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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： M. Bhanumathi M. Sangeetha F. Mary Harin Fernandez A. Ganesh Ramachandran S.K. Saravanan Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India Department of Computer Engineering University of Technology and Applied Sciences Nizwa Sultanate of Oman Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computer Science Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computers Science and Engineering BigData and Network Security Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

In the study, three leading reinforcement learning algorithms Deep Q-Network (DQN), Proximal Policy Optimization (PPO) and Asynchronous Advantage Actor – Critic (A3Cc are applied in a number of real-world robotics tasks. The focus of our analysis is on performance indicators, computational efficiencies across multiple environments as well with respect to customer satisfaction rating. The DQN demonstrated good performance, with the success rate of up to 85% in object manipulation tasks and confirmed its reliability under controlled lab conditions where it made only a 5 percent error. PPO excelled in autonomous navigation (82%) and was the most energy-efficient option, requiring just 10 hours for training and using only twelve KWH of power. Compared with A3C, the method performed to a higher level of adaptability and robustness, especially in object manipulation (90% success rate) and dynamic environments (10% error rates applicable for outdoor variable weather conditions). But A3C was also the most automatable, which required 15 hours to train and consumed an astonishing amount of electricity – 18 kWh. Regarding customer satisfaction, PPO performs the best in end-user rating on user interface; A3C shows a preference with engineers due to its advanced features and flexibility. The focus of this study is significant trade-offs between performance, efficiency and user preference in real robotics applications that advocate for individual tailoring selections as well as deployments based on task specifics nature.

关键词： Training Error analysis Heuristic algorithms Customer satisfaction Reinforcement learning Robustness Computational efficiency

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Retraction Note: A Systematic Survey on Implementation of Fuzzy Regression Models for Real Life Applications

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Archives of Computational Methods in engineering 2025年 1-2页

作者： Khan, Mufala Kumar, Rakesh Aledaily, Arwa N. Kariri, Elham Viriyasitavat, Wattana Yadav, Kusum Dhiman, Gaurav Kaur, Amandeep Sharma, Ashutosh Vimal, S. Department of Mathematics Lovely Professional University Phagwara India College of Computer Science and Engineering University of Ha’il Hail Saudi Arabia College of Computer Science and Engineering Prince Sattam Bin Abdulaziz University Al‑Kharj Saudi Arabia Business Information Technology Division Department of Statistics Faculty of Commerce and Accountancy Chulalongkorn University Bangkok Thailand Department of Computer Science Government Bikram College of Commerce Patiala India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Gharuan India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajaplayam India

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Empowering Patient Care: A Novel Patient Drug Recommendation System Using Artificial Intelligence with Modified Learning Strategy 1

Empowering Patient Care: A Novel Patient Drug Recommendation...

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1st IEEE International Conference on Computing and Data Science, ICCDS 2024

作者： Samuthira Pandi, V. Kavitha, V.R. Shinev, S. Arumai Roopa Chandrika, R. Valarmathy, A.S. Glory, E. Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Tamil Nadu Chennai India R.M.K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Chennai India S A Engineering College Department of CSE Tamil Nadu Chennai India Karpagam Academy of Higher Education Department of CSE TamilNadu Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Saveetha University Department of Biosciences Chennai India

