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2nd International Conference on Multidisciplinary engineering and Applied science, ICMEAS 2023

作者： Ifijeh, Ayodele H. Adetiba, Emmanuel Adewale, Adeyinka Thakur, Surrendra Moyo, Sibusiso Emmanuel, Dare O. Ukpong, Udeme C. Dept. of Electrical and Information Engineering Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Covenant University Dept. of Electrical and Information Engineering Ota Canaanland Nigeria Durban University of Technology KZN E-skills CoLab Durban4001 South Africa School for Data Science and Computational Thinking Stellenbosch University Department of Mathematical Sciences Stellenbosch7602 South Africa

ISBN: (纸本)9798350358834

Advancement in wireless transmission has enabled the communication among multiple devices all over the world, as well as expanding the frontiers of wireless Internet access. As a result of these advancement, numerous devices are questing for wireless connectivity thereby congesting the existing spectrum. If new measures are not taken to reduce depletion of the reserved radio resource, new allocations of wireless Internet access will be impossible, thereby risking the development of the entire wireless ecosystem. The demands for resourceful spectrum consumption have continually driven researchers and stakeholders in the wireless industry to search for spare radio spectrum to offer affordable and reliable wireless connectivity. The TV broadcasting is not fully utilizing the spectrum in some geographical zone. These TV unused spaces called TV White Spaces (TVWS), are well-thought-out to be the probable future resolutions for unavailability of spectrum. The low frequency band (470 to 694 MHz) of the unutilized TV channel has exceptional broadcast features over extensive distances and enables transmissions through high-rise buildings, trees, and mountains. These features allow the applications of TVWS in Internet of Things (IoT), wireless sensor network, wireless connectivity, wireless switching, smart grid and cities, automated transportation, and logistics as well as connectivity in cloud infrastructure. This paper presents a study on the advantages and benefits of television white space as an alternative to wireless broadband connectivity. © 2023 IEEE.

关键词： Tall buildings

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Hotel Booking Cancellation Prediction Using Applied Bayesian Models

arXiv

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arXiv 2024年

作者： Jishan, Md Asifuzzaman Singh, Vikas Ghosh, Ayan Kumar Alam, Md Shahabub Mahmud, Khan Raqib Paul, Bijan Institute of Computer Science and Computational Science Universität Potsdam Germany Faculty of Statistics Technische Universität Dortmund Germany Department of Computer Science Louisiana Tech University RustonLA712723 United States Department of Computer Science and Engineering University of Nebraska-Lincoln LincolnNE685034 United States

This study applies Bayesian models to predict hotel booking cancellations, a key challenge affecting resource allocation, revenue, and customer satisfaction in the hospitality industry. Using a Kaggle dataset with 36,285 observations and 17 features, Bayesian Logistic Regression and Beta-Binomial models were implemented. The logistic model, applied to 12 features and 5,000 randomly selected observations, outperformed the Beta-Binomial model in predictive accuracy. Key predictors included the number of adults, children, stay duration, lead time, car parking space, room type, and special requests. Model evaluation using Leave-One-Out Cross-Validation (LOO-CV) confirmed strong alignment between observed and predicted outcomes, demonstrating the model’s robustness. Special requests and parking availability were found to be the strongest predictors of cancellation. This Bayesian approach provides a valuable tool for improving booking management and operational efficiency in the hotel industry. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

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Big Data Solutions for Predictive Healthcare Analytics: Enhancing Patient Outcomes and Operational Efficiency 3

Big Data Solutions for Predictive Healthcare Analytics: Enha...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Kumar, Nishi Kant Nandhini, J. Vinitha, S. Madhubala, P. Trimukhe, Dipmala Bhagwat Salunkhe, Kalpana Birla Institute of Technology Department of Computer Science and Engineering Mesra Off Campus Jharkhand Deoghar India Jai Shriram Engineering College Department of Ece Tirupur India Jerusalem College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India Bharathiyar Institute of Engineering for Women Department of Computer Science and Engineering Salem India Government College of Engineering & Research Department of Instrumentation Avasari Khurd India School of Computational Sciences Jspm University Wagholi Pune India

ISBN: (纸本)9798331542375

The embracing of Big Data solutions in healthcare comes with widely prospect to enhance patient satisfaction and increase organizational effectiveness. The research aims to use Predictive techniques and models coupled with modern data integration systems to improve the health care services. The components of the suggested methodology are deep neural networks for patient monitoring and prognosis, wearable devices, and operational prognosis models for resources use. From the practice of this approach, we have noticed marked increase in the patient monitoring frequency, efficiency of timely intervention, and generalization of health care organization productivity factors such as wait time and other resource utilization. In addition, the Onshape plan carried measurable benefits for the patient results such as satisfaction, reduced mortality, improved chronic disease, and the lesser hospital readmission. The results emphasize that the opportunities that Big Data and predictive analytics offer for healthcare can be indeed immense - offering improved care and more effective outcomes at a lower total cost. This research focuses on how Big Data solutions can help create a better future of healthcare improving the clinical and operational activities in this sphere and, thus, the population's health. © 2025 IEEE.

