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An Investigative Analysis for IoT Based Supply Chain Coordination and Control Through Machine Learning 5th

An Investigative Analysis for IoT Based Supply Chain Coordin...

5th International Conference on Emerging technologies in computer Engineering: Cognitive Computing and Intelligent IoT, ICETCE 2022

作者： Veerasamy, K. Sanyal, Shouvik Almahirah, Mohammad Salameh Saxena, Monika Manohar Bhanushali, Mahesh Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Department of Marketing and Entrepreneurship Dhofar University Salalah Oman Business Administration Department Isra University Amman Jordan Computer Science Banasthali Vidyapith Rajasthan Aliyabad India Management Studies-Operations VPM’s Dr. V.N. Bedekar Institute of Management Studies Maharashtra Thane India

ISBN: (纸本)9783031070112

The use of the Internet of Things (IoT) has brought about radical changes in the construction and business sectors, and companies are now using technology to remain competitive, support the exploitation of competitive advantages and increase growth and profitability. The use of the next generation of computers facilitated industrial change in all areas, and IoT helped shape the production structure, build an efficient value chain and achieve economic growth points. It can be argued that the introduction of IoT has changed the way we create value in the supply chain, which creates better opportunities for companies to improve scalability, make faster decisions and achieve better profits and growth. Although there are few challenges for the company, such as optimizing resources, investing in IoT and related digital technology, changing the production process and supply chain, etc., these new problems tend to change the organization’s bases and change the traditional way of doing things. Business. digital environment for effective customer engagement. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Regression analysis

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Climate Impact Prediction: Whale-Optimized Conv-XGBoost with Remote Sensing and Sociological Data

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 443-456页

作者： Jayakarthik, R. Chandrashekhara K.T Sampath, O. Kumar, Deepak Biban, Laxmi Maroor, Jnaneshwar Pai Malluvalasa, S. N. Lakshmi Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Chennai India Department of Information Science and Engineering BMS Institute of Technology and Management Bangalore India Department of CSE RGM College of Engineering and Technology Nandyal India Department of Applied Sciences Manav Rachna International Institute of Research and Studies Faridabad India Department of Botany Pt. C.L.S. Govt. College Haryana Karnal India Department of Business Administration Nitte Institute of Professional Education Nitte (Deemed to be University) Mangaluru Karnataka India Department of Artificial Intelligence & amp Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur District Andhra Pradesh India

Climate change poses significant challenges globally, impacting ecosystems, economies, and societies. Accurate prediction of climate impacts is crucial for developing effective mitigation strategies and informed policy decisions. Traditional predictive models often struggle to capture the complex interactions between environmental and socio-economic factors, necessitating advanced approaches that leverage diverse data sources and sophisticated algorithms. This study presents a robust predictive framework integrating convolutional neural networks (CNN) and XGBoost, optimized by the whale optimization algorithm (WOA). The proposed model leverages high-resolution satellite imagery and socio-economic data, combining spatial feature extraction with advanced machine learning techniques to enhance prediction accuracy and efficiency. We evaluate the model’s performance against baseline models and various optimization techniques, including particle swarm optimization (PSO) and genetic algorithm (GA). The integrated CNN and XGBoost model optimized by WOA significantly outperforms these benchmarks, achieving an accuracy of 98%, with substantial improvements in precision, recall, F1-score, and redundant phrases, and focuses directly on the core points to enhance clarity such as mean squared error (MSE), area under the curve (AUC), specificity, sensitivity, mean absolute error (MAE), and R². The results highlight the efficacy of the proposed approach in capturing complex environmental and socio-economic interactions, providing a powerful tool for climate impact assessment and decision-making. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate Impact Prediction CNN Convolutional Neural Networks GA Genetic Algorithm Machine Learning Model Optimization Particle Swarm Optimization PSO Spatial Feature Extraction Whale Optimization Algorithm WOA XGBoost

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Multi-class Classification of Motor Imagery EEG Signals Using Deep Learning Models

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SN computer science 2024年第5期5卷 444页

作者： Khemakhem, Rafik Belgacem, Sana Echtioui, Amira Ghorbel, Mohamed Ben Hamida, Ahmed Kammoun, Ines Advanced Technologies for Medicine and Signals Laboratory ‘ATMS’ National Engineering School of Sfax (ENIS) Sfax University Sfax Tunisia Higher Institute of Management of Gabès Gabès University Gabès Tunisia Department IS College of Computer Science King Khaled University ‘KKU’ Abha Saudi Arabia Functional Exploration Department of Habib Bourguiba Hospital Sfax University Sfax Tunisia