ISBN: (纸本)9798350365337

In the era of personalized medicine, the demand for efficient and accurate drug recommendation systems has escalated. This paper introduces a pioneering approach to enhance patient care through a novel Patient Drug Recommendation System (PDRS). Leveraging the power of machine learning (ML), specifically Cascaded Multi-Layer Perceptrons (MLP), our system aims to provide precise and personalized drug recommendations based on an extensive Drug Review Dataset. The proposed PDRS capitalizes on the inherent complexities of drug reviews, extracting valuable insights and patterns from patient feedback. The cascaded MLP architecture is designed to sequentially process and analyze diverse aspects of drug reviews, capturing nuanced information and correlations that may be overlooked by traditional recommendation systems. Our methodology involves preprocessing the drug review dataset to address noise and ensure data quality. Feature extraction techniques are applied to distill meaningful information, and the cascaded MLP model is trained using a robust set of labeled data. The system's performance is evaluated on various metrics, with a remarkable accuracy of 97%, signifying its efficacy in predicting suitable drug recommendations for patients. The cascaded MLP architecture demonstrates superior adaptability, allowing the system to comprehend intricate relationships between patients, drugs, and their respective effects. The achieved accuracy underscores the potential of our PDRS to revolutionize clinical decision-making, fostering a more personalized and efficient approach to prescribing medications. © 2024 IEEE.

关键词： Feature extraction

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Algorithmic Innovations in Machine Learning for Drug Discovery and Pharmaceutical Innovations

Algorithmic Innovations in Machine Learning for Drug Discove...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： M. Sangeetha F. Mary Harin Fernandez A. Ganesh Ramachandran S. K. Saravanan M. Bhanumathi Department of Computer Engineering University of Technology and Applied Sciences Nizwa Sultanate of Oman Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computer Science Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computers Science and Engineering BigData and Network Security Saveetha School of Engineering SIMATS Chennai Tamil Nadu Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

Algorithmic advancements in machine learning have transformed drug discovery and pharmaceutical innovation. This study examines how machine learning might speed medication development and pharmaceutical innovation. Data from genomes, chemistry, and clinical trials is collected to start the research. Careful data cleansing, normalization, and feature engineering prepare the data for model building. Random Forest, SVM, NN, Decision Tree, and Logistic Regression are tested for prediction. ROC AUC results show that Random Forest is the most accurate model at 86%. Ensemble approaches have drug discovery potential. Beyond accuracy, models are assessed for precision, false positive, and false negative rates. Drug discovery relies on precision to measure positive predictions. The Random Forest model makes the most accurate positive predictions at 0.87. However, false positives and negatives also matter in drug discovery. The Decision Tree model contains the falsest positives (35), underlining the need to reduce them to save resources. The Neural Network approach reduces false negatives to 12, ensuring good medication candidates are not overlooked. The results show that different machine learning models have trade-offs, underscoring the necessity for a personalized strategy depending on objectives and restrictions. Algorithmic breakthroughs in pharmaceutical innovation reduce medication development time by 40% and cost by 25%, according to the study.

关键词： Support vector machines Drugs Technological innovation Logistic regression Machine learning algorithms Genomics Prediction algorithms

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Machine Learning-Assisted Protein Structure Prediction: An AI Approach for Biochemical Insights 9

Machine Learning-Assisted Protein Structure Prediction: An A...

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9th International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Mahendran, S. Radha Dogra, Akriti Mendhe, Dinesh Babu, S. B. G. Tilak Dixit, Shriniket Singh, Surya Pratap Vels Institute of Science Technology And Advanced Studies Department of Bioinformatics Chennai Pallavaram India Manav Rachna Dental College Department of Oral & Maxillofacial Surgery Haryana Faridabad India Rutgers State University of New Jersey Office of Research Computing New BrunswickNJ United States Aditya Engineering College Department of Ece Surampalem India School of Computer Science Engineering Vellore Institute of Technology Tamil Nadu Vellore India Upums Department of Multi-Disciplinary Research Unit UP Etawah India

ISBN: (纸本)9798350365092

Recently, machine learning has changed the field of bioinformatics by making it possible to identify and understand protein structures in ways that were not possible before. This paper gives a thorough examination of a unique way for guessing protein structure. It uses complex machine learning algorithms to give you deep biological information. We use cutting-edge deep learning models along with traditional computer methods to improve the accuracy and usefulness of predicting protein structures. The suggested AI-assisted method uses a variety of protein structures and attention processes to find complex spatial connections and long-range interactions within protein sequences. It does this by using convolutional neural networks (CNNs), recurrent neural networks (RNNs), and CNNs. Using machine learning to predict protein structures can be very useful, and this work shows how it can effectively connect computational biology and experimental research. Our method not only makes predictive modeling better, but it also gives researchers a useful way to learn more about the complex links between protein structure and function as the field grows. The study's results could speed up the process of finding new drugs and help us learn more about how different diseases work at the molecular level. © 2024 IEEE.

