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Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

computer Modeling in engineering & Sciences 2024年第5期139卷 1455-1486页

作者： Kolli Ramujee Pooja Sadula Golla Madhu Sandeep Kautish Abdulaziz S.Almazyad Guojiang Xiong Ali Wagdy Mohamed Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science LBEF Campus(Asia Pacific University of Technology&InnovationMalaysia)Kathmandu44600Nepal Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmmanJordan

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental *** attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation *** this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering *** achieve this goal,a new approach using convolutional neural networks(CNNs)has been *** study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly *** selection of optimal input parameters is guided by two distinct *** first criterion leverages insights garnered from previous research on the influence of individual features on compressive *** second criterion scrutinizes the impact of these features within the model’s predictive *** to enhancing the CNN model’s performance is the meticulous determination of the optimal *** a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s *** model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score ***,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction *** unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rat

关键词： Class F fly ash compressive strength geopolymer concrete prediction deep learning convolutional neural network

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Design and Implementation of Renewable Energy Applications Based Bi-Directional Buck-Boost Converter 3

Design and Implementation of Renewable Energy Applications B...

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3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Ramana, K. Venkata Arulkumar, S. Marathe, Asmita Beshir, Kedir Jaiganesh, V. Tamilselvi, K. Sudhakar, M. Vnr Vignana Jyothi Institute of Engineering & Technology Department of Computer Scinece and Engineering Hyderabad India Wachemo University Centre for Energy Studies Department of Electrical and Computer Engineering Ethiopia Savitribai Phule Pune University Department of Technology Pune India Wachemo University Department of Electrical and Computer Engineering 667 Ethiopia Bharath Institute of Higher Education and Research Department of Mechanical Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Sai Ram Engineering College Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798350336238

Our main focus was on creating a Two directional power converter for a standalone photo-voltaic energy-generating technology as well as technology energy control when a lead-acid energy storage device is used to control the energy supply. We utilized a light bulb as the load to evaluate the effectiveness of the directional power converter developed as part of our project as well as the functionality of energy control technologies. The suggested technology was composed of an energy control technology, a lead-acid battery, a bulb, a two-directional buck-boost converter, an utmost power point tracking controller, and these components. An energy control technique was developed to grow the rate at which the Photo-voltaic power producing technology consumed energy. The circuits are intended to charge the battery between upper and lower voltage limits, as well as to continuously check the battery's state of charge and add or release current as necessary. The bidirectional buck-boost converter's ability to function as a charge controller on its own, along with the use of particular BUCK and BOOST converter properties to optimize the home application, is the primary distinction between the method used in the proposed technology and other techniques used in the past. The battery is charged and discharged using a bidirectional dc-dc converter. Measurement data are employed to support the viability of a photovoltaic air conditioning technology. © 2023 IEEE.

关键词： Buck-Boost converter

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Intelligent Energy Management Energy Storage Systems Using Machine Learning

Intelligent Energy Management Energy Storage Systems Using M...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Govindasamy, Chinnaraj Dayana, T.R Nisha Adkar, Siddhika Dilip Prakash, D. Shyam Gupta, A. Shilpa Trivedi, Sanjeev Kumar Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamilnadu Chennai602105 India Vels Institute of Science Technology and Advance Studies Department of Computer Science Chennai India School of Computer Science Engineering and Applications D y Patil International University Akurdi India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore India Department of Electrical and Electronics Engineering Aurora's Technological Research Institute Uppal Hyderabad India Khwaja Moinuddin Chishti Language University Faculty of Engineering and Technology Department of Electronics Lucknow India

ISBN: (纸本)9798350375237

A nevertheless-emerging generation called cloud computing permits customers to pay for services on a usage-based foundation. Internet-primarily based IT offerings are supplied through cloud computing, at the same time as virtualization enables the availability of PC sources. The muse of cloud computing is the information center, which is made up of networked computers, cables, electricity components, and different components that host and shop corporate records. With cloud facts centers, high overall performance has continually been the most critical concern, yet it compromises strength utilization. The key hassle is to lessen power consumption at the same time as preserving provider nice and performance a good way to stability device performance and strength intake. A detailed grasp of strength use styles within the cloud environment is needed for our suggested technique. We look at power consumption tendencies and reveal how, through the use of the right optimization standards based on our strength intake models, we can keep more strength in cloud records facilities. All through the prediction section, tablet optimization, which has a 97 percent accuracy rate, permits this era to provide extra correct future price forecasts. © 2024 IEEE.

