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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Almohammedi, Akram A. Yusof, Mohammad Hafiz Mohd Bin Tuan Muda, Saiyed Rasol Shepelev, Vladimir Balfaqih, Mohammed Shamsuddin, Solahuddin Karabuk University Electrical & Electronic Engineering Department 78050 Turkey Taiz University AlSaeed Faculty for Engineering and Information Technology Taiz Yemen Universiti Teknologi Mara UiTM Faculty of Computer and Mathematical Sciences Tapah Malaysia South Ural State University Automobile Transportation Department Chelyabinsk454080 Russia College of Computer Science and Engineering University of Jeddah Department of Computer and Network Engineering Jeddah23890 Saudi Arabia Menara Cyber Axis Jalan Impact Selangor Darul Ehsan Cyberjaya Cyberjaya63000 Malaysia

ISBN: (纸本)9798350305333

Intrusion Detection System (IDS) must be developed based on latest live traffic dataset. This will facilitate the study on new attributes of current threat. Once again, researchers are locked on the same vicious circle of inability to access live traffic dataset. This is coupled with public domain logged dataset may not be useful for a new threat research. Due to these standing hurdles, Pseudo Random Binary Sequence (PRBS) is used to generate synthetic dataset in IDS studies. One of its components is the Digital to Analog (D/A) converter. There are two types of D/A implementations;1) through Weighted and 2) R2R resistors. This paper compares the advantages and the disadvantages of designing D/A converter by using the abovementioned resistors. It is advisable to opt for R2R version over Weighted Resistor since it is easier and practical to choose the multiple types of resistors during circuit design process. Furthermore, the shifted parallel output from the circuit can be synchronized by the proposed PLL circuit. © 2023 IEEE.

关键词： Deep learning

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Estimating the AGV load and a battery lifetime based on the current measurement and random forest application

Estimating the AGV load and a battery lifetime based on the ...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Hanzel, Krzysztof Grzechca, Damian Ziebinski, Adam Chruszczyk, Lukas Janus, Artur Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Telecommunications and Teleinformatics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Electronics Electrical Engineering and Microelectronics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice Poland Silesian University of Technology Student of Electrical EngineeringFaculty of Electrical Engineering Gliwice Poland

ISBN: (纸本)9798350324457

The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and reliable information about power consumption and battery life estimation for autonomous guided vehicles (AGV). The authors propose a two-step algorithm. In the first step, a linear classifier was proposed. Then, the KNN classifier was tested;however, it did not give satisfactory results, so it was finally decided to use the random forest to estimate the load and PWL. The time domain current measurement is evaluated, and the beforementioned algorithms process the selected statistical measures. It has been proven that a two-step algorithm allows for achieving high accuracy. Based on the current observation, the paper is a good starting point for further investigation of the AGV because it is usually implemented in the AGV - so it does not require additional hardware. Moreover, it can lead to better energy management and increase battery lifetime. © 2023 IEEE.

关键词： AGV load estimation machine learning Prediction of energy consumption random forest

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Towards Holistic Crime Management Through Descriptive Analysis and Predictive Forecasting

Towards Holistic Crime Management Through Descriptive Analys...

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Advancements in Computing (ICAC), International Conference on

作者： Tinaya Wijethunge Sampath Edirisinghe Mansik Jeon Jeehyun Kim Ruchire Wijesinghe Bhagya Silva Department of Computer Engineering Faculty of Engineering Nugegoda Sri Lanka Daegu Republic of Korea Department of Electrical and Electronic Engineering Faculty of Engineering Malabe Sri Lanka Center for Excellence in Informatics Electronics Transmission (CIET) Malabe Sri Lanka Department of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Throughout the evolution of humankind, devising effective crime prevention strategies has been a concern in fostering safer communities. Although achieving a zero-crime rate is not possible yet, using data management methods can contribute to preventing and minimizing crimes. This article presents an integrated approach to proactive crime prevention. Data insights derived from analysis and visualization provide a comprehensive overview of historical trends and scenarios, aiding understanding of past and present situations. The forecasting of crime rates extends this comprehension to the future, spotlighting the severity of the potential threat to the safety of the community. Three distinct prediction models are presented to predict the occurrence of violent/non-violent crimes, the most probable crime type, and the most probable crime location in a spatial-temporal context, facilitating optimal resource allocation of police patrolling. Combining these three analytical components, an application is designed to present this holistic solution for effective crime prevention, with a 73% accuracy in predicting violent crimes. To achieve higher accuracy in predicting the most probable crime types and locations, it is essential to go beyond spatiotemporal context and augment data on other factors that affect the occurrence of crimes.

