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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Stanczyk, Urszula Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

In the context of data imbalance probably the most investigated problem is imbalance of classes, as learning from the data with this characteristic makes detection of existing patterns for all classes more difficult. However, other problems related to imbalance also exists and the paper addresses such cases where classes are balanced, but there is in-class imbalance. Such imbalance can be caused by uneven representation of sub-concepts. When there is a noticeable difference between the numbers of samples belonging to sub-concepts, this can turn the under-represented sub-concepts into disjuncts. Data irregularities of this type can hinder recognition, therefore actions are typically taken to restore balance. In the investigations described, the issue was studied in the stylometric domain and various classifiers were applied to the data that was balanced, then imbalanced, and finally with restored balance. The experiments show that the specifics of the domain of application can put its own mark on the data which is difficult to overcome by standard processing such as under- or oversampling. Observed dependence on a learner and dataset makes the issue even more complex and layered, and shows the need for deeper studies. © 2024 The Authors.

关键词： Data assimilation

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Using Machine Learning Algorithms to Increase the Supplier Selection Process Efficiency in Supply Chain 4.0 1

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International Conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2022

作者： Abouloifa, Houria Bahaj, Mohamed Mathematics and Computer Science Department Faculty of Sciences and Techniques Lab L.M.I.E.T Hassan Ist University Settat Morocco

ISBN: (数字)9783031263842

ISBN: (纸本)9783031263835

Supply Chain in one of the processes that took an important part of the attention given to Industry 4.0 because of its information flows which are dispersed in different directions. To that purpose, many new technologies have seen the day to improve the flows between the different links of the Supply Chain: Machine learning is being introduced into the manufacturing environment, which initiated a fourth industrial revolution. As such, this paper explore the application of machine learning algorithm to the supplier selection process based on past supplier orders and choices. The proposed model will enable firms to predict the good suppliers from the bad ones using supervised learning algorithms. To this purpose, we start by using three supervised machine learning tasks: AdaBoost, SVM and KNN. AdaBoost shows the most reliable results with an accuracy of 98% and an F1-score of 86%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Industry 4.0

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Lightweight Authentication Scheme Based on ECC for IoT

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SN computer science 2024年第7期5卷 1-10页

作者： Mishra, Sweta Mondal, Bhaskar Jha, Rishi Kumar Department of Mathematics National Institute of Technology Patna Bihar Patna 800005 India Department of Computer Science and Engineering National Institute of Technology Patna 800005 Bihar Patna India

The presence of the Internet of Things (IoT) enables the exponential growth of IoT devices. The IoT commonly involves many limited resources that are incapable of processing classical cryptographic protocols and vulnerable to threats like privacy loss, Distributed Denial of Service (DDoS) Attacks, and quantum computers. One-time passwords (OTPs) are essential in IoT for enhancing security by ensuring single-use authentication codes and preventing unauthorized access while being lightweight, scalable, and suitable for resource-constrained devices. The proposed work is a novel OTP generation scheme designed on isogeny between two Montgomery elliptic curves, such as translation maps and isogeny computed by the V élu formula to provide secure communication. This scheme is secure and lightweight due to the utilization of the Montgomery curve, known for its computational efficiency in elliptic curve operations, to significantly reduce the complexity and increase the speed of OTP generation. The security is proven by the hardness of computational Deffie Hellman (CDH) over elliptic curves and isogenies. Hence, the scheme is lightweight and suitable for use in secure IoT device communications. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Authentication Elliptic curve cryptography Internet of things Isogeny One-time password Smart city

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Where Do CABs Exist? Verification of a specific region containing concave Actin Bundles (CABs) in a 3-Dimensional confocal image

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In Silico Biology 2023年第1-2期15卷 1-10页

作者： Park, Doyoung Department of Mathematics Computer Science Information State University of New York at Old Westbury Old Westbury NY United States

CABs (Concave Actin Bundles) are oriented against the scaffold transversally in a manner different from traditional longitudinal F-actin bundles. CABs are present in a specific area, and do not exist in random areas. Biologically, CABs are developed to attach cells to fibers firmly so that CABs are found near cells. Based on this knowledge, we closely examined 3D confocal microcopy images containing fiber scaffolds, actin, and cells. Then, we assumed that the areas containing high values of compactness of fiber, compactness of actin, and density of cells would have many numbers of CABs. In this research, we wanted to prove this assumption. We first incorporated a two-point correlation function to define a measure of compactness. Then, we used the Bayes' theorem to prove the above assumption. As the assumption, our results verified that CABs exist in an area of high compactness of a fiber network, high compactness of actin distribution, and high density of cells. Thus, we concluded that CABs are developed to attach cells to a fibrillar scaffold firmly. This finding may be further verified mathematically in future studies. © 2023 - The authors.

