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7th International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2023

作者： Huseyinov, Ilham Diallo, Issa Raed, Mhd Wasim Software Engineering Department Istanbul Aydin University Istanbul Turkey Graduate Institute of Science Artificial Intelligence and Data Science Program Istanbul Aydin University Istanbul Turkey Computer Engineering Department Istanbul Aydin University Istanbul Turkey

ISBN: (纸本)9783031437885

One way to save time and resources in the human recruitment and hiring process is to post open job positions on the Internet, but the overload of applications creates challenges for hiring managers and companies to select an adequate candidate. One of the solutions is to apply intelligent tools such as Deep Learning and recommender system algorithms to speed up the hiring process and identify the right candidates. In this paper, we propose a two-fold algorithmic approach to 1) building an RNN classifier for resume classification;2) using cosine similarity for resume recommendation to find a candidate that fits job requirements best after selecting all the resumes that belong to the right category. The performance of the proposed RNN classifier is evaluated in terms of accuracy, precision, recall, F1-score, and confusion matrix criteria. The experiment results have shown that the RNN classifier performs better than the other classifiers such as GNB, Linear SVM, RF, and BERT on the same dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Recommender systems

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Sensor Data Analysis in a Real-Time IoT and Blockchain-Based System for Monitoring Driver Alcohol Levels 16

Sensor Data Analysis in a Real-Time IoT and Blockchain-Based...

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16th International Symposium on Electronics and telecommunications, ISETC 2024

作者： Gavrila, Mihaela Fotache, Marin Barbuta, Delia-Elena Alexandrescu, Adrian Murariu, Madalina-Giorgiana Ambrozie, Roxana-Emanuela Tarniceriu, Daniela 'Gheorghe Asachi' Technical University Department of Telecommunication and Information Technologies Iaşi Romania Alexandru Ioan Cuza University Department of Accounting Business Information Systems and Statistics Iaşi Romania 'Gheorghe Asachi' Technical University Department of Computer Science and Engineering Iaşi Romania

ISBN: (纸本)9798350390865

Monitoring alcohol levels of drivers is essential for maintaining public safety, especially for preventing traffic accidents. This paper presents the development and evaluation of a sensor-based module designed to predict breath alcohol levels using a modular sensor battery, integrated with blockchain technology and a data collection platform. The proposed system monitors the driver's location and alcohol level in real-time, preserving data integrity through the use of blockchain. The experimental setup involved varying alcohol concentrations and measuring sensor responses at different distances from the source. The core of this research is the analysis of the collected data, using machine learning techniques, specifically Random Forest and Extreme Gradient Boosting, to assess the reliability and accuracy of the sensor readings. The analysis confirmed a strong correlation between the sensor's readings and actual alcohol levels, demonstrating that the system achieves high accuracy when the sensors are placed within 1000 mm of the source. © 2024 IEEE.

关键词： Data integrity

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Deep Learning for Multivariate Prediction of Building Energy Performance of Residential Buildings

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computers, Materials & Continua 2023年第6期75卷 5947-5964页

作者： Ibrahim Aliyu Tai-Won Um Sang-Joon Lee Chang Gyoon Lim Jinsul Kim Department of ICT Convergence System Engineering Chonnam National UniversityGwangju61186Korea Graduate School of Data Science Chonnam National UniversityGwangju61186Korea Interdisciplinary Program of Digital Future Convergence Service Chonnam National UniversityGwangju61186Korea Department of Computer Engineering Chonnam National UniversityYeosu59626Korea

