2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)最新文献_第9页

Using Learning Analytics to Improve Students' Enrollments in Higher Education 运用学习分析提高高等教育学生入学率

2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2022-03-03 DOI: 10.1109/IRASET52964.2022.9737993

Nabila Sghir, Amina Adadi, Zakariyaa Ait El Mouden, Mohammed Lahmer

{"title":"Using Learning Analytics to Improve Students' Enrollments in Higher Education","authors":"Nabila Sghir, Amina Adadi, Zakariyaa Ait El Mouden, Mohammed Lahmer","doi":"10.1109/IRASET52964.2022.9737993","DOIUrl":"https://doi.org/10.1109/IRASET52964.2022.9737993","url":null,"abstract":"In the last years there has been a growing interest in adopting learning analytics (LA) in higher and further education systems. LA assists the institutional stakeholders to enhance the learning process, ameliorate the teaching activities, make adequate decisions and take appropriate actions based on the collection, analysis, and reporting of data generated from individual learners. The learning analytics approach aims to achieve many objectives, one of them is prediction which is the center of this research. In this paper, we conduct a comparative study between three machine learning algorithms; Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM); to predict the stream of new enrollments in the first year of higher education. As a case study, the predictive model is applied to new enrollments in the first year of the University Diploma of Technology (DUT) at the Higher School of Technology in Meknes, Morocco (ESTM). The performance of the classifiers is tested with and without the use of SMOTE data balancing on a dataset of 53554 students collected between 2016 and 2019. The obtained results show the best algorithm to predict the most accurate enrollments of students.","PeriodicalId":377115,"journal":{"name":"2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122028880","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

Implementation of CNN-Inception Deep Learning for Cognitive Radio Based on Modulation Classifications 基于调制分类的认知无线电CNN-Inception深度学习实现

2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2022-03-03 DOI: 10.1109/IRASET52964.2022.9738405

Mohamed Ben mohammed mahieddine, N. Mellah, A. Bassou, Mustapha Khelifi, S. A. Chouakri

{"title":"Implementation of CNN-Inception Deep Learning for Cognitive Radio Based on Modulation Classifications","authors":"Mohamed Ben mohammed mahieddine, N. Mellah, A. Bassou, Mustapha Khelifi, S. A. Chouakri","doi":"10.1109/IRASET52964.2022.9738405","DOIUrl":"https://doi.org/10.1109/IRASET52964.2022.9738405","url":null,"abstract":"With the growing demand for new wireless services and applications, as well as the growing number of wireless users, the available spectrum is becoming increasingly scarce. As a result, the Federal Communications Commission (FCC) explored new ways to manage radio frequency resources. Cognitive radio technology is an innovative radio design philosophy that aims to increase the maximum exploitation of the physical spectrum by harnessing unused and underutilized spectrum in dynamic environments. Modulation recognition is the most important part of the spectrum sensing for cognitive radio. In this paper, we proposed and implement a developed Convolutional Neural Network technique called CNN-inception for modulation classification, the introduced model is a new module inspired by the idea of a naive version of the initiation module. We examine it with two types of inputs first one is the complex (I/Q) time-domain, the second one is the FFT of the signals as features to train the neurons. We test the proposed model by using two popular datasets MIGOU-MOD dataset and the RadioML2016.10a dataset, the results show that we were able to achieve maximum accuracy of 93.22% which is very competitive and better than many other proposed techniques","PeriodicalId":377115,"journal":{"name":"2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125925280","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

Towards an Artificial Intelligence Framework for Early Diagnosis and Prediction of Lung Cancer 构建肺癌早期诊断与预测的人工智能框架

2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2022-03-03 DOI: 10.1109/IRASET52964.2022.9738317

Inssaf El Guabassi, Zakaria Bousalem, R. Marah, Abdellatif Haj

引用次数: 0

TBER and SINR Evaluation for Interference Sensitivity Assessment in 5G NR Networks for Bands above 6 GHz 6ghz以上频段5G NR网络干扰灵敏度评估的TBER和SINR评估

2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2022-03-03 DOI: 10.1109/IRASET52964.2022.9738278

