2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)最新文献

{"title":"IoT Bus Monitoring System via Mobile Application","authors":"Muhammad Fareez Mohd Ainul Hakeem, N. Sulaiman, M. Kassim, N. M. Isa","doi":"10.1109/I2CACIS54679.2022.9815268","DOIUrl":"https://doi.org/10.1109/I2CACIS54679.2022.9815268","url":null,"abstract":"Bus transportation is important for public users and bus waiting is important in time and schedule management. Today, bus transportation schedules are crucial, especially in identifying the available passengers on the bus and the intended passengers cannot view the available seats on the bus. Another problem is tracking bus location sometimes takes time causing passengers to wait for a long time. This paper presents a simple Internet of Things (IoT) prototype for users to view or for authorities to monitor the bus activity via a mobile application on the available bus seats, bus schedule, and bus activities. The design prototype is using the NodeMCU ESP32 controller which communicates using Wi-Fi. IR sensor and GPS module are used for the input sensors. Blynk and cloud applications are used to present the data analysis on mobile apps. The mobile application was designed where users can view the number of passengers on the bus and the location of the bus. The online database is designed to capture all records of the bus passengers entering and leaving the bus. the result presents the GPS module able to get the exact location of the bus and detect its latitude and longitude. Passengers’ activities on entering and leaving the bus are recorded every 5 seconds. The number of passengers has increased to 20 passengers in 3 minutes at one bus stop. The number of passengers leaving the bus also are recorded and analyzed. These activities can be monitored by the authorities which helps for good services, time, and management for the bus transport services.","PeriodicalId":332297,"journal":{"name":"2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114452317","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":"Optimization Algorithms: Who own the Crown in Predicting Multi-Output Key Performance Index of LTE Handover","authors":"Noormadinah Allias, M. M. Noor, Mohd. Taha Ismail, M. Ismail","doi":"10.1109/i2cacis54679.2022.9815466","DOIUrl":"https://doi.org/10.1109/i2cacis54679.2022.9815466","url":null,"abstract":"The Long-Term Evolution network (LTE) has been introduced to cater to the rich content applications of multimedia services. With its ability to support lower latency and higher Throughput, the LTE network can provide faster data download speeds. However, once the mobile user moves from one location to another, the performance tends to degrade. Thus, it required the handover from the serving base station to the target base station. Therefore, the telecommunication service providers must provide a further service enhancement to increase the network quality. As a result, the Key Performance Index (KPI) modeling and predictions can be utilized to achieve this objective. In this article, the Extreme Gradient Boosting regressor algorithm has been selected. However, the hyper-parameter associated with this algorithm needs to be optimized first to produce good prediction results. Three optimization algorithms have been chosen: the Annealing Search, Random Search, and the Tree Parzen Estimator. The experiment results show that the Extreme Gradient Boosting with Annealing Search outperformed the Random Search and the Tree Parzen Estimator by producing the lowest MAE and RMSE and higher R2.","PeriodicalId":332297,"journal":{"name":"2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122071325","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":"Deep Transfer Learning Based Real Time Fitness Movement Identification","authors":"Kuan-Yu Chen, Jungpil Shin, Md. Al Mehedi Hasan, Jiun-Jian Liaw","doi":"10.1109/i2cacis54679.2022.9815456","DOIUrl":"https://doi.org/10.1109/i2cacis54679.2022.9815456","url":null,"abstract":"Sports are full of people’s lives, and regular exercise has become an indicator of people’s health. Due to the high price, most people who exercise at home will not hire fitness trainers, but learn about fitness through media communities. This is likely to lead to the wrong posture of fitness, which can lead to injury. A cheap, simple, and accurate fitness recognition system could increase fitness awareness. This paper proposes a deep transfer learning method that uses Yolov4 to classify fitness movements, which can instantly recognize fitness movements with only one network camera. We built a database, which contains 20 users and online fitness photos, a total of 16302 images, including 12 kinds of fitness movements. 10 user and online photos are used to train Yolov4, and another 10 user photos are used for testing. In the experiment based on Yolov4 to detect fitness, mAP is 99.71%, Precision is 97.9%, Recall is 98.56%, and F1-score is 98.23%. The results show that fitness movements can be detected accurately and quickly using this method.","PeriodicalId":332297,"journal":{"name":"2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124540573","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":"Brain-Computer Interface: Feature Extraction and Classification of Motor Imagery-Based Cognitive Tasks","authors":"H. Nisar, Kee Wee Boon, Yeap Kim Ho, Teoh Shen Khang","doi":"10.1109/i2cacis54679.2022.9815460","DOIUrl":"https://doi.org/10.1109/i2cacis54679.2022.9815460","url":null,"abstract":"Decoding motor imagery (MI) signals accurately is important for Brain-Computer Interface (BCI) systems for healthcare applications. Electroencephalography (EEG) decoding is a challenging task because of its