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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Bhuiyan, Md Shoaib Suzuka University of Medical Science Faculty of Medical Engineering Dept. Health Data Science Mie Suzuka Japan

ISBN: (纸本)9781665492324

There are some models of cars that remain popular with the consumers, even if pre-used. However, they sell at a wide variety of prices. We built a dataset of past prices for two popular Toyota SUVs, Harrier and RAV4 using the final selling price obtained from a large Japanese auction house, USS. We then tried to predict a useful range of prices for each of these two models of pre-used Toyota SUVs, based on the parameters of that particular car like how many kilometers has it already travelled, whether the powertrain is hybrid or runs on gasoline only, which year was it originally built in, 2019 or in 2020 or later, etc. We also tried to find out whether options like having Sunroof/moonroof had any effect on the final bid of auction prices. Feature Selection and Feature Transformation were done using the Classification Learner App of the MATLAB Statistics and Machine Learning Toolbox version R2020b. Our analysis shows that we can establish a useful range of prices. © 2022 IEEE.

关键词： Costs

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Enhancing Printed Circuit Board Defect Detection through Ensemble Learning 1

Enhancing Printed Circuit Board Defect Detection through Ens...

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1st IEEE International Workshop on Future Intelligent Technologies for Young Researchers, FITYR 2024

作者： Law, Ka Nam Canaan Yu, Mingshuo Zhang, Lianglei Zhang, Yiyi Xu, Peng Gao, Jerry Liu, Jun San José State University Department of Applied Data Science San José United States Virginia Tech Dept. of ISE Blacksburg United States San José State University Dept. of Computer Engineering San José United States

ISBN: (纸本)9798331539757

The quality control of printed circuit boards (PCBs) is paramount in advancing electronic device technology. While numerous machine learning methodologies have been utilized to augment defect detection efficiency and accuracy, previous studies have predominantly focused on optimizing individual models for specific defect types, often overlooking the potential synergies between different approaches. This paper introduces a comprehensive inspection framework leveraging an ensemble learning strategy to address this gap. Initially, we utilize four distinct PCB defect detection models utilizing state-of-The-Art methods: EfficientDet, MobileNet SSDv2, Faster RCNN, and YOLOv5. Each method is capable of identifying PCB defects independently. Subsequently, we integrate these models into an ensemble learning framework to enhance detection performance. A comparative analysis reveals that our ensemble learning framework significantly outperforms individual methods, achieving a 95% accuracy in detecting diverse PCB defects. These findings underscore the efficacy of our proposed ensemble learning framework in enhancing PCB quality control processes. © 2024 IEEE.

关键词： Contrastive Learning

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Exploring Television White Space as an Alternative for Wireless Broadband Connectivity 2

Exploring Television White Space as an Alternative for Wirel...

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2nd International Conference on Multidisciplinary engineering and Applied science, ICMEAS 2023

作者： Ifijeh, Ayodele H. Adetiba, Emmanuel Adewale, Adeyinka Thakur, Surrendra Moyo, Sibusiso Emmanuel, Dare O. Ukpong, Udeme C. Dept. of Electrical and Information Engineering Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Covenant University Dept. of Electrical and Information Engineering Ota Canaanland Nigeria Durban University of Technology KZN E-skills CoLab Durban4001 South Africa School for Data Science and Computational Thinking Stellenbosch University Department of Mathematical Sciences Stellenbosch7602 South Africa

ISBN: (纸本)9798350358834

Advancement in wireless transmission has enabled the communication among multiple devices all over the world, as well as expanding the frontiers of wireless Internet access. As a result of these advancement, numerous devices are questing for wireless connectivity thereby congesting the existing spectrum. If new measures are not taken to reduce depletion of the reserved radio resource, new allocations of wireless Internet access will be impossible, thereby risking the development of the entire wireless ecosystem. The demands for resourceful spectrum consumption have continually driven researchers and stakeholders in the wireless industry to search for spare radio spectrum to offer affordable and reliable wireless connectivity. The TV broadcasting is not fully utilizing the spectrum in some geographical zone. These TV unused spaces called TV White Spaces (TVWS), are well-thought-out to be the probable future resolutions for unavailability of spectrum. The low frequency band (470 to 694 MHz) of the unutilized TV channel has exceptional broadcast features over extensive distances and enables transmissions through high-rise buildings, trees, and mountains. These features allow the applications of TVWS in Internet of Things (IoT), wireless sensor network, wireless connectivity, wireless switching, smart grid and cities, automated transportation, and logistics as well as connectivity in cloud infrastructure. This paper presents a study on the advantages and benefits of television white space as an alternative to wireless broadband connectivity. © 2023 IEEE.

关键词： Tall buildings

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Combating Cyber Attacks by Malware Classification Using Deep Neural Networks

Combating Cyber Attacks by Malware Classification Using Deep...

