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49th EPS Conference on Plasma Physics, EPS 2023

作者： Pastore, F. Felici, F. Sauter, O. Bosman, T.O.S.J. Galperti, C. Vincent, B. Pau, A. Poels, Y. Ravensbergen, T. Vu, N.M.T. Lausanne Switzerland DIFFER - Dutch Institute for Fundamental Energy Research Energy Systems and Control Group Eindhoven Netherlands Eindhoven University of Technology Dept. of Mechanical Engineering Control Systems Technology Group Eindhoven Netherlands Eindhoven University of Technology Dept. of Mathematics and Computer Science Data Mining Group Eindhoven Netherlands ITER Organization Route de Vinon-sur-Verdon St. Paul Lez Durance13067 Cedex France

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Trade-off between payoff and model rewards in shapley-fair collaborative machine learning 22

Trade-off between payoff and model rewards in shapley-fair c...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Quoc Phong Nguyen Bryan Kian Hsiang Low Patrick Jaillet Institute of Data Science National University of Singapore Republic of Singapore Dept. of Electrical Engineering and Computer Science MIT Dept. of Computer Science National University of Singapore Republic of Singapore

ISBN: (纸本)9781713871088

This paper investigates the problem of fairly trading off between payoff and model rewards in collaborative machine learning (ML) where parties aggregate their datasets together to obtain improved ML models over that of each party. Supposing parties can afford the optimal model trained on the aggregated dataset, we propose an allocation scheme that distributes the payoff fairly. Notably, the same scheme can be derived from two different approaches based on (a) desirable properties of the parties' payoffs or (b) that of the underlying payoff flows from one party to another. While the former is conceptually simpler, the latter can be used to handle the practical constraint on the budgets of parties. In particular, we propose desirable properties for achieving a fair adjustment of the payoff flows that can trade off between the model reward's performance and the payoff reward. We empirically demonstrate that our proposed scheme is a sensible solution in several scenarios of collaborative ML with different budget constraints.

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ANFIS Model for Cost Analysis in a Dual Source Multi-Destination System 4

ANFIS Model for Cost Analysis in a Dual Source Multi-Destina...

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4th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2022

作者： Okwu, M.O. Tartibu, L.K. Ojo, E.O. Adume, S. Gidiagba, J.O. Fadeyi, J. Department of Mechanical Industrial Engineering Tech. University of Johannesburg South Africa Department of Mechanical Engineering Federal University of Petroleum Resources Effurun Nigeria Mathematics and Data Science Dept. Computing Science and Mathematics Division Faculty of Natural Science University of Stirling United Kingdom Faculty of Engineering and Applied Sc. Memorial University Newfoundland Canada Department of Industrial and Systems Engineering University of Buffalo United States

Managers face uncertainties while making allocation decisions, especially in dual or multi-source multi-destination inventory systems. Regrettably, many studies focus on classical methods as a technique for product distribution, which has never guaranteed a satisfactory solution. Real-life problems are non-deterministic polynomial-time hard (NP-hard), and solving such problems is relatively challenging. Such complicated problems need an efficient and robust computational hybrid algorithm. This study emphasises the need for a hybrid intelligent technique for effective product distribution. Soft computing hybrid algorithm, ANFIS was applied to product distribution in a double source multi-destination system. Rules were developed from available input datasets. Distributing products from dual manufacturing plants to fifteen available depots using the creative algorithm resulted in an overall 13.5% decrease in cost compared to the existing method adopted by the company. The result showed that the proposed method is relatively satisfactory and adequate for cost modelling. In addition, it is easy to use and outperforms the classical approach. © 2022 The Authors. Published by ELSEVIER B.V.

