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

作者： Sultana, Tamanna Jahan, Meskat Uddin, Md. Kamal Kobayashi, Yoshinori Hasan, Mahmudul Comilla University Department of Computer Science and Engineering Cumilla Bangladesh NSTU Department of Computer Science & Telecommunication Engineering Noakhali Bangladesh Saitama University Interactive Systems Lab. Saitama Japan

ISBN: (纸本)9798350359015

The field of emotion recognition in artificial intelligence focuses on enabling machines to comprehend and react to the range of emotions experienced by humans. This paper presents a novel approach that integrates the Convolution Neural Network (CNN) with audio and visual modalities. The study employs the RAVDESS database as a resource to train two distinct models for the analysis of both video and audio data. When it comes to audio pre-processing, advanced signal-processing techniques are applied to extract relevant elements and capture basic acoustic characteristics. A one-dimensional Convolutional Neural Network (CNN) architecture receives the audio data as input, enabling the model to learn complicated patterns and representations from the audio domain. In the context of video pre-processing, sophisticated algorithms are employed to extract essential facial characteristics. In order to capture the changing periods of facial expressions, the video frames are analyzed using a three-dimensional CNN framework following that they have been compressed and converted to grayscale. The fusion technique involves concatenating and extending the outputs of the audio and visual models. The fused features are subsequently sent into a softmax layer, which facilitates the development of a resilient emotion identification system. © 2023 IEEE.

关键词： Audio-visual recognition Convolution Neural Networks Emotion recognition Multi-modal fusion

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Wav2Vn: Scalable Unsupervised Pre-training and Adaptation for Multi-Accent Vietnamese ASR 9

Wav2Vn: Scalable Unsupervised Pre-training and Adaptation fo...

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9th International Conference on Applying New Technology in Green Buildings, ATiGB 2024

作者： Kim, Viet Nguyen Ngoc, Huyen Nguyen Thi Minh, Hieu Dam Khoi, Nguyen Do Hoang Do, Tien-Dung Dung, Cao Truong Posts and Telecommunication Institute of Technology Department of Information Technology Ha Noi Viet Nam Swinburne University Department of Computer Science Ha Noi Viet Nam Posts and Telecommunication Institute of Technology Department of Computer Science Ha Noi Viet Nam Posts and Telecommunication Institute of Technology Faculty of Information Ha Noi Viet Nam Posts and Telecommunication Institute of Technology Department of Electronic Engineering Ha Noi Viet Nam

ISBN: (纸本)9798331505042

Automatic speech recognition (ASR) systems have seen significant advancements in recent years, driven by deep learning and large-scale pre-training methods. However, achieving high accuracy across multiple accents remains a challenging task, especially for languages with rich phonetic diversity like Vietnamese. The Vietnamese language encompasses a wide range of regional accents, each with unique phonetic characteristics that can hinder the performance of conventional ASR. To address this issue, we present a Vietnamese language processing model called Wav2Vn as a novel approach to scalable unsupervised pretraining and adaptation for ASR targeting multiple Vietnamese accents. Our method leverages the power of the Mixture of Experts (MoE) to effectively handle the diverse phonetic variations across different Vietnamese accents. Our results demonstrate a significant performance improvement in terms of word error rate (WER) and robustness to accent variability compared to the current state-of-the-art PhoWhisper. © 2024 IEEE.

关键词： Deep learning

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Solar Power Generation Technique and its Challenges - A Comprehensive Review 15

Solar Power Generation Technique and its Challenges - A Comp...

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15th International Conference on Advances in Computing, Control, and telecommunication Technologies, ACT 2024

作者： Kitey, Himanshu Chandankhde, Pankaj Jajulwar, Kapil Shaikh, Mohammad Shahnawaz Fatinge, Pragati M. M. Tech Communication Engineering Department of Electronics and Telecommunication Engineering G H Raisoni College of Engineering Nagpur Nagpur India Department of Electronics and Telecommunication Engineering G H Raisoni College of Engineering Nagpur India Department of Computer Science and Engineering G H Raisoni Institute of Engineering and Technology Nagpur India

