Vol. 1 No. 1 (2023): Vol. 1 No. 1 (2023)-- Publication of the first issue

					View Vol. 1 No. 1 (2023): Vol. 1 No. 1 (2023)-- Publication of the first issue

We are thrilled to announce the publication of the first issue of "Current Trends in Computing (CTC)" journal. This groundbreaking edition marks the advent of a new platform for researchers to explore and disseminate cutting-edge research in the field of computer science.

Vol. 1 No. 1 (2023): Vol. 1 No. 1 (2023)-- Publication of the first issue

Cover and Contents

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Articles

Please click here to access the articles in the first issue of the journal :

ONE TO ALL BROADCASTING ALGORITHM FOR HIERARCHICAL HONEYCOMB MESHES

Burhan Selçuk, Ayşe Nur A.Tankül, Ali Karcı

Abstact: In the nature, a honeycomb structure is found frequently and man-made honeycomb structures are used in many different areas due to its features. The use of honeycomb meshes provides advantages when building hierarchical structures. In this paper, one to all broadcasting algorithm are studied for hierarchical honeycomb meshes (HHM) using a new strategy.

1-9

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COMMON NEIGHBORHOOD-BASED LINK PREDICTION IN SPORTS NETWORKS

Emrah Özkaynak, Mine Keleş

Abstact: Link prediction has been among the popular topics in social network analysis studies in recent years. The prediction of new links that may arise in the future, depending on the analysis of the relations between the entities, has started to be used frequently, especially in recommendation systems. Link prediction methods, especially used in social networks, mostly use the topological features of complex networks in terms of application. This situation has also paved the way for link prediction methods to be preferred in almost all kinds of networks of complex network structures. The increased trend in link prediction studies has also allowed many methods to be proposed and used in this field. The differences in the formation of the network and the link types prevent the developed methods from giving the same performance for every complex network. This situation has increased the importance of choosing the appropriate link prediction method depending on the structure of the complex network. This study applied neighborhood-based link prediction methods in networks created from different sports competitions. Furthermore, The most suitable neighborhood-based link prediction method that could be used in sports networks has been investigated. Link prediction methods were applied to the networks formed with different time periods formed from different sports branches such as tennis tournaments, football competitions, and billiards competitions, and the accuracy performances of the methods were determined. The results obtained from the AUC metric in the experimental studies show that the neighborhood-based link prediction methods successfully predict the new connections that may arise in the future in sports networks.

10-21

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FIREWALLS AND INTERNET OF THINGS SECURITY: A SURVEY

Mostafa Raeisi Varzaneh, Adib Habbal, Omar Dakkak

Abstract: One way to define the Internet of Things is as a network of objects, data, and the internet. Things can be referred to as objects, whether an appliance, a car, a human, an animal, or a plant. Connected devices, manufacturers, and operators can exchange data over the Internet of Things to monitor and control their functions. According to analysts, thousands of things are predicted to be connected to the Internet of Things. Consequently, these devices generate a great deal of data. This enormous amount of data is described as Big Data. In addition to its volume and velocity, this data is diverse and varied. This data is at risk of being compromised. Firewalls are security devices that monitor, and control network traffic flow based on a set of predefined rules. More proactive firewalls are needed to block current and emerging threats such as botnets and targeted attacks. This paper provides a comprehensive overview of the information security issues and demonstrates how firewalls can mitigate these challenges in IoT applications.

22-43

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NAVIGATR: DETECTING AND RECOGNIZING TURKISH TRAFFIC SIGNS USING A NEW DATASET BASED ON DEEP LEARNING

Erdi Tuna, Kasım Özacar

Abstract: While cars are becoming smarter than ever with built-in sensing technologies, thanks to the spreading availability of low-cost wearable devices, millions of cars in traffic lack such technologies. However, detecting and recognizing traffic signs is essential in ensuring the safety of pedestrians and drivers. To provide this safety, we conducted a study first to prepare a dataset using collected data in different weather conditions. Then, we used TensorFlow’s Object Detection API to detect and recognize traffic signs in Turkey. Initially, we collected over 5000 pieces of data for training. We labeled the data in the dataset using a web-based helper application and selected a suitable deep-learning model. After the training process, we evaluated the results of the models and assessed the quality of our prepared dataset. After training the model, we imported it into an Android application that we developed. This application helps navigate drivers by providing information about the signs in front of their cars using text-to-speech technology.

44-53

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SURVEY OF THE DEVELOPMENT PROCESSES AND EVOLUTION OF THE INTERNATIONAL CLASSIFICATION OF DISEASES

Ibrahım I. Alkhateeb, Kürşat Mustafa Karaoğlan, Hakan Kutucu

Abstract: The International Classification of Diseases (ICDs) has a global application in epidemiological research, health administration, and diagnostic studies. The ICD is a classification system within the healthcare system, developed and endorsed by the World Health Organization (WHO) to provide a comprehensive range of diagnostic codes for categorizing diseases. It encompasses exhaustive classifications of diverse indications, manifestations, abnormal findings, grievances, societal circumstances, and extrinsic factors contributing to injury or illness. This system relied entirely on clinical data sets that are collected by officials, on the basis of which the International Classification of Diseases is coded for many purposes such as billing systems, determining the type of disease, and the types of treatments used. Recently, what is known as the electronic health record system appeared to be adopted in writing clinical notes, which led researchers to integrate modern technology in Natural Language Processing in addition to Machine Learning and deep learning techniques to code the International Classification of Diseases in a more effective and accurate way. The factors mentioned helped shed more light on the importance of this system, its objectives, and the developments that have been made on it since its inception. and also led us in this paper to conduct a comprehensive survey on the latest technologies prepared by researchers in the field of classification and coding of diseases and what are the processes that have been adopted in this regard.

54-74

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AN ATTEMPT TO VISUALISING TWITTER DATA FOR ANALYSIS

İlhami M. Orak, Muhammed Selman Dönmez

Abstract: Social media is attracting so many people from different demographics. Facebook, YouTube, TikTok, WhatsApp, Instagram, and LinkedIn are some of those. Each one has some similarities as well as some differences. Twitter is one of the most popular social media platforms. It was publicly launched to be used for micro-blogging in 2006. Originally, it was limited to a maximum of 140 characters. It extended its usage capacity and became one of the most highly used social media platforms. Twitter users reached around 401 million by 2022. According to statistics, the number of daily tweets shared on Twitter is 500 million. The contents of shared tweets are so valuable for data analytic since they provide information about such as users' social lives, feelings, political opinions, and social environment. In this study, it is intended to introduce a visualization platform for Twitter data to provide better analysis. By using this tool, it will be possible to have a better understanding of the big data produced by Twitter posts. Analysis of all of the actions of Twitter such as hashtags and retweets is visualized. The visualization tool will also enable the reflection of the social network for targeted users and their followings and followers.

75-82

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Published: 2023-06-30

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