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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Tony E. Astuhuaman Davila Daniel B. Hier Tayo Obafemi-Ajayi Computer Science Dept. Missouri State University Springfield MO USA Kummer Institute Missouri University of Science & Technology Rolla MO USA Engineering Program Missouri State University Springfield MO USA

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Dimension reduction methods are used to visualize the output of unsupervised learning models when applied to complex data. These techniques improve interpretability by transforming a high-dimension space to a lower-dimension space (usually 2D or 3D). The results are typically viewed as 2D scatter plots, and class centroids may be added to increase interpretability. Although useful, the relationship of these class centroids to the underlying feature space remains opaque. The innovative aspect of this work is to create a strong link between the dimension-reduced space and the underlying high-dimension feature space by adding selected feature centroids to the 2D scatter plots. This approach simultaneously visualizes the centers for the classes and the features on the same 2D scatter plot. Since classes are often imbalanced, we provide a method to balance class sizes. We present an automated framework that performs a grid search to find the optimal dimension reduction parameters, balances the class sizes, uses an ensemble approach to find the most important features, and adds class centroids and selected feature centroids to 2D dimension-reduced plots. This is especially useful when applied to complex, feature-rich biomedical data, as addition of feature centroids to 2D scatter plots serve as landmarks for the previously featureless dimension-reduced space. The utility of this approach is demonstrated by its application to seven classes of neurogenetic diseases with 31 defining phenotypic features.

关键词： Dimensionality reduction Three-dimensional displays Phenotypes Data visualization Data models Ensemble learning Bioinformatics Unsupervised learning Computational intelligence Diseases

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White Bread Mold Detection using K-Means Clustering Based on Grey Level Co-Occurrence Matrix and Region of Interest

White Bread Mold Detection using K-Means Clustering Based on...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Qori Alfatah Putra Christy Atika Sari Eko Hari Rachmawanto Nur Ryan Dwi Cahyo Edy Mulyanto Mohamed Ayad Alkhafaji Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Department of Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Department of Computer Science National University of Science and Technology DhiQar Iraq

This research paper presents a bread mold identification system that utilizes digital image processing techniques, specifically K-means clustering and thresholding, to accurately detect and classify mold-infected areas on bread. The system aims to provide valuable information regarding the safety of consuming mold-contaminated bread, enabling consumers to make informed decisions and reduce the risk of health issues associated with consuming mold-infested bread. the K-means clustering algorithm for mold classification, considering factors such as the number of iterations and the distance metric within the feature space to determine its accuracy. The GLCM feature extraction technique is employed to extract texture information from bread images, encompassing features such as standard deviation (110.62), contrast (1.46), energy, and homogeneity (0.96 and 0.77, respectively). Experimental results demonstrate that the Euclidean Distance yielded the highest result of 72.73, the Cosine Distance obtained the highest result of 8.25, and the City Block Distance obtained the highest result of 92.11. These findings showcase the effectiveness of the system in accurately detecting and classifying mold-infected areas on bread, thereby aiding in ensuring food safety and reducing health risks for consumers.

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The importance of mobile applications for companies' brand image: A study using structural equations 9th

The importance of mobile applications for companies' brand i...

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9th International Conference on Information Technology and Quantitative Management, ITQM 2022

作者： Mariano, Ari Melo Silva, Marcel Carneiro Mello, Tarcilla Mariano Santos, Maíra Rocha Department of Production Engineering University of Brasília Brazil Department of Computer Science University of Brasilia Brazil Postgraduate Program in Communication University of Brasília Brazil Center for Advanced Multidisciplinary Studies University of Brasilia Brazil

Because of the advancement of new technologies and the popularity of mobile devices, this study was designed to identify whether apps have a representative influence on companies' brand image. To fulfill this objective, descriptive research based on the structural equation model was carried out with the help of the statistical software Adanco. The study was conducted in Brasilia and applied in a virtual environment in the Google forms platform. The link was made available and disseminated to students of one university and through the WhatsApp and Facebook social networks, having 350 mobile device users as participants. The questionnaire was validated with α=0.81. The results of this research suggest that applications can influence 30.5% of a brand's image, thus raising the discussion that mobile applications will soon have equal or even greater importance than a website and social networks, tools that were once trends and are now essential requirements for organizations. Thus, for a company to be successful, it must not only be seen as a company with high performance and quality standards, but it must also represent a solid brand with a good reputation in the market and create and cultivate an image that connects emotionally with its consumer public. © 2022 The Authors. Published by Elsevier B.V.