关键词： Adaptation models Accuracy Uncertainty Prevention and mitigation Computational modeling Predictive models Data models Spatiotemporal phenomena Forecasting Meteorology

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ExPDrug: Integration of an interpretable neural network and knowledge graph for pathway-based drug repurposing

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computers in Biology and Medicine 2025年 187卷 109729-109729页

作者： Kim, Junku Jang, Hojoong Park, Youngjun Jung, Inuk Jo, Kyuri Department of Computer Engineering Chungbuk National University Cheongju Korea Republic of Department of Medical Informatics University Medical Center Göttingen Göttingen Germany School of Computer Science and Engineering Kyungpook National University Daegu Korea Republic of

Precision medicine aims to provide personalized therapies by analyzing patient molecular profiles, often focusing on gene expression data. However, effectively linking these data to actionable drug discovery for clinical application remains challenging. In this paper, we introduce ExPDrug, a neural network (NN) model that integrates biological pathways from transcriptomic data with a biomedical knowledge graph to facilitate pathway-based drug repurposing. ExPDrug enhances disease phenotype prediction by capturing the complex relationships between genes and pathways. Using layer-wise relevance propagation (LRP), the model interprets the contribution of each pathway using relevance scores applied in a random walk-with-restart (RWR) algorithm to prioritize potential drug candidates in the biomedical network. ExPDrug outperforms existing methods in predicting phenotypes for the three diseases and identifying drug candidates, as supported by the literature. This model offers a transformative approach for advancing precision medicine by linking transcriptomic insights directly to clinical drug repurposing, thereby potentially improving treatment strategies for complex diseases. © 2025 Elsevier Ltd

关键词： Knowledge graph

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Data Hiding in AMBTC Images Using Selective XOR Hiding Scheme

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computers, Materials & Continua 2022年第6期71卷 5167-5182页

作者： Yung-Yao Chen Yu-Chen Hu Ting-Kai Yang You-An Wang Department Electronic and Computer Engineering National Taiwan U.Science and TechnologyTaipei106Taiwan Department Computer Science and Information Management Providence UniversityTaichung433Taiwan Grad.Inst.Automation Technology National Taipei University of TechnologyTaipei106Taiwan

Nowadays since the Internet is ubiquitous,the frequency of data transfer through the public network is *** secure data in these transmitted data has emerged broad security issue,such as authentication and copyright *** the other hand,considering the transmission efficiency issue,image transmission usually involves image compression in Internet-based *** address both issues,this paper presents a data hiding scheme for the image compression method called absolute moment block truncation coding(AMBTC).First,an image is divided into nonoverlapping blocks through AMBTC compression,the blocks are classified four types,namely smooth,semi-smooth,semi-complex,and *** secret data are embedded into the smooth blocks by using a simple replacement *** proposed method respectively embeds nine bits(and five bits)of secret data into the bitmap of the semi-smooth blocks(and semicomplex blocks)through the exclusive-or(XOR)*** secret data are embedded into the complex blocks by using a hidden *** the embedding phase,the direct binary search(DBS)method is performed to improve the image qualitywithout damaging the secret *** experimental results demonstrate that the proposed method yields higher quality and hiding capacity than other reference methods.

关键词： Content protection technology security for image data absolute moment block truncation coding(AMBTC) direct binary search(DBS)

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Machine Learning-assisted Label-Free Cytometry Based on Laser Light Scattering of Single Cells at Multiple Wavelengths and Multiple Directions

Machine Learning-assisted Label-Free Cytometry Based on Lase...