The accurate classification of Motor Imagery (MI) electroencephalography (EEG) signals is crucial for advancing Brain-computer Interface (BCI) technologies, particularly for individuals with disabilities. In this study, we present a sophisticated deep learning methodology that systematically evaluates three models CNN, RNN, and BiLSTM, to identify the optimal approach for MI signal classification. Leveraging the BCI Competition IV 2a dataset, we applied a pre-processing step removing three EOG channels and retaining 22 EEG channels, extracting 288 MI epochs, each lasting 3 s. Our findings highlight the superior performance of the proposed RNN model, achieving a remarkable maximum accuracy of 98%. This outcome signifies a significant advancement in MI signal classification, demonstrating the potential of deep learning techniques to enhance BCI precision. The study contributes by introducing a novel methodology and showcasing its efficacy through rigorous evaluation against benchmarks, providing valuable insights for the development of more robust and accurate BCI systems. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： BCI BiLSTM CNN EEG Motor imagery RNN

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The Performance Analysis of Daffier-Hellman Key Exchange Algorithm for Secure Transmission of Data in Cryptography Networks 3

The Performance Analysis of Daffier-Hellman Key Exchange Alg...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Ahmad, Arsalan Sakhare, Nitin N. Mondal, Dipannita Vigenesh, M. Indira, G. Futane, Pravin R. Mangalayatan University Department of Management Aligarh India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Dr. D.Y. Patil College of Engineering and Innovation Artificial Intelligence and Data Science Department Talegaon India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India

ISBN: (纸本)9798350319125

This paper examines the overall performance of the Daffier-Hellman key trade algorithm for the relaxed transmission of data in cryptography networks. Two types of Daffier-Hellman key exchanges are considered: the traditional scheme without elliptic curves and the current model utilizing elliptic curves. The parameters and their respective performance of both schemes are compared. In addition, it is located that the elliptic curve-primarily based Daffier-Hellman set of rules offers better overall performance than the conventional one, with better security and a drastically shorter key-status quo time. An in-depth analysis of the velocity and protection of both algorithms is supplied. The consequences of the have look indicate that the elliptic curve model of DaffierHellman is the fine-acting set of rules for secure records switches in cryptography networks. Moreover, the paper additionally discusses the safety problems and challenges arising from implementing the Daffier-Hellman key trade algorithm and indicates some possible solutions. Ultimately, the paper also offers an end on the overall performance of DaffierHellman Key's alternate set of rules regarding pace, safety, and reliability. © 2023 IEEE.

关键词： algorithm consequences performance possible safety

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A Comparative Study of Feed Forward Hybrid Neuro-Computing Framework with Multilayer Perceptron Model for Prediction of Breast Cancer 3rd

A Comparative Study of Feed Forward Hybrid Neuro-Computing F...

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3rd International Conference on Communications and Cyber Physical Engineering, ICCCE 2020

作者： Narasingarao, M.R. Satya Aruna, V. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur India Department of Computer Science and Engineering Lingaya’s Institute of Management and Technology Nunna Vijayawada India

ISBN: (纸本)9789811579608

Cancer originates when cells start to grow disorderly and there is a possibility that all the cells of the body have the scope to become cancer and can spread to other areas of body. A malignant tumor that initiates in the cells of breast turns out to be breast cancer. The presence of tumor anywhere in the body can be either malignant or benign i.e. cancerous and non-cancerous. Many research works have been carried out to diagnose the cancer disease. In this proposed research, A hybrid neural network model (SOM and LVQ) has been proposed. The output of SOM has been fed as an input to LVQ model. The classification of the dataset is done with SOM network using a competitive learning algorithm where as LVQ is trained with a vector quantization method. The patient’s data set contains 9 attributes which have been considered as input to the model. The inputs are then given to an SOM where each data point is classified into various clusters by using competitive learning process. Gradually, the classes obtained from SOM are appended back to the training input data for the training of supervised LVQ. After training, LVQ can be used to classify any unknown input data. The output thus obtained from this supervised learning algorithm is used to diagnose the presence of tumor leading to breast cancer. The labelled data from SOM is given as input to Multilayer Perceptron (MLP) and performance of the network is compared with the hybrid network. It has been observed that, The hybrid model performed equally well with that of the MLP model in diagnosing the cancer disease. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multilayer neural networks