关键词： Social networking (online)

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Challenges on Cloud Computing Migration Strategy for Music Industry: A Systematic Literature Review

Challenges on Cloud Computing Migration Strategy for Music I...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Hijir Della Wirasti Henki Seta Deden Witarsyah Hamonagan K Prabu Ayu Azzahro Bayu Hananto Faculty of Engineering Industry Telkom University Bandung Indonesia Informatics Study Program Computer Science Faculty Universitas Pembangunan Nasional Veteran Jakarta

Cloud computing has emerged as a transformative technology that offers numerous benefits to various industries, including the music industry. Cloud computing has revolutionized the way businesses operate and has had a significant impact on various industries, including the music industry Cloud computing, with its scalability, flexibility, and cost-effectiveness, presents an opportunity for the music industry to leverage technology to enhance its operations and meet the evolving demands of the digital era. This paper presents a systematic literature review on cloud computing migration strategies specifically tailored for the music industry. The review aims to identify existing research, frameworks, and best practices related to cloud migration in the music industry, as well as highlight the challenges and opportunities associated with such migrations. Through an analysis of relevant literature, this study provides valuable insights to assist music industry stakeholders in developing effective cloud migration strategies.

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Van der Waals device integration beyond the limits of van der Waals forces using adhesive matrix transfer

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Nature Electronics 2024年第1期7卷 17-28页

作者： Satterthwaite, Peter F. Zhu, Weikun Jastrzebska-Perfect, Patricia Tang, Melbourne Spector, Sarah O. Gao, Hongze Kitadai, Hikari Lu, Ang-Yu Tan, Qishuo Tang, Shin-Yi Chueh, Yu-Lun Kuo, Chia-Nung Lue, Chin Shan Kong, Jing Ling, Xi Niroui, Farnaz Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA United States Department of Chemical Engineering Massachusetts Institute of Technology CambridgeMA United States Department of Physics Massachusetts Institute of Technology CambridgeMA United States Department of Chemistry Boston University BostonMA United States Department of Materials Science and Engineering National Tsing Hua University Hsinchu Taiwan Semiconductor Research Center Hon Hai Research Institute Taipei Taiwan College of Semiconductor Research National Tsing Hua University Hsinchu Taiwan Department of Physics National Cheng Kung University Tainan Taiwan Taiwan Consortium of Emergent Crystalline Materials National Science and Technology Council Taipei Taiwan Program on Key Materials Academy of Innovative Semiconductor and Sustainable Manufacturing National Cheng Kung University Tainan Taiwan Division of Materials Science and Engineering Boston University BostonMA United States The Photonics Center Boston University BostonMA United States

Pristine van der Waals (vdW) heterostructures formed between two-dimensional (2D) and other materials can be used to create optical and electronic devices. However, the weak vdW forces alone do not allow direct physical stacking of arbitrary layers. As a result, vdW heterostructure fabrication typically requires solvents, sacrificial layers or high temperatures, which can introduce damage and contaminants. Here, we show that adhesive matrix transfer can eliminate these limitations and can provide vdW integration beyond the limits of vdW forces. In the approach, a hybrid high- and low-adhesion surface is used to decouple the forces driving the transfer from the vdW forces defining the heterostructure of interest. We show that the technique can be used to achieve direct vdW integration of diverse 2D materials (MoS2, WSe2, PtS2 and GaS) with dielectrics (SiO2 and Al2O3), which is conventionally forbidden but critical for active devices and scalable, aligned heterostructure formation. The approach also allows single-step 2D material-to-device integration, which we illustrate by fabricating arrays of monolayer MoS2 transistors. As any exposure to solvents or polymers is avoided, the interfaces and surfaces remain pristine. Thus, intrinsic 2D material properties can be probed without the influence of processing steps. The materials can be further engineered through surface treatments and used to fabricate unconventional device form factors. © 2023, The Author(s), under exclusive licence to Springer Nature Limited.

