2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)最新文献

Literature Study of Face Recognition using The Viola-Jones Algorithm 基于Viola-Jones算法的人脸识别研究

2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS) Pub Date : 2021-04-28 DOI: 10.1109/AIMS52415.2021.9466010

M. F. Hirzi, S. Efendi, R. Sembiring

{"title":"Literature Study of Face Recognition using The Viola-Jones Algorithm","authors":"M. F. Hirzi, S. Efendi, R. Sembiring","doi":"10.1109/AIMS52415.2021.9466010","DOIUrl":"https://doi.org/10.1109/AIMS52415.2021.9466010","url":null,"abstract":"The face is the front part of a human expression comprising the eyes, nose, lips, cheeks, forehead, and chin. These characters are uniquely placed according to a human's pattern of face. The Viola-Jones algorithm is used to recognize and detect objects, including this human face. It consists of several stages, such as Haar-like Filter, Integral Image, Adaboost Algorithm, and Cascade. Haar-Like filter is used to determine feature values from images containing certain objects. Furthermore, integral image helps to find the feature value to quicken the calculation process. Adaboost algorithm processes feature selection by determining the threshold value in order to determine the existing object. Meanwhile, cascade performs an image selection process that contains or excludes objects with large amounts of test data. It directly discards the figure when no objects are detected to produce images containing objects. This study is a literature review on facial recognition using the Viola-Jones algorithm. It contributes to the search for the suitability of using the Viola-Jones Algorithm in certain cases. The research contribution also lies in the researcher's idea for future research, namely testing the Viola-Jones algorithm in recognizing objects other than facial images. Furthermore, five studies are analyzed and described the application of the Viola-Jones algorithm for facial recognition with their respective advantages. The first study had a very good accuracy level of 85%–95% in detecting faces. The second study had accuracy, precision, recall, and achievement times of 0.74, 0.73, 0.76, and 15 seconds in recognizing a person's emotions through facial expressions. Meanwhile, the third study had a very good accuracy level of 94.5% in recognizing faces that are 1 meter away.","PeriodicalId":299121,"journal":{"name":"2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124413149","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: 5

Rating Prediction of Product Reviews of Bangla Language using Machine Learning Algorithms 基于机器学习算法的孟加拉语产品评论评级预测

2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS) Pub Date : 2021-04-28 DOI: 10.1109/AIMS52415.2021.9466022

Md. Iqbal Hossain, Maqsudur Rahman, M. T. Ahmed, Md. Saifur Rahman, A. Z. M. T. Islam

{"title":"Rating Prediction of Product Reviews of Bangla Language using Machine Learning Algorithms","authors":"Md. Iqbal Hossain, Maqsudur Rahman, M. T. Ahmed, Md. Saifur Rahman, A. Z. M. T. Islam","doi":"10.1109/AIMS52415.2021.9466022","DOIUrl":"https://doi.org/10.1109/AIMS52415.2021.9466022","url":null,"abstract":"The only way to provide feedback about a product is through reviews. When a new shopper proceeds to an online shop to purchase a product but does not have adequate time to study the reviews provided by other shoppers to get an opinion about the product, the shopper determines whether to buy the product or not on the number rating. Through reviews, shoppers can acquaint everyone about the product's quality and the manufacturer can enhance their products and business by interpreting that review. However, manufacturers demand a number review more than a text review for business analysis. This paper represents a machine learning based model for predicting the number rating from written text for Bangla product review. This study performs on a dataset collected manually from Daraz.com.bd, a Bangladeshi leading e-commerce shop. We have implemented Support Vector Machine (SVM), Random Forest, XGBoost, and Logistic Regression with Term Frequency-Inverse Document Frequency (TF-IDF) Vectorizer on our collected dataset and record all the performance metrics like accuracy, precision, recall and f1-score. From these above four algorithms, SVM showed more outstanding results than others in terms of performance metrics. SVM achieved 90% accuracy on the applied dataset. The other SVM performance metrics are 0.90, 0.92, and 0.91 for precision, recall and f1-score respectively.","PeriodicalId":299121,"journal":{"name":"2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)","volume":"473 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125837153","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

Embedded System Real Time Data Acquisition System Using FPGA Technology for Detection and Counting of PD Signal from PICO Pulse Generator 基于FPGA技术的嵌入式系统实时数据采集系统对PICO脉冲发生器PD信号的检测与计数

2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS) Pub Date : 2021-04-28 DOI: 10.1109/AIMS52415.2021.9466063

