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A review on multi-lingual sentiment analysis by machine learning methods

Journal of Engineering science and technology Review 2020年第2期13卷 154-166页

作者： Sagnika, Santwana Pattanaik, Anshuman Mishra, Bhabani Shankar Prasad Meher, Saroj K. School of Computer Engineering Kalinga Institute of Industrial Technology Deemed to be University Bhubaneswar Odisha India Systems Science And Informatics Unit Indian Statistical Institute Bangalore Karnataka India

The arrival of e-commerce and the multitude of information presented by the web have established the internet as a principal destination for consumers looking for truthful opinions and multiple viewpoints for some product, news, topic, or trend in the markets. Thus, it is desirable to make this search easier by using systems which sift through the mass of data and summarize the available opinions for easy understanding of the seeker. This task, known as sentiment analysis, is currently a prominent area of research. Sentiment analysis can be useful for businesses, data analysts and data scientists, as well as customers. Even though many methods are designed to perform this task on English data, there is a lack of systems that can analyze data in other languages. This paper attempts to provide a detailed study on the sentiment analysis methods applied on languages other than English. The tools used, pros and cons, and efficiency of all methods is covered. The associated challenges are also discussed. The paper covers methods that analyze translated data as well as methods that analyze available data in the target language. © 2020 School of science, IHU.

关键词： Sentiment analysis

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AI-Based Virtual Screening for Identifying Novel Drug Candidates 5

AI-Based Virtual Screening for Identifying Novel Drug Candid...

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5th IEEE International Conference on Recent Trends in computer science and technology, ICRTCST 2024

作者： Radhamahendran, S. Deshmukh, Sheetal Vishal Thiyagasundaram, T. Sivakumar, K. Tilak Babu, S.B.G. Singh, Surya Pratap Vels Institute of Science Technology and Advanced Studies School of Life Sciences Department of Bioinformatics Pallavaram Chennai India Bharati Vidyapeeth Yashwantrao Mohite Institute of Management Department of Computer Applications Maharashtra Karad India Sri Sairam Siddha Medical College Department of NoiNaadal Chennai India Saveetha University Simats School of Engineering Department of Mathematics Chennai Tamilnadu Thandalam India Aditya Engineering College Department of ECE Surampalem India UPUMS Department of Multi-Disciplinary Research Unit UP Etawah India

ISBN: (纸本)9798350351378

VR screening uses computers to find large databases of chemical compounds and predict their potential as medications based on factors such as binding affinity, pharmacokinetics, and toxicity. This accelerates the process of discovering novel medications by concentrating on compounds with a higher likelihood of efficacy. This results in decreased expenses and increased effectiveness over an extended period. The study demonstrates the utilization of many artificial intelligence techniques in virtual screening, including deep learning networks, machine learning models, and other sophisticated computer technologies. The analysis examines the training of AI models on diverse datasets, incorporating data from existing databases, research publications, and experimental studies. This demonstrates the potential of AI in discovering novel chemicals for the treatment of cancer, infectious diseases, neurological disorders, and uncommon ailments. There are limitations associated with utilizing Molecular structure-based CADD methods. This research reviews current tools, applications, and methods for medication production speed and cost reduction. Structure-Based Virtual Screening (SBVS) is essential to medication development, according to research. © 2024 IEEE.

关键词： Diseases

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Time-series Data Imputation for Improved Prediction of PM10 Pollutant in Northern Thailand

Time-series Data Imputation for Improved Prediction of PM10 ...

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International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)

作者： Seng Nu Pan Kumgyi Somrudee Deepaisarn Thepchai Supnithi Tsuhoshi Isshiki Titaporn Supasri Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Pathum Thani Thailand Department of Information and Communication Engineering Tokyo Institute of Technology Tokyo Japan Atmospheric Research Unit National Astronomical Research Institute of Thailand Chiang Mai Thailand

