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arXiv 2023年

作者： rapetti, Lorenzo Sartore, Carlotta Elobaid, Mohamed Tirupachuri, Yeshasvi draicchio, Francesco Kawakami, Tomohiro Yoshiike, Takahide Pucci, daniele Artificial and Mechanical Intelligence Italian Insititute of Technology Center for Robotics and Intelligent Systems Genoa Italy Machine Learning and Optimisation The University of Manchester Manchester United Kingdom Department of Occupational and Environmental Medicine Epidemiology and Hygiene INAIL Roma Italy Honda R&D Co. Ltd. Saitama Japan

Collaborative robots can relief human operators from excessive efforts during payload lifting activities. Modelling the human partner allows the design of safe and efficient collaborative strategies. In this paper, we present a control approach for human-robot collaboration based on human monitoring through whole-body wearable sensors, and interaction modelling through coupled rigid-body dynamics. Moreover, a trajectory advancement strategy is proposed, allowing for online adaptation of the robot trajectory depending on the human motion. The resulting framework allows us to perform payload lifting tasks, taking into account the ergonomic requirements of the agents. Validation has been performed in an experimental scenario using the iCub3 humanoid robot and a human subject sensorized with the iFeel wearable system. Copyright © 2023, The Authors. All rights reserved.

关键词： Ergonomics

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IoT and machine learning Technology based Smart Shopping System 3

IoT and Machine Learning Technology based Smart Shopping Sys...

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3rd International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022

作者： Arul, U. Prasath, A. Arul Mishra, Suman Shirisha, J. Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India CMR Engineering College Department of Electronics and Communication Engineering Telangana Hyderabad India Department of Electronics and Communication Engineering Telangana Hyderabad India

ISBN: (纸本)9781665462754

In today's environment, traditional shopping is a time-consuming activity. Customers must wait in huge lines at the pay counter in traditional shopping. As a result, both the client and the clerk waste a lot of time and energy. To combat this interaction, we're pushing a fantastic shopping framework built on an Android app. The user must first download and install the software on his or her Android phone in this framework. He then scans the item's Qr code and enters the required amount. After that, the object is put onto the car. Customers buy the items and put them into the trucks in this manner. The client can add or remove items from the vehicle. The customer will be given the recommended items as well as the most convenient method of obtaining them. When the items are purchased, the bill is generated. This bill might be sent to the client's mobile device via internet banking, allowing the consumer to make a quick payment and leave the business sooner. © 2022 IEEE.

关键词： machine learning

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MOdELS MATTEr: THE IMPACT OF SINGLE-STEP rETrOSYNTHESIS ON SYNTHESIS PLANNING

arXiv

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arXiv 2023年

作者： Torren-Peraire, Paula Hassen, Alan Kai Genheden, Samuel Verhoeven, Jonas Clevert, djork-Arné Preuss, Mike Tetko, Igor Institute of Structural Biology Molecular Targets and Therapeutics Center Helmholtz Zentrum München Neuherberg Germany In-Silico Discovery and External Innovation Janssen Research & Development Janssen Pharmaceutica N.V Beerse Belgium Leiden Institute of Advanced Computer Science Leiden University Leiden Netherlands Machine Learning Research Pfizer Worldwide Research Development and Medical Berlin Germany Molecular AI Discovery Sciences R&D AstraZeneca Gothenburg Sweden

retrosynthesis consists of breaking down a chemical compound recursively step-by-step into molecular precursors until a set of commercially available molecules is found with the goal to provide a synthesis route. Its two primary research directions, single-step retrosynthesis prediction, which models the chemical reaction logic, and multi-step synthesis planning, which tries to find the correct sequence of reactions, are inherently intertwined. Still, this connection is not reflected in contemporary research. In this work, we combine these two major research directions by applying multiple single-step retrosynthesis models within multi-step synthesis planning and analyzing their impact using public and proprietary reaction data. We find a disconnection between high single-step performance and potential route-finding success, suggesting that single-step models must be evaluated within synthesis planning in the future. Furthermore, we show that the commonly used single-step retrosynthesis benchmark dataset USPTO-50k is insufficient as this evaluation task does not represent model performance and scalability on larger and more diverse datasets. For multi-step synthesis planning, we show that the choice of the single-step model can improve the overall success rate of synthesis planning by up to +28% compared to the commonly used baseline model. Finally, we show that each single-step model finds unique synthesis routes, and differs in aspects such as route-finding success, the number of found synthesis routes, and chemical validity, making the combination of single-step retrosynthesis prediction and multi-step synthesis planning a crucial aspect when developing future methods. © 2023, CC BY.