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Biophotonics Congress: Optics in the Life Sciences 2023, OMA, NTM, BODA, OMP, BRAIN 2023 - Part of Optica Biophotonics Congress: Optics in the Life Sciences

作者： Liu, Xiaoxuan Liu, Lina Islam, Md Zahurul Gupta, Manisha Rozmus, Wojciech Mandal, Mrinal Tsui, Ying Yin Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G2R3 Canada Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka1000 Bangladesh Department of Physics University of Alberta EdmontonABT6G2R3 Canada

We present a laser light scattering study using image classification techniques on simulated cell model patterns for label-free cytometry development. Simulation parameters include mitochondria number, surface roughne... 详细信息

ISBN: (纸本)9781957171210

关键词： Surface roughness

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Feature Selection and Prediction of Heart Disease Using Machine Learning Approaches 1

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6th International Conference on Electrical, Control and computer engineering, InECCE 2021

作者： Molla, M. M. Imran Islam, Md. Sakirul Shafi, A.S.M. Alam, Mohammad Khurshed Islam, Md. Tarequl Jui, Julakha Jahan Department of Computer Science and Engineering Khwaja Yunus Ali University Enayetpur Bangladesh Faculty of Electrical and Electronic Engineering Technology University Malaysia Pahang Pahang Pekan Malaysia Department of Computer Science and Engineering Pabna University of Science and Technology Pabna Bangladesh

ISBN: (数字)9789811686900

ISBN: (纸本)9789811686894

Heart Disease (HD) is the world's most serious illness that seriously impacts human life. The heart does not push blood to other areas of the body in cardiac disease. For the prevention and treatment of cardiac failure, accurate and timely diagnosis of heart disease is critical. The diagnosis of cardiac disease has been considered via conventional medical history. Non-invasive approaches like machine learning are effective and powerful to categorize healthy people and people with heart disease. In the proposed research, by using the cardiovascular disease dataset, we created a machine-learning model to predict cardiac disease. In this paper, it is capable of recognizing and classifying the heart disease patient from healthy people by using three standard machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). In addition, the Area Under Curve (AUC) value is calculated for each classification algorithms. In the proposed scheme, we also used the feature selection algorithm to reduce dimensions over a qualified heart disease dataset. After that, the whole structure for the classification of heart disease has been created. On complete features and reduced features, the performance of the proposed approach has been verified. The decrease in features affects the accuracy and time of execution of the classifiers. With the selected features, the highest classification accuracy is obtained for the KNN algorithm is about 93%, with a sensitivity is 0.9750 and specificity is 0.8529. Therefore, with the complete features, the classification accuracy is about 91%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diagnosis

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Open-Vocabulary Calibration for Fine-tuned CLIP 41

Open-Vocabulary Calibration for Fine-tuned CLIP

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41st International Conference on Machine Learning, ICML 2024

作者： Wang, Shuoyuan Wang, Jindong Wang, Guoqing Zhang, Bob Zhou, Kaiyang Wei, Hongxin Department of Statistics and Data Science Southern University of Science and Technology Shenzhen China Department of Computer and Information Science University of Macau Taipa China William & Mary WilliamsburgVA United States School of Computer Science and Engineering University of Electronic Science and Technology of China China Department of Computer Science Hong Kong Baptist University Hong Kong

Vision-language models (VLMs) have emerged as formidable tools, showing their strong capability in handling various open-vocabulary tasks in image recognition, text-driven visual content generation, and visual chatbots, to name a few. In recent years, considerable efforts and resources have been devoted to adaptation methods for improving the downstream performance of VLMs, particularly on parameter-efficient fine-tuning methods like prompt learning. However, a crucial aspect that has been largely overlooked is the confidence calibration problem in fine-tuned VLMs, which could greatly reduce reliability when deploying such models in the real world. This paper bridges the gap by systematically investigating the confidence calibration problem in the context of prompt learning and reveals that existing calibration methods are insufficient to address the problem, especially in the open-vocabulary setting. To solve the problem, we present a simple and effective approach called Distance-Aware Calibration (DAC), which is based on scaling the temperature using as guidance the distance between predicted text labels and base classes. The experiments with 7 distinct prompt learning methods applied across 11 diverse downstream datasets demonstrate the effectiveness of DAC, which achieves high efficacy without sacrificing the inference speed. Our code is available at https://***/mlstat-Sustech/CLIP Calibration. Copyright 2024 by the author(s)

