Pub Date : 2023-01-01DOI: 10.5455/jjcit.71-1667394363
M. Ndenga
Detecting and controlling propagation of hate-speech over social media platforms is a challenge. This problem is exacerbated by extreme fast flow, readily available audience, and relative permanence of information on social media. The objective of this research is to propose a model that could be used to detect political hate speech that is propagated through social media platforms in Kenya. Using Twitter textual data and Keras TensorFlow Decision Forests (TF-DF), three models were developed i.e., Gradient Boosted Trees with Universal Sentence Embeddings(USE), Gradient Boosted Trees, and Random Forest respectively. The Gradient Boosted Trees with USE model exhibited a superior performance with an accuracy of 98.86%, recall of 0.9587, precision of 0.9831, and AUC of 0.9984. Therefore, this model can be utilized for detecting hate speech on social media platforms.
{"title":"A Deep Decision Forests Model for Hate Speech Detection","authors":"M. Ndenga","doi":"10.5455/jjcit.71-1667394363","DOIUrl":"https://doi.org/10.5455/jjcit.71-1667394363","url":null,"abstract":"Detecting and controlling propagation of hate-speech over social media platforms is a challenge. This problem is exacerbated by extreme fast flow, readily available audience, and relative permanence of information on social media. The objective of this research is to propose a model that could be used to detect political hate speech that is propagated through social media platforms in Kenya. Using Twitter textual data and Keras TensorFlow Decision Forests (TF-DF), three models were developed i.e., Gradient Boosted Trees with Universal Sentence Embeddings(USE), Gradient Boosted Trees, and Random Forest respectively. The Gradient Boosted Trees with USE model exhibited a superior performance with an accuracy of 98.86%, recall of 0.9587, precision of 0.9831, and AUC of 0.9984. Therefore, this model can be utilized for detecting hate speech on social media platforms.","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70821355","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 : 2023-01-01DOI: 10.5455/jjcit.71-1670862654
Ahmad Taani, Ishraq Dagamseh
Pneumonia is a life-threatening disease and early detection can save lives, many automated systems have contributed to the detection of this disease and currently deep learning models have become one of the most widely used models for building these systems. In this study, two deep learning models are combined: DenseNet169 and pre-activation ResNet models, and used for automatic detection of pneumonia. DenseNet169 model is an extension of the ResNet model, while the second is a modified version the ResNet model, these models achieved good results in the field of medical imaging. Two methods are used to deal with the problem of unbalanced data: class weight, which enables to control the percentage of data to be used from the original data for each class of data, while the other method is resampling, in which modified images are produced with an equal distribution using data augmentation. The performance of the proposed model is evaluated using a balanced dataset consists of 5856 images. Achieved results were promising compared to several previous studies. The model achieved a precision value of 98%, an area under curve (AUC) based on ROC of 97%, and a loss value of 0.23.
{"title":"AUTOMATIC DETECTION OF PNEUMONIA USING CONCATENATED CONVOLUTIONAL NEURAL NETWORK","authors":"Ahmad Taani, Ishraq Dagamseh","doi":"10.5455/jjcit.71-1670862654","DOIUrl":"https://doi.org/10.5455/jjcit.71-1670862654","url":null,"abstract":"Pneumonia is a life-threatening disease and early detection can save lives, many automated systems have contributed to the detection of this disease and currently deep learning models have become one of the most widely used models for building these systems. In this study, two deep learning models are combined: DenseNet169 and pre-activation ResNet models, and used for automatic detection of pneumonia. DenseNet169 model is an extension of the ResNet model, while the second is a modified version the ResNet model, these models achieved good results in the field of medical imaging. Two methods are used to deal with the problem of unbalanced data: class weight, which enables to control the percentage of data to be used from the original data for each class of data, while the other method is resampling, in which modified images are produced with an equal distribution using data augmentation. The performance of the proposed model is evaluated using a balanced dataset consists of 5856 images. Achieved results were promising compared to several previous studies. The model achieved a precision value of 98%, an area under curve (AUC) based on ROC of 97%, and a loss value of 0.23.","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70820989","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 : 2023-01-01DOI: 10.5455/jjcit.71-1667637331
Zahra Mahmoudi, Elham Darbanian, M. Nickray
Cloud computing plays an essential role in development of the Internet of Things, which provides data processing and storage services. Fog computing is the evolution of cloud computing, which helps provide solutions to cloud computing challenges such as latency, location awareness, and real-time mobility support. Fog computing fills the gap between the cloud and IoT devices within the close vicinity of IoT devices. So, computation, networking, storage, data management, and decision making occur along the path between the cloud and IoT devices. The automatic and intelligent management of fog node resources and achieving an effective scheduling policy in the computing model is a necessary requirement and will lead to the improvement of the overall performance of fog computing. Some optimization problems are modeled by mixed-integer nonlinear programming (MINLP). In this paper, a model, i.e. an MINLP optimization problem on fog computing, is designed. Our model has two goals: to increase Cost Performance as well as to reduce energy consumption. Cost Performance is the price, users are charged as benefit/revenue. In other words Cost Performance is defined as the ratio of the average data rate of each user to its cost. Then the exact mathematical method with the GAMS program was used to prove its logical process. In the next step, we solved the model with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing+GA (SA+GA), Teaching–Learning-Based Optimization (TLBO), Grey Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), and random method. According to the TOPSIS comparison, the SA+GA method with a value of 0.23 is the best compared to other methods. Then GWO, GA, TLBO, PSO, and GOA methods are better, respectively.
