with an acceptable bias(time of peak<±1 h).Analysis of grid configurations(resolutions of 4 vs 9/3/1 km)revealed that higher resolution does not imply an improvement in the wind gust forecast for this particular *** regard to lead time,a shorter lead time does not necessarily result in more accurate ***,it is beneficial to conduct multiple sensitivity runs in order to obtain and understand the dispersion of forecasted wind speeds.

关键词： extreme wind wind event weather research and forecasting(WRF)model wind gust forecast cold front lead time

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Pushing AI to wireless network edge: an overview on integrated sensing, communication, and computation towards 6G

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Science China(Information Sciences) 2023年第3期66卷 7-25页

作者： Guangxu ZHU Zhonghao LYU Xiang JIAO Peixi LIU Mingzhe CHEN Jie XU Shuguang CUI Ping ZHANG Shenzhen Research Institute of Big Data Future Network of Intelligence Institute (FNii) The Chinese University of Hong Kong (Shenzhen) School of Science and Engineering (SSE) The Chinese University of Hong Kong (Shenzhen) State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

Pushing artificial intelligence(AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things(AIoT) in the sixth-generation(6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the vast amount of data scattered at the wireless network edge. Typically, realizing edge intelligence corresponds to the processes of sensing, communication,and computation, which are coupled ingredients for data generation, exchanging, and processing, ***, conventional wireless networks design the three mentioned ingredients separately in a task-agnostic manner, which leads to difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications like auto-driving and metaverse. This thus prompts a new design paradigm of seamlessly integrated sensing, communication, and computation(ISCC) in a taskoriented manner, which comprehensively accounts for the use of the data in downstream AI tasks. In view of its growing interest, this study provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art advancements, and shedding light on the road ahead.

关键词： sixth-generation (6G) edge intelligence artificial intelligence of things (AIoT) integrated sensing communication and computation (ISCC)

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In situ and operando infrared spectroscopy of battery systems:Progress and opportunities

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Journal of Energy Chemistry 2023年第6期81卷 472-491,I0011页

作者： Murilo M.Amaral Carla G.Real Victor Y.Yukuhiro Gustavo Doubek Pablo S.Fernandez Gurpreet Singh Hudson Zanin Advanced Energy Storage Division Center for Innovation on New EnergiesSchool of Electrical and Computer EngineeringUniversity of CampinasCampinasSP 13083-852Brazil Alan Levin Department of Mechanical and Nuclear Engineering Kansas State UniversityManhattanKS 66506USA Chemistry Institute Center for Innovation on New EnergiesUniversity of CampinasCampinasSP 13083-970Brazil Advanced Energy Storage Division Center for Innovation on New EnergiesSchool of Chemical EngineeringUniversity of CampinasCampinasSP 13083-852Brazil

In situ and operando infrared spectroscopies are powerful techniques to support the design of novel materials for batteries and the development of new battery *** techniques can support the study of batteries by identifying the formation of new species and monitoring electrochemical energy ***,few works have employed these techniques,which can be used to investigate various materials,including systems beyond lithium-ion technology,in the research of ***,this review presents a comprehensive overview focusing on the main contributions of in situ and operando infrared spectroscopy for lithium-ion batteries(LIBs)and other battery *** techniques can successfully identify the formation of species during the electrolyte reduction,electrode degradation,and the formation of the solid-electrolyte interphase(SEI)*** these outcomes,it is possible to conclude that this characterization approach should be employed as a protocol to overcome remaining issues in batteries,consequently supporting battery *** review aims to be a guide on how infrared spectroscopy can contribute to monitoring battery systems and to lead researchers interested in applying this technique.

关键词： In situ spectroscopy Operando spectroscopy FTIR Spectro-electrochemical cells Batteries

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A Model for the Analytical Performance of Data Lake in Stock Market Analysis with Databricks Delta Lake

A Model for the Analytical Performance of Data Lake in Stock...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Kamalakkannan, S. Yasmin, A. Arunkumar, R. Kavitha, P. School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Department of Information Technology Chennai Pallavaram India Jbas College for Women Department of Computer Science Chennai India University of South Wales Faculty of Computing Engineering and Science Treforest/Newport Campus United Kingdom School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Department of Computer Applications Chennai Pallavaram India

ISBN: (纸本)9798350300857

Stock market investments are highly rewarding but also high in risk. Modern investors use variety of tools to take informed investment decisions. In the current era of digital world, financial service industry has generated huge volume and immense verities of data with extreme speed. Due to the rapid growth in data collection and the heterogeneous nature and complexity of the data, there is a need for Big Data analytical solution that would be able to deal with the stock market data. Large volumes of unstructured, heterogeneous raw data can be stored in a massively scalable manner using data lakes, which are the ideal solution to the big data storage conundrum. The ability of a data lake to preserve data in its original format while processing it at runtime using a schema on-read technique is its key feature. The challenge faced in the data lake is performing analytics which is a significant tool to calculate and analyze the stock market. The proposed architecture of Azure Databricks DeltaLake (ADDL) with Azure DataLake Storage Generation 2 (ADLSG2) is used for analytical processes like Fibonacci retracement for better stock analysis, which aid in forecasting the market price for better investment. As a result, the research focus is to produce a storage having read as well as write capabilities by taking into consideration the Extract-Load-Transform (ELT) operation on the datasource. In this experimental databricks implementation, runtime is performed using open source of Apache Spark API and a highly improved execution engine, which results in a significant performance improvement when comparing to the standard source of Apache Spark available on the ADLS platform. Additionally, the Fibonacci retracement level calculation is achieved with the analytics and forecasting of test close price with various ML and DL techniques such as KNN, LSTM are compared with original price of the test data for better prediction of forecast close price. © 2023 IEEE.

关键词： Big data

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Catalysts for electrochemical ammonia oxidation:Trend,challenge,and promise

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Science China Materials 2022年第12期65卷 3334-3352页

作者： Sol A Lee Mi Gyoung Lee Ho Won Jang Department of Materials Science and Engineering Seoul National UniversitySeoul 08826Republic of Korea Department of Electrical and Computer Engineering University of TorontoToronto M5S 3G4OntarioCanada Advanced Institute of Convergence Technology Seoul National UniversitySuwon 16229Republic of Korea

Developing sustainable and clean energy technology is critial to address the current energy and environmental *** an energy carrier,hydrogen has been getting attention due to its abundance,high energy density,and zero carbon *** to the difficulty of hydrogen storage and transport,the electro-oxidation of ammonia shows great potential for renewable energy since it satisfies both energy supply and environmental protection as a carbon-free fuel with high hydrogen ***,ammonia electro-oxidation is a sluggish reaction and shows low *** the complicated mechanism and intermediates is conducive to developing efficient electrocatalysts with high catalytic activity and *** review begins with the ammonia oxidation technologies and their *** two typical mechanisms for ammonia electro-oxidation,Oswin-Salomon mechanism and Gerischer-Mauerer mechanism,are discussed,and attention to the poisoning species is ***,the electrocatalysts for ammonia reaction are classified into Pt-based catalysts and non-noble metal-based catalysts,and their performances are reviewed with various *** on the progress on the electrochemical ammonia oxidation catalysts,the challenges and future insight on ammonia oxidation reaction are discussed and proposed.

关键词： NH3 oxidation reaction electrocatalysts green hydrogen

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