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E-Health and Bioengineering Conference (EHB)

作者： Mohammed Mujahid Ulla Faiz Saumya Kareem Reni Izzet Kale ECE Department Presidency University Bengaluru India Department of Computer Science and Engineering Applied DSP and VLSI Research Group University of Westminster London United Kingdom

ISBN: (数字)9781665485579

ISBN: (纸本)9781665485586

In this article, a new fixed point Leaky Sign Regressor Least Mean Mixed Norm (LSRLMMN) powered adaptive noise cancellation technique is being used for eliminating the Power Line Interference (PLI) noise embedded in the ElectroCardioGram (ECG) signal. The fixed point LSRLMMN powered noise cancellation technique used in this article has been completely quantized. The intention for the extensive quantization study and modeling approach was with a view to the physical integrated circuit implementation. All the modeling and simulation studies were carried out at the bit-level with various loss of precision schemes to ensure compliance with the set specification. The filter coefficients and all the data paths are quantized in order to establish at a high-level behavioral level of the parameters for a decreased complexity in integrated circuit implementation.

关键词： Quantization (signal) Upper bound Adaptive filters Interference Filtering algorithms Electrocardiography Noise cancellation

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EEG-Based VR Scene Adaptive Generation System for Regulating Emotion

EEG-Based VR Scene Adaptive Generation System for Regulating...

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Virtual Reality (ICVR), IEEE International Conference on

作者： Hui Liang ShiQing Liu Yi Wang Junjun Pan Jialin Fu Yingkai Yuan Department of Software Engineering Zhengzhou University of Lightindustry Zheng zhou China Department of Rehabilitation General Hospital of Pingmei Shenma Group Pingdingshan China Department of Computer Science and Engineering Beihang University Peking China College of Economics and Management Zhengzhou University of LightIndustry Zheng zhou China College of Computer and Communication Engineering Zhengzhou University of LightIndustry Zheng zhou China

Research based on electroencephalogram (EEG) and virtual reality (VR) has developed rapidly in recent years. The integration of EEG and VR technology has been broadly employed in psychological intervention to treat various psychological conditions and alleviate stress. At present, there exist commercial-level EEG emotion recognition products, however, research on the correlation between the content of VR scenarios and emotions remains unavailable. Therefore, this work investigates VR scene construction based on EEG, which incorporates the recovery environment theory and virtual reality technology to construct different three-dimensional dynamic virtual reality interaction scenes. EEG equipment constructs an emotion-adaptive VR system based on EEG signals with the aid of VR interaction equipment. Herein, EEG data feedback was applied to automatically generate VR scenes, and the experimental findings revealed that the system has certain feasibility and application value.

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A Dynamic SDN-based Privacy-Preserving Approach for Smart City Using Trust Technique 9

A Dynamic SDN-based Privacy-Preserving Approach for Smart Ci...

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9th Iranian Joint Congress on Fuzzy and Intelligent Systems, CFIS 2022

作者： Alzubi, Jafar A. Movassagh, AliAkbar Gheisari, Mehdi Najafabadi, Hamid Esmaeili Abbasi, Aaqif Afzaal Liu, Yang Pingmei, Zhou Izadpanahkakhk, Mahdieh Najafabadi, AmirHossein Pourishaban Al-Balqa Applied University Faculty of Engineering Salt Jordan Tehran University of Medical Sciences School of Medicine Department of Medical Physics and Biomedical Engineering Tehran Iran Islamic Azad University Young Researchers and Elite Club Parand Branch Parand Iran University of Calgary Canada Foundation University Department of Software Engineering Islamabad44000 Pakistan Harbin Institute of Technology Department of Computer Science and Technology Shenzhen China Shenzhen BKD Company Shenzhen China La Sapienza University DIET Department Rome Italy University of Yazd Department of Electrical and Computer Engineering Yazd Iran

