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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Komal Pokale Shiv Nath Chaudhri Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology DMIHER (DU) Maharashtra India Department of Computer Science and Design Faculty of Engineering and Technology DMIHER (DU) Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Fine-tuning a pre-trained model is a type of transfer learning: a pre-trained model that works on a large dataset is transferred onto a particular task or a smaller dataset, such as a hyperspectral image. The efficacy of such a technique is most visible in hyperspectral imaging since the data is usually too high-dimensional with a very scarce number of available annotated datasets. By using the pre-trained model, fine-tuning enables spatial and spectral features to be captured with lower computational overheads than fully training models. Domain adaptation approaches, including feature alignment and adversarial training, are also adapted to overcome limitations caused by the domain shift phenomenon due to the variability of the sensor, environmental changes, and temporal changes. Hyperspectral imaging is applied in diverse fields, including agriculture, environmental monitoring, mineral exploration, defense, medical diagnostics, and food quality assessment. Such applications are unique in the sense that HSI can integrate spatial and spectral information to achieve detailed material analysis. Advanced learning techniques will enhance the effectiveness of HSI models in real-world scenarios.

关键词： Training Deep learning Adaptation models Computational modeling Transfer learning data models Agriculture Medical diagnosis Environmental monitoring Hyperspectral imaging

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AI-Driven Sentiment Analysis for Music Composition 18th

AI-Driven Sentiment Analysis for Music Composition

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18th EAI International Conference on Tools for design, Implementation and Verification of Emerging Information Technologies, TRIDENTCOM 2023

作者： Wang, Qinyuan Qu, Youyang Cheng, Haibo Yu, Yonghao Wang, Xiaodong Gu, Bruce Sydney Conservatorium of Music University of Sydney Sydney Australia Jinan China Faculty of Data Science City University of Macau China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Engineering Design and Construction Melbourne Polytechnic Melbourne Australia

ISBN: (纸本)9783031513985

In the realm of music composition, sentiment plays a pivotal role in connecting compositions with their audience, evoking emotions and memories. With the rapid evolution of artificial intelligence (AI), there exists a burgeoning interest in utilizing AI for sentiment analysis in various domains, including textual data, social media, and film. This paper delves into the novel application of AI-driven sentiment analysis specifically tailored for music composition. Leveraging diverse music datasets across multiple genres and eras, we introduce an innovative methodology that breaks down music into foundational features such as melody, rhythm, timbre, and harmony. Through the application of advanced AI techniques, including neural networks and Long Short-Term Memory (LSTM) models, we aim to accurately map these features to a wide spectrum of sentiments. Our results showcase not only the potential accuracy and precision of our chosen models but also the richness of music compositions they can produce, underscoring the viability of AI in enhancing the emotional depth of musical works. The implications of this research stretch from aiding composers in creating more resonant pieces to the potential therapeutic applications of AI-composed music, tailored to specific emotional needs. © 2024, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Sentiment analysis

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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis 6

Mapping the Scientific Landscape of Integrated Scheduling To...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Putri Ardyanti, Anak Agung Ayu Xie, Zhiqiang Karnyoto, Andrea Stevens Pardamean, Bens Estiyanti, Ni Made School of Computer Science and Technology Harbin University of Science and Technology Harbin150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, Computer science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions. © 2025 The Authors. Published by Elsevier B.V.

关键词： Job shop scheduling

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γ-Butyrolactone Synthesis from Allylic Alcohols Using the CO_(2)Radical Anion

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Precision Chemistry 2024年第3期2卷 88-95页

作者： Saeesh R.Mangaonkar Hiroki Hayashi Wataru Kanna Suvankar Debbarma Yu Harabuchi Satoshi Maeda Tsuyoshi Mita Institute for Chemical Reaction Design and Discovery(WPI-ICReDD) Hokkaido UniversitySapporoHokkaido 001-0021Japan JST ERATO Maeda Artificial Intelligence in Chemical Reaction Design and Discovery ProjectSapporoHokkaido 060-0810Japan Department of Chemistry Faculty of ScienceHokkaido UniversitySapporoHokkaido 060-0810Japan Research and Services Division of Materials Data and Integrated System(MaDIS) National Institute for Materials Science(NIMS)TsukubaIbaraki 305-0044Japan

γ-Butyrolactone structures are commonly found in various natural products and serve as crucial building blocks in organic ***,the development of methods for synthesizingγ-butyrolactones has garnered significant interest within the organic synthesis *** this study,we present a direct and highly efficient approach for the synthesis ofγ-butyrolactones from allylic ***,this study represents the first instance ofγ-butyrolactone synthesis initiated by radical hydrocarboxylation using CO_(2)^(•–),generated from metal formates,followed by *** two-step process is achieved through the synergistic interaction of photoredox and hydrogen atom transfer(HAT)catalysis,resulting in the production ofγ-butyrolactones with exceptional ***,when employingα,α-diaryl allylic alcohol derivatives as substrates,the reaction involves 1,2-aryl migration,which occurs concomitantly with CO_(2)^(•–)addition,leading to the formation of 4,5-substituted lactones in a good *** artificial force induced reaction(AFIR)method identified the preferred 1,2-aryl migration pathway along with potential byproduct pathways,in which the targeted 1,2-migration was found to be the most plausible pathway.

