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Sensor Array system Based on Electronic Nose to Detect Borax in Meatballs with Artificial Neural Network

Journal of Electrical and computer Engineering 2023年第1期2023卷

作者： Pradhana, Anak Agung Surya Astuti, Suryani Dyah Fauziah Permatasari, Perwira Annissa Dyah Agustina, Riskia Yaqubi, Ahmad Khalil Setyawati, Harsasi Winarno Putra, Cendra Devayana Department of Computer System Faculty of Technology and Informatics Indonesian Institute of Business and Technology Denpasar80225 Indonesia Department of Physics Faculty of Science and Technology Airlangga University Surabaya60115 Indonesia Department of Mathematics Faculty of Science and Technology Airlangga University Surabaya60115 Indonesia Magister of Forensic Science Post Graduate School Airlangga University Surabaya60115 Indonesia Faculty of Science and Technology Airlangga University Surabaya60115 Indonesia Department of Chemistry Faculty of Science and Technology Airlangga University Surabaya60115 Indonesia Institute of Information Management National Cheng Kung University Tainan Taiwan

The categorization of odors utilizing gas sensor arrays with various meatball borax concentrations has been studied. The samples included meatballs with a borax content of 0.05%, 0.10%, 0.15%, 0.20%, and 0.25% (%mm) and meatballs without any borax. Six TGS gas sensors with a baseline of 10 seconds, a detecting period of 120 seconds, and a purging period of 250 seconds make up the gas sensor array used in this work. Artificial neural networks (ANNs) and principal component analysis (PCA), which are beneficial for feature extraction and classification, are used to handle the collected data based on machine learning approaches. Two models were produced by the data analysis: model 1, which only used the PCA approach, and model 2, which only used the ANN methodology. 90.33% is the total variance value of PC from model 1. In addition, the multilayer perceptron artificial neural network (ANN-MLP) technique for model 2 yielded accuracy values of 95%. © 2023 Anak Agung Surya Pradhana et al.

关键词： Principal component analysis

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Manufacturing A Low-Cost Telegram and Optical Character Recognition-Based Indoor Air Quality Monitoring Data Logger

Manufacturing A Low-Cost Telegram and Optical Character Reco...

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International Conference on Information Management and technology (ICIMTech)

作者： Andi Pramono Satrio Arif Budiman Muchammad Farchan Andi Baso Mappaturi Interior Design Department School of Design Bina Nusantara University Jakarta Indonesia Information System Computer Science Faculty Brawijaya University Malang Indonesia Department of Electrical Engineering Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Architecture Department Faculty of Science and Technology Islamic State University of Maulana Malik Ibrahim Malang Indonesia

Urban air is frequently contaminated with CO, CO2, VOC, HCHO, PM 2.5, and PM 10. Rural regions are at a lower risk than those near roads and industrial areas that produce emissions. Air pollutants negatively affect its inhabitants' health, particularly the respiratory system. Most people will spend more time indoors, requiring a method for monitoring indoor air quality (IAQ). Urban communities need IAQ monitoring instruments as an early warning system for their health. This research seeks to produce inexpensive IAQ that can be utilized by all levels of society, particularly urban communities. This research utilizes a qualitative, design-thinking-based methodology. The research diverged from Empatize, which asserts that urban communities require a method for determining the local air quality. Moreover, it is determined at the define stage that IAQ monitoring, which includes a data log feature, is still comparatively expensive, so a solution is developed at the ideate stage. Creating a prototype is the next step in realizing the specified solution. In this investigation, they decided to use IAQ monitoring, which is relatively inexpensive but lacks log data storage. The author uses the ESP32-cam microcontroller to capture the screen display device, and the resulting image is sent to a telegram and processed with optical character recognition (OCR) to generate a data log. The results of prototype testing are utilized for prototype development.

