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A Stochastic Gradient Descent Method for Computational Design of Random Rough Surfaces in Solar Cells

Communications in Computational Physics 2023年第10期34卷 1361-1390页

作者： Qiang Li Gang Bao Yanzhao Cao Junshan Lin Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China Department of Mathematics Zhejiang UniversityChina Department of Mathematics and Statistics Auburn UniversityAuburnAL 36849USA

In this work,we develop a stochastic gradient descent method for the computational optimal design of random rough surfaces in thin-film solar *** formulate the design problems as random PDE-constrained optimization problems and seek the optimal statistical parameters for the random *** optimizations at fixed frequency as well as at multiple frequencies and multiple incident angles are *** evaluate the gradient of the objective function,we derive the shape derivatives for the interfaces and apply the adjoint state method to perform the *** stochastic gradient descent method evaluates the gradient of the objective function only at a few samples for each iteration,which reduces the computational cost *** numerical experiments are conducted to illustrate the efficiency of the method and significant increases of the absorptance for the optimal random *** also examine the convergence of the stochastic gradient descent algorithm theoretically and prove that the numerical method is convergent under certain assumptions for the random interfaces.

关键词： Optimal design random rough surface solar cell Helmholtz equation stochastic gradient descent method

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Optimized Hybrid CNN-GRU Approach for Accurate Prediction of Cardiovascular Diseases: Integrating Medical Imaging and Clinical Data 3

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of...

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3rd International Conference on Communication, Control, and Intelligent systems, CCIS 2024

作者： Prasanna, V. Suresh Kumar, N. Divyaprabha, P. Sundaram, R. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Data Science and Business Systems Chennai India Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Ganesh College of Engineering Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798331528201

As the primary cause of death globally, cardiovascular diseases (CVDs) demand precise and timely prediction to enhance patient outcomes. Other examples of conventional approaches for CVD prediction include statistical models as well as machine learning models such as a single model that often struggle to address complex and comprehensive data from clinical records and Medical Imaging. To address these issues, the concept proposed here is to integrate the medical imaging data with clinical data as new apart from utilizing the hybrid CNN-GRU model to predict cardiovascular diseases with higher accuracy and interpretability. While the GRU module deals with the time sequences of clinical data including past history and other test results, the CNN module is to extract the spatial features from the medical images. Thus, by integrating these modules, it is more accurate to predict the data of the patient as a synergy of spatial and temporal arrangements. Cross-validation techniques and performance indices are used for model training and testing on a diverse data set containing imaging and other clinical-related data. Experimental results depict that the suggested hybrid CNN-GRU model performs much better than the traditional approaches and has higher levels of predicted accuracy and robustness. It is also feasible to generalize this integrated strategy to other medical disorder where multi-modal data analysis is crucial, besides enhancing the capacity to estimate CVDs. The findings underline the importance of integrating doctors' information with radiological images and applying new patterns of neural networks to augmenting clinical judgment. The suggested work is implemented using python. © 2024 IEEE.

关键词： Recurrent neural networks

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Numerical Modelling of Multi-Layered Capacitive Electrodes for Biomedical Signals Measurement 45

Numerical Modelling of Multi-Layered Capacitive Electrodes f...

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45th Jubilee International Convention on Information, Communication and Electronic Technology, MIPRO 2022

作者： Klaic, L. Stanesic, A. Cifrek, M. Faculty of Electrical Engineering and Computing Department of Electronic Systems and Information Processing Zagreb Croatia

ISBN: (纸本)9789532331035

In this paper, a concentric cylinder model of the upper arm is used in order to perform a stationary and low-frequency analysis of the electrical field distribution on the capacitive surface electromyography (sEMG) electrode. The four-layered capacitive electrode is implemented, along with the preprocessing printed circuit. The goal of this research is to explore the quality of capacitive coupling between the electrode and skin covered with fabric, as well as the behavior of the implemented electronics, and to appraise the utility of simulation methods. For this purpose, the finite element method within the CST Studio Suite® software is used. The results have confirmed the purpose of implemented electronic components, as well as shown that the proximity of the electrode and thinner fabric layer with greater dielectric permittivity are expected to create stronger capacitive coupling. © 2022 Croatian Society MIPRO.