关键词： CO_(2) lactone radical photoredox visible light carboxylation calculation DFT

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Estimation of the Burden Factors of Order Pickers by Bayesian Network: Approach Based on Heart Rate Variability Analysis

Estimation of the Burden Factors of Order Pickers by Bayesia...

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SICE Annual Conference

作者： Yurika Ono Aya Ishigaki Aya Saito Department of Industrial and Systems Engineering Tokyo University of Science Chiba Japan Department of Faculty of Design and Data Science Tokyo City University Kanagawa Japan

ISBN: (数字)9784907764838

ISBN: (纸本)9798331544461

This paper presents a Bayesian network for estimating the burden factors of order pickers in a robotic mobile fulfillment system (RMFS). Recently, many logistics companies have introduced RMFS to improve the order-picking efficiency. However, they have ignored the fact that RMFS is a human-robot coordinated system, and thus have not been able to design human-friendly operations. Therefore, we replicate the order-picking operation in RMFS and measure the picker workload. The workload is assessed subjectively (via the NASA task load index(NASA- Tlx)) and objectively (via heart rate variability (HRV) analysis). In addition, a Bayesian network is used to identify the elements of order picking that cause stress and HRV indices that could estimate subjective workload. The results revealed the conditions under which the picker workload increased. The Mean NNI (the mean of RR intervals on electrocardiogram) was also observed to be a valid HRV index for estimating physical demands, a subscale of the NASA-TLX.

关键词： Robot kinematics NASA Estimation Artificial neural networks Particle measurements Time measurement Iron Bayes methods Heart rate variability Logistics

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Artificial Intelligence Cultivation: Transforming Agriculture for a Smart and Sustainable Future

Artificial Intelligence Cultivation: Transforming Agricultur...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Bhushan Fulkar Samir Mendhe Pawan Patil Department of Artificial Intelligence and Data science Faculty of Engineering and Technology DMIHER(DU) Wardha Maharashtra India Computer Science and Design Faculty of Engineering and Technology DMIHER(DU) Wardha Maharashtra India Artificial Intelligence and Data science Faculty of Engineering and Technology DMIHER(DU) Wardha Maharashtra India

The growing global population is posing unprecedented challenges for the agriculture industry, requiring it to produce more food while also addressing resource constraints and environmental concerns. Artificial intelligence (AI) has changed the game in agriculture by offering creative solutions to increase productivity, optimize resource use, and promote sustainability. Artificial intelligence (AI) has been used in the agriculture sector more and more recently. A few of the problems the industry faces in attempting to maximize its produce are improper soil treatment, disease and pest infestation, the use of driverless tractors, big data requirements, and the knowledge gap between farmers and technology. This work provides an overview of the applications of artificial intelligence in crop, weed, and disease management. The proposed work aims to review various AI techniques, including artificial neural networks (ANN) and fuzzy logic (FL).

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Deep Learning Approach and its Application in the Cybersecurity Domain

Deep Learning Approach and its Application in the Cybersecur...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Pawan Dilip Chaudhari Amit Gudadhe Faculty of Engineering and Technology(DMIHER) Artificial Intelligence and Data Science Sawangi(M) Wardha Maharashtra India Faculty of Engineering and Technology(DMIHER) Computer Science and Design Sawangi(M) Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Cyber Security has become especially prominent and critical for organizations all over the world because of the fast advancements in technology and the increasing sophistication of modern cyber threats. Most traditional methods related to cyber security are unable to match the scale and complexity of modern cyber-attacks. Deep learning has emerged in this context as a powerful tool for offering brand-new approaches and avenues toward threat detection, anomaly identification, and protection of data. In this paper, we investigate the basic principles of deep learning and its significant architectures Convolutional Neural Networks, Recurrent Neural Networks, and Generative Adversarial Networks which provide models for solving various issues in the field of cybersecurity. We describe the contribution of deep learning to intrusion detection systems, malware detection, phishing protection, DDoS attack mitigation, and fraud detection. Deep learning also presents some advantages in cybersecurity. In this paper, I reviewed the basic applications, challenges, and potential solutions for enhancing cybersecurity systems by techniques of deep learning. Every organization, right from financial institutes to the entire globe employing itself to reach a developmental stage, has been forced to consider the implications of cybersecurity threats carefully. Most traditional methods relevant to cybersecurity have been ineffective at combatting modern cyber-attacks' scale and complexity.

关键词： Deep learning Recurrent neural networks Prevention and mitigation Phishing Organizations Threat assessment Complexity theory Computer security Protection Cyberattack

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Optimal Decision Support System Model for Breast Cancer Diagnosis

Optimal Decision Support System Model for Breast Cancer Diag...