关键词： Patient treatment

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Enhancing Sentiment Analysis on SEED-IV Dataset with Vision Transformers: A Comparative Study 23

Enhancing Sentiment Analysis on SEED-IV Dataset with Vision ...

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Proceedings of the 2023 11th International Conference on Information Technology: IoT and Smart City

作者： Imad Eddine Tibermacine Ahmed Tibermacine Walid Guettala Christian NAPOLI Samuele Russo Department of Computer Automation and Management Engineering Sapienza Unviersity of Rome Italy Department of Computer Science University of Biskra Algeria Department of Computer Automation and Management Engineering Sapienza Unviersity of Rome Italy and Institute for Systems Analysis and computer Science Italian National Research Council Italy and Department of Computational Intelligence Czestochowa University of Technology Poland Department of Psychology Sapienza Unviersity of Rome Italy

ISBN: (纸本)9798400709043

This paper introduces a new approach to emotion classification utilising deep learning models, specifically the Vision Transformer (ViT) model, in the analysis of electroencephalogram (EEG) signals. A dual-feature extraction approach was implemented in our study, utilising Power Spectral Density and Differential Entropy, to analyse the SEED IV dataset. This methodology resulted in the detailed classification of four distinct emotional states. The ViT model, which was originally designed for image processing, has been successfully applied to EEG signal analysis. It demonstrated remarkable performance by attaining a test accuracy of 99.02% with little variance. Notably, it outperformed conventional models like GRUs, LSTMs, and CNNs in this context. The findings of our study indicate that the ViT model has a high level of effectiveness in accurately identifying complex patterns present in EEG data. Specifically, the precision and recall rates achieved by the model surpass 98%, while the F1 score is estimated to be about 98.9%. The results of this study not only demonstrate the efficacy of transformer-based models in analysing cognitive states, but also indicate their considerable potential in improving systems for sympathetic human-computer interaction.

关键词： Classification Deep Learning EEG Signal Analysis Vision Transformer

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Storage Solution and Security Transmission in Image Sensing Using Blockchain Technology in Internet of Things

Storage Solution and Security Transmission in Image Sensing ...

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Communication, computer sciences and engineering (IC3SE), International Conference on

作者： Vijaya Gunturu Nekkanti Renu Navdeep Dhaliwal Ippa Sumalatha Anandhi R J Gourab Dutta Department of ECE School of Engineering SR University Warangal Telangana India Department of Computer Science and Engineering Vignan's Institute of Information Technology (A) Duvvada Visakhapatnam Lovely Professional University Phagwara India Institute of Aeronautical Engineering Dundigal Hyderabad Department of Information Science Engineering New Horizon College of Engineering Bangalore Department of Computational Sciences Brainware University Kolkata West Bengal India Department of Computer Science & Engineering University of Kalyani Kalyani West Bengal India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

The number of Internet of Things (IoT) devices has grown dramatically with the technology's rapid development. Higher security standards have so been proposed for the administration, transfer, and archiving of vast amounts of IoT data. But security problems like data theft and forgeries are likely to happen while IoT data is being transmitted. Furthermore, the majority of data storage options now in use are centralized, meaning that a centralized server handles both data maintenance and storage. The confidentiality of IoT data would be seriously jeopardized once a hostile assault targets the server. Given the aforementioned security concerns, a secure transmission as well as storage solution for blockchain sensing images in the Internet of Things is put forth. Therefore, to enable effective secure data storage in Internet of Things-related smart computing systems, develop and build a novel blockchain-based artificial intelligence model. We also demonstrated the operation of the system framework. Upon conducting a thorough security study, we have determined that our suggested solution possesses a strong potential to address the majority of security issues that conventional systems encounter. Furthermore, our suggested method can be used for any file-changing wireless Internet of things network that requires the exchange of multimedia data, including traffic data from smart cities, wearable device data, healthcare data, etc.