关键词： Finite element method

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Robust Human Interaction Recognition Using Extended Kalman Filter

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Computers, Materials & Continua 2024年第11期81卷 2987-3002页

作者： Tanvir Fatima Naik Bukht Abdulwahab Alazeb Naif Al Mudawi Bayan Alabdullah Khaled Alnowaiser Ahmad Jalal Hui Liu Faculty of Computing&Artificial Intelligence Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

In the field of computer vision and pattern recognition,knowledge based on images of human activity has gained popularity as a research *** recognition is the process of determining human behavior based on an *** implemented an Extended Kalman filter to create an activity recognition system *** proposed method applies an HSI color transformation in its initial stages to improve the clarity of the frame of the *** minimize noise,we use Gaussian *** of silhouette using the statistical *** use Binary Robust Invariant Scalable Keypoints(BRISK)and SIFT for feature *** next step is to perform feature discrimination using Gray *** that,the features are input into the Extended Kalman filter and classified into relevant human activities according to their definitive *** experimental procedure uses the SUB-Interaction and HMDB51 datasets to a 0.88%and 0.86%recognition rate.

关键词： Pattern recognition geometric features activity recognition full-body texture

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RubberMate - Integrated Platform for Rubber Crop Harvest and Production 5

RubberMate - Integrated Platform for Rubber Crop Harvest and...

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5th International Conference on Advancements in computing, ICAC 2023

作者： Perera, K.A.Y. Mandawela, M.K.C.N. Nilumika, H.L.G. Rasanjana, A.H.P. Mahaadikara, M.M.D.J.T. Hettiarachchi, Sathira Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

Rubber cultivation plays a vital role in the global economy as a primary natural rubber source necessitates efficient plantation management for sustainability and latex quality. This paper introduces an integrated mobile app that enhances rubber plantation management through real-time monitoring of tapping, disease identification via image processing, weed detection, and latex quality assessment. Addressing these challenges becomes pivotal to improving latex quality, minimizing waste, and promoting sustainable practices in the rubber industry. In conclusion, the proposed mobile application serves as a prospective solution for estate owners, utilizing selected algorithms to tackle these challenges effectively. © 2023 IEEE.

关键词： Sustainable development

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Success Factors of Requirement Elicitation in the Field of Software engineering 4

Success Factors of Requirement Elicitation in the Field of S...

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4th International Conference on Advancements in computing, ICAC 2022

作者： Attanayaka, Buddhima Nawinna, Dasuni Manathunga, Kalpani Abeygunawardhana, Pradeep K.W. Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka Department of Computer Systems & Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (纸本)9798350398090

Requirement elicitation (RE) is a cognitively challenging and time-consuming task in software development due to the numerous challenges associated with it including conflicting requirements, unspoken, or assumed requirements, difficulty meeting with relevant stakeholders, stakeholder resistance to change, and not enough time set aside for meetings with all stakeholders. The prime causes of software implementation failure have been identified as inadequacies in the treatment of requirements. Without collecting the quality requirement, cannot achieve the goal of a quality software product. Through identifying the success factors affecting requirement elicitation, the paths to the quality requirements can be identified. The success factors identify through this research are experience, business analyst skills, stakeholder relationship, organizational elicitation process. This study aims to identify the factors affecting requirement elicitation based on the data collected from business analysts and similar positions in the software industry through a survey, interviews, and analyzed data to provide the initial validation for the identified factors. Through the analysis, we identified the main factors affecting successful requirement elicitation with a perfect significance value of less than 0.05 for all factors. © 2022 IEEE.

关键词： Requirements engineering

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Autism Spectrum Disorder Detection from EEG Through Hjorth Parameters and Classification Using Neural Network 1st

Autism Spectrum Disorder Detection from EEG Through Hjorth P...

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1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Peya, Zahrul Jannat Zaman, Bipasha Akhand, M.A.H. Siddique, Nazmul Department of Computer Science and Engineering Khulna University of Engineering and Technology Khulna9203 Bangladesh School of Computing Engineering and Intelligent Systems Ulster University Northern Ireland Londonderry United Kingdom

ISBN: (纸本)9783031346217

Autism spectrum disorders (ASD) are a collection of neurodevelopmental disorders. Even though ASD has no cure, early detection and intervention can help developing language, behavior, and communication skills. Research shows that ASD can be diagnosed from brain signals, particularly electroencephalography (EEG) where the brain activity is recorded over time as an EEG signal from humans’ scalp and then used to study neuropsychiatric disorders. This study investigates the classification performance of the Neural Networks (NN) model with Hjorth features namely activity, mobility, and complexity extracted from EEG signals through selected channels using the XGBoost algorithm. The classification accuracy is 80% using all 19 channels of EEG data whereas the accuracy of the model using activity, mobility and complexity reached 100% with selected channels. Hjorth parameter-based NN model with channel selection seems to be promising that improves the computation complexity significantly. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Electroencephalography

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Enhancing Agricultural Yield Predictions with Real-Time IoT Sensor Data and Machine Learning Integration 5

Enhancing Agricultural Yield Predictions with Real-Time IoT ...

