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International Electronics Symposium (IES)

作者： Tresna Maulana Fahrudin Iwan Syarif Ali Ridho Barakbah Department of Information and Computer Engineering Graduate Program of Engineering Technology Surabaya Indonesia

ISBN: (纸本)9781509016419

Development of microarray technology makes the number of research about Bioinformatics will increase as well. Microarray dataset contains genetic information and can be used to analyze thousands of samples and features. Especially in cancer research, the cancer data is generated by microarray technology, and will be a primary data for training and testing in the machine learning process. The main difficulties lie in the nature of microarray gene expression data which usually are noisy and high-dimensional. Microarray dataset usually has a large number of attributes or features, but it has a small number of samples. This condition makes the learning process microarray dataset has become harder because of the curse of dimensionality, where the machine will be difficult to handle a number of data with a very high-dimensional. The solution to handle a high-dimensional dataset and improve the accuracy of microarray dataset is using feature selection. The method of feature selection is followed the principle of natural selection, called Evolutionary Algorithms. We proposed to implement some Evolutionary Algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), which ACO improved F-measure of 9 datasets and ROC Area of 7 datasets from 11 datasets existing in the cancer research.

关键词： Classification algorithms DNA Cancer Ant colony optimization Evolutionary computation Genetic algorithms Measurement

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A Hybrid Inference Engine to Predict Uncertain Sugar Content for Sugar Industry 9th

A Hybrid Inference Engine to Predict Uncertain Sugar Content...

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9th International Conference on computer Science and Computational Intelligence, ICCSCI 2024

作者： Asrol, Muhammad Napthaleni Utama, Ditdit Nugeraha Suharjito Harito, Christian Syafrudin, Muhammad Industrial Engineering Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia Department of Artificial Intelligence and Data Science Sejong University Seoul05006 Korea Republic of

The sugar industry is facing challenges in increasing productivity to meet consumer demand. One opportunity for productivity improvement lies in ensuring sugar content. This study proposes a hybrid model to predict sugar content by considering uncertainty factors. A hybrid model combining fuzzy subtractive clustering, and a fuzzy inference system is proposed to predict sugar content. The clustering results using silhouette and fuzzy subtractive clustering successfully identified 6 cluster centres from 2225 datasets collected in a sugar industry in East Java Province. The hybrid inference engine model is designed with fuzzy rules derived from the clustered data. Two inference models are developed: triangular and Gaussian fuzzy numbers. The testing results indicate that the hybrid model with triangular fuzzy numbers shows the smallest error with an R2 value of 0.95. This model is possible to applied in the sugar industry for decision makers in improving productivity with taking attention into uncertain factors influencing sugar content. © 2024 The Authors.

关键词： Prediction models

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A Comprehensive Technique Based on Machine Learning for Device and Circuit Modeling of Gate-All-Around Nanosheet Transistors

IEEE Open Journal of Nanotechnology

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IEEE Open Journal of Nanotechnology 2023年 4卷 181-194页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering the Institute of Biomedical Engineering the Department of Electronics and Electrical Engineering the Institute of Pioneer Semiconductor Innovation and the Institute of Artificial Intelligence Innovation Hsinchu300093 Taiwan

Machine learning (ML) is poised to play an important part in advancing the predicting capability in semiconductor device compact modeling domain. One major advantage of ML-based compact modeling is its ability to capture complex relationships and patterns in large datasets. Therefore, in this paper a novel design scheme based on dynamically adaptive neural network (DANN) is proposed to develop fast and accurate compact model (CM). This framework constitutes a powerful yet computationally efficient methodology and exhibits emergent dynamic behaviors. This paper demonstrates that the compact model based on ML can be designed to replicate the performance of conventional compact model for nanodevices. For this work, gate-all-around (GAA) nanosheet (NS) device characteristics are comprehensively analyzed for process variability sources using the proposed model. The device geometry parameters such as channel length, nanosheet width and nanosheet thickness are fed as input features to the DANN model. The adaptive neural network learns dynamically by updating weights of the model in accordance with the input features and achieves accurate neural weight convergence. The proposed model predicted the electrical characteristics of NS devices with less than 1% error rate. The model is also implemented and validated for the simulations of digital circuit designs such as inverter, and logic gates. ©;2023 The Authors.

关键词： Circuit simulation

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Investigating the Impact of Data Augmentation for Fine-Grained Grocery Product Classification Based on Vision Transformer

Investigating the Impact of Data Augmentation for Fine-Grain...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Rahmania, Rissa Heryadi, Yaya Lukas, Lukas Suparta, Wayan Sonata, Ilvico Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350327472

The existence of fine-grained image classification supporting smart retail provides effectiveness in recognizing products with high similarity. However, the generic classification method performs poorly in identifying products from a subordinate category. This paper aims to identify augmentation techniques to leverage the Vision Transformer (ViT) model to classify the fine-grained grocery product, which involves embedding patches and transformer encoders to extract the main features. First, we develop a fine-grained image dataset with ColorJitter, CutOut, and combining both augmentations. Secondly, we perform experiments and analysis of ViT size, embedded patch size and image size in the patch embedding process. Lastly, the ViT model are evaluated according to the image sizes 224, 384, and 512 in accuracy, loss, and confusion matrix. The highest accuracy was obtained at 0.9922. The ColorJitter and CutOut improved the confusion matrix in ViT-B/16 and ViT-L/16 with an image size of 384 and 512. The results show that both augmentations in the ViT model are able to distinguish fine-grained grocery products. © 2023 IEEE.