Air pollution has become a big issue in the Northern part of Thailand due to forest fires, human activities, and seasonal climate. The air pollutant particulate matter (PM10) measurements from the Pollution Control Department (PCD) stations contain some missing values, which limit the capability in time-series prediction of future pollutant quantity. This research performs a preliminary study of PM10 time-series data imputation in three provinces in Thailand, including Chiang Mai, Lampang, and Nan. The evaluation was performed using three imputation methods: k-nearest neighbours (KNN), last observation carried forward (LOCF), and nearest observation carried backward (NOCB). NOCB obtains the highest R 2 , the lowest mean absolute error (MAE), and the lowest root mean squared error (RMSE) when tested with mimic missing data at varied proportions, ranging from 5% to 25% of the entire data. Imputed data as a result of NOCB underwent time-series data prediction of PM10 using long short-term memory (LSTM)-based neural networks. Prediction models were trained and tested based on the data splitting ratio of 80:20 from 2019 to 2020. The PM10 prediction model trained using imputed data leads to accurate prediction of unseen future data, achieving the R 2 values of approximately 0.92.

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Generic Architecture for Multisource Physiological Signal Acquisition, Processing and Classification Based on Microservices 1

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20th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2023

作者： Sánchez-Reolid, Roberto Sánchez-Reolid, Daniel Ayora, Clara de la Vara, José Luis Pereira, António Fernández-Caballero, Antonio Departamento de Sistemas Informáticos Universidad de Castilla-La Mancha Albacete Spain Neurocognition and Emotion Unit Instituto de Investigación en Informática de Albacete Albacete Spain Computer Science and Communications Research Centre School of Technology and Management Polytechnic Institute of Leiria Leiria Portugal INOV INESC INOVAÇÃO Institute of New Technologies—Leiria Office Leiria Portugal Madrid28016 Spain

ISBN: (数字)9783031383335

ISBN: (纸本)9783031383328

The use of IoT devices is increasing and their integration into healthcare is growing. Therefore, there is a need to develop microservice-oriented hardware-software architectures that integrate all the stages from the acquisition of physiological signals to their processing and classification. In addition, the integration of physiological signals from different sources is a must in order to increase the knowledge of the monitored person’s condition. In this context, the focus of this work has been to identify all the necessary workflow phases in this type of architecture, focusing mainly on scalability, replication and redundancy of the different services. This work proposes an architecture generic in terms of the number of sensors to be included and their acquisition requirements (sampling frequency and latency). We have chosen to include network protocols such as the Laboratory Stream Layer for data synchronisation and streaming. To this end, infrastructure as a service and as a machine have been included. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Network architecture

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Enhancing Functional and Extra Motor Abilities: A Focus Group Study on the Re-Design of an Extra-Robotic Finger

Enhancing Functional and Extra Motor Abilities: A Focus Grou...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Sjoerd Hendriks Basma Hasanen Naqash Afzal Irfan Hussain Mohammad Obaid Department of Computer Science and Engineering Interaction Design Unit Chalmers University of Technology Gothenburg Sweden Department of Mechanical & Nuclear Engineering Khalifa University Abu Dhabi United Arab Emirates Center for Autonomous Robotic Systems Khalifa University Abu Dhabi United Arab Emirates

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

The utilization of extra-robotic limbs to regain and extend motor abilities poses challenges. Nonetheless, the evidence suggesting the extension of these abilities without functional interference holds promise for the advancement of extra-robotic fingers or limbs. To gain insights into design improvement from diverse perspectives, we conducted a focus group study utilizing a prototype supernumerary robotic finger (SRF). The main purpose of the study is to gain knowledge on the current prototype, and then use the outcomes in a future co-designing process of SRF having the potential to be used as an assistive and augmented tool. Fifteen healthy subjects with an engineering background participated in the current study. We analyzed the collected data through a thematic analysis process to generate key design recommendations. These recommendations contain suggestions for designing for personal and social acceptance, adaptability, and multi-modal interaction. The results serve as a subset of future subsequent studies to be carried out with other stakeholders of the prototype, including patients, clinicians, and designers. Future work focuses on using the recommendations in a co-design process to inform the prototyping of SRF technologies supporting and extending motor abilities in upper limbs.