complexity, and dynamic nature. By improving EEG signal classification, the performance of MI-based BCI can be enhanced. In this paper, five features (Band Power (BP), Approximate Entropy (ApEn), statistical features, wavelet-based features, and Common Spatial Pattern (CSP)) are extracted from EEG signals. For classification, Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Artificial Neural Network (ANN) are used. These methods are tested on a publicly available Physionet motor imagery database. The EEG signals are recorded from 64 channels for 50 subjects, while the subject is performing four different MI tasks. The proposed method achieved an accuracy of 98.53% for left and right hands MI tasks with ApEn feature (overlapping ratio~ 0.8) and SVM classifier. Hence the proposed method shows better results than several EEG MI classification methods proposed in the literature.","PeriodicalId":332297,"journal":{"name":"2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121430007","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":"Modelling Workforce For Transportation Sector In Malaysia (Considering Covid-19 Pandemic)","authors":"Mohd Fikri Hadrawi, S. Shariff, Nur Ashikin Muhamad, Nurin Alya Abdullah, Nurshafiqah Ahmad Damanhuri","doi":"10.1109/i2cacis54679.2022.9815486","DOIUrl":"https://doi.org/10.1109/i2cacis54679.2022.9815486","url":null,"abstract":"The Covid-19 pandemic is worrying the workforce, especially in the transportation sector since transportation has been one of Malaysia's crucial sectors. The problem of losing jobs during the Covid-19 pandemic largely contributes to low economic Malaysians, especially in the urgent need for change. Thus, adopting a strategic approach is needed to plan and manage workforce trends to prevent a drop in the economy. This study examines the workforce pattern in the transportation sector in Malaysia, comparing them using time series models and forecasting them using the best fit time series model. It studies explicitly the export and import volume in Malaysia from the year 2010 until 2020 and the number of workforces in the transportation sector in Malaysia from 2012 until 2020. The data were used to model and forecast the export and import volume and the number of workers in the transportation sector in Malaysia. It is found that ARIMA (0, 1, 1) model was able to produce the forecasted values for the year 2020 for export volume in Malaysia based on the values of RMSE and Holt’s (α = 0.34, β = 0.01, γ = 0.3) were able to forecast for export volume in Malaysia when the MAE and MAPE values were considered. Also, it is found that ARIMA (2, 1, 3) model was able to produce the forecast value for import volume in Malaysia for 2020 when the MAE and RMSE were used while Holt’s model (α = 0.41, β = 0.04, γ = 0.5) when MAPE value was considered. Lastly, ARIMA (1,1,1) was used as the selection criteria for forecasting the number of workers in the transportation sector in Malaysia for 2020 when RMSE and MAPE were used Holt’s (α =0.62, β = 0.00000000000000034694) model meanwhile when MAE value was considered.","PeriodicalId":332297,"journal":{"name":"2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121669570","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":"Customer Satisfaction and Service Experience in Big Data Analytics for Automotive Service Advisor","authors":"Syahrul Nizam Samsudin, B. Abdullah, Noriah Yusoff","doi":"10.1109/i2cacis54679.2022.9815482","DOIUrl":"https://doi.org/10.1109/i2cacis54679.2022.9815482","url":null,"abstract":"Service Advisor in Automotive Service Centre plays an important role as the frontline in providing exceptional services. The automotive service centre has to adopt big data applications in understanding customers’ needs by collecting data promptly and analysing scientifically. The objective of this paper is to evaluate Customer Satisfaction (CS) and Service Advisor Experience (SAE) scores via an online survey based on big data analytics. Thus, applying a Quadrifid graph in identifying focus regions for improvement activities. The application of big data online survey platforms is an efficient way of gathering customer feedback for continuous improvement activities. The study focused on Service Advisor (SA) services throughout Malaysia with selected one automotive brand. It explains the definition of customer process and customer satisfaction by comparing high-density customer regions namely Central, Northern and Southern regions with low-density customer regions namely East Coast and East Malaysia regions. There are five steps in deriving the output, which are the consolidation of customer data, customer selection, survey execution, score calculation and analytical report. Thus, the big data applications analyse the expectation SA gap and propose recommendation actions. The online survey results achieved a minimum of 879.90 points for Customer Satisfaction while Service Advisor Experience was minimum at 73%. SA achieved a high score for portraying courtesy and professionalism, while a lack of performing the visual inspection is the main gap for all regions. Detailed analysis using Quadrifid graph interpreted Southern region recorded the lowest correlation with R-square value less than 0.1 and level of CS & SAE below the average value of 800 relates to response towards needs by SA. In this paper, the outcome of the execution is centralization of customer information, Service Level Agreement standard, customer handling norms and work efficiency improvement. Such indicators lead to the SA’s professionalism in managing customer expectations.","PeriodicalId":332297,"journal":{"name":"2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132652351","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":"Background