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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Keren Persis, P. Kondekar, Vipul H. Sankaran, D. Suresh Kumar, K. Saveetha School of Engineering Simats Department of Coding Linguistics Chennai India Walchand Institute of Technology Department Of Electronics and Telecommunication Engineering Solapur India St. Joseph's College of Engineering Department Of Electronics and Instrumentation Engineering Chennai India Chennai Institute of Technology Department Of Computer and Science Engineering Chennai India

ISBN: (纸本)9798350376517

This research explores both Batch Gradient Descent and Stochastic Gradient Descent to optimize polynomial regression models at degrees 2 and 3, assessing the utility of this method in estimating the Remaining Useful Life (RUL) of lithium-ion batteries. After conducting an investigation, it was determined that the degree 2 polynomial regression optimized by Batch Gradient Descent had the maximum accuracy. As a result, it was chosen to be combined with Fully Convolutional Networks (FCN) in order to improve RUL prediction. Using Polynomial Feature Transformation and degree 2 polynomial regression, the 6,000 record dataset produces an R-squared (R) score of 0.9550 and a Mean Squared Error (MSE) of *** the other hand, an MSE of 0.0741, an Explained Variance Score of 0.9847, and a R2 of 0.9847 are obtained by the combination model of Batch Gradient Descent polynomial regression and FCN using a simple average. In comparison, the merging of the two models yields an impressive improvement in the accuracy of RUL forecasts for lithium-ion batteries, with an MSE decrease of around 66.1 percent from 0.2177 to 0.0741, and an improvement in R2 of nearly 3.1 percent. © 2024 IEEE.

关键词： Convolutional neural networks

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Augmented Reality in Higher Education: Literature Review 25

Augmented Reality in Higher Education: Literature Review

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25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024

作者： Maulana, Fairuz Iqbal Rahayu, Anita Zamahsari, Gamal Kusuma Adi, Puput Dani Prasetyo Arifuddin, Rahman Dirgantara, Wahyu Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Humanities Digital Language Learning Center Communication Science Department Jakarta11480 Indonesia Research Center for Telecommunication Bandung Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9798350378573

Augmented Reality (AR) is rapidly transforming educational landscapes, offering an immersive and interactive learning experience. This literature mapping review explores the current landscape of AR in higher education research. By systematically analyzing peer-reviewed publications in the last five years from 2019 to 2023, the review identifies key trends, application areas, author collaboration patterns, and research foci, were extracted, analyzed, and visualized using R and VOSViewer. The analysis reveals a growing body of research, with a surge in publications after 2020. science, Technology, engineering, and Mathematics (STEM) subjects dominate AR applications, highlighting its potential to visualize abstract concepts. The review further investigates the impact of AR on student outcomes, including engagement and learning processes. It also examines the geographical distribution of research and identifies knowledge gaps for future exploration. Overall, this review provides a comprehensive map of AR in higher education research, laying the groundwork for further investigation into its pedagogical effectiveness and broader implementation across disciplines. © 2024 IEEE.

关键词： Adversarial machine learning

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Response Analysis of Varied Q-Power Values of Cosine Distribution in Spatial Correlation

Response Analysis of Varied Q-Power Values of Cosine Distrib...

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio science Meeting, AP-S/URSI 2022

作者： Affum, E.A. Ajagbe, S.A. Boateng, K.A. Adigun, M.O. Addo, E. Kwame Nkrumah University of Science and Technology Department of Telecommunication Engineering Ghana University of Zululand Department of Computer Science South Africa

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

This paper investigates the impact of Q-power values of cosine distribution on the spatial correlation of cylindrical array using the maximum power of offset distribution. Our results showed significant mismatch between the simulation and theoretical coefficient for arbitrary Q-power values above ten(10) as compared to that of the Von Mises distribution. Even though high Q-power values are the key parameters for system performance, there is a need to carefully select arbitrary Q-power values above ten (10) to minimize errors in channel modeling. © 2022 IEEE.

关键词： Analytical models Correlation System performance Conferences

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Gender-Based Bimodal Analysis of Speech and Text in Non-Contextual Emotion Recognition

Gender-Based Bimodal Analysis of Speech and Text in Non-Cont...

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2024 Beyond Technology Summit on Informatics International Conference, BTS-I2C 2024

作者： Angkasa, Abby Rasya Zahra, Amalia Fung, Wai-Keung Binus Graduate Program Computer Science Department Jakarta11480 Indonesia Cardiff Metropolitan University Cardiff School of Technologies Engineering Department Cardiff United Kingdom

