<abstract xmlns="http://www.w3.org/1999/xhtml">

In the present era of the internet and social media, the way of information dissemination has changed. However, due to rapid growth in the amount of news generated regularly and the unsupervised nature of social media, fake news turns out to be a big problem. Fake news can easily build a false positive or negative perception about a person, or an event. Fake news was also used as a tool by propagandists during the Coronavirus (COVID-19) pandemic. Thus, there is a need to use technology to tag fake news and prevent its dissemination. Previously, different algorithms were designed to detect fake news but without considering the semantic meaning and long sentence dependence. This research work proposes a new approach to the detection of fake news in the context of COVID-19. The suggested approach uses a combination of Bidirectional Encoder Representations from Transformers (BERT) for extracting context meaning from sentences, SVM for pattern identification to detect fake news in a better way from the COVID-19 dataset, and an evolutionary algorithm called Non-dominated Sorting Genetic Algorithm II (NSGA-II) to distribute text for Support Vector Machine (SVM) classification. The suggested approach improves accuracy by 5.2 % by removing a certain amount of ambiguity from sentences.
</abstract>

National University of Computer and Emerging Sciences

An Improved FakeBERT for Fake News Detection

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A method of heuristically solving large and extremely large traveling salesman problems is suggested. The solver is a specific genetic algorithm producing approximately shortest routes the fastest of known heuristics without losing much in accuracy. The method consists in parallelizing the problem by clustering its nodes and finding the best route passing through the centroids of the clusters. The open-loop subroutes of the clusters are connected via specific nodes. These specific nodes referred to as connectors are determined as those for which the distance to the depot is maximal and the distance to the cluster of the following subproblem is minimal. Thus, a bunch of smaller open-loop problems is solved instead of solving the whole (closed loop) problem. Extremely large problems should be clustered manually by imposing a mesh of rotated square cells. In this case, the connectors should be determined manually as well. A connector can also be approximated by a node which is the closest to the line connecting the centroids of the two clusters. The suggested parallelization can produce a very significant speedup depending on how many processor cores are simultaneously available. The factual speedup by the parallelization depends on the availability of processor cores, memory, and the processor clock frequency. The efficiency of the parallelization is maintained for a few hundred to a few million nodes by any number of clusters being less than the size of the average cluster.
</abstract>

Parallelization of the Traveling Salesman Problem by Clustering its Nodes and Finding the Best Route Passing through the Centroids

<abstract xmlns="http://www.w3.org/1999/xhtml">

Content-based image retrieval system (CBIR) is a well-known and widely used system for image retrieval. Most of the current CBIR systems are either command-based or specific to applications. However, due to the availability of a good computing facility, a graphical way of retrieving images may prove to be very useful for both industrial and research purposes. This paper proposes a generic and user-friendly graphical user interface (GUI) for CBIR framework. With the proposed GUI, any user with or without knowledge of CBIR can operate and retrieve images of their choice among a huge number of images. The GUI gives a vast range of facilities for selecting options. The proposed GUI is implemented and verified on a well-known image database.
</abstract>

Generic Graphical User Interface for CBIR Framework

<abstract xmlns="http://www.w3.org/1999/xhtml">

The paper proposes an Energy-Harvested Transmitter-Initiated MAC Protocol for WSNs (ETI-MAC). ETIMAC takes advantage of the benefits of transmitter-initiated schemes and employs the low power listening (LPL) method with small preamble messages so that each sensor node in the network can predict its next sleep duration based on the harvested energy rate value, thereby lowering the duty cycle by making use of its accumulating residual energy. The simulation results show that the proposed protocol outperforms compared to the old PSEHWSN scheme.
</abstract>

ETI-MAC: An Energy-Harvested Transmitter-Initiated MAC Protocol for Wireless Sensor Networks

