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10th International Conference on Digital Arts, Media and technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2025

作者： Intharasompong, Sornthun Jitpattanakul, Anuchit Mekruksavanich, Sakorn Wongwiwat, Piyada Phaphan, Wikanda King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Thailand University of Phayao School of Information and Communication Department of Computer Engineering 56000 Thailand Suansunandha Rajabhat University Faculty of Science and Technology Bangkok10800 Thailand Research Group in Statistical Learning and Inference Thailand

ISBN: (纸本)9798331543273

Small and medium-sized enterprises (SMEs) are frequently considered high credit risk, making it difficult to obtain loans from banks. A viable substitute for assessing borrower creditworthiness more impartially is using machine learning-based credit risk assessment (CRA) algorithms. This study evaluated the performance of five machine learning techniques: decision tree, support vector machine, gradient boosting, K-nearest neighbor, and Naïve Bayes. We evaluated each model with K-fold cross-validation and utilized the Stepwise selection method for the variable selection. To address the underlying data imbalance, we balanced the dataset with the SMOTE technique, which showed that normal loans were more widespread than Non-Performing Loans (NPLs). After balancing the data, the Gradient Boosting model displayed the best accuracy, F-measure, and robustness without variable selection, making it the most effective model for predicting SME loans. © 2025 IEEE.

关键词： Risk assessment

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Maize Crop Yield Prediction Using Machine Learning Regression Approach 5th

Maize Crop Yield Prediction Using Machine Learning Regressio...

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5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Bedane, Tarikwa Tesfa Kebede, Kidistie Bizuneh Mohapatra, Sudhir Kumar Das, Tapan Kumar Tripathy, Asis Kumar Department of Software Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Agricultural Transformation Institute Addis Ababa Ethiopia Faculty of Emerging Technologies Sri Sri University Cuttack India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819735228

Given maize’s significant role as a staple crop, it becomes imperative to carry out precise crop yield prediction to ensure food security. This research employs machine learning algorithms to analyze historical data related to maize crop yields, with the aim of developing a predictive model. The outcome of this study can serve as a valuable tool for pre-harvest planning and measuring crop production per unit of land area. The dataset utilized in this research spans from 2010 to 2021 and was sourced from Ethiopia’s Central Statistical Agency. Three machine learning regression algorithms—Logistic Regression, XGBoost, and Random Forest—were employed to design the predictive model. After data preparation and preprocessing, a total of 436,358 records were used for model development. The models were further trained with reduced features using principal component analysis (PCA). The experimental results demonstrate that the Random Forest algorithm, trained with a reduced number of features through PCA, yielded superior performance, with a lower error rate and a high R2 score of 0.98. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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The Necessary Requirements for Utilizing AI in Data Access Setting 15

The Necessary Requirements for Utilizing AI in Data Access S...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Swapnil, K. Sharma, Bhuvnesh Singh, Manpreet Yadav, Rakesh Kumar Devaraj Verma, C. Umarani, K. Vishwakarma Institute of Technology Department of Artificial Intelligence &data Science Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India Maharishi University of Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Ramnagar District562112 India Chitkara University Institute of Engineering and technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

AI has been an increasing number of adopted in records get admission to settings to improve the overall performance of statistics get admission to and extract significant insights, using extended productiveness and value-effectiveness. However, the success implementation of AI calls for positive essential requirements to be met, together with facts accuracy, connectivity, security, and scalability. First, facts accuracy is critical for AI applications in facts get entry to settings. information gathered need to be of excessive quality and accuracy, as AI working on erroneous facts can yield unreliable and probably harmful effects. Records should be of suitable granularity, taking pictures the most relevant elements, to make certain a complete and steady training performance.2nd, linked statistics assets, networks, and services are had to leverage the capability of AI in information get entry to settings. This gives an interconnected and up to date dataset at scale, while requiring included answers for uninterrupted get entry to. Moreover, single sign-on and alertness orchestration abilities are vital to decorate user revel in. © 2024 IEEE.

关键词： Connectivity Gathered Increasing Productiveness Security

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Development of Remote Laboratory system with Multi-degree Viewing Access in Support for Distance Learning Initiative 7th

Development of Remote Laboratory System with Multi-degree Vi...

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7th International Conference on Mechanical, Manufacturing and Plant Engineering, ICMMPE 2021

作者： MohdZubir, Mohd Nashrul Mahmud, Faisal Hakeem Azman, Azfar Yazed Ahmad, Mohd Obaidellah, Unaizah Abdullah, Zuraidah Adib Kadri, Nahrizul Ahmad, Norhafizan Ali, Mohammed A. H. Department of Mechanical Engineering Faculty of Engineering Universiti Malaya Kuala Lumpur Malaysia Faculty of Computer Science and Technology Universiti Malaya Kuala Lumpur Malaysia Department of Biomedical Engineering Faculty of Engineering Universiti Malaya Kuala Lumpur Malaysia Department of Artificial Intelligence Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Educational Management Planning and Policy Faculty of Education Universiti Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9789811933066