Ismail Angri, Mohammed Mahfoudi, A. Najid

{"title":"TBER and SINR Evaluation for Interference Sensitivity Assessment in 5G NR Networks for Bands above 6 GHz","authors":"Ismail Angri, Mohammed Mahfoudi, A. Najid","doi":"10.1109/IRASET52964.2022.9738278","DOIUrl":"https://doi.org/10.1109/IRASET52964.2022.9738278","url":null,"abstract":"For high mobility users in a 5G NR network, interference management becomes a big challenge. Efficient RRM (Radio Resource Management) plays a primary role in increasing the Signal to Interference and Noise Ratio (SINR) values, and also in decreasing signal Transport Block Error Rate (TBER). To this end, the evaluation of these two parameters actively participates in the propagation channel sensitivity prediction. Thus, radio resources allocation must be efficient and powerful. Therefore, eight scheduling algorithms were developed and programmed into the mmWave model of NS-3 simulator. The simulations were run for different types of data flows, and the achieved results were evaluated in terms of SINR and TBER. The performance of most schemes in a typical 5G NR environment is satisfactory. In terms of SINR, and for the RT (Real Time) flows, MLWDF, EXP-Rule and our algorithm EXP-MLWDF ensure more reliability compared to other schemes, with values of around 25 dB for VOIP traffic and around 30 dB for video streams, whereas PF and Max-Rate allow better values for BE flows, with achievements greater than 20 dB, which presents improvements of about 60% compared to other schemes. On the other hand, PF, EXP-PF, EXP-Rule, Log-Rule and EXP-MLWDF ensure optimal values of TBER for RT flows, by achieving stable performance with a rate of less than 0.5% for VOIP, and around 0.1% for Vi5G. These values represent a reduction of 100% compared to other scheduling strategies. For NRT (Non-RT) flows, EXP-Rule and MLWDF have the best achievements and they quickly reach stability, with values between 0.4 and 1.4%, which represents an improvement of 70% in comparison with the concurrent schemes.","PeriodicalId":377115,"journal":{"name":"2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127393755","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

Covid-19 Classification Using HOG-SVM and Deep Learning Models 基于HOG-SVM和深度学习模型的Covid-19分类

2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2022-03-03 DOI: 10.1109/IRASET52964.2022.9738357

Nafisa Labiba Ishrat Huda, Md. Ashraful Islam, Md. Osman Goni, N. Begum

{"title":"Covid-19 Classification Using HOG-SVM and Deep Learning Models","authors":"Nafisa Labiba Ishrat Huda, Md. Ashraful Islam, Md. Osman Goni, N. Begum","doi":"10.1109/IRASET52964.2022.9738357","DOIUrl":"https://doi.org/10.1109/IRASET52964.2022.9738357","url":null,"abstract":"COVID-19 is measured as the biggest hazardous and fast infectious grief for the human body which has a severe impact on lives, health, and the community all over the world. It is still spreading throughout the world with different variants which is silently killing many lives globally. Thus, earlier diagnosis and accurate detection of COVID-19 cases are essential to protect global lives. Diagnosis COVID-19 through chest X-ray images is one of the best solutions to detect the virus in the infected person properly and quickly at a low cost. Encouraged by the existing research, in this paper, we proposed a hybrid model to classify the Covid cases and non-Covid cases with chest X-ray images based on feature extraction, machine learning and deep learning techniques. Two feature extractors, Histogram Oriented Gradient (HOG) and CNN (MobileNetV2, Sequential, ResNet152V2) are used to train the model. For the classification, we utilized two approaches: Support Vector Machine (SVM) for machine learning and CNN (MobileNetV2, Sequential, ResNet152V2) classifiers for deep learning. The experimental result analysis shows that the Sequential model and the ResNet152V2 model achieve 100% and 82.6% accuracy respectively which is satisfactory. On the other hand, the HOG-SVM method successfully detects all the test images correctly which provides the best result with 100% accuracy, specificity, and responsiveness over a limited public dataset.","PeriodicalId":377115,"journal":{"name":"2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129293215","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

Analysis of Distance Measures for WiFi-based Indoor Positioning in Different Settings 基于wifi的室内定位在不同环境下的距离度量分析

2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2022-03-03 DOI: 10.1109/IRASET52964.2022.9737848

