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River 2024年第3期3卷 260-271页

作者： Kourosh Nosrati Hossein Afzalimehr Hamidreza Raeisifar Mohammad Nazari-Sharabian Moses Karakouzian Faculty of Civil Engineering Iran University of Science and TechnologyTehranIran Department of Mathematics Engineeringand Computer ScienceWest Virginia State UniversityInstituteWest VirginiaUSA Department of Civil and Environmental Engineering University of Nevada Las VegasLas VegasNevadaUSA

This research is centered on a comprehensive investigation into the impact of turbulence on the movement and dispersion of materials within a three-dimensional(3D)bedform,speciﬁcally when there is a continuous presence of rigid vegetation submerged in theﬂ*** achieve our research objectives,we conducted extensive velocity measurements within a channel featuring this submerged *** measurements were carried out using an Acoustic Doppler Velocimeter(ADV).Additionally,our study delved into the intricate structures and turbulent characteristics of theﬂow,considering the coexistence of submerged vegetation and a 3D gravel *** pool featured entrance and exit slopes measuring 3 and 2.5°,*** experimental setup took place in a straightﬂume,measuring 14 m in length,0.9 m in width,and 0.6 m in ***ﬂume was equipped with transparent side walls to facilitate ***,our investigation extended to the spatial variations in velocity and turbulence *** analyzed various parameters including turbulence kinetic energy,integral turbulence lengths,dispersion coefﬁcients,and advective *** results revealed that integral length scales offer key insights into turbulent eddy *** the presence of vegetation and a 3D bedform,turbulent eddies undergo notable changes,ﬂattening in the longitudinal direction and expanding in the transverse and vertical ***,longitudinal advection is notably higher compared toﬂows without vegetation in a uniformﬂow or bare channel,especially for z/H>*** indicates that the presence of vegetation and a 3D bedform leads to an increase in turbulent kinetic energy(k values)that surpasses the reduction in the time-averaged velocity component(“U”)in the U×k term,thereby enhancing longitudinal advection.

关键词： 3D pool advection dispersion integral turbulent length submerged rigid vegetation

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Utilizing Transfer Learning and Class-Selective Image Processing for COVID-19 Detection 26

Utilizing Transfer Learning and Class-Selective Image Proces...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Tabassum, Noshin Parvin, Farjana Rajshahi University of Engineering & Technology Department of Computer Science and Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350359015

The year 2019 marked the emergence of a novel contagious illness named COVID-19 which was brought about by the SARS-CoV-2 virus. This newly identified virus swiftly spread across the globe, ultimately resulting in the unprecedented COVID-19 pandemic, that impacted populations, healthcare systems, and economies worldwide. Globally, there is a growing need for COVID-19 non-invasive detection by medical image analysis. In the realm of medical diagnostics, deep learning stands out as a supremely efficient approach for analyzing extensive collections of chest X-ray (CXR) images, particularly influencing the realm of COVID-19 screening. A specific technique within deep learning, known as Convolutional Neural Network (CNN), holds a preeminent position due to its effectiveness. In the scope of this research, Convolutional Neural Networks (CNN) have been utilized to differentiate COVID-19 cases from both normal cases and instances of Pneumonia, leveraging information extracted from chest X-ray images. This utilization underscores the potency of advanced machine learning in confronting pivotal healthcare hurdles. The approach involved fine-tuning a pre-trained CNN model, InceptionV3, via transfer learning, wherein certain layers were adjusted before training on the dataset. The initial classification accuracy exhibited strong performance across all image classes, except for one category that displayed a marginally lower accuracy rate. A straightforward yet efficient approach to enhance image contrast has been utilized exclusively in the lower-performance class. This was done to enhance the image quality within that particular class, leading to improved accuracy. Since the entire dataset did not have to go through image processing, the model was computationally efficient and yielded an overall accuracy of 99.92% outperforming existing works. © 2023 IEEE.

关键词： chest X-ray Convolutional Neural Network COVID-19 image processing Pneumonia transfer learning

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Exploring Integration of Multimodal Deep Learning Approaches for Enhanced Alzheimer's Disease Diagnosis: A Review of Recent Literature

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SN computer science 2024年第7期5卷 852页

作者： Deshpande, Sonali Kulkarni, Nilima Computer Science & amp Engineering Department MIT SoC MIT Art Design and Technology University Pune India

Alzheimer's disease (AD), is the most common form of dementia that affects the nervous system. In the past few years, non-invasive early AD diagnosis has become more popular as a way to improve patient care and treatment results. Imaging methods, electroencephalogram (EEG) tests, and sound evaluations are some of the new ways that researchers have looked into. This review covers 60 papers published from 2020. They are compared in terms of how they use basic deep learning models such as CNN, LSTM, Alex Net, Inception Net, VGG19, and ResNet to identify AD. But not many studies use more than one method together, like image and EEG, EEG and sounds, or images and sounds. The information from the Scopus database makes it easy to look at the newest information and work. This means that using more than one method to find AD isn't getting as much attention. Our review says that combining the best parts of each method in a mixed way could make Alzheimer's research much more useful and lead to better ways to diagnose. The paper talks about problems and opportunities in the field right now as well as possible study topics and issues for the future. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Alzheimer's disease (AD) Deep learning Electroencephalography Machine learning Magnetic resonance imaging Neurological disorders Sound

