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

作者： Lok-In Tsui Te-Cheng Hsu Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Institute of Communication Engineering National Tsing-Hua University Hsinchu Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Computational and Systems Biology and Center for Biotechnology NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

Drug–target affinity (DTA) prediction is crucial to speed up drug development. The advance in deep learning allows accurate DTA prediction. However, most deep learning methods treat protein as a 1D string which is not informative to models compared to a graph representation. In this paper, we present a deep-learning-based DTA prediction method called N-gram Graph DTA (NG-DTA) that takes molecular graphs of drugs and n-gram molecular sub-graphs of proteins as inputs which are then processed by graph neural networks (GNNs). Without using any prediction tool for protein structure, NG-DTA performs better than other methods on two datasets in terms of concordance index (CI) and mean square error (MSE) (CI: 0.905, MSE: 0.196 for the Davis dataset; CI: 0.904, MSE: 0.120 for Kiba dataset). Our results showed that using n-gram molecular sub-graphs of proteins as input improves deep learning models’ performance in DTA prediction.

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COMPETITIVE ADVANTAGE MODEL THROUGH KNOWLEDGE management systems IMPLEMENTATION TO OPTIMIZE BUSINESS SUSTAINABILITY 9

COMPETITIVE ADVANTAGE MODEL THROUGH KNOWLEDGE MANAGEMENT SYS...

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9th International Conference on Information Technology: IoT and Smart City, ICIT 2021

作者： Sardjono, Wahyu Lusia, Erma Utomo, Hargo Sukardi, Samudra Rahmasari, Aninda Regent Montororing, Yuri Delano Information Systems Management Department Binus Graduate Program Bina Nusantara University Indonesia Research Capacity Building Research & Development Bina Nusantara University Indonesia Department of Management Faculty of Economic and Business Gajah Mada University Indonesia Graduate Program Faculty of Economy and Business University of Indonesia Indonesia International Relations Department Faculty of Humanities Bina Nusantara University Indonesia Industrial Engineering Study Program Bhayangkara University Indonesia

ISBN: (纸本)9781450384971

Complaint resolution that arise due to internal and external factors in a company can be monitored through the Service Recovery Index (SRI), and SRI is developed through a number of factors that influence it. Meanwhile, the SRI model needs to be developed to see the business sustainability through implementation knowledge management system in the organization. The data collection method was carried out, starting with building a research instrument based on knowledge management theory and continued with observation and distributing questionnaires to respondents who were involved in the business process of managing SRI. The data analysis method was used to process the data from the questionnaire, factor analysis method was used and the method to build the model used was the regression analysis method. The results obtained from this study are the discovery of 4 (four) factors with a number of indicators that are grouped on each formed factor, which can then be made and built a readiness model that reflects current conditions, so that it can be used to build predictions in the future related to readiness. implementation of knowledge management system development in order to support increased understanding of the service recovery index in the organization. © 2021 ACM.

关键词： Knowledge management

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Performance Analysis of Building Integrated Photovoltaic (BIPV): MANOVA Analysis

Performance Analysis of Building Integrated Photovoltaic (BI...

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Solar Conference (MENA-SC), Middle East and North Africa

作者： Wadhah Alzahmi Alyaa Alyaarbi Tahani Alnaqbi Ayman Alzaatreh Dubai Electricity and Water Authority Dubai United Arab Emirates Department of Industrial Engineering Engineering Systems Management Program American University of Sharjah Sharjah Sharjah United Arab Emirates Department of Mathematics and Statistics College of Arts and Sciences American University of Sharjah Sharjah Sharjah United Arab Emirates

Global energy consumption has increased over the years due to population growth, economic expansion, and the pursuit of a higher quality of life. The building sector is a critical sector for high consumption, contributing to about 36% of the total global energy consumption in 2020. The global interest in reducing energy consumption allows different sectors to adopt renewable resources such as solar photovoltaics (PV). However, utilizing solar PV in buildings has limitations due to rooftop space constraints in newly designed buildings. Due to this limitation and continuous innovation in solar PV technologies, Building Integrated Photovoltaics (BIPV) have been introduced as a potential alternative for solar PV in buildings. As such, BIPV energy performance and panel temperatures are affected by many factors such as orientation setup and technology type. Therefore, this research aims to analyze the impact of technology type (indium gallium selenide (CIGS) and monocrystalline (MONO) & orientation on BIPV energy production & module temperature. The data used for this study is based on a project completed in Dubai for BIPV in building applications. The results indicate that BIPV type and system orientation during each session significantly affect BIPV energy production and BIPV module temperature.

