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2023 IEEE World AI IoT Congress, AIIoT 2023

作者： Pasha, Syed Tangim Islam, Ashraful Rahman, Mohammed Masudur Ahmed, Eshtiak Ahmed Foysal, Md. Ferdouse Zahangir Alam, Md Daffodil International University Department of Computing and Information System Bangladesh Independent University Center for Computational and Data Sciences Bangladesh Independent University Department of Computer Science and Engineering Bangladesh International Standard University Department of Computer Science and Engineering Bangladesh Daffodil International University Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350337617

The computational analysis of the Bangla poems is a challenging task due to the diverse linguistic, stylistic, and semantic features of the Bangla language. In this work, we prepared a dataset of 1311 Bangla poems of two separate categories: Love and Miscellaneous poem, which contain 500 and 811 poems respectively. We used word or semantic-based features to classify Bangla poems using the TF-IDF feature techniques. We used Logistic Regression, Naïve Bayes (NB), and Support Vector Machine (SVM) models for classification through machine learning, and we used Bayesian optimization techniques for hyperparameters tuning of these three models. We also used LSTM, CNN, and transformer models for this research. For the performance evaluation of the classification models, we used four evaluation metrics of precision, recall, F1-score, and accuracy. We also used the ROC-AUC curve to distinguish between all the machine learning and deep learning models. The experimental results expressed that, the transformer model achieved the highest accuracy compared to all the typical machine learning and deep learning models with an accuracy of 87%. © 2023 IEEE.

关键词： Semantics

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Revolutionizing COVID-19 Diagnosis with Radiographic Images and Deep Learning: A Paradigm Shift in Healthcare

Revolutionizing COVID-19 Diagnosis with Radiographic Images ...

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2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

作者： Mohanaprakash, T.A. Saranya, S. Abinaya, M. Rajheshwari, K.C. Elangovan, D. Gobinath, M. Panimalar Engineeering College Department of Computer Science and Engineering Chennai India Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Chennai India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Sona College of Technology Department of Computer Science and Engineering TamilNadu Salem India Sri Manakula Vinayagar Engineering College Departement of Computer and Communication Engineering Puducherry India

ISBN: (纸本)9798350319088

Amid the relentless battle against the COVID-19 pandemic, a cadre of researchers and scientists is diligently crafting innovative tools to expedite diagnosis and treatment. One standout innovation is the integration of radiographic images and deep convolutional neuronal networks (CNN), offering deeper insights into COVID-19. Early, accurate diagnosis is paramount in managing the COVID-19 crisis. It not only facilitates timely treatment but also curbs the virus's spread. The amalgamation of radiographic images and advanced machine learning techniques emerges as a transformative force. Traditional COVID-19 diagnostics rely heavily on molecular tests, like Polymerase Chain Reaction (PCR) tests. While highly accurate, these tests often involve protracted processing times and substantial resource demands. They may also produce false negatives in early infections. Radiographic imaging, especially chest X-rays, addresses these limitations. Researchers have harnessed deep convolutional neuronal networks, specialized for image analysis, to create an innovative diagnostic platform. This platform meticulously scrutinizes chest X-ray images, distinguishing COVID-19 pneumonia from other forms with exceptional precision. The deep CNN model undergoes intensive training on a diverse dataset, including chest X-rays from both COVID-19-positive and COVID-19-negative patients. It employs intricate processes of feature extraction and pattern recognition, discerning subtle nuances indicative of COVID-19-related lung abnormalities. This proficiency empowers the model to categorize new chest X-ray images, assisting radiologists in decision-making. The merits of this groundbreaking approach are multifaceted. Foremost is the substantial reduction in diagnosis time. Unlike molecular tests, which can take hours, chest X-ray analysis yields results within minutes, facilitating swift patient triage. It holds particular promise in resource-constrained settings where molecular testing may be scarc

关键词： COVID-19

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Nonlinearity Estimation and Compensation for Accurate PMSM Modeling and Voltage Prediction

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CES Transactions on Electrical Machines and Systems 2024年第4期8卷 394-403页

作者： Beichen Ding Yuting Lu Chunyan Lai Weiwen Peng Kaide Huang Guodong Feng IEEE School of Advanced Manufacturing Sun Yat-sen UniversityShenzhen 510275China School of Intelligent Systems Engineering Sun Yat-sen UniversityShenzhen 510275China Department of Electrical and Computer Engineering Concordia UniversityMontrealCanada School of Mathematics and Big Data Foshan UniversityFoshan 528000China

