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4th IEEE International Conference in Power Engineering Applications, ICPEA 2024

作者： Fry, Matthew Islam, Muhammad Usama Islam, Syed Zahurul Uddin, Jasim Cardiff Metropolitan University Department of Applied Computing and Engineering United Kingdom University of Louisiana at Lafayette School of Computing and Informatics United States Universiti Tun Hussein Onn Faculty of Electrical and Electronics Engineering Malaysia

ISBN: (纸本)9798350344578

Recently, the Electric Vehicle (EV) market has experienced significant growth and is projected to expand exponentially with the advancement of technology. Major industries are increasingly adopting the concept of producing hybrid or fully electric cars, resulting in electric vehicles becoming a common sight in today's era. This research examines the potential of wireless power transfer systems as an alternative method of charging electric automobiles. Despite the widespread use of traditional plug-in charging, wireless charging proves to be more effective and convenient. The study evaluates various approaches to integrating wireless charging into an electric vehicle prototype, represented by a remote-control car. It also explores the background and advancements of wireless charging technology. By optimising the design of the transmitter and receiver, based on circuits used for mobile phone chargers, the necessary power (5V, 2A) is provided to the remote-controlled car. Voltage regulators are utilized to manage the output voltage of the receiver circuit during simulations, which assess the circuit's performance under different conditions. The results suggest that wireless charging could serve as a practical and environmentally friendly alternative to conventional charging methods. © 2024 IEEE.

关键词： Transmitters

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A Brief Comparative of Indoor Positioning System 1

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8th International Conference on Computational Science and Technology, ICCST 2021

作者： Safwan, Mohd Aidil Ng, Giap Weng Hasnah, Siti Faculty Computing and Informatics University Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (数字)9789811685156

ISBN: (纸本)9789811685149

Indoor positioning systems is one of the most challenging future for Internet of Things (IoT) applications, especially in smart buildings. On spatial nature of tasks in a built environment, target users localize spatial location with a frame of reference (for example, way-finding location based services at airport using indoor positioning systems). This paper presents a brief study of various Indoor Positioning System (IPS). Comparison of the IPS are carried out and this study finds that the most ideal for current technology used is iBeacon, (BLE) for indoor positioning. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Optimizing Foreign Exchange Trading Performance Through Reinforcement Machine Learning Framework 14

Optimizing Foreign Exchange Trading Performance Through Rein...

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14th International Conference on Computer and Knowledge Engineering, ICCKE 2024

作者： Moung, Ervin Gubin Sufian, Maisarah Mohd Farzamnia, Ali Murnizam, Hani Yasmin binti Liaw, Valentino Angeline, Lorita Faculty of Computing and Informatics University Malaysia Sabah Sabah Kota Kinabalu Malaysia School of Computing and Engineering University of Huddersfield West YorkshireHD1 3DH United Kingdom Faculty of Engineering Universiti Malaysia Sabah Sabah Malaysia

ISBN: (纸本)9798331511272

The ever-changing financial market of foreign exchange attracts many traders. Traders must make wise decisions to avoid significant losses when buying and selling currencies. This project intends to reduce the chance of suffering from loss by providing a trading strategy. The research on developing a trading strategy specifically for the foreign exchange market is still lacking due to the limitation in selecting the best model to create a trading strategy, which is still a working area. Even with current research on trading strategy, it tends not to work overtime due to unpredictable market trends. Therefore, this paper proposed three models using the algorithms A2C, PPO & DQN to find the best strategy in foreign exchange trading, analyze the impact of individual features on the trading strategy and identify the most influential features to develop the best trading strategy using reinforcement learning and finally evaluate the performance on unseen data using Sharpe Ratio, Sortino Ratio, Omega Ratio, Profit & Loss (%), Maximum Drawdown (%) and Cumulative Score. The experiment result showed that the PPO algorithm performed best on 2 of the currency pairs which is GBP/USD and USD/JPY, with a Sharpe Ratio of 0.23 and 0.70, respectively, and a Profit & Loss of 7.4% and 16.78%, respectively, when tested on unseen data. Meanwhile, when tested on unseen data, the A2C model performed the best on the EUR/USD currency pair with a Sharpe Ratio of 0.16 and a Profit & Loss of 3.34%. ©2024 IEEE.

关键词： Reinforcement learning

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Journaling System with Embedded Machine Learning Text Depression Detection Alert 4

Journaling System with Embedded Machine Learning Text Depres...

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4th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2022

作者： Hung, Lai Po Tadius, Esther Mary Universiti Malaysia Sabah Kota Faculty of Computing and Informatics Kinabalu Malaysia

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

Depression is a common mental issue that is suffered by many whether they realised it or not. Early on set of depression could be easily ignored, however if it turns severe, it could result in more dangerous behaviors. Therefore, it is helpful if there could be some warning signs to indicate to a person whether they are depressed or not and enable them to seek help soon. In this work, a journaling system with embedded text depression detection is proposed. Although it cannot be used as a formal diagnosis, but it serves as a warning to the users. A personal journal or diary enables someone to release and express all their inner thoughts and feelings. It is where there are genuine expressions of feelings. By implementing text depression detection in the journal system, honest feelings are able to be captured and analysed to detect any early signs of onset depression. The system will embed a machine learning model which analyses and classifies texts as depressed or non-depressed. An experiment is performed to compare the performance between several machine learners and Logistic Regression showed better performance and was implemented in the system. Tests were performed on the system to evaluate the system functionality and it was seen that the system could perform well and classify texts accurately. © 2022 IEEE.

