Pub Date : 2020-11-24DOI: 10.1109/CloudTech49835.2020.9365883
Ismail Angri, A. Najid, Mohammed Mahfoudi
The new standard of mobile technologies called 5G allows enormous improvements, comparing to the previous telecommunication network system LTE, in terms of user requirements by offering different use cases (eMBB, URLLC and mMTC). With the use of the Internet of Things (IoT) by 5G networks, the number of radio devices by each user will drop from 2 to around 7 to 10 devices. Despite this, the saturation of the system does not arise, thanks to the connected equipment’s high density, offered by Massive machine type communications (mMTC). A Radio Resource Management RRM procedure for efficient distribution of available radio resources between those devices is essential for 5G systems. In this article, we have studied the behavior of scheduling algorithms in a 5G environment, for a large number of connected objects and for different types of data flows, while limiting to small cells (Femtocells) with a speed of 3 km/h of the User Equipment (UE). In this objective, we program in C++ two new scheduling algorithms at the base station gNb, namely Exponential PF (EXP/PF) and Exponential Rule (EXP-rule), in addition to those already existing (Maximum-Weight (MW) and Proportional Fair (PF)), using the mmWave model of the famous NS-3 simulator. The performance comparison of the different 5G scheduler schemes was inspected via two important parameters, which are the user throughput and the Signal-to-Interference-plus-Noise Ratio (SINR). Consequently, we have demonstrated that the scheduling algorithms used by LTE networks can be implemented at the 5G gNB level. The results of our simulations have shown that the EXP-rule algorithm provides the best SINR and DataRate values for voice, video and data streams.
{"title":"Performance Evaluation of Newly Implemented Resource Blocks (RB) Allocation Schemes on NS-3 simulator for mMTC 5G NR (New Radio) Femtocells","authors":"Ismail Angri, A. Najid, Mohammed Mahfoudi","doi":"10.1109/CloudTech49835.2020.9365883","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365883","url":null,"abstract":"The new standard of mobile technologies called 5G allows enormous improvements, comparing to the previous telecommunication network system LTE, in terms of user requirements by offering different use cases (eMBB, URLLC and mMTC). With the use of the Internet of Things (IoT) by 5G networks, the number of radio devices by each user will drop from 2 to around 7 to 10 devices. Despite this, the saturation of the system does not arise, thanks to the connected equipment’s high density, offered by Massive machine type communications (mMTC). A Radio Resource Management RRM procedure for efficient distribution of available radio resources between those devices is essential for 5G systems. In this article, we have studied the behavior of scheduling algorithms in a 5G environment, for a large number of connected objects and for different types of data flows, while limiting to small cells (Femtocells) with a speed of 3 km/h of the User Equipment (UE). In this objective, we program in C++ two new scheduling algorithms at the base station gNb, namely Exponential PF (EXP/PF) and Exponential Rule (EXP-rule), in addition to those already existing (Maximum-Weight (MW) and Proportional Fair (PF)), using the mmWave model of the famous NS-3 simulator. The performance comparison of the different 5G scheduler schemes was inspected via two important parameters, which are the user throughput and the Signal-to-Interference-plus-Noise Ratio (SINR). Consequently, we have demonstrated that the scheduling algorithms used by LTE networks can be implemented at the 5G gNB level. The results of our simulations have shown that the EXP-rule algorithm provides the best SINR and DataRate values for voice, video and data streams.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"281 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122536831","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}
Pub Date : 2020-11-24DOI: 10.1109/CloudTech49835.2020.9365898
Mounira Belmabrouk, M. Marrakchi
In this paper, we focus on parallel planning applied to a 2-step graph with a constant task cost which is the precedence graph of the algorithm solving a triangular system. We sort the tasks of 2-steps graph using critical path scheduling and we present a new schedule without and with some availability constraints. Some processors may not be available for some time interval. For each described scheduling, we determine the theoretical value of its makespan. Finally, we expose some experimental results.