关键词： Van der Waals forces

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Multi-Object RANSAC: Efficient Plane Clustering Method in a Clutter

arXiv

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arXiv 2024年

作者： Lim, Seunghyeon Yoo, Youngjae Lee, Jun Ki Zhang, Byoung-Tak Interdisciplinary Program in Cognitive Science Seoul National University Seoul Korea Republic of Department of Computer Science and Engineering Seoul National University Seoul Korea Republic of AIIS Seoul National University Seoul Korea Republic of

In this paper, we propose a novel method for plane clustering specialized in cluttered scenes using an RGB-D camera and validate its effectiveness through robot grasping experiments. Unlike existing methods, which focus on large-scale indoor structures, our approach—Multi-Object RANSAC emphasizes cluttered environments that contain a wide range of objects with different scales. It enhances plane segmentation by generating subplanes in Deep Plane Clustering (DPC) module, which are then merged with the final planes by post-processing. DPC rearranges the point cloud by voting layers to make subplane clusters, trained in a self-supervised manner using pseudo-labels generated from RANSAC. Multi-Object RANSAC demonstrates superior plane instance segmentation performances over other recent RANSAC applications. We conducted an experiment on robot suction-based grasping, comparing our method with vision-based grasping network and RANSAC applications. The results from this real-world scenario showed its remarkable performance surpassing the baseline methods, highlighting its potential for advanced scene understanding and manipulation. Copyright © 2024, The Authors. All rights reserved.

关键词： Clutter (information theory)

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Smart Dome 4.0: Low-Cost, Independent, Automated Energy System for Agricultural Purposes enabled by Machine Learning 2

Smart Dome 4.0: Low-Cost, Independent, Automated Energy Syst...

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2021 2nd International Symposium on Automation, Information and Computing, ISAIC 2021

作者： Budiman, Arief S Gunawan, Fergyanto Djuana, Endang Pardamean, Bens Romeli, Sugiarto Putri, Dianing Nn Aji, Daisman Pb Rahardjo, Kuat Stevanus Ilham Wibowo, M. Daffa, Nadiev Owen, Raymond Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia PT. Impack Pratama Industri Tbk Jakarta Indonesia Mechanical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

Many economically essential crops in Indonesia (such as coffee, tea, chocolate, or copra) require storage or drying under certain environmental conditions, especially temperature and humidity. The solar dryer dome, typically used for agricultural purposes in Indonesia, produces a sufficient amount of heat to increase the evaporation rate inside the dome and reduce the moisture content of the commodity. A hybrid solar dryer accompanied by a photovoltaic panel, fan, and ventilation system is generally suitable. The system can provide an optimum environment with minimum control. However, as the outdoor temperature and humidity change dramatically, such as at night time, more control is required. Based on Industry 4.0 technologies, we have developed a new kind of hybrid solar dryer that provides an optimum environment 24/7. The system, called Smart Dome 4.0, is an intelligent, low-cost, self-sufficient drying and storage system to support Indonesia Agriculture 4.0. The system has a local power generation unit to self-sustain the required energy and operate without connecting to the electricity grid. The system utilizes a machine-learning algorithm to predict the environmental condition and optimally uses self-generated electric power. The developed Smart Dome 4.0 technology is critical to producing a sustainable solar dome under drastic environmental dynamics. © Published under licence by IOP Publishing Ltd.

关键词： Learning algorithms

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Advanced Receiver Operating Characteristic Curve Analysis to Identify Outliers in Binary Machine Learning Classifications for Precision Medicine

Advanced Receiver Operating Characteristic Curve Analysis to...

