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3rd International Conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2024

作者： Gupta, Rakhi Siddharth Tumrate, Charanjeet Singh Department of English JECRC University Vidhani India Department of Computer Science JECRC University Vidhani India Department of Civil Engineering Manipal University Jaipur Jaipur India Multiscale Simulation and Research Centre Manipal University Jaipur Jaipur India

ISBN: (纸本)9789819739905

The student’s academic response analysis during the COVID-19 pandemic is vital in comprehending their cognitive aspects towards the online teaching methodology adopted by the educational centres. This study provides the application of machine learning classification techniques to highlight the student’s response towards the online teaching pedagogy. The student response data considered is analysed using machine learning based random forest and gradient boosting machine classification techniques. The considered data is divided into two cases to increase the variation and importance accuracy in classification outcome. Based on the feature importance values obtained, less time spent on the self-study and the online lectures were the main reasons for the low performance among the students. Moreover, the various indoor stress busting activities played a huge role in safeguarding the mental health of students during pandemic. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Transforming Cognitive Maps into Markov Process Models for Software Development Projects

Transforming Cognitive Maps into Markov Process Models for S...

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2023 IEEE International Conference on Smart Information Systems and Technologies, SIST 2023

作者： Kolesnikova, Kateryna Myrzakerimova, Alua Alpysbay, Nursultan Olekh, Tetiana International It University Department of Information Systems Almaty Kazakhstan International It University Department of Computer Engineering Almaty Kazakhstan Odessa National Polytechnic University Department of Higher Mathematics and Systems Simulation Odessa Ukraine

ISBN: (纸本)9798350335040

The complexity of managing software development projects in organizational and technical systems is due to a number of features. Such projects are weakly structured systems. The parameters of weakly structured systems form a complex 'web' of connections and states that change over time. The development of projects in such a multifactorial environment can only be represented in the form of qualitative models. The use of cognitive maps with their subsequent presentation in the form of Markov chains allows quantifying the course of project processes, which are the main condition for the success of the project. At the same time, quantitative estimates represent not only a multi-vector picture of the state of projects, but also have the property of forecasting. The scope of the proposed method can be significantly expanded through the use in the educational process in the training of professionals in the field of programming and project management. © 2023 IEEE.

关键词： Directed graphs

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Million-scale data integrated deep neural network for phonon properties of heuslers spanning the periodic table

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npj Computational Materials 2023年第1期9卷 2155-2166页

作者： Alejandro Rodriguez Changpeng Lin Hongao Yang Mohammed Al-Fahdi Chen Shen Kamal Choudhary Yong Zhao Jianjun Hu Bingyang Cao Hongbin Zhang Ming Hu Department of Mechanical Engineering University of South CarolinaColumbiaSC 29208USA Theory and Simulation of Materials(THEOS) École Polytechnique Fédérale de LausanneCH-1015 LausanneSwitzerland Key Laboratory for Thermal Science and Power Engineering of Ministry of Education Department of Engineering MechanicsTsinghua UniversityBeijing 100084China Institute of Materials Science Technical University of DarmstadtDarmstadt 64287Germany Materials Science and Engineering Division National Institute of Standards and TechnologyGaithersburgMD 20899USA Department of Computer Science and Engineering University of South CarolinaColumbiaSC 29208USA

Existing machine learning potentials for predicting phonon properties of crystals are typically limited on a material-to-materialbasis, primarily due to the exponential scaling of model complexity with the number of atomic species. We address this bottleneckwith the developed Elemental Spatial Density Neural Network Force Field, namely Elemental-SDNNFF. The effectiveness andprecision of our Elemental-SDNNFF approach are demonstrated on 11,866 full, half, and quaternary Heusler structures spanning 55elements in the periodic table by prediction of complete phonon properties. Self-improvement schemes including active learningand data augmentation techniques provide an abundant 9.4 million atomic data for training. Deep insight into predicted ultralowlattice thermal conductivity (<1 Wm^(−1) K^(−1)) of 774 Heusler structures is gained by p–d orbital hybridization analysis. Additionally, aclass of two-band charge-2 Weyl points, referred to as “double Weyl points”, are found in 68% and 87% of 1662 half and 1550quaternary Heuslers, respectively.

