ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal <p dir="ltr">The <a title="adcaij" href="" target="_blank" rel="noopener">Advances in Distributed Computing and Artificial Intelligence Journal</a> (ISSN: 2255-2863) is an open access journal that publishes articles which contribute new results associated with distributed computing and artificial intelligence, and their application in different areas, such as the Internet, electronic commerce, mobile communications, wireless devices, distributed computing and so on.&nbsp;These technologies are changing constantly as a result of the large research and technical effort being undertaken in both universities and businesses. Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of computing.</p> <p dir="ltr">Adcaij focuses attention in the exchange of ideas between scientists and technicians. &nbsp;Both, academic and business areas, are essential to facilitate the development of systems that meet the demands of today's society. The journal is supported by the research group and start-up value <a title="bisite" href="" target="_blank" rel="noopener">BISITE</a>.</p> <p dir="ltr">The journal commenced publication in 2012; has quarterly periodicity and has published 192 articles with peer review. All the articles are written in scientific English language.</p> <p dir="ltr">It has indexed in DOAJ, ProQuest, Scholar, WorldCat, Dialnet, Sherpa ROMEO, Dulcinea, UlrichWeb, Emerging Sources Citation Index of Thomson Reuters, BASE y Academic Journals Database.</p> en-US <h3>Ethical standards for the ADCAIJ journal</h3><p>Upon submitting original contributions to the ADCAIJ joumal, the authors agree to accept the standard procedures for the scientific community: contributions will be original in nature, neither published nor under consideration by other journals. Likewise, any original material sent to ADCAIJ will not be sent to other publications until our joumal has completed its evaluation process.</p><p>The authors will adhere to international copyright standards for written, graphic and other materials included in their writings sent to ADCAIJ for publication. For their part, the editors, the editorial staff, and journal reviewers will ensure that the integrity of the research is upheld, an effort that corresponds first and foremost to the authors. Consequently, authors will be asked to continue avoiding the practice of plagiarism and self-­?plagiarism. The Corresponding Author signs for and accepts responsibility for releasing this material on behalf of any and all Co-Authors.</p> (Juan M. CORCHADO) (Ángel REDERO (Ediciones Universidad de Salamanca)) Tue, 05 Oct 2021 17:57:37 +0200 OJS 60 Index ADCAIJ editorial Team Copyright (c) 2021 ADCAIJ equipo editorial Tue, 05 Oct 2021 00:00:00 +0200 An Agent-Based Simulation to Explore Communication in a System to Control Urban Traffic with Smart Traffic Lights <p>Populational growth increases the number of cars and makes the transport infrastructure increasingly saturated. The control of traffic lights by intelligent software is a promising way to solve the problem caused by this situation. This article addresses this problem that occurs in urban traffic. An agent-based simulation of an urban traffic control system is proposed. The solution is offered as intelligent traffic lights as agents to alleviate traffic congestion at a given location. Each agent controls a crossing and maintains communication with agents from other corners. Thus, they can have greater control of a larger area and identify patterns that can help them to solve congestion problems. The results of our simulated experiments point to the improvement of the urban traffic when using the proposed Multiagent System, in comparison with an approach that uses crossing agents without communication and other that implements static traffic lights.</p> Dr. Marcos de Oliveira, Robson Teixeira, Roberta Sousa, Dr. Enyo Gonçalves Copyright (c) 2021 ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal Tue, 05 Oct 2021 00:00:00 +0200 An Empirical Analysis on Software Development Efforts Estimation in Machine Learning Perspective <p>The prediction of effort estimation is a vital factor in the success of any software development project. The available of expert systems for the software effort estimation supports in minimization of effort and cost for every software project at same time leads to timely completion and proper resource management of the project. This article supports software project managers and decision makers by providing the state-of-the-art empirical analysis of effort estimation methods based on machine learning approaches. In this paper ?ve machine learning techniques; polynomial linear regression, ridge regression, decision trees, support vector regression and Multilayer Perceptron (MLP) are investigated for the purpose software development effort estimation by using bench mark publicly available data sets. The empirical performance of machine learning methods for software effort estimation is investigated on seven standard data sets i.e. Albretch, Desharnais, COCOMO81, NASA, Kemerer, China and Kitchenham. Furthermore, the performance of software effort estimation approaches are evaluated statistically applying the performance metrics i.e. MMRE, PRED (25), R2-score, MMRE, Pred(25). The empirical results reveal that the decision tree-based techniques on Deshnaris, COCOMO, China and kitchenham data sets produce more adequate results in terms of all three-performance metrics. On the Albretch and nasa datasets, the ridge regression method outperformed then other techniques except pred(25) metric where decision trees performed better.