2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)最新文献_第5页

MIMO-NOMA and MIMO-OMA: Outage Probability Analysis and BER comparative study MIMO-NOMA和MIMO-OMA:中断概率分析和误码率比较研究

2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2023-05-18 DOI: 10.1109/IRASET57153.2023.10153065

Abdelbari Falloun, Anass Deroussi, Abdessalam Ait Madi

{"title":"MIMO-NOMA and MIMO-OMA: Outage Probability Analysis and BER comparative study","authors":"Abdelbari Falloun, Anass Deroussi, Abdessalam Ait Madi","doi":"10.1109/IRASET57153.2023.10153065","DOIUrl":"https://doi.org/10.1109/IRASET57153.2023.10153065","url":null,"abstract":"5G wireless technology is available to many more users than other cellular networks. For users, there are several ways to connect to base stations, including Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Orthogonal Multiple Access (OMA), and Non-orthogonal multiple access (NOMA). This last one is considered the best option for 5G, which enables sharing the same resources (e.g., frequency, time, and code domain resources) with multiple users. Furthermore, NOMA permits massive connections and increases the efficiency of the spectrum network by using strategies for radio resource optimization and interference control that involves successive interference cancellation (SIC). There are two types of users: strong and weak. A high data rate is required by strong users, while a low one is sufficient for weak users. Throughout this paper, we compare the rate achievable capacity, outage probability (OP), and Bit Error Rate (BER) indicatively with transmit power, of those two users' type. [Moreover, we also compare the outage probability per fair and fixed power allocation (PA), applied respectively to NOMA and OMA.. This study centers on creating a MATLAB framework for systems using non-orthogonal multiple access (NOMA). All schemes are run in MATLAB Version R2021a. The obtained results concluds that NOMA is the better option. Furthermore, we suggest MIMO as an alternative solution to increase performance and decrease the outage probability. In addition, we compare MIMO-NOMA and MIMO-OMA in terms of performance for the two types of users to demonstrate the advantages of the MIMO-NOMA design.","PeriodicalId":228989,"journal":{"name":"2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116512385","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

Investigation of corrosion behavior of ZrN-based PVD coatings on Ti-29Nb-13Ta-4.6Zr alloy in Hank's solution zrn基PVD涂层对Ti-29Nb-13Ta-4.6Zr合金在Hank’s溶液中的腐蚀行为研究

2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2023-05-18 DOI: 10.1109/IRASET57153.2023.10152937

Leila Ait Ali, N. Aslan, H. Yilmazer, M. Niinomi, Anıl Şen, B. Dikici

引用次数: 0

A humanoid robot assistant for the classification of students according to their type of dysgraphia 一个人形机器人助手，根据学生的书写困难类型进行分类

2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2023-05-18 DOI: 10.1109/IRASET57153.2023.10152933

Soukaina Gouraguine, Mustapha Riad, Mohamed Rafik, Mohammed Qbadou, K. Mansouri

{"title":"A humanoid robot assistant for the classification of students according to their type of dysgraphia","authors":"Soukaina Gouraguine, Mustapha Riad, Mohamed Rafik, Mohammed Qbadou, K. Mansouri","doi":"10.1109/IRASET57153.2023.10152933","DOIUrl":"https://doi.org/10.1109/IRASET57153.2023.10152933","url":null,"abstract":"Students diagnosed with dysgraphia, a learning disability that affects handwriting and fine motor skills, are unable to follow the learning process correctly. The majority of regular teachers cannot support the correct learning of these students. Our present work aims to develop and implement a knowledge-learning model for a robot assistant to provide a diagnosis to dysgraphic students, in order to categorize them according to their types of dysgraphia. The conception of the general process of categorization is based on exploiting the expertise of specialists in the field in order to design a matrix of correspondence between symptoms and types of dysgraphia. This correspondence provides an automated technique for the efficient formation of decision trees. A decision tree classification is a popular approach to the problem, resulting in an accurate, flexible, and efficient classification. Our classification strategy has reached an advanced level; it uses a severity filtering procedure to divide dysgraphic learners into 3 categories: dysgraphic learners with mild dysgraphia, dysgraphic learners with a moderate type of dysgraphia, and dysgraphic learners with a severe type of dysgraphia. Therefore, the results indicate that 92.3 % of the finding of our assistant robot's diagnosis are similar to the finding of the experts' diagnosis. The results indicate that the robot was able to classify dysgraphic students according to their type of dysgraphia, giving the degree of severity that a learner attains in that type so that the human tutor can assign the students to the appropriate rehabilitation program depending on their specific needs.","PeriodicalId":228989,"journal":{"name":"2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121829068","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

