2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC)最新文献_第8页

Emotion Recognition from Facial Expression using CNN 基于CNN的面部表情情感识别

2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2021-09-30 DOI: 10.1109/R10-HTC53172.2021.9641578

Ishika Agrawal, Adarsh Kumar, DG Swathi, V. Yashwanthi, Rajeshwari Hegde

引用次数: 4

Classification of Faults in Power System with Probabilistic Neural Networks: An Imbalanced Learning Approach 基于概率神经网络的电力系统故障分类:一种不平衡学习方法

2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2021-09-30 DOI: 10.1109/R10-HTC53172.2021.9641580

Debottam Mukherjee, Samrat Chakraborty

{"title":"Classification of Faults in Power System with Probabilistic Neural Networks: An Imbalanced Learning Approach","authors":"Debottam Mukherjee, Samrat Chakraborty","doi":"10.1109/R10-HTC53172.2021.9641580","DOIUrl":"https://doi.org/10.1109/R10-HTC53172.2021.9641580","url":null,"abstract":"Modern day power grids with its inherent operating characteristics are susceptible to faults. Grid operators must detect as well as classify the current system operating conditions like normal or faulty from the current raw sets of measurement data available at supervisory control and data acquisition (SCADA) system. With the rapid deployment of micro PMUs, faults are detected from the raw measurements in real time, but their classification still possess a challenging task. This paper focuses on a diligent comparison between several deep and machine learning techniques for classifying faults in real time. In real life scenarios, line to ground (L-G) faults being the most frequent one while three phase to ground (LLL-G) faults being rare, an imbalanced dataset is generally developed for supervised learning approach leading to biased classification of faults. In order to alleviate this current concern, data oversampling policy over the imbalanced dataset based on synthetic minority oversampling technique (SMOTE) is proposed. The dataset used in this work is derived from the Drexel University's Reconfigurable Distribution Automation and Control (RDAC) software/hardware laboratory.","PeriodicalId":117626,"journal":{"name":"2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC)","volume":"65 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133009007","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

Impact of E-Learning System User Interface Design on User Satisfaction E-Learning系统用户界面设计对用户满意度的影响

2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2021-09-30 DOI: 10.1109/R10-HTC53172.2021.9641570

G. Senevirathne, K. Manathunga

{"title":"Impact of E-Learning System User Interface Design on User Satisfaction","authors":"G. Senevirathne, K. Manathunga","doi":"10.1109/R10-HTC53172.2021.9641570","DOIUrl":"https://doi.org/10.1109/R10-HTC53172.2021.9641570","url":null,"abstract":"Interface design is a mandatory aspect influencing the success of an e-Learning system implementation. User interface (UI) design of e-learning is a point of interaction between user and computer software. Users prefer more attractive and simpler interface designs rather than dull or complex designs. This study aims to outline the impact of UI design on the satisfaction of learners. Specifically, this study will be evaluating different user interaction design strategies such as ease of navigation, ease of resource discoverability, ease of configuring integrated tools etc. in e-learning platforms such as learning management systems, and massive open online courses (MOOCs). Further, this research aims to find answers for the challenges and issues faced by students and teachers when using e-learning platforms. A comprehensive questionnaire was distributed among teachers and students. Collected data was analyzed to get an idea about main interface design problems that frustrate the learners and teachers and distract them from educational tasks. Using this statistical analysis results, a model is proposed indicating success factors and failure factors that may affect to e-learning system interface designing. Moreover, this research also results in a set of guidelines or suggestions that can be followed to improve UI designing in e-learning platforms. Finally, an initial prototype implementation capable of recommending suggestions intelligently for e-learning platform designers and users is proposed after modelling the user satisfaction factors.","PeriodicalId":117626,"journal":{"name":"2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133608772","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

Early Diagnosis and Comparative Analysis of Different Machine Learning Algorithms for Myocardial Infarction Prediction 不同机器学习算法在心肌梗死预测中的早期诊断与比较分析

2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2021-09-30 DOI: 10.1109/R10-HTC53172.2021.9641080

