2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)最新文献_第6页

Real Time Bangla License Plate Recognition with Deep Learning Techniques 实时孟加拉车牌识别与深度学习技术

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936764

Mahmudol H. Tusar, Md. T. Bhuiya, M. S. Hossain, Anika Tabassum, R. Khan

{"title":"Real Time Bangla License Plate Recognition with Deep Learning Techniques","authors":"Mahmudol H. Tusar, Md. T. Bhuiya, M. S. Hossain, Anika Tabassum, R. Khan","doi":"10.1109/IICAIET55139.2022.9936764","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936764","url":null,"abstract":"Automatic license plate recognition now plays a critical role in vehicle monitoring and administration system. This system may be applied to car parking and toll collection system, vehicle security, road management, etc. It is one of the most cost-effective solutions for managing or regulating cars on the road or in a car parking area. This paper develops an automatic license plate detection and recognition system using deep learning and transfer learning approaches. Transfer learning was used to educate the model. The open-source dataset of the vehicles has been collected from Kaggle. We also created a custom dataset of our own Bangla license plates, containing around 1 thousand pictures of vehicles. Next, a deep learning model has been used to detect license plates from an image and the optical character recognition technique to extract the information from the detected plates. We choose the You Only Look Once version 5 framework for detecting license plates and EasyOCR to recognize the characters in the number plate. Numerical results demonstrate that the accuracy of license plate detection for YOLOv5 is 98%, and the EasyOCR reached 78% accuracy in recognizing the characters. Finally, the implemented system deployed with Raspberry Pi and Pi camera successfully detects and recognizes the license plate. The overall cost to build this project was approximately USD 200$.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"429 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":"131611318","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}

引用次数: 2

Extracting Graphological Features for Identifying Personality Traits using Agglomerative Hierarchical Clustering Algorithm 基于聚类层次聚类算法的人格特征提取

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936858

N. Yusof, N. Z. Zulkarnain, Sharifah Sakinah Syed Ahmad, Zuraini Othman, Azura Hanim Hashim

引用次数: 0

A Hybrid Biosignal Compression Model for Healthcare Sensor Networks 医疗传感器网络的混合生物信号压缩模型

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936793

T. Dheepa, K. Sekar, Satish Kumar Satti, Goluguri N. V. Rajareddy

{"title":"A Hybrid Biosignal Compression Model for Healthcare Sensor Networks","authors":"T. Dheepa, K. Sekar, Satish Kumar Satti, Goluguri N. V. Rajareddy","doi":"10.1109/IICAIET55139.2022.9936793","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936793","url":null,"abstract":"Recent development in wearable sensor technology helps to collect biological signals at a low cost. Collecting and analyzing different biomarkers are anticipated to improve the preventative health care system through customized medical applications. The wearable sensors are battery-operated and based on technology with restricted resources, and they must use simple approaches to handle storage and energy properly. To achieve this goal, apply a lossy predictive coding-based method to compress signals at the sensors to reduce the energy needed to transmit data, minimize the storage space required, and extend battery life. This paper proposes a combination of Long-Short-Term-Memory(LSTM) and XGBoost-based hybrid model to address the challenge of sparse signal reconstruction in terms of multiple sampling vectors under compressed sensing, based on the assumption that the signal vectors are jointly correlated. The Proposed model achieves better compression efficiency than the baseline models considered for comparison and minimizes the energy consumption and storage space required. The performance results show that the proposed model extends the lifetime of the sensors and HSN.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"3 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120818662","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}

引用次数: 1

Modelling and Simulation of Bioretention System with HYDRUS-1D 基于HYDRUS-1D的生物滞留系统建模与仿真

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936812

Jason Lowell Jitolis, Farrell Nereus Aegidius, N. Bolong

{"title":"Modelling and Simulation of Bioretention System with HYDRUS-1D","authors":"Jason Lowell Jitolis, Farrell Nereus Aegidius, N. Bolong","doi":"10.1109/IICAIET55139.2022.9936812","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936812","url":null,"abstract":"The design of lab-scale bioretention cell column was constructed based on Urban Stormwater Management Manual for Malaysia (MSMA) specifications. The stormwater runoff flowrate applied to each column was calculated to mimic the actual scale impervious area for generation of runoff. An inflow and outflow of water was measured using water flow sensor, simulating rainfall runoff correlation with depth and hydraulic conductivity parameters effects. A model to simulate the water movement beneath the engineered soil media, one dimensional (l-D) model of water flow was used to study the effect of different media depth and rainfall intensity on hydraulic conductivity parameter value. It resulted that at lower rainfall intensity of 5.3mm/min small percentage of runoff volume reduction was observed at low height of media (150mm) with a total of 11% compared to 55% of 250 mm media height. The recommended media depth values for moderate storm event are <250mm but not less than 150mm to achieve half of the volume to be treated and maintain the acceptable contact time for higher treatment capabilities. An average of 0.03mm/min hydraulic conductivity is suitable for moderate rainfall scenario as simulate by HYDRUS 1D. On the other hand, results at higher rainfall intensity (12mm/min), no large deviation was observed in terms of percentage of runoff volume reduction between both heights. The thickness ranges are not within the required control volume runoff. Thus, further optimization at lower depth is essential.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"46 9 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":"125706770","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}