Primary Prediction of Heart Disease using Machine Learning Algorithms and SMOTE 使用机器学习算法和SMOTE进行心脏病的初步预测

2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2023-05-18 DOI: 10.1109/IRASET57153.2023.10153064

Mohammed Amine Bouqentar, O. Terrada, Driss Lamrani, Asmae Ouhmida, B. Cherradi, A. Raihani

{"title":"Primary Prediction of Heart Disease using Machine Learning Algorithms and SMOTE","authors":"Mohammed Amine Bouqentar, O. Terrada, Driss Lamrani, Asmae Ouhmida, B. Cherradi, A. Raihani","doi":"10.1109/IRASET57153.2023.10153064","DOIUrl":"https://doi.org/10.1109/IRASET57153.2023.10153064","url":null,"abstract":"The cardiovascular disease is one of the largest field where Artificial Intelligence and Machine Learning tools can be used to help in early prediction and diagnosis. As the first concern of doctors is to make the one percent precise diagnosis, these tools can be more than helpful. Lately, Physicians and searchers are performing systems capable of predicting the occurrence of such diseases. In this paper, we aimed to build a heart disease prediction system by conducting a comparison over two Machine Learning Classifier: Logistic regression (LR) and Naïve Bayes (NB). These two methods were applied on the 13 attributes and 1 target feature of Cleveland dataset and 10 features of Hungarian dataset from UCI repository. To compare the methods, six performance metrics were calculated: accuracy (ACC), Recall (RE), precision(P), F1_score (F1score) and the Area Under the ROC curve (AUC). Thus, in order to enhance the accuracy of our models, Synthetic Minority Over-Sampling Technique (SMOTE) is used. Furthermore, the results were compared to the results of previous works from the literature. Afterwards, it is found that applied to the Cleveland dataset the accuracy of LR and NB classifiers reached respectively 90% and 89 % and for the Hungarian dataset, the accuracy of the NB and LR algorithm increased to 85% and 81 % Finally, after applying the SMOTE methods the accuracy was improved to 92% and 90% for the Cleveland dataset and 85% and 82% using the Hungarian dataset.","PeriodicalId":228989,"journal":{"name":"2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130698077","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

Artificial Intelligence-based MPPT Techniques in Wind Energy Systems: A Literature Review 风能系统中基于人工智能的MPPT技术:文献综述

2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2023-05-18 DOI: 10.1109/IRASET57153.2023.10153057

Driss Belkhiri, M. Ajaamoum, Khalide Cherifi, Abdellah Elidrissi, Moulay Rachid El Moutawakil Alaoui

{"title":"Artificial Intelligence-based MPPT Techniques in Wind Energy Systems: A Literature Review","authors":"Driss Belkhiri, M. Ajaamoum, Khalide Cherifi, Abdellah Elidrissi, Moulay Rachid El Moutawakil Alaoui","doi":"10.1109/IRASET57153.2023.10153057","DOIUrl":"https://doi.org/10.1109/IRASET57153.2023.10153057","url":null,"abstract":"The global opportunity to accelerate the world's renewable energy in future is technological progress. Today, artificial intelligence (AI) is used to drive the fastest revolution in technology. Thus, the highest possible level of energy conversion efficiency is required for wind turbines in order to fully use wind power. This paper introduces an overview of AI-based Maximum-Power-Point- Tracking (MPPT) for wind energy systems (WES). Given that wind energy conversion systems (WECS) are becoming increasingly complex and unexpected changing in wind speed conditions. Indeed, working conditions have a major impact on yield. Achieving the ideal yield is typically difficult. For this reason, MPPT controllers are attracting a lot of attention. This review provides a thorough analysis of both Artificial Neural Networks (ANN) and Fuzzy Logic (FL) used in WES. And identifies the approaches used most frequently to provide the best possible yield in many situations. Both ANN and FL may be utilised instead of more traditional approaches for control and optimisation, for which the approaches are collected and analysed based on the application. Both techniques in this area and its potential developments are statistically presented. A thorough bibliography is done, ideas for further study are offered, and a number of difficulties have been risen. Ultimately, future research in wind generation systems should find this platform helpful to academics, energy planners, and politicians.","PeriodicalId":228989,"journal":{"name":"2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130968706","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