ISBN: (纸本)9798331508579

Emotion recognition can help human-computer interactions by enabling systems to respond empathetically and adapt to users' emotional conditions. This capability improves user experience, supporting the development of a more intuitive and emotionally responsive communication system. This study analyzes a bimodal approach based on gender (male and female) to recognize emotions without contextual information in dialogue analysis. Utilizing the Multimodal EmoryNLP dataset extracted from the TV series Friends with acted speech, we focused on four primary emotions: Angry, Neutral, Joy, and Scared. The model used in this study for text classification is RoBERTa, and wav2vec 2.0 is used for audio feature extraction with the Bi-LSTM model for classification. The experiment results using weighted F1-score reveal that data augmentation enhanced the performance of analyzing the original dataset from 0.46% to 0.52% and the male dataset from 0.43% to 0.51 %. In comparison, the female dataset remained consistent at 0.46%. The weighted F1-score and Unweighted Averaged Recall (UAR) from the male dataset are higher, 51 % and 48%, respectively, than those from the female dataset, 46% and 47%, respectively. Gender-based analysis indicated that male and female datasets exhibited distinct performance patterns, highlighting variations in emotional expression and recognition between genders. These findings underscore the effectiveness of multimodal strategies in emotion recognition and suggest that gender-specific factors play a significant role in enhancing classification performance. While these results highlight performance trends, further validation through repeated trials and statistical analyses could provide stronger generalizations and insights into gender-based differences. © 2024 IEEE.

关键词： Emotion Recognition

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High resolution and high sensitivity imaging magnetometer with magnetic nanoparticle and optical heterodyne detection

High resolution and high sensitivity imaging magnetometer wi...

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2024 Frontiers in Optics, FiO 2024

作者： Bartos, Jan Tis, Taleb Ba Park, Wounjhang Huang, Shu-Wei Department of Electrical Computer and Energy Engineering University of Colorado Boulder80309 United States Material Science and Engineering Program University of Colorado Boulder80309 United States

We present progress towards an imaging magnetometer using a magneto-optical polymer nanocomposite thin film, experimentally demonstrating 29nT/√Hz sensitivity with © Optica Publishing Group 2024, © 2024 The... 详细信息

关键词： Heterodyne detection

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Beyond Words: Unraveling Text Complexity with Novel Dataset and A Classifier Application 26

Beyond Words: Unraveling Text Complexity with Novel Dataset ...

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

作者： Islam, Mohammad Shariful Rony, Mohammad Abu Tareq Saha, Pritom Ahammad, Mejbah Nazmul Alam, Shah Md Saifur Rahman, Md Computer Science and Telecommunication Engineering Bangladesh Department of Statistics Noakhali3814 Bangladesh Daffodil International University Computer Science and Engineering Dhaka1216 Bangladesh American International University Bangladesh Computer Science and Engineering Kuril Dhaka Bangladesh Masters of Computer Science University:Arizona State University United States Strategic Management University:University of the Cumberlands United States

ISBN: (纸本)9798350359015

Text classification is a fundamental aspect of Natural Language Processing (NLP). This research presents a novel human-annotated English sentence dataset categorized into four classes (simple, complex, compound, complex-compound) containing 22331 sentences and a sophisticated sentence classifier tool offering the capability to analyze and classify sentences within English text with particular relevance to literature writing. This study explores its performance using three distinct feature representation methods: Bag-of-Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), and Word Embedding Features. The study involves the evaluation of four machine learning and two deep learning classifier models. BoW combined with Support Vector Classifier (SVC) and Logistic Regression (LR) demonstrated impressive accuracy rates, excelling in distinguishing sentence complexity. Word Embedding Features, specifically LSTM and RNN, offer a more profound semantic representation. LSTM stands out with the highest accuracy of 98.03% and balanced precision and recall, yielding an average F1-score of 97%. RNN, slightly less accurate at 97.75%, nevertheless exhibits competence in grasping sentence structure dependencies. It offers valuable insights for practical applications and contributes to the broader understanding of sentence structures and semantics. © 2023 IEEE.

关键词： Classifer NLP Performance Sentence Web app

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Detection of Omicron Caused Pneumonia from Radiology Images Using Convolution Neural Network(CNN)

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computers, Materials & Continua 2023年第2期74卷 3743-3761页

作者： Arfat Ahmad Khan Malik Muhammad Ali Shahid Rab Nawaz Bashir Salman Iqbal Arshad Shehzad Ahmad Shahid Javeria Maqbool Chitapong Wechtaisong College of Computing Khon Kaen UniverityKhon Kaen40000Thailand Department of Computer Science COMSATS University IslamabadVehari61100Pakistan Department of Petroleum&Gas Engineering University of Engineering&Technology54890Pakistan Electrical Engineering Department SBASSELahore University of Management SciencesLahore54890Pakistan School of Telecommunication Engineering Suranaree University of TechnologyNakhon Ratchasima30000Thailand

COVID-19 disease caused by the SARS-CoV-2 virus has created social and economic disruption across *** ability of the COVID-19 virus to quickly mutate and transfer has created serious concerns across the *** is essential to detectCOVID-19 infection caused by different variants to take preventive measures *** existing method of detection of infections caused by COVID-19 and its variants is costly and *** impacts of theCOVID-19 pandemic in developing countries are very drastic due to the unavailability of medical facilities and infrastructure to handle the *** is the major symptom of COVID-19 *** radiology of the lungs in varies in the case of bacterial pneumonia as compared to COVID-19-caused *** pattern of pneumonia in lungs in radiology images can also be used to identify the cause associated with *** this paper,we propose the methodology of identifying the cause(either due to COVID-19 or other types of infections)of pneumonia from radiology ***,because different variants of COVID-19 lead to different patterns of pneumonia,the proposed methodology identifies pneumonia,the COVID-19 caused pneumonia,and Omicron caused pneumonia from the radiology *** fulfill the above-mentioned tasks,we have used three Convolution Neural Networks(CNNs)at each stage of the proposed *** results unveil that the proposed step-by-step solution enhances the accuracy of pneumonia detection along with finding its cause,despite having a limited dataset.