Emilliano, C. Chakrabarty, A. Basri, A. Ramasamy, N. Suhendi

{"title":"Embedded System Real Time Data Acquisition System Using FPGA Technology for Detection and Counting of PD Signal from PICO Pulse Generator","authors":"Emilliano, C. Chakrabarty, A. Basri, A. Ramasamy, N. Suhendi","doi":"10.1109/AIMS52415.2021.9466063","DOIUrl":"https://doi.org/10.1109/AIMS52415.2021.9466063","url":null,"abstract":"The FPGA (Field Programmable Gate Array) technology is widely used today for signal processing and control owing to its fast digital processing capabilities. Using this, a test-bed high-speed data acquisition system that combines a commercial FPGA board (ML405) with the ADC in PIC micro-controller 16F877A that has 8 bit in resolution, sampling rate of 20 MS/s and bandwidth of 10 MHz for counting very high speed transient signals has been developed and successfully tested in the lab. This system enables direct measurement and counting of the transient signals at 50–100 ns pulse width at a sampling frequency 20MHz from Pico pulse generator as simulator of Partial Discharge signals on the site. All the results are shown to proof the concept for detecting partial discharge with pulse width of 5 ns in high power underground cables. The advantage of this system is that it can easily be deployed and count simulated Partial Discharge signal without the use of an oscilloscope and PC. The work in this paper comprises of using VHDL programming in FPGA to capture and discriminate the high-speed transient signals that has been digitized by the ADC in PIC micro-controller. In implementation of the test in the laboratory for the detection circuit, it is shown that it can detect the amount of impulses quite accurately. This is shown in PD detector system whereby LCD reads 84,746 impulses per second when it was set at 84.746 KHz repetitive using Pico pulse generator. The result show that the output peak detector can detect peak signal from input signal of the ADC when the pulse width of PD signal more than 30 ns using the ADC of the PIC Microcontroller 16F877A. Several features such as counting and discrimination between pulses are integrated in the system are also shown. This concept will be used in the future to detect real partial discharge generated in high power underground cables in the field.","PeriodicalId":299121,"journal":{"name":"2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123798520","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 Comparative Analysis of Multiple Biasing Techniques for $Q_{biased}$ Softmax Regression Algorithm $Q_{biased}$ Softmax回归算法的多偏置技术比较分析

2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS) Pub Date : 2021-04-28 DOI: 10.1109/AIMS52415.2021.9466049

Muhammad Moiz, Hazique Malik, Muhammad Bilal, Noman Naseer

{"title":"A Comparative Analysis of Multiple Biasing Techniques for $Q_{biased}$ Softmax Regression Algorithm","authors":"Muhammad Moiz, Hazique Malik, Muhammad Bilal, Noman Naseer","doi":"10.1109/AIMS52415.2021.9466049","DOIUrl":"https://doi.org/10.1109/AIMS52415.2021.9466049","url":null,"abstract":"Over the past many years the popularity of robotic workers has seen a tremendous surge. Several tasks which were previously considered insurmountable are able to be performed by robots efficiently, with much ease. This is mainly due to the advances made in the field of control systems and artificial intelligence in recent years. Lately, we have seen Reinforcement Learning (RL) capture the spotlight, in the field of robotics. Instead of explicitly specifying the solution of a particular task, RL enables the robot (agent) to explore its environment and through trial and error choose the appropriate response. In this paper, a comparative analysis of biasing techniques for the Q-biased softmax regression (QBIASSR) algorithm has been presented. In QBIASSR, decision-making for un-explored states depends upon the set of previously explored states. This algorithm improves the learning process when the robot reaches unexplored states. A vector bias(s) is calculated on the basis of variable values of experienced states and added to the Q-value function for action selection. To obtain the optimized reward, different techniques to calculate bias(s) are adopted. The performance of all the techniques has been evaluated and compared for obstacle avoidance in the case of a mobile robot. In the end, we have demonstrated that the cumulative reward generated by the technique proposed in our paper is at least 2 times greater than the baseline.","PeriodicalId":299121,"journal":{"name":"2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126599883","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

One-Dimensional Convolutional Neural Network Method as The Predicting Model for Interactions Between Drug and Protein on Heterogeneous Network 基于一维卷积神经网络的药物与蛋白质相互作用预测模型

2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS) Pub Date : 2021-04-28 DOI: 10.1109/AIMS52415.2021.9466059