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Production-Level Artificial Intelligence Applications in Semiconductor Supply Chains

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IEEE Transactions on Semiconductor Manufacturing 2023年第4期36卷 560-569页

作者： Chien, Chen-Fu Ehm, Hans Fowler, John W. Kempf, Karl G. Monch, Lars Wu, Cheng-Hung National Tsing Hua University Department of Industrial Engineering and Engineering Management Hsinchu30013 Taiwan Infineon Technologies Ag Supply Chain Innovation Department Neubiberg85579 Germany Arizona State University Department of Supply Chain Management TempeAZ85287 United States Intel Corporation Decision Engineering Group ChandlerAZ85226 United States University of Hagen Department of Mathematics and Computer Science Hagen58097 Germany National Taiwan University Institute of Industrial Engineering The Department of Business Administration The Department of Mechanical Engineering Taipei City106 Taiwan

This is a panel paper that discusses the use of Artificial Intelligence (AI) technologies to address production and supply chain level problems in semiconductor manufacturing. We have gathered a group of expert semiconductor researchers and practitioners from around the world who have developed AI solutions for various semiconductor problems. This paper aims to provide their answers to an initial set of questions and provide an overview of the AI developments and empirical studies to make suggestions for future directions in this arena. © 1988-2012 IEEE.

关键词： Semiconductor device manufacture

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Lightweight AI and Blockchain Optimization for Enhancing Consumer Electronics Decision-Making

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IEEE Transactions on Consumer Electronics 2025年

作者： Byeon, Haewon Alsaadi, Mahmood Keshta, Ismail Ahanger, Tariq Ahamed Safarova, Nodira Aldawsari, Hamad Cascone, Lucia Shabaz, Mohammad Korea University of Technology and Education Convergence Department Cheonan Korea Republic of University of Al Maarif College of Sciences Department of Computer Sciences Al Anbar31001 Iraq AlMaarefa University College of Applied Sciences Computer Science and Information Systems Department Riyadh Saudi Arabia CoBA Prince Sattam Bin Abdulaziz University Department of Management Information Systems Saudi Arabia Tashkent State University of Economics Department of Finance and Financial technologies Tashkent Uzbekistan University of Tabuk Department of Computer Science Haql University College Saudi Arabia University of Salerno Dept. of Computer Science Fisciano Italy Model Institute of Engineering and Technology J and K Jammu India

Under the "dual carbon" background, consumerelectronics consumption has become deeply ingrained in people'sminds. However, consumers often distrust the sustainabilityclaims of consumer electronics products. Artificial intelligence(AI) and blockchain technology can address this trust deficitthrough transparency and traceability mechanisms. This studyintegrates blockchain technology into traditional consumerelectronics supply chains, considering consumers' preferences andtrust in these products. An AI-based game model is proposed toanalyze the interactions among supply chain members before andafter implementing blockchain technology, under varying EdgeComputing-based power structures. This model quantitativelyevaluates emission reduction and pricing strategies, aiming tooptimize consumer surplus and total social welfare. By leveragingLightweight AI and blockchain, smart wholesale and cost-sharingcontracts are designed to establish reasonable ranges for wholesaleprices and optimal cost-sharing ratios, enhancing enterpriseoperational efficiency and achieving supply chain *** demonstrate that when consumers exhibit a strongerpreference for consumer electronics products, the adoption ofLightweight AI and blockchain delivers greater benefits across thesupply chain. Furthermore, as consumer willingness to purchasethese products increases, the advantages become morepronounced. Numerical analysis highlights that smart contractscan better coordinate the supply chain, particularly in retailer-dominated scenarios. Finally, empirical cases validate theeffectiveness of the proposed strategies and models. © 1975-2011 IEEE.

关键词： Supply chain management

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Sustainable Agriculture-Based Climate Change Training Models using Remote Hyperspectral Image with Machine Learning Model

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 261-270页

作者： Durairaj, M. Rubenraju, Kasapaka Krishna, B. V. Rama Yajid, Mohd Shukri Ab Jackson, Beulah Babu, Jampani Satish Padmavathi, Kodali Lakshmi Department of ECE Saveetha School of Engineering SIMATS Chennai India Department of Data Science & amp IT Malla Reddy University Telangana Hyderabad 500100 India Department of CSE Aditya College of Engineering and Technology Andhra PradeshSurampalem India Management and Science University Selangor Shah Alam Malaysia Department of ECE Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram A.P. Guntur 522302 India Department of Artificial Intelligence and Data Science Lakireddy Bali Reddy College of Engineering Andhra Pradesh India