{"title":"OPTIMAL ENERGY CONSUMPTION AND COST PERFORMANCE SOLUTION WITH DELAY CONSTRAINTS ON FOG COMPUTING","authors":"Zahra Mahmoudi, Elham Darbanian, M. Nickray","doi":"10.5455/jjcit.71-1667637331","DOIUrl":"https://doi.org/10.5455/jjcit.71-1667637331","url":null,"abstract":"Cloud computing plays an essential role in development of the Internet of Things, which provides data processing and storage services. Fog computing is the evolution of cloud computing, which helps provide solutions to cloud computing challenges such as latency, location awareness, and real-time mobility support. Fog computing fills the gap between the cloud and IoT devices within the close vicinity of IoT devices. So, computation, networking, storage, data management, and decision making occur along the path between the cloud and IoT devices. The automatic and intelligent management of fog node resources and achieving an effective scheduling policy in the computing model is a necessary requirement and will lead to the improvement of the overall performance of fog computing. Some optimization problems are modeled by mixed-integer nonlinear programming (MINLP). In this paper, a model, i.e. an MINLP optimization problem on fog computing, is designed. Our model has two goals: to increase Cost Performance as well as to reduce energy consumption. Cost Performance is the price, users are charged as benefit/revenue. In other words Cost Performance is defined as the ratio of the average data rate of each user to its cost. Then the exact mathematical method with the GAMS program was used to prove its logical process. In the next step, we solved the model with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing+GA (SA+GA), Teaching–Learning-Based Optimization (TLBO), Grey Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), and random method. According to the TOPSIS comparison, the SA+GA method with a value of 0.23 is the best compared to other methods. Then GWO, GA, TLBO, PSO, and GOA methods are better, respectively.","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70820913","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 : 2023-01-01DOI: 10.5455/jjcit.71-1673581703
A. Editya, T. Ahmad, H. Studiawan
Drones are one of devices that are used in many different activities. There is a time when drones have accidents, and authorities need to find the cause. Drone forensics is used to determine the cause of an accident. The analysis phase of drone forensics is one of the most important steps in determining accident causes. In this paper, we applied deep learning technique to classify drone collisions. We investigate the use of the InceptionV3 as the deep learning framework. Additionally, this study compares the performance of the proposed method with other techniques, such as MobileNet, VGG, and ResNet, in classifying drone collisions. In this experiment, we also implement transfer learning as well as its fine tuning to speed up the training process and to improve the accuracy value. Additionally, our investigation shows that InceptionV3 outperforms others in terms of accuracy, precision, and F1 scores.