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Intelligent & Innovative Practices in engineering & Management (IIPEM), International Conference on

作者： Deepthi Tabitha Bennet Preethi Samantha Bennet Anitha Gracy. J Sharmila. P Dept. of Applied Mathematics and Computational Science Thiagarajar College of Engineering Madurai India

ISBN: (数字)9798350390049

ISBN: (纸本)9798350390056

The detection and classification of malware pose significant challenges in cybersecurity due to the ever-evolving nature of malicious threats. In this study, we propose a novel approach for classifying malware using deep neural networks (DNNs). Our dataset comprises of 25 distinct classes of malware samples. After developing and testing 4 DNNs, including ResNet50, InceptionResNetV2, VGG16, DenseNet169, a robust and efficient model capable of accurately identifying and categorizing malware instances is built using DenseNet169. The process involves feature extraction from byteplot *** proposed malware detection model uses DenseNet169 as it has shown very high accuracy (95%), precision, recall and F1-score for the *** research contributes to advancing the state-of-the-art in malware classification, providing insights into the efficacy of deep neural networks for combating sophisticated cyber threats in modern computing environments.

关键词： Training Accuracy computational modeling Transfer learning Training data Feature extraction Malware data models Testing Residual neural networks

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Facial Expression Recognition Using Multi-Block Deep CNN 5

Facial Expression Recognition Using Multi-Block Deep CNN

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Priyadarshini, V. Srinivasulu Reddy, U. Reddy Chirra, Venkata Rami Mrudula, M. Suneetha, M. National Institute of Technology Dept. of Computer Science & Engineering Andhra Pradesh India Machine Learning & Data Analytics Lab National Institute of Technology Dept. of Computer Applications Tiruchirappalli India School of Computer Science & Engineering VIT-AP University Amaravathi India Malla Reddy College of Engineering Department of CSE Telangana India Independent Researcher Amaravati India

ISBN: (纸本)9798331528539

Emotions play a crucial role in decision-making, moral judgments, and other cognitive processes. The goal of this work is to identify people's facial emotions from face images. Facial expression recognition (FER), a highly diverse field with many commonalities between classes, is a challenging problem. The capabilities of current FER systems are limited in this regard. In this work, a multi-block Deep Convolutional Neural Networks (DCNN) model was proposed to identify facial expressions. Five blocks with different computational components were defined in a multi-block DCNN to extract the unique characteristics from input images. The second model (DCNN-SVM) is developed with an ensemble of SVM classifier to improve predictability and stability. The third model (DCNN-MV) is based on the majority voting with 4 classifiers SVM, KNN, Random Forest, and Decision Tree. The CK+ dataset was utilized and expanded via image data augmentation to enhance model performance and generalization. By adjusting hyperparameters, the suggested DCNN model's accuracy was investigated. The DCNN model reports an accuracy score of 97.71%. Both DCNN-SVM and DCNN-MV models achieved 97.79% accuracy. On the CK+ dataset, the proposed models demonstrated good accuracy in identifying facial expressions. When compared to existing models, the proposed model outperformed existing models in terms of accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploring BioClinical BERT’s NLP Capabilities with Explainability Techniques

Exploring BioClinical BERT’s NLP Capabilities with Explaina...

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Emerging Technologies in Computer science for Interdisciplinary Applications (ICETCS), International Conference on

作者： Aditi Sankaranarayanan Diya Shetty Kirti Chetwani B R Shambhavi Dept. of Information Science and Engineering BMS College of Engineering Bengaluru India Dept. of CSE (Data Science) BMS College of Engineering Bengaluru India

ISBN: (数字)9798350372502

ISBN: (纸本)9798350372519

Natural Language Processing has gained significant attention in the world of Information Technology and is experiencing rapid growth. Its applications span across various domains, with a notable surge in its utilization within the healthcare industry. This has fueled the development of language models that are trained on a large amount of clinical data, like Bio+Clinical BERT. However, there remains a crucial concern regarding the trustworthiness of NLP models. Traditional accuracy measures alone may not be sufficient to instill confidence in their reliability. In this study, we address this concern by exploring the application of Explainability to NLP tasks in the healthcare sector. We employ Explainability techniques to gain deeper insights into the underlying mechanisms and evaluate the trustworthiness of fine-tuned models based on Bio+Clinical BERT. Specifically, LIME and SHAP techniques have been utilized to produce explanations, which prove instrumental in enhancing our understanding of the models, as well as identifying their strengths and weaknesses.

关键词： Measurement Biological system modeling computational modeling Medical services Predictive models Natural language processing data models

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Risk Estimation in a Markov Cost Process: Lower and Upper Bounds 41

Risk Estimation in a Markov Cost Process: Lower and Upper Bo...

