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PPM-HC: A method for helping project portfolio management based on topic hierarchy learnings

International Journal of Business Intelligence and Data Mining 2021年第3期18卷 364-382页

作者： Pinto, Ricardo A.M. Bernardini, Flavia Marcacini, Ricardo M. Graduate Program in Industrial Engineering and Computing Systems Fluminense Federal University Jardim Bela Vista Rio das OstrasRJ Brazil Computing Institute Fluminense Federal University São Domingos NiteróiRJ Brazil Information Systems Federal University of Mato Grosso do Sul P.O. Box 210 Três LagoasMS Brazil

The projects categorisation is a crucial step in the project portfolio management (PPM). Categorising projects allows the organisation to identify categories with a lack or excess of projects, according to its strategic objectives. In this work, we present a new method for project portfolio management based on hierarchical clustering (PPM-HC) to organise the projects at several levels of abstraction. In the PPM-HC, similar projects are allocated to the same clusters and subclusters. PPM-HC automatically learns an understandable topic hierarchy from the project portfolio dataset, thereby facilitating the (human) task of exploring, analysing and prioritising the projects of the organisation. We also proposed a card sorting-based technique which allows the evaluation of the projects categorisation using an intuitive visual map. We carried out an experimental evaluation based on a benchmark dataset and we also presented a real-world case study. The results show that the proposed PPM-HC method is promising. © 2021 Inderscience Enterprises Ltd.

关键词： Investments

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Multi-task Learning Graph Neural Networks for Cancer Prognosis Prediction with Genomic Data

Multi-task Learning Graph Neural Networks for Cancer Prognos...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Tsung-Wei Lin Sofia Ormazabal Arriagada Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taiwan International Graduate Program AIoT NTU Research Center for Information Technology Innovation Academia Sinica Department of Electrical Engineering NTU Center for Advanced Computing and Imaging in Biomedicine NTU Smart Medicine and Health Informatics Program NTU

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Providing robust prognosis predictions for cancers with limited data samples remains a challenge for precision oncology. In this study, we propose a novel approach that combines multi-task learning (MTL) and graph neural networks (GNNs) to address this issue. By representing gene-gene interactions as a graph network, our approach leverages multi-task learning to effectively capture the relationships of genes relevant to the oncogenesis and progression of breast, lung, and colon cancer. We demonstrate that our approach improves the cancer prognosis prediction for cancers with fewer samples, such as colon adenocarcinoma, by leveraging the shared gene-gene interactions across different cancer types, obtaining increases in the area under the precision-recall curve (AUPRC) of 24%. Our work contributes to the field of smart healthcare by demonstrating the potential of MTL and GNNs for enhancing cancer prognosis prediction, even with limited data samples.

关键词： Lungs Genomics Medical services Colorectal cancer Breast Multitasking Oncology Graph neural networks Prognostics and health management Cancer

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Few-shot learning for biotic stress classification of coffee leaves

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Artificial Intelligence in Agriculture 2022年第1期6卷 55-67页

作者： Lucas M.Tassis Renato A.Krohling LABCIN-Nature Inspired Computing Lab Federal University of Espirito SantoAv.Fernando Ferrari514CEP29075-910 VitóriaEspırito SantoESBrazil PPGI-Graduate Program in Computer Science Federal University of Espirito SantoAv.Fernando Ferrari514CEP 29075-910VitóriaEspírito SantoESBrazil

In the last few years,deep neural networks have achieved promising results in several ***,one of the main limitations of these methods is the need for large-scale datasets to properly ***-shot learning methods emerged as an attempt to solve this *** the few-shot learning methods,there is a class of methods known as embedding learning or metric *** methods tackle the classification problem by learning to compare,needing fewer training *** of the main problems in plant diseases and pests recognition is the lack of large public datasets *** to this difficulty,the field emerges as an intriguing application to evaluate the few-shot learning *** field is also relevant due to the social and economic importance of agriculture in several *** this work,datasets consisting of biotic stresses in coffee leaves are used as a case study to evaluate the performance of few-shot learning in classification and severity estimation *** achieved competitive results compared with the ones reported in the literature in the classification task,with accuracy values close to 96%.Furthermore,we achieved superior results in the severity estimation task,obtaining 6.74%greater accuracy than the baseline.