{"title":"Forensic Analysis of Drone Collision with Transfer Learning","authors":"A. Editya, T. Ahmad, H. Studiawan","doi":"10.5455/jjcit.71-1673581703","DOIUrl":"https://doi.org/10.5455/jjcit.71-1673581703","url":null,"abstract":"Drones are one of devices that are used in many different activities. There is a time when drones have accidents, and authorities need to find the cause. Drone forensics is used to determine the cause of an accident. The analysis phase of drone forensics is one of the most important steps in determining accident causes. In this paper, we applied deep learning technique to classify drone collisions. We investigate the use of the InceptionV3 as the deep learning framework. Additionally, this study compares the performance of the proposed method with other techniques, such as MobileNet, VGG, and ResNet, in classifying drone collisions. In this experiment, we also implement transfer learning as well as its fine tuning to speed up the training process and to improve the accuracy value. Additionally, our investigation shows that InceptionV3 outperforms others in terms of accuracy, precision, and F1 scores.","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70821169","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 : 2023-01-01DOI: 10.5455/jjcit.71-1689717889
Lakhdar Laimeche, Issam Djellab, Mohamed Redjimi
The field of computer vision and pattern recognition has shown great interest in facial recognition due to its wide range of applications. These applications span across historical and genealogical research, forensic science, searching for missing family members, analyzing social media, automatically managing and annotating image databases, and identifying kinship relationships. This research paper aims to address the challenges associated with facial recognition by introducing two innovative approaches: Fusion-based Classifier Combination (FCC) and Sequential CNN Deep learning-based face recognition (S-CNN). In the first part of the study, we assess the effectiveness of three techniques: Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), and a hand-crafted learned technique called Compact Binary Facial Descriptors (CBFD). To overcome these challenges, we employ a classification step that utilizes a novel multi-classifier combination model. In the second part, we propose a novel method where we extract high-level features from multiple image regions treated as sequential data using ensemble of Convolutional Neural Networks (CNNs). These features are then fed into a Deep Neural Network (DNN) for facial recognition. The experimental results obtained from well-known face databases, including Labeled Faces in the Wild (LFW) and ORL, highlight the competitive performance of both the proposed multi-classifier combination model and the S-CNN deep learning model when compared to state-of-the-art methods .
人脸识别由于其广泛的应用，引起了计算机视觉和模式识别领域的极大兴趣。这些应用涵盖了历史和家谱研究、法医学、寻找失踪的家庭成员、分析社交媒体、自动管理和注释图像数据库以及识别亲属关系。本文旨在通过引入两种创新方法来解决与面部识别相关的挑战:基于融合的分类器组合(FCC)和基于顺序CNN深度学习的面部识别(S-CNN)。在研究的第一部分，我们评估了三种技术的有效性:局部二值模式(LBP)、定向梯度直方图(HOG)和一种称为紧凑二值面部描述符(CBFD)的手工学习技术。为了克服这些挑战，我们采用了一种利用新型多分类器组合模型的分类步骤。在第二部分中，我们提出了一种新的方法，我们使用卷积神经网络(cnn)的集合从多个图像区域中提取高级特征，这些图像区域被视为序列数据。然后将这些特征输入深度神经网络(DNN)进行面部识别。从知名的人脸数据库(包括Labeled Faces in The Wild (LFW)和ORL)中获得的实验结果表明，与最先进的方法相比，所提出的多分类器组合模型和S-CNN深度学习模型具有竞争力。
{"title":"CAN THE COMBINATION OF FACIAL FEATURES ENHANCE THE PERFORMANCE OF FACE RECOGNITION?","authors":"Lakhdar Laimeche, Issam Djellab, Mohamed Redjimi","doi":"10.5455/jjcit.71-1689717889","DOIUrl":"https://doi.org/10.5455/jjcit.71-1689717889","url":null,"abstract":"The field of computer vision and pattern recognition has shown great interest in facial recognition due to its wide range of applications. These applications span across historical and genealogical research, forensic science, searching for missing family members, analyzing social media, automatically managing and annotating image databases, and identifying kinship relationships. This research paper aims to address the challenges associated with facial recognition by introducing two innovative approaches: Fusion-based Classifier Combination (FCC) and Sequential CNN Deep learning-based face recognition (S-CNN). In the first part of the study, we assess the effectiveness of three techniques: Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), and a hand-crafted learned technique called Compact Binary Facial Descriptors (CBFD). To overcome these challenges, we employ a classification step that utilizes a novel multi-classifier combination model. In the second part, we propose a novel method where we extract high-level features from multiple image regions treated as sequential data using ensemble of Convolutional Neural Networks (CNNs). These features are then fed into a Deep Neural Network (DNN) for facial recognition. The experimental results obtained from well-known face databases, including Labeled Faces in the Wild (LFW) and ORL, highlight the competitive performance of both the proposed multi-classifier combination model and the S-CNN deep learning model when compared to state-of-the-art methods .","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135502111","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 : 2023-01-01DOI: 10.5455/jjcit.71-1672251891