关键词： properties phonon spanning

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A Monte Carlo Based COVID-19 Detection Framework for Smart Healthcare

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computers, Materials & Continua 2022年第2期70卷 2365-2380页

作者： Tallat Jabeen Ishrat Jabeen Humaira Ashraf Nz Jhanjhi Mamoona Humayun Mehedi Masud Sultan Aljahdali Research Center for Modelling and Simulation(RCMS)NUST Islamabad44000Pakistan Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan School of Computer Science and Engineering SCE Taylor’s UniversitySubang Jaya47500Malaysia College of Computer Science and Information Technology Jouf UniversityAl JoufSaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

COVID-19 is a novel coronavirus disease that has been declared as a global pandemic in *** affects the whole world through personto-person *** virus spreads by the droplets of coughs and sneezing,which are quickly falling over the ***,anyone can get easily affected by breathing in the vicinity of the COVID-19 ***,vaccine for the disease is under clinical investigation in different pharmaceutical *** now,multiple medical companies have delivered health monitoring ***,a wireless body area network(WBAN)is a healthcare system that consists of nano sensors used to detect the real-time health condition of the *** proposed approach delineates is to fill a gap between recent technology trends and healthcare *** COVID-19 affected patient is monitored through WBAN sensors and network,a physician or a doctor can guide the patient at the right timewith the correct possible *** scenario helps the community to maintain social distancing and avoids an unpleasant environment for hospitalized patients Herein,a Monte Carlo algorithm guided protocol is developed to probe a secured cipher *** cipher helps to avoid wireless network issues like packet loss,network attacks,network interference,and routing *** Carlo based covid-19 detection technique gives 90%better results in terms of time complexity,performance,and *** indicate that Monte Carlo based covid-19 detection technique with edge computing idea is robust in terms of time complexity,performance,and efficiency and thus,is advocated as a significant application for lessening hospital expenses.

关键词： COVID-19 coronavirus cryptography Monte Carlo algorithm edge computing

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Uplink Control Information Based Clustered Access and Discontinuous Reception Management for mMTC

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IEEE Communications Letters 2025年第6期29卷 1380-1384页

作者： Wang, Shao-Di Wang, Chang-Long Wang, Hui-Ming Zhou, Feng Leung, Victor C. M. Xidian University Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education Xi’an710071 China Xi’an Jiaotong University School of Information and Communication Engineering Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an710049 China Shenzhen MSU BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Uplink control information (UCI) and discontinuous reception (DRX) play important roles for massive machine type communication (mMTC). Despite their standalone significance, a conspicuous gap exists in comprehensively exploring the synergy between UCI and DRX concerning both energy conservation and latency reduction for mMTC. In this letter, we propose a UCI-based clustered access and DRX management (UCADM) scheme that strikes a balance between these two critical performance metrics, reducing energy usage while preserving low latency in uplink-downlink interaction for mMTC. Specifically, we present a strictly feasible solution for the proposed UCADM scheme by using the spectral radius and the constraint projection, which can effectively solve DRX switching operations as indicated by scheduling information from UCI. simulation results confirm the effectiveness of our scheme. © 1997-2012 IEEE.

关键词： Information management

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An Anonymous IoT-Based E-Health Monitoring System Using Blockchain Technology

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IEEE Systems Journal 2023年第2期17卷 2422-2433页

作者： Samuel, Omaji Omojo, Akogwu Blessing Mohsin, Syed Muhammad Tiwari, Prayag Gupta, Deepak Band, Shahab S. Department of Computer Science Osara264103 Nigeria Edo State University Uzairue300281 Nigeria Shestco Applied Mathematics and Simulation Advanced Research Centre Abuja186 Nigeria Comsats University Islamabad Department of Computer Science Islamabad45550 Pakistan Aalto University Department of Computer Science Espoo02150 Finland Department of Computer Science and Engineering Maharaja Agrasen Institute of Technology Delhi110086 India Research Advisor Ucrd Chandigarh University GharuanPunjab Mohali140413 India National Yunlin University of Science and Technology Future Technology Research Center Douliou64002 Taiwan