关键词： Fingers Prototypes Interference Motors Stakeholders Robots

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AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive Modelling

arXiv

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arXiv 2024年

作者： Capstick, Alexander Krishnan, Rahul G. Barnaghi, Payam Department of Brain Sciences Imperial College London United Kingdom UK Dementia Research Institute Care Research and Technology Centre United Kingdom Vector Institute Canada Department of Computer Science University of Toronto Canada Unit The Great Ormond Street Hospital United Kingdom

Large language models (LLMs) acquire a breadth of information across various domains. However, their computational complexity, cost, and lack of transparency often hinder their direct application for predictive tasks where privacy and interpretability are paramount. In fields such as healthcare, biology, and finance, specialised and interpretable linear models still hold considerable value. In such domains, labelled data may be scarce or expensive to obtain. Well-specified prior distributions over model parameters can reduce the sample complexity of learning through Bayesian inference;however, eliciting expert priors can be time-consuming. We therefore introduce AutoElicit to extract knowledge from LLMs and construct priors for predictive models. We show these priors are informative and can be refined using natural language. We perform a careful study contrasting AutoElicit with in-context learning and demonstrate how to perform model selection between the two methods. We find that AutoElicit yields priors that can substantially reduce error over uninformative priors, using fewer labels, and consistently outperform in-context learning. We show that AutoElicit saves over 6 months of labelling effort when building a new predictive model for urinary tract infections from sensor recordings of people living with dementia. © 2024, CC BY.

关键词： Modeling languages

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Deep Learning for Localization of Damaged Photovoltaic Panels with Hotspot Detection

Deep Learning for Localization of Damaged Photovoltaic Panel...

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International Universities Power Engineering Conference (UPEC)

作者： Nur Khalida Maisyara Mohd Shaharin Madiah Binti Omar Nurliana Farhana Binti Salehuddin Rosdiazli Bin Ibrahim Mohamed Nordin Bin Zakaria Mochammad Faqih Computer & Information Science Department Universiti Teknologi PETRONAS Perak Malaysia Chemical Engineering Department Universiti Teknologi PETRONAS Perak Malaysia Renewable Energy Green Technology Unit Kajang Selangor Malaysia Electrical and Electronic Engineering Department Universiti Teknologi PETRONAS Perak Malaysia

ISBN: (数字)9798350379730

ISBN: (纸本)9798350379747

The use of photovoltaic panels has been increasing over the last decade resulting in a growing need for advanced and robust condition monitoring systems. Implementation of photovoltaic panels in a large-scale farm requires predictive maintenance to ensure their reliability and effectiveness. In this study, a deep learning model has been developed for hotspot detection in photovoltaic panels through the processing of thermal images captured by a thermal imaging drone. An advanced convolutional neural network model is used wherein the task of the model is to detect hotspots on the photovoltaic panel, identify the region of interest of the hotspot, and provide a confidence level to determine the quality of hotspots. The associated GPS data will then be employed to geolocate and align these thermal images, creating a comprehensive thermal representation of the entire solar farm. This innovative approach allows for the precise localization of damaged photovoltaic panels within the solar farm, facilitating targeted maintenance and repair interventions, thereby optimizing farm performance and efficiency. This study leverages cutting-edge artificial intelligence and autonomous drone technology to revolutionize solar farm maintenance. Through a well-trained deep learning model, we can achieve precise hotspot detection in photovoltaic panels, minimizing physical interaction for workers and eliminating the need for extensive manual interventions. A deep learning model has been developed that provides hotspot localization with accuracy using bounding box predictions and confidence intervals. The model can reliably identify hotspots in thermal images with accuracy of 80% and confidence level above 90%.

关键词： Photovoltaic systems Deep learning Location awareness Accuracy Solar farms Imaging Predictive models Maintenance Drones Predictive maintenance

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Prediction of Stroke Risk Within 7-Years Follow Up Using Machine Learning Models

Prediction of Stroke Risk Within 7-Years Follow Up Using Mac...

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2023 IEEE EMBS Special Topic Conference on Data science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Tsarapatsani, Konstantina-Helen Sakellarios, Antonis Tsakanikas, Vasilis D. Rudolf, Henrik Trampisch, Hans J. Pezoulas, Vasileios C. Matsopoulos, George K. Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering IoanninaGR 45110 Greece Biomedical Research Institute Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany National Technical University of Athens School of Electrical and Computer Engineering Athens Greece