关键词： Proteins

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Detection of Malicious Social Bots with the Aid of Learning Automata on Twitter 5

Detection of Malicious Social Bots with the Aid of Learning ...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Vashisht, Swati Gupta, Sushil Kumar Fegade, Atul Dhondiyal, Shiv Ashish Kumar, Rohit Revathy, G. Amity University Uttar Pradesh Greater Noida India Maharashtra Pune India Balaji Institute of Modern Management Department of Marketing Maharashtra Pune India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Lloyd Institute of Engineering and Technology Department of Humanities Uttar Pradesh Greater Noida India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350398267

Violent social bots automate social interactions, create fictitious profiles to spread destructive propaganda, or assume the identities of followers to make misleading tweets. Furthermore, malicious social bots disseminate malicious root URLs, which reroute requests from online social media agents to certain malicious servers. Therefore, one of the most crucial jobs of the Twitter network is to distinguish between actual drug users and active social bots. Instead of taking as long to remove as social graph-based features, URL-based features can identify the cruel conduct of social bots. It's difficult for malicious social bots to change URL redirect chains. This part offers a literacy automaton-grounded vicious social bot discovery (LA-MSBD) for safe (drug) agents on the Twitter network by fusing URL-based functionality with a trust computation model. The research discussed in this paper focuses on designing, utilizing, and evaluating robotic sensors based on deep literacy models rather than adding metadata about position or birthpoint counting. This paper also demonstrates how deep literacy models can compete with conventional machine-ability idioms. The findings of this study demonstrate that in-depth comprehension models can be made more complex by utilizing pre-trained models. © 2022 IEEE.

关键词： Graphic methods

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Optimizing complex event forecasting 22

Optimizing complex event forecasting

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16th ACM International Conference on Distributed and Event-Based Systems, DEBS 2022

作者： Stavropoulos, Vasileios Alevizos, Elias Giatrakos, Nikos Artikis, Alexander Institute of Informatics and Telecommunications Ncsr Demokritos Greece School of Electrical and Computer Engineering Technical University of Crete Greece Athena Research Center Institute for Management of Information Systems Greece Department of Maritime Studies University of Piraeus Greece

ISBN: (纸本)9781450393089

In Complex Event Recognition (CER), applications express business rules in the form of patterns and deploy them in a CER Engine which seeks the occurrence of such patterns on incoming streams. This is useful for practical applications which rely on the timely detection of patterns to support critical decisions. One step further, stakeholders want to act proactively, accurately forecasting the occurrence of patterns on raw streams well ahead of time to better schedule their decisions. This calls for making the transition from CER to Complex Event Forecasting (CEF). In CEF, stochastic models of future behavior are embedded into the event processing loop to project into the future the sequence of events that have occurred so far and to estimate the likelihood of the imminent occurrence of more complex patterns. CEF performance engages the stochastic model's training speed and forecast accuracy. In turn, these performance dimensions are affected by few parameters. However, CEF parameter tuning so that optimal CEF performance is achieved is a non-trivial task. This is due to the fact that there is an infinite number of possible parameter combinations, each affecting CEF performance in ways which are hard to predict. In this work, we introduce the first CEF Optimizer that gracefully automates CEF parameter tuning decisions, rapidly cherry picking good CEF configurations. We detail the novel internal architecture of our CEF Optimizer and present an elaborate empirical analysis on two applications that illustrates the effectiveness of our optimization approach. © 2022 ACM.