In order to help farmers and crop managers better understand the elements influencing crop status and growth, hyperspectral and multispectral data processing methods have shown to be beneficial. Utilising advanced computational methods via machine learning is one strategy that has been in use recently. This method can forecast satellite image data based on the circumstances of mapping different types of land and vegetation in the field. This research proposes novel technique in sustainable agriculture-based climate change detection using hyperspectral image analysis with machine learning model. Here, the hyperspectral image of agricultural field is collected as input and processed for smoothening with normalisation. The proposed image analysis model is carried out in two stages which is feature extraction and classification. In stage 1, the feature extraction of processed input hyperspectral image is carried out using multilayer Bayesian encoder vector model (MBEV). The second stage of this proposed model is to classify the extracted image using deep convolutional belief neural networks (DCBNN). The experimental analysis has been carried out for various agriculture-based hyperspectral image datasets in terms of training accuracy, sensitivity, specificity, and AUC. The experimental findings demonstrate that, when compared to other ways, the suggested strategy performed exceptionally well. Proposed technique attained training accuracy of 97%, AUC of 85%, sensitivity of 96%, and specificity of 93%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Classification Climate change Hyperspectral image analysis Machine learning Sustainable agriculture

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DLIDAP: Experimental Evaluation of Drug Prediction System Using Modified Deep Learning Strategy with Intelligent Data Analyzer Principles 4

DLIDAP: Experimental Evaluation of Drug Prediction System Us...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Patan, Muzeeb Khan Kar, Nihar Ranjan Chakraborty, Ankita Thatipudi, Jayendra Gopal Mahadik, Ramchandra Kumar, Solleti Phani Sagi Rama Krishnam Raju Engineering College Department of Eee Bhimavaram India Centurion University of Technology and Management School of Pharmacy Odisha India Nowgong Polytechnic Assam India University of North Texas Department of Computer Science United States Deemed to be University Bharati Vidyapeeth Pune India Koneru Lakshmaiah Education Foundation Department of Cse Andhra Pradesh India

ISBN: (纸本)9798331508845

Sentiment analysis, or the act of deducing an individual's view on a topic from their written expression, is a rapidly developing field within the larger field of natural language processing. Medication sentiment analysis has gained traction in recent years as researchers have discovered that classifying medicines by their efficacy through the evaluation of user reviews can help potential future consumers learn more about and make better judgments about a drug. To improve the precision with which pharmaceuticals may be classified according to their efficacy, the suggested research seeks for the keywords by tokenization and lemmatization. Such labeling can be useful in the fight against disease and the promotion of health and happiness. A drug review dataset was constructed using information from the UCI machine learning repository, subsequently four machine learning methods were implemented for binary categorization and one was utilized for multiclass categorization. The effectiveness of these four classifier algorithms has been examined by analyzing the results they produce. Between each of these techniques (Naive Bayes classifier, Random Forest, and Support Vector Classifier), the multilayer perceptron had been shown to have the greatest effectiveness, with an accuracy of 98%. © 2024 IEEE.

关键词： Adversarial machine learning

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Empowering the Future Workforce: The Impact of AI on education and Skill Development

Empowering the Future Workforce: The Impact of AI on Educati...

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Communication, Computing and Energy Efficient technologies (I3CEET), International Conference on

作者： Shikha Gupta Eti Jain Amit Gupta Ashish Garg Department of Management Management Education & Research Institute(MERI) Affiliated to GGSIP University New Delhi India Department of Management Delhi Metropolitan Education(DME) Affiliated to GGSIP University Noida Uttar Pradesh India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The use of AI has surged in most aspects of our lives, and the education and skill development era is no different. As we look to a future where AI will enhance the workforce, ensuring that the next generation has the skills it needs to adapt to this rapidly changing job market is essential. education AI can leverage education to personalize learning and provide real-time feedback, making the learning experience more streamlined and efficient. Finally, applying AI to skill development programs may enable more reliable assessments and job-relevant skills, giving individuals a competitive edge in the job market. In summary, AI promises to help the future workforce be ready by providing them with the skills they will need in a world powered by technology..

关键词： Ethics education Learning (artificial intelligence) Real-time systems Telecommunication computing Reliability Problem-solving Artificial intelligence Next generation networking Testing

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