关键词： computational modeling Memory Data models Blockchains Encryption Sensors Security

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Patient-specific coronary angioplasty simulations – a mixed-dimensional finite element modeling approach

arXiv

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arXiv 2024年

作者： Datz, Janina C. Steinbrecher, Ivo Meier, Christoph Hagmeyer, Nora Engel, Leif-Christopher Popp, Alexander Pfaller, Martin R. Schunkert, Heribert Wall, Wolfgang A. Institute for Computational Mechanics Technical University of Munich Germany Department of Cardiology Deutsches Herzzentrum München Technical University of Munich Germany Institute for Mathematics and Computer-Based Simulation University of the Bundeswehr Munich Germany Pediatric Cardiology Cardiovascular Institute Institute for Computational and Mathematical Engineering Stanford University United States Munich Institute of Biomedical Engineering Technical University of Munich Germany

Coronary angioplasty with stent implantation is the most frequently used interventional treatment for coronary artery disease. However, reocclusion within the stent, referred to as in-stent restenosis, occurs in up to 10% of lesions. It is widely accepted that mechanical loads on the vessel wall strongly affect adaptive and maladaptive mechanisms. Yet, the role of procedural and lesion-specific influence on restenosis risk remains understudied. computational modeling of the stenting procedure can provide new mechanistic insights, such as local stresses, that play a significant role in tissue growth and remodeling. Previous simulation studies often featured simplified artery and stent geometries and cannot be applied to real-world examples. Realistic simulations were computationally expensive since they featured fully resolved stenting device models. The aim of this work is to develop and present a mixed-dimensional formulation to simulate the patient-specific stenting procedure with a reduced-dimensional beam model for the stent and 3D models for the artery. In addition to presenting the numerical approach, we apply it to realistic cases to study the intervention’s mechanical effect on the artery and correlate the findings with potential high-risk locations for in-stent restenosis. We found that high artery wall stresses develop during the coronary intervention in severely stenosed areas and at the stent boundaries. Herewith, we lay the groundwork for further studies towards preventing in-stent restenosis after coronary angioplasty. Copyright © 2024, The Authors. All rights reserved.

关键词： Stents

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MoSS: Monocular Shape Sensing for Continuum Robots

arXiv

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arXiv 2023年

作者： Shentu, Chengnan Li, Enxu Chen, Chaojun Dewi, Puspita Triana Lindell, David B. Burgner-Kahrs, Jessica Department of Computer Science University of Toronto Canada Department of Mechanical and Industrial Engineering University of Toronto Canada Continuum Robotics Laboratory Department of Mathematical and Computational Sciences University of Toronto Canada Waabi Canada

Continuum robots are promising candidates for interactive tasks in medical and industrial applications due to their unique shape, compliance, and miniaturization capability. Accurate and real-time shape sensing is essential for such tasks yet remains a challenge. Embedded shape sensing has high hardware complexity and cost, while vision-based methods require stereo setup and struggle to achieve real-time performance. This paper proposes a novel eye-to-hand monocular approach to continuum robot shape sensing. Utilizing a deep encoder-decoder network, our method, MoSSNet, eliminates the computation cost of stereo matching and reduces requirements on sensing hardware. In particular, MoSSNet comprises an encoder and three parallel decoders to uncover spatial, length, and contour information from a single RGB image, and then obtains the 3D shape through curve fitting. A two-segment tendon-driven continuum robot is used for data collection and testing, demonstrating accurate (mean shape error of 0.91 mm, or 0.36% of robot length) and real-time (70 fps) shape sensing on real-world data. Additionally, the method is optimized end-to-end and does not require fiducial markers, manual segmentation, or camera calibration. Code and datasets will be made available at https://github. com/ContinuumRoboticsLab/MoSSNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Curve fitting

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Optimized Transfer Learning Model for Lung Cancer Stage Classification Using Computed Tomography Images