ISBN: (纸本)9798331300579

In the contemporary world, energy stands as an essential driving force behind socioeconomic development. However, with the alarming levels of environmental pollution, there has been a growing focus on renewable energy sources. These sustainable alternatives are gaining increasing attention, particularly as the depletion of fossil fuels becomes more ap-parent. Natural components such as sunlight, wind, and rainfall are used to generate renewable energy. Among these non-traditional renewable sources, solar energy emerges as a prominent contender for power generation. Solar power generation has gained recognition as a promising and environmentally sustain-able renewable energy source to meet growing global energy demands while minimizing cli-mate change consequences. This paper extensively examines solar power generation techniques, encompassing Photovoltaic (PV) Systems and Solar Thermal Technologies. The paper explores the present state of solar power generation technology, outlines its advantages, and researches the various challenges obstructing its widespread adoption. Furthermore, the study offers insights into potential solutions and outlines future directions for advancing solar power generation techniques. © Grenze Scientific Society, 2024.

关键词： Photovoltaic (PV) System Renewable Energy Socio-economic Solar Power Solar Thermal Technologies

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Development of Machine Learning Model for Medicinal Plant Identification 7

Development of Machine Learning Model for Medicinal Plant Id...

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7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Ranjani, G. Balaji, S. Rv College of Engineering Department of Electronics and Telecommunication Bengaluru India School of Computer Science and Engineering Rv University Bengaluru India

ISBN: (纸本)9798350343144

Accurate identification of medicinal plants is of paramount importance in pharmaceutical research, traditional medicine, and conservation efforts. This work presents the development of a machine learning model for the identification of medicinal plants. A comprehensive standard image of diverse medicinal plant species were collected and preprocessed for model training and evaluation. The model development process involved training and fine-tuning a deep CNN architecture on the medicinal plant dataset. Data augmentation techniques were employed to enhance the model's generalization and robustness. The trained model demonstrated impressive accuracy and effectiveness in identifying medicinal plants, achieving high classification performance across different species with diverse morphological variations and characteristics. © 2023 IEEE.

关键词： Machine learning

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ConvNeXt Model for Breast Cancer Image Classification 6

ConvNeXt Model for Breast Cancer Image Classification

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Setiawan, Devin Karnyoto, Andrea Stevens Intan, Indo Pardamean, Bens Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Informatics Engineering Universitas Dipa Makassar Makassar Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

Breast cancer is an occurrence of cancer that attacks breast tissue and is the most common cancer among women worldwide, affecting one in eight women. In this modern world, breast cancer image classification simplifies the process of analyzing, providing objective and accurate results. By leveraging machine learning algorithms and computer vision techniques, we developed breast cancer detection. The dataset is histopathology dataset from BreakHis and UNHAS Hospital. We chose the ConvNeXt-Tiny model then modified the classifier head as the proposed method. Before the dataset is processed by the model, we augment the images by applying random horizontal and vertical flips, adjustments to brightness, contrast, saturation, and hue using color jitter. The augmentation process simulates real-world variance and enhances the model's ability to generalize to unseen data. Our proposed model gained better performance (accuracy, F1-Score) results compared two other techniques to VGG16 and SVM. According to our experiments, the F1-Score for the ConvNeXt-Tiny model with classifier head modification is higher at 0.9516, than the gain for VGG16 at 0.9292, and the gain for the SVM at 0.83. © 2024 IEEE.

关键词： Lung cancer

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Navigating Bengali Linguistics: Insights from Machine and Deep Learning Perspectives for Categorization of Sentences 26

Navigating Bengali Linguistics: Insights from Machine and De...