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Model for Anomaly Detection in Reducing Water Losses in the Distribution Network of Peru using Machine Learning Algorithms

Model for Anomaly Detection in Reducing Water Losses in the ...

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Software engineering and Computer Science (CSECS), International Conference on

作者： Mayra Rivera Pricila Lucero José Luis Castillo-Sequera Lenis Wong Information Systems Engineering Program Universidad Peruana de Ciencias Aplicadas Lima Peru Department of Computer Science Group: Health Computing and Intelligent System Universidad de Alcalá Alcalá de Henares Spain

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

Water leakage in distribution networks is a significant challenge, especially in regions with limited infrastructure like Huancayo, Peru, where losses account for 32.82% of the distributed volume. This study introduces a machine learning-based approach to detect leaks using four algorithms: Autoencoder LSTM, Isolation Forest, One-Class SVM, and K-Nearest Neighbors (KNN). The methodology involved preprocessing historical consumption data (2018–2024) into 12-month temporal sequences per client and evaluating the models based on F1 Score, Precision, and Mean Absolute Error (MAE). Among the algorithms, the Autoencoder LSTM demonstrated superior performance with the highest precision (0.89) and the lowest MAE (0.00402). Its robustness in high-variability contexts enables early and reliable leak detection, providing a cost-effective solution for optimizing water management in resource-constrained environments.

关键词： Support vector machines Machine learning algorithms Autoencoders Distribution networks Forestry Nearest neighbor methods Robustness Leak detection Long short term memory Water resources

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Multimodal Sentiment Analysis using Kernel Based Support Vector Machine 2

Multimodal Sentiment Analysis using Kernel Based Support Vec...

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2nd IEEE International Conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication engineering, ICATIECE 2022

作者： Kumar, V Sunil Pareek, Piyush Kumar De Albuquerque, Victor Hugo Costa Khanna, Ashish Gupta, Deepak Madhumala, R.B. Nitte Meenakshi Institute of Technology Department Artificial Intelligence and Machine Learning Bengaluru India Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceará Fortaleza Brazil Federal University of Ceará Department of Teleinformatics Engineering Brazil Maharaja Agrasen Institute of Technology GGSIPU CSE Dept India Dayananda Sagar Academy of Technology and Management Department of ISE Bangalore India

ISBN: (纸本)9781665493963

There has been a steady expansion in the number of social websites, electronic media outlets, and blogging services, all of which contribute a large number of user messages such as comments, reviews, and opinions from customers. The quantity of these signals, which are used to evaluate the quality of a wide variety of goods and services, is rather substantial. As a result, it is challenging to monitor and identify the feelings of customers. As a result, a method known as Sentiment Analysis (SA), also known as Opinion Mining (OM), was developed. This method gathers data from the internet by using Natural Language Processing (NLP) strategies and then analyses people's thoughts. It does this by extracting subjective information from the words, assessing opinions, and quantifying positive and negative evaluations. The end result is a conclusion about the specific entity, which may be an organisation, the public, or a product. The NLP first gathers the views, and then those opinions are sorted into positive, neutral, and negative categories. Both the marketing promotion and the customer's ability to choose the most appropriate product for their needs may be improved by doing an analysis. The purpose of this study is to evaluate the use of different machine learning and swarm intelligence optimization methods to perform sentiment analysis on data derived from movie reviews. This study helps many businesses increase their profits while simultaneously lowering the number of problems caused by a certain product. Within the scope of this investigation, three distinct models of sentiment analysis were investigated. An effective method for doing sentiment analysis known as a Hybrid Kernel based Support Vector Machine (HKSVM) has been presented. The preprocessing that is described here is meant to cut down on the data inconsistencies and get rid of the data that isn't needed. After that, the Expectation Maximization (EM) technique is used in order to identify an outlier. After t

关键词： Sentiment analysis

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Robust Closed-Loop Supply Chain Model with Return management System for Circular Economy

SSRN

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SSRN 2024年

作者： Shin, Youngchul Kim, Gwang Jeong, Yoonjea Department of Industrial Engineering Ajou University Gyeonggi-do Suwon-si16499 Korea Republic of Division of Business Administration Chosun University Gwangju61452 Korea Republic of Division of Systems Management and Safety Engineering Pukyong National University Busan48513 Korea Republic of

The significance of the circular economy (CE) is increasingly recognized worldwide due to its substantial global environmental impacts. To address this critical issue from a supply chain management perspective, we propose an optimization model for a closed-loop supply chain that integrates CE operations, including reuse, remanufacturing, and recycling processes. To capture correlations between uncertain end-customer demand and three types of return processes, we adopt a factor-based demand model. Since these types of uncertain correlated parameters make the optimization problem intractable, we utilize a linear decision rule and a distributionally robust bound to retain computational tractability from a deterministic formulation. Consequently, we derive a deterministic second-order cone program, which is solvable using an interior point method within reasonable times for moderate-size data. The computational results show that the proposed model achieves significant cost savings effects, contributing positively to the CE operations. © 2024, The Authors. All rights reserved.