In the quest to minimize energy waste,the energy performance of buildings(EPB)has been a focus because building appliances,such as heating,ventilation,and air conditioning,consume the highest ***,effective design and planning for estimating heating load(HL)and cooling load(CL)for energy saving have become *** this vein,efforts have been made to predict the HL and CL using a univariate ***,this approach necessitates two models for learning HL and CL,requiring more computational ***,the one-dimensional(1D)convolutional neural network(CNN)has gained popularity due to its nominal computa-tional complexity,high performance,and low-cost hardware *** this paper,we formulate the prediction as a multivariate regression problem in which the HL and CL are simultaneously predicted using the 1D *** the building shape characteristics,one kernel size is adopted to create the receptive fields of the 1D CNN to extract the feature maps,a dense layer to interpret the maps,and an output layer with two neurons to predict the two real-valued responses,HL and *** the 1D data are not affected by excessive parameters,the pooling layer is not applied in this ***,the use of pooling has been questioned by recent *** performance of the proposed model displays a comparative advantage over existing models in terms of the mean squared error(MSE).Thus,the proposed model is effective for EPB prediction because it reduces computational time and significantly lowers the MSE.

关键词： Artificial intelligence(AI) convolutional neural network(CNN) cooling load deep learning energy energy load energy building performance heating load prediction

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Permanent Magnet Wind Generator with Double Excitation for Smart Grids 5

Permanent Magnet Wind Generator with Double Excitation for S...

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5th International Youth Conference on Radio Electronics, Electrical and Power engineering, REEPE 2023

作者： Amuhaya, L.L. Muchuka, Nicasio M. Akuru, Udochukwu B. Kamper, M.J. Obe, E.S. Botswana International University of Science and Technology Department of Electrical Computer and Telecommunication Engineering Palapye Botswana Egerton University Department of Electrical and Control Engineering Nakuru Kenya Tshwane University of Technology Pretoria Campus Department of Electrical Engineering Gauteng South Africa Stellenbosch University Department of Electric and Electronic Engineering Stellenbosch South Africa

ISBN: (纸本)9798350399523

The contribution begins with the attention that there is attention of directly grid connected wind turbine systems with generator designs adapting to the grid constraints. This paper considers permanent magnet synchronous generator topologies with rotor field coils to achieve reduced mass with good efficiency for smart grids. Conventional double-excited synchronous generators have surface mounted permanent magnets. However, the low relative permeability of PMs results in an increased air-gap size. This increased air-gap size limits the minimal copper-mass of the rotor-field-winding. To improve on this limitation, Interior permanent magnets (IPM) are used. Three rotor topologies with IPM are presented in this paper. The analysis of their 2D FEM results and optimization are shown considering their magnetizing current, torque, Back EMF for smart grids application. The three rotor topologies are compared on the overall active mass and their performance by considering PM and rotor copper mass, torque capabilities and efficiency. The spoke-type topology with rectangular magnets achieves the lowest total rotor mass reduction of 39% total mass reduction when compared to the topology with PMs that are buried horizontally. The reduced copper mass and flux variation capabilities are good characteristics in wind energy conversion generators with direct grid connections for smart grid. © 2023 IEEE.

关键词： Topology

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7th International Conference of computer and Informatics engineering, IC2IE 2024

作者： Wandy, Wandy Adi, Kusworo Ayu, Media Anugerah Diponegoro University Doctoral Program of Information Systems School of Postgraduate Studies Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science South Jakarta Indonesia

ISBN: (纸本)9798331505530

Full-marathon and Half-marathon distances are categorized as road running. Full-marathon running is becoming increasingly popular, and Half-marathon is increasing worldwide in both sexes and all age groups. Some aspects might relate to Full-marathon and Half-marathon running performance during training and races. Technology also plays an essential role in supporting runners and running races. Technology like artificial intelligence (AI) now supports the running athlete, not only predicting performance and results. It can also be used later to help the coach generate training programs for the athlete. This research aimed to find many aspects of marathons and performance and analyze them to see if artificial intelligence could later support them. It used secondary data and a systematic literature review proposed by Kitchenham. Out of the 58 articles, 21 of them (36.21%) received a score of 1 from Q1. Additionally, 19 articles (32.76%) received a score of 1 from both Q2 and Q3. Among the 58 articles, 9 (15.52%) received a total score of 3, with all three Q1, Q2, and Q3 scores being 1. This indicates that artificial intelligence will likely support the content of these nine articles. Several factors were also discovered to be connected to marathons and athletic performance. These findings suggested that additional investigation into marathons and performance, later backed by artificial intelligence, remained pertinent and essential. © 2024 IEEE.