关键词： Machine learning

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Microwave-induced cooling in double quantum dots: Achieving millikelvin temperatures to reduce thermal noise around spin qubits

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Physical Review B 2025年第1期111卷 014305-014305页

作者： Daryoosh Vashaee Jahanfar Abouie Department of Materials Science and Engineering and Department of Electrical and Computer Engineering North Carolina State University Raleigh North Carolina 27606 USA Department of Physics Institute for Advanced Studies in Basic Sciences Zanjan 45137-66731 Iran

Spin qubits in gate-defined quantum dots are emerging as a leading technology due to their scalability and long coherence times. However, maintaining these qubits at ultralow temperatures typically requires complex cryogenic systems. This paper proposes a gate-defined double quantum dot (DQD) cooling system, where the DQDs act as refrigerants to reduce the local phonon environment around computational qubits. The cooling process occurs in two distinct stages: the first step involves microwave-induced state depopulation combined with fast cyclic detuning to transfer the DQD's population to the ground state, effectively lowering the DQD's temperature. In the second step, the cooled DQD interacts with and absorbs phonons resonant with the DQD spin energy, thereby filtering out these phonons that contribute to spin-lattice relaxation in the surrounding environment. This study focuses on the first step, presenting detailed calculations and numerical results that demonstrate the feasibility of achieving local DQD temperatures below 10mK at a bath temperature of 1K. The sensitivity of the cooling performance to detuning energy, magnetic field strength, spin-orbit couplings, and diabatic return time is analyzed, while the phonon filtering in the second step will require further investigation.

关键词： Semiconductor quantum dots

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Utilizing Gradient Boosting Models to Identify Risk Factors for Stroke 2

Utilizing Gradient Boosting Models to Identify Risk Factors ...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Tamilselvi, P. Harini, C. Rao, Guduri Padma Narasimha Raju, P.V. Subbulakshmi, R. Channabasava, Ugranada Department of Computer Science Vels Institute of Science Technology and Advanced Studies Chennai India Maisammaguda Secunderabad India SRKR Engineering College Andhra Pradesh Bhimavaram India Sagi Rama Krishnam Raju Engineering College Bhimavaram India Department of Computer Science and Engineering Karpagam Institute of Technology Coimbatore India Department of Information Science and Engineering Don Bosco Institute of Technology VTU Karnataka Belagavi India

ISBN: (纸本)9798350383867

This research looks at the usage of the models of gradient boosting to find out the ones that are relevant in the stroke incidence. With the help of a large dataset ranging from demographics to clinical and imaging characteristics, the three most effective gradient boosting algorithms (XGBoost with LightGBM and CatBoost) were evaluated in terms of their ability for stroke risk prediction. Model performance metrics, including accuracy, area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, precision, and F1 score, were estimated through re-scheduling validation proses. Through feature importance analysis, I discovered which of predictors, such as age, hypertension, diabetes and others contribute to the stroke risk prediction and where age and hypertension occupy the highest positions. The result of external validation in separate cohorts was the confirmation of the predictive properties of the data assortment as well as the generalizability of the presented models in variety of populations. Also, the role of these models has been examined from the perspective of their clinical usefulness and ability to inform the implementation of targeted preventive interventions that will result in improved outcomes regarding heart health. We evaluated various lifestyle adjustments, medications and precision monitoring plans as being the most likely to have a positive effect on stroke rates and patient outcomes. Overall the present study proves the problem-solving capability of the gradient boosting model in a tailored risk assessment of stroke and stroke prevention, enabling us to implement the necessary interventions to minimize the impact of the diseases on the healthcare systems and individuals. © 2024 IEEE.