Sharmin Akter, Mahdia Amina, N. Mansoor

{"title":"Early Diagnosis and Comparative Analysis of Different Machine Learning Algorithms for Myocardial Infarction Prediction","authors":"Sharmin Akter, Mahdia Amina, N. Mansoor","doi":"10.1109/R10-HTC53172.2021.9641080","DOIUrl":"https://doi.org/10.1109/R10-HTC53172.2021.9641080","url":null,"abstract":"Heart attack alternatively known as Myocardial Infarction is one of the primary reasons of morbidity on the planet. Therefore, the diagnosis and prediction of heart disease is persuading many researchers to develop intelligent medical decision support systems. Machine Learning has been demonstrated to be viable in helping with decision making and predictions from the huge amount of clinical data delivered by the medical care. This paper aims to improve the accuracy of machine learning models which can help to make informed decision and prediction of heart attack. We have applied six machine learning classification algorithms: Support Vector Machine, Random Forest, K Nearest Neighbors, Gaussian Naive Bayes, Decision Tree and Logistic Regression. Additionally, an extensive comparison of machine learning techniques has been carried out. Our research work suggests that machine learning methods with data balancing techniques are effective tools for stroke prediction with imbalanced data. Therefore, Synthetic Minority Over-Sampling Technique (SMOTE) has been applied in our model. Hence, it is anticipated that Random Forest excels with the highest accuracy of 96% in heart attack prediction regarding performance metrics.","PeriodicalId":117626,"journal":{"name":"2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC)","volume":"132 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133686231","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

Seed Architectural Phenes Prediction and Variety Classification of Dry Beans (Phaseolus vulgaris) Using Machine Learning Algorithms 基于机器学习算法的干豆种子结构特征预测与品种分类

2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2021-09-30 DOI: 10.1109/R10-HTC53172.2021.9641554

Christan Hail R. Mendigoria, Ronnie S. Concepcion, E. Dadios, Heinrick L. Aquino, Oliver John Alaias, E. Sybingco, A. Bandala, R. R. Vicerra, J. Cuello

{"title":"Seed Architectural Phenes Prediction and Variety Classification of Dry Beans (Phaseolus vulgaris) Using Machine Learning Algorithms","authors":"Christan Hail R. Mendigoria, Ronnie S. Concepcion, E. Dadios, Heinrick L. Aquino, Oliver John Alaias, E. Sybingco, A. Bandala, R. R. Vicerra, J. Cuello","doi":"10.1109/R10-HTC53172.2021.9641554","DOIUrl":"https://doi.org/10.1109/R10-HTC53172.2021.9641554","url":null,"abstract":"Proper identification and categorization of seeds at an earlier stage of the cultivation process is an imperative procedure that contributes to better crop quality and higher production yield. As a strategy to supplement this procedure, integration of computer vision approach and machine learning algorithms including gaussian process regression (GPR), decision trees for regression (RT) and classification (CT), support vector machine regression (SVMR), k-nearest neighbors (KNN), linear discriminant analysis (LDA) classifier, and Naïve Bayes (NB) classifier are explored in this study to predict the extended morphological features (solidity, roundness, compactness) and variety classification of dry bean (Phaseolus vulgaris L.). A total of 13,611 image samples were used. CIELab color channel thresholding was applied in segmenting bean pixels and region properties for extracting the morphological features (bean biomass area, perimeter, major and minor axis lengths, convex area, eccentricity, extent, equivalent diameter, and axis length proportionality, shape factors, roundness, solidity, compactness). Based on RMSE and MAE performances, the optimized GPR is the most reliable model for predicting seed solidity, and regression tree for both seed roundness and compactness. Classification models with seven morphological predictors (LDA7, KNN7, CT7, NB7) exhibited sensitive classification performance, all having accuracies greater than 90%. Further, KNN7 bested out other models with 93.69% accuracy, 93.64% precision, 93.66% specificity, and 93.69% f1-score. The developed machine learning models are innovative approaches in the seed variety classification and phenotyping of dry bean seeds.","PeriodicalId":117626,"journal":{"name":"2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134030869","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