Ahmad Rimi, A. Zugari, A. Mchbal, M. Ouahabi, M. Khalladi
A compact broadband antenna of dimensions 27 mm x 28 mm x 1.6 mm and with good impedance matching is designed for high-bandwidth radio systems with a short range. To improve the impedance matching, two rectangular slots are created on the radiating element, and the ground plane size is reduced to extend the ultra-wideband frequency band. The antenna bandwidth and radiation performance are analysed using characteristic mode theory (TCM). The performance is compared to the desired specifications, and the shape and size are modified to produce efficient radiation and dominant radiation patterns. The findings clearly demonstrate that the six modes are resonant with (λn = 0). This implies that the eigenvalues of the six modes contribute strongly to dominant electromagnetic radiation and have high modal significance values around 1 at their respective frequencies. Furthermore, the characteristic angle indicates that the antenna resonates at 180°, since the six modes intersect the axis line at 180° at their respective frequencies. Experimental results show a bandwidth of 109.7% between 5.64 and 19.34 GHz, a maximum gain of 6.3 dB, and a maximum efficiency of approximately 86.5%. These results make this antenna a versatile and effective choice for a wide variety of communications and electronics applications and easy to install in narrow spaces due to its easy design characteristics, small size, and light weight.
一种尺寸为27mm x 28mm x 1.6 mm的紧凑型宽带天线，具有良好的阻抗匹配，适用于短距离的高带宽无线电系统。为了改善阻抗匹配，在辐射元件上创建了两个矩形槽，减小了接地面尺寸，扩展了超宽带频段。利用特征模理论对天线的带宽和辐射性能进行了分析。将性能与期望的规格进行比较，并修改形状和尺寸以产生有效的辐射和主导辐射模式。结果表明，6种模态均与λn = 0共振，说明6种模态的特征值对主导电磁辐射的贡献较大，且在各自频率处模态显著性值均在1左右。此外，特征角表明天线在180°共振，因为六个模式在各自的频率下以180°相交轴线。实验结果表明，该系统在5.64 ~ 19.34 GHz之间的带宽为109.7%，最大增益为6.3 dB，最大效率约为86.5%。这些结果使该天线成为各种通信和电子应用的通用和有效的选择，并且由于其易于设计的特点，小尺寸和重量轻，易于安装在狭窄的空间中。
{"title":"DESIGN OF A COMPACT BROADBAND ANTENNA USING \u0000CHARACTERISTIC MODE ANALYSIS FOR MICROWAVE \u0000APPLICATIONS","authors":"Ahmad Rimi, A. Zugari, A. Mchbal, M. Ouahabi, M. Khalladi","doi":"10.5455/jjcit.71-1672251891","DOIUrl":"https://doi.org/10.5455/jjcit.71-1672251891","url":null,"abstract":"A compact broadband antenna of dimensions 27 mm x 28 mm x 1.6 mm and with good impedance matching is designed for high-bandwidth radio systems with a short range. To improve the impedance matching, two rectangular slots are created on the radiating element, and the ground plane size is reduced to extend the ultra-wideband frequency band. The antenna bandwidth and radiation performance are analysed using characteristic mode theory (TCM). The performance is compared to the desired specifications, and the shape and size are modified to produce efficient radiation and dominant radiation patterns. The findings clearly demonstrate that the six modes are resonant with (λn = 0). This implies that the eigenvalues of the six modes contribute strongly to dominant electromagnetic radiation and have high modal significance values around 1 at their respective frequencies. Furthermore, the characteristic angle indicates that the antenna resonates at 180°, since the six modes intersect the axis line at 180° at their respective frequencies. Experimental results show a bandwidth of 109.7% between 5.64 and 19.34 GHz, a maximum gain of 6.3 dB, and a maximum efficiency of approximately 86.5%. These results make this antenna a versatile and effective choice for a wide variety of communications and electronics applications and easy to install in narrow spaces due to its easy design characteristics, small size, and light weight.","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70821045","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 : 2023-01-01DOI: 10.5455/jjcit.71-1674653544
Ahcène Abed, A. Bouchekhlal, A. Amrouche, R. Bendoumia
This paper describes a substrate integrated waveguide (SIW) filter built on an Fr4-Epoxy substrate with a dielectric constant 4.4 and a height of h=1.6mm. SIW-based devices have piqued the interest of researchers in recent years due to their low loss, small size, and low cost. The goal is to simulate and realize a SIW filter for C-band applications. The designed filter is analyzed using the reflection coefficient S11 and electric field distributions. We used the HFSS simulator. According to the findings, there is a high degree of agreement between the simulated and realized filters. The results also show that the filter has a very good response. It also shows a bandwidth of 6.3GHz around the C-band frequencies. This filter's pass-band ranges from 5.39 to 6.83GHz, with an insertion loss of 4.2dB and a return loss of 45.49dB.
