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Global Conference for Advancement in Technology (GCAT)

作者： Daniel Nareshkumar. M Desidi Narsimha Reddy A. Sowmiya V. Samuthira Pandi Rayala Sateesh N. Hindumathy Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS University Chennai Tamil Nadu India Data Consultant (Data Governance Data Analytics: Enterprise Performance Management AI&ML) Soniks Consulting LLC Texas USA Department of EEE Rajalakshmi Engineering College Chennai Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of ECE MLR Institute of Technology Hyderabad Department of Artificial Intelligence and Machine Learning Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

Autonomous vehicles (AVs) are a game-changer in the transportation industry; they will alter cityscapes, cut down on accidents, and reimagine how people experience mobility. This study investigates the complex relationship between autonomous vehicles and high-tech AI systems, looking at how these two technologies can influence transportation in the future. The paper highlights crucial developments in sensor fusion, machine learning algorithms, and real-time data processing that are essential for the development of fully autonomous systems by analyzing the present status of AV technology. In the first part of the study, the basic technologies that support AVs are described. These technologies include LiDAR, radar, cameras, and GPS. When taken together, they allow AVs to perceive their surroundings and locate themselves. After that, it delves into how AI is helping AVs understand sensor data, make judgments in real-time, and carry out complicated maneuvers, showcasing how advancements in deep learning, neural networks, and predictive analytics are powering these capabilities. Integrating AVs into current transportation infrastructures presents both potential and obstacles, which the paper delves into. Critical situation safety, legal frameworks, cybersecurity risks, and AI decision-making ethics are all covered. In addition to the environmental advantages of improved traffic flow and decreased emissions, the study delves into the possible socioeconomic implications, including shifts in urban design, public transit, and the labor market. The research delves into the existing deployment tactics and the path for scaling AV operations through a thorough analysis of case studies, pilot projects, and industry reports. It determines how prepared different groups are to accept this revolutionary technology, including the general public, lawmakers, and manufacturers. To sum up, the study highlights how important it is for academic institutions, business executives, and governm

关键词： Ethics machine learning algorithms Urban planning Decision making Sensor fusion data processing Real-time systems Safety Computer security Autonomous vehicles

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Computational Approach for Respiratory Pressure Parameters in Neonatal Ventilation

Computational Approach for Respiratory Pressure Parameters i...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Daria Lipchak Aleksey Chupov Anton Dolganov Aleksei Zhdanov Medical Devices R&D Bureau Joint Stock Company “Production Association “Urals Optical & Mechanical Plant” named after Mr. E. S. Yalamov” Yekaterinburg Russia Engineering School of Information Technologies Telecommunications and Control Systems Ural Federal University named after the first President of Russia B.N. Yeltsin Yekaterinburg Russia Machine Learning and Data Analytics Lab University of Erlangen-Nuremberg Erlangen Germany

In the context of mechanical ventilation, the accurate calculation of secondary pressure parameters is essential for clinicians to effectively assess a patient's clinical condition. In the case of neonatal resuscitation with the T-piece, both the inspiratory and expiratory triggers are manually operated. The device provides a constant flow at the initiation of inspiration and reduces the flow to limit output pressure according to the set peak inspiratory pressure. However, without an algorithmic classification of the inspiration and expiration pressure, data analysis may only be performed postfactum. To address the need for the separate trend analysis of PIP and PEEP in non-invasive mechanical ventilation with a T-piece, there is a requirement to develop and implement an accurate algorithm for respiratory pressure parameters calculation. In this study, we review the current state-of-the-art methods for the calculation of respiratory pressure parameters for non-invasive mechanical ventilation with a T-piece. We then present the development and implementation of an algorithm for respiratory pressure parameters calculation and discuss the results of the analysis. This algorithm is designed to provide accurate and reliable measurements of PIP and PEEP in neonatal resuscitation with the T-piece, allowing clinicians to more effectively monitor and assess a patient's clinical condition during mechanical ventilation. The results demonstrate the effectiveness of the algorithm in accurately calculating PIP and PEEP values, providing valuable insights into the patient's respiratory mechanics and helping to inform clinical decision-making.