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Plant Disease Detection Using Convolution Neural Network 2

Plant Disease Detection Using Convolution Neural Network

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Rani, K. Pushpa Lavanya, G. Madhuri, K. Sai Vamsinath, J. Sabitha, Ch. Rao, K. Srinivas Department of Computer Science and Engineering MLR Institute of Technology Dundigal Hyderabad 500043 Assistant Professor Department of CSE VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur 522302

ISBN: (纸本)9798350370188

Today the persistence of plant and plant diseases is a substantial problem for cultivators, disease diagnosis is important in the farm. To preserve the production of crops, adequate consideration is a must. To isolate and detect infections in conventional situation, a lot of workforce, time, and frequent surveillance of the farm is involved. If no proper safety measures are not taken in protecting the crops, then it may damage the production often diseases make the plants weak by interrupting the photosynthesis. This paper shows in-depth study methods used in disease diagnosing. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comparison of Machine Learning Techniques for Analyzing Infant Mortality in Bangladesh 2

A Comparison of Machine Learning Techniques for Analyzing In...

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2nd International Conference on Information and Communication technology for Sustainable Development, ICICT4SD 2023

作者： Sultana, Irin Islam, Md. Rabiul Salehin, Md. Nazmus Rajshahi University of Engineering and Technology Department of Computer Science and Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350358667

The Infant Mortality Rate (IMR) is a significant measure used to evaluate the health and socioeconomic status of developing nations, particularly Bangladesh. It is important to carefully examine the various risk factors associated with IMR. This study aims to identify potential risk variables that contribute to infant mortality in Bangladesh and utilize machine learning techniques to develop prediction models. We have analyzed data from the World Bank's 'World Development Indicator (WDI) 2021' repository. By employing multiple stepwise linear regression, Support Vector Machine, and Random Forest algorithms, we have identified relevant components and modeled the data. Through cross-validation techniques and evaluating performance using Mean Absolute Error, Mean Square Error, and Root Mean Square Error, we have determined that linear regression and support vector regression show superior performance. The final prediction model's effectiveness yields significant results, potentially leading to a significant reduction in Bangladesh's infant mortality rate significantly as well as improving the country's socio-economic conditions among other developing nations worldwide. © 2023 IEEE.

关键词： Mean square error

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Big Data Analytics for Quality Control in The Pharmaceutical Industry 10

Big Data Analytics for Quality Control in The Pharmaceutical...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Malathi, P. Jansi, S. Prabhakaran, M.V. Senbagam, K. Sujatha, M.P. Sadaieswaran, R. Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Department of Data Analytics Tamil Nadu Chennai India Acharya college of Engineering and Technology Department of Computer science and Engineering Achariyapuram Puducherry India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Mamallapuram India Agni College of Technology Department of Computer Science and Engineering Chennai Thalambur India Tagore College of arts and science Department of Computer Applications Chennai Chromepet India

ISBN: (纸本)9798350384369

In order to ensure the safety and manufacturing efficiency of pharmaceuticals, rigorous quality control measures are necessary. However, existing quality control systems often struggle with industrial process complexity and regulatory requirements. The proposed study highlights the need for a revolutionary strategy to overcome the limitations of existing systems. Quality control systems in existing systems have limitations such as real-time monitoring, insufficient data analysis for pattern identification, and limited predictive capabilities. These limitations have an impact on the industry's ability to recognize and manage quality concerns, posing a danger to medicine manufacturing. For addressing these issues, a novel approach to maintain quality control in the pharmaceutical sector using big data analytics is proposed. Advanced analytics are used for real-time monitoring, data analysis and predictive modeling in the proposed system. The results and analysis through extensive comparisons shows proposed big data analytics system achieves 95% fault detection, 40% downtime reduction, and 35% waste reduction in pharmaceutical manufacturing. The proposed system's ability to handle massive datasets allows it to identify complicated patterns and abnormalities, enabling proactive quality control. The implementation exceeds industry standards, addressing data privacy and security concerns. © 2024 IEEE.

关键词： Data quality

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Bayesian Deep Learning for Dynamic Power System State Prediction Considering Renewable Energy Uncertainty

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Journal of Modern Power Systems and Clean Energy 2022年第4期10卷 913-922页

作者： Shiyao Zhang James J.Q.Yu Academy for Advanced Interdisciplinary Studies Southern University of Science and TechnologyShenzhen 518055China Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China

Modern power systems are incorporated with distributed energy sources to be environmental-friendly and ***,due to the uncertainties of the system integrated with renewable energy sources,effective strategies need to be adopted to stabilize the entire power ***,the system operators need accurate prediction tools to forecast the dynamic system states *** this paper,we propose a Bayesian deep learning approach to predict the dynamic system state in a general power ***,the input system dataset with multiple system features requires the data pre-processing ***,we obtain the dynamic state matrix of a general power system through the Newton-Raphson power flow ***,by incorporating the state matrix with the system features,we propose a Bayesian long short-term memory(BLSTM)network to predict the dynamic system state variables *** results show that the accurate prediction can be achieved at different scales of power systems through the proposed Bayesian deep learning approach.