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Two-Layer SVM, Towards Deep Statistical Learning

Two-Layer SVM, Towards Deep Statistical Learning

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International Engineering Conference on

作者： Alireza Kazemi Reza Boostani Mahmoud Odeh Mohammad Rasmi AL-Mousa Mathematics Department Faculty of Science Shiraz University Shiraz Fars Iran CSE&IT Dept. Electrical and Computer Engineering School Shiraz University Shiraz Fars Iran Department of Management Information System Zarqa University Jordan Department of Cyber Security Faculty of Information Technology Zarqa University Zarqa Jordan

Support Vector Machine (SVM) is originally a binary large-margin classifier emerged from the concept of structural risk minimization. Multiple solutions such as one-versus-one and one-versus-all have been proposed for creating multi-class SVM using elementary binary SVMs. Also multiple solutions have been proposed for SVM model selection, adjusting margin-parameter C and the Gaussian kernel variance. Here, an improved classifier named SVM-SVM is proposed for multi-class problems which increases accuracy and decreases dependency to margin-parameter selection. SVM-SVM adopts two K-class one-vs-one SVMs in a cascaded two-layer structure. In the first layer, input features are fed to one-vs-one SVM with non-linear kernels. We introduce this layer as a large-margin non-linear feature transform that maps input feature space to a discriminative K*(K-1)/2 dimensional space. To assess our hierarchical classifier, some datasets from the UCI repository are evaluated. Standard one-vs-one SVM and one-vs-one fuzzy SVM are used as reference classifiers in experiments. Results show significant improvements of our proposed method in terms of test accuracy and robustness to the model (margin and kernel) parameters in comparison with the reference classifiers. Our observations suggest that a multi-layer (deep) SVM structures can gain the same benefits as is seen in the deep neural nets (DNNs).

关键词： Support vector machines Electric potential Statistical learning Neural networks Transforms Nonhomogeneous media Robustness

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XoT (Xducation of Things): Harnessing AI and edge computing to educate all things

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IEEE Access 2024年

作者： Nurtantyana, Rio Hwang, Wu-Yuin Hariyanti, Uun Research Center for Data and Information Sciences Bandung Indonesia Telkom University School of Computing Indonesia Telkom University Center of Excellence Artificial Intelligence for Learning and Optimization Bandung Indonesia National Dong Hwa University College of Science and Engineering Department of Computer Science and Information Engineering Taiwan National Central University Graduate Institute of Network Learning Technology Taiwan Universitas Brawijaya Faculty of Computer Science Department of Information System Indonesia

Most English foreign language (EFL) studies focus solely on human beings. This research explores how edge computing can facilitate learning for all things. The XoT (Xducation of Things) framework was proposed to educate both human and all things. All things encompass two terms: AI-Agent and smartthings (covering physical and digital smart objects). At the core of this framework is Smart Question Answer Forwarding Mechanism (SQA-Forwarding), specifically designed to assist all things in building knowledge. To demonstrate this, the smartXoT environment was developed based on XoT framework, and its impacts on EFL learners was assessed. A quasi-experimental study involving 26 EFL learners, divided into an experimental group (EG) and a control group (CG), examined the differences in learning achievement of smartthings and EFL learners when using the smartXoT environment with/without SQA-Forwarding. Findings, on one hand, indicated that smartthings in the EG developed knowledge bases greater than those in the CG. On the other hand, the interaction between EFL learners and smartthings with SQA-Forwarding significantly improved learners' writing skills, with revisions playing a crucial role in enhancing writing quality. Thus, the XoT framework offers a novel and promising approach to educating both humans and all things. © 2013 IEEE.

关键词： Contrastive Learning

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Next-generation machine vision systems incorporating two-dimensional materials: Progress and perspectives

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InfoMat 2022年第1期4卷 23-39页

作者： Peisong Wu Ting He He Zhu Yang Wang Qing Li Zhen Wang Xiao Fu Fang Wang Peng Wang Chongxin Shan Zhiyong Fan Lei Liao Peng Zhou Weida Hu State Key Laboratory of Infrared Physics Shanghai Institute of Technical PhysicsChinese Academy of SciencesShanghaiChina University of Chinese Academy of Sciences BeijingChina Hangzhou Institute for Advanced Study University of Chinese Academy of SciencesHangzhouChina State Key Laboratory of ASIC and System School of MicroelectronicsFudan UniversityShanghaiChina Henan Key Laboratory of Diamond Optoelectronic Materials and Devices School of Physics and EngineeringZhengzhou UniversityZhengzhouChina Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong Kong SARChina School of Physics and Electronics Hunan UniversityChangshaChina

Machine vision systems(MVSs)are an important component of intelligent systems,such as autonomous vehicles and ***,with the continuous increase in data and new application scenarios,new requirements are put forward for the next generation of *** is an urgent need to find new material systems to complement the existing semiconductor technology based on thin-film materials,and new architectures must be explored to improve *** of their unique physical properties,two-dimensional(2D)materials have received extensive attention for use in MVSs,especially in biomimetic ones:the human visual system,which can process complex visual information with low power consumption,provides a model for next-generation *** review paper summarizes the progress and challenges of applying 2D material photodetectors in sense-memory-computational integration and biomimetic image sensors for machine vision.