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DSGD-CECA: Decentralized SGD with Communication-Optimal Exact Consensus Algorithm

arXiv

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arXiv 2023年

作者： Ding, Lisang Jin, Kexin Ying, Bicheng Yuan, Kun Yin, Wotao Department of Mathematics University of California Los AngelesCA United States Department of Mathematics Princeton University PrincetonNJ United States Google Inc. Los AngelesCA United States Center for Machine Learning Research Peking University Beijing China AI for Science Institute Beijing China National Engineering Labratory for Big Data Analytics and Applications Beijing China Decision Intelligence Lab. Alibaba US BellevueWA United States

Decentralized Stochastic Gradient Descent (SGD) is an emerging neural network training approach that enables multiple agents to train a model collaboratively and simultaneously. Rather than using a central parameter server to collect gradients from all the agents, each agent keeps a copy of the model parameters and communicates with a small number of other agents to exchange model updates. Their communication, governed by the communication topology and gossip weight matrices, facilitates the exchange of model updates. The state-of-the-art approach uses the dynamic one-peer exponential-2 topology, achieving faster training times and improved scalability than the ring, grid, torus, and hypercube topologies. However, this approach requires a power-of-2 number of agents, which is impractical at scale. In this paper, we remove this restriction and propose Decentralized SGD with Communication-optimal Exact Consensus Algorithm (DSGD-CECA), which works for any number of agents while still achieving state-of-the-art properties. In particular, DSGD-CECA incurs a unit per-iteration communication overhead and an Õ(n3) transient iteration complexity. Our proof is based on newly discovered properties of gossip weight matrices and a novel approach to combine them with DSGD’s convergence analysis. Numerical experiments show the efficiency of DSGD-CECA. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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Retraction Note: Efficient user authentication protocol for distributed multimedia mobile cloud environment

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 275-275页

作者： Vivekanandan, Manojkumar Sastry, V. N. Srinivasulu Reddy, U. Center for Mobile Banking (CMB) Institute for Development and Research in Banking Technology (IDRBT) Hyderabad India Machine Learning and Data Analytics Lab Department of Computer Applications National Institute of Technology Tiruchirappalli India

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Importance of Wearable Health Monitoring Systems Using IoMT;Requirements, Advantages, Disadvantages and Challenges

Importance of Wearable Health Monitoring Systems Using IoMT;...

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International Symposium on Computational Intelligence and Informatics

作者： Fahime Khozeimeh Mohamad Roshanzamir Afshin Shoeibi Mohammad Tayarani Darbandy Roohallah Alizadehsani Hamid Alinejad-Rokny Davood Ahmadian Abbas Khosravi Saeid Nahavandi Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Victoria Australia Department of Computer Engineering Fasa University Fasa Iran Internship in Health Data Analytics Program AI-enabled Processes (AIP) Research Centre Macquarie University Sydney Australia School of Architecture Islamic Azad University Taft Taft Iran BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney Sydney NSW Australia Faculty of Mathematics Statistics and Computer Sciences University of Tabriz Tabriz Iran

ISBN: (纸本)9798350398830

Providing better quality of service is one of the most important goals in medical/healthcare systems. The services provided should have features such as low latency, appropriate geographic distribution, and real-time interaction. Internet of Medical Things (IoMT) technology is one of the solutions to achieve high quality of services with these features. The wearable health monitoring systems which have become popular in the last decade are one of the foundations of achievement to these features. In this paper, the importance of wearable devices under IoMT technology is investigated. There are several reasons for using wearable devices under IoMT technology, such as reducing healthcare costs and long waiting times. Therefore, anyone working on wearable devices needs to have a general understanding of how they are designed and function. In fact, the integration of new devices to monitor and alert patients about their physical health is a promising option for improving health care systems around the world. Overcrowding in medical centers will be prevented by upgrading the facilities for the virtual presence of patients. It can also ensure real-time monitoring and reduce operating costs. In addition, it can accelerate treatment of patients and reduce the cost of investment and increase its coverage.