关键词： Embeddings

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Cross-camera vehicle tracking via affine invariant object matching for video forensics applications

Cross-camera vehicle tracking via affine invariant object ma...

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2013 IEEE International Conference on Multimedia and Expo, ICME 2013

作者： Hsu, Chao-Yung Kang, Li-Wei Mark Liao, Hong-Yuan Institute of Information Science Academia Sinica Taipei Taiwan Graduate School of Engineering Science and Technology-Doctoral Program Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan

ISBN: (纸本)9781479900152

The recent deployment of very large-scale camera networks consisting of fixed/moving surveillance cameras and vehicle video recorders, has led to a novel field in object tracking problem. The major goal is to detect and track each vehicle within a large area, which can be applied to video forensics. For example, a suspected vehicle can be automatically identified for mining digital criminal evidences from a large amount of video data. In this paper, we propose an efficient cross-camera vehicle tracking technique via affine invariant object matching. More specifically, we formulate the problem as invariant image feature matching among different viewpoints of cameras. To achieve vehicle matching, we first extract invariant image feature based on ASIFT (affine and scale-invariant feature transform) for each detected vehicle in a camera network. Then, to improve the accuracy of ASIFT feature matching between images from different viewpoints, we propose to efficiently match feature points based on our observed spatially invariant property of ASIFT, as well as the min-hash technique. As a result, cross-camera vehicle tracking can be efficiently and accurately achieved. Experimental results demonstrate the efficacy of the proposed algorithm and the feasibility to video forensics applications. © 2013 IEEE.

关键词： Tracking (position)

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Student engagement through mathematical applications in electrical power systems

Student engagement through mathematical applications in elec...

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113th Annual ASEE Conference and Exposition, 2006

作者： Osorno, Bruno California State University Northridge United States Department of Electrical and Computer Engineering California State University Northridge United States Electric Power Systems Graduate and Undergraduate Program

Historically, electrical engineering students have been very proficient in the application of mathematics. At California State University we have experienced, recently, a negative shift of mathematical interest and therefore a lack of progress in the understanding of basic concepts that require a strong mathematical background. This problem stems from the lack of mathematical skills and studying habits. One way to solve this problem is to engage our students into the application of mathematics with simple, yet, powerful problems. An assessment of such engagement occurs every week with a quiz that spans the material covered during the previous week and tests some of the mathematical and engineering skills. This paper attempts to show the results of such assessment and the outcome of a class in which such approach was tasted. During this semester we are testing this technique again in a different course. © American Society for engineering Education, 2006.

关键词： Students

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High Resolution of City-Level Climate Simulation by GPU with Multi-physical Phenomena 18th

High Resolution of City-Level Climate Simulation by GPU with...

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18th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2021

作者： Watanabe, Koei Kikuchi, Kohei Boku, Taisuke Sato, Takuto Kusaka, Hiroyuki Master’s Program in Computer Science Graduate School of Systems and Information Engineering University of Tsukuba Tsukubashi Japan Center for Computational Sciences University of Tsukuba Tsukubashi Tennodi Japan

ISBN: (纸本)9783030935702

In this paper, we describe the Graphics Processing Unit (GPU) implementation of our City-LES code on detailed large eddy simulations, including the multi-physical phenomena on fluid dynamics, heat absorption and reflection by surface and building materials, cloud effects, and even sunlight effect. Because a detailed simulation involving these phenomena is required for analyses at the street level and several meters of resolution, the computation amount is enormous, and ordinary CPU computation cannot provide sufficient performance. Therefore, we implemented the entire code on GPU clusters with large-scale computing. We applied OpenACC coding to incrementally implement relatively easy programming and eliminate data transfers between the CPU and GPU memories. Based on this research, we determined that the elimination of data transfers is effective, even in the case where a part of the code execution on the GPU is slower than the CPU, owing to the absence of spatial parallelism. The objective of this study is to perform a complete climate simulation on a few square-kilometers field around the Tokyo Station, considering the finest resolution of the original highlighted area of the Marathon race in the Olympic Games Tokyo 2020. We successfully transferred the entire code to the GPU to provide approximately eight times the performance of CPU-only computation on multi-GPU per node with a large scale cluster. © 2022, IFIP International Federation for Information Processing.