关键词： Machine learning

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Multiclass Intent Classification for Chatbot Based on Machine Learning Algorithm 7

Multiclass Intent Classification for Chatbot Based on Machin...

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7th International Conference on informatics and computing, ICIC 2022

作者： Hamzah, W. M. Amir Fazamin W. Yusof, Mohd Kamir Ismail, Ismahafezi Makhtar, Mokhairi Nawang, Hasnah Aziz, Azwa Abdul Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Terengganu Malaysia

ISBN: (纸本)9798350345711

In recent years, the use of Chatbots has grown significantly in various industries, including support systems, education, health care, tourism, entertainment, and banking. Chatbot for education can provide instant feedback in interactions session with students. The responses from Chatbot can be based on machine learning algorithms or multiple heuristics techniques to select responses from the predefined library. The generated response depends on the user's intent of using the Chatbot. There are various classes or categories in the user's intent. However, the user's intent class cannot simultaneously belong to multiple classes. Therefore, this research proposed a multiclass intent classification for the Chatbot based on the machine learning algorithm. The findings of this research showed that Linear SVC is the best machine learning algorithm model for multiclass intent classification. The results of the analysis proved the accuracy of the prediction using Linear SVC. © 2022 IEEE.

关键词： Classification (of information)

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An approach for measuring spatial similarity among COVID-19 epicenters

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Geo-Spatial Information Science 2023年第3期26卷 496-513页

作者： Neda Kaffash Charandabi Abolghasem Sadeghi-Niaraki Soo-Mi Choi Tamer Abuhmed Geomatic Department Marand Technical FacultyUniversity of TabrizTabrizIran Department of Computer Science and Engineering and Convergence Engineering for Intelligent Drone Sejong UniversitySeoulSouth Korea College of Computing and Informatics Sungkyunkwan UniversitySuwonSouth Korea

Most countries and territories worldwide are affected by coronavirus disease 2019(COVID-19),and some cities have become known as epicenters owing to high *** of the changeable and unknown nature of the virus,managers of different cities could learn from the experiences of cities that have been successful in controlling COVID-19 instead of wasting time exploring different *** would be even more beneficial if they analyzed the experiences of similar *** similarity of such cities could be examined within a geographic information system based on various *** study investigated the similarities among eight cities-Wuhan,Tehran,Bergamo,Madrid,Paris,Daegu,New York,and Berlin-in terms of the COVID-19 situation(target)in these locations based on proximity factors,weather,and demographic ***,the factor and target layers were prepared,and then similar cities were identified using a similarity model and different distance *** results were aggregated using the Copeland method because of the different outcomes for each *** most similar city was identified for each selected city,and its similarity level was determined based on these *** results suggested the following pairs of similar cities:Wuhan-Berlin,Tehran-Berlin,Daegu-Wuhan,Bergamo-Madrid,Paris-Madrid,and New York-Paris based on COVID-19 related data up to 15 April 2020(target T1),and Daegu-Wuhan,Tehran-Madrid,Bergamo-Paris,Berlin-Paris,and New York-Madrid up to 8 December 2021(target T2)with a minimum and maximum similarity rate of 82.85%and 92.36%,*** similar cities,the most similar factors among the proximity criteria are the distance from bus and metro stations;among weather,the criteria are humidity and pressure;and among demographics,the criteria are male and female population ratios,literacy ratio,and death ratio from asthma and cancer,with a minimum and maximum difference of 0%and 64.94%,*** addition,according to the rando

关键词： COVID-19 GIS spatial similarity environmental factors demographic factors

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Ontology-based Recommender System with Descriptive Analytics in e-Commerce 2

Ontology-based Recommender System with Descriptive Analytics...

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2nd International Conference on Big Data Engineering and Education, BDEE 2022

作者： Haw, Su-Cheng Chew, Lit-Jiew Ng, Kok-Why Naveen, Palanichamy Gandhi, Arfive Martha, Ati Suci Dian Multimedia University Faculty of Computing and Informatics Cyberjaya Malaysia School of Computing Telkom University Bandung Indonesia

ISBN: (数字)9781665481847

ISBN: (纸本)9781665481847

The amount of information and users has been increasing at a remarkable rate in recent years. This is where the recommender system comes in, recommender system is a system that generates a list of recommended products for the user. Recommender system has outshined as one of the important features in an e-Commerce portal. Several recommender techniques have been proposed, yet, problems such as cold-start item problem, cold-start user problem and data sparsity problem still existed. The expected outcomes of this paper are an ontology-based recommender system, combining the collaborative and content-based filtering approaches. In addition, the recommender system also generates recommendations by considering user preferences based on social network association. The experimental evaluation indicated that the proposed approach works more efficiently and effectively improves the recommendation. © 2022 IEEE.