ISBN: (纸本)9781665478724

A smart city is an Internet-based application of things that automates city management with no need for human interference. Exchanging data via devices obviate some challenges in intelligent cities. In a smart city, Internet-of-Things (IoT) devices may detect sensitive data, posing a risk of privacy violation and system harm. We discover that existing solutions are either too expensive or ineffective at limiting unintended disclosure of sensitive data to build a dependable, smart city. The fact that they create static surroundings is the fundamental reason behind this. software-Defined Networking (SDN) technology has recently evolved to configure the network for performance and monitoring improvement. This study offers a work-in-progress that uses the SDN to protect the privacy of IoT devices by creating a dynamic SDN-based privacy-preserving ecology. The mechanism of the SDN controller performs under the nodes' mutual trust;it chooses various routes from the IoT device to the Cloud space destination dependent on the level of confidence. The packet is re-routed if the SDN controller identifies a device that does not trust its neighbor. Then it instructs the owner to deliver data over a different path. To demonstrate its improved performance, we are currently evaluating it from the perspective of overhead criteria in the future. © 2022 IEEE.

关键词： Smart city

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Modeling the One Class of Radionuclide Migration Processes in Soils under Non-Isothermal Conditions Using Perturbation Theory Methods

Modeling the One Class of Radionuclide Migration Processes i...

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International Conference on Advanced computer Information Technologies (ACIT)

作者： Andrii Bomba Oksana Ostapchuk Roman Vovk Miroslav Kopnický Department of Computer Science and Applied Mathematics National University of Water and Environmental Engineering Rivne Ukraine Department of Software Engineering Ivano-Frankivsk National Technical University of Oil and Gas Ivano-Frankivsk Ukraine Department of Catholic Theology Catholic University in Ružomberok Spišská Kapitula Slovakia

Based on the synthesis of complex analysis methods, perturbation theory, and characteristics, a new approach has been developed for accounting for osmosis and temperature in predicting the migration processes of radionuclides under non-isothermal conditions in quasi-ideal porous media (in curved regions) bounded by streamlines and equipotential lines. The solution to the corresponding degenerate problem was obtained based on the developed method of nonlinear inversion of boundary value problem solutions into quasi-conformal mappings. An algorithm for calculating a uniformly dynamic grid, streamlines, and speed of ideal filtration field was constructed. With the developed method of characteristics, formulas for the approximate solution of diffusion-convection mass transfer problems in perturbed osmosis and heat filtration fields were obtained.

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An intelligent framework for sarcasm detection in Arabic tweets using deep learning with Al-Biruni earth radius optimization algorithm

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Alexandria engineering Journal 2025年 127卷 562-572页

作者： AL-anazi, Reema G. A. Alzaidi, Muhammad Swaileh Eltahir, Majdy M. Almukadi, Wafa Sulaiman Alajmani, Samah Hazzaa Darem, Abdulbasit A. Alshahrani, Mohammed Alhebaishi, Nawaf Department of Arabic Language and Literature College of Humanities and Social Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of English Language College of Language Sciences King Saud University P. O. Box 145111 Riyadh Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Department of Software Engineering College of Engineering and Computer Science University of Jeddah Saudi Arabia Department of Information Technology College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Computer Science College of Science Northern Border University Arar Saudi Arabia Computer Department Applied College Najran University Najran66462 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

Social media networks play a major role in expressing people's feelings, reviews, and thoughts. One of the popular linguistic patterns to express or criticize one's ideas with ridicule is sarcasm, where they have unintended and intended meanings in written text. The sarcastic text is used to reverse the polarity of the sentiment. As a result, sarcasm detection in the text positively affects the sentimental analysis (SA) tasks and improves accuracy. The sarcasm recognition of Arabic content is constrained even though Arabic is the most popular language for web content sharing, and yet still naive owing to various challenges, such as the insufficient data sources, the multiple dialects, and the morphological structure of the Arabic language. This study presents an Artificial Intelligence Driven Sarcasm Detection using Optimal Deep Learning (AIDSD-ODL) technique on Arabic tweets. In the AIDSD-ODL technique, a hyperparameter-tuned DL model is utilized to identify sarcastic or non-sarcastic Arabic tweets. At the initial stage, the AIDSD-ODL method undergoes data pre-processing to convert the input tweets into a compatible format. In the next phase, the Glove word embedding process is used in the AIDSD-ODL technique. The self-attention bidirectional long short-term memory (SA-BiLSTM) model is utilized for sarcasm detection. The Al-Biruni Earth Radius (BER) model is implemented for the hyperparameter selection process to enhance the performance of the SA-BiLSTM network. The experimental results of the AIDSD-ODL method are examined under the Arabic tweets dataset. The comparison study of the AIDSD-ODL method portrayed a superior accuracy value of 94.20 % over existing models. © 2025 The Authors

关键词： Deep learning

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Influence of Lung Cancer Model Characteristics on Nanodrugs Tumor Targeting Behavior Based on Convolutional Kernel Extreme Learning Machine Model

Influence of Lung Cancer Model Characteristics on Nanodrugs ...