关键词： Graphics processing unit

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Unraveling the genetic mechanisms of UV radiation resistance in Bacillus through biofilm formation, sporulation, and carotenoid production

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Genomics 2025年 111066页

作者： Prasansah Shrestha Byeollee Kim So-Ra Han Hyun Lee Tae-Jin Oh Department of Life Science and Biochemical Engineering Graduate School SunMoon University Asan 31460 Republic of Korea Bio Big Data-based Chungnam Smart Clean Research Leader Training Program SunMoon University Asan 31460 Republic of Korea Genome-based BioIT Convergence Institute Asan 31460 Republic of Korea Division of Computer Science and Engineering SunMoon University Asan 31460 Republic of Korea Department of Pharmaceutical Engineering and Biotechnology SunMoon University Asan 31460 Republic of Korea

Bacillus species are Gram-positive bacteria that are rod-shaped, endospore-forming, and aerobic or facultatively anaerobic. With over 300 recognized species, Bacillus subtilis stands out as a well-studied model organism. The genus's various species exhibit a wide range of physiological capabilities, allowing them to thrive in diverse environmental conditions. Each cell produces a single endospore, which is highly resistant to heat, cold, radiation, desiccation, and disinfectants. Among Bacillus strains, those capable of producing spores, biofilms, and carotenoids demonstrate significant resilience to UV light. This review examines the genes involved in spore formation, biofilm development, and carotenoid synthesis, emphasizing their roles in UV radiation survival. We explore the interconnections between these processes and their combined contribution to UV resistance, focusing on the underlying genetic mechanisms. These insights will benefit researchers studying the genetic basis of UV radiation resistance in Bacillus species. Importance Bacteria employ adaptive strategies in extreme environments through rapid changes in gene expression, altering their phenotype for survival. Bacillus species, for example, defend against UV radiation by making spores, creating biofilms, and producing pigments . During sporulation, sigma factors (σ F , σ E , σ G , and σ K ) regulate gene expression to adapt to environmental shifts. It has been found that the spores of some species may contain pigments that strongly absorb UV radiation, playing a crucial role in spore UV resistance. UV light penetrates biofilm matrices minimally, mainly affecting surface cells, which produce compounds like mycosporine-like amino acids and carotenoids to shield against UV damage.

关键词： Bacillus UV-radiation resistance Spore Biofilm Carotenoid

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Model for Estimating the Probability of a Customer to Have a Transaction 7

Model for Estimating the Probability of a Customer to Have a...

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7th International Conference on computer Science and engineering, UBMK 2022

作者： Sayar, Alperen Bozkan, Tunaban Cakar, Tuna Ertugrul, Seyit Mef University Information Technology Graduate Program Istanbul Turkey Mef University Computer Engineering Department Istanbul Turkey Mef University Department of Information Technology Istanbul Turkey

ISBN: (数字)9781665470100

ISBN: (纸本)9781665470100

In this study, it is aimed to estimate the probability of a customer who comes to the institution for the first time to make a transaction in the next 3 months, using data-driven machine learning models, in order to provide financing to the seller company by assigning the receivables arising from the sale of goods and services in a company actively operating in the factoring sector. Accordingly, it was aimed to directly contribute to the transaction volume on a business basis by acting and taking action with more effective, efficient and correct approaches by finding high-potential and low-potential customers. In this context, provided by KKB (Credit Registration Bureau);The data set to he used in machine learning models was created with feature engineering and exploratory data analysis, using the Risk, Mersis, GIB information of the prospective customers and the historical information of the customers, check issuers, customer representatives and branches kept in the database. Since the leads coming to the institution are in two different types of organizations (Individual and Legal), two different forecasting models were applied. Multiple classification models were tried, and the highest F1-Score of 86% for private companies was obtained with the Random Forest model, and the highest F1- Score for commercial companies was obtained with the Random Forest model with 82%. © 2022 IEEE.

关键词： Machine learning

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Exploring Shifted Domain Problem within MNIST Dataset

Exploring Shifted Domain Problem within MNIST Dataset

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2023 International Conference on computer Science, Information Technology and engineering, ICCoSITE 2023

作者： Hidayaturrahman Heryadi, Yaya Lukas Suparta, Wayan Arifin, Yulyani Bina Nusatara University BINUS Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350320954

Domain data can be shifted in any direction so it will be shared in different distributions to its original domain. This could be a problem since the model was trained with different distributions. It is found that adversarial domain adaptation using domain adversarial neural networks (DANN) can help to solve this problem on some scale. DANN can minimize the discrepancy between source and target data so the model can work well in both domains. The experiment is done by utilizing MNIST dataset that shifted into some conditions. In a condition when the shifting of distribution is too far, DANN is struggling to maintain the knowledge extracted from source data which leads to underperformance in the source and target domain. In contrast, when the shifting is closer, DANN can easily fit the model so it can perform well in both domains. It proves DANN is one of the good approaches to performing domain adaptation in small discrepancies. © 2023 IEEE.

关键词： Adversarial Domain Adaptation Domain Shifting Problem MNIST Transfer Learning

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