The project highlights the work on establishing a remote laboratory system that can be operated via the web as part of the effort to support online distance learning programs. To achieve this objective, integration of the hardware, software, and network communication modules was performed, enabling real-time remote access and interaction between the user and the remote infrastructure. The entire system was locally developed which requires linking between a web application software and server software to ensure uninterrupted online communication during the remote activities. The physical interaction within the lab was realized via human interface machine and microcontroller which translate the command given by the user from the web into set of instructions to control and operate the lab. The current prototype has solved the persisting challenges in implementing web-based remote control systems namely data lagging and false data acquisition via the use of server systems and data transfer algorithms. This strategy ensures data integrity and reliability to be displayed to the user in real-time. A new robotic viewing system has been developed and incorporated within the laboratory infrastructure. This new feature allows more freedom in coordinating user viewing flexibility which is crucial in capturing and mimicking the user presence in the lab and during the experimental conduct. The above results provide welcoming perception on the impact of implementing remote labs as part of the continuous efforts to liberate the education platform and promote education without boundaries. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Application programs

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Nonlinear Inverse Heat Transfer Problem 2

Nonlinear Inverse Heat Transfer Problem

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2021 2nd International Symposium on Automation, information and Computing, ISAIC 2021

作者： Rysbaiuly, Bolatbek Alpar, Sultan Mathematical and Computer Modeling Department International Information Technology University Faculty of Computer Technology and Cybersecurity 34/1 Manas street Almaty050040 Kazakhstan

In this work, a method for finding nonlinear heat-conducting characteristics of soil is developed. Two-layer complexes of containers were created, the side faces of which are thermally insulated, so the 1D thermal conductivity equation is used. A temperature sensor is placed at the junction of the two media, and a mixed boundary value problem is solved in each area. In order to provide the inverse coefficient problem with initial data, two temperature sensors are used: one sensor was placed at the open border of the container and recorded the soil temperature at this border, and the second sensor was placed at a short distance from the border, which recorded the air temperature. The measurements were carried out in the time interval (0,4t max ). First, the initial-boundary value problem of thermal conductivity with nonlinear coefficients is investigated by the finite difference method. Two types of difference schemes are constructed: linearized and nonlinear. The linearized difference scheme is implemented numerically by the scalar Thomas method, and the nonlinear difference problem is solved by the Newton method. The solution of the linearized difference problem was taken as the initial approximation of the Newton method. To find the thermophysical parameters, the corresponding functional is minimized using the gradient descent method. In addition, all thermophysical characteristics (8 coefficients) were found for a two-layer container with sand and chernozem. © Published under licence by IOP Publishing Ltd.

关键词： Containers

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Corporate Reputation Risk in Social Media 2

Corporate Reputation Risk in Social Media

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2nd International Conference on computer science and Engineering, IC2SE 2021

作者： Hamidi, Saidatul Rahah Akmar Ismail, Maizatul Shuhidan, Shuhaida Mohamed Faculty of Computer Science and Information Technology Department of Information System Kuala Lumpur50 603 Malaysia Faculty of Computer and Mathematical Sciences Department of Computer Science Shah Alam Selangor40450 Malaysia

ISBN: (纸本)9781665400459

In today's world, digital platforms such as social media have become one of the most influential media sources. This may give an impact on company reputation due to massive user-generated content. However, due to its vast volume and unstructured nature, manual classification of these digital contents is difficult. Previous studies, evaluate corporate reputation using machine learning-focused mostly on positive, neutral, and negative aspects and lacking the multidimensional nature of corporate reputation. Therefore, this paper aimed to explore the integration of the domain ontology approach with machine learning approaches to achieve multi-dimensional classification for online corporate reputation. Overall research route to measure corporate reputation in social media is discussed. The proposed research route starts from retrieving corporate content from digital media for selected domain, next preprocessing data before topics and bag-of-words generated from the extracted data and mapping according to corporate reputation dimensions. The proposed approach is expected to produce a framework for an online corporate reputation that can be used across multiple business domains. © 2021 IEEE.

关键词： Sentiment analysis

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Facial Emotion Based Automatic Music Recommender system

Facial Emotion Based Automatic Music Recommender System

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2023 International Conference on Sustainable Emerging Innovations in Engineering and technology, ICSEIET 2023

作者： Singh, Mahesh Kumar Singh, Pushpendra Sharma, Amit ABES Engineering College Computer Science Department UP Ghaziabad India Ims Engineering College Information Technology Department UP Ghaziabad India

ISBN: (纸本)9798350329186

Listening to music can change the mood of any individual based on type of song like classic, pop or hip-hop. Music can have very strong influence on an individual's emotions. These emotions can be recognized by extracting various facial image features like Eye or Lip movement. Using facial expressions, meaning and context of words can be clearly understood by Humans. With the quick improvement of versatile systems and advanced interactive media innovations, advanced music has gotten to be the standard customer substance looked for by numerous youthful people. People frequently utilize music for mood regulation, more specifically to change a bad mood, to get motivated, to focus on their work, to increase energy levels or to reduce tension. Moreover, tuning in to the correct kind of music at the proper time may progress mental wellbeing. Recommendation of different songs based on an individual's current facial expressions could be latest research problem within the field of image processing. Very few researchers have proposed the solution of this research problem through automatic facial recognition using convolution neural network (CNN) models. In this paper, the proposed system based on CNN model focused on detecting human emotions from face image to identify list of songs to be played through music player to swing user's mood. The objective of this study is to improve the accuracy and reduce time and cost of proposed system for music recommendation. © 2023 IEEE.

