Computer Science and Information Technologies (E-Journal)
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Optimization of an inset-fed calculations for rectangular microstrip antenna
Get PDFThis paper offers an alternative solution to produce a formula to calculate the inset-fed (????????) feed distance from the edge of the patch in order to obtain the impedance of a rectangular patch microstrip antenna as close to 50 Ohm as possible. The basic calculation refers to Ramesh's calculation formula to calculate the inset-fed (????????) on a rectangular patch microstrip antenna. From this research it is hoped that a number of correction factors will be obtained which will be multiplied by the Ramesh formula in order to obtain an inset-fed calculation which results in a lower return loss (????????????) and voltage standing wave ratio (VSWR). From several calculation attempts, the approximate value for the correction factor is s=0.83477. This s correction factor is then multiplied by Ramesh's calculation formula. In this experiment, a microstrip antenna was simulated using an FR4 epoxy printed circuit board (PCB) with a relative permittivity, ???????? = ????. ????, with a thickness of h=1.6 mm. The specified antenna nominal input impedance is 50 Ohms. The transmission line used is a microstrip line with a characteristic impedance of 50 Ohm. The test method used is to compare the results of simulation calculations using the Ramesh formula with the results of simulation calculations using the Ramesh formula multiplied by the correction factor. Tests are carried out using varying working frequencies. From the experimental results it can be seen that the average Return Loss (????????????) and VSWR of the antenna are lower when using the Ramesh formula with a correction factor compared to the original Ramesh formula
Constraint satisfaction algorithms: edition of timetables in the license-master-doctorate system
Get PDFIn this paper, we studied some algorithms for solving constraint satisfaction problem (CSP) and then applied them to solve the problem of generating schedules in a university setting. In other words, we studied the genetic algorithm, the simulated annealing, the hill climbing, a hybridization of the genetic algorithm and the simulated annealing as well as a hybridization of the genetic algorithm and the hill climbing. These algorithms have been tested on the problem of scheduling in a university environment. The hybrid uses hill climbing or simulated annealing to improve each individual in the starting population to a certain stopping point. These individuals are then sent to the genetic algorithm. Our results show that the hybridization of the genetic algorithm with a metaheuristic gives better execution time and performs better as the problem size increases compared to the classical genetic algorithm
Agile adoption challenges in insurance: a systematic literature and expert review
Get PDFThe drawback of agile is struggled to function in large businesses like banks, insurance companies, and government agencies, which are frequently associated with cumbersome processes. Traditional software development techniques were cumbersome and pay more attention to standardization and industry, this leads to high costs and prolonged costs. The insurance company does not embrace change and agility may find themselves distracted and lose customers to agile competitors who are more relevant and customer-centric. Thus, to investigate the challenges and to recognize the prospect of agile adoption in insurance industry, a systematic literature review (SLR) in this study was organized and validated by expert review from professional with expertise in agile. The project performance domain from project management body of knowledge (PMBOK) was applied to align the challenges and the solution. Academicians and practitioners can acquire the perception and knowledge in having exceeded understanding about the challenge and solution of agile adoption from the results
Antispoofing in face biometrics: a comprehensive study on software-based techniques
Get PDFThe vulnerability of the face recognition system to spoofing attacks has piqued the biometric community's interest, motivating them to develop antispoofing techniques to secure it. Photo, video, or mask attacks can compromise face biometric systems (types of presentation attacks). Spoofing attacks are detected using liveness detection techniques, which determine whether the facial image presented at a biometric system is a live face or a fake version of it. We discuss the classification of face anti-spoofing techniques in this paper. Anti-spoofing techniques are divided into two categories: hardware and software methods. Hardware-based techniques are summarized briefly. A comprehensive study on software-based countermeasures for presentation attacks is discussed, which are further divided into static and dynamic methods. We cited a few publicly available presentation attack datasets and calculated a few metrics to demonstrate the value of anti-spoofing techniques
The effect of segmentation on the performance of machine learning methods on the morphological classification of Friesien Holstein dairy cows
Get PDFMany classification algorithms are in the form of image pattern recognition; the approach to the complexity of the problem should be a feature of feasibility for representing images. The morphology of dairy cows greatly affects their health and milk production. The paper will apply several classification methods based on the morphology of Holstein Friesian dairy cows. To improve the accuracy of the model used, the segmentation process is the right step. In this paper, we compare several machine learning algorithms to get optimal accuracy. The algorithm used a support vector machine (SVM), artificial neural networks, random forests and logistic regression. Segmentation methods used are mask region-based convolutional neural network (R-CNN) and Canny; optimal accuracy is expected to create intelligent applications. The success of the method is measured with accuracy, precision, recall, and F1 Score, as well as testing by conducting a training-testing ratio of 90:10 and 80:20. This study discovered an artificial neural network optimal model with Canny with an accuracy of 82.50%, precision of 87.00%, recall of 79.00%, F1-score of 81.62%, and testing ratio of 90:10. While the models with the highest 80:20 ratio achieved 84.39% accuracy, 88.46% precision, 80.47%, and 83.00% F1-score with mask RCNN with logistic regression