关键词： Bayesian deep learning data analytics NewtonRaphson power flow renewable energy source system state

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Secure Sharing of Student Credentials Using Blockchain 4th

Secure Sharing of Student Credentials Using Blockchain

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4th International Conference on Communication, Devices and Computing (ICCDC 2023)

作者： Sarkar, Puja Kalita, Psarag Jyoti Goswami, Mugdhatanu Dev Saha, Sanchita Nag, Amitava Department of Computer Science and Engineering Central Institute of Technology Assam Kokrajhar783370 India Department of Computer Science and Engineering Haldia Institute of Technology Haldia721657 India

ISBN: (纸本)9789819927098

Sharing students’ credentials is an essential process for any educational ecosystem that includes diverse stakeholders such as students, teachers, various committees of institutes, administrations, government agencies, and many more. All of these stakeholders take extensive measures to ensure the authenticity and confidentiality of students’ testimonials when those are being shared. In spite of these concerns, the distribution of students’ credentials is a vulnerable, potentially complex, and sometimes insecure process. The goal is to reduce the existing security-related issues when sharing the students’ credentials with the help of blockchain technology. The technology of blockchain can be used effectively to solve the issue of securely exchanging student credentials. We proposed a secure, immutable, authentic, tamper-proof, privacy-preserving, and easily shareable blockchain-based mechanism for securely exchanging students’ credentials. In order to make the process of sharing credentials scalable, we proposed a secure off-chain store mechanism. A decentralized application based on Ethereum technology is used for conducting an analysis of the architecture with regard to its performance as well as its potential feasibility. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smart contract

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Real-time monitoring and fault detection in smart grids using IoT sensors and deep learning algorithm 4

Real-time monitoring and fault detection in smart grids usin...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Arnika Tyagi, Reshu Kumar, Pawan Sagar, Pramod Kumar Saraswat, Manish Bansal, Abhinav Manav Rachna University Department of Computer Science and Technology Haryana Faridabad India Abes Institute of Technology Department of Information Technology UP Ghaziabad India Hi-Tech Institute of Engineering and Technology Computer Science & Engineering UP Ghaziabad India Raj Kumar Goel Institute of Technology Computer Science & Engineering UP Ghaziabad India Lloyd Institute of Engineering & Technology Department of Mechanical Engineering UP Greater Noida India Raj Kumar Goel Institute of Technology Electronics & Communication Engineering UP Ghaziabad India

ISBN: (纸本)9798350364729

The concept of smart grids was released a long time ago, and since then the development in clever grids have changed our conventional energy machine with new advanced features like real-time monitoring power distribution control. This increased complexity brings with it the danger of errors and failures which can destabilize the system, leading to massive financial losses as well as social problems. In order to address this problem, we propose a real-time monitoring and fault detection system for smart grids via IoT sensors along with deep learning algorithms. This is able to encompass a network of Iot sensors which might be deployed in the course of the grid for real time facts on numerous parameters together with voltage, current and power glide. That number is then input into a deep getting to know set of rules, capable of detecting abnormal behavior and anomalies in the grid that can highlight faults or outages. In other words, the device is studying and learning about these facts all the time so it can be used as warning signs for grid operators who might still act proactively by avoiding any kind of matrix breakages. In addition the device can provide actual time color coded grid performance maps providing operators with the ability to track health of their own particular targeted area. Having green third-party maintenance will make your life and environment easier, decreasing downtime as well cost. Using an autonomous and durable strength supply, our suggested device is environmentally friendly along with a dependable answer for real-time tracking etc. fault detection in smart grids. © 2024 IEEE.

关键词： Smart power grids

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A novel optimised GWOAN algorithm for scheduling task and power consumption in MEC network

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International Journal of Intelligent Systems Technologies and Applications 2023年第4期21卷 386-401页

作者： Sivakumar, P. Murugan, Murali Somasundaram, S.K. Prabhu, S. Nagendra School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Engineering Retail Business Professional Georgia United States School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu Chennai632014 India

In recent decades, the 5G and internet of things (IoT) are occupied with several applications like face recognition, traffic control, video surveillance and telecommunication, etc. Mobile-edge computing (MEC) is a promising paradigm in wireless communication which is carried by the computational scheduling of mobile devices. To enhance the computation design, the AlexNet of the DL model is applied which is based on the convolutional neural network (CNN) used to train a large number of attributes. To provide an optimal solution, the metaheuristics algorithm of grey wolf optimisation (GWO) is combined with an AlexNet which is named a novel grey wolf optimisation-based AlexNet (GWOAN) algorithm. In the proposed GWOAN algorithm, the AlexNet hyperparameters (weights, biases and other parameters) are fine-tuned by GWO and then performed a classification. As a result, the GWOAN has achieved a higher scheduling task and low latency than the standard AlexNet, ResNet-18 and VGGNet-16 respectively. © 2023 Inderscience Enterprises Ltd.

关键词： Optimization

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