For permanent magnet synchronous machine(PMSM),the machine model is critical to predict the operating states for motor control,which,however,can be greatly affected by system ***,this paper investigates accurate machine modeling for control and parameter *** the proposed approach,the PMSM model with saturated inductances is used as the base model,and this paper investigates modeling and compensation of the offsets to the base model due to system nonlinearities such as saturation and core loss effects for accurate machine modeling and voltage ***,the offsets to the base model are modeled using nonlinear functions with variable coefficients to compensate saturation and core loss effect,which can achieve better accuracy without changing the model structure.A differential estimation model is derived to estimate the model coefficients from a small amount of measurements with simplified ***,the model offset calculation is both computation and memory efficient with simplified *** contribution is to improve the machine model accuracy and achieve precise voltage prediction for practical ***,comparisons and the application to temperature estimation are conducted on a test interior PMSM to validate the proposed approach.

关键词： Core loss Differential modeling Multiparameter estimation PMSM

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Short Paper: "It’s Too Hot Under the Sun": Unveiling the Impact of Prolonged Heat Exposure on Rickshaw Pullers’ Brainwaves 24

Short Paper: "It’s Too Hot Under the Sun": Unveiling the Im...

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11th International Conference on Networking, Systems and Security, NSysS 2024

作者： Hasana, Maoyejatun Rahaman, Masfiqur Abedin, Razin Reaz Tahmid, Md Toki Tarin, Ishika Saha, Sudipa Tithi, Sutapa Dey Promi, Zarin Tasnim Noor, Kazi Abdun Sanjid, M.D. Zahidul Islam Dhrubo, Mahir Shahriar Akter, Samira Rahman, Tauhidur Al Islam, A.B.M. Alim Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Halıcıoğlu Data Science Institute University of California San Diego San DiegoCA United States Department of Computer Science and Engineering Brac University Dhaka Bangladesh Halıcıoğlu Data Science Institute University of California San Diego La Jolla CA United States

ISBN: (纸本)9798400711589

The changing climate is anticipated to result in more frequent and extended heat waves having catastrophic effects on urban areas. Prolonged exposure to extreme heat is believed to adversely affect both physiological and psychological well-being, particularly for outdoor workers. This study analyzed the effects of hot weather conditions on the brainwaves of rickshaw pullers by examining changes in EEG signals before and after driving rickshaws at different air temperatures and relative humidity levels. We selected six rickshaw pullers as participants for six levels of air temperature (°C) and humidity condition (%) in various outdoor areas in Dhaka city, Bangladesh, while driving rickshaws for a duration of 20 to 30 minutes. The acquired EEG data assists in assessing the effects of heat exposure on cerebral activity. Our statistical analysis shows that there is a positive association between heat stress and the alpha band power of brainwaves. © 2024 Copyright held by the owner/author(s).

关键词： Brain

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Prediction of AIDS/HIV Infection using Custom Artificial Neural Networks Model 1

Prediction of AIDS/HIV Infection using Custom Artificial Neu...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Sneha, B.J. Shriyashreshta Kodipalli, Ashwini Martis, Roshan Joy Ushasree, A. Devamane, Shridhar B. Global Academy of Technology Department of Ai & Data Science Bangalore India Manipal Institute of Technology Department of Electronics & Communication Engineering Bangalore India Gokaraju Lailavathi Engineering College Department of Computer Science & Engineering Hyderabad India Amrita Vishwavidyapeetam School of Computing Department of Cs Mysuru India

ISBN: (纸本)9798331529246

HIV is a serious disease that impairs immunity. Without treatment, the infection may progress through three stages, which might drastically shorten a person's life. Artificial neural networks (ANNs) are utilized to detect the HIV infection and even to find treatments for it. The AIDS viral infection is predicted in this review. The aim of ANN is to precisely classify patients according to whether or not they have HIV/AIDS. We have formed a custom ANN model and applied it to the Kaggle dataset that is accessible to the public, compared which optimizers offer the best accuracy and whether employing dropouts yields improved accuracy. With Gradient Descent, we achieved the maximum accuracy of 86.92% it is also one of the most commonly used optimizers due to its simplicity and effectiveness, followed by SGD with 86.45% it is a variation of gradient descent in which a random subset of data as opposed to the complete dataset is used to update the parameter and RMSprop with 86.21% uses the average of recent gradient magnitudes to modify the learning rate for each parameter. This aids with training stabilization, particularly when dealing with sparse or noisy data. The accuracy increased to 87.62% after we introduced dropouts to the hidden layer with optimizer as gradient descent, which produced the best results. Loss vs. Accuracy graphs and ROC curve have been plotted. With dropouts, the custom ANN model performed even better on the Gradient Descent optimizer. © 2025 IEEE.