Offline Simulation of Motion Planning of a Planar Manipulator in RoboAnalyzer and its Integration with a Physical Prototype 机器人分析仪中平面机械臂运动规划的离线仿真及其与物理样机的集成

2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2021-09-30 DOI: 10.1109/R10-HTC53172.2021.9641544

Medha Shruti, Rajeevlochana G. Chittawadigi

{"title":"Offline Simulation of Motion Planning of a Planar Manipulator in RoboAnalyzer and its Integration with a Physical Prototype","authors":"Medha Shruti, Rajeevlochana G. Chittawadigi","doi":"10.1109/R10-HTC53172.2021.9641544","DOIUrl":"https://doi.org/10.1109/R10-HTC53172.2021.9641544","url":null,"abstract":"Thereare two important aspects of robotics education. First, learning the mathematics of a robot and second, hands on development of a physical prototype of the robot. There are plenty of resources and software available to help in teaching the mathematics involved and there are fewer resources available to help the students to work with the physical prototypes. Still less is the number of resources which combine both the aspects into one. This paper proposes a new motion planning module in RoboAnalyzer, a 3D model based software to teach robotics. It has a simpler interface to input the parameters of a 2-Revolute planar manipulator and perform kinematic analysis and motion planning to achieve linear and circular motion of the end-effector point. Once the offline simulation is satisfactorily observed, the joint trajectories can be sent to Arduino IDE through serial port communication. From Arduino IDE, one can send the joint trajectories to drive motors of a physical prototype. As a demonstration, the trajectories have been sent to a prototype developed using Dynamixel motors. The authors feel that such integrated approach of learning robotics would yield better results and also enable the students to become industry ready.","PeriodicalId":117626,"journal":{"name":"2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117326373","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

Smart Sustainable Mini Weather Station 智能可持续迷你气象站

2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2021-09-30 DOI: 10.1109/R10-HTC53172.2021.9641672

Anshika Gupta, Ankita Tripathi, Reuben Coutinho, Rhea Rodrigues, Prachi Raut

引用次数: 1

Comparative ANFIS Models for Stochastic On-road Vehicle CO2 Emission using Grid Partitioning, Subtractive, and Fuzzy C-means Clustering 基于网格划分、减法和模糊c均值聚类的随机道路车辆CO2排放ANFIS模型比较

2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2021-09-30 DOI: 10.1109/R10-HTC53172.2021.9641644

Maria Gemel B. Palconit, Ronnie S. Conception, Jonnel D. Alejandrino, Warren A. Nunez, A. Bandala, E. Dadios

{"title":"Comparative ANFIS Models for Stochastic On-road Vehicle CO2 Emission using Grid Partitioning, Subtractive, and Fuzzy C-means Clustering","authors":"Maria Gemel B. Palconit, Ronnie S. Conception, Jonnel D. Alejandrino, Warren A. Nunez, A. Bandala, E. Dadios","doi":"10.1109/R10-HTC53172.2021.9641644","DOIUrl":"https://doi.org/10.1109/R10-HTC53172.2021.9641644","url":null,"abstract":"On-road vehicle CO2 emission is stochastic and is presently not feasible to be solved using hard computing methodologies due to computational cost. This paper presents an on-road paratransit vehicle CO2 emission estimation model using an adaptive neuro-fuzzy inference system (ANFIS). With input parameters, namely, the speed, slope, and acceleration, three ANFIS clustering types were utilized. Results have shown that Fuzzy-C means clustering method (FCM) obtained the best performance concerning error rates and computation simplicity. Specifically, it has yielded 13.38% NRMSE using five membership functions per input and five fuzzy rules. The grid partitioning (GP) obtained the worst prediction output while the subtractive clustering method (SCM) has comparable prediction accuracy with FCM but has a higher computational cost compared to the latter. The proposed estimation model is beneficial for paratransit vehicles wherein the state-of-the-art on-road emission models are deemed unsuitable.","PeriodicalId":117626,"journal":{"name":"2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131363930","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: 8