关键词： artificial intelligence aspects marathon performance

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An Improvement of A Robust Authentication Protocol for Multi-server Architecture Using Elliptic Curve Cryptography

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International Journal of Network Security 2024年第4期26卷 713-718页

作者： Hwang, Min-Shiang Li, Hou-Wen Yang, Cheng-Ying Department of Computer Science & Information Engineering Asia University Taiwan Fintech and Blockchain Research Center Asia University Taiwan The Ph.D. Program in Artificial Intelligence Asia University Taiwan 500 Lioufeng Rd. Wufeng Taichung Taichung41354 Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung40402 Taiwan Department of Computer Science University of Taipei Taipei Taiwan

The user authentication scheme of the multi-server architecture is mainly that the user only needs to register once to access many distributed servers. This architecture solves the problem of repeated multiple registrations in many servers and can provide a scalable solution. Recently, Zhang et al. proposed a secure, robust remote user authentication scheme for multi-servers using elliptic curve cryptography. However, their proposed scheme can solve many severe flaws and authenticate messages while keeping users anonymous. However, we will show that their scheme couldn’t withstand online password-guessing with a user’s smart card attack and a denial of service attack. This article proposes improving user authentication protocol against these vulnerabilities in Zhang et al.’s protocol. © (2024) All rights reserved.

关键词： Smart cards

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A Quantitative Performance Evaluation Of Machine Learning Algorithms For Analysing Sentiments Of Emoticons 12

A Quantitative Performance Evaluation Of Machine Learning Al...

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12th International Conference on Cloud Computing, Data science and engineering, Confluence 2022

作者： Aditi, G Sharma, Utsav Kumar, Sumit Jadon, Jitendra Singh Amity University Department Of Computer Science And Engineering Uttar Pradesh Noida India Amity University Department Of Electronics And Telecommunication Uttar Pradesh Noida India

ISBN: (纸本)9781665437011

Twitter is one among the most popular platforms for people to express their opinions on a certain issue or product. As it consists of a multitude of users, Twitter is a significant source of data for companies to analyze product reviews, or general public opinion. On Twitter, a tweet can consist of 280 characters. Due to this limitation, users have resorted to using emoticons in a more efficient fashion. This has been achieved by replacing multiple words that indicate an emotion, with a single emoji. Traditional sentiment analysis algorithms and classifiers are highly efficient for usage on text data that does not contain any emojis. This paper evaluates the existing sentiment analysis algorithms in their capability to analyse emoticons in text. © 2022 IEEE.

关键词： Sentiment analysis

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FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable Stocks

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IEEE Transactions on Knowledge and Data engineering 2023年第1期35卷 469-481页

作者： Hsu, Yi-Ling Tsai, Yu-Che Li, Cheng-Te National Taiwan University Master Program in Statistics Taipei10617 Taiwan National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan National Cheng Kung University Institute of Data Science Tainan701 Taiwan

Financial technology (FinTech) has drawn much attention among investors and companies. While conventional stock analysis in FinTech targets at predicting stock prices, less effort is made for profitable stock recommendation. Besides, in existing approaches on modeling time series of stock prices, the relationships among stocks and sectors (i.e., categories of stocks) are either neglected or pre-defined. Ignoring stock relationships will miss the information shared between stocks while using pre-defined relationships cannot depict the latent interactions or influence of stock prices between stocks. In this work, we aim at recommending the top-K profitable stocks in terms of return ratio using time series of stock prices and sector information. We propose a novel deep learning-based model, Financial Graph Attention Networks (FinGAT), to tackle the task under the setting that no pre-defined relationships between stocks are given. The idea of FinGAT is three-fold. First, we devise a hierarchical learning component to learn short-term and long-term sequential patterns from stock time series. Second, a fully-connected graph between stocks and a fully-connected graph between sectors are constructed, along with graph attention networks, to learn the latent interactions among stocks and sectors. Third, a multi-task objective is devised to jointly recommend the profitable stocks and predict the stock movement. Experiments conducted on Taiwan Stock, S&P 500, and NASDAQ datasets exhibit remarkable recommendation performance of our FinGAT, comparing to state-of-the-art methods. © 1989-2012 IEEE.