Ninh Duong-Bao, Jing He, L. Thi, K. Nguyen-Huu

{"title":"Analysis of Distance Measures for WiFi-based Indoor Positioning in Different Settings","authors":"Ninh Duong-Bao, Jing He, L. Thi, K. Nguyen-Huu","doi":"10.1109/IRASET52964.2022.9737848","DOIUrl":"https://doi.org/10.1109/IRASET52964.2022.9737848","url":null,"abstract":"Recently, indoor positioning systems based on wireless technologies such as WiFi fingerprinting become more popular. The nearest neighbor-based algorithms using Euclidean distance are very common and used in many fingerprinting systems. Thus, the distance measure is very important and it affects much to the tracking result. In this paper, we present an analytical study of using different distance measures for the weighted K-nearest neighbor algorithm to determine the position of a user. We implement five distance measures and compare the positioning results of each measure to find out the best one. To check the robustness of the measures, we change some settings when creating the radio map in the offline phase such as the number of access points or the distance between two reference points. From the experiments, it is shown that the Chi-Squared distance outperforms other distance measures since it achieves the mean error of 1.13 meters in a simple test case and 1.20 meters in a more complicated test case. Even when we change the settings, Chi-Squared distance remains the best positioning result.","PeriodicalId":377115,"journal":{"name":"2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127827741","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

A Novel Intelligent Technique Based on Metaheuristic Algorithms and Artificial Neural Networks: Application on a Photovoltaic Panel 一种基于元启发式算法和人工神经网络的智能新技术:在光伏板上的应用

2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2022-03-03 DOI: 10.1109/IRASET52964.2022.9738106

N. Ncir, Saliha Sebbane, Nabil El Akchioui

引用次数: 4

DSP In the Loop implementation of an enhanced Direct Torque Control for Induction Motor drive DSP在环路中实现了一种增强的直接转矩控制，用于感应电机驱动

2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2022-03-03 DOI: 10.1109/IRASET52964.2022.9738073

Mohammed El haissouf, Mustapha El Haroussi, A. Ba-Razzouk

引用次数: 3

Learner personality type effect on attitude toward social assistive robotics in learning for children with disabilities 学习者人格类型对残疾儿童社会辅助机器人学习态度的影响

2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2022-03-03 DOI: 10.1109/IRASET52964.2022.9738138

Soukaina Gouraguine, Intissar Salhi, Mohammed Qbadou, K. Mansouri

{"title":"Learner personality type effect on attitude toward social assistive robotics in learning for children with disabilities","authors":"Soukaina Gouraguine, Intissar Salhi, Mohammed Qbadou, K. Mansouri","doi":"10.1109/IRASET52964.2022.9738138","DOIUrl":"https://doi.org/10.1109/IRASET52964.2022.9738138","url":null,"abstract":"Social robots' presence in the educational sector becomes increasingly important especially to support people with specific needs, it is essential to evaluate their effectiveness in different contexts and to examine the impact of this creative modality of teaching on the psychic of learners studying their personalities. In this work, we are exploring the use of a social assistance robot to perform interactions with the learners in order to determine the type of their personalities and observe the psychological change of the learners during the student-robot interaction according to their personalities. To realize our study, we have deployed a model for the integration of a humanoid assistant robot in an educational environment. The assistant robot should supervise, assist, encourage, and socially interact with learners to acquire information about their personality types. The Negative Attitude Scale toward Robots (NARS) and the Myers Briggs Type Indicator MBTI personality test was used to collect the data. The results indicate the personality types of the participants that are most dominant. The results also show a positive correlation value that should not be neglected between personality type and negative attitudes towards robots. The findings of the study can be used to encourage teachers to better understand the personality type of their students so that they can integrate robots into appropriate child-robot interaction situations.","PeriodicalId":377115,"journal":{"name":"2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115344768","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

Advanced Torque and Speed Control Techniques for Induction Motor Drives: A Review 感应电机驱动的先进转矩和速度控制技术:综述

2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2022-03-03 DOI: 10.1109/IRASET52964.2022.9738368

Siham Mencou, Majid Ben Yakhlef, El Bachir Tazi

引用次数: 6