Design and Implementation of an Audio Fingerprinting System for the Identification of Audio Recordings 音频指纹识别系统的设计与实现

2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2021-09-30 DOI: 10.1109/R10-HTC53172.2021.9641681

A. Patil, Lakshmi J Itagi, Ashika Cs, Ambika G, Mallika Ravi

{"title":"Design and Implementation of an Audio Fingerprinting System for the Identification of Audio Recordings","authors":"A. Patil, Lakshmi J Itagi, Ashika Cs, Ambika G, Mallika Ravi","doi":"10.1109/R10-HTC53172.2021.9641681","DOIUrl":"https://doi.org/10.1109/R10-HTC53172.2021.9641681","url":null,"abstract":"Rapid growth in the multimedia industry due to various streaming platforms has increased audio and video traffic enormously. This traffic generates a massive amount of data that requires efficient algorithms to retrieve the desired data in a short amount of time. Thus, there is a need for efficient audio retrieval methods such as audio fingerprinting. Audio fingerprinting systems mainly use audio signals after processing to obtain a representative hash called an audio fingerprint. The fingerprint holds content information of a recording that can distinguish one recording from another by extracting relevant features from the audio content. Some applications of audio fingerprinting include Music Retrieval, Copyright infringement, digital watermarking and broadcast monitoring. We propose to build a reliable audio fingerprinting system, which uses a robust audio fingerprint extraction method and an efficient search strategy, which uses only limited computing resources, with minimized search time for recognition of audio content, by considering other musical features. Accuracy, confidence, efficiency in storing the fingerprints and speed is used to measure the system's performance. The accuracy of the system depends on the confidence level of the match found. The accuracy varies with the recording time. The ideal recording time is found to be 5 seconds that recognizes the song with accuracy of 83.3%. The system performs well even in the presence of noise with reduced false positives.","PeriodicalId":117626,"journal":{"name":"2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121165850","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

Prospects and Design Assessment of a Hybrid Renewable Energy Microgrid for the Thuisa Para Indigenous Community in Bangladesh 孟加拉国Thuisa Para土著社区混合可再生能源微电网的前景和设计评估

2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2021-09-30 DOI: 10.1109/R10-HTC53172.2021.9641079

M. Hossain, Abdullah Al Kayes, Rayhan Suny

{"title":"Prospects and Design Assessment of a Hybrid Renewable Energy Microgrid for the Thuisa Para Indigenous Community in Bangladesh","authors":"M. Hossain, Abdullah Al Kayes, Rayhan Suny","doi":"10.1109/R10-HTC53172.2021.9641079","DOIUrl":"https://doi.org/10.1109/R10-HTC53172.2021.9641079","url":null,"abstract":"The Thuisa Para indigenous community living in the hill tracts of Bandarban, Bangladesh, has never experienced the miracles of electricity, as most of the remote hilly areas of Bandarban are still not under the National Energy Grid coverage. These indigenous people are deprived of the blessings of electricity and, their socio-economic advancement is being obstructed. The Sustainable Development Goal 7 (SDG7) program under the UN aims to diminish such energy access inequalities. It is possible for communities like Thuisa Para, which are located in remote areas, to acquire an adequate supply of electricity by utilizing the available renewable energy resources. But before implementation, thorough analyses regarding the geographical factors, cost-effectiveness and durability for a particular location is required to ensure that the energy system fulfills the demands adequately. Therefore, this paper aims to propose the most affordable and most reliable hybrid renewable energy micro grid design for the Thuisa Para Community upon completion of thorough comparative analyses of the available design options. For design and simulation purposes, HOMER software has been used. Elements considered for this microgrid are specifically solar-PV panels, kinetic batteries, wind turbine, diesel generator and converter. The results obtained from the simulation were used to compare the viable design choices in terms of their respective energy production capabilities, per-unit electricity costs, net present costs (NPC) and a few other important factors. Additionally, a multiyear sensitivity analysis regarding the net present cost has been conducted for the ease of choosing the suitable project lifetime of the Thuisa Para microgrid.","PeriodicalId":117626,"journal":{"name":"2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124484018","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