关键词： Plant diseases and pests classification Image classification Few-shot learning Meta-learning Convolutional neural networks

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Calibration of Logical Effort Transistor Sizing for On-the-Fly Low-Power Supergate Design

Calibration of Logical Effort Transistor Sizing for On-the-F...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Henrique Kessler Murilo Bohlke Leomar S. da Rosa Marcelo Porto Viní cius V. Camargo Postgraduate Program in Computing - PPGC Federal University of Pelotas - UFPel Pelotas Brazil Center for Technological Development - CDTec*

Gate sizing has been widely studied to improve power dissipation and performance characteristics in VLSI design. Recent developments allow the automatic design of static CMOS complex gates for a reduction in power dissipation. It is possible to observe a lack of different transistor sizing in these works using minimum transistor dimension or the Logical Effort technique. In this work, we propose a methodology to adapt the Logical Effort technique for low-power applications. Results show significant improvements on up to 99.9% of the studied cases in power-performance trade-off across multiple simulation environments for a 45nm CMOS technology.

关键词： Semiconductor device modeling Circuits and systems Logic gates Very large scale integration Benchmark testing CMOS technology Libraries

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Deep STI: Deep Stochastic Time-series Imputation on Electronic Health Records 46

Deep STI: Deep Stochastic Time-series Imputation on Electron...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Cheng, Ming-Che Hsieh, Yi-Hsien Hsu, Te-Cheng Su, Tung-Hung Lin, Che Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan Taipei10617 Taiwan Division of Gastroenterolgy and Hepatology Dept. of Internal Medicine National Taiwan University Hospital Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei Taiwan Smart Medicine and Health Informatics Program Ntu Taipei Taiwan

ISBN: (纸本)9798350371499

Electronic Health Records (EHRs) are a cornerstone of modern healthcare analytics, offering rich datasets for various disease analyses through advanced deep learning algorithms. However, the pervasive issue of missing values in EHRs significantly hampers the development and performance of these models. Addressing this challenge is crucial for enhancing clinical decision-making and patient care. Existing methods for handling missing data, ranging from simple imputation to more sophisticated approaches, often fall short of capturing the temporal dynamics inherent in EHRs. To bridge this gap, we introduce the Deep Stochastic Time-series Imputation (Deep STI) algorithm, an innovative end-to-end deep learning model that seamlessly integrates a sequence-to-sequence generative network with a prediction network. Deep STI is designed to leverage the observed time-series data in EHRs, learning to infer missing values from the temporal context with high accuracy. We evaluated Deep STI on the liver cancer data from the National Taiwan University Hospital (NTUH), Taiwan. Our results showed that Deep STI achieved better 5-year hepatocellular carcinoma predictions (19.21% in the area under the precision-recall curve) than extreme gradient boosting (18.15%) and Transformer (18.09%). The ablation study also illustrates the efficacy of our generative architecture design compared to regular imputations. This approach not only promises to improve the reliability of disease analysis in the presence of incomplete data but also sets a new standard for utilizing EHRs in predictive healthcare. Our work aims to advance the field of healthcare analytics and open new avenues for research in deep learning applications to EHRs. © 2024 IEEE.