关键词： Soft computing

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Easy Variational Inference for Categorical Models via an Independent Binary Approximation

arXiv

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arXiv 2022年

作者： Wojnowicz, Michael T. Aeron, Shuchin Miller, Eric L. Hughes, Michael C. Tufts University MedfordMA United States Data Intensive Studies Center Dept. of Electrical and Computer Engineering Dept. of Computer Science

We pursue tractable Bayesian analysis of generalized linear models (GLMs) for categorical data. Thus far, GLMs are difficult to scale to more than a few dozen categories due to non-conjugacy or strong posterior dependencies when using conjugate auxiliary variable methods. We define a new class of GLMs for categorical data called categorical-from-binary (CB) models. Each CB model has a likelihood that is bounded by the product of binary likelihoods, suggesting a natural posterior approximation. This approximation makes inference straightforward and fast;using well-known auxiliary variables for probit or logistic regression, the product of binary models admits conjugate closed-form variational inference that is embarrassingly parallel across categories and invariant to category ordering. Moreover, an independent binary model simultaneously approximates multiple CB models. Bayesian model averaging over these can improve the quality of the approximation for any given dataset. We show that our approach scales to thousands of categories, outperforming posterior estimation competitors like Automatic Differentiation Variational Inference (ADVI) and No U-Turn Sampling (NUTS) in the time required to achieve fixed prediction quality. © 2022, CC BY.

关键词： Bayesian networks

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De Novo Drug Property Prediction using Graph Convolutional Neural Networks 9

De Novo Drug Property Prediction using Graph Convolutional N...

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9th Edition of IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2021

作者： Shishir, Fairuz Shadmani Hasib, Khan Md. Sakib, Shadman Maitra, Shithi Shah, Faisal Muhammad BJIT Group SD-06 | Dept. of AI Sylhet Dhaka Bangladesh Dept. of Computer Science and Engineering Sylhet Dhaka Bangladesh Ahsanullah University of Science and Technology Leading University Sylhet Dhaka Bangladesh Data and BI Engineering SureCash Sylhet Dhaka Bangladesh

ISBN: (纸本)9781665432405

Drug property prediction poses a complex task in the healthcare domain, because the drug-like molecules greatly vary in chemical structures. Inhibitory concentration (IC50) prediction is essential to reduce the cost and labor of a drug's preclinical and clinical trials, because a lot of drug evaluations (cellular, animal, and clinical) can be spared due to the predicted IC50. In the era of Artificial Intelligence, Drug Discovery processes can benefit from deep learning, which has been widely used in computational chemistry and bioinformatics with state-of-the-art performance. In this paper, we propose a novel (in other words, de novo) graph convolutional network approach, cross-validated by traditional methods like Lipinski's rule of five and PaDEL-described one-hot encoding. The experiment has been carried out on the ChEMBL bioactivity dataset of Acetylcholinesterase protein, achieving an R2 score of 0.52. © 2021 IEEE.

关键词： Bioinformatics

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Interpretable Analysis of Oral Mucosal Lesions Using U-Net and Quantum Approaches

Interpretable Analysis of Oral Mucosal Lesions Using U-Net a...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Aparna M Anna Liza Sibi Prachi Kumari Golla Ram Senthil Kumar T. Somasundaram K. Vindhya Savithri Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Dept. of Artificial Intelligence and Data Science School of AI Amrita Vishwa Vidyapeetham Coimbatore India Department of Mathematics Amrita School of Physical Sciences Amrita Vishwa Vidyapeetham Coimbatore India Dept. of Oral & Maxillofacial Pathology and Microbiology Amrita School of Dentistry AIMS Ponekkara PO Kochi India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Oral mucosal lesions are abnormal changes found in the tissue of the mouth. They can be painful and may include abnormal cell growths, as well as rare tongue and hard- palate disorders. They may result from infections, inflamma- tory processes, trauma, autoimmune conditions, or precancerous changes. A recent study found a 59.6% prevalence of oral mucosal lesions among elderly individuals, highlighting a signif- icant association with factors such as age over 65, male gender, tobacco habits, and denture usage. Thus, lesions can significantly impact a patient's quality of life, necessitating accurate diagnosis and timely intervention. In this study, we use a combination of two advanced technologies-U-Net, a deep learning model, and quantum computing-to analyze medical images of oral health. We use a publicly available dataset containing labeled images classified as ‘Lesion’ and ‘Oral Cavity,’ focusing only on lesion detection. Our results show that while the U-Net model achieves 95.02% segmentation accuracy, the quantum approach using QCNN achieves 74.5% accuracy. Although the quantum model does not outperform U-Net in segmentation, it demonstrates potential for future research in quantum-enhanced medical imaging. To measure how well our method works, we use different evaluation techniques, including Dice Coefficient, Intersection over Union (IoU), accuracy, precision, recall, and F1-score. Our results indicate that while quantum computing has shown high accuracy in previous research for classification tasks, its application to segmentation-particularly in oral lesion detection-faces challenges such as lower accuracy, quantum noise, and hardware constraints. This research highlights the potential of integrating AI and quantum approaches for medical diagnosis while acknowledging the need for further improvements in quantum seamcntation models.