{"title":"High-performance computing under availability constraints to solve dense triangular system","authors":"Mounira Belmabrouk, M. Marrakchi","doi":"10.1109/CloudTech49835.2020.9365898","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365898","url":null,"abstract":"In this paper, we focus on parallel planning applied to a 2-step graph with a constant task cost which is the precedence graph of the algorithm solving a triangular system. We sort the tasks of 2-steps graph using critical path scheduling and we present a new schedule without and with some availability constraints. Some processors may not be available for some time interval. For each described scheduling, we determine the theoretical value of its makespan. Finally, we expose some experimental results.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123763861","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}
Pub Date : 2020-11-24DOI: 10.1109/CloudTech49835.2020.9365909
An Braeken, P. Porambage, Amirthan Puvaneswaran, Madhusanka Liyanage
The recent advances in mobile devices and wireless communication sector transformed Mobile Augmented Reality (MAR) from science fiction to reality. Among the other MAR use cases, the incorporation of this MAR technology in the healthcare sector can elevate the quality of diagnosis and treatment for the patients. However, due to the highly sensitive nature of the data available in this process, it is also highly vulnerable to all types of security threats. In this paper, an edge-based secure architecture is presented for a MAR healthcare application. Based on the ESSMAR architecture, a secure key management scheme is proposed for both the registration and authentication phases. Then the security of the proposed scheme is validated using formal and informal verification methods.
{"title":"ESSMAR: Edge Supportive Secure Mobile Augmented Reality Architecture for Healthcare","authors":"An Braeken, P. Porambage, Amirthan Puvaneswaran, Madhusanka Liyanage","doi":"10.1109/CloudTech49835.2020.9365909","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365909","url":null,"abstract":"The recent advances in mobile devices and wireless communication sector transformed Mobile Augmented Reality (MAR) from science fiction to reality. Among the other MAR use cases, the incorporation of this MAR technology in the healthcare sector can elevate the quality of diagnosis and treatment for the patients. However, due to the highly sensitive nature of the data available in this process, it is also highly vulnerable to all types of security threats. In this paper, an edge-based secure architecture is presented for a MAR healthcare application. Based on the ESSMAR architecture, a secure key management scheme is proposed for both the registration and authentication phases. Then the security of the proposed scheme is validated using formal and informal verification methods.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132508667","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}
Pub Date : 2020-11-24DOI: 10.1109/CloudTech49835.2020.9365914
Abderraouf Dembri, Mohamed Gharzouli
Nowadays, several companies use social media marketing to increase profit and control the market. The customer’s feedback has a powerful influence on company reputation by conveying their experience in social media. Customers exchange their feedback about the services using electronic Word-of-Mouth (e-WOM). Negative feedback could help companies improve their service to increase profit. In this work, we propose an approach to determine the effect of negative e-WOM relating to a company’s products or services. Firstly, we apply a machine-learning algorithm called random forest to classify e-WOM on three classes based on polarity: Positive, negative, or neutral. Secondly, we group negative e-WOM into different clusters based on their topics using a similarity method named cosine similarity. Thirdly, we generate an influence graph of negative e-WOM based on time precedence and social ties. Finally, we analyze the resulted graph to identify risk patterns and convey useful information. The provided method is implemented using Python and is tested with collected data.