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Advancement in Technology (ICONAT), International Conference for

作者： Manthan S. Manavadaria Srikanth Chintakindi Rajasree Rajamohanan S Krishna Prasad P. Janardhan Saikumar Neerav Nishant EC Department CSPIT CHARUSAT Changa Petlad Gujarat India Associate Project Manager Cloud Software Group Department of Computer Science (Artificial intelligence and Data Science) Karpagam Academy of Higher Education Coimbatore India Department of Mechanical Engineering Nitte (Deemed to be University) NMAM Institute of Technology Nitte Udupi India Department of ECE Audisankara College of Engineering and Technology Gudur India Department of CSE School of Engineering Babu Banarasi Das University Lucknow India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

One of the main killers on a worldwide basis is cancer. In terms of global persistence, lung cancer stands out among the most frequent cancers. For lung cancer, the standard medical approach includes chemotherapy, surgical removal, and radiation therapy. When compared to other methods, this one isn’t extremely targeted and may even damage surrounding healthy cells. There has been recent recognition of nanotechnology as a possible tool for the treatment and management of lung cancer. Preprocessing, feature selection, segmentation, and model training must be executed in this precise sequence to ensure accuracy. As part of the preprocessing, the proposed approach employ the kernel correlation approach. Splitting the input image into smaller pieces with the goal of reducing the overall representation of the image is called image segmentation. To find features with high predictive power and remove redundant ones, feature selection was done in the training cohort. The model was trained using a Convolutional-KELM. As compared to CNN and ELM, the suggested approach performs better. Following implementation of the technique, the accuracy increased by 94.25%.

关键词： Training Image segmentation Accuracy Lungs Lung cancer Feature extraction Convolutional neural networks Kernel Medical diagnostic imaging Tumors

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Student Perceptions of AI-Enhanced Adaptive Learning Systems: A Pilot Survey

Student Perceptions of AI-Enhanced Adaptive Learning Systems...

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International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Klara Ida Katonane Gyonyoru Jozsef Katona Department of Organizational Development and Communication Science Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

Artificial Intelligence (AI) significantly enhances adaptive learning by personalizing and tailoring instruction to individual student needs. AI analyzes data in real-time to create personalized learning paths based on students' strengths, weaknesses, and preferences, which keeps students engaged and motivated. A major benefit of AI in adaptive learning is the provision of real-time feedback and assessment, allowing students to correct mistakes promptly and understand concepts more thoroughly. AI-based intelligent tutoring systems are primarily intended to simulate personalized tutoring processes that guide students in complex problem-solving and answering questions. It is convenient in teaching mathematics, sciences, and languages. AI also supports inclusive education, dealing with diversified learning requirements and styles, such as those of learners with disabilities. For the teacher, AI acts as a reflector of student performance so that one can intervene early and make adjustments in the method of instruction by creating effective learning environments. AI technology is a field in constant development and harbors the potential to change the face of adaptive learning, bringing an upswing in educational outcomes. This article will summarize the advantages and features that merit improvement of the AI-embedded adaptive learning systems with student feedback.

关键词： Surveys Adaptive learning Power engineering Data security Real-time systems Mathematics Problem-solving Artificial intelligence Faces

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Two-Dimensional Thin Layer Convective Flow and Heat Transfer of a Dusty Fluid on a Nonlinear Stretching Sheet

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Multiscale science and engineering 2022年第3期4卷 111-118页

作者： Islam, Md. Robiul Ferdows, M. Misra, J. C. Murtaza, M. G. Research Group of Fluid Flow Modeling and Simulation Department of Applied Mathematics University of Dhaka Dhaka Bangladesh Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Centre for Healthcare Science and Technology Indian Institute of Engineering Science and Technology Howrah India Department of Mathematics Comilla University Cumilla Bangladesh

In this study, steady two-dimensional boundary layer flow and heat transfer of a dusty fluid over a nonlinearly stretching sheet with variable thickness, $${u}_{w} (x)={U}_{0}{(x+b)}^{m}$$ has been considered. A varia...