关键词： bayes' theorem CABs concave actin bundles fiber network two-point correlation function

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Hyperbolic Hopfield Neural Network for Enhanced Solar Power Generation Forecasting 4

Hyperbolic Hopfield Neural Network for Enhanced Solar Power ...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Gupta, Raj Kumar Naveen, K.N. Sukumar, P. Yamini, C. Raghapriya, N.R. Dwivedi, Vijay Kumar Department of Physics Bihar India Dayananda sagar College of Engineering Department of Electronics and Communication Engineering Bengaluru India Department of Computer Science and Engineering Erode India Psna College of Engineering and Technology Department of Mathematics Dindigul India Erode Sengunthar Engineering College Department of Computer Science and Engineering Erode India Vishwavidyalaya Engineering College Department of Mathematics Surguja India

ISBN: (纸本)9798350352931

Solar power is growing more essential for renewable energy, but measuring its output is difficult due to irradiance, temperature, and weather fluctuations. Planning and controlling energy distribution requires accurate short-term estimates. An innovative Hyperbolic Hopfield Neural Network (HHNN) model for short-term solar power prediction is presented in this paper. The procedure includes preprocessing, feature extraction, and model training. Remove linearly dependent characteristics and filter nighttime data during preprocessing. The HHNN model is trained after feature selection is optimised using PCA and wavelet decomposition. The new HHNN model surpassed the HNN and DNN models in solar power forecasting with an average accuracy of 92.41%.HHNN is a solid method for calculating solar power output. This model has improved solar power management systems by increasing projected accuracy. © 2024 IEEE.

关键词： hyperbolic hopfield neural network (HHNN) principal component analysis (PCA) solar power generation forecasting

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IoT-enabled monitoring and comparative analysis of machine learning techniques for detection of plant diseases in marigolds

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International Journal of Information Technology (Singapore) 2024年第5期17卷 2941-2948页

作者： Jomsri, Pijitra Prangchumpol, Dulyawit Poonsilp, Kittiya Department of Information Technology Faculty of Science and Technology Suan Sunandha Rajabhat University 1 U-Thong Nok Road Dusit Bangkok Thailand Department of Computer Science Faculty of Science and Technology Suan Sunandha Rajabhat University 1 U-Thong Nok Road Dusit Bangkok Thailand

Ornamental flower cultivation is highly popular and a critical career that creates income for the farmers. Among these marigold flower is a top-selling flowering plant across most of the countries. Further, this plant has many insect-repellent properties. Despite farming these plants economic benefits, multiple risks are associated with cultivating them. Plant disease plays a critical role which reduces the quality and quantity of flower production flowers and contributes primarily to the loss of production or exports. Manual disease detection strategies are expensive and highly prone to error. Thus, multiple early detection of plant diseases by the Internet of Things (IoT) and Artificial Intelligence (AI) technologies are introduced to reduce the burden of the disease. Most of them are complex and cost high. In conjunction with these, a sensor model suitable for collecting data on marigold leaf diseases is presented in this research. A Machine Learning (ML) technique based on the latest Neural Network (NN) called EFFICIENTNET is employed to isolate the diseased plants from the healthier plants. Experiments conducted and comparisons with existing K-Nearest Neighbor (KNN) techniques showed that the EFFICIENTNET-B0 technique had shown remarkable performance with 97.70% accuracy. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： EfficientNet-B0 and K-Nearest Neighbor algorithm Images Analysis Machine Learning Marigold Leaf Disease

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A Comparative Study of Machine Learning Models for Plant Disease Identification 19th

A Comparative Study of Machine Learning Models for Plant D...

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19th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2024

作者： Mara, Măcelaru Pop, Petrică Barata, José Department of Mathematics and Computer Science North University Center at Baia Mare Technical University of Cluj-Napoca Cluj-Napoca Romania Electrical and Computer Engineering Department School of Sciences and Technology NOVA University of Lisbon Lisbon Portugal

ISBN: (纸本)9783031750090

Early identification of plant diseases plays an important role in reducing the financial losses for farmers and enhancing the productivity and quality of crops. The present work proposes five machine learning (ML) algorithms: K-Nearest Neighbors, Random Forest, Iterative Dichotomizer 3, Adaptive Boosting and Logistic Regression for plant disease identification. In order to test the proposed ML algorithms, we used a large data set containing 74798 images mainly from PlantVillage dataset. The achieved experimental results show that the proposed ML techniques can be used to reliably identify various plant diseases. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Re-rank Search Engine Results via Machine Learning 9