关键词： Costs Medical devices Wearable computers Quality of service Organizations Real-time systems Security

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Fractal Interpolation Densities

SSRN

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SSRN 2023年

作者： Igudesman, Konstantin Tumakov, Maksim Snegina, Svetlana Tumakov, Dmitrii Department of Data Analysis and Machine Learning Financial University under the Government of Russian Federation 4th Veshnyakovsky pr. 4 Moscow111395 Russia Centre for Econometrics and Business Analytics St. Petersburg State University Uni-versitetskaya nab. 7/9 St. Petersburg199034 Russia Kazan Federal University Institute of Computational Mathematics and Information Technologies Russia Department of Legal Regulation of Economic Operations University under the Government of Russian Federation 4th Veshnyakovsky pr. 4 Moscow111395 Russia

Necessary and sufficient conditions are found under which a continuous fractal interpolation function is the distribution density of a continuous random variable. Examples of fractal interpolation densities are consid... 详细信息

关键词： Interpolation

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A Smart Electronic Wheelchair Design Using Artificial Intelligence and Intelligent Sensors Association

A Smart Electronic Wheelchair Design Using Artificial Intell...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： R Balamurugan D. Kanchana B. Anni Princy J Jaisudha P. Malathi E Glory Department of Artificial Intelligence and Machine Learning AJK College of Arts and Science Coimbatore Tamil Nadu India Department of Computer Applications SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of ECE Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences SIMATS Chennai Tamil Nadu India Department of Biosciences Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

Since the beginning of the seventeenth century, wheelchairs have been used to transport hospital patients and those with mobility impairments. The power of people is what spreads wheelchairs. Wheelchair users propel themselves forward using their upper bodies or, more commonly, require the assistance of another person to propel them. In this work, we examine how to build an autonomous electric wheelchair for the disabled at the lowest possible cost without sacrificing quality of design or control. This research presents the planning and development of an AI-enhanced electric wheelchair that can be controlled automatically to climb stairs, making it accessible to those with mobility impairments. The chair’s controls allow the user to move it in any direction and raise the landing and staircases to varying heights. The chair’s DC power source (battery used) may be recharged, allowing it to function for an extended period of time. In addition, a safety system was created that monitors and provides feedback on the user’s pulse rate, as well as the wheelchair’s functioning state (including acceleration, climate, and inclination position). This proposed AI based Wheelchair (AIWC) design is suitable for any aged people and the proposed model is cross-validated with the conventional wheelchair design using mobile based operations handling system called Mobile-Wheelchair (MWC) to test the efficiency of the proposed design in clear manner.

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Tracking one-in-a-million: Large-scale benchmark for microbial single-cell tracking with experiment-aware robustness metrics

arXiv

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arXiv 2024年

作者： Seiffarth, Johannes Blöbaum, Luisa Paul, Richard D. Friederich, Nils Yamachui Sitcheu, Angelo Jovin Mikut, Ralf Scharr, Hanno Grünberger, Alexander Nöh, Katharina Institute of Bio- and Geosciences IBG-1: Biotechnology Forschungszentrum Jülich GmbH Jülich Germany RWTH Aachen University Aachen Germany Multiscale Bioengineering Bielefeld University Bielefeld Germany Institute for Advanced Simulation IAS-8: Data Analytics and Machine Learning Forschungszentrum Jülich GmbH Jülich Germany Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany Institute of Biological and Chemical Systems Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany Microsystems in Bioprocess Engineering Karlsruhe Institute of Technology Karlsruhe Germany

Tracking the development of living cells in live-cell time-lapses reveals crucial insights into single-cell behavior and presents tremendous potential for biomedical and biotechnological applications. In microbial live-cell imaging (MLCI), a few to thousands of cells have to be detected and tracked within dozens of growing cell colonies. The challenge of tracking cells is heavily influenced by the experiment parameters, namely the imaging interval and maximal cell number. For now, tracking benchmarks are not widely available in MLCI and the effect of these parameters on the tracking performance are not yet known. Therefore, we present the largest publicly available and annotated dataset for MLCI, containing more than 1.4 million cell instances, 29k cell tracks, and 14k cell divisions. With this dataset at hand, we generalize existing tracking metrics to incorporate relevant imaging and experiment parameters into experiment-aware metrics. These metrics reveal that current cell tracking methods crucially depend on the choice of the experiment parameters, where their performance deteriorates at high imaging intervals and large cell colonies. Thus, our new benchmark quantifies the influence of experiment parameters on the tracking quality, and gives the opportunity to develop new data-driven methods that generalize across imaging and experiment parameters. The benchmark dataset is publicly available at https://***/doi/10.5281/zenodo.7260136. © 2024, CC BY.