引用次数: 0

A Feature-based Stochastic Morphological Analyzer for Filipino Affixed Words 基于特征的菲文贴贴词随机形态分析仪

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936850

G. A. Ong, Melvin A. Ballera

引用次数: 0

Adapting Perturbation Voltage For Variable Speed Micro-Hydro Using Particle Swarm Optimization (PSO) 基于粒子群优化(PSO)的变速微水电扰动电压自适应

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936753

Kit Guan Lim, Mohd Izzat Fikri Md Zainal, M. K. Tan, A. Haron, C. Chai, K. Teo

{"title":"Adapting Perturbation Voltage For Variable Speed Micro-Hydro Using Particle Swarm Optimization (PSO)","authors":"Kit Guan Lim, Mohd Izzat Fikri Md Zainal, M. K. Tan, A. Haron, C. Chai, K. Teo","doi":"10.1109/IICAIET55139.2022.9936753","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936753","url":null,"abstract":"The aim of this research is to explore a technique that can be implemented to the Variable Speed Micro-hydro Power Generation (VS-MHPG) system to search the optimum operating point for the maximum power extraction. Micro-Hydro that operates in variable speed mode are sensitive to the changes of flow rate and proved to have wide operating point. The Perturb and Observe (P&O) based maximum power point tracking (MPPT) was applied to the VS-MH and based on simulation. However, oscillation occur at maximum point due to the large perturbation speed. The existing Micro-hydro Power Generation (MHPG) system commonly suffers from the non-optimal input control as the controller estimate the changes of flow rate without anticipating the global maximum power curve. Hence the implementation of P&O based MPPT is expected to improve the efficiency of MHPG system while reducing the fluctuation of output power. Results show that the value of perturbation speed affects the performance of MPPT algorithm to search the maximum operating point. Low perturbation signal requires many numbers of iteration before it reaches the steady state. Meanwhile, high perturbation signal will cause the fluctuation that led to unstable power production. Thus, new method was introduced which is Particle Swarm Optimization (PSO) that is expected to improve the performance of conventional MPPT. Simulation result shows that PSO based MPPT was able to track the global maximum point under extreme condition with no power fluctuation compared to P&O MPPT. Also, PSO based MPPT provides adaptive perturbation speed that show improvement in maximum power tracking by 20.88%.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"358 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":"134500744","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}

引用次数: 0

Computer-assisted Table Tennis Posture Analysis using Machine Learning 使用机器学习的计算机辅助乒乓球姿势分析

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936806

Mel Jay Llanos, Jecee Ryn Obrero, Lhora Mae Alvarez, Chun-Hung Yang, Chris Jordan G. Aliac

{"title":"Computer-assisted Table Tennis Posture Analysis using Machine Learning","authors":"Mel Jay Llanos, Jecee Ryn Obrero, Lhora Mae Alvarez, Chun-Hung Yang, Chris Jordan G. Aliac","doi":"10.1109/IICAIET55139.2022.9936806","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936806","url":null,"abstract":"Manual assessments for table tennis players, done in person or virtually, can be tedious, inefficient, and error-prone. Existing machine learning software tries to eliminate these gaps; however, its capability is only limited to one technical skill at a time. In this study, a software was developed to help assess the key technical skills of a table tennis player: (1) upper body position (leaning and not leaning), (2) lower body position (knees bending and straight), (3) basic hand strokes (forehand and backhand), and (4) footwork (side-to-side and in-and-out); these four would be used as performance metrics for the video input. Datasets of five OpenTTGames videos depicting professional player's postures and three videos from YouTube portraying amateur player's postures were extracted into frames, resulting to 32,395 frames. Posture detection was first carried out on the extracted frames using OpenPose library, generating a total of 519,320 key points. Then, various machine learning models were trained using the key points for posture analysis, and their performances were compared and benchmarked. The models with the highest accuracies would be integrated into one assessing model. Among Backpropagation, SVM-Linear, SVM-RBF, and SVM-Polynomial models, the SVM-RBF model yielded the highest in all performance metrics: 95.03% for upper body, 95.28% for lower body, 95.72% for hand stroke, and 92.78% for footwork. These results indicated that the software successfully assessed the player's posture, providing relevant data for coach's assessment of the player's performance. This software will help coaches and players analyze and evaluate their performance for improvements in lacking areas.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"73 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":"114477602","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}

引用次数: 0

A Safe Overtaking Control Scheme for Autonomous Vehicles using Rapid-Exploration Random Tree 基于快速探索随机树的自动驾驶汽车安全超车控制方案