Re-rank Search Engine Results via Machine Learning

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9th International Conference on Web Research, ICWR 2023

作者： Ataei Monfared, Amir Hussein Avizheh, Amir Hossein Kharazmi University Faculty of Mathematical Sciences and Computer Tehran Iran Shahid Beheshti University Department of Data and Computer Science Tehran Iran

ISBN: (纸本)9798350399691

Research on search engines has demonstrated their significant impact on information search and user behavior and highlighted the need for continued investigation of their effectiveness, efficiency, and ethical implications. Moreover, search engine optimization (SEO) has emerged as a crucial aspect of online content creation and marketing, as it involves optimizing websites and online materials to improve their visibility and ranking on search engine results pages, thereby increasing their reach and impact. SEO metrics provide valuable insight into a website's search engine performance. However, the relative importance of these metrics remains unclear as search engine algorithms and keyword competitiveness can vary widely. Therefore, ongoing research is needed to better understand the relationship between SEO metrics and search engine rankings. Given the challenge of determining the relative importance of SEO metrics using traditional statistical methods, interested in applying machine learning techniques. By training models with custom datasets of three search engine results (Google, Bing, and DuckDuckGo) and their associated SEO metrics, we hope to gain a better understanding of the complex relationships between these variables. The first two pages of each search engine return the most relevant results. In this article, we propose two types of methods to do this. First, classify the page index of web search results. Second, learning ranking methods to rank them. LambdaRank has the best NDCG compared to other methods for Google and DuckDuckGo which are 0.86 and 0.93 respectively. RankNet is the best learning-to-rank method for Bing with 0.85 as the NDCG value. Logistic regression has the highest accuracy compared to other methods in page index classification for Bing and Google, which are 61.29% and 64.71%, respectively. K-nearest neighbor performs best for DuckDuckGo with an accuracy of 66.67%. © 2023 IEEE.

关键词： Websites

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Navigating the Nexus: Exploring the Fusion of AI and Nanotechnology for Cutting-Edge Advances 1

Navigating the Nexus: Exploring the Fusion of AI and Nanotec...

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2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Izanker, Sakshi V. Dhole, Aman Kumar, Praveen Faculty of Engineering and Technology Department of Computer and Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798350338423

The convergence of artificial intelligence (AI) and nanotechnology has initiated a transformative journey, introducing innovative possibilities across various fields. This review article explores the dynamic interaction between these two disciplines, emphasizing their collaborative potential, notable accomplishments, and prevailing challenges. The integration of AI algorithms with nanotechnology has expedited material discovery, nanoscale characterization, personalized medical applications, and nano- electronics. AI's predictive capabilities have revolutionized the discovery of novel nanomaterials with customized properties, the enhancement of drug delivery systems, and the design of intricate nano-electronic components. However, this synergy is accompanied by challenges such as data quality, multidisciplinary cooperation, ethical considerations, and computational complexity. The fusion of AI and nanotechnology is poised to transform industries, unlock quantum advancements, and influence sustainable materials. As AI's capacities evolve, scholars are prepared to address challenges and harness the transformative potential of AI- nanotechnology integration. © 2023 IEEE.

关键词： Medical applications

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A Review of Grid-Connected Inverters and Control Methods Under Unbalanced Grid Conditions 16

A Review of Grid-Connected Inverters and Control Methods Und...

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16th Annual Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2025

作者： Marangalu, Milad Ghavipanjeh Rehman, Fazal Ur Kurkdjian, Naser Vosoughi Computer Queen's University Dep. Of Electrical and Computer Engineering KingstonON Canada University of L'Aquila Dep. Information Engineering Computer Science and Mathematics L'Aquila Italy San Diego State University Faculty of Electrical and Computer Engineering San DiegoCA92182-0001 United States

ISBN: (纸本)9798331533946

Grid-connected inverters play a pivotal role in integrating renewable energy sources into modern power systems. However, the presence of unbalanced grid conditions poses significant challenges to the stable operation of these inverters. This review paper provides a comprehensive overview of grid-connected inverters and control methods tailored to address unbalanced grid conditions. Beginning with an introduction to the fundamentals of grid-connected inverters, the paper elucidates the impact of unbalanced grid voltages on their performance. Various control strategies, including voltage and current control methods, are examined in detail, highlighting their strengths and limitations in mitigating the effects of grid imbalance. Lastly, the review identifies emerging trends and future research directions in the field, emphasizing the need for innovative solutions to address the evolving challenges posed by unbalanced grid conditions in the context of renewable energy integration. © 2025 IEEE.

关键词： control strategy fault conditions grid-connected inverters Photovoltaic systems

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