关键词： Forecasting

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Ontologies in Design: How Imagining a Tree Reveals Possibilities and Assumptions in Large Language Models 25

Ontologies in Design: How Imagining a Tree Reveals Possibili...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Haghighi, Nava Yu, Sunny Landay, James A. Rosner, Daniela Computer Science Stanford University StanfordCA United States Symbolic Systems Program Stanford University StanfordCA United States Computer Science Department Stanford University StanfordCA United States Human Centered Design & Engineering University of Washington SeattleWA United States

ISBN: (纸本)9798400713941

Amid the recent uptake of Generative AI, sociotechnical scholars and critics have traced a multitude of resulting harms, with analyses largely focused on values and axiology (e.g., bias). While value-based analyses are crucial, we argue that ontologies - concerning what we allow ourselves to think or talk about - is a vital but under-recognized dimension in analyzing these systems. Proposing a need for a practice-based engagement with ontologies, we offer four orientations for considering ontologies in design: pluralism, groundedness, liveliness, and enactment. We share examples of potentialities that are opened up through these orientations across the entire LLM development pipeline by conducting two ontological analyses: examining the responses of four LLM-based chatbots in a prompting exercise, and analyzing the architecture of an LLM-based agent simulation. We conclude by sharing opportunities and limitations of working with ontologies in the design and development of sociotechnical systems. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Ontology

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Correlating the Ambient Conditions and Performance Indicators of the LoRaWAN via Surrogate Gaussian Process-Based Bidirectional LSTM Stacked Autoencoder

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IEEE Transactions on Network and Service Management 2023年第3期20卷 3413-3427页

作者： Bhat, Showkat Ahmad Huang, Nen-Fu Hussain, Imtiyaz Sajjad, Uzair National Tsing Hua University College of Electrical and Computer Science Hsinchu1300044 Taiwan National Tsing Hua University Department of Computer Science Hsinchu1300044 Taiwan National Taipei University of Technology Graduate Program in Energy and Opto-Electronic Materials Taipei10608 Taiwan National Taipei University of Technology Department of Energy and Refrigerating Airconditioning Engineering Taipei10608 Taiwan

LoRa's biggest advantage is its flexibility, which is the ability to increase or decrease data rate and range while decreasing or increasing sensitivity. Whenever propagation conditions change frequently, this function allows the spreading factor to be modified accordingly. Despite their efficiency and scalability, adaptive data rate algorithms ignore and fail to factor in the complex correlation between ambient weather parameters influencing the communication channel design. In this research, a Bayesian surrogate Gaussian process-based bidirectional LSTM stacked autoencoder model (BSGP-BLSTM-SAE) is proposed to estimate the channel performance indicators such as received signal strength indicator (RSSI) and signal-to-noise ratio (SNR) and to determine the correlation between the ambient weather conditions and performance indicators for the LoRaWAN network. Bayesian optimization algorithm has been used to optimize the hyper-parameters of the developed model. A LoRaWAN experimental multivariate time series dataset has been used for the evaluation of the developed model, which upon testing and validation produces high accuracy in predicting the channel performance indicators and ambient conditions of the experimental LoRaWAN network. The mean absolute error of the developed model was around 0.45. Thus, the proposed model can predict the link performance indicators and thereby assist in real-time optimization of the transmission parameters to enhance the network performance in LoRaWAN-based systems at different ambient conditions. © 2004-2012 IEEE.

关键词： Internet of things

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