The Internet of Things (IoT) has made it possible for health institutions to have remote diagnosis, reliable, preventive, and real-time decision-making. However, the anonymity and privacy of patients are not considered in IoT. Therefore, this article proposes a blockchain-based anonymous system, known as GarliMediChain, for providing anonymity and privacy during COVID-19 information sharing. In GarliMediChain, garlic routing and blockchain are integrated to provide low-latency communication, privacy, anonymity, trust, and security. Also, COVID-19 information is encrypted multiple times before transmitting to a series of nodes in the network. To ensure that COVID-19 information is successfully shared, a blockchain-based coalition system is proposed. The coalition system enables health institutions to share information while maximizing their payoffs. In addition, each institution uses the proposed fictitious play to study the strategies of others in order to update its belief by selecting the best responses from them. Furthermore, simulation results show that the proposed system is resistant to security-related attacks and is robust, efficient, and adaptive. From the results, the proposed proof-of-epidemiology-of-interest consensus protocol has 15.93% less computational cost than 26.30% of proof-of-work and 57.77% proof-of-authority consensus protocol, respectively. Nonetheless, the proposed GarliMediChain system promotes global collaborations by combining existing anonymity and trust solutions with the support of blockchain technology. © 2007-2012 IEEE.

关键词： COVID-19

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Performance of Adaptive Neuro-Fuzzy Inference System State-of-Charge Estimation of Lithium-Ion Batteries for Electric Ship 8

Performance of Adaptive Neuro-Fuzzy Inference System State-o...

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8th International Conference on Computational Intelligence and Applications, ICCIA 2023

作者： Marisno, Denny Chin, Cheng Siong Shan, Chunlai See, Simon Newcastle University in Singapore Faculty of Science Agriculture Engineering Singapore Northwest Institute of Mechanical and Electrical Engineering Simulation and Test Technology Department Xianyang China Shanghai Jiao Tong University 800 Dongchuan RD. Minhang District Department of Computer Science and Engineering Shanghai200240 China

ISBN: (纸本)9798350339741

Estimating the State of Charge (SoC) is a crucial part of a battery management system (BMS) for the electric ship. An Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the SoC using the ANFIS under different ambient temperature variations is proposed. A five-layer Artificial Neural Network for SoC estimation is used. The ANFIS achieves the lowest prediction error compared to the Neural Network, electrical circuit model, coulomb counting, Kalman Filter, Fuzzy Neural Network, and Support Vector Machine method. © 2023 IEEE.

关键词： Battery management systems

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Probabilistic Multi-Objective Optimal Power Flow: A Grey Wolf Optimizer Approach Considering Load and Wind Turbine Uncertainties

Probabilistic Multi-Objective Optimal Power Flow: A Grey Wol...

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Canadian Conference on Electrical and computer engineering (CCECE)

作者： Shiva Amini Innocent Kamwa Shabbo Nahvi Hêmin Golpîra Department of Electrical and Computer Science Engineering Laval University Quebec Canada Department of Electrical and Computer Engineering University of Kurdistan Sanandaj Iran Department of Electrical and Computer Engineering Power Systems Modeling & Simulation Lab University of Kurdistan Sanandaj Iran

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

This article presents a novel approach to tackle the challenge of Probabilistic Multi-Objective Optimal Power Flow (PMOOPF) by utilizing the Grey Wolf Optimizer (GWO). The methodology incorporates uncertainties related to both load and wind turbine generation, modeling load uncertainty with a normal distribution and wind speed uncertainty with a Weibull distribution and employs Monte Carlo simulation (MCS) to establish the probability distribution function (PDF) for both the load and power generated by the hybrid sources. The PMOOPF problem aims to concurrently minimize thermal generation costs, real power losses, and maximize voltage stability. The proposed approach is tested on the IEEE 26-bus system, and the outcomes are compared with the Gravitational Search Algorithm (GSA). The contribution of this paper lies in effectively addressing uncertainties in load and wind turbine generation, incorporating diverse PMOOPF objectives, and employing the GWO optimization technique. The simulation results demonstrate the method's efficacy and efficiency in achieving economic and technical benefits.