关键词： Electronic health record

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Data Selection Based on Phoneme Affinity Matrix for Electrolarynx Speech Recognition

Data Selection Based on Phoneme Affinity Matrix for Electrol...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： I-Ting Hsieh Chung-Hsien Wu Shu-Wei Tsa Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University Tainan Taiwan Dept. of Otolaryngology National Cheng Kung University Hospital Taiwan

Electrolarynx (EL) is a communicative aid for the patient after laryngectomy to generate communicable speech. Since EL speech exhibits low speech intelligibility and produces loud noise, understanding the content of the speech remains challenging for listeners, even if the patient is proficient in using the EL device. Accordingly, it is important to develop the tools that offer additional communication methods. Automatic speech recognition (ASR) of EL speech emerges as a method worth considering in this regard. However, the problem of under-resourced data dramatically degrades the recognition performance of EL speech. Data augmentation is one of the viable solutions for addressing the issue of under-resourced speech data. However, even with an increased health training corpus, the improvement in EL speech recognition may not be satisfactory. Because the characteristics of the EL speech still differ significantly from those of health speech. This paper proposes a data selection method using the phoneme affinity matrix to prioritize the selection of health speech that closely resembles EL speech for data augmentation. The affinity between two phonemes is defined as the similarity of the Phone Posteriorgrams(PPGs) of the two phonemes, considering the phoneme models. The experimental results demonstrate that the approach utilizing data selection based on the phoneme affinity matrix yields superior results compared to both the baseline and the method employing random sampling to select the augmented health speech corpus.

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Computer Vision and Eyetracking Approach for Assessing Visual Disorders in Premature Infants

Computer Vision and Eyetracking Approach for Assessing Visua...

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Annual IEEE Symposium on Computer-Based Medical Systems

作者： Rodrigo Ferraz Souza Manoella Rockembach Bruna Samantha Marchi Marcelo Fernandes Costa Cristiane Aparecida Moran Antonio Carlos Sobieranski Department of Computing - DEC Federal University of Santa Catarina Araranguá Brazil Department of Health Sciences Federal University of Santa Catarina Araranguá Brazil Neurosciences and Behavior Postgrad. Program Institute of Psychology São Paulo University São Paulo Brazil Department of Rehabilitation Sciences Federal University of Santa Catarina Araranguá Brazil Department of Computing - DEC Graduate Program in Information and Communication Technologies - PPGTIC Federal University of Santa Catarina Araranguá Brazil

ISBN: (数字)9798350384727

ISBN: (纸本)9798350384734

Premature infants are subjected to an elevated risk of abnormal visual development, leading to potential visual impairments such as field defects, optic nerve dysfunctions, eye movement abnormalities, and nystagmus. To mitigate these dangers, early and exact assessment is important. This paper presents a computer vision approach that uses face mapping and eye-tracking technologies to objectively and accurately assess further visual disorders in infants. Our methodology integrates eye-tracking with facial recognition techniques to quantify eye movements and gaze stability accurately. Detailed video recordings of infants responses to visual stimuli are analyzed using a custom-developed algorithm, which processes eye and head movements to generate precise metrics of visual function. The results demonstrate that our computational tool significantly enhances the diagnostic capabilities by providing quantifiable and objective metrics that supports the clinical observations, thus assisting healthcare professionals in identifying and addressing potential visual abnormalities at early stages effectively.

关键词： Measurement Visualization Pediatrics Computer vision Face recognition Visual impairment Gaze tracking

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Modeling the Percentage of NEET in Indonesia with Spatial Cauchy Regression through the Bayesian Analysis Approach

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IAENG International Journal of Applied Mathematics 2024年第7期54卷 1288-1295页

作者： Rantini, Dwi Fakhruzzaman, Muhammad Noor Ningrum, Ratih Ardiati Othman, Fazidah Choir, Achmad Syahrul Ramadan, Arip Alya, Najma Attaqiya Putri, Elfira Rahma Pratama, Muhammad Alfian Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology andMultidiscipline Universitas Airlangga Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia Statistical Computing StudyProgram Politeknik Statistika STIS Indonesia Information System Study Program School of Industrial and System Engineering Telkom University Indonesia