{"title":"LOW-COST C-BAND SIW BANDPASS FILTER USING FR4-EPOXY SUBSTRATE","authors":"Ahcène Abed, A. Bouchekhlal, A. Amrouche, R. Bendoumia","doi":"10.5455/jjcit.71-1674653544","DOIUrl":"https://doi.org/10.5455/jjcit.71-1674653544","url":null,"abstract":"This paper describes a substrate integrated waveguide (SIW) filter built on an Fr4-Epoxy substrate with a dielectric constant 4.4 and a height of h=1.6mm. SIW-based devices have piqued the interest of researchers in recent years due to their low loss, small size, and low cost. The goal is to simulate and realize a SIW filter for C-band applications. The designed filter is analyzed using the reflection coefficient S11 and electric field distributions. We used the HFSS simulator. According to the findings, there is a high degree of agreement between the simulated and realized filters. The results also show that the filter has a very good response. It also shows a bandwidth of 6.3GHz around the C-band frequencies. This filter's pass-band ranges from 5.39 to 6.83GHz, with an insertion loss of 4.2dB and a return loss of 45.49dB.","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70821471","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 : 2023-01-01DOI: 10.5455/jjcit.71-1682778947
N.A.H. Sultan, Rawaa Qasha
Blockchain technology has garnered interest in several scientific and engineering fields. To improve blockchain services, its execution challenges must be addressed. Container-based virtualization enables running isolated apps on a shared OS where blockchain technology can leverage this technology to run numerous nodes, smart contracts, and decentralized apps in distinct containers allowing resource isolation and allocation, faster deployment and scalability, and improved security through limited host OS and other container access. This article covers container-based virtualization for Blockchain technology, including current methodologies, prospects, and future perspectives. Initially, this study explains blockchain and containerization as well as the reason for their integration. Then reviews container virtualization services to address blockchain complexity, size, scalability, and security. Conversely, Container technology uses blockchain to protect data and enhance resource management. Next, analyzes the latest containerization and blockchain integration studies. Finally, difficulties and future directions are considered to advance this promising research.
{"title":"CONTAINER-BASED VIRTUALIZATION FOR BLOCKCHAIN TECHNOLOGY: A SURVEY","authors":"N.A.H. Sultan, Rawaa Qasha","doi":"10.5455/jjcit.71-1682778947","DOIUrl":"https://doi.org/10.5455/jjcit.71-1682778947","url":null,"abstract":"Blockchain technology has garnered interest in several scientific and engineering fields. To improve blockchain services, its execution challenges must be addressed. Container-based virtualization enables running isolated apps on a shared OS where blockchain technology can leverage this technology to run numerous nodes, smart contracts, and decentralized apps in distinct containers allowing resource isolation and allocation, faster deployment and scalability, and improved security through limited host OS and other container access. This article covers container-based virtualization for Blockchain technology, including current methodologies, prospects, and future perspectives. Initially, this study explains blockchain and containerization as well as the reason for their integration. Then reviews container virtualization services to address blockchain complexity, size, scalability, and security. Conversely, Container technology uses blockchain to protect data and enhance resource management. Next, analyzes the latest containerization and blockchain integration studies. Finally, difficulties and future directions are considered to advance this promising research.","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70821689","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 : 2023-01-01DOI: 10.5455/jjcit.71-1688749546
Rachida Boufouss, Abdellah Najid
In this paper, a microstrip bandpass filter for dual-band sub-6 GHz 5G mobile communications is designed. The two first resonance frequencies of the stepped-impedance resonator are used as the operating frequencies of the two passbands. Furthermore, the stepped-impedance resonator is folded to form open-loop stepped-impedance resonator for compactness. This form of the resonator generates a second transmission zero in the upper stopband which improve the out of-band rejection. A 50 Ω tapped-line input/output is used to feed the filter. The proposed structure is designed, analyzed and manufactured and their measured results are found to be in good agreement with the simulation results. From the measured results, it is found that the proposed filter achieved return loss of 21 dB and 28.3 dB, insertion loss of 0.4 dB and 1.7 dB and bandwidth of 12.5 % and 10.81% at 3.61 GHz and 5.55 GHz, respectively. In addition, the proposed filter has a compact size which make it suitable for sub-6 GHz 5G mobile communications.