<abstract xmlns="http://www.w3.org/1999/xhtml">

Source code constitutes the static and human-readable component of a software system. It comprises an array of artifacts and features that collectively execute a specific set of tasks. Coding behaviours and patterns are formulated through the orchestrated utilization of distinct features in a specified sequence, fostering inter-dependencies among these features. This study seeks to explore into the presence of specific coding behaviours and patterns within Java, which could potentially unveil the extent to which developers endeavour to leverage the facilities and services that exist in the programming language aggregatively. In pursuit of investigating behaviours and patterns, 436 open-source Java projects are selected, each having more than 150 Java files (Classes and Interfaces), in a semi-randomized manner. For every project, 39 features have been chosen, and the frequency of each individual feature has been independently assessed. By employing linear regression, the interrelationships among all features across the complete array of projects are scrutinized. This analysis intends to uncover the manifestation of distinct coding behaviours and patterns. Based on the selected features, preliminary findings suggest a notable collective incorporation of diverse coding behaviours among programmers, encompassing Encapsulation and Polymorphism. The findings also point to a distinct preference for using a specific commenting mechanism, JavaDoc, and the potential existence of Code-Clone and dead code. Overall, the results indicate a clear tendency among programmers to strongly adhere to the fundamental principles of Object -Oriented programming. However, certain less obvious attributes of object-oriented languages appear to receive relatively less attention from programmers.
</abstract>

Source Code Features and their Dependencies: An Aggregative Statistical Analysis on Open-Source Java Software Systems

<abstract xmlns="http://www.w3.org/1999/xhtml">

Living standards are rising due to a more developed society, and recreation, particularly tourism, is becoming more critical. Expanding the tourist industry is one of the most significant concerns in economic growth. Tourism revenue has helped increase residents’ income, leading to socio-economic development. In recent years, emerging Vietnamese tourism spots like Hon Son, Sapa, Hue, Phu Quoc in Vietnam, and others have consistently drawn travellers to visit and experience through social networking platforms. Tourism potential is tremendous, but foreign visitors’ information about tourist destinations still needs to be improved. This work proposes an approach to integrating machine learning algorithms into an information system to consult tourism traveling. Machine learning algorithms can classify question topics, predict user intent, and predict conversation scenarios to give appropriate responses. Our method is evaluated on the dataset, including 7319 samples on 11 topics collected from the TWCS dataset, using three algorithms: Bag of Words, BERT, and RoBERTa. BERT achieved the highest performance among the surveyed algorithms with 90 % in accuracy and 90.1 % in F1-Score. From the trained model, the team built a mobile application on Android to deploy the chatbot application with the Flutter framework based on Dart, an object-oriented programming language developed by Google using the concept of containers. The system’s functionality serves two primary user groups: administrators and application users. Administrators can utilize the application’s primary functions to manage content set up, and train a chatbot. Users can access information about locations, read location articles, check hotel prices, and use chatbots to find answers to their location-related questions. Administrators can also train the chatbot model to expand its knowledge.
</abstract>

AI Chatbot for Tourist Recommendations: A Case Study in Vietnam

<abstract xmlns="http://www.w3.org/1999/xhtml">

Comparable corpora are the right resources for extracting parallel data due to their abundant availability. It is of great importance where parallel data are scarce. In this study, the focus is placed on building of parallel data for Punjabi and English language pair. The raw data were collected from web contents of “Mann Ki Baat”, which is a collection of textual speeches of Prime Minister of India Mr. Narendra Modi broadcasted every last Sunday of the month. Data were cleaned and pre-processed using a natural language toolkit. An alignment model using BERT was built that aligned two textual files on a sentence level. Furthermore, extraction of noun forms with the help of NLTK library in Python programming was performed. The noun aligned dataset was built for English-Punjabi language pair and made available at Mendeley data repository.
</abstract>

Building English – Punjabi Aligned Parallel Corpora of Nouns from Comparable Corpora