Subtraction for Accurate Palm Oil Fruitlet Ripeness Detection","authors":"David Nathan Arulnathan, Brenda Chia Wen Koay, W. Lai, T. K. Ong, Li Li Lim","doi":"10.1109/i2cacis54679.2022.9815275","DOIUrl":"https://doi.org/10.1109/i2cacis54679.2022.9815275","url":null,"abstract":"Image background subtraction is an important and essential process in many computer vision applications as allows for a more effective processing of the foreground objects. Various methods have been proposed for performing background subtraction in the literature. In this study, we investigated various background subtraction to automatically identify the correct class of the foreground objects. There are only a few major producers of palm oil and Malaysia is the world’s second-largest producer and exporter of palm oil in terms of volume. In 2019, the gross domestic product (GDP) contribution from palm oil in Malaysia was estimated to be around 37.6 billion ringgit to Malaysia’s economy or at 2.7 percent of the country’s GDP. Among the many major industries, it is one of Malaysia’s primary industries, and a main agricultural export. There are various studies to automatically identify fruit ripeness, ranging from mangos to strawberries, etc. In addition, there have been some work in recent years to identify the maturity of the palm oil fruit bunches, and the use of Raman spectroscopy on individual fruitlets, etc. This study investigates the effect of background subtraction on the performance of a deep neural network to accurately identify the ripeness of palm oil fruitlets i.e. ripe, unripe and over ripe. This was compared with a feature based probabilistic approach.","PeriodicalId":332297,"journal":{"name":"2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114955309","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":"Malaysian Banknotes Counterfeit Detection Algorithm for Ten Ringgit and Twenty Ringgit","authors":"Turki Khaled Salem, Wai Kit Wong, Thu Soe Min, E. K. Wong","doi":"10.1109/I2CACIS54679.2022.9815463","DOIUrl":"https://doi.org/10.1109/I2CACIS54679.2022.9815463","url":null,"abstract":"The counterfeit problem with Ringgit become a significant challenge especially with nowadays high definition color printing technology. Hence, watermark-based image processing techniques is crucial to the Ringgit counterfeit detection. This paper presents a Malaysian banknotes counterfeit detection algorithm using fuzzy logic and image processing techniques for ten Ringgit and twenty Ringgit. The algorithm will first identify the currency values of the inserted banknote, perform banknotes position detection and re-adjustment, detect the three watermarks (Watermark Portrait, Perfect See-Through Register, and Color Shifting Security Thread) and uses the Fuzzy IF-THEN conditional statements to inference and make decision whether the inserted banknote is a real ten Ringgit, a real twenty Ringgit or none of them.","PeriodicalId":332297,"journal":{"name":"2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130207953","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":"Use of Fuzzy Logic System for Assessing Optically-Detected NPK Levels","authors":"Vince Andrei S. Hu, M. A. Latina, Jaymick Bryan A. Monido","doi":"10.1109/i2cacis54679.2022.9815270","DOIUrl":"https://doi.org/10.1109/i2cacis54679.2022.9815270","url":null,"abstract":"Soil is the core of agriculture, and the quality of the soil often influences which crops the farmers can and cannot cultivate in a particular area. The inaccessibility of modern agricultural equipment, such as those used to detect soil quality, is one of the factors leading to the Philippines' agriculture sector's downfall. Nitrogen (N), phosphorus (P), and potassium (K) are all significant soil quality indicators since they aid in plant growth and development. In this paper, the researchers formed a technique for determining NPK levels in a given soil sample. NPK levels were determined with an optical transducer and compared to lab-acquired values from soil samples. With a Pearson Correlation Coefficient R-value greater than 0.5, the device reading corresponds and correlates with the findings from the recognized soil testing facility in all nutrients Nitrogen (N), Phosphorus (P), and Potassium (K).","PeriodicalId":332297,"journal":{"name":"2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122410821","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":"Smart Water Meter with Cloud Database and Water Bill Consumption Monitoring via SMS and Mobile Application","authors":"R. S. Alejandrino, Maria Carmela G. Diomampo, Jessie R. Balbin","doi":"10.1109/i2cacis54679.2022.9815483","DOIUrl":"https://doi.org/10.1109/i2cacis54679.2022.9815483","url":null,"abstract":"This study delves upon the design and development of a smart water meter system that provides an IoT-based platform which provides consumption and billing reports in real time. The system is capable of automated data collection and upload phases in times of meter inactivity. Google Apps Scripting (GAS) was used to provide interfacing between the physical prototype, Google Sheets, and a mobile application. A calibrated equation for the determination of water consumption and flow rate is generated using MATLAB Curve Fitting Tool. Having undergone statistical analysis, the volume measurement methods deployed were of no significant difference with each other. Overall, the system was verified to be functional in all aspects of its operation.","PeriodicalId":332297,"journal":{"name":"2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120947485","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}