关键词： Supply chain management

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L-Type Calcium Channel Modulates Low-Intensity Pulsed Ultrasound-Induced Excitation in Cultured Hippocampal Neurons

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Neuroscience Bulletin 2024年第7期40卷 921-936页

作者： Wen-Yong Fan Yi-Ming Chen Yi-Fan Wang Yu-Qi Wang Jia-Qi Hu Wen-Xu Tang Yi Feng Qian Cheng Lei Xue State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Fudan UniversityShanghai 200433China Department of Physiology and Neurobiology School of Life SciencesFudan UniversityShanghai 200438China Institute of Acoustics School of Physics Science and EngineeringTongji UniversityShanghai 200092China Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education Tongji UniversityShanghai 200070China Center for Rehabilitation Medicine Department of Pain ManagementZhejiang Provincial People's HospitalAffliated People's HospitalHangzhou Medical CollegeHangzhou 310014China Department of Critical Care Medicine Shanghai General HospitalShanghai Jiaotong University School of MedicineShanghai 200080China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 201210China Research Institute of Intelligent Complex Systems Fudan UniversityShanghai 200433China

As a noninvasive technique,ultrasound stimulation is known to modulate neuronal activity both in vitro and in *** latest explanation of this phenomenon is that the acoustic wave can activate the ion channels and further impact the electrophysiological properties of targeted ***,the underlying mechanism of low-intensity pulsed ultrasound(LIPUS)-induced neuro-modulation effects is still ***,we characterize the excitatory effects of LIPUS on spontaneous activity and the intracellular Ca^(2+)homeostasis in cultured hippocampal *** whole-cell patch clamp recording,we found that 15 min of 1-MHz LIPUS boosts the frequency of both spontaneous action potentials and spontaneous excitatory synaptic currents(sEPSCs)and also increases the amplitude of sEPSCs in hippocampal *** phenomenon lasts for>10 min after LIPUS *** with Ca^(2+)imaging,we clarified that LIPUS increases the[Ca^(2+)]cyto level by facilitating L-type Ca^(2+)channels(LTCCs).In addition,due to the[Ca^(2+)]cyto elevation by LIPUS exposure,the Ca^(2+)-dependent CaMKII-CREB pathway can be activated within 30 min to further regulate the gene transcription and protein *** work suggests that LIPUS regulates neuronal activity in a Ca^(2+)-dependent manner via *** may also explain the multi-activation effects of LIPUS beyond *** stimulation potentiates spontaneous neuronal activity by increasing Ca^(2+)influx.

关键词： Low-intensity pulsed ultrasound Neuromodulation L-type calcium channel Hippocampal neuron

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HAEE: Question Classification Using Hierarchical Intra-Attention Enhancement Encoder

HAEE: Question Classification Using Hierarchical Intra-Atten...

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26th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2021

作者： Wang, Jen-Wei Chen, Kai-Hsiang Huang, Jen-Wei National Cheng Kung University Department of Electrical Engineering Tainan Taiwan National Cheng Kung University and Academia Sinica Graduate Program for Multimedia Systems and Intelligent Computing Taiwan

ISBN: (纸本)9781665408257

With the development of E-commerce, an Automated Question-Answering system takes a crucial part in customer service. Question classification, which assigns labels to questions according to the answer types, is one of the tasks in question answering. Previous methods usually used handcraft features like named entity recognition, but it needs the predefined dictionary or tools. The machine learning approaches are recently applied to this task and achieve high accuracy. In this paper, we proposed HAEE, a Hierarchical intra-Attention Enhancement Encoder which composed of bidirectional GRUs and intra-attentions. In addition, we adopt the character input to address the issue of the OOV (Out-Of-Vocabulary) problem and create multiple intra-attentions to simulate the certain relationships between characters (Chinese) or words (English) to enhance the influence of tokens on the sentence. We evaluate the HAEE model in an actual corporate setting and several datasets. As shown in the experimental results, our HAEE model outperforms the existing state-of-the-art models on question classification tasks, especially for the Chinese corpus. © 2021 IEEE.