Optimized Transfer Learning Model for Lung Cancer Stage Clas...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： R Deepika P. Shanmugam K. Moorthi P. K. Manoj Kumar S. L. Swarna Ramnath. K Department of Artificial Intelligence and Data Science B V Raju Institute of Technology Narsapur Telangana India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Electronics and Communication Engineering Sri Sairam Engineering College Chennai India School of Computational Intelligence Joy University Tirunelveli India Department of Computer Science and Engineering Sona college of Technology Salem Tamilnadu India Department of Computer Science and Engineering K.L.N College of Information Technology Madurai Tamilnadu India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Lung cancer has become one of the primary causes of death worldwide due to cancer, which indicates the necessity of early diagnosis. In this regard, the present work proposes an optimized model of transfer learning based on characterizing different stages of lung cancer by using computed tomography images acquired from the IQ-OTH/NCCD lung cancer database downloadable from Kaggle. It makes use of a two-step process: first, a U-Net model is used on the patient's CT images to reconstruct anatomical features and reduce noise; second, a ShuffleNet architecture is used to predict the patient's CT stage using the reconstructed image. In addition, to enhance classification, PSO was incorporated into the model so that it can optimize the hyperparameters. The accuracy of the model was at 97.85 percent, and it suggests that the model can become a very useful tool for clinicians to bring about earliest identification and staging for lung cancer.

关键词： Accuracy Databases computational modeling Computed tomography Transfer learning Noise Lung cancer Predictive models Image reconstruction Optimization

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On computing probabilistic explanations for decision trees 22

On computing probabilistic explanations for decision trees

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

作者： Marcelo Arenas Pablo Barceló Miguel Romero Bernardo Subercaseaux Department of Computer Science PUC Chile and Institute for Mathematical and Computational Engineering PUC Chile and Millenium Institute for Foundational Research on Data Chile Institute for Mathematical and Computational Engineering PUC Chile and Millenium Institute for Foundational Research on Data Chile and CENIA Chile Faculty of Engineering and Science UAI Chile and CENIA Chile Carnegie Mellon University Pittsburgh

ISBN: (纸本)9781713871088

Formal XAI (explainable AI) is a growing area that focuses on computing explanations with mathematical guarantees for the decisions made by ML models. Inside formal XAI, one of the most studied cases is that of explaining the choices taken by decision trees, as they are traditionally deemed as one of the most interpretable classes of models. Recent work has focused on studying the computation of sufficient reasons, a kind of explanation in which given a decision tree T and an instance x, one explains the decision T(x) by providing a subset y of the features of x such that for any other instance z compatible with y, it holds that T (z) = T (x), intuitively meaning that the features in y are already enough to fully justify the classification of x by T. It has been argued, however, that sufficient reasons constitute a restrictive notion of explanation. For such a reason, the community has started to study their probabilistic counterpart, in which one requires that the probability of T(z) = T(x) must be at least some value δ ∈ (0, 1], where z is a random instance that is compatible with y. Our paper settles the computational complexity of δ-sufficient-reasons over decision trees, showing that both (1) finding δ-sufficient-reasons that are minimal in size, and (2) finding δ-sufficient-reasons that are minimal inclusion-wise, do not admit polynomial-time algorithms (unless PTIME = NP). This is in stark contrast with the deterministic case (δ =1) where inclusion-wise minimal sufficient-reasons are easy to compute. By doing this, we answer two open problems originally raised by Izza et al., and extend the hardness of explanations for Boolean circuits presented by Wäldchen et al. to the more restricted case of decision trees. On the positive side, we identify structural restrictions of decision trees that make the problem tractable, and show how SAT solvers might be able to tackle these problems in practical settings.

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Digital Twin of a Vibration System for Active Geomonitoring of the Environment

Digital Twin of a Vibration System for Active Geomonitoring ...

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International Conference on Actual Problems of Electronic Instrument engineering (APEIE)

作者： Marat S. Khairetdinov Alexander A. Yakimenko Evgenii V. Nishlyaev Institute of Computational Mathematics and Mathematical Geophysics of SB RAS Novosibirsk State Technical University Novosibirsk Russia Department of Computer Science Novosibirsk State Technical University Novosibirsk Russia

The problem of constructing a digital twin of a vibration geophysical system for active geophysical monitoring of the environment is considered. The system is used to probe complex media in the "lithosphere-atmosphere-hydrosphere" complex in order to monitor the dynamically changing geophysical characteristics of media in time and space. First of all, this concerns seismic-volcano-hazardous zones, landslide areas, areas of quarry and nuclear explosions, etc. in order to develop methods for predicting catastrophic phenomena - earthquakes, volcanic eruptions, etc. An example of such a seismically active zone is the Baikal Rift Zone. The functioning of the virtual part of the digital twin of a vibration geophysical system is based on multifactor equations for the propagation and processing of vibration oscillations in complex environments. Such equations determine the main components of the digital twin, the connections between them and the simulation modes that underlie the digital twin. The implementation of the virtual digital model is provided using the Manufacturing and Transportation Simulation System simulation modeling system. The system is a visually interactive processor-oriented simulation modeling system for technical systems and technological processes.

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