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, 08 Feb 2022 00:00:00 +0100 OJS 60 Taking FANET to Next Level <p>Flying Ad-hoc Network (FANET) is a special member/class of Mobile Ad-hoc Network (MANET) in which the movable nodes are known as by the name of Unmanned Aerial Vehicles (UAVs) that are operated from a long remote distance in which there is no human personnel involved. It is an ad-hoc network in which the UAVs can more in 3D ways simultaneously in the air without any onboard pilot. In other words, this is a pilot free ad-hoc network also known as Unmanned Aerial System (UAS) and the component introduced for such a system is known as UAV. There are many single UAV applications but using multiple UAVs system cooperating can be helpful in many ways in the field of wireless communication. Deployments of these small UAVs are quick and flexible which overcome the limitation of traditional ad hoc networks. FANETs differ from other kinds of ad hoc networks and envisioned to play an important role where infrastructure operations are not available and assigned tasks are too dull, dirty, or dangerous for humans. Moreover, setting up to bolster the range and performance of small UAV in ad hoc network lead to emergent evolution with its high stability, quick deployment, and ease-of-use for the formation of the network. Routing and task allocation are the challenging research areas of the network with ad hoc nodes. The paper overview based on the study of biological inspired routing protocols (Moth-and-Ant and Bee Ad-Hoc) routing protocols.</p> Altaf Hussain, Habib Ullah Khan, Shah Nazir, Ijaz Ullah, Tariq Hussain Copyright (c) 2021 ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal Tue, 08 Feb 2022 00:00:00 +0100 The Approach of Data Mining <p><em>The concept of data mining is to classify and analyze the given data and to examine it clearly understandable and discoverable for the learners and researchers. The different types of classifiers are there exist to classify a data accordingly for the best and accurate results. Taking a primary data, and then classifying it into different portions of parts, then to analyze and remove any ambiguities from it and finally make it possible for understanding. With this process, that data will become secondary from primary and will called information. So, the classifiers are doing the same strategy for the solution and accuracy of the data. In this paper, different data mining approaches have been used by applying different classifiers on the taken data set. The data-set consists of 500 candidates’ segregated data for the analysis and evaluation to perfectly classify and to show the accurate results by using the proposed Algorithms. The data mining approaches have been used in which HUGO (Highly Undetectable steGO) Algorithm, Naïve Bayes Classification, k-nearest neighbors and Logistic Regression are used with the extension of the other classification methods that are Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) as classifiers. These classifiers are given names for further analysis that are Classifier-1 and Classifier-2 respectively. Along with these, a tool is used named WEKA (Waikato Environment for Knowledge Analysis) for the analysis of the classifier-1 and 2. For performance evaluation and analysis the parameters are used for best classification that which classifier has given best performance and why. These parameters are RRSE (Root Relative Square Error), RAE (Relative Absolute Error), MAE (Mean Absolute Error), and RMSE (Root Mean Square Error). For the best and outstanding accuracy of the proposed work, these parameters have been tested under the simulation environment along with the incorrect, correct classifying and the %age has been witnessed and calculated. From simulation results based on RRSE, RAE, MAE and RMSE, it has been shown that classifier-1 has given outstanding performance among the others and has been placed in highest priority.</em></p> Altaf Hussain, Ijaz Ullah, Tariq Hussain Copyright (c) 2021 ALTAF HUSSAIN Scholar Tue, 08 Feb 2022 00:00:00 +0100 Review on recent Computer Vision Methods for Human Action Recognition <div class="page" title="Page 1"> <div class="layoutArea"> <div class="column"> <p>The subject of human activity recognition is considered an important goal in the domain of computer vision from the beginning of its development and has reached new levels. It is also thought of as a simple procedure. Problems arise in fast-moving and advanced scenes, and the numerical analysis of artificial intelligence (AI) through activity prediction mistreatment increased the attention of researchers to study. Having decent methodological and content related variations, several datasets were created to address the evaluation of these ways. Human activities play an important role but with challenging characteristic in various fields. Many applications exist in this field, such as smart home, helpful AI, HCI (Human-Computer Interaction), advancements in protection in applications such as transportation, education, security, and medication management, including falling or helping elderly in medical drug consumption. The positive impact of deep learning techniques on many vision applications leads to deploying these ways in video processing. Analysis of human behavior activities involves major challenges when human presence is concerned. One individual can be represented in multiple video sequences through skeleton, motion and/or abstract characteristics. This work aims to address human presence by combining many options and utilizing a new RNN structure for activities. The paper focuses on recent advances in machine learning-assisted action recognition.</p> <p>Existing modern techniques for the recognition of actions and prediction similarly because the future scope for the analysis is mentioned accuracy within the review paper.