关键词： Emotion Recognition

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ThermalEye: A Low-Resolution Thermal-Imaging Based Artificial Intelligence Self-Harm Behavior Detection system in Psychiatric Seclusion Rooms 13

ThermalEye: A Low-Resolution Thermal-Imaging Based Artificia...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Chang, Wan-Jung Chen, Liang-Bi Chen, Ming-Che Chen, Yi-Jia Su, Jian-Ping Liao, Bo-Hao Chou, Hsin-Hus Tsai, Pei-Ching National Kaohsiung University of Science and Technology Department of Electronic Engineering Kaohsiung Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu Taiwan Southern Taiwan University of Science and Technology Department of Electronic Engineering Tainan Taiwan Ditmanson Medical Foundation Chia-Yi Christian Hospital Chia-Yi Taiwan

ISBN: (纸本)9798350355079

This paper proposes an Artificial Intelligence (AI) self-harm behavior detection system called ThermalEye. By using AI to recognize the skeleton of human body in low-resolution thermal images, and self-harm behavior recognition algorithms, the ThermalEye system can instantly recognize the self-harm behaviors of patients in the psychiatric seclusion rooms of the hospital. Experimental results demonstrate that the ThermalEye system can significantly improve the accuracy of skeleton recognition in low-resolution thermal human images and accurately and immediately detect when patients engage in self-harm behavior, thereby improving the hospitalization safety of people with a mental health condition. © 2024 IEEE.

关键词： Musculoskeletal system

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Optimization Ensemble Weights Model for Wind Forecasting system

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computers, Materials & Continua 2022年第11期73卷 2619-2635页

作者： Amel Ali Alhussan El-Sayed M.El-kenawy Hussah Nasser AlEisa M.El-SAID Sayed A.Ward Doaa Sami Khafaga Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Electrical Engineering Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Delta Higher Institute of Engineering and Technology(DHIET) Mansoura35111Egypt Department of Electrical Engineering Faculty of Engineering at ShoubraBenha UniversityCairo11629Egypt Faculty of Engineering Delta University for Science and TechnologyGamasaMansoura11152Egypt

Effective technology for wind direction forecasting can be realized using the recent advances in machine ***,the stability and safety of power systems are expected to be significantly ***,the unstable and unpredictable qualities of the wind predict the wind direction a challenging *** paper proposes a practical forecasting approach based on the weighted ensemble of machine learning *** weighted ensemble is optimized using a whale optimization algorithm guided by particle swarm optimization(PSO-Guided WOA).The proposed optimized weighted ensemble predicts the wind direction given a set of input *** conducted experiments employed the wind power forecasting dataset,freely available on Kaggle and developed to predict the regular power generation at seven wind farms over forty-eight *** recorded results of the conducted experiments emphasize the effectiveness of the proposed ensemble in achieving accurate predictions of the wind *** addition,a comparison is established between the proposed optimized ensemble and other competing optimized ensembles to prove its ***,statistical analysis using one-way analysis of variance(ANOVA)and Wilcoxon’s rank-sum are provided based on the recorded results to confirm the excellent accuracy achieved by the proposed optimized weighted ensemble.

关键词： Guided Whale Optimization Algorithm(Guided WOA) forecasting machine learning weighted ensemble model wind direction

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Analysis of Duplicating the Human Mind Suspicions in AI in Various Divisions

Analysis of Duplicating the Human Mind Suspicions in AI in V...

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2025 International Conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Santhoshkumar, S.P. Karthika, R. Vijayakumar, N. Gajalakshmi, S. Jayanthi, K. Hariharasudhan, S. School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India School of Computing Department of Information Technology Rathinam Technical Campus Coimbatore India Pollachi Institute of Engineering and Technology Pollachi Coimbatore India

ISBN: (纸本)9798331509828

Uncertainty in AI refers to the degree of confidence or probability associated with the accuracy or effectiveness of an AI system's output. It is a measure of how much the AI system is unsure about the correctness of its output or decision-making. Uncertainty can arise in AI systems due to a variety of factors, including the quality of input data, variations in data formats, variations in context or environment, and the complexity of the underlying algorithms. Different AI applications can have specific types of uncertainty associated with them, as we discussed in previous questions about uncertainty in AI image recognition, natural language processing, data extraction, expert systems, planning and optimization, and robotics. To mitigate uncertainty in AI systems, researchers and developers use various methods, such as incorporating more data and diverse inputs, improving the quality of input data, enhancing the algorithms, and using feedback mechanisms to learn from errors and improve the system over time. However, complete elimination of uncertainty is often challenging or impossible, and users should always be aware of the potential for errors or unexpected behaviour in AI systems. In this paper, analysis of artificial intelligence uncertainty in various fields. © 2025 IEEE.

关键词： Expert systems

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