关键词： Calibration

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An electronic Othello Game Board based on Voltage Divider Rule 3

An Electronic Othello Game Board based on Voltage Divider Ru...

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3rd International Conference on engineering Education and Information Technology, EEIT 2023

作者： Lee, Hsiao Ping Liu, Junqi Su, Chien-Jung Sheu, Tzu-Fang Chung Shan Medical University Department of Medical Informatics Taichung Taiwan Chung Shan Medical University Hospital Department of Medical Research Taiwan Providence University Department of Computer Science and Communication Engineering Taichung Taiwan

ISBN: (纸本)9798350326840

With the development of technology, people can now play board games with their electronic devices. Although playing board games is not limited to entity game boards, many people still prefer doing it. Due to the development cost, current electronic entity game board products are more expensive than traditional entity game boards. Moreover, most current products are designed for Go, chess, and Chinese chess. Some scholars have discovered that these three kinds of board games are difficult for beginners or young children to learn. Therefore, we developed an electronic Othello game board based on the voltage divider rule. Using only Arduino UNO, resistors, LED strip lights, and the game board software we designed, the cost of this game board is much cheaper than the sales price of the current product. While taking advantage of the feature of the Othello game, it also remains the advantage of the entity game board. © 2023 IEEE.

关键词： Voltage dividers

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Evaluation of Deep Convolutional Neural Network-Based Tattoo Detection

Evaluation of Deep Convolutional Neural Network-Based Tattoo...

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Inteligencia Ambiental, Ingeniería de Software y Salud Electrónica y Móvil (AmITIC), V Congreso Internacional en

作者： E. Jiménez-Delgado C. Quesada-López A Méndez-Porras A Lara-Petitdemange J Alfaro-Velasco Graduate Program in Computer Science and Informatics University of Costa Rica Computer Engineering Department Technological Institute of Costa Rica School of Computer Science and Informatics University of Costa Rica

ISBN: (数字)9798350364538

ISBN: (纸本)9798350364545

In this study, two deep learning models for automatic tattoo detection were analyzed; a modified Convolutional Neural Network (CNN) and pre-trained ResNet-50 model. In order to achieve this, ResNet-50 uses transfer learning with fine-tuning. The purpose of this study was to evaluate the accuracy, precision, recall, F1-score, and computational efficiency of the system being considered. To augment the dataset included 1000 photos that were equally divided between those showing tattoos and those that did not show tattoos. A k-fold cross-validation approach was employed in training and testing the models. Although custom CNNs are effective, utilizing pre-trained ones like ResNet-50 can offer even better outcomes. Specifically, ResNet-50 attained a higher accuracy (0.86 compared to 0.79), precision (0.85 versus 0.78), recall (0.91 against 0.86), and F1-score (0.91 vis-a-vis 0.86) as compared to custom CNNs. In selecting these models for examination, two main motivations were considered. The first motivation is to see whether transfer learning with a pre-trained ResNet-50 model does well when compared with a customized CNN designed specifically for tattoo detection. Secondly,the intent of this study is to know what advantages can be derived from each approach and their demerits too. Furthermore, it seeks to determine if transfer learning can provide an alternative in contrast to the common CNN techniques with regards to precision and computational efficiency. In this research, two models will be evaluated in order to answer the question of what is better for tattoo detection: transfer learning or designing custom architectures.

关键词： Training Analytical models Accuracy Computational modeling Transfer learning computer architecture Real-time systems Computational efficiency Convolutional neural networks Tuning

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