</p> </div> </div> </div> Azhee Wria Muhamada, Aree A. Mohammed Copyright (c) 2021 ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal Tue, 08 Feb 2022 00:00:00 +0100 Urdu News Clustering Using K-Mean Algorithm On The Basis Of Jaccard Coefficient And Dice Coefficient Similarity <p><em>Clustering is the unsupervised machine learning process that group data objects into clusters such that objects within the same cluster are highly similar to one another. Every day the quantity of Urdu text is increasing at a high speed on the internet. Grouping Urdu news manually is almost impossible, and there is an utmost need to device a mechanism which cluster Urdu news documents based on their similarity. Clustering Urdu news documents with accuracy is a research issue and it can be solved by using similarity techniques i.e., Jaccard and Dice coefficient, and clustering k-mean algorithm. In this research, the Jaccard and Dice coefficient has been used to find the similarity score of Urdu News documents in python programming language. For the purpose of clustering, the similarity results have been loaded to Waikato Environment for Knowledge Analysis (WEKA), by using k-mean algorithm the Urdu news documents have been clustered into five clusters. The obtained cluster’s results were evaluated in terms of Accuracy and Mean Square Error (MSE). The Accuracy and MSE of Jaccard was 85% and 44.4%, while the Accuracy and MSE of Dice coefficient was 87% and 35.76%. The experimental result shows that Dice coefficient is better as compared to Jaccard similarity on the basis of Accuracy and MSE.</em></p> Zahid Rahman, Altaf Hussain, Hussain Shah, Muhammad Arshad Copyright (c) 2021 Zahid Rahman, Altaf Hussain, Hussain Shah, Muhammad Arshad Tue, 08 Feb 2022 00:00:00 +0100 A Proposed Hybrid model for Sentiment Classification using CovNet-DualLSTM Techniques <p><em>The fast growth of Internet and social media has resulted in a significant quantity of texts based review&nbsp;that is&nbsp;posted on the platforms like social media. In the age of social media, analyzing the emotional context of comments using machine learning technology helps in understanding of QoS for any product or service. Analysis and classification of user’s review helps in improving the QoS (Quality of Services). Machine Learning techniques have evolved as a great tool for performing sentiment analysis of user’s. In contrast to traditional&nbsp;classification models. Bidirectional Long Short-Term Memory (BiLSTM) has obtained substantial outcomes and Convolution Neural Network (CNN) has shown promising outcomes in sentiment classification. CNN can successfully retrieve local information by utilizing convolutions and pooling layers. BiLSTM employs dual LSTM orientations for increasing the background knowledge accessible to deep learning based models. The hybrid model proposed here is to utilize the advantages of these two deep learning based models. Tweets of users for reviews of Indian Railway Services have been used as data source for analysis and classification. Keras Embedding technique is used as input source to the proposed hybrid model. The proposed model receives inputs and generates features with lower dimensions which generate a classification result. The performance of proposed hybrid model was&nbsp;&nbsp; compared using Keras and Word2Vec and observed effective improvement in the response of the proposed model with an accuracy of 95.19%.</em></p> Roop Ranjan, AK Daniel Copyright (c) 2021 Roop Ranjan, A K Daniel Tue, 08 Feb 2022 00:00:00 +0100 Ensemble Boosted Tree based Mammogram image classification using Texture features and extracted smart features of Deep Neural Network <table> <tbody> <tr> <td width="432"> <p>&nbsp;</p> <p><em>&nbsp; This work proposes a technique of breast cancer detection from mammogram images. It is a multistage process which classifies the mammogram images into benign or malignant category. During preprocessing, images of Mammographic Image Analysis Society (MIAS) database are passed through a couple of filters for noise removal, thresholding and cropping techniques to extract the region of interest, followed by augmentation process on database to enhance its size. Features from Deep Convolution Neural Network (DCNN) are merged with texture features to form final feature vector. Using transfer learning, deep features are extracted from a modified DCNN, whose training is performed on 69% of randomly selected images of database from both categories. Features of Grey Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP) are merged to form texture features. Mean and variance of four parameters (contrast, correlation, homogeneity and entropy) of GLCM are computed in four angular directions, at ten distances. Ensemble Boosted Tree classifier using five-fold cross-validation mode, achieved an accuracy, sensitivity, specificity of 98.8%, 100% and 92.55% respectively on this feature vector.</em></p> </td> </tr> </tbody> </table> Bhanu Prakash Sharma, Ravindra Kumar Purwar Copyright (c) 2021 ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal Tue, 08 Feb 2022 00:00:00 +0100 EpilNet: A Novel Approach to IoT based Epileptic Seizure Prediction and Diagnosis System using Artificial Intelligence <p>Epilepsy is one of the most occurring neurological diseases. The main characteristic of this disease is a frequent seizure, which is an electrical imbalance in the brain. It is generally accompanied by shaking of body parts and even leads (fainting). In the past few years, many treatments have come up. These mainly involve the use of anti-seizure drugs for controlling seizures. But in 70% of cases, these drugs are not effective, and surgery is the only solution when the condition worsens. So patients need to take care of themselves while having a seizure and be safe. Wearable electroencephalogram (EEG) devices have come up with the development in medical science and technology. These devices help in the analysis of brain electrical activities. EEG helps in locating the affected cortical region. The most important is that it can predict any seizure in advance on-site. This has resulted in a sudden increase in demand for effective and efficient seizure prediction and diagnosis systems. A novel approach to epileptic seizure prediction and diagnosis system “EpilNet” is proposed in the present paper. It is a one-dimensional (1D) convolution neural network. EpilNet gives the testing accuracy of 79.13% for five classes, leading to a significant increase of about 6-7% compared to related works. The developed Web API helps in bringing EpilNet into practical use. Thus, it is an integrated system for both patients and doctors. The system will help patients prevent injury or accidents and increase the efficiency of the treatment process by doctors in the hospitals.</p> Shivam Gupta, Virender Ranga, Priyansh Agrawal Copyright (c) 2021 ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal Tue, 08 Feb 2022 00:00:00 +0100 Staff Secretaría de Redacción ADCAIJ Copyright (c) 2021 Tue, 08 Feb 2022 00:00:00 +0100 Index ADCAIJ editorial Team Copyright (c) 2022 ADCAIJ editorial Team Tue, 08 Feb 2022 00:00:00 +0100