{"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":null,"pages":null},"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}
{"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":null,"pages":null},"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}
{"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":null,"pages":null},"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}
{"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":null,"pages":null},"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}
{"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":null,"pages":null},"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}
Ahmad Rimi, A. Zugari, A. Mchbal, M. Ouahabi, M. Khalladi
{"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":null,"pages":null},"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}