关键词： Signal encoding

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DeepCAPE: A Deep Convolutional Neural Network for the Accurate Prediction of Enhancers

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Genomics, Proteomics & Bioinformatics 2021年第4期19卷 565-577页

作者： Shengquan Chen Mingxin Gan Hairong Lv Rui Jiang Ministry of Education Key Laboratory of Bioinformatics Bioinformatics Division at the Beijing National Research Center for Information Science and TechnologyCenter for Synthetic and Systems BiologyDepartment of AutomationTsinghua UniversityBeijing 100084China Department of Management Science and Engineering School of Economics and ManagementUniversity of Science and Technology BeijingBeijing 100083China

The establishment of a landscape of enhancers across human cells is crucial to deciphering the mechanism of gene regulation,cell differentiation,and disease ***-throughput experimental approaches,which contain successfully reported enhancers in typical cell lines,are still too costly and time-consuming to perform systematic identification of enhancers specific to different cell *** computational methods,capable of predicting regulatory elements purely relying on DNA sequences,lack the power of cell line-specific *** studies have suggested that chromatin accessibility of a DNA segment is closely related to its potential function in regulation,and thus may provide useful information in identifying regulatory *** by the aforementioned understanding,we integrate DNA sequences and chromatin accessibility data to accurately predict enhancers in a cell line-specific *** proposed Deep CAPE,a deep convolutional neural network to predict enhancers via the integration of DNA sequences and DNase-seq *** from the well-designed feature extraction mechanism and skip connection strategy,our model not only consistently outperforms existing methods in the imbalanced classification of cell line-specific enhancers against background sequences,but also has the ability to self-adapt to different sizes of ***,with the adoption of autoencoder,our model is capable of making cross-cell line *** further visualize kernels of the first convolutional layer and show the match of identified sequence signatures and known *** finally demonstrate the potential ability of our model to explain functional implications of putative disease-associated genetic variants and discriminate diseaserelated *** source code and detailed tutorial of Deep CAPE are freely available at https://***/Shengquan Chen/DeepCAPE.

关键词： Enhancer prediction Chromatin accessibility Data integration Transcription factor binding motif Disease-associated regulatory element

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Intelligent Phase Contrast Meta-Microscope System

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Nano Letters 2023年第24期23卷 11630-11637页

作者： Chu, Cheng Hung Chia, Yu-Hsin Hsu, Hung-Chuan Vyas, Sunil Tsai, Chen-Ming Yamaguchi, Takeshi Tanaka, Takuo Chen, Huei-Wen Luo, Yuan Yang, Pan-Chyr Tsai, Din Ping YongLin Institute of Health National Taiwan University Taipei10672 Taiwan Institute of Medical Device and Imaging National Taiwan University Taipei10051 Taiwan Department of Biomedical Engineering National Taiwan University Taipei10051 Taiwan Department of Mechanical Engineering National Taiwan University Taipei10617 Taiwan Innovative Photon Manipulation Research Team RIKEN Center for Advanced Photonics Saitama351-0198 Japan Graduate Institute of Toxicology College of Medicine National Taiwan University Taipei100 Taiwan Genome and Systems Biology Degree Program National Taiwan University and Academia Sinica Taipei100 Taiwan Program for Precision Health and Intelligent Medicine National Taiwan University Taipei106319 Taiwan Department of Internal Medicine National Taiwan University Hospital National Taiwan University Taipei10002 Taiwan Institute of Biomedical Sciences Academia Sinica Taipei11529 Taiwan Department of Electrical Engineering City University of Hong Kong Kowloon999077 Hong Kong Centre for Biosystems Neuroscience and Nanotechnology City University of Hong Kong Kowloon999077 Hong Kong The State Key Laboratory of Terahertz and Millimeter Waves City University of Hong Kong Kowloon99907 Hong Kong

Phase contrast imaging techniques enable the visualization of disparities in the refractive index among various materials. However, these techniques usually come with a cost: the need for bulky, inflexible, and complicated configurations. Here, we propose and experimentally demonstrate an ultracompact meta-microscope, a novel imaging platform designed to accomplish both optical and digital phase contrast imaging. The optical phase contrast imaging system is composed of a pair of metalenses and an intermediate spiral phase metasurface located at the Fourier plane. The performance of the system in generating edge-enhanced images is validated by imaging a variety of human cells, including lung cell lines BEAS-2B, CLY1, and H1299 and other types. Additionally, we integrate the ResNet deep learning model into the meta-microscope to transform bright-field images into edge-enhanced images with high contrast accuracy. This technology promises to aid in the development of innovative miniature optical systems for biomedical and clinical applications. © 2023 American Chemical Society.

关键词： Microscopes

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