关键词： Forecasting

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An Enhanced Traffic Incident detection using Factor Analysis and Weighted random Forest Algorithm 5

An Enhanced Traffic Incident Detection using Factor Analysis...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kanna, P. rajesh Vanithamani, S. Karunakaran, P. Pandiaraja, P. Tamilarasi, N. Nithin, P. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India M. Kumarasamy College of Engineering Department of Master of Computer Applications Tamil Nadu Karur India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Erode India Vel Tech Rangarajan Dr. Sagunthala Department of Computer Science and Engineering R&D Institute of Science and Technology Tamil Nadu Chennai India Nehru Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu Erode India

ISBN: (纸本)9798331518097

Efficient and precise traffic incident detection is essential to reduce casualties and property damage. To address the issue of unbalanced event data, this work offers a novel methodology known as FA-WrF (Factor Analysis and Weighted random Forest). This approach combines dimensionality reduction through factor analysis with classification using weighted random forests, data preparation through the Synthetic Minority Over-Sampling Technique (SMOTE), and data preparation with SMOTE. The included feature of severity detection is highlighted, as are the performance metrics of the FA-WrF model using well-established metrics such as detection level rate, number of false count alarm rate, classification type rate, and area specification under the receiver operating characteristic curve (AUC). The superiority of the FA-WrF model is illustrated using real-world expressway traffic data represented by imbalanced incidents through thorough comparisons with various machine learning-based algorithms such as Support Vector machine, k-nearest neighbours algorithms, Logistic regression algorithms, and decision-making trees. In addition to advancing incident detection, this technique has encouraging prospects for enhancing traffic management procedures and well-informed decision-making procedures in transportation networks. © 2024 IEEE.

关键词： dimensionality reduction

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Exploring the role of Natural Language Processing in Educational Chatbots: A machine learning Approach

Exploring the Role of Natural Language Processing in Educati...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： G Swamy reddy Haider Ali rizvi Harmeet Kaur Kochhar Ajim Shaikh r. Udhayakumar Joshuva Arockia dhanraj School of Sciences SR University Warangal India Artificial Intelligence and Machine Learning Yogananda School of Artificial Intelligence Shoolini University University Institute of Media Studies Chandigarh University Yashwantrao Mohite Institute of Management Bharati Vidyapeeth (Deemed to be University) Pune VelTech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Avadi Chennai TamilNadu Research and Development Cell Lovely Professional University Jalandhar-Delhi Grand Trunk Road Phagwara Punjab India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

AI in the classroom instruction of languages an exciting potential effect that is altering teaching strategies and student involvement. Everything is simply achievable, including chatbots and natural-language processing (NLP) technology. Therefore, in order to verify the influence of AI on language acquisition, this study will evaluate the research, concentrating on programs like ChatGPT that provide individualized and engaged educational opportunities. Inversely, AI-driven chatbots that imitate real-world interactions, such as ChatGPT, supply very adaptable and accessible language training tools. Yet, forthcoming problems with correctness, societal prejudices, and the most suitable usage of AI stress their early promise. These demand that teachers and students improve their digital skills in order to use AI technology in a responsible and effective way. With a focus on present developments and potential futures, it highlights the crucial role artificial intelligence (AI) is playing in revolutionizing language teaching.

关键词： Training Computational modeling machine learning Chatbots Artificial intelligence Informatics Stress

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Hybrid Attention-Based Transformers-CNN Model for Seizure Prediction Through Electronic Health records

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INTErNATIONAL JOUrNAL OF AdVANCEd COMPUTEr SCIENCE ANd APPLICATIONS 2025年第2期16卷 1111-1120页

作者： ramesh, Janjhyam Venkata Naga Misba, M. Balaji, S. Kumar, K. Kiran Muniyandy, Elangovan El-Ebiary, Yousef A. Baker Bala, B. Kiran Elbasir, radwan Abdulhadi . M. Koneru Lakshmaiah Educ Fdn Dept CSE Vaddeswaram Andhra Pradesh India Graph Era Hill Univ Dept CSE Dehra Dun 248002 India Graph Era Deemed Be Univ Dept CSE Dehra Dun 248002 Uttarakhand India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Dept Artificial lntelligence & Machine Learning Chennai India Panimalar Engn Coll Dept CSE Chennai India Koneru Lakshmaiah Educ Fdn Dept CSE Vaddeswaram AP India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Biosci Chennai India Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan UniSZA Univ Fac Informat & Comp Terengganu Malaysia K Ramakrishnan Coll Engn Dept AI & DS Head Dept Trichy India Libyan Author Sci Res Adv Ctr Technol Tripoli State Of Libya Libya

