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IEEE International Conference on Bioinformatics and Biomedicine

作者： Jaime Seguel Marie Lluberes Department of Electrical and Computer Engineering University of Puerto Rico at Mayaguez Doctoral Program in Computing and Information Sciences and Engineering University of Puerto Rico at Mayaguez

ISBN: (纸本)9781467368001

The problem of deciding whether or not a concentration of messenger RNA corresponds to a gene in expressed state is crucial in the Boolean representation of gene regulatory networks. Since there is no independent mathematical description of gene expressions, the computational answers of this problem are affected by uncertainties in the model used for the design of the computer algorithms, and by the number of concentration measurements available. In this article, we handle these uncertainties by computing their probability distributions and unifying heterogeneous algorithmic models with aggregation rules. This approach renders an algorithmic framework that computes a set of probabilistic strings of states and, within it, the elected string, which results from the collective decision of the algorithms on the state of the gene in each of the concentration measurements. The set of probabilistic strings is endowed with a metric and a pseudo metric function that makes it possible to analyze the elected string against the expected and the highest probability strings in the set, among other considerations. The method is applied on biological data sets and the results are compared with those of four previously published algorithms.

关键词： Area measurement Biology Arrays

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Haze effect removal from image via haze density estimation in optical model

Haze effect removal from image via haze density estimation i...

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作者： Yeh, Chia-Hung Kang, Li-Wei Lee, Ming-Sui Lin, Cheng-Yang Department of Electrical Engineering National Sun Yat-sen University 804 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 640 Taiwan Graduate Institute of Networking and Multimedia Department of Computer Science and Information Engineering National Taiwan University Taipei 106 Taiwan

Images/videos captured from optical devices are usually degraded by turbid media such as haze, smoke, fog, rain and snow. Haze is the most common problem in outdoor scenes because of the atmosphere conditions. This paper proposes a novel single image-based dehazing framework to remove haze artifacts from images, where we propose two novel image priors, called the pixel-based dark channel prior and the pixel-based bright channel prior. Based on the two priors with the haze optical model, we propose to estimate atmospheric light via haze density analysis. We can then estimate transmission map, followed by refining it via the bilateral filter. As a result, high-quality haze-free images can be recovered with lower computational complexity compared with the state-of-the-art approach based on patch-based dark channel prior. ©2013 Optical Society of America.

关键词： Pixels

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ORGANIZING THE RECOGNIZED CONCEPTS OF THE WORK MIND BY SELF-ORGANIZING MAP − Comprehensive indicators that contribute to design and operate a better workplace −

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AIJ Journal of Technology and Design 2022年第70期28卷 1390-1395页

作者： Ishiyama, Nozomi Matsumoto, Yuji Doctoral Program of Engineering Design Graduate School of Science and Technology Kyoto Institute of Technology Japan Faculty of Design and Architecture Kyoto Institute of Technology Japan

The purpose of this study is to focus on the indicators related to motivation in the Work Mind, and to organize the concepts by Pile Sort experiments using the sentences of the question items that compose each indicator. By polymerizing the personal conceptual system by Self-Organizing Map, the map of the Work Mind with some degree of completeness and generality is drawn. It is expected that this map will be a material for getting the general picture of the Work Mind and will be treated as a ruler when designing and operating a better workplace. © 2022 Architectural Institute of Japan. All rights reserved.

关键词： Piles

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Transforming the academic workplace: An evaluation of the ADVANCE program in colleges of engineering (2001-2008)

Transforming the academic workplace: An evaluation of the AD...

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2010 ASEE Annual Conference and Exposition

作者： Zajicek, Anna M. Rencis, Joseph Morimoto, Shauna A. Hunt, Valerie H. Department of Sociology University of Arkansas United States Department of Mechanical Engineering University of Arkansas Fayetteville United States Department of Sociology and Criminal Justice University of Arkansas United States Doctoral Study in the Interdisciplinary Public Policy Program University of Arkansas Fayetteville United States

For over two decades, the US government has supported gender equity programs in Science, Technology, engineering, and Mathematics (STEM) disciplines. In 2001, the National Science Foundation (NSF) initiated the ADVANCE Institutional Transformation (IT) program with the primary goal to increase the representation of women in STEM. Since 2001, 37 institutions of higher education have received the NSF ADVANCE IT awards, and 19 have completed their five-year projects. Using the American Society of engineering Education (ASEE) data, we assess the changes in the representation of full-time tenure-track women faculty in engineering colleges. While earlier cross-institutional studies of the ADVANCE IT program focus only on ADVANCE institutions, we also compare engineering colleges at ADVANCE institutions to their university peers. Our analysis suggests that while the average gains in the percentage of full-time tenure-track engineering women faculty for Cohort 2 were almost twice the national average, the gains were uneven not only between the two cohorts, but also among the engineering colleges in each cohort. Similarly, the ADVANCE institutions showed an uneven performance when compared with their peers. We therefore raise important questions regarding how success is defined and conclude by highlighting the most interesting instances of successful and not so successful efforts along with the importance of conducting multi-level comparative analyses of ADVANCE and non-ADVANCE institutions. © American Society for engineering Education, 2010.