关键词： ANN dropouts HIV/AIDS optimizers

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Pattern Recognition of Modulation Signal Classification Using Deep Neural Networks

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computer Systems Science & engineering 2022年第11期43卷 545-558页

作者： D.Venugopal V.Mohan S.Ramesh S.Janupriya Sangsoon Lim Seifedine Kadry Department of Electronics and Communication Engineering KPR Institute of Engineering and TechnologyCoimbatore641048India Department of Electronics and Communication Engineering Saranathan College of EngineeringTrichy620012India Department of Computer Science and Engineering Krishnasamy College of Engineering&TechnologyCuddalore607109India Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Engineering Sungkyul UniversityAnyangKorea Department of Applied Data Science Noroff University CollegeKristiansandNorway

In recent times,pattern recognition of communication modulation signals has gained significant attention in several application areas such as military,civilian field,*** becomes essential to design a safe and robust feature extraction(FE)approach to efficiently identify the various signal modulation types in a complex *** works have derived new techniques to extract the feature parameters namely instant features,fractal features,and so *** addition,machine learning(ML)and deep learning(DL)approaches can be commonly employed for modulation signal *** this view,this paper designs pattern recognition of communication signal modulation using fractal features with deep neural networks(CSM-FFDNN).The goal of the CSM-FFDNN model is to classify the different types of digitally modulated *** proposed CSM-FFDNN model involves two major processes namely FE and *** proposed model uses Sevcik Fractal Dimension(SFD)technique to extract the fractal features from the digital modulated ***,the extracted features are fed into the DNN model for modulation signal *** improve the classification performance of the DNN model,a barnacles mating optimizer(BMO)is used for the hyperparameter tuning of the DNN model in such a way that the DNN performance can be raised.A wide range of simulations takes place to highlight the enhanced performance of the CSM-FFDNN *** experimental outcomes pointed out the superior recognition rate of the CSM-FFDNN model over the recent state of art methods interms of different evaluation parameters.

关键词： Pattern recognition signal modulation communication signals deep learning feature extraction

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A novel multi-method framework for 3D printed fiber-reinforced polymer concrete utilizing advance additive manufacturing techniques

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Asian Journal of Civil engineering 2025年第4期26卷 1655-1668页

作者： Raut, Jayant M. Pimpalshende, Anjusha Chandak, Mayuri A. Patil, Tejas R. Pinjarkar, Latika Nair, Sruthi Department of Civil Engineering Yeshwantrao Chavan College of Engineering Maharashtra Nagpur 441110 India Department of Computer Science and Engineering Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology Hyderabad 500090 India Department of Civil Engineering Priyadarshini College of Engineering Maharashtra Nagpur 440019 India Department of Civil Engineering Priyadarshini Bhagwati College of Engineering Maharashtra Nagpur 440024 India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed) University Maharashtra Pune 440008 India Department of Computer Science (Data Science) Ramdeobaba College of Engineering and Management Maharashtra Nagpur 440013 India

Growing demands for customized, sustainable, and high-performance infrastructure urgently require innovative construction methodologies. Conventional methods for fiber-reinforced polymer concrete are not efficient in material usage, are inconsistent in their mechanical properties, and fail to satisfy the complex structural demands. The current methods of 3D printing are often affected by delamination of layers, bad alignment of fibers, and relatively high rates of defects, which adversely affect the structural integrity and efficiency of the printed components. To address these challenges, we propose a novel multi-method framework utilizing advance additive manufacturing techniques for 3D printed fiber-reinforced polymer concrete. Our research introduces four additional mechanisms: GCMME (Gradient-Controlled Deposition via Multi-Material Extrusion) for smooth material transitions with functional graded properties, DFAM (Directional Fiber Alignment Mechanism) for optimal reinforcement along stress trajectories, ARCS (Adaptive Rheology Control System) for viscosity modulation and self-healing capabilities, and AQA-PDM (AI-Based Quality Assurance and Predictive Defect Mitigation) for real-time defect detection and quality control. All the above-mentioned mechanisms can be used simultaneously to allow for the mass production of customised structural parts with outstanding mechanical properties. Significant results include tensile strength greater than 12 MPa, compressive strength greater than 50 MPa, enhanced flexural strength by about 35%, and the defects density of less than 0.5%. The material wastage is minimized by up to 25%. Moreover, self-healing efficiency in closure is more than 60% as well. This integrated method significantly enhances performance, accuracy, and sustainability in modular construction and thus provides a transforming solution for the infrastructure development process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 20

关键词： 3D printing Adaptive rheology Fiber-reinforced polymer concrete Gradient deposition Quality assurances