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

作者： Islam, Md Minhazul Jubaer, Tanbeer Srizon, Azmain Yakin Rakib Hossain, Md. Mahedy Hasan, S.M. Farukuzzaman Faruk, Md. Minhazur Rahman, A.F.M. Rajshahi University of Engineering and Technology Department of Computer Science & Engineering Rajshahi Bangladesh Rajshahi University of Engineering and Technology Department of Electronics and Telecommunication Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350359015

The paper presents an innovative exploration into the classification of Bengali sentences, an essential aspect of advancing natural language processing (NLP) for languages characterized by rich linguistic diversity. In this comprehensive study, various machine learning and deep learning methodologies were meticulously applied to navigate the intricate linguistic landscape of the Bengali language. The research draws upon and synthesizes insights from previous studies that have focused on aspects such as sentence attributes, simplification algorithms, context-free grammar applications, and grammar-rule based transformations. The core of this study is anchored in the deployment of three distinct vectorization approaches - BERT-based, Glove-based, and TF-IDF-based - for the nuanced classification of Bengali sentences into simple, complex, and compound categories. A series of classifiers were applied, with the BERT-based vectorization demonstrating exemplary proficiency, registering an impressive 98.07% overall test accuracy. This finding underscores the model's robustness and versatility, illuminating its potential applicability beyond the designated dataset to encompass bespoke, real-life datasets. The research is set against the backdrop of a recently published large-scale Bengali sentence dataset titled Bangla Transformation of Sentence Dataset (BTSD) consisting of 3793 samples, which has filled a significant void in the existing literature, enabling a more intricate and detailed analysis. The findings of this study not only contribute to the enriched understanding of Bengali sentence structures but also hold implications for the broader field of computational linguistics and NLP. © 2023 IEEE.

关键词： Bengali Text Processing BERT Deep Learning Natural Language Processing Sentence Classification

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Comprehensive Forecasting of Smart Grid Stability Using Advanced Deep Learning and Machine Learning Models

Comprehensive Forecasting of Smart Grid Stability Using Adva...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Zaman, Snigdha Nur, Abdullah Hafez Butt, Usman Sulaiman, Rejwan Bin Farhan, Maruf International Islamic University Chittagong Department of Computer Science and Engineering Chittagong Bangladesh International Islamic University Chittagong Department of Electronic and Telecommunication Engineering Chittagong Bangladesh The British University in Dubai Dubai United Arab Emirates Northumbria University Department of Computer Science & Technology Northumbria United Kingdom Northumbria University Department of Computer Science and Engineering Northumbria United Kingdom

ISBN: (纸本)9798350367560

This study comprehensively examines the ability to forecast the stability of smart grids using sophisticated deep learning and machine learning models. We investigate different approaches, such as Bidirectional Gated Recurrent Unit (BiGRU), Multi-Layer Perceptron with Extreme Learning Machine (MLP-ELM), and innovative fusion models that combine Artificial Neural Networks (ANN) with Support Vector Machines (SVM). The performance of these models was evaluated by comparing parameters such as accuracy, precision, recall, and F-measure. The dataset is an improved version of the Electrical Grid Stability Simulated Dataset from the Karlsruher Institut für Technologie. It consists of 60,000 observations and includes twelve primary prediction variables. Before training and testing the models, we conducted thorough exploratory data analysis (EDA) and preprocessing. The solution we present utilizes a Sequential model that incorporates multiple optimizers and activation functions. This approach achieves a maximum accuracy of 99.48% when using the Adamax optimizer. The results indicate the substantial capacity of deep learning methods to improve the dependability and effectiveness of smart grid stability forecasts. This work offers a novel deep learning methodology for predicting the stability of smart grids, attaining a prediction accuracy of 99.48 % and exhibiting enhanced performance relative to leading methodologies. Comprehensive testing across several grid settings demonstrates the effectiveness of the proposed tactics, rendering it very beneficial for real-time stability forecasts. © 2024 IEEE.

关键词： Adversarial machine learning

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Beyond p-y method:A review of artificial intelligence approaches for predicting lateral capacity of drilled shafts in clayey soils

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Journal of Rock Mechanics and Geotechnical engineering 2024年第9期16卷 3812-3840页

作者： M.E.Al-Atroush A.E.Aboelela Ezz El-Din Hemdan Civil and Environmental Engineering Program Engineering Management DepartmentCollege of EngineeringPrince Sultan UniversityRiyadhSaudi Arabia Department of Structural Engineering Faculty of EngineeringAin Shams UniversityCairoEgypt Structure and Materials Research Lab Prince Sultan UniversityRiyadhSaudi Arabia Computer Science and Engineering Department Faculty of Electronic EngineeringMenoufia UniversityMenoufiaEgypt