关键词： Professional aspects

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Correction: Optimized framework for evaluating F3 transgressive segregants in cayenne pepper

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BMC plant biology 2025年第1期25卷 300页

作者： Ifayanti Ridwan Muh Farid Feranita Haring Nuniek Widiayani Ahmad Yani Nirwansyah Amier Muhammad Alfan Ikhlasul Amal Jekvy Hendra Nawab Ali Mekhled M Alenazi Mahmoud F Seleiman Willy Bayuardi Suwarno Muhammad Fuad Anshori Department of Agronomy Faculty of Agriculture Hasanuddin University Makassar 90245 Indonesia. Agrotechnology Bachelor Study Program Faculty of Agriculture Hasanuddin University Makassar 90245 Indonesia. Agrotechnology Master Study Program Faculty of Agriculture Hasanuddin University Makassar 90245 Indonesia. Agricultural Science Doctoral Program Graduate School Hasanuddin University Makassar 90245 Indonesia. Directorate of Horticultural Protection Agricultural Ministry Jakarta 12520 Indonesia. Department of Biosystems and Agricultural Engineering (BAE) College of Agriculture and Natural Resources Michigan State University East Lansing USA. Department of Plant Production College of Food and Agriculture Sciences King Saud University Riyadh Saudi Arabia. Department of Agronomy and Horticulture Faculty of Agriculture IPB University Bogor 11680 Indonesia. Department of Agronomy Faculty of Agriculture Hasanuddin University Makassar 90245 Indonesia. fuad.anshori@unhas.ac.id.

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Visual Localization of Intersections on Autonomous Vehicles Based on HD Map 23

Visual Localization of Intersections on Autonomous Vehicles ...

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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Utsula, Bizza S. Ashedananta, Muhammad D. Azis, Nadana A. Nazaruddin, Yul Y. Nadhira, Vebi Institut Teknologi Bandung Faculty of Industrial Technology Department of Engineering Physics Bandung40132 Indonesia Institut Teknologi Bandung Engineering Physics Doctoral Program Faculty of Industrial Technology Bandung40132 Indonesia Instrumentation and Control Research Group Institut Teknologi Bandung Faculty of Industrial Technology Bandung40132 Indonesia

ISBN: (纸本)9788993215274

The increasing number of autonomous vehicles will result in a safety factor that must be maintained. One of the crucial prerequisites for this is proper localization, especially at intersections where most road accidents occur each year. This research is proposed to improve the localization process, which has drawbacks such as the inaccurate Global Positioning System (GPS) when satellite signals are blocked, and Light Detection and Ranging (LiDAR), which is expensive and needs heavy computing time. This is done by implementing the High Definition Map (HD Map) method, which maps the condition of an area that is stored as memory on an autonomous vehicle computer. The proposed system uses a monocular camera to be processed into a semantic segmentation feature map at the pixel level, then uses point cloud to reconstruct a 3D intersection model, which is then segmented semantically as well so that position data is obtained after merging using the occupancy grid and scoring function. The results of this CNN-based approach localization produce less accuracy and Mean Intersection over Union (MIoU) values with 60% average accuracy and 62.3 MioU when compared to GPS data and other 3D semantic segmentation systems, but the proposed approach can save costs and computational burden. © 2023 ICROS.

关键词： Autonomous vehicles

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Multi Task Classification Using Deep Learning Approaches for Big Five Personality Traits Prediction: A Review 11

Multi Task Classification Using Deep Learning Approaches for...

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11th International Conference on Information Technology, Computer and Electrical engineering, ICITACEE 2024

作者： Nudin, Salamun Rohman Gernowo, Rahmat Somantri, Maman Wibowo, Adi Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia Diponegoro University Faculty of Engineering Department of Electrical Engineering Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Informatics Semarang Indonesia

ISBN: (纸本)9798350389289

This paper presents a comprehensive review of deep learning methods for the Big Five personality traits prediction using multi-task classification. The purpose of the review is to determine the performance of models developed in previous research to describe research opportunities and challenges in the future. The Big Five model is a framework in psychology that categorizes human personality into five dimensions: openness, conscientiousness, extraversion, agreeableness, and neuroticism. The review begins by analyzing 22 articles from the Science Direct, IEEE Xplore, and Scopus databases by applying screening using the Big Five Personality keywords. Then study the concepts of deep learning for model development such as: input, architecture, method, dataset, and performance to identify the strengths and limitations of each approach. The review results of the highest average accuracy by modality such as unimodal text with essay dataset (80.98%), unimodal image with ImageNet dataset (95.36%), unimodal audio with SSPNet dataset (79.48%), bimodal image and audio with Chalearn Video First Impression dataset (91.67%), multimodal text, image, and audio with Chalearn Video First Impression dataset (91.88%). The average accuracy of the Big Five Personality traits was highest (95.36%) and lowest (59%). This highlights the need for standard benchmarks, interpretable models and interdisciplinary collaboration to improve the accuracy, reliability and ethical implications of personality prediction. Future research is expected to pave the way for advancements in understanding human personality through deep learning approaches using other modalities such as EEG, GSR, and PPG. © 2024 IEEE.