Indonesia has entered a period of demographic bonus. Human resources must be optimized. The number of children who do not in employment, education or training (NEET) in each province needs attention. Several factors that could contribute to the decline in the NEET percentage are literacy rates and the number of adolescents with computer skills. Increasing these two factors is believed to be able to reduce the percentage of NEET in every province in Indonesia. To find out the relationship between these two factors and how much influence they have on the percentage of NEET, this research is modeled by Cauchy regression and includes spatial effects. The results of the analysis show that the best model is when the spatial effect is modeled by Fernandez Steel Skew Normal conditionally autoregressive (FSSN CAR). This result is seen from the smallest value of the Watanabe Akaike Information Criterion (WAIC) in this model, which is 190.5. The parameter estimated shows that the higher the literacy rate and the number of adolescents with computer skills, the lower the percentage of NEET in each province in Indonesia. The results of this research can be useful for the Indonesian government to increase the number of educational facilities related to these two factors. © (2024), (International Association of Engineers). All rights reserved.

关键词： Bayesian CAR Cauchy Education NEET

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Low Complexity Broadband Array Processing in Dynamic Scenarios with Jamming

Low Complexity Broadband Array Processing in Dynamic Scenari...

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IEEE Conference on Vehicular Technology (VTC)

作者： Daniel V. De Lima João Paulo J. Da Costa Ricardo K. Miranda Antonio Arlis S. Da Silva Giovanni A. Santos José Alfredo R. Vargas Auzuir R. De Alexandria Postgraduate Program in Telecommunications Engineering (PPGET) IFCE Fortaleza Brazil Hamm-Lippstadt University of Applied Sciences (HSHL) Lippstadt Germany Graduate School for Applied Research in North Rhine-Westphalia (PKNRW) Bochum Germany Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Graduate Program in Computing (PPGC) UFRGS Porto Alegre Brazil Postgraduate Program in Mechatronics Engineering (PPMEC) UnB Brasilia Brazil

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

Vehicle-to-Everything (V2X) networks require low-latency communications utilizing a broad spectrum while operating under jamming. In this sense, low complexity antenna array-based broadband jamming mitigation schemes are crucial in order to allow low latency communication and low-cost hardware. In this paper we propose low-complexity algorithms for signal recovery in broadband processing scenario applied to V2X. Numerical simulation of a jamming scenario demonstrate the proposed algorithm achieving the same performance in terms of signal-to-interference plus noise ratio (SINR) as the state-of-the-art while taking signiflcantly less time to compute.

关键词： Heuristic algorithms Prevention and mitigation Interference Numerical simulation Broadband communication Broadband antennas Jamming

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Analysis of Shopping Mall Tourist Satisfaction in Bangkok Using Word Cloud of Online Reviews

Analysis of Shopping Mall Tourist Satisfaction in Bangkok Us...

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2023 International Conference on Cyber Management and Engineering, CyMaEn 2023

作者： Ladkoom, Kobthong Tarigan, Emiya Fefayosa Br Yasirandi, Rahmat Suwastika, Novian Anggis Anom, Rahmat Indra Pratama Makky, Muhammad Al Chandrakasem Rajabhat University Faculty of Science Bangkok Thailand Telkom University School of Computing Bandung Indonesia King Mongkut's Institute of Technology Ladkrabang Faculty of Information Technology Bangkok Thailand Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Kuala Lumpur Malaysia Doctoral Program of Tourism Trisakti School of Tourism Jakarta Indonesia

ISBN: (纸本)9781665493291

The tourism industry is very important to prioritize, especially for developing countries like Thailand. The increase in tourist visits to Bangkok also triggers the potential for a rise in Bangkok tourism during the pandemic. In this study, shopping malls are one of the attractive attractions for a capital city like Bangkok. Tourist expectations of attraction must be realized so that satisfaction can be realized and created revisit. In the digitalization era, the internet's use to find and disseminate information has become commonplace. *** is one of the leading review sites, which already has many reviewers, and is the case study in this research. Using Word Cloud as an analysis technique and clustering of data reviews, several important factors influence tourist satisfaction. Based on the 1186 clustered reviewers, several factors were produced, such as Place Quality, Location Access, Support Facilities, Price, and Seller Attitude. These factors can influence the decision to visit shopping malls in Bangkok because they represent tourist satisfaction. © 2023 IEEE.

关键词： Shopping centers

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