Seizures are a serious neurological disease, and proper prognosis by electroencephalography (EEG) dramatically enhances patient outcomes. Current seizure prediction methods fail to deal with big data and usually need intensive preprocessing. recent breakthroughs in deep learning can automatically extract features and detect seizures. This work suggests a CNN- Transformer model for epileptic seizure prediction from EEG data with the goal of increasing precision and prediction rates by investigating spatial and temporal relationships within data. The innovation is in employing CNN for spatial feature extraction and a Transformer-based architecture for temporal dependencies over the long term. In contrast to conventional methods that depend on hand-crafted features, this method uses an optimization approach to enhance predictive performance for large-scale EEG datasets. The dataset, which was obtained from Kaggle, consists of EEG signals from 500 subjects with 4097 data points per subject in 23.6 seconds. CNN layers extract spatial characteristics, while the Transformer takes temporal sequences in through a Self- Attention Profiler to process EEG's temporality. The suggested CNN-Transformer model also performs well with 98.3% accuracy, 97.9% precision, 98.73% F1-score, 98.21% specificity, and 98.5% sensitivity. These outcomes show how the model identifies seizures while being low on false positives. The results indicate how the hybrid CNN-Transformer model is effective at utilizing spatiotemporal EEG features in seizure prediction. Its high sensitivity and accuracy indicate important clinical promise for early intervention, enhancing treatment for epilepsy patients. This method improves seizure prediction, allowing for better management and early therapeutic response in the clinic.

关键词： Epileptic seizure prediction EEG signal analysis CNN-Transformer model deep learning in healthcare spatiotemporal feature extraction neural network optimization

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Efficacious human metapneumovirus vaccine based on AI-guided engineering of a closed prefusion trimer

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NATUrE COMMUNICATIONS 2024年第1期15卷 6270页

作者： Bakkers, Mark J. G. ritschel, Tina Tiemessen, Machteld dijkman, Jacobus Zuffiano, Angelo A. Yu, Xiaodi van Overveld, daan Le, Lam Voorzaat, richard van Haaren, Marlies M. de Man, Martijn Tamara, Sem van der Fits, Leslie Zahn, roland Juraszek, Jarek Langedijk, Johannes P. M. Janssen Vaccines & Prevent BV Leiden Netherlands Janssen Res & Dev Struct & Prot Sci Spring House PA 19044 USA ForgeBio BV Amsterdam Netherlands J&J Innovat Med Technol R&D New Brunswick NJ USA Univ Amsterdam Van t Hoff Inst Mol Sci Amsterdam Netherlands Univ Amsterdam Informat Inst Amsterdam Machine Learning Lab Amsterdam Netherlands Promaton BV Amsterdam Netherlands

The prefusion conformation of human metapneumovirus fusion protein (hMPV Pre-F) is critical for eliciting the most potent neutralizing antibodies and is the preferred immunogen for an efficacious vaccine against hMPV respiratory infections. Here we show that an additional cleavage event in the F protein allows closure and correct folding of the trimer. We therefore engineered the F protein to undergo double cleavage, which enabled screening for Pre-F stabilizing substitutions at the natively folded protomer interfaces. To identify these substitutions, we developed an AI convolutional classifier that successfully predicts complex polar interactions often overlooked by physics-based methods and visual inspection. The combination of additional processing, stabilization of interface regions and stabilization of the membrane-proximal stem, resulted in a Pre-F protein vaccine candidate without the need for a heterologous trimerization domain that exhibited high expression yields and thermostability. Cryo-EM analysis shows the complete ectodomain structure, including the stem, and a specific interaction of the newly identified cleaved C-terminus with the adjacent protomer. Importantly, the protein induces high and cross-neutralizing antibody responses resulting in near complete protection against hMPV challenge in cotton rats, making the highly stable, double-cleaved hMPV Pre-F trimer an attractive vaccine candidate. The authors report an efficacious vaccine for human metapneumovirus based on a prefusion-stabilized fusion protein trimer that does not require a trimerization domain, designed using an AI convolutional classifier trained to predict optimized complex polar interactions.

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Advancing Vascular dementia diagnosis Through Salp Swarm Optimized Convolutional Neural Network