关键词： Risk assessment

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Artificial Driving and Spectral Graph Theory for Automated Plant Leaf Disease Classification

Artificial Driving and Spectral Graph Theory for Automated P...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Bajpai, Chhavi Gaur, Manish Suganthi, J Roselin Vinayagam, S. Kaliappan, S. Manoj Senthil, K. Centre for Advanced Studies Department of Computer Science & Engineering Lucknow India Institute of Engineering and Technology Department of Computer Science & Engineering Lucknow India K. Ramakrishnan College of Engineering Department of Ece Trichirapalli India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Bioscience Chennai India Lovely Professional University Department of Research and Development Jalandhar India Kongu Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (纸本)9798350375442

Having a certified plant pathologist visually inspect the diseased leaves is now the gold standard for disease diagnosis in plants. This is because symptoms are present in the vast majority of plant illnesses. Because manual disease diagnosis is slow and results are highly dependent on the skill of the pathologist, this is an ideal domain for computer-aided diagnostic *** one of the suggested methods are preprocessing, step two is feature extraction, and step three is training the model. Pictures are acquired, then processed, enhanced, resized, segmented, and finally converted from RGB to HSI as part of the preprocessing phase. The topic of this study is the extraction of numerous features for the purpose of leaf disease categorization. Hexagons of colors are retrieved for classification purposes. The model was trained using Spectral Graph Theory. On average, this state-of-the-art technique achieves a higher accuracy rate of 92.48% than CNN and SVM. © 2024 IEEE.

关键词： Graph theory

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A Reliable Clinical Decision Support System for Posttraumatic Stress Disorder Using Functional Magnetic Resonance Imaging Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5605-5615页

作者： Bhattacharya, J. Gupta, A. Dretsch, M.N. Denney, T.S. Deshpande, G. TIET Department of Computer Science and Engineering Patiala147004 India Bennett University School of Computer Science Engineering and Technology Greater Noida201310 India Walter Reed Army Institute of Research U.S. Army Medical Research Directorate-West Joint Base Lewis McChordWA98433 United States Auburn University Auburn University Neuroimaging Center Department of Electrical and Computer Engineering AuburnAL36849 United States Alabama Advanced Imaging Consortium BirminghamAL36849 United States National Institute of Mental Health and Neurosciences Department of Psychiatry Bangalore560030 India Indian Institute of Technology Department of Heritage Science and Technology Hyderabad502284 India

In recent years, there has been an upsurge in artificial intelligence (AI) systems. These systems, along with efficient performance and predictability, also need to incorporate the power of explainability and interpretability. This can significantly aid clinical decision support by providing explainable predictions to assist clinicians. Explainability generally involves uncovering key input features important for classification. However, characterizing the uncertainty underlying the decisions of the AI system is an important aspect needed for interpreting the decisions. This is especially important in clinical decision support systems, given considerations of medical ethics such as nonmaleficence and beneficence. In this study, we develop methods for characterizing the decision certainty of machine learning (ML)-based clinical decision support systems. As an illustrative example, we introduce a framework for ML-based posttraumatic stress disorder (PTSD) diagnostic classification that classifies the subjects into pure and mixed classes. Accordingly, a clinician can have very high confidence (95% probability) about the diagnosis of a subject in a pure PTSD or combat control class. Remaining sample points for which the AI classification tool does not have very high confidence ( © 2024 IEEE.

关键词： Decision support systems

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Harvesting Knowledge: An Innovative Ontology Framework for Agricultural Advancement using NPL methodologies 3

Harvesting Knowledge: An Innovative Ontology Framework for A...

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3rd International Conference on electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Deepa, R. Das, Manisha Thilakavathy, P. Bennet Prabhu, A. Surendran, R. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India Andhrapradesh Rajam India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Computer Science and Engineering Chennai India Kone Elevator India Technology & Engineering Centre Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350369083

This paper introduces Agricultural Advancement using NLP (AA-NLP) techniques as a strategy to overcome the limitations of traditional agricultural management information systems. There is a lot of unstructured data available but this situation makes agricultural research inefficient because of its knowledge extraction and usage failure. This problem is addressed by combining an ontology framework developed for agricultural information management with Natural Language Processing (NLP) methods. The solution, AA-NLP, utilizes modern NLP algorithms which extract, classify and evaluate agricultural information from many textual sources that include research papers, reports and online repositories. AA-NLP achieves effective information retrieval and exploitation through named entity identification, sentiment analysis and semantic similarity as well as other techniques to convert unstructured input into structured ontological representations. Applying AA-NLP has several benefits such as increased discoverability of relevant agricultural information, automatic classification of scientific findings, and generation of actionable insights for decision making in farming practices, crop management and formulation of agricultural policies. The effectiveness of AA-NLP was analyzed in this paper by measuring the precision associated with knowledge extraction and ontology creation through various performance metrics including accuracy, sustainability farming practices, agricultural productivity and precision. © 2024 IEEE.