Iswahyuli, A. Bustamam, Arry Yanuar, W. Mangunwardoyo

{"title":"One-Dimensional Convolutional Neural Network Method as The Predicting Model for Interactions Between Drug and Protein on Heterogeneous Network","authors":"Iswahyuli, A. Bustamam, Arry Yanuar, W. Mangunwardoyo","doi":"10.1109/AIMS52415.2021.9466059","DOIUrl":"https://doi.org/10.1109/AIMS52415.2021.9466059","url":null,"abstract":"Prediction task of drug-target interactions (DTI) is an important step of drug development and repositioning. Experimental identification of drugs and target interactions is expensive and time-consuming. Therefore, predictive drug-target interactions with computational approaches are being developed to alleviate work in drug development. In recent years, many computational approaches aimed at predicting drug-target interactions have been developed. One of the most popular models for predicting drug interactions and targets in recent times is the machine learning-based approach and homogeneous network information. However, the accuracy and efficiency of the methods used still need to be improved. Therefore, this research aims to propose a deep learning-based prediction model for DTI implemented in heterogeneous networks. We use 12,015 nodes and 1,895,445 edges that extract from several databases to build the heterogeneous network. The model of DTI prediction that we proposed implements the random walk with restart (RWR) algorithm to build a heterogeneous network of drug and protein targets, and utilizes diffusion component analysis (DCA) algorithm to obtain low-dimensional vectors. Furthermore, a one-dimensional convolutional neural network (1D-CNN) was used as a predictive model between drug and target. The results show that our proposed model provides good performance with a mean score of AUROC was 0.9332, and a mean score of AUPR was 0.9402.","PeriodicalId":299121,"journal":{"name":"2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115199201","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

System Design and Integration for Exploration Mission on Autonomous Underwater Glider 自主水下滑翔机探测任务系统设计与集成

2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS) Pub Date : 2021-04-28 DOI: 10.1109/AIMS52415.2021.9466075

D. P. Wicaksa, B. Trilaksono, E. Hidayat

引用次数: 0

fNIRS Based Multi-Class Mental Workload Classification Using Recurrence Plots and CNN-LSTM 基于递归图和CNN-LSTM的fNIRS多类心理负荷分类

2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS) Pub Date : 2021-04-28 DOI: 10.1109/AIMS52415.2021.9466084

Nabeeha Ehsan Mughal, Khurram Khalil, Muhammad Jawad Khan

{"title":"fNIRS Based Multi-Class Mental Workload Classification Using Recurrence Plots and CNN-LSTM","authors":"Nabeeha Ehsan Mughal, Khurram Khalil, Muhammad Jawad Khan","doi":"10.1109/AIMS52415.2021.9466084","DOIUrl":"https://doi.org/10.1109/AIMS52415.2021.9466084","url":null,"abstract":"The ever-increasing human-machine interaction and advancement in socio-technical systems have made it essential to analyze the vital human factors such as mental workload, vigilance, fatigue, stress, etc., via monitoring brain states. Similarly, brain signals are becoming paramount for rehabilitation and assistive purposes in fields such as brain- computer interface (BCI), closed-loop neuromodulation for neurological disorders, etc. The complex, non-stationary, and very low signal-to-noise ratio of brain signals poses a significant challenge for researchers to design robust and reliable BCI systems outside the laboratory environment. In this study, we present novel recurrence plots (RPs) based on convolutional neural network and long short term memory (CNN-LSTM) algorithm for four class functional near-infrared spectroscopy (fNIRS) BCI. The acquired brain signals are projected into a non-linear dimension with RPs and fed into the CNN, which extracts the important features. Then LSTM learns the chronological and time-dependent relations. The average accuracy achieved with the proposed model is 77.7%, while the maximum accuracy is 85.9%. The results confirm the viability of RPs based deep learning algorithms for successful BCI systems.","PeriodicalId":299121,"journal":{"name":"2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134265064","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: 4

Programming Language Translator For Integration Client Application With Web APIs 编程语言翻译器集成客户端应用程序与Web api

2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS) Pub Date : 2021-04-28 DOI: 10.1109/AIMS52415.2021.9466033

Mudiarta Tauda, Z. Zainuddin, Z. Tahir

引用次数: 0

Design and Fabrication of Exoskeleton for Power Augmentation of Arm using Intuitive Control 基于直觉控制的机械臂动力增强外骨骼设计与制造

2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS) Pub Date : 2021-04-28 DOI: 10.1109/AIMS52415.2021.9466021

Syed Usama Bin Sabir, Kashif Ahmed, Usama Sabir, Noman Naseer

引用次数: 0

Automatic Counting Shrimp Larvae Based You Only Look Once (YOLO) 基于You Only Look Once (YOLO)的虾仔自动计数

2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS) Pub Date : 2021-04-28 DOI: 10.1109/AIMS52415.2021.9466058

Siska Armalivia, Z. Zainuddin, A. Achmad, Muh. Arief Wicaksono

引用次数: 2