</p> Zulfiqar Ali, Israr ur Rehman, Zahoor Jaan Copyright (c) 2021 ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal Tue, 05 Oct 2021 00:00:00 +0200 Advance Approach for Detection of DNS Tunneling Attack from Network Packets Using Deep Learning Algorithms <p>Domain Name System (DNS) is a protocol for converting numeric IP addresses of websites into a human-readable form. With the development of technology, to transfer information, a method like DNS tunneling is used which includes data encryption into DNS queries. The ability of the DNS tunneling method of transferring data attracts attackers to establish bidirectional communication with machines infected with malwares. This can lead to sending instructions in an obfuscated way or can lead to data exfiltration. Since firewalls and intrusion detection systems detect only specific types of tunneling, were as the Machine Learning Algorithms can analyze and predict based on previous data provided to it, it is being adopted by researchers to detect and predict the occurrence of DNS Tunneling. The identification of anomalies in Network packets can be done by using Natural Language Processing (NLP) technique. The experimental test accuracy showed that the feature extraction method in NLP for detecting DNS tunneling in network packets was found to be 98.42% on the generated Dataset. This paper makes a comparative study of 1 Dimensional Convolution Neural Network (1-D CNN), Simple Recurrent Neural Network (Simple RNN), Long Short-Term Memory (LSTM) algorithm, Gated Recurrent Unit (GRU) algorithm for detecting DNS Tunneling over the generated dataset. To detect this threat of DNS tunneling attack, good quality of the dataset is required. This paper also proposes the generation of a good quality dataset that contains network packets, by the recreation of DNS Tunneling attack using tool dnscat2.</p> Dr. Gopal Sakarkar, Mahesh Kumar H Kolekar, Ketan Paithankar, Gaurav Patil, Prateek Dutta, Ruchi Chaturvedi, Shivam Kumar Copyright (c) 2021 ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal Tue, 05 Oct 2021 00:00:00 +0200 Brain Inspired Computing Approach for the Optimization of the Thin Film Thickness of Polystyrene on the Glass Substrates <p>Advent in machine learning is leaving deep impact on various sectors including material science domain. The present paper highlights the application of various supervised machine learning regression algorithms such as polynomial regression, decision tree regression algorithm, random forest algorithm, support vector regression algorithm and artificial neural network algorithm to determine the thin film thickness of Polystyrene on the glass substrates. The results showed that polynomial regression machine learning algorithm outperforms all other machine learning models by yielding the coefficient of determination of 0.96 approximately and mean square error of 0.04 respectively.</p> Akshansh Mishra, Devarrishi Dixit Copyright (c) 2021 ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal Tue, 05 Oct 2021 00:00:00 +0200 Crime Detection Using Sentiment Analysis <p><em>Women and girls have been subjected to a great deal of violence and harassment in public locations around the country, ranging from stalking to abuse harassment and assault. This research paper examines the role of social media in improving women's safety in Indian cities, with a focus on the use of social media websites and apps such as Twitter, Facebook, and Instagram. This research also looks at how ordinary Indians can develop a sense of responsibility in Indian society so that we can focus on the protection of women in their surroundings. Tweets on the safety of women in Indian cities, which often include images and text as well as written phrases and quotations, can be used to send a message to the Indian youth culture and encourage them to take harsh action and punish those who harass women. Twitter and other Twitter handles that feature hash tag messages are extensively used throughout the world as a channel for women to share their feelings about how they feel when going to work or travelling by public transportation and what is their mental condition when they are surrounded by unknown males, and do they feel safe or not?</em></p> Ruba Khan, Shadab Siddiqui, Abhishek Rastogi Copyright (c) 2021 ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal Tue, 05 Oct 2021 00:00:00 +0200 Projects Distribution Algorithms for Regional Development <p>This paper aims to find an efficient method to assign different projects to several regions seeking an equitable distribution of the expected revenue of projects. The solutions to this problem are discussed in this paper. This problem is NP-hard. For this work, the constraint is to suppose that all regions have the same socio-economic proprieties. Given a set of regions and a set of projects. Each project is expected to elaborate a fixed revenue. The goal of this paper is to minimize the summation of the total difference between the total revenues of each region and the minimum total revenue assigned to regions. An appropriate schedule of projects is the schedule that ensures an equitable distribution of the total revenues between regions. In this paper, we give a mathematical formulation of the objective function and propose several algorithms to solve the studied problem. An experimental result is presented to discuss the comparison between all implemented algorithms.</p> Mahdi Jemmali Copyright (c) 2021 ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal Tue, 05 Oct 2021 00:00:00 +0200 Deep Convolutional Neural Network Modeling and Laplace Transformation Algorithm for the Analysis of Surface Quality of Friction Stir Welded Joints <p>The quality of Friction Stir Welded joint depends on the input parameters like tool rotational speed, tool traverse speed (mm/min), tool tilt angle, and an axial plunge force. If there is any variation in these input parameters then there will be a chance of formation of various surface defects such as groovy edges, flash formation, and non-homogeneous mixing of alloys. The main objective of the present work is to use machine learning algorithms such as Deep Convolutional Neural Network (DCNN) and Laplace transformation algorithm to detect these surface defects present on the Friction Stir Welded joint. The results showed that the used algorithms can easily detect such surface defects with good accuracy.</p> Akshansh Mishra, Anusri Patti Copyright (c) 2021 ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal Tue, 05 Oct 2021 00:00:00 +0200