关键词： Deep learning Image segmentation Quantum computing Accuracy Computational modeling Hardware Computational efficiency Lesions Artificial intelligence Medical diagnostic imaging

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Auto Text Correction Using NLP Techniques

Auto Text Correction Using NLP Techniques

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Nalla Srivarsha Gummadi Nithin Shanmugasundaram Hariharan Bibhuti Bhusan Dash Subrata Chowdhury Sudhansu Shekhar Patra Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India School of Computer Applications Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar India Dept. of Computer Science and Engineering Sreenivasa Institute of Technology and Management Studies(A) Chittoor Andhra Pradesh India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Auto text correction is a fundamental tool in modern Natural Language Processing (NLP) that upgrades user involvement by decreasing typographical and linguistic errors in composed content. This extends leverages progressed NLP procedures to create a strong and proficient auto-correction framework that precisely predicts and corrects content input errors in real-time. The framework utilizes a combination of deep learning models, such as sequence-to-sequence models and transformers, in conjunction with conventional strategies like n-grams and edit distance algorithms. By analyzing the context and structure of input sentences, the model is able to offer relevant rectifications, making it versatile to different dialects and user composing styles. Moreover, we investigate diverse assessment measurements and compare different calculations to optimize the precision and productivity of the correction system. The arrangement has potential applications in word processors, mobile keyboards, and other content input platforms where high accuracy and low latency are crucial.

关键词： Productivity Program processors Keyboards Linguistics Transformers Prediction algorithms Natural language processing Real-time systems Low latency communication Mobile computing

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Hybrid Deep Learning Approach for Nonfunctional Software Requirements Classifications

Hybrid Deep Learning Approach for Nonfunctional Software Req...

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International Conference on Communication, Computing and Digital Systems (C-CODE)

作者： Kiramat Rahman Anwar Ghani Rashid Ahmad Syed Haseeb Sajjad Dept. of Software Engineering International Islamic University Islamabad Pakistan Big Data Research Center Jeju National University Jeju South Korea Dept. of Computer Science Inernational Islamic University Islamabad Pakistan

This paper explores the concept of algorithmic hybridization, which involves combining various machine learning (ML) algorithms to enhance performance by utilizing the benefits of both simultaneously. This study presents a framework that utilizes a combination of long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) with artificial neural networks (ANN) to classify non-functional requirements (NFR). The task of NFR classification is challenging due to the scarcity of supervised learning data. The effectiveness of the proposed approach was assessed by comparing the performance of our integrated model with that of single LSTM and BiLSTM models. To conduct this evaluation, we combined two datasets consisting of 1000 non-functional requirements (NFR). The experimental findings revealed that the proposed approach is effective as the hybrid models exhibited better precision, recall, and F-1 score compared to its counterparts.

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Design and Behavioral Analysis of Students during Examinations using Distributed Machine Learning

Design and Behavioral Analysis of Students during Examinatio...