关键词： Semantics

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Recycled silicon solar cell-derived nanostructured p-black silicon device for high performance NO₂ gas sensor applications

RSC Applied Interfaces

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RSC Applied Interfaces 2024年第1期2卷 220-229页

作者： Aleem, Mahaboobbatcha Vishnuraj, Ramakrishnan Pullithadathil, Biji Nanosensors & Clean Energy Laboratory Department of Chemistry & Nanoscience and Technology PSG Institute of Advanced Studies Coimbatore 641 India Advanced Self-Powered Systems of Integrated Sensors and Technology Center Department of Electrical and Computer Engineering North Carolina State University Raleigh 27695 NC United States Center of Excellence in Advanced Materials and Green Technologies Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Department of Chemical Engineering and Materials Science Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India

Nitrogen dioxide (NO2) is a toxic gas that can cause respiratory problems, and sensing its presence is crucial for environmental monitoring and industrial safety. This investigation presents a novel approach towards s...

Nitrogen dioxide (NO₂) is a toxic gas that can cause respiratory problems, and sensing its presence is crucial for environmental monitoring and industrial safety. This investigation presents a novel approach towards sensing NO₂ gas by utilizing partially completed/recycled silicon solar cells employing a metal-assisted etching process to fabricate a high-performance p-black-silicon based sensor. Structural and morphological analyses using X-ray diffraction patterns, Raman spectroscopy and cross sectional FESEM characterization confirm the integrity of the p-B-silicon sensor. By combining recycling techniques with advanced fabrication methods, the resulting sensor exhibits exceptional sensitivity, a low detection limit of 1 ppm, and rapid response times (12-14 s) when exposed to NO₂ gas concentrations ranging from 1 to 5 ppm. The enhanced sensitivity is attributed to the unique nanostructured comb-like morphology of the sensor material, which facilitates fast charge transport mechanisms, and a plausible sensing mechanism has been proposed and explained using a depletion model diagram and energy model diagram. This eco-friendly and cost-effective solution not only addresses electronic waste concerns but also highlights the potential of sustainable practices in scientific research. The findings emphasize on the importance of environmental consciousness and innovation, showcasing a promising future for gas sensing technology. By utilizing recycled materials and advanced fabrication techniques, this study contributes to the development of efficient, eco-friendly sensors for environmental monitoring applications, paving the way for a more sustainable and technologically advanced future in the field of gas sensors. © 2025 RSC.

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Design of Semiconductor Laser Drive Circuit Based on STM32 7

Design of Semiconductor Laser Drive Circuit Based on STM32

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7th International Conference on Power and Energy Applications, ICPEA 2024

作者： Wang, Shangkun Zhang, Junchao Yang, Xiaofeng Guo, Shijia Yan, Huanshuai Xu, Bingshe College of Electrical and Power Engineering Taiyuan University of Technology Taiyuan China Shanxi Vocational University Engineering Science and Technical College Department of Computer Engineering Yuci China Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering Taiyuan China

ISBN: (纸本)9798350356113

A circuit using STM32 chip as the main control is designed to address the issue of semiconductor laser performance being easily affected by injection current and operating temperature. It can achieve high-precision and high stability constant current drive and constant temperature control, and has complete protection functions. The system adopts LT3080 The AD5160, TS5A3159 chips and sampling resistors have been designed with a constant current negative feedback circuit;Design a constant voltage limiting module using LM317 chip to ensure that the forward voltage of the semiconductor laser does not exceed the rated maximum value, and the slow start module slowly increases the current to the set value;The electrostatic protection circuit effectively avoids the damage of high-voltage static electricity and surge current to the laser;Design a temperature sampling module with ADN8834 as the core, and use incremental digital PID technology to control the heating or cooling of TEC. The experimental results show that this driving circuit can continuously adjust the current of the semiconductor laser, with a range of 0 to 100 mA. The current regulation accuracy has reached 0.01mA, and the stability has reached 0.02%;The temperature control accuracy within 10-40 °C is 0.01 °C, and the stability can reach 0.12%. © 2024 IEEE.

关键词： Integrated circuit manufacture

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