关键词： in-sensor computing machine vision systems photodetectors two-dimensional materials

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Optimal Deep Transfer Learning-Based Human-Centric Biomedical Diagnosis for Acute Lymphoblastic Leukemia Detection

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Computational Intelligence and Neuroscience 2022年第0期2022卷 7954111页

作者： Hamza, Manar Ahmed Albraikan, Amani Abdulrahman Alzahrani, Jaber S. Dhahbi, Sami Al-Turaiki, Isra Al Duhayyim, Mesfer Yaseen, Ishfaq Eldesouki, Mohamed I. Department of Computer and Self Development Preparatory Year Deanship Prince Sattam Bin Abdulaziz University AlKharj Saudi Arabia Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Industrial Engineering College of Engineering at Alqunfudah Umm Al-Qura University Mecca Saudi Arabia Department of Computer Science College of Science & Art at Mahayil King Khalid University Abha Saudi Arabia Department of Information Technology College of Computer and Information Sciences King Saud University P.O. BOX 145111 Riyadh4545 Saudi Arabia Department of Computer Science College of Sciences and Humanities- Aflaj Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Department of Information System College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University AlKharj Saudi Arabia

Human-centric biomedical diagnosis (HCBD) becomes a hot research topic in the healthcare sector, which assists physicians in the disease diagnosis and decision-making process. Leukemia is a pathology that affects younger people and adults, instigating early death and a number of other symptoms. computer-aided detection models are found to be useful for reducing the probability of recommending unsuitable treatments and helping physicians in the disease detection process. Besides, the rapid development of deep learning (DL) models assists in the detection and classification of medical-imaging-related problems. Since the training of DL models necessitates massive datasets, transfer learning models can be employed for image feature extraction. In this view, this study develops an optimal deep transfer learning-based human-centric biomedical diagnosis model for acute lymphoblastic detection (ODLHBD-ALLD). The presented ODLHBD-ALLD model mainly intends to detect and classify acute lymphoblastic leukemia using blood smear images. To accomplish this, the ODLHBD-ALLD model involves the Gabor filtering (GF) technique as a noise removal step. In addition, it makes use of a modified fuzzy c-means (MFCM) based segmentation approach for segmenting the images. Besides, the competitive swarm optimization (CSO) algorithm with the EfficientNetB0 model is utilized as a feature extractor. Lastly, the attention-based long-short term memory (ABiLSTM) model is employed for the proper identification of class labels. For investigating the enhanced performance of the ODLHBD-ALLD approach, a wide range of simulations were executed on open access dataset. The comparative analysis reported the betterment of the ODLHBD-ALLD model over the other existing approaches. © 2022 Manar Ahmed Hamza et al.

关键词： Deep learning

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An Interdisciplinary Approach to Designing Adaptive Lighting Environments

An Interdisciplinary Approach to Designing Adaptive Lighting...

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International Conference on Intelligent Environments (IE)

作者： Remco Magielse Philip Ross Sunder Rao Tanir Ozcelebi Paola Jaramillo Oliver Amft Designing Quality in Interaction (DQI) Group Department of Industrial Design Intelligent Lighting Institute (ILI) Eindhoven University of Technology Eindhoven Netherlands System Architecture and Networks (SAN) Group Department of Mathematics and Computer Science Intelligent Lighting Institute (ILI) Eindhoven University of Technology Eindhoven Netherlands Signal Processing Systems (SPS) Group Department of Electrical Engineering Intelligent Lighting Institute (ILI) Eindhoven University of Technology Eindhoven Netherlands

Due to advancements in lighting technologies new opportunities for application emerge. In this paper an interdisciplinary study towards the development of adaptive lighting environments is presented. An implementation of an adaptive lighting environment is made in the domain of office work. For evaluation, experts from the domains of human-system interaction, activity and context recognition, and system architecture design are interviewed. Contributions are made with regard to the implementation of the adaptive lighting in office environments and an evaluation method that is in line with the interdisciplinary approach. From the evaluation method insights in the fields of human-computer interaction, activity and context recognition and system architecture and (wireless) networking and in topics that span across these fields are gained.