{"title":"Design of Microstrip Dual-Band Bandpass Filter for Sub-6 GHz 5G Mobile Communications.","authors":"Rachida Boufouss, Abdellah Najid","doi":"10.5455/jjcit.71-1688749546","DOIUrl":"https://doi.org/10.5455/jjcit.71-1688749546","url":null,"abstract":"In this paper, a microstrip bandpass filter for dual-band sub-6 GHz 5G mobile communications is designed. The two first resonance frequencies of the stepped-impedance resonator are used as the operating frequencies of the two passbands. Furthermore, the stepped-impedance resonator is folded to form open-loop stepped-impedance resonator for compactness. This form of the resonator generates a second transmission zero in the upper stopband which improve the out of-band rejection. A 50 Ω tapped-line input/output is used to feed the filter. The proposed structure is designed, analyzed and manufactured and their measured results are found to be in good agreement with the simulation results. From the measured results, it is found that the proposed filter achieved return loss of 21 dB and 28.3 dB, insertion loss of 0.4 dB and 1.7 dB and bandwidth of 12.5 % and 10.81% at 3.61 GHz and 5.55 GHz, respectively. In addition, the proposed filter has a compact size which make it suitable for sub-6 GHz 5G mobile communications.","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135839866","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 : 2023-01-01DOI: 10.5455/jjcit.71-1689174557
Hariyady Hariyady, Ag Ibrahim, Jason Teo, Ng Weng, Azhana Ahmad, Fouziah Yassin, Carolyn Salimun
Emotion identification has received a lot of interest in recent years, with applications in mental health, education, and marketing. This systematic literature review aimed to provide an up-to-date overview of trends and technological advancements in the use of media stimuli for emotion recognition. A comprehensive search yielded 720 relevant studies from 2018 to 2023, which employed various media stimuli to induce and measure emotional responses. The main findings indicate that audios and videos are the most used media stimuli for emotion recognition. However, there is a growing trend toward exploring other forms of media, such as physiological signals and wearables. This review highlights the varying ecological validity of different stimulus types and emphasizes the potential of virtual reality for more objective emotion recognition. These findings offer valuable insights for future research and practical applications in the field by synthesizing knowledge to inform advancements in media stimuli for emotion recognition.
{"title":"MEDIA STIMULI OF EMOTION RECOGNITION: A STATE-OF-THE-ART REVIEW OF CURRENT TRENDS AND TECHNOLOGY","authors":"Hariyady Hariyady, Ag Ibrahim, Jason Teo, Ng Weng, Azhana Ahmad, Fouziah Yassin, Carolyn Salimun","doi":"10.5455/jjcit.71-1689174557","DOIUrl":"https://doi.org/10.5455/jjcit.71-1689174557","url":null,"abstract":"Emotion identification has received a lot of interest in recent years, with applications in mental health, education, and marketing. This systematic literature review aimed to provide an up-to-date overview of trends and technological advancements in the use of media stimuli for emotion recognition. A comprehensive search yielded 720 relevant studies from 2018 to 2023, which employed various media stimuli to induce and measure emotional responses. The main findings indicate that audios and videos are the most used media stimuli for emotion recognition. However, there is a growing trend toward exploring other forms of media, such as physiological signals and wearables. This review highlights the varying ecological validity of different stimulus types and emphasizes the potential of virtual reality for more objective emotion recognition. These findings offer valuable insights for future research and practical applications in the field by synthesizing knowledge to inform advancements in media stimuli for emotion recognition.","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135561618","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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