2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)最新文献

{"title":"Classification of Cacao Pod if Healthy or Attack by Pest or Black Pod Disease Using Deep Learning Algorithm","authors":"Rey Anthony G. Godmalin, Chris Jordan G. Aliac, L. Feliscuzo","doi":"10.1109/IICAIET55139.2022.9936817","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936817","url":null,"abstract":"Cacao farming is a worldwide industry and a vital resource for some businesses. But it is constantly threatened by diseases and pest attacks that can cause significant loss to cacao farmers. Using Artificial Intelligence and Deep Learning Algorithm, an automated recognition of these attacks can help the farmers respond immediately to control this event. This paper used Deep Learning Algorithm to address the automatic classification of a cacao pod condition. An experimental research design method is utilized, and a convolutional neural network is used for training. The model can classify three conditions of a given cacao pod image: healthy, black pod disease attack, and pest attack. Under controlled conditions, the model correctly classifies the cacao pod condition with an accuracy of 94 Thus, using the trained lightweight model, it is possible to accurately and automate the classification of cacao pod conditions. Further study is recommended to integrate it with hardware monitoring/surveillance devices to perform real-time classification of the cacao pod condition on the actual field. With this in place, it can then support fast and immediate responses mitigating the loss of production.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126036769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application Water Level Prediction Through Seasonal Autoregressive Integrated Moving Average: Red Hills Reservoir Case Study","authors":"A. Azad, R. Sokkalingam, H. Daud, S. Adhikary","doi":"10.1109/IICAIET55139.2022.9936784","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936784","url":null,"abstract":"Predicting water levels has become difficult because of spatiotemporal variations in meteorological circumstances and complex physical processes. The Red Hill Reservoir (RHR) serves as an essential derivation of the water system in its locality. It is also anticipated that it would be transformed into other useful services. Climate change in the region, on the other hand, is predicted to have an impact on the RHR's prospects. In a nutshell, accurate water level forecasting is crucial for the reservoir to meet the needs of the population. In this paper, the time series modeling technique is suggested for the water level prediction in RHR using Box-Jenkins autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) models. The models were trained using average monthly water level data from January 2004 to November 2020. The models' performance was analysed with the Akaike information criterion (AIC), mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (R2). The results revealed that among the models, the SARIMA model performed better than the ARIMA model. The selected SARIMA model was further used for forecasting the water level in RHR for 25 months starting from December 2020 to December 2022. The model well predicted the future reservoir levels data.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124736852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of Social Media Hashtag Hijacking Using Dictionary-based and Machine Learning Methods","authors":"Wei Ling Cheah, Hui Na Chua","doi":"10.1109/IICAIET55139.2022.9936788","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936788","url":null,"abstract":"Nowadays, hashtags are widely utilized on all social media platforms since they deliver numerous benefits, particularly for corporations aiming to reach a larger audience. However, hashtag exploitation has resulted in the problem of hashtag hijacking, which is a type of cyber content threat that anyone or any organization can carry out. As a result, this research presents a framework for detecting social media hashtag hijacking through machine learning algorithms. This paper aims to identify methods to classify relevant and irrelevant hashtags to their contents. This paper demonstrates the unsupervised machine learning method, namely the dictionary-based approach, to classify the relevance of hashtags with the content of tweets on an unlabeled dataset, and also the implementation of supervised machine learning methods, including the Support Vector Machine (SVM), Naive Bayes classifier, and Decision Tree algorithms, to classify the relevance of hashtags used with their contents and compare the machine's performances on labeled datasets. Our results showed that the Support Vector Machine (SVM) performs the best in classifying the relevance of hashtags with an accuracy of 93.36%, an F1 score of 96.19% and ROC-AVC score of 97.22 %. The findings of the study present an automated detection framework for hashtag hijacking that can overcome the limitations of previous studies and adapt to external threats with high performance over time.