关键词： Indexes

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Soft Set Based Clustering and Its Comparison on Categorical Data

Soft Set Based Clustering and Its Comparison on Categorical ...

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Information technology International Seminar (ITIS)

作者： Iwan Tri Riyadi Yanto Cheah WaiShiang Rahmat Hidayat Rofiul Wahyudi Suprihatin Ani Apriani Information System Department Universitas Ahmad Dahlan Yogyakarta Indonesia Faculty of Computer Science & IT Universiti Malaysia Sarawak Samarahan Sarawak Malaysia Information Technology Department Politeknik Negeri Padang Padang Indonesia Islamic Banking Department Universitas Ahmad Dahlan Yogyakarta Indonesia Institute Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Categorical data clustering is problematic since it is difficult or complex to determine how comparable the data is. Several methods, most recently centroid-based strategies, have been developed to reduce the complexity of the similarity of categorical data. These methods nevertheless result in lengthy processing durations. Another method, soft set-based clustering (SSC), based on the probability function of multivariate multinomial distributions, is suggested in this article. Soft sets are used to represent the data, and each soft set has a probability for each object. The joint cluster distribution function determines the probability for each object after the multivariate multinomial distribution function. The connected cluster would receive the highest likelihood. Benchmark data sets from UCI machine learning are used to compare the performance of the approach to the baseline techniques. The outcomes demonstrate that the suggested strategy performed better in purity, rank index, and calculation time.

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Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis

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Energy and AI 2021年第3期5卷 53-66页

作者： Meisam Booshehri Lukas Emele Simon Flügel Hannah Förster Johannes Frey Ulrich Frey Martin Glauer Janna Hastings Christian Hofmann Carsten Hoyer-Klick Ludwig Hülk Anna Kleinau Kevin Knosala Leander Kotzur Patrick Kuckertz Till Mossakowski Christoph Muschner Fabian Neuhaus Michaja Pehl Martin Robinius Vera Sehn Mirjam Stappel Institute for Applied Ecology BerlinGermany Institute for Applied Informatics(InfAI)/Leipzig University Germany Department of Energy Systems Analysis Institute of Networked Energy SystemsGerman Aerospace Center(DLR)StuttgartGermany Faculty of Computer Science Otto-von-Guericke-Universität MagdeburgGermany Reiner Lemoine Institut BerlinGermany Institute of Techno-economic Systems Analysis(IEK-3) Forschungszentrum JülichJülichGermany Institute of Energy Economics and Rational Energy Use University of StuttgartGermany Potsdam Institute for Climate Impact Research PotsdamGermany Fraunhofer Institute for Energy Economics and Energy System Technology KasselGermany

Heterogeneous data,different definitions and incompatible models are a huge problem in many domains,with no exception for the field of energy systems ***,it is hard to re-use results,compare model results or couple models at *** provide a precisely defined vocabulary to build a common and shared conceptu-alisation of the energy ***,we present the Open Energy Ontology(OEO)developed for the domain of energy systems *** the OEO provides several benefits for the ***,it enables consistent annotation of large amounts of data from various research *** example is the Open Energy Platform(OEP).Adding such annotations makes data semantically searchable,exchangeable,re-usable and ***,computational model coupling becomes much *** advantages of using an ontology such as the OEO are demonstrated with three use cases:data representation,data annotation and interface *** also describe how the ontology can be used for linked open data(LOD).

关键词： Collaborative ontology development Linked open data Metadata annotation Energy systems analysis

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Multiple neural network ensembles method based on learning knowledge database

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Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and technology) 2009年第SUPPL. 1期40卷 1-9页

作者： An, Jin-Xia Zhu, Ji-Hong Yuan, Xia-Ming National Key Laboratory of Intelligent Technology and System Department of Computer Science and Technology Tsinghua University Beijing 100084 China Jiuquan Satellite Launch Center Jiuquan 732750 China

The architecture of multiple neural network ensembles based on learning knowledge database was proposed. By decomposing the complex system into subsystems, a complex problem may be disassembled and solved by methods of neural network ensembles. Compared with the other technologies, this method can increase the training rate, reduce the neural network complexity and training accuracy. The training experience and knowledge of neural networks for some representative problems are stored in database and can be exploited repeatedly. Two complex non-linear-function approximation examples show that the architecture and the method are effective and applicable for solving some real complex tasks, and increasing the generalize-ability of neural network.

关键词： Complex networks

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