Deep Learning Technique for Classification of Breast Cancer using Ultrasound Images 基于超声图像的乳腺癌分类的深度学习技术

2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2023-05-18 DOI: 10.1109/IRASET57153.2023.10153069

Mobarak Zourhri, S. Hamida, Nouhaila Akouz, B. Cherradi, H. Nhaila, M. E. Khaili

{"title":"Deep Learning Technique for Classification of Breast Cancer using Ultrasound Images","authors":"Mobarak Zourhri, S. Hamida, Nouhaila Akouz, B. Cherradi, H. Nhaila, M. E. Khaili","doi":"10.1109/IRASET57153.2023.10153069","DOIUrl":"https://doi.org/10.1109/IRASET57153.2023.10153069","url":null,"abstract":"Breast cancer is a significant medical and social issue that draws attention from the global scientific community. The classification of ultrasound images of breast cancer plays a crucial role in computer-aided diagnostic systems. In this paper, we present a deep learning technique for the diagnosis of breast cancer using ultrasound images. The proposed system leverages Transfer Learning, a machine learning approach that allows the reuse of pre-trained models for new tasks. Four pre-trained models, namely VGG16, VGG19, MobileNetV2, and ResNet50V2, were considered for building the system. Our study uses a public dataset that contains 9016 ultrasound images related to benign and malignant breast cancers. Results indicate that the VGG19 network is the most accurate model in classifying breast cancer as benign or malignant. The proposed system showed remarkable results, with an accuracy rate of 98.44% as obtained from the VGG19 model. This study emphasizes the ability of Transfer Learning to improve the precision of breast tumor classification based on Ultrasound Images.","PeriodicalId":228989,"journal":{"name":"2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114188277","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

Determination of chaos in different jerk systems: dynamical study, circuit design 不同激振系统混沌的确定:动力学研究，电路设计

2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2023-05-18 DOI: 10.1109/IRASET57153.2023.10152980

Ali Abou El Qassime, Hasna Nhaila, L. Bahatti

引用次数: 0

Static Analysis of Polymer Nanocomposite Beams Reinforced with Functionally Graded Single-Walled Boron Nitride Nanotubes Resting on Elastic Foundations 弹性地基上功能梯度单壁氮化硼纳米管增强聚合物纳米复合梁的静力分析

2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2023-05-18 DOI: 10.1109/IRASET57153.2023.10153019

Mehdi Bilah Ouassim, Fouaidi Mustapha, Zahiri Laidi, Belouaggadia Naoual, El Ouardi Soufiane

{"title":"Static Analysis of Polymer Nanocomposite Beams Reinforced with Functionally Graded Single-Walled Boron Nitride Nanotubes Resting on Elastic Foundations","authors":"Mehdi Bilah Ouassim, Fouaidi Mustapha, Zahiri Laidi, Belouaggadia Naoual, El Ouardi Soufiane","doi":"10.1109/IRASET57153.2023.10153019","DOIUrl":"https://doi.org/10.1109/IRASET57153.2023.10153019","url":null,"abstract":"Polymer nanocomposites reinforced with nanofillers are widely used in different engineering fields, due to their attractive mechanical properties. In this study, the static response of polymer nanocomposite beams (PNB) reinforced with functionally graded single-walled boron nitride nanotubes (FG-SWBNNT) and resting on elastic foundations (EF) is investigated using the finite element method (FEM) based on the first-order shear deformation theory (FSDT) of beams. The PNB are submitted to static mechanical loads (SL) with different boundary conditions (BC) and rested on EF modeled using the Pasternak-Winkler (PW) foundation model. The FSDT is used to model the PNB reinforced with FG-SWBNNT. The effective material properties of the PNB are determined using the rule of mixture (ROM). The equilibrium equations and associated boundary conditions of the PNB strengthened with FG-SWBNNT are derived using the principle of minimum total potential energy. Comparison studies are performed using the proposed numerical model. Parametric studies are conducted to analyze the influence of several parameters on the bending behavior of FG-SWBNNT reinforced PNB.","PeriodicalId":228989,"journal":{"name":"2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127884008","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 a Smart and Sustainable Industry : Cycle Time optimization 走向智能和可持续工业:周期时间优化