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128848134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conceptualizing A Battery Swapping Station: A Case Study in Malaysia","authors":"Elvin Cheah Ee Sheng, Christina Chin May May, N. Sakundarini, A. Garg","doi":"10.1109/IICAIET55139.2022.9936865","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936865","url":null,"abstract":"One of the key aspects for the successful implementation of electric vehicles (EVs) is a fast and convenient way to recharge the batteries. Currently, there are two solutions (i) a battery swapping station (BSS); and (ii) a charging station. BSS may well be the key to shift Malaysian attention from internal combustion engine (ICE) vehicles to EVs as BSS can refuel depleted batteries faster by swapping it. This paper aims to study the current status of BSS technology in the market and propose a conceptual BSS design suitable for EVs operating conditions in Malaysia. An e-survey was conducted to determine the consumer needs for BSS and its corresponding challenges leading to a conceptual design of BSS for a 4-wheeler vehicle in Malaysia. The BSS was designed via SolidWorks along with its operating framework. The findings also provide appropriate solutions to the challenges discussed in order to encourage the growth of the EVs market in Malaysia.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127845747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of an Electronic Sensor-Based Agility Test System for Badminton Players' Development","authors":"Jeckta Emmi Marrylin Yalin, Hazlihan Haris, M. Hasnan, I. Saad","doi":"10.1109/IICAIET55139.2022.9936872","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936872","url":null,"abstract":"Badminton requires quick movements, high-intensity repeated actions, and precise foot movements to maximize shot accuracy. This emphasizes the need for agility in badminton games. Numerous tests are used to assess agility. However, badminton lacked a specialized agility test based on the game's nature. This project aims to create a modified version of an agility test system specified for amateur badminton players that emphasizes footwork, change of direction speed (CODS), and reactive agility (RA). The ATmega328, Adjustable Infrared Sensor Switch, Force Sensitive Resistor, and RF communication module created Badminton Agility System (BAS). This combination allows players to control the test flow according to their abilities. This study presents a new technological approach to sensor system design for sports measurement, focusing on measuring parameters for assessing agility in amateur athletes. Second is the capability to perform precise and accurate semi-automatic measures in sync with the player's abilities. The system's functioning was tested using a replica court prototype and the actual prototype. The data tested for five participants showed that the mean total time for each direction was between four and five seconds, with a maximum of 25 seconds and a minimum of 15 seconds required to complete the test. This data indicates that the Badminton Agility System optimizes badminton agility testing. Due to the reactive agility factor, the reaction time to different targets revealed that amateur players need more time for forward movement than backward movement. The reaching time and return time have demonstrated significant variances, and the system could communicate between the control part without substantial changes. In addition, the total time spent at each position revealed the participant's areas for future improvement. The test results show that the system can perform the agility process reliably. All three components considered while constructing this test might well be studied in the future.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115871450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Text Classification of Medical Transcriptions using N-Gram Machine Learning Approach","authors":"Lee Kah Win, Gan Keng Hoon","doi":"10.1109/IICAIET55139.2022.9936867","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936867","url":null,"abstract":"Medical domain is in a data rich environment that a variety of knowledge can be extracted for positive outcomes. This research work will show multiclass classification of medical transcriptions using a real dataset. The objective of this paper is to classify medical transcriptions based on the medical specialty labels, namely Discharge Summary, Neurosurgery and ENT. Text normalisation has performed followed by extracting five different n-gram feature representations are. Moreover, three supervised learning classifiers were trained on each of the n-gram feature representations, namely K-Nearest Neighbours, Decision Tree, and Random Forest. The classification performance was evaluated by the metric score of macro F1. The best score achieved was 0.93 macro F1 on testing set using tuned Random Forest and unigram feature vectors.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130843961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Retinal Blood Vessel Feature Extraction in Digital Fundus Images","authors":"Z. H. Nasiruddin, W. Zaki, S. A. Hudaibah, A. H. N. Asyiqin","doi":"10.1109/IICAIET55139.2022.9936842","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936842","url":null,"abstract":"The retinal microvascular network manifests the well-being of other systems and organs as they are structurally and physiologically similar. It offers a unique window to assess numerous disorders such as hypertension, heart disease and nervous system illnesses. However, manually analysing retinal blood vessels in digital fundus images is challenging. In addition, the low contrast images limit the diagnosis of retinal blood vessel-related eye diseases. Thus, this work uses the digital image processing approach to automate the extraction and selection of significant blood vessel features, i.e., the width and pixel intensity of the artery and vein. The digital fundus images are collected from the Digital Retinal Images for Vessel Extraction (DRIVE) database, consisting of twenty 584×565-pixel digital fundus and ground truth images. The proposed method automatically extracts the retinal width and intensity based on the identified coordinates of the blood vessel's skeleton images. Using a one-way ANOVA statistical test computation, we found that the width and the green channel intensity pixel are significant features (p-value <0.005) that can be used to differentiate artery and vein in digital fundus images.