关键词： Contrastive Learning

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Motivation increase of mathematics students in engineering-A proposal from Game Based Learning

Motivation increase of mathematics students in Engineering-A...

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2019 International Symposium on engineering Accreditation and Education, ICACIT 2019

作者： Zabala-Vargas, Sergio A. Garcia-Mora, Lewis H. Ardila-Segovia, Dayan A. De Benito-Crosetti, Barbara L. Universidad Santo Tomás Faculty of Telecommunications Engineering Bucaramanga Colombia Universidad Santo Tomás Curriculum Development Unit and Teacher Training Bucaramanga Colombia Universidad de Las Islas Baleares Doctoral Program in Educational Technology Palma de Mallorca Spain

ISBN: (纸本)9781728135670

Training and capacity building for students in engineering programs, including developing competencies and skills in mathematics, have been well-established in literature. There is less known about lowering rates of desertion (understood as the non-completion of an academic program). While it is generally agreed that there are multiple causes (individual, institutional, and state), several authors suggest that low academic results and low motivation in an educational process are important causes. In the case of Santo Tomás University, the site of study for this research project, the desertion index in engineering programs is close to 45%. The course with the highest failure rates is introductory mathematics (offered in the first year). Given this context, this research project proposed innovative pedagogical strategies and mediations, targeting the motivation indexes of university-aged students enrolled in mathematics courses. Game-based learning and mixed methods were employed to design a set of two didactic strategies: (A) Kahoot Online Testing Tool, and (B) Online Crossword. The target population was represented by 20 students enrolled in a differential calculus course, as part of the first-year engineering program. For benchmarking purposes, the participants were given a diagnostic calculus test. Next, an adaptation of Instructional Material Motivation Survey was applied, which allowed me to measure the dimensions of attention, confidence, satisfaction, and relevance. The results demonstrated that the pedagogical innovation positively impacted student motivation. The most significant results include the importance of planning rigorous learning activities, incorporating problems from the real student environment, and the importance of offering immediate feedback. © 2019 IEEE.

关键词： Students

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The Effect of Road Network and the Freight Transport Operation to the Development of the Region and Welfare of the Society in Langkat Regency 2

The Effect of Road Network and the Freight Transport Operati...

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2nd International Conference on Sciences and Technology Applications, ICOSTA 2020

作者： Tarigan, Harimin Rahim Matondang, A. Lubis, Suwardi Sirojuzilam Doctoral Program in Regional Planning Universitas Sumatera Utara Medan Indonesia Department of Industrial Engineering Universitas Sumatera Utara Medan Indonesia Department of Communication Science Universitas Sumatera Utara Medan Indonesia Department of Development Economics Universitas Sumatera Utara Medan Indonesia

This study aims to determine the effect of the road network and the implementation of freight transportation on regional development and the community welfare in Langkat Regency. This type of research is associative and explanatory research whose data are obtained through surveys. The study population was drivers of motor vehicles transporting goods in Langkat Regency. The number of samples is 305 respondents by means of probability sampling and data analysis techniques using Structural Equation Modelling (SEM). The results showed that the road network variables did not significantly influence the community welfare, but the road network had a significant and positive effect on the community welfare through the development of the Langkat Regency. The implementation of freight transportation has a significant and positive effect on the community welfare partially, directly and through the development of the Langkat Regency. Regional development has a significant and positive effect on the community welfare of Langkat Regency. © Published under licence by IOP Publishing Ltd.

关键词： Freight transportation

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Exploring Regression and Classification in Big Data with Deep Learning

Exploring Regression and Classification in Big Data with Dee...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Pandey, Ravindra Rongali, Aruna Sri Gopinath, S. Anute, Nilesh Nama, Forda Pund, Sachin Hult International Business School United States University of the Cumberlands Doctoral Information Technology Information Technology Emphasis Information Technology United States Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India ASM's Institute of Business Management and Research Pune India University of Lampung Doctoral Program of Environtmental Science Department of Informatics Bandar Lampung Indonesia Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India

ISBN: (纸本)9798350357769

Deep gaining knowledge is a synthetic intelligence approach used to recognize complex facts. It uses many facts to recognize the complicated relationships between various factors. With deep learning, models can be built which might be capable of understanding the complicated patterns within big datasets. In this paper, we can explore using deep mastering in regression and type tasks on large datasets. In particular, we can use deep learning strategies to build a regression model that predicts a target value primarily based on the functions in a massive dataset and a classification version that can classify an object into one of the many classes of an extensive dataset. We can then discuss the overall performance of the fashions in comparison to standard models and talk about capability areas for development. Moreover, we will analyze the scalability of deep mastering strategies used on large datasets and discuss exceptional practices for building deep mastering fashions on massive datasets. Finally, we will summarize our findings and offer our conclusions approximately the use of deep studying in regression and type obligations on large datasets. © 2023 IEEE.

关键词： approximately gaining knowledge mastering performance regression

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