{"title":"Graph-based Model for Negative e-WOM Influence in Social Media","authors":"Abderraouf Dembri, Mohamed Gharzouli","doi":"10.1109/CloudTech49835.2020.9365914","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365914","url":null,"abstract":"Nowadays, several companies use social media marketing to increase profit and control the market. The customer’s feedback has a powerful influence on company reputation by conveying their experience in social media. Customers exchange their feedback about the services using electronic Word-of-Mouth (e-WOM). Negative feedback could help companies improve their service to increase profit. In this work, we propose an approach to determine the effect of negative e-WOM relating to a company’s products or services. Firstly, we apply a machine-learning algorithm called random forest to classify e-WOM on three classes based on polarity: Positive, negative, or neutral. Secondly, we group negative e-WOM into different clusters based on their topics using a similarity method named cosine similarity. Thirdly, we generate an influence graph of negative e-WOM based on time precedence and social ties. Finally, we analyze the resulted graph to identify risk patterns and convey useful information. The provided method is implemented using Python and is tested with collected data.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115650992","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}
Pub Date : 2020-11-24DOI: 10.1109/CloudTech49835.2020.9365890
Yassine Amkrane, M. Adoui, M. Benjelloun
With breast cancer being one of the recurring diseases affecting women around the globe, the World Health Organization disclosed that more than 620,000 women died from breast cancer in the world in 2018 alone, which represents approximately 15% of all female cancer deaths. Thus, breast cancer diagnosis presents one of the main challenges that need to get timely treatments. In this context, multiple image modalities, namely mammography, echography and magnetic resonance Imaging (MRI) are used for breast tumor diagnosis. One of the main treatments of this pathology is chemotherapy. However, several secondary effects (hair loss, osteoporosis, vomiting, etc.) can occur due this treatment, and cancer can not respond to it. This paper aims to suggest a novel method to predict breast tumor response to treatment, using three main steps: 1. Tumor segmentation from MR images ; 2. Extraction of features from segmented tumors in order to generate a complete and exploitable database ; 3. The use of deep and machine learning architectures to compute tumor-response prediction models. Experimental results are applied using a public QIN Breast DCE-MRI dataset of breast cancer patients.
{"title":"Towards Breast Cancer Response Prediction using Artificial Intelligence and Radiomics","authors":"Yassine Amkrane, M. Adoui, M. Benjelloun","doi":"10.1109/CloudTech49835.2020.9365890","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365890","url":null,"abstract":"With breast cancer being one of the recurring diseases affecting women around the globe, the World Health Organization disclosed that more than 620,000 women died from breast cancer in the world in 2018 alone, which represents approximately 15% of all female cancer deaths. Thus, breast cancer diagnosis presents one of the main challenges that need to get timely treatments. In this context, multiple image modalities, namely mammography, echography and magnetic resonance Imaging (MRI) are used for breast tumor diagnosis. One of the main treatments of this pathology is chemotherapy. However, several secondary effects (hair loss, osteoporosis, vomiting, etc.) can occur due this treatment, and cancer can not respond to it. This paper aims to suggest a novel method to predict breast tumor response to treatment, using three main steps: 1. Tumor segmentation from MR images ; 2. Extraction of features from segmented tumors in order to generate a complete and exploitable database ; 3. The use of deep and machine learning architectures to compute tumor-response prediction models. Experimental results are applied using a public QIN Breast DCE-MRI dataset of breast cancer patients.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"310 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115909869","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}
Pub Date : 2020-11-24DOI: 10.1109/cloudtech49835.2020.9365891
{"title":"CloudTech 2020 Contents","authors":"","doi":"10.1109/cloudtech49835.2020.9365891","DOIUrl":"https://doi.org/10.1109/cloudtech49835.2020.9365891","url":null,"abstract":"","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130655682","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}
Pub Date : 2020-11-24DOI: 10.1109/CloudTech49835.2020.9365919
Alae-Eddine Bouaouad, A. Cherradi, Assoul Saliha, N. Souissi
The IoT architectures proposed in the literature and which can be deployed in a Cloud environment are diverse and multiple. These architectures are organized in several layers that differ in terms of functionality and number. It is therefore necessary to analyze these architectures and their layers in order to identify the key layers to define a complete and exhaustive architecture. This paper presents the result of the analysis of the different IoT architectures in a Cloud environment, proposed in the literature, and thus allows us to identify initially twelve layers cited in the thirty-two architectures studied. The results of this analysis show that six key layers are relevant to build a new reference architecture of an IoT system in a Cloud environment.