2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2023-05-18 DOI: 10.1109/IRASET57153.2023.10153046

Youssef Raouf, Zoubida Benmamoun, Hanaa Hachimi, Ikhel Jebbor, Mehdi Haqqi, Mehdi Akikiz

{"title":"Towards a Smart and Sustainable Industry : Cycle Time optimization","authors":"Youssef Raouf, Zoubida Benmamoun, Hanaa Hachimi, Ikhel Jebbor, Mehdi Haqqi, Mehdi Akikiz","doi":"10.1109/IRASET57153.2023.10153046","DOIUrl":"https://doi.org/10.1109/IRASET57153.2023.10153046","url":null,"abstract":"Beyond a simple concern for the company's picture, the global competition requires cost reduction, process rationalization and on-time delivery of customers to remain economically viable and competitive. This needs a continuous improvement and a sustainable strategy, but before this phase, it is necessary to find the influential parameters and put them under control in order to stabilize the process. For this, we referred to Lean Six-Sigma (LSS) tools. In this paper, we concentrate on the wastes that affect the cycle times to produce the good within a process recently implemented. We opted to apply the LSS for the reduction of manufacturing time overruns using the DMAIC concept (Define, Measure, Analyze, Improve, and Control). The proposed case is tested in an aeronautic company. First, we identified the bottleneck in the process. Secondly, we measured the time of each operation of this process and map them via a Value Stream Mapping (VSM). We identified the root causes in the third section. Finally, we established an action plan to improve the process and control the different solutions. As a result, the process cycle efficiency was increased by 16 per cent. Thus, the effectiveness of the proposed method was demonstrated.","PeriodicalId":228989,"journal":{"name":"2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122540594","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

Arabic Speech Commands Recognition with LSTM & GRU Models Using CUDA Toolkit Implementation 使用CUDA工具包实现的LSTM和GRU模型的阿拉伯语语音命令识别

2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2023-05-18 DOI: 10.1109/IRASET57153.2023.10152979

Omayma Mahmoudi, Mouncef Filali Bouami

{"title":"Arabic Speech Commands Recognition with LSTM & GRU Models Using CUDA Toolkit Implementation","authors":"Omayma Mahmoudi, Mouncef Filali Bouami","doi":"10.1109/IRASET57153.2023.10152979","DOIUrl":"https://doi.org/10.1109/IRASET57153.2023.10152979","url":null,"abstract":"Speech commands recognition, especially in the Arabic language, is a fertile field for research, due to the severe shortage of datasets in the Arabic language, unlike the English language, available on the most famous set of data for spoken commands, which is the Google Speech Commands dataset. This has sped up research and given rise to numerous fresh deep-learning methods involving keyword discovery. This paper introduces the classification of two classes of Arabic speech commands taken from the Arabic Speech Commands Dataset (v1.0) using LSTM & GRU models. Furthermore, the model will be trained on a GPU using NVIDIA's CUDA to reduce training time. Several experiments were run during training to investigate how various factors affect the system's performance and ensure that our model's parameters are the best. The outcomes reveal that the proposed method had good training, validation, and testing accuracy, and because of the innovation (CUDA Toolkit), the training was completed quite quickly, and the model accurately identified the commands.","PeriodicalId":228989,"journal":{"name":"2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)","volume":"538 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123913650","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