In 2023,pivotal advancements in artificial intelligence(AI)have significantly *** that in mind,traditional methodologies,notably the p-y approach,have struggled to accurately model the complex,nonlinear soil-structure interactions of laterally loaded large-diameter drilled *** study undertakes a rigorous evaluation of machine learning(ML)and deep learning(DL)techniques,offering a comprehensive review of their application in addressing this geotechnical challenge.A thorough review and comparative analysis have been carried out to investigate various AI models such as artificial neural networks(ANNs),relevance vector machines(RVMs),and least squares support vector machines(LSSVMs).It was found that despite ML approaches outperforming classic methods in predicting the lateral behavior of piles,their‘black box'nature and reliance only on a data-driven approach made their results showcase statistical robustness rather than clear geotechnical insights,a fact underscored by the mathematical equations derived from these ***,the research identified a gap in the availability of drilled shaft datasets,limiting the extendibility of current findings to large-diameter *** extensive dataset,compiled from a series of lateral loading tests on free-head drilled shaft with varying properties and geometries,was introduced to bridge this *** paper concluded with a direction for future research,proposes the integration of physics-informed neural networks(PINNs),combining data-driven models with fundamental geotechnical principles to improve both the interpretability and predictive accuracy of AI applications in geotechnical engineering,marking a novel contribution to the field.

关键词： Laterally loaded drilled shaft load transfer and failure mechanisms Physics-informed neural networks(PINNs) P-y curves Artificial intelligence(AI) Dataset

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Reversible data hiding based on pixel segmentation and prediction for improving hiding capacity

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Signal, Image and Video Processing 2024年第6-7期18卷 5043-5059页

作者： Huang, Cheng-Ta Weng, Chi-Yao Zheng, Guo-Ting Weng, Hao-Yu International Bachelor Program in Informatics Yuan Ze University Taoyuan320315 Taiwan Department of Information Management Yuan Ze University Taoyuan320315 Taiwan Department of Computer Science and Information Engineering National Chiayi University Chiayi City600355 Taiwan Department of Computer Science and Artificial Intelligence National Pingtung University Pingtung900391 Taiwan Department of Computer Science and Information Engineering National Central University Taoyuan City320317 Taiwan

The idea of the traditional histogram shifting technique is to hide a message within the cover-image pixel distribution. However, the embedding capacity is limited by the peak point occurrences. To solve this problem, many reversible data hiding (RDH) techniques are developed to combine pixel prediction and histogram shifting to increase the embedding payload. In this paper, a RDH scheme based on chessboard prediction and pixel partitioning is proposed for enhancing the hiding payload. Pixel partitioning is used to separate a pixel into two parts: most significant bit and least significant bit. Then, the chessboard predictor is adopted to generate prediction errors and separate the pixels. Thereafter, histogram shifting is applied to embed the secret messages into prediction errors. Due to the high similarity of adjacent pixels in an image, the generated prediction errors are highly close to zero, resulting in a large embedding space. The experimental results indicate that the proposed method has superior performance in embedding capacity with acceptable image visual quality compared to existing schemes since it adopts the adaptive adjustment strategy to achieve this improved balance. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Pixels

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Boost DC-DC converter with regenerative snubber

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Energy Reports 2024年 12卷 798-819页

作者： Yau, Yeu-Torng Department of Ph.D. Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung Taiwan Department of Electrical Engineering National Chin-Yi University of Technology Taichung Taiwan

To accommodate the wide range of input voltages supplied by redundant batteries and ensure an adequate hold-up time for communication systems during utility power failures, power supplies used in 5 G base stations typically include a front-end boost converter as the first stage and a second-stage isolated converter. In this paper, a new topology for a regenerative snubber in boost converters is proposed. Compared to traditional active or passive snubber topologies, the proposed snubber offers the advantages of having fewer components and occupying less space. Moreover, it does not require additional active devices and can be driven by widely-used commercial PWM control integrated circuits, achieving a higher level of cost-effectiveness and offering increased industrial applications. The final prototype of a power supply for a networking switch hub (as used in 5 G base stations) is fabricated to demonstrate the feasibility of the proposed method. © 2024 The Authors

关键词： Topology

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