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41st International Conference on Machine Learning, ICML 2024

作者： Thoppe, Gugan Prashanth, L.A. Bhat, Sanjay P. Bengaluru India Robert Bosch Centre for Data Science and Artificial Intelligence IIT Madras Chennai India Dept. of Computer Science and Engineering Indian Institute of Technology Madras Chennai India TCS Research Hyderabad India

We tackle the problem of estimating risk measures of the infinite-horizon discounted cost of a Markov cost process. The risk measures we study include variance, Value-at-Risk (VaR), and Conditional Value-at-Risk (CVaR). First, we show that estimating any of these risk measures with ϵ-accuracy, either in expected or high-probability sense, requires at least Ω(1/ϵ2) samples. Then, using a truncation scheme, we derive an upper bound for the CVaR and variance estimation. This bound matches our lower bound up to logarithmic factors. Finally, we discuss an extension of our estimation scheme that covers more general risk measures satisfying a certain continuity criterion, such as spectral risk measures and utility-based shortfall risk. To the best of our knowledge, our work is the first to provide lower and upper bounds for estimating any risk measure beyond the mean within a Markovian setting. Our lower bounds also extend to the infinite-horizon discounted costs’ mean. Even in that case, our lower bound of Ω(1/ϵ2) improves upon the existing Ω(1/ϵ) bound (Metelli et al., 2023). Copyright 2024 by the author(s)

关键词： Cost estimating

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An Algorithm for Belief Rule Induction with Partial Ignorance

SSRN

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SSRN 2024年

作者： Li, Yangxue Pérez, Ignacio Javier Cabrerizo, Francisco Javier Morente-Molinera, Juan Antonio Andalusian Research Institute in Data Science and Computational Intelligence Dept. of Computer Science and AI University of Granada Granada18071 Spain

Belief rules are an extension of fuzzy rules that consider belief degrees. They are widely applied due to their traceability, interpretability, and flexibility. However, current belief rules only consider residual support and global ignorance while ignoring partial ignorance. In this paper, we proposed a novel belief rule-based classification system called partial ignorance belief rule induction algorithm (PIBRIA). The consequent part of belief rules takes into account not only the residual support and global ignorance but also partial ignorance of evidence. The belief degrees in the consequent part of each belief rule collectively form a genuine basic probability assignment (BPA). Based on fuzzy unordered rule induction algorithm (FURIA), we propose the learning algorithm for belief rules with partial ignorance, which helps the novel proposed PRIBIA achieve a balance between accuracy and computational complexity. The inference method employs the Dempster's combination rule and Shafer's discounting operation, where the discounting operation can resolve evidence conflicts in the combination rule. The effectiveness and superiority of PIBRIA in handling classification tasks have been validated by comparing it to some state-of-the-art rule-based models. The results of statistical tests show that PIBRIA outperforms all other methods in terms of classification accuracy. Whereas its computational complexity is moderate. © 2024, The Authors. All rights reserved.

关键词： computational complexity

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Robust Self-Guided Deep Image Prior 48

Robust Self-Guided Deep Image Prior

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Bell, Evan Liang, Shijun Qu, Qing Ravishankar, Saiprasad Michigan State University Dept. of Mathematics East LansingMI United States University of Michigan Dept. of Electrical and Computer Engineering Ann ArborMI United States Michigan State University Dept. of Biomedical Engineering East LansingMI United States Michigan State University Dept. of Computational Mathematics Science and Engineering East LansingMI United States

ISBN: (纸本)9781728163277

In this work, we study the deep image prior (DIP) for reconstruction problems in magnetic resonance imaging (MRI). DIP has become a popular approach for image reconstruction, where it recovers the clear image by fitting an overparameterized convolutional neural network (CNN) to the corrupted/undersampled measurements. To improve the performance of DIP, recent work shows that using a reference image as an input often leads to improved reconstruction results compared to vanilla DIP with random input. However, obtaining the reference input image often requires supervision and hence is difficult in practice. In this work, we propose a self-guided reconstruction scheme that uses no training data other than the set of undersampled measurements to simultaneously estimate the network weights and input (reference). We introduce a new regularization that aids the joint estimation by requiring the CNN to act as a powerful denoiser. The proposed self-guided method gives significantly improved image reconstructions for MRI with limited measurements compared to the conventional DIP and the reference-guided method while eliminating the need for any additional data. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Real-Time Detection of Cattle Behavior Using YOLOv9 with AutoAugment and Mixup data Augmentation Techniques

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Transactions of the Korean Institute of Electrical Engineers 2024年第12期73卷 2326-2332页

作者： Lee, Su-Bin Park, Jae-Beom Cho, Hyun-Chong Dept. of Electronics Engineering Dept. of Data Science Kangwon National University Korea Republic of Dept. of Data Science Kangwon National University Korea Republic of Dept. of Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of

As the number of livestock farms in Korea decreases, the scale of individual farms has grown, necessitating advanced methods for efficient livestock management. Recent studies have increasingly applied computer vision and deep learning to monitor livestock behavior, health, and disease, aiming to enhance productivity and animal welfare. This study introduces a real-time cattle behavior detection system utilizing YOLOv9, a state-of-the-art object detection algorithm known for its high accuracy and efficiency. The YOLOv9-s model, optimized for real-time detection with minimal computational overhead, was employed. To further enhance detection accuracy, we incorporated AutoAugment, a data augmentation technique that automatically selects the optimal augmentation policies, and mixup, a method that improves model generalization by creating new training samples through linear interpolation of existing ones. The application of AutoAugment improved the mean Average Precision (mAP) from 0.926 to 0.941, while the addition of mixup raised the mAP to 0.947, representing a 2.1% performance increase. These results confirm the system's capability for accurate and efficient real-time detection of cattle behavior. Copyright © The Korean Institute of Electrical Engineers.

关键词： Livestock

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