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"93 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113977689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved VGG Architecture in CNNs for Image Classification","authors":"Nurzarinah Zakaria, Yana Mazwin Mohmad Hassim","doi":"10.1109/IICAIET55139.2022.9936735","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936735","url":null,"abstract":"Apart from computer vision, deep learning has brought the concept to a new era of machine learning. One of the deep learning approaches for classification analysis is Convolutional Neural Networks (CNNs), a model of artificial neural network that has often been the most popular approach in computer vision. In recent decades, many approaches for image classification have been proposed. To obtain high accuracy, most studies focused on deepening and enlarging the CNNs architecture such as the VGG network. However, deep and complex architecture, on the other hand, can result in extraordinarily long execution time. This study primarily aims to classify images using the improved VGG architecture to minimize the execution time and enhance the classification performance. The comparative experiments of the proposed architecture with another three existing architectures have been made and trained with six different datasets from Kaggle. As a result, the execution time and the classification accuracy of the proposed architecture is better than the other three existing architecture. Hence, the proposed architecture indicates that the execution time and the classification performance can be improved by downsized the VGG architecture.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122152075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of Smart Elderly Care Mobile Application for Health Management System","authors":"Z. Dahari, Choong Chee Jun, Poh Jin Ze, Nurul Najwa Binti Mohd Zakir, Mohd Noor Faidhi Bin Mohd Fauzi, Muhammad Hafiz Syazwan Bin Mohamad Azam, Nurhaniza Hamiri","doi":"10.1109/IICAIET55139.2022.9936853","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936853","url":null,"abstract":"Elderly care management has great potential to be further developed and shaped with smart healthcare solutions. In general, elderly care is defined as a service that serves the needs and requirements of senior citizens. As age progresses, the physical strength, health condition and mental stability deteriorate, especially when people reached a certain age (also known as elderly). It also brings about more medical appointments, medication and health issues. For elderlies, it is not easy to have a systematic management on medical appointments, medications and their health status on their own. It would be difficult for them to update or explain in details on their health conditions to their children or authorized caregivers. In most cases, family members are taking turns and responsibility to take their parents for medical appointments and check-up. It is quite a challenging task to keep up with the health status, medication details and others if more than one person act as the caregivers. This project proposes a smart health management system called Smart Elderly Care App (SECA). The main objective of this project is to develop a mobile apps to facilitate the health management issues. In general, SECA has five main features which are medical appointment, medication, daily health data, health summary, and elderly profile. By having these features, users can shape elderly care with innovative healthcare solutions to help the elderly people and their caregivers in the health management.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123125210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-Time Trajectory Tracking Control of an Electro-Hydraulic System Using a Fuzzy Logic Sliding Mode Controller","authors":"M. F. Ghani, R. Ghazali, H. Jaafar, C. C. Soon","doi":"10.1109/IICAIET55139.2022.9936783","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936783","url":null,"abstract":"This paper presents the trajectory tracking control of an electro-hydraulic actuator (EHA) system using a fuzzy logic sliding mode control approach. To establish the proposed controller, a linear model of the EHA system is determined using the parametric Grey-box identification technique, and the model's parameters are estimated using the MATLAB System Identification Toolbox. Then, a fuzzy logic sliding mode controller is proposed by substituting the signum function with the fuzzy logic function in the conventional sliding mode control algorithm, and the MATLAB Fuzzy Logic toolbox was utilized to design the continuous fuzzy logic function. The stability of the closed-loop system with the proposed controller is assessed using Lyapunov's theory of stability. The control output for the tracking control system was acquired through simulation and real-time implementation in order to evaluate the trajectory tracking control performance. The real-time implementation for Sinusoidal trajectory tracking was conducted on an EHA workbench equipped with a PCIe-6321 card. For trajectory tracking control, experimental results indicate that the proposed controller is more effective than the conventional sliding mode controller.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122858753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}