关键词： Cytology

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A case-control study of reaction time deficits in a 3D virtual reality in patients with Post-COVID syndrome

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Scientific Reports 2024年第1期14卷 1-12页

作者： Güttes, Moritz Lucio, Marianna Skornia, Adam Rühl, Eva Steußloff, Fritz Zott, Julia Mardin, Christian Mehringer, Wolfgang Ganslmayer, Marion Michelson, Georg Hohberger, Bettina Department of Ophthalmology Universitätsklinikum Erlangen Friedrich-Alexander-Universität Erlangen- Nürnberg Erlangen Germany Research Unit Analytical BioGeoChemistry Helmholtz Zentrum München Neuherberg Germany Department Artificial Intelligence in Biomedical Engineering (AIBE) Machine Learning and Data Analytics Lab (MaD Lab) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Department of Internal Medicine 1 Universitätsklinikum Erlangen Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

Following the Coronavirus disease 2019 (COVID-19) pandemic, a large number of people continue to report Post-COVID symptoms (PCS). A wide variety of symptoms are described, including fatigue, post-exertional malaise and cognitive impairment. However, adequate objective diagnostic tests for PCS are not yet available. Since the neurotropism of SARS-CoV-2 could be a possible factor for cognitive impairment, the aim of this study was to clarify whether visual reaction time (RT) in a stereoscopic setting can be a marker in PCS diagnostics. The Virtual-Reality-Oculomotor-Test-System (VR-OTS) was used testing binocular vision in 9 gaze directions via stereoscopic stimuli displayed in a virtual reality (VR)-environment (disparity: 275″, 550″, 1100″) in 179 individuals: 130 patients with PCS and 49 healthy controls. The results from the generalized linear models indicated that both group membership (PCS vs. control) and covariates (age and sex) yielded statistically significant different RT across the models. Accounting for the effect of covariates a statistically significant difference of RT was observed between patients with PCS and controls (disparity 275″ p-value = 0.001;550″ p-value = 0.001;1100″ p-value = 0.003). Patients with PCS performed worse in RT in all gaze directions, respectively. Adjusting for the influence of covariates, correct responses (CR) differed significantly between patients with PCS and controls (disparity 275″ p-value < 0.001;550″ p-value = 0.003;1100″ p-value = 0.019). Statistically significant effects of covariates on RT were observed for sex (disparity 275″ p-value = 0.047;550″ p-value = 0.012;1100″ p-value = 0.005) and age (disparity 275″ p-value < 0.001;550″ p-value < 0.001;1100″ p-value < 0.001). However, regarding covariates, no significant effects were found for CR, except for age at disparity 275″ (p-value = 0.035). The present data suggested that the mentioned variables uniquely contributed to explain the variation of the response variabl

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T-Cell Receptor Optimization with Reinforcement learning and Mutation Policies for Precision Immunotherapy

arXiv

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arXiv 2023年

作者： Chen, Ziqi Min, Martin Renqiang Guo, Hongyu Cheng, Chao Clancy, Trevor Ning, Xia Computer Science and Engineering The Ohio State University ColumbusOH43210 United States Machine Learning Department NEC Labs PrincetonNJ08540 United States Digital Technologies Research Centre National Research Council Canada ON Canada Department of Medicine Baylor College of Medicine HoustonTX77030 United States NEC Oncolmmunity AS Oslo Cancer Cluster Innovation Park Ullernchausséen 64 Oslo0379 Norway Biomedical Informatics The Ohio State University ColumbusOH43210 United States Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States

T cells monitor the health status of cells by identifying foreign peptides displayed on their surface. T-cell receptors (TCRs), which are protein complexes found on the surface of T cells, are able to bind to these peptides. This process is known as TCR recognition and constitutes a key step for immune response. Optimizing TCR sequences for TCR recognition represents a fundamental step towards the development of personalized treatments to trigger immune responses killing cancerous or virus-infected cells. In this paper, we formulated the search for these optimized TCRs as a reinforcement learning (RL) problem, and presented a framework TCRPPO with a mutation policy using proximal policy optimization. TCRPPO mutates TCRs into effective ones that can recognize given peptides. TCRPPO leverages a reward function that combines the likelihoods of mutated sequences being valid TCRs measured by a new scoring function based on deep autoencoders, with the probabilities of mutated sequences recognizing peptides from a peptide-TCR interaction predictor. We compared TCRPPO with multiple baseline methods and demonstrated that TCRPPO significantly outperforms all the baseline methods to generate positive binding and valid TCRs. These results demonstrate the potential of TCRPPO for both precision immunotherapy and peptide-recognizing TCR motif discovery. © 2023, CC BY.

关键词： Reinforcement learning

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