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2023 IEEE International Conference on Research Methodologies in knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Patil, Sangram Sungheetha, Akey Bansode, G.S. Kalaivaani, P.T. Kandaswamy, Vijay Anand Jagannathan, Sharath Kumar Department of Computer Science & Engineering D Y Patil Agricultural and Technical University Talsande Maharashtra Kolhapur India Dept. of CSE Alliance College of Engineering and Design Alliance University Karnataka Bangalore India University Guntur AP Vaddeswaram India Tamilnadu Tiruchengode India Electronics and Communication Engineering R.M.K. Engineering College Chennai India Data Science Institute Frank J. Guarini School of Business Saint Peter's University New Jersery NJ07306 United States

ISBN: (纸本)9798350305708

This research focuses on the design and behavioral analysis of students during examinations through the utilization of Distributed Machine Learning (DML). With the increasing integration of technology in education, the ability to monitor and understand student behavior during exams has become crucial for educators and institutions. DML offers a powerful approach to process and analyze data collected from various sources, such as online exams, wearable devices, and eye-tracking sensors. This study proposes a framework that employs DML techniques to gather, process, and interpret student behavioral data in real-time. The collected data encompasses factors like gaze patterns, typing speed, hesitation intervals, and physiological responses. By employing machine learning algorithms distributed across multiple nodes, the system aims to provide insights into student engagement, cognitive load, and potential instances of academic misconduct. The analysis of behavioral patterns during examinations can aid educators in adapting teaching methodologies, providing timely interventions, and enhancing the overall learning experience. The proposed approach highlights the potential of DML in the field of education and contributes to the broader discourse on leveraging technology for pedagogical improvement. © 2023 IEEE.

关键词： Cognitive Load Distributed Machine Learning Educational Technology Examination Monitoring Online Learning Student Behavior Analysis

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A Novel Design of Secured BlockChain Assisted Land Registration Mechanism by using Enhanced Verification Principles

A Novel Design of Secured BlockChain Assisted Land Registrat...

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Research Methodologies in knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： R. T. Prabu S. Diwakaran R. Hemalatha V. Senthilkumar Madhini. M B. Thiyaneswaran Dept. Electronics and Communication Saveetha School of Engineering Chennai India Dept. Master of Comp. Applns Kalasalingam Academy of Res. & Edn. Srivilliputhur India Dept. Computer Science Engineering St.Joseph College of Engineering Chennai India Department of Data Science VelTech Multitech Engg. College Chennai India Dept. Electronics and Communication S.A. Engineering College Chennai India Dept. Electronics & Communication Sona College of Technology Salem India

In the contemporary world, safeguarding data has assumed paramount importance, prompting numerous industrial sectors to fortify their defenses against potential hackers. In this landscape, blockchain has emerged as a cutting-edge technology that empowers peers to securely exchange various assets, including currency, financial documents, and land properties. Operating as an open-source public network, blockchain eliminates the need for a central authority. Instead, it facilitates peer-to-peer interactions, ensuring that transactions, value transfers, and data sharing are authenticated by all interconnected nodes within the network. The conventional process of land registration, characterized by its sluggishness and complexity, involves multiple intermediaries and is susceptible to instances of fraudulent and counterfeit land transfers. To address these challenges, the paper introduces a conceptual framework for blockchain-based land registry systems. By analyzing existing frameworks and scrutinizing the robust strategies and concepts they employ, the paper establishes connections that culminate in the creation of this conceptual framework. The proposed solution in the paper revolves around the secure transfer of land ownership through blockchain technology. This solution circumvents the need for intermediaries, allowing buyers and sellers to conduct land ownership transactions seamlessly using the Ethereum network. By leveraging the power of blockchain, this solution aims to mitigate the vulnerabilities and inefficiencies inherent in traditional land registration processes, ushering in a more secure, efficient, and transparent approach to managing land ownership transactions.

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