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936834

Yincong Ma, Kit Guan Lim, M. K. Tan, H. S. Chuo, L. Angeline, K. Teo

{"title":"A Safe Overtaking Control Scheme for Autonomous Vehicles using Rapid-Exploration Random Tree","authors":"Yincong Ma, Kit Guan Lim, M. K. Tan, H. S. Chuo, L. Angeline, K. Teo","doi":"10.1109/IICAIET55139.2022.9936834","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936834","url":null,"abstract":"In order to enhance the commuting ability of autonomous vehicles on the road and ensure the comfort and safety of passengers, the Rapid-exploration Random Tree (RRT) algorithm is applied to the research of safe overtaking control of autonomous vehicles. Firstly, the kinematics and dynamic model of the vehicle are implemented. Secondly, the RRT algorithm and the A-star algorithm are expounded, and the idea of the A-star algorithm is applied to the RRT algorithm for improvement. The improved algorithm is used to obtain the rough obstacle avoidance of the vehicle. The rough path is optimized by applying the cubic spline interpolation method to solve the problem that the path cannot be applied to the actual vehicle driving task. Finally, the simulation of the overtaking scheme is carried out. The results reveal that the safe overtaking scheme based on RRT algorithm achieves the predetermined requirements in the two actual cases under the premise of ensuring safety, controlling the swing of the sideslip angle of the vehicle's center of mass within a reasonable range. It has certain practical significance for ensuring the road safety of autonomous vehicles.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"101 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":"128087073","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}

引用次数: 0

River Water Level Prediction for Flood Risk Assessment using NARX Neural Network 基于NARX神经网络的洪水风险评估中河流水位预测

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936739

Zizi Zulaikha Zulkifli, Mazlina Mamat, H. T. Yew

{"title":"River Water Level Prediction for Flood Risk Assessment using NARX Neural Network","authors":"Zizi Zulaikha Zulkifli, Mazlina Mamat, H. T. Yew","doi":"10.1109/IICAIET55139.2022.9936739","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936739","url":null,"abstract":"Flood is one of the primary natural disasters in Malaysia and becoming more frequent and on a large scale lately. Not excluded, Sabah encounters repeated floods caused by river overflow. Therefore, an efficient mechanism for flood risk assessment is needed until a more viable solution exists. This paper proposes using the Nonlinear Autoregressive with Exogenous Input (NARX) neural network to model the river water level as an approach for assessing flood risk. The NARX was trained, validated, and tested using the hydrological data obtained at the target areas: Wariu River (Sungai Wariu), Kota Belud, and Padas River (Sungai Padas), Beaufort. Inputs to the NARX are the current and previous water levels at the upstream and downstream rivers and rainfall at the target area. The output is the predicted water level at the downstream river that can be used to assess flood risk. Results show that NARX trained with the Levenberg-Marquardt training algorithm (trainlm) performs best compared to other training algorithms. Results also show that the NARX could predict up to thirty days ahead of water level prediction, with an R2 of 0.75 and above. However, it is more safe to conclude that a reliable prediction for up to five days ahead, with R2 above 0.85 can be obtained.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"72 6 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":"133190487","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}

引用次数: 0

Depression Detection on Social Media With User Network and Engagement Features Using Machine Learning Methods 使用机器学习方法在具有用户网络和参与特征的社交媒体上检测抑郁症

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936814

Aik Seng Liaw, Hui Na Chua

{"title":"Depression Detection on Social Media With User Network and Engagement Features Using Machine Learning Methods","authors":"Aik Seng Liaw, Hui Na Chua","doi":"10.1109/IICAIET55139.2022.9936814","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936814","url":null,"abstract":"Depression is a complicated mental health disorder with many different forms and symptoms. Traditional methods face barriers when detecting and diagnosing depression, including social stigma and societal labeling. As social media platforms became commonplace for information sharing, their anonymity meant that the barriers had considerably lessened. An alternative method to depression detection being researched is using social media data to build machine learning models for depression detection. To that end, this research uses machine learning models to incorporate new user networks and user engagement features into depression detection on Twitter users. These two features provide an additional understanding of users and may significantly affect depression detection. A Twitter dataset is constructed to include additional data on users' following list and the history of liked tweets not examined in prior studies. Ten machine learning models are constructed using five different machine learning algorithms tested on two sets of features. Models with proposed features outperformed other machine learning models without proposed features, with the best model yielding 82.05% performance for both accuracy and F1 score. This study discovered that the most important feature is the number of depression keywords in liked tweets, with at least twice the gain compared to 88% of other features used. Topic modelling features for liked tweets also have high gain and are important in detecting depression. Additionally, features derived from original tweets, replies, and liked tweets have higher gain and are more important than retweets and quote tweets in detecting depression.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"23 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":"116771252","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}

引用次数: 2