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2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Namdar, Khashayar Khalvati, Farzad Department of Diagnostic & Interventional Radiology The Hospital for Sick Children TorontoON Canada SickKids Research Institute Neurosciences & Mental Health Research Program TorontoON Canada Department of Medical Imaging University of Toronto TorontoON Canada Institute of Medical Science University of Toronto TorontoON Canada University of Toronto Department of Computer Science TorontoON Canada Department of Mechanical and Industrial Engineering University of Toronto TorontoON Canada Vector Institute TorontoON Canada

ISBN: (纸本)9798350351552

The Receiver Operating Characteristic (ROC) curve is a critical tool for binary classification analysis in medicine, with the Area Under the ROC Curve (AUROC) serving as a widely accepted metric to evaluate the performance of binary classifiers. This study conducts a comprehensive review of the ROC curve with a focus on its utility in outlier identification. We introduce a novel scoring method to rank actual positives and actual negatives within a test set, according to their impact on AUROC degradation. We bridge the scoring system with the ROC curve analysis to quantify each data point's contribution to AUROC loss. Furthermore, we introduce the IMICS ROC Analyzer, a graphical user interface-based software, embedded with our innovative algorithms. Through the use of an open-source prostate cancer dataset, we illustrate the application of our algorithms for practical outlier detection in binary classification tasks. The IMICS ROC Analyzer enhances the field of precision medicine by allowing for measuring an individual's contributions (be it patients, lesions, or samples) to the overall AUROC, thus facilitating confidence measurement of Machine Learning (ML) classifiers for individual cases of interest in a cohort. © 2024 IEEE.

关键词： Adversarial machine learning

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Artificial Intelligence Assisted IoT Data Intrusion Detection 4

Artificial Intelligence Assisted IoT Data Intrusion Detectio...

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4th International Conference on Computing and Communications Technologies, ICCCT 2021

作者： Shukla, Amogh Ahamad, Shahanawaj Rao, G.Nageswara Al-Asadi, Avein Jabar Gupta, Ankur Kumbhkar, Makhan Research Assistant Vellore Institute of Technology Vellore India College of Computer Science and Engineering University of Hail Hail City Saudi Arabia Lakireddy Bali Reddy College of Engineering Dept. of EEE Mylavaram India Computer Institute of Sulaimani Dept. of Computer Program Iraq Vaish College of Engineering Dept. of Computer Science and Engineering Rohtak India Christian Eminent College Dept. of Computer Science Indore India

ISBN: (纸本)9781665414470

It has become increasingly important since the introduction of Internet of Things (IoT) technology to ensure the security of IoT networks. Network anomalies and threats can be identified and predicted using a variety of intrusion detection systems (IDS). Despite the fact that the Internet of Things is vulnerable to attacks, early detection of malicious behavior can prevent data from being compromised. In this work, the primary goal is to develop machine artificial intelligence energy efficient models that can be used to detect attacks on the Internet of Things network. Normal and attack data from the IoT environment must be collected in order to construct a model. The Bayesian Network, the Artificial Neural Network, and the Support Vector Machine are considered to have the most promise. Using roundtrip time and power consumption data, a traditional three-layer Artificial Neural Network is put through its paces in the real world. © 2021 IEEE.

关键词： Internet of things

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Using textile capacitive sensors to train synchronized movement of hands

Using textile capacitive sensors to train synchronized movem...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Chang-Ming Yang Shu-Cing Wu Shih-Hung Chen Ze-We Wu Kuo-Cheng Liao Chi-Chun Chen Ming Young Biomedical Corp. Miaoli Taiwan Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung Taiwan Dept. of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung Taiwan Dept. of Electronic Engineering National Chin-Yi University of Technology Taichung Taiwan

This study introduced a capacitive sensing interactive game platform aimed at promoting emotional stability, which we have named the “Sunrise and Sunset” game. This game primarily consists of two pieces of regular textile fabric enveloping conductive silver fabric. A microcontroller was employed to extract the sensed capacitive values, and a game named “Sunrise and Sunset” is designed to complement the slow raising and lowering of both hands. The development of this gaming platform has the potential to offer a novel method of emotional management, particularly in high-stress living environments. It can serve as an effective relaxation tool, aiding individuals in emotional balance, anxiety reduction, and stress alleviation. Simultaneously, this platform can contribute to the promotion of mental well-being, providing an engaging and beneficial means for people to manage their emotions and moods.

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