In this study, steady two-dimensional boundary layer flow and heat transfer of a dusty fluid over a nonlinearly stretching sheet with variable thickness, $${u}_{w} (x)={U}_applied{(x+b)}^{m}$$ has been considered. A variable magnetic field is applied transversely to the direction of the flow. The general problem has been reduced to a system of coupled non-linear ordinary differential equations of two-dimensional boundary layer. By means of similarity transformation, the basic governing equations are transformed into a set of nonlinear ordinary differential equation and then are solved numerically by dsolve routine in MAPLE. Results of the present study have been duly validated by adopting an appropriate procedure. The influence of various parameters such as velocity power index, wall thickness parameter, magnetic parameter fluid-particle interaction parameter for velocity, fluid-particle interaction parameter for temperature, mass concentration of dust particle, Prandtl number, Eckert number has been investigated. The effects are then presented graphically both for dust phase and fluid phase. It is clear that flow field and other quantities of physical interest are significantly influenced by these parameters. Also the effects of the parameters on the skin-friction coefficients and rate of heat transfer in terms of the Nusselt number, which are of physical interest, are shown graphically/in tabular form. The study reveals that the impact of the parameters for the dusty fluid on Nusselt number is considerably higher than that for a clean fluid.

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DIFFERENTIABLE FOLDING FOR NEAREST NEIGHBOR MODEL OPTIMIZATION

arXiv

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arXiv 2025年

作者： Krueger, Ryan K. Aviran, Sharon Mathews, David H. Zuber, Jeffrey Ward, Max School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Biomedical Engineering and Genome Center University of California Davis DavisCA95616 United States Department of Biochemistry and Biophysics and Center for RNA Biology University of the Rochester Medical Center RochesterNY14642 United States Department of Computer Science and Software Engineering The University of Western Australia PerthWA Australia

The Nearest Neighbor model is the de facto thermodynamic model of RNA secondary structure formation and is a cornerstone of RNA structure prediction and sequence design. The current functional form (Turner 2004) contains ≈ 13, 000 underlying thermodynamic parameters, and fitting these to both experimental and structural data is computationally challenging. Here, we leverage recent advances in differentiable folding, a method for directly computing gradients of the RNA folding algorithms, to devise an efficient, scalable, and flexible means of parameter optimization that uses known RNA structures and thermodynamic experiments. Our method yields a significantly improved parameter set that outperforms existing baselines on all metrics, including an increase in the average predicted probability of ground-truth sequence-structure pairs for a single RNA family by over 23 orders of magnitude. Our framework provides a path towards drastically improved RNA models, enabling the flexible incorporation of new experimental data, definition of novel loss terms, large training sets, and even treatment as a module in larger deep learning pipelines. We make available a new database, RNAometer, with experimentally-determined stabilities for small RNA model systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Nearest neighbor search

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An adaptive solution strategy for Richards' equation

arXiv

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arXiv 2023年

作者： Stokke, Jakob S. Mitra, Koondanibha Storvik, Erlend Both, Jakub W. Radu, Florin A. Center for Modeling of Coupled Subsurface Dynamics Department of Mathematics University of Bergen Norway Computational Mathematics Group Hasselt University Belgium Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Norway

Flow in variably saturated porous media is typically modelled by the Richards equation, a nonlinear elliptic-parabolic equation which is notoriously challenging to solve numerically. In this paper, we propose a robust and fast iterative solver for Richards’ equation. The solver relies on an adaptive switching algorithm, based on rigorously derived a posteriori indicators, between two linearization methods: L-scheme and Newton. Although a combined L-scheme/Newton strategy was introduced previously in [1], here, for the first time we propose a reliable and robust criteria for switching between these schemes. The performance of the solver, which can be in principle applied to any spatial discretization and linearization methods, is illustrated through several numerical *** Codes 65M12, 65J08, 65M50, 76Sxx © 2023, CC BY.

关键词： Nonlinear equations

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