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science and Information Technology in Smart Administration (ICSINTESA), International Conference of

作者： Hanin Saadah Amani Yousef Owda Lampros Stergioulas Majdi Owda Department of Natural Engineering and Technology Sciences Faculty of Graduate Studies Arab American University Ramallah West Bank Palestine Faculty of IT & Design UNESCO Chair on AI and Data Science The Hague University of Applied Sciences The Hague The Netherlands Faculty of Data Science UNESCO Chair on Data Science for Sustainable Development Arab American University Ramallah Palestine

ISBN: (数字)9798350376111

ISBN: (纸本)9798350376128

Breast cancer is a common cancer among women worldwide, and early diagnosis is crucial for improving the chances of successful treatment. Decision support systems (DSS) are computer-based systems that help and guide users in making decisions. This paper discusses the characteristics of an optimal DSS for diagnosing breast cancer, including accuracy and reliability. This paper also introduces an optimal DSS for diagnosing breast cancer that should be able to provide an accurate, reliable, and comprehensive guide to users while being up-to-date, and flexible. The results obtained from the R-squared in the model succeeded in explaining 70% of the variation in the outputs. In contrast, the initial value of R-squared was 0.705091558 and then improved to 0.700018844. Such a system has the potential to improve the early diagnosis and treatment of breast cancer significantly and could ultimately save lives.

关键词： Decision support systems Accuracy Breast cancer Reliability Information technology

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Advanced Lung Cancer Diagnosis and Treatment with Artificial Intelligence: Applications, Methods, and Future Directions

Advanced Lung Cancer Diagnosis and Treatment with Artificial...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Pawan Dilip Chaudhari Amit Gudadhe Artificial Intelligence and Data Science Faculty of Engineering and Technology(DMIHER) Sawangi(M) Wardha Maharashtra India Computer Science and Design Faculty of Engineering and Technology(DMIHER)Sawangi(M) Wardha Maharashtra India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

AI technology revolutionizes the treatment of lung cancer and other related lung cancer diagnoses, hence becoming the game-changing technology in the diagnosis and treatment of this serious disease. As one of the most prevalent and deadliest cancers globally, early identification, precise diagnosis, and efficient planning for the treatment of lung cancer have always been difficult. The therapy using AI-based methods is introducing another novel way of addressing the problems concerning lung cancer via advanced imaging analysis, predictive modeling, and tailored plans of treatment, the most important among which is of course, imaging analysis. What is certainly most exciting among the applications of AI in lung cancer treatment is in the realm of imaging analysis. AI has been seen to be extremely clever in the interpretation of images rather than using machine learning or deep learning but has been much faster and more accurate than a human being while doing it. Besides, these systems also increase the general accurateness of diagnosis through a reduction of the error in diagnosis and consistent results. Alluring promises are made for AI soon for the treatment of lung cancer. In this paper, we discuss how AI has been transformed into imaging, diagnosis, and treatment planning and how this will improve patient outcomes and completely transform the way healthcare is delivered. We also consider barriers still to be cleared for realizing fully the potential benefits of AI. These include problems such as the quality of available data used in model interpretation and generalizability across different healthcare settings. Therefore, we end and suggest some future research on collaboration by emphasizing the safe, effective, and ethical entry of AI into normal clinical practice.

关键词： Analytical models Renewable energy sources Ethics Lung cancer Imaging Medical treatment Transforms Predictive models Planning Artificial intelligence

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Forecasting Solar Power Generation Using Extreme Gradient Boosting: A Machine Learning Approach

Forecasting Solar Power Generation Using Extreme Gradient Bo...

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science and Information Technology in Smart Administration (ICSINTESA), International Conference of

作者： Mobarak Abumohsen Amani Yousef Owda Majdi Owda Ahmad Abumihsan Lampros Stergioulas Department of Natural Engineering and Technology Sciences Faculty of Graduate Studies Arab American University Ramallah West Bank Palestine Faculty of Data Science UNESCO Chair in Data Science for Sustainable Development Arab American University Ramallah West Bank Palestine Faculty of Data Science Arab American University Ramallah Palestine Faculty of IT & Design The Hague University of Applied Sciences The Hague The Netherlands

ISBN: (数字)9798350376111

ISBN: (纸本)9798350376128

The swift expansion of solar photovoltaic (PV) technology has introduced significant challenges for those overseeing electricity distributions due to its reliance on weather conditions. In this study, predictive modeling techniques including Multilayer Perceptron (MLP) Regression, Gradient Boosting Regression, and Extreme Gradient Boosting (XGB) Regression were employed to enhance solar PV output forecasting. Comparative analysis utilizing performance metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-square revealed that the XGB model outperforms others. Specifically, the XGB model demonstrated an MAE of 0.0653, RMSE of 0.10461, and R-squared of 88.66%, showcasing its superior accuracy and efficiency in solar power management. This synergy between machine learning and energy efficiency heralds a promising direction towards achieving a more sustainable, decarbonized, and digitalized energy sector.

关键词： Photovoltaic systems Measurement Analytical models Predictive models Multilayer perceptrons Boosting Energy efficiency Forecasting Information technology Meteorology

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