关键词： Stochastic models

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Two-Stage Representation Refinement Based on Convex Combination for 3-D Human Poses Estimation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6500-6508页

作者： Chen, Luefeng Cao, Wei Zheng, Biao Wu, Min Pedrycz, Witold Hirota, Kaoru China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Macau University of Science and Technology Institute of Systems Engineering Taipa999078 China Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Tokyo Institute of Technology Yokohama226-8502 Japan

In the human pose estimation task, on the one hand, 3-D pose always has difficulty in dividing different 2-D poses if the view is limited;on the other hand, it is hard to reduce the lifting ambiguity because of the lack of depth information, it is an important and challenging problem. Therefore, two-stage representation refinement based on the convex combination for 3-D human pose estimation is proposed, in which the two-stage method includes a dense-spatial-temporal convolutional network and a local-to-refine network. The former is applied to determine the features between each video frame;the latter is used to get the different scales of pose details. It aims to address the difficulty of estimating 3-D human pose from 2-D image sequences. In such a way, it can better use the relations between every frame in the sequence of the pose video to produce more accurate results. Finally, we combine the above network with a block called convex combination to help refine the 3-D pose location. We test the proposed approach on both Human3.6m and MPII datasets. The result confirms that our method can achieve better performance than improved CNN supervision, a simple yet effective baseline, and coarse-to-fine volumetric prediction. Besides, a robustness test experiment is carried out for the proposed method while the input is interrupted. The result verifies that our method shows better robustness. © 2024 IEEE.

关键词： Convolution

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Sparse-view CBCT reconstruction using meta-learned neural attenuation field and hash-encoding regularization

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computers in Biology and Medicine 2025年 189卷 109900-109900页

作者： Shin, Heejun Kim, Taehee Lee, Jongho Chun, Se Young Cho, Seungryong Shin, Dongmyung Artificial Intelligence Engineering Division Radisen Co. Ltd. Seoul Korea Republic of Laboratory for Imaging Science and Technology Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Intelligent Computational Imaging Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Medical Imaging and Radiotherapy Laboratory Department of Nuclear and Quantum Engineering Korea Advanced Institute of Science and Technology Daejean Korea Republic of

Cone beam computed tomography (CBCT) is an emerging medical imaging technique to visualize the internal anatomical structures of patients. During a CBCT scan, several projection images of different angles or views are collectively utilized to reconstruct a tomographic image. However, reducing the number of projections in a CBCT scan while preserving the quality of a reconstructed image is challenging due to the nature of an ill-posed inverse problem. Recently, a neural attenuation field (NAF) method was proposed by adopting a neural radiance field algorithm as a new way for CBCT reconstruction, demonstrating fast and promising results using only 50 views. However, decreasing the number of projections is still preferable to reduce potential radiation exposure, and a faster reconstruction time is required considering a typical scan time. In this work, we propose a fast and accurate sparse-view CBCT reconstruction (FACT) method to provide better reconstruction quality and faster optimization speed in the minimal number of view acquisitions ( © 2025 Elsevier Ltd

关键词： Image reconstruction

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A Review of Clinical Applications for Super-resolution Ultrasound Localization Microscopy

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Current Medical Science 2022年第1期42卷 1-16页

作者： Hui-ming YI Matthew RLowerison Peng-fei SONG Wei ZHANG eDpartment of Medical Ultrasound Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Eckman Institute for Advanced Science and Technology University of Illinois at Urbana-ChampaignUrbana 61801USA Department of Electrical and Computer Engineering University of Illinois at Urbana-ChampaignUrbana 61801USA

Microvascular structure and hemodynamics are important indicators for the diagnosis and assessment of many diseases and *** structural and functional imaging of tissue microvasculature in vivo is a clinically significant objective for the development of many imaging ***-enhanced ultrasound(CEUS)is a popular clinical tool for characterizing tissue microvasculature,due to the moderate cost,wide accessibility,and absence of ionizing radiation of ultrasound.

关键词： contrast-enhanced ultrasound super-resolution ultrasound microvascular imaging microbubbles

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