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5th International Conference on IoT Based Control Networks and Intelligent systems, ICICNIS 2024

作者： Chandiraprakash, N. Chinnasamy, A. Ashok, M. Malla Reddy College of Engineering Department of Artificial Intelligence and Machine Learning Hyderabad India School of Computing Department of Data Science and Business Systems SRMIST kattankulathur campus Chennai India Malla Reddy College of Engineering Hyderabad India

ISBN: (纸本)9798331518097

This research extends previous studies on the effects of climate change on agricultural yield predictions by integrating advanced Machine learning (ML) methods with real time environmental sensing data. It builds on the basic usage of Long Short-Term Memory (LSTM) and Random Forest (RF) methods by employing real time data streams from IoT based field sensors that measure soil moisture, nutrient levels and pest activity. The aim is to enhance the prediction model's adaption to changing climatic circumstances, therefore increasing their utility in agricultural management. This study proposes a dynamic adaptation feature that uses real time data to make timely adjustments to crop management predictions. Moreover, the integration of Convolutional Neural Network (CNN) with LSTM is being investigated to increase the spatial and temporal accuracy of these predictions. By integrating conventional climatic data with detailed micro environmental elements from IoT sensors, this method obtains a aspects which affects the crop growth. This study also focuses on developing a userfriendly interface that allows farmers to easily access predictive developments and recommendations, allowing for more informed decision making. This technique is predicted to make substantial advances in precision agriculture, giving more effective methods for regulating agricultural productivity in dynamic climatic variability. These results may improve food security and sustainability by optimizing resource utilization and reducing the environmental impact of farming operations. © 2024 IEEE.

关键词： Smart agriculture

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DeepDect: A Facial Deepfake Video Detection Application using Ensemble Learning 12

DeepDect: A Facial Deepfake Video Detection Application usin...

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12th IEEE Conference on systems, Process and Control, ICSPC 2024

作者： Tay, Wan Xin Chua, Hui Na Jasser, Muhammed Basheer Issa, Bayan Wong, Richard T.K. Sunway University No. 5 Jalan Universiti School of Engineering and Technology Department of Computing and Information Systems Selangor Darul Ehsan Bandar Sunway47500 Malaysia University of Aleppo Faculty of Informatics Engineering Syria

ISBN: (纸本)9798350391398

The emerging technology of deepfake video poses significant threats to information integrity and public trust. Deepfake videos come in various forms, including face swaps, lip-syncing, and full-body simulations. Detection algorithms may struggle to generalize across these different types of deepfakes, leading to lower effectiveness in real-world applications. An advanced deepfake detection system is developed using an ensemble approach, combining the strengths of three pre-trained Convolutional Neural Networks (CNNs)ResNeXt50, Xception, and EfficientNet-v2-with Long ShortTerm Memory (LSTM) layers to capture both spatial and temporal features from videos to improve detection accuracy. The ensemble model is further enhanced by incorporating additional features such as lip movements, eye blinks, head poses, and color contrasts to identify subtle inconsistencies in deepfake videos that are often ignored by traditional detection methods. The ensemble model achieves an accuracy of 84.06%, with a good balance between precision and recall, indicating its effectiveness in real-world applications. We developed DeepDect, a web application embedded with the ensemble model, to provide a user-friendly interface for deepfake detection by uploading videos or submitting URL links. The application includes educational content about deepfake techniques to raise user awareness about the potential dangers of deepfakes. © 2024 IEEE.

关键词： Convolutional neural networks

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QSroute : A QoS Aware Routing Scheme for Software Defined Networking 8

QSroute : A QoS Aware Routing Scheme for Software Defined Ne...

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8th IEEE International Conference on Software engineering and Computer systems, ICSECS 2023

作者： Kamarudin, Imran Edzereiq Ameedeen, Mohamed Ariff Umar Ong, Muhamad Idaham Zabidi, Azlee Universiti Malaysia Pahang AL-Sultan Abdullah Faculty of Computing Department of Computer Systems and Networking Pekan Malaysia Department of Software Engineering Faculty of Computing Universiti Malaysia Pahang AL-Sultan Abdullah Pekan Malaysia

ISBN: (纸本)9798350310931

The increasing demand for bandwidth-intensive network applications, such as video streaming, multimedia, and Internet of Things (IoT) applications, necessitates improved resource management to protect the network without compromising Quality of Service (QoS). Meeting these challenges requires a centralized view of all available network resources. Software Defined Networking (SDN), an emerging technology, provides a centralized view and control of network resources. This feature enables administrators to programmatically define and manage network behavior, including routing, making it more flexible and adaptable. This study proposes a QoS-aware routing scheme for SDN that considers available bandwidth, packet delay, and packet loss to determine the optimal routing path. Optimal paths are selected based on meeting predefined threshold criteria. The study concludes by discussing potential directions for future research in this field. © 2023 IEEE.

关键词： Quality of service

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