Advancing Vascular Dementia Diagnosis Through Salp Swarm Opt...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Nivedita Manohara Mathkunti U Ananthanagu Vidya N Abdulpur d. Mangaiyarkarasi Anitha S Department of Computer Science & Engineering Alliance School of Advanced Computing Alliance Univerisity Bengaluru Karnataka India Department of Electrical Engineering Dr.D.Y Patil Pratishthan's College of Engineering Kolhapur Maharastra India Computer Science & Engineering Vel Tech Rangarajan Dr.Sangunthala R&D Institute of Science and Technology Avadi India Department of Artificial Intelligence & Machine Learning Sri Sairam College of Engineering Anekal (VTU) Bengaluru Karnataka India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Vascular dementia (Vd) is a form of cognitive impairment seen in individuals, restricting from problems with blood vessels supplying the brain. These issues lead to strokes or reduced blood flow and gradually lead to the development of vascular dementia. Since it tends to progress slowly, diagnosis often comes after significant brain damage making prevention is crucial. Early identification of risk factors are vital to mitigate the risk of irreversible cognitive decline associated with vascular dementia. Accordingly, this research proposes an optimized neural network model for predicting vascular dementia at its earlier to improve the quality of life. The initial stage in this research involves preprocessing, where the input data is cleaned and normalized. These steps ensure that the vascular dementia dataset is cleaned of inconsistencies and prepared with normalized features for accurate analysis and modeling. Following preprocessing, relevant data features are selected using the chi-square approach, making the classification task easier. Finally, classification is performed utilizing a Convolutional Neural Network (CNN) and for fine-tuning of CNN parameters Salp Swarm Optimization (SSO) Algorithm is implemented. The execution of proposed work is processed using Python software and the outcome reveals superior performance in terms of accuracy and precision. Thereby the proposed method provides better performance with an accuracy of 94.5%, contrasted to existing methods in predicting Vd.

关键词： Accuracy Software algorithms data preprocessing Predictive models Software Classification algorithms Convolutional neural networks Biological neural networks dementia Python

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Affective Computing Has Changed: The Foundation Model disruption

arXiv

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arXiv 2024年

作者： Schuller, Björn Mallol-ragolta, Adria Almansa, Alejandro Peña Tsangko, Iosif Amin, Mostafa M. Semertzidou, Anastasia Christ, Lukas Amiriparian, Shahin MRI Technical University of Munich Germany MDSI - Munich Data Science Institute Germany MCML - Munich Center for Machine Learning Germany GLAM - Group on Language Audio & Music Imperial College London United Kingdom EIHW - Embedded Intelligence for Health Care & Wellbeing University of Augsburg Germany School of Engineering Universidad Autonoma de Madrid Spain AI R&D Team SyncPilot GmbH Germany

The dawn of Foundation Models has on the one hand revolutionised a wide range of research problems, and, on the other hand, democratised the access and use of AI-based tools by the general public. We even observe an incursion of these models into disciplines related to human psychology, such as the Affective Computing domain, suggesting their affective, emerging capabilities. In this work, we aim to raise awareness of the power of Foundation Models in the field of Affective Computing by synthetically generating and analysing multimodal affective data, focusing on vision, linguistics, and speech (acoustics). We also discuss some fundamental problems, such as ethical issues and regulatory aspects, related to the use of Foundation Models in this research area. © 2024, CC BY-NC-SA.

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A review of biomedical datasets relating to drug discovery: a knowledge graph perspective

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BrIEFINGS IN BIOINFOrMATICS 2022年第6期23卷 bbac404-bbac404页

作者： Bonner, Stephen Barrett, Ian P. Ye, Cheng Swiers, rowan Engkvist, Ola Bender, Andreas Hoyt, Charles Tapley Hamilton, William L. AstraZeneca Cambridge Data Sci & Quantitat Biol Discovery Sci Cambridge England AstraZeneca Cambridge Data Sci & Quantitat Biol Dept Discovery Sci Cambridge England R&D AstraZeneca Data Sci & Quantitat Biol Dept Cambridge England AstraZeneca Cambridge England AstraZeneca Gothenburg Mol AI Dept Discovery Sci Gothenburg Sweden Chalmers Univ Technol Machine Learning AI Mol Design Gothenburg Sweden Univ Cambridge Mol Informat Cambridge England Harvard Med Sch Lab Syst Pharmacol Boston MA 02115 USA McGill Univ Comp Sci Montreal PQ Canada Citadel LLC Chicago IL USA

drug discovery and development is a complex and costly process. machine learning approaches are being investigated to help improve the effectiveness and speed of multiple stages of the drug discovery pipeline. Of these, those that use Knowledge Graphs (KG) have promise in many tasks, including drug repurposing, drug toxicity prediction and target gene-disease prioritization. In a drug discovery KG, crucial elements including genes, diseases and drugs are represented as entities, while relationships between them indicate an interaction. However, to construct high-quality KGs, suitable data are required. In this review, we detail publicly available sources suitable for use in constructing drug discovery focused KGs. We aim to help guide machine learning and KG practitioners who are interested in applying new techniques to the drug discovery field, but who may be unfamiliar with the relevant data sources. The datasets are selected via strict criteria, categorized according to the primary type of information contained within and are considered based upon what information could be extracted to build a KG. We then present a comparative analysis of existing public drug discovery KGs and an evaluation of selected motivating case studies from the literature. Additionally, we raise numerous and unique challenges and issues associated with the domain and its datasets, while also highlighting key future research directions. We hope this review will motivate KGs use in solving key and emerging questions in the drug discovery domain.

关键词： knowledge graph embeddings disease-gene prediction drug-target discovery

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