ISBN: (纸本)9798350383386

A stroke, also known as brain attack, occurs when blood supply to your brain is interrupted. Primary prevention relies on prompt prediction of a stroke. While currently there are several clinical risk scores, machine learning (ML) models seems to be more suitable tools for accurate prediction of stroke events. Therefore, this work focuses on the prediction of stroke within 7 years follow-up in patients who have not suffered from a stroke or TIA event at baseline. LightGBM (LGBM), Extreme Grading Boosting (XGBoost), Support Vector Machine (SVM) and Decision Tree were employed in the getABI dataset, which includes 5,897 participants. The performance of models was calculated by Accuracy (ACC), Sensitivity (SENS), Specificity (SPE) and area under the receiver operating characteristic curve (AUC) of each model. According to the comparison analysis's results, LGBM has been shown to be the most trustworthy algorithm, with accuracy 68 %. Moreover, sex, age, status of peripheral artery disease (PAD), history of myocardial infarction, angina pectoris, amputation and diabetes and pulse status of different arteries can be used as a simple and cost-effective way to predict *** Relevance: A fatal medical emergency, stroke may be anticipated using artificial intelligence, and the sooner it is predicted, the more cerebrovascular disease occurrences can be avoided. © 2023 IEEE.

关键词： Forecasting

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GIS data support technique for forest fire management and decision support system: A Sebangau National Park, Kalimantan case

GIS data support technique for forest fire management and de...

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International Conference on Information and Communications technology (ICOIACT)

作者： Kumara Ari Yuana Kusrini Kusrini Arief Setyanto Arif Dwi Laksito Zauvik Rizaldi Maruf Muhammad Zuhdi Fikri Johari Gardyas Bidari Adninda Renindya Azizza Kartikakirana Fitria Nucifera Wiwi Widayani Theofanis Orphanoudakis Faculty of Computer Science Universitas Amikom Yogyakarta Yogyakarta Indonesia Magister of Informatic Engineering Universitas Amikom Yogyakarta Yogyakarta Indonesia Faculty of Science and Technology Universitas Amikom Yogyakarta Yogyakarta Indonesia Netcompany-Intrasoft A. Research & Innovation Development Unit L1253 Luxembourg

Numerous science and engineering fields are involved when handling forest fire disasters. Social sciences contribute to analyze the human behavior behind the forest fire phenomena, while science and engineering principles can be applied to investigate the physical and environment variables involved. The use of a Geographic Information System (GIS) as a computer system that manages the comprehensive variables influence on the forest fire is proposed is proposed. Capturing, processing and displaying the data generating valuable information related to specific locations is possible through satellite and computer utilities, which turn into powerful assistants for forest fire management with the aid of a decision support system. Data fusion from different sources with different formats and the relevant processing requirements with a large variety of complexities are the challenges that are addressed in the present work. Our proposed method results in a specific informative map with specific characteristics of variables of the forest. These maps are very useful for mitigating forest fire disaster as well as for making informed decisions about optimal resources allocation.

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A New Defect Detection Method for Improving Text Detection and Recognition Performances in Natural Scene Images

A New Defect Detection Method for Improving Text Detection a...

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2020 Swedish Workshop on Data science, SweDS 2020

作者： Mokayed, Hamam Shivakumara, Palaiahnakote Liwicki, Marcus Pal, Umapada University of Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia Lulea University of Technology Department of Computer Science Electrical and Space Engineering Sweden Indian Statistical Institute Computer Vision and Pattern Recognition Unit Kolkata India

ISBN: (纸本)9781728192048

This paper presents a new idea for improving text detection and recognition performances by detecting defects in the text detection results. Despite the rapid development of powerful deep learning based models for scene text detection and recognition in the wild, in complex situations (logos or decorated components connected with text), existing methods do not yield satisfactory results. In this paper, we propose to use post-processing method to improve the text detection and recognition performance. The proposed method extracts features, namely phase congruency, entropy and compactness for the text detection results. To strengthen discriminative power for feature extraction, we explore the combination of SVM classifier and Gaussian distribution of text components to determine proper weight, which represents true text component. The weights are multiplied with the features to detect defect components though clustering. The bounding boxes are redrawn, which results proper bounding box without defects components. Experimental results show that the proposed defect detection reports satisfactory results. To validate the effectiveness of defect detection, we conduct experiments on benchmark datasets of MSRA-TD-500 and SVT for detection and recognition before and after defect detection. The result shows that the performance of text detection and recognition improves significantly after defect detection. © 2020 IEEE.

关键词： Gaussian distribution

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