关键词： Uncertainty Costs Wind speed Voltage Probabilistic logic Stability analysis Wind turbines

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A hybrid physics-informed data-driven neural network for CO_(2) storage in depleted shale reservoirs

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Petroleum Science 2024年第1期21卷 286-301页

作者： Yan-Wei Wang Zhen-Xue Dai Gui-Sheng Wang Li Chen Yu-Zhou Xia Yu-Hao Zhou College of Construction Engineering Jilin UniversityChangchun130026JilinChina Institute of Intelligent Simulation and Early Warning for Subsurface Environment Jilin UniversityChangchun130026JilinChina School of Energy Resources China University of GeosciencesBeijing100083China Department of Safety Environmental Protection and Quality ManagementShengli OilfieldSinopecDongying257000ShandongChina Key Laboratory of Thermo-Fluid Science and Engineering of MOE School of Energy and Power EngineeringXi'an Jiaotong UniversityXi'an710049ShaanxiChina College of Computer Science and Technology China University of Petroleum(East China)Qingdao266580ShandongChina Department of Physics University of FribourgFribourg1700Switzerland

To reduce CO_(2) emissions in response to global climate change,shale reservoirs could be ideal candidates for long-term carbon geo-sequestration involving multi-scale transport ***,most current CO_(2) sequestration models do not adequately consider multiple transport ***,the evaluation of CO_(2) storage processes usually involves laborious and time-consuming numerical simulations unsuitable for practical prediction and *** this paper,an integrated model involving gas diffusion,adsorption,dissolution,slip flow,and Darcy flow is proposed to accurately characterize CO_(2) storage in depleted shale reservoirs,supporting the establishment of a training *** this basis,a hybrid physics-informed data-driven neural network(HPDNN)is developed as a deep learning surrogate for prediction and *** incorporating multiple sources of scientific knowledge,the HPDNN can be configured with limited simulation resources,significantly accelerating the forward and inversion ***,the HPDNN can more intelligently predict injection performance,precisely perform reservoir parameter inversion,and reasonably evaluate the CO_(2) storage capacity under complicated *** validation and test results demonstrate that the HPDNN can ensure high accuracy and strong robustness across an extensive applicability range when dealing with field data with multiple noise *** study has tremendous potential to replace traditional modeling tools for predicting and making decisions about CO_(2) storage projects in depleted shale reservoirs.

关键词： Deep learning Physics-informed data-driven neural network Depleted shale reservoirs CO_(2)storage Transport mechanisms

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Detection of Detonators in Baggage Using Deep CNN Architecture: A Comparative Study of Contrast Enhancement and Feature Extraction Methods 8

Detection of Detonators in Baggage Using Deep CNN Architectu...

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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Lynda, Oulhissane Mostefa, Merah Moldovanu, Simona Moraru, Luminita LSS Faculty of Science and Technology Abdelhamid Ibn Badis University Laboratory of Signals and Systems Mostaganem Algeria Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Modelling & Simulation Laboratory MSlab Department of Computer Science and Information Technology Galati Romania Faculty of Sciences and Environment Modelling & Simulation Laboratory Dunarea de Jos University of Galati MSlab Department of Chemistry Physics & Environment Galati Romania

ISBN: (纸本)9798350309249

Visual inspection of dual-energy X-ray radiographic images of cabin baggage requires high performance, but is hindered by various challenges such as low target prevalence, variability in target visibility, possible presence of multiple targets, and security personnel fatigue and inattention. Artificial intelligence (AI) techniques, particularly deep convolutional neural networks (CNNs), have shown promise in improving the automatic detection of explosives, even with low-resolution radiographic images, especially in high baggage throughput scenarios. In this paper, we focus on the detection of detonators as components of improvised explosive devices. The proposed approach involves comparing two experiments implemented in a deep CNN architecture using TensorFlow and Keras libraries. In the first experiment, raw dual-energy radiographic images without any enhancement were used. The second experiment includes three methods for contrast enhancement and feature extraction: the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, the wavelet transform-based method, and the mixed CLAHE RGB-Wavelet method. In the latter two methods, Haar, Db2, Coif2, and Sym2 mother wavelet functions at two levels (HH and HL) were employed. The analysis of results focuses on a comparative study of performance measures such as accuracy, precision, recall, and F1 score. It was found that the preprocessing methods used in experiment 2, for the two evaluated classes (detonator and no detonator), achieved higher accuracy compared to the raw radiographic images used in experiment 1 (98.08%). The highest accuracies in experiment 2, with a value of 100%, were obtained with the CLAHE method (green channel in grayscale, blue channel in grayscale, and RGB channels) and the wavelet transform method with Haar mother wavelet at two levels HL © 2024 IEEE.

关键词： Feature extraction

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