关键词： Recommender systems

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Comparative analysis of Start-up Success Rate Prediction Using Machine Learning Techniques 3

Comparative analysis of Start-up Success Rate Prediction Usi...

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3rd International Conference on Smart Technologies and Systems for Next Generation computing, ICSTSN 2024

作者： Belgaum, Mohammad Riyaz Viswanath, Ediga Kasi Chakradhar, Pavukolla Goud, Ediga Chenna Kesava Chandu, Kuruva G. Pullaiah College of Engineering and Technology Department of Computer Science and Engineering Kurnool India Multimedia University Faculty of Computing and Informatics Cyberjaya Malaysia

ISBN: (纸本)9798350391565

Software startups create innovative products or services using programming, which can lead to a need for products from potential customers. Researchers suggest that trial and error methodology can increase the success rate of these organizations by evaluating assumptions about client needs before fostering a product. However, programming new businesses typically do not require trial and error. The study aims to predict the success of startup companies in their early development stages by analyzing their success based on initial funding. A study of 106 unsuccessful software start-ups was conducted to predict the success rate. The experimentation progression model (X Pro) was developed to show that effective experimentation requires understanding trial and error, setting objectives, conducting tests, analyzing results, and acting based on new knowledge. The findings provide insights into supporting procedures and approaches that enhance trial- and-error reception in developing new business models. Researchers can become aware of potential factors leading to startup disappointment and take precautions to avoid them. The research focuses on using web-based open sources, including social networking services and the web, to model the task. The study uses machine learning techniques like Random Forest, Gradient Boost Classifier, XG Boost, and Ada Boost Classifier. The analysis provides insights into the types of useful signals discovered on the web and market mechanisms underlying the funding process. This approach offers a more comprehensive approach to predicting startup success. © 2024 IEEE.

关键词： Ada (programming language)

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Parkinson's Disease Induced Gain Freezing Detection using Gated Recurrent Units Optimized by Modified Crayfish Optimization Algorithm 5

Parkinson's Disease Induced Gain Freezing Detection using Ga...

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5th International Conference on Mobile computing and Sustainable informatics, ICMCSI 2024

作者： Bacanin, Nebojsa Petrovic, Aleksandar Jovanovic, Luka Zivkovic, Miodrag Zivkovic, Tamara Sarac, Marko Singidunum University Department of Informatics and Computing Belgrade Serbia Singidunum University Faculty of Technical Sciences Belgrade Serbia School of Electrical Engineering Belgrade Serbia

ISBN: (纸本)9798350395235

Parkinson's disease belongs to the group of health problems that are incurable but can be mitigated if treated properly. While there is no way of curing the damage caused by the disease, patient's life quality can be improved if diagnosed and treated properly and on time. The role of artificial intelligence (AI) in medicine is increasing. Deep learning algorithms may be utilized to automatically detect freezing of gait episodes. This study is focused on Parkinson's disease diagnosis based on the disturbances in the patient's gait which is affected by this disease. A hybrid deep and machine learning AI solution that employs the gated recurrent unit (GRU) neural network optimized by a hybrid swarm intelligence solution between the crayfish optimization algorithm and the firefly algorithm has been proposed. The proposed solution is compared to other high-performing optimization algorithms to establish objective grounds for comparison. The proposed framework results in the overall best performance and confirms the made improvements. The best-constructed model attained an accuracy of 87.08%. © 2024 IEEE.

关键词： Patient treatment

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Sarcasm Detection on Social Media Text Using Major Voting Ensemble Approach 4th

Sarcasm Detection on Social Media Text Using Major Voting En...

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4th International Conference on Advances in Computational Science and Engineering, ICACSE 2023

作者： Ibrahim, Muhammad Irham Sia, Florence Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS 88400 Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9789819729760

With the increasing popularity of social media platforms such as Facebook, Instagram, and Twitter, people tend to use those platforms to share their ideas, reviews, and opinions through text. Their comments posted on social media platforms have become a critical source of feedback for many organizations on their programs and services. They may comment freely that occasionally transmit their opposing opinions implicitly through sarcastic text. This can cause the risk of misinterpreting the sarcastic comment which may lead to misunderstanding the context of the comment. Machine learning methods have been widely applied to automatically detect sarcasm in text which improves the analysis of sentiment in the text. The ensemble of these methods has showed the ability to detect sarcasm well compared to the single method. Majority voting is a simple yet widely used ensemble technique for improving classification performance. However, the effectiveness of the ensemble machine learning method based on majority voting has not yet been thoroughly explored in detecting sarcasm on social media text data. This paper proposes an ensemble machine learning model using majority voting for sarcasm detection on Twitter text data. Several experiments have been conducted to evaluate the accuracy performance of the majority voting ensemble-based methods to detect sarcasm in real world datasets consisting of Twitter text. The results show that the ensemble method using hard voting particularly outperformed the single methods in sarcasm detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tweets

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