{"title":"The key layers of IoT architecture","authors":"Alae-Eddine Bouaouad, A. Cherradi, Assoul Saliha, N. Souissi","doi":"10.1109/CloudTech49835.2020.9365919","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365919","url":null,"abstract":"The IoT architectures proposed in the literature and which can be deployed in a Cloud environment are diverse and multiple. These architectures are organized in several layers that differ in terms of functionality and number. It is therefore necessary to analyze these architectures and their layers in order to identify the key layers to define a complete and exhaustive architecture. This paper presents the result of the analysis of the different IoT architectures in a Cloud environment, proposed in the literature, and thus allows us to identify initially twelve layers cited in the thirty-two architectures studied. The results of this analysis show that six key layers are relevant to build a new reference architecture of an IoT system in a Cloud environment.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132123355","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}
Pub Date : 2020-11-24DOI: 10.1109/cloudtech49835.2020.9365894
{"title":"CloudTech 2020 Copyright Page","authors":"","doi":"10.1109/cloudtech49835.2020.9365894","DOIUrl":"https://doi.org/10.1109/cloudtech49835.2020.9365894","url":null,"abstract":"","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114299794","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}
Pub Date : 2020-11-24DOI: 10.1109/CloudTech49835.2020.9365884
Najat Tissir, S. E. Kafhali, N. Aboutabit
Cloud Computing is an evolving term that is subject to security threats, vulnerabilities, and attacks. Latterly, various classifications and taxonomies have been suggested to characterize and classify cloud security issues. Some of them are based on general security factors, such as the CIA triad (confidentiality, integrity, and availability), while others specify cloud security classes. Most of these classes are determined by the cloud’s attributes, such as Cloud service models, cloud deployment models, and cloud actors. In this paper, we explore the already existing criteria and dimensions considered in the development of cloud computing security classification/taxonomy. Then, we study and compare their strengths and characteristics. Thereafter, our objective is to provide and develop exhaustive cloud security taxonomy and push researchers to better comprehend the nature of any newly introduced threat or attack, categorize them, and explain the relationship between threats and other categories or subcategories.
{"title":"Cloud Computing security classifications and taxonomies: a comprehensive study and comparison","authors":"Najat Tissir, S. E. Kafhali, N. Aboutabit","doi":"10.1109/CloudTech49835.2020.9365884","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365884","url":null,"abstract":"Cloud Computing is an evolving term that is subject to security threats, vulnerabilities, and attacks. Latterly, various classifications and taxonomies have been suggested to characterize and classify cloud security issues. Some of them are based on general security factors, such as the CIA triad (confidentiality, integrity, and availability), while others specify cloud security classes. Most of these classes are determined by the cloud’s attributes, such as Cloud service models, cloud deployment models, and cloud actors. In this paper, we explore the already existing criteria and dimensions considered in the development of cloud computing security classification/taxonomy. Then, we study and compare their strengths and characteristics. Thereafter, our objective is to provide and develop exhaustive cloud security taxonomy and push researchers to better comprehend the nature of any newly introduced threat or attack, categorize them, and explain the relationship between threats and other categories or subcategories.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128640350","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}
Pub Date : 2020-11-24DOI: 10.1109/CloudTech49835.2020.9365906
Soufiane El Mrabti, M. Lazaar, Mohammed Al Achhab, Hicham Omara
In this paper, we propose a Novel Convex Polyhedron classifier (NCPC) based on the geometric concept convex hull. NCPC is basically a linear piecewise classifier (LPC). It partitions linearly non-separable data into various linearly separable subsets. For each of these subset of data, a linear hyperplane is used to classify them. We evaluate the performance of this classifier by combining it with two feature selection methods (Chi- squared and Anova F-value). Using two datasets, the results indicate that our proposed classifier outperforms other LPC- based classifiers.
{"title":"Novel Convex Polyhedron Classifier for Sentiment Analysis","authors":"Soufiane El Mrabti, M. Lazaar, Mohammed Al Achhab, Hicham Omara","doi":"10.1109/CloudTech49835.2020.9365906","DOIUrl":"https://doi.org/10.1109/CloudTech49835.2020.9365906","url":null,"abstract":"In this paper, we propose a Novel Convex Polyhedron classifier (NCPC) based on the geometric concept convex hull. NCPC is basically a linear piecewise classifier (LPC). It partitions linearly non-separable data into various linearly separable subsets. For each of these subset of data, a linear hyperplane is used to classify them. We evaluate the performance of this classifier by combining it with two feature selection methods (Chi- squared and Anova F-value). Using two datasets, the results indicate that our proposed classifier outperforms other LPC- based classifiers.","PeriodicalId":272860,"journal":{"name":"2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128222192","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}
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