Exploring network security threats through text mining techniques: a comprehensive analysis
Get PDFIn response to the escalating cybersecurity threats, this research focuses on leveraging text mining techniques to analyze network security data effectively. The study utilizes user-generated reports detailing attacks on server networks. Employing clustering algorithms, these reports are grouped based on threat levels. Additionally, a classification algorithm discerns whether network activities pose security risks. The research achieves a noteworthy 93% accuracy in text classification, showcasing the efficacy of these techniques. The novelty lies in classifying security threat report logs according to their threat levels. Prioritizing high-risk threats, this approach aids network management in strategic focus. By enabling swift identification and categorization of network security threats, this research equips organizations to take prompt, targeted actions, enhancing overall network security
Virtual reality's effects on air crash accident investigation learning interaction
Get PDFThe objective of this study is to gain a deeper comprehension of the factors that play a role in the evolution of virtual reality (VR) for application in the investigation of aviation disasters. This study was motivated by the concept of utilising VR to create an illusion of the procedures involved in an anviation accident. A conceptual model has been presented, to create the scene that will be simple to understand the procedure of accident following an air crash. The idea is to compile a series of steps that an investigation team will go through and then present them in VR. This stage entails obtaining complete views of the object before it crashes and at ground level. This study contributes to the process of creating the groundwork for adopting VR in air crash investigations to provide instructional experiences. The idea that was presented in this research focuses on feature of the surrounding area of the crash or accident, wreckage distribution, wreckage above the ground, wreckage in motion, wreckage at the ground, spatial view effect, and full view projection as the primary VR features that are required for teaching and learning about air crash accident investigations using VR
Development reference model to build management reporter using dynamics great plain aggregated
Get PDFThe digital technology transformation impacts changes in business patterns that require companies to innovate to act appropriately in making strategic decisions quickly, precisely, and accurately to increase efficiency, be practical company performance, and impacts changes in business patterns that require companies to innovate to act appropriately in making strategic decisions quickly to improve the performance. An enterprise resource planning (ERP) system is one step toward achieving performance. ERP system is one step to achieving performance. ERP system is essential for companies to automate the efficiency of business processes. The decisions from management in implementing the ERP system are necessary for ERP implementation to be successful. However, in practice, companies still experience complexity. For that, it needs to be considered related a business process reference model is essential to enhance efficiency in implementing the ERP used. This research discusses the business process reference model based on the ERP dynamics great plain (GP) application aggregated using management reporter (MR) to help users better understand the practical overview. The methodology utilizes a reference model based on Microsoft Dynamics GP guidelines with a business process redesign approach. This contributes to developing business processes to help users understand using the ERP dynamics GP application
Circularly polarized metamaterial Antenna in energy harvesting wearable communication systems
Get PDFWhen battery powered sensors are spread out in places that are sometimes hard to reach, sustaining them become difficult. Therefore, to develop this technology on a large scale such as in the internet of things (IoT) scenario, it is necessary to figure out how to power them. The proffered solution in this work, is to get energy from the environment using energy harvesting Antennas. This work presents a wearable circular polarized efficient receiving and transmitting sensors for medical, IoT, and communication systems at the frequency range of WLAN, and GSM from 900 MHz up to 6 GHz. Using a cascaded system block of a circularly polarized Antenna, a rectifier and t-matching network, the design was successfully simulated. A DC charging voltage of 2.8V was achieved to power-up batteries of the wearable and IoT sensors. The major contribution of this work is the tri-band Antenna system which is able to harvest reflected Wi-Fi frequencies and also GSM frequencies combined in a miniaturized manner. This innovative configuration is a step forward in building devices with over 80% duty cycle
An ensemble approach for the identification and classification of crime tweets in the English language
Get PDFTwitter is a famous social media platform, which supports short posts limited to 280 characters. Users tweet about many topics like movie reviews, customer service, meals they just ate, and awareness posts. Tweets carrying information about some crime scenes are crime tweets. Crime tweets are crucial and informative and separate classification is required. Identification and classification of crime tweets is a challenging task and has been the researcher’s latest interest. The researchers used different approaches to identify and classify crime tweets. This research has used an ensemble approach for the identification and classification of crime tweets. Tweepy and Twint libraries were used to collect datasets from Twitter. Both libraries use contrasting methods for extracting tweets from Twitter. This research has applied many ensemble approaches for the identification and classification of crime tweets. Logistic regression (LR), support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), and random forest (RF) Classifier assigned with the weights of 1,2,1,1 and 1 respectively ensemble together by a soft weighted Voting classifier along with term frequency – inverse document frequency (TF-IDF) vectorizer gives the best performance with an accuracy of 96.2% on the testing dataset