<abstract xmlns="http://www.w3.org/1999/xhtml">

Image registration is an essential pre-processing step required for many image processing applications such as medical imaging and computer vision. The aim is to geometrically align two or more images of the same scene by establishing a mapping that relies on each point from one image to its corresponding point of another image. Scale invariant feature transform (SIFT) and speeded up robust features (SURF) are well-liked local features descriptors that have been extensively utilised for feature-based image registration due to their inherent properties such as invariance, changes in illumination, and noise. Moreover, the task of registration can be viewed as an optimization problem that can be solved by applying genetic algorithms (GAs). This paper presents an efficient feature image registration method based on combined local features and GAs. Firstly, the procedure consists of extracting the local features from the images by combining SIFT and SURF algorithms and matching them to refine the feature set data. Therefore, an adaptive GA based on fitness sharing and elitism techniques is employed to find the optimal rigid transformation parameters that best align the feature points by minimizing a distance metric. The suggested method is applied for registering medical images and the obtained results are significant compared to other feature-based approaches with reasonable computation time.
</abstract>

A Rigid Image Registration by Combined Local Features and Genetic Algorithms

<abstract xmlns="http://www.w3.org/1999/xhtml">

Worldwide websites publish enormous amounts of text, audio, and video content every day. This valuable information allows for the assessment of regional trends and general public opinion. Based on consumers’ online behavioural habits, businesses are showing them their chosen ads. It is difficult to carefully analyse these raw data to find valuable trends, especially for a language with limited resources like Urdu. There have not been many studies or efforts to create language resources for the Urdu language and analyse people’s sentiment, even though there are more than 169 million Urdu speakers in the world and a sizable amount of Urdu data is generated on various social media platforms every day. However, there has been relatively little research on sentiment analysis in Urdu. Researchers have primarily performed studies in English and Chinese. In response to this gap, we suggest an emotion analyser for Urdu, the primary language of Asia, in this research study. In this paper, we propose to assess sentiment in Urdu review texts by integrating a bidirectional long short-term memory (BiLSTM) model with a convolutional neural network (CNN). We contrast the CNN, LSTM, BiLSTM, and CNN-LSTM models with the CNN-BiLSTM model. With an accuracy rate of 0.99 %, the CNN-BiLSTM model performed better than the other models in an initial investigation.
</abstract>

Sentiment Analysis Based on Urdu Reviews Using Hybrid Deep Learning Models

AHEAD OF PRINT

Volume 28 (2023): Issue 2 (December 2023)

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Applied Computer Systems

 The Journal Applied Computer Systems is an open access scientific journal published biannually in May and December. The journal serves for experience generation in computer science and information technologies use in different applications and is a source of information for the industry practitioners, for academic personnel and students.  The journal publishes research papers describing new results, original ideas and applications or reports on the development and maintenance of actual systems addressing issues concerning theory, development, management and applications of advanced applied computer science.  The topics covered by the journal are:   Artificial Intelligence  Computer Applications  Data  Information Technology and Systems  Programming Technologies  Software Engineering   Besides, the reader will find papers on conception of framework for automated creation of graph visualisation systems, theoretical basis and practical usage of simulation for creating solutions for data management systems, modelling of software systems and business processes, comparison of programming languages, intelligent tutoring systems, semantic web development, applications of cloud-based services, issues of dynamic wireless networks, as well as surveys and papers focused on different other specific topics.  The aim of the publishers is to encourage the applied research by granting free publication possibilities to professionals in the software field, young scientists and doctoral students.  The authors of articles are researchers from various educational and research institutions and companies not only from different European countries, but also from Africa, North and South America, Asia and Australia.  Rejection Rate   47%   Publication History 　  Currently known as Applied Computer Systems 2012 – current, ISSN 2255-8683 (print), ISSN 2255-8691 (online).Formerly known as Scientific Journal of Riga Technical University, Series Computer Science. Applied Computer Systems 2000–2011, ISSN 1407-7493 (print).In 2012 the journal Applied Computer Systems separated from the RTU journal series Computer Science and the present volume numbering reflects the number of journals published within the series since 2000.   Archiving  Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections.  Plagiarism Policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process. 