关键词： COVID-19 pneumonia radiology images omicron convolution neural network(CNN) microscopy

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Asynchronous I/O Persistence for In-Memory Database Servers: Leveraging io_uring to Optimize Redis Persistence 17th

Asynchronous I/O Persistence for In-Memory Database Servers:...

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17th International Conference on Cloud Computing, CLOUD 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Chen, Le-Gao Li, Yanzhi Laohakangvalvit, Tipporn Sugaya, Midori Innovative Global Program College of Engineering Shibaura Institute of Technology Tokyo Koto City Japan Department of Computer Science and Engineering College of Engineering Shibaura Institute of Technology Tokyo Koto City Japan

ISBN: (纸本)9783031771521

With constant hardware improvements allowing for increasingly large memory sizes, in-memory database servers have become an attractive option for various cloud applications. Even though in-memory database servers store all data in memory, there is still a need to persist data onto an I/O device for maintenance, updates, or protection against server crashes. In high throughput environments, persistence happens often, significantly hindering server throughput performance. Although popular in-memory database servers such as Redis or Dragonfly DB have adopted asynchronous I/O for socket operations, none have extended this approach for persistence. This results in significantly longer time taken for persistence, leading to prolonged reduced throughput. Given the recent introduction of io_uring, which has demonstrated significant performance improvements over past asynchronous I/O APIs, we have decided to implement it into Redis to evaluate its effectiveness in reducing these performance overheads. Our findings indicate that io_uring can significantly enhance performance in use cases with medium to large payload sizes but may hinder performances in use cases involving small payload sizes due to higher complexity and higher operation cost per datapoint. This method can likely be applied to other servers, such as Dragonfly DB and Memcached, to achieve similar benefits in appropriate scenarios. Additionally, in-memory database server persistence can serve as valuable benchmarks for further io_uring development and optimization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Benchmarking

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High resolution and high sensitivity imaging magnetometer with magnetic nanoparticle and optical heterodyne detection

High resolution and high sensitivity imaging magnetometer wi...

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2024 Laser science, LS 2024

作者： Bartos, Jan Ba Tis, Taleb Park, Wounjhang Huang, Shu-Wei Department of Electrical Computer and Energy Engineering University of Colorado Boulder80309 United States Material Science and Engineering Program University of Colorado Boulder80309 United States

We present progress towards an imaging magnetometer using a magneto-optical polymer nanocomposite thin film, experimentally demonstrating 29nT/√Hz sensitivity with © 2024 The Author(s). © Optica Publishing ... 详细信息

关键词： Heterodyne detection

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KGR-Rainfall: Temperature-Based Rainfall Prediction in Bangladesh with Novel KGR Stacking Ensemble

KGR-Rainfall: Temperature-Based Rainfall Prediction in Bangl...

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2023 International Conference on Artificial Intelligence and Applications, ICAIA 2023 and Alliance Technology Conference, ATCON-1 2023

作者： Bitto, Abu Kowshir Rubi, Maksuda Akter Bijoy, Md. Hasan Imam Shuvo, Subrata Das Das, Aka Chowdhury, Amit Daffodil International University Department of Software Engineering Dhaka Bangladesh Development Daffodil International University Department of General Educational Dhaka Bangladesh Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh Chittagong University of Engineering and Technology Department of Electronics and Telecommunication Engineering Chattogram Bangladesh Premier University Department of Computer Science and Engineering Chattogram Bangladesh

ISBN: (纸本)9781665456272

Climate change factors such as wet or dry, cold or warm seasons have a significant impact on both the economy and culture. Extreme rainfall events have historically posed a major threat to many parts of the world. In Bangladesh, during monsoon seasons, wet southern airflows from the Bay of Bengal collide with dry mainland air, causing heavy rainfall that negatively affects various socio-economic sectors. These include agriculture, food production, urban planning, energy, water resource management, fisheries, forest management, healthcare, disaster management, transportation, tourism, sports, and leisure. To address this issue, the paper proposes a machine-learning approach to forecast rainfall in Bangladesh using multiple regression models and a novel Stacked Ensemble Model (KGR Stacking). The study also investigates the relationship between rainfall and temperature. The KGR Stacking model outperforms the other 12 regression models, achieving an accuracy of 86.43% and lower error. © 2023 IEEE.

关键词： Machine learning

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