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Robust graph‐based localization for industrial Internet of things in the presence of flipping ambiguities

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1140-1149页

作者： Mian Imtiaz ul Haq Ruhul Amin Khalil Muhannad Almutiry Ahmad Sawalmeh Tanveer Ahmad Nasir Saeed Faculty of Electrical Engineering Swedish College of Engineering and TechnologyWah CanttPakistan Department of Electrical Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawarPakistan Department of Electrical Engineering Remote Sensing UnitNorthern Border UniversityArarSaudi Arabia Data Science and Artificial Intelligence Department‐College of Science and Information Technology Irbid National UniversityIrbadJordan Innovation Education and Research Center for On‐Device AI Software(Bk21) Department of Computer Science and EngineeringChungnam National UniversityDaejeonRepublic of Korea Department of Electrical and Communication Engineering United Arab Emirates University(UAEU)Al AinUnited Arab Emirates

Localisation of machines in harsh Industrial Internet of Things(IIoT)environment is necessary for various ***,a novel localisation algorithm is proposed for noisy range measurements in IIoT *** position of an unknown machine device in the network is estimated using the relative distances between blind machines(BMs)and anchor machines(AMs).Moreover,a more practical and challenging scenario with the erroneous position of AM is considered,which brings additional uncertainty to the final position ***,the AMs selection algorithm for the localisation of BMs in the IIoT network is *** those AMs will participate in the localisation process,which increases the accuracy of the final location ***,the closed‐form expression of the proposed greedy successive anchorization process is derived,which prevents possible local convergence,reduces computation,and achieves Cramér‐Rao lower bound accuracy for white Gaussian measurement *** results are compared with the state‐of‐the‐art and verified through numerous simulations.

关键词： Cramér‐Rao lower bound greedy successive anchorization industrial internet of things localization

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Development of an Efficient CNN model with Hyperparameter tuning for Early Prediction of Lung Diseases

Development of an Efficient CNN model with Hyperparameter tu...

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2023 International Conference on data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Meenakshi, D. Anbarasan, M. Murugesan, S. Selvalakshmi, B. Tagore Engineering College Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai India Tagore Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350348910

Deep learning (DL), an evolving technology, allows computers to learn autonomously from historical data. Furthermore, it employs a number of methodologies to generate the precise models and create predictions based on facts or old data. A deep learning system produces the predictions based on mathematical models of prediction which is built from past information once it gets new information. Nowadays, the use of deep learning in medical systems is crucial in various applications such as recognition, segmentation, and classification. Worldwide, lung illness is a prevalent occurrence in human beings which includes Covid-19, pneumonia, emphysema, asthma, lung cancer, etc. The early identification of lung diseases helps to detect the disease more accurately. Several deep learning techniques, including convolutional neural network, vanilla neural networks, visual geometry group-based networks and Caps Net, are used to predict the lung illness. To forecast different lung diseases from chest X-ray and CT scan pictures, typical CNN's performance and learning framework is insufficient. This proposed work aims to build a lung disease detection model using CNN with various hyperparameter tuning to enhance the accuracy of the model. Also, the accuracy is associated with advanced methods to enhance the performance of the proposed work. © 2023 IEEE.

关键词： Convolutional neural networks

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Multi-Stage Predict+Optimize for (Mixed Integer) Linear Programs 38

Multi-Stage Predict+Optimize for (Mixed Integer) Linear Prog...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hu, Xinyi Lee, Jasper C.H. Lee, Jimmy H.M. Stuckey, Peter J. Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin N.T. Hong Kong Department of Computer Science University of California Davis United States Department of Data Science and AI Monash University Clayton Australia

The recently-proposed framework of Predict+Optimize tackles optimization problems with parameters that are unknown at solving time, in a supervised learning setting. Prior frameworks consider only the scenario where all unknown parameters are (eventually) revealed at the same time. In this work, we propose Multi-Stage Predict+Optimize, a novel extension catering to applications where unknown parameters are instead revealed in sequential stages, with optimization decisions made in between. We further develop three training algorithms for neural networks (NNs) for our framework as proof of concept, all of which can handle mixed integer linear programs. The first baseline algorithm is a natural extension of prior work, training a single NN which makes a single prediction of unknown parameters. The second and third algorithms instead leverage the possibility of updating parameter predictions between stages, and trains one NN per stage. To handle the interdependency between the NNs, we adopt a sequential and parallelized versions of coordinate descent for training. Experimentation on three benchmarks demonstrates the superior learning performance of our methods over classical approaches. © 2024 Neural information processing systems foundation. All rights reserved.

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