Recent Advances in Computer Science and Communications - Volume 16, Issue 8, 2023

Background: The trend of software development has always been challenging for industry experts and software developers. There is tremendous growth in software development methodologies under the influence of evolving technologies and the rising demands of society. The 2019 pandemic forced software developers to shut down their offices and begin working from home, thereby, highlighting the critical necessity for a shared development and operations teams platform. As a result, the development trend moves from waterfall and Agile towards DevOps. Objective: The objective of the research is to review and comparatively analyze the availability factor of different selective and required features in software development methodologies. Software development industries will be benefited in appropriate methodology selection based on the requirement. Methods: The analysis is based on review of different development methodologies based on existing literature study, Google, and Stack Overflow Trends followed by tabular comparison of Waterfall, Iterative, Prototype, Spiral development models under Traditional and Rapid Application Development (RAD), Scrum, Kanban, XP for Agile methods with DevOps automation culture on essential features. Results: The moving trend towards DevOps, from Traditional and Agile development, demonstrate the most recent market swings for these models. Although Traditional models adhere to outdated software development methodologies, they are included in this high-quality survey and evaluation because of their widespread use in the software industry and prominent researcher’s survey work. Conclusion: Software developers, students, and researchers will all find it simple to comprehend the workings of development processes as a result of this analytical review. Additionally, it will also make it easier for these target audiences to choose relevant and effective models for software development.

Aims: Text classification emerged as an important approach to advancing Natural Language Processing (NLP) applications concerning the available text on the web. To analyze the text, many applications are proposed in the literature. Background: The NLP, with the help of deep learning, has achieved great success in automatically sorting text data in predefined classes, but this process is expensive and time-consuming. Objectives: To overcome this problem, in this paper, various Machine Learning techniques are studied & implemented to generate an automated system for movie review classification. Methodology: The proposed methodology uses the Bidirectional Encoder Representations of the Transformer (BERT) model for data preparation and predictions using various machine learning algorithms like XG boost, support vector machine, logistic regression, naïve Bayes, and neural network. The algorithms are analyzed based on various performance metrics like accuracy, precision, recall and F1 score. Result: The results reveal that the 2-hidden layer neural network outperforms the other models by achieving more than 0.90 F1 score in the first 15 epochs and 0.99 in just 40 epochs on the IMDB dataset, thus reducing the time to a great extent. Conclusion: 100% accuracy is attained using a neural network, resulting in a 15% accuracy improvement and 14.6% F1 score improvement over logistic regression.

Background: SLAM plays an important role in the navigation of robots, unmanned aerial vehicles, and unmanned vehicles. The positioning accuracy will affect the accuracy of obstacle avoidance. The quality of map construction directly affects the performance of subsequent path planning and other algorithms. It is the core algorithm of the intelligent mobile application. Therefore, robot vision SLAM has great research value and will be an important research direction in the future. Objective: By reviewing the latest development and patent of Computer Vision SLAM, this paper provides references to researchers in related fields. Methods: Computer Vision SLAM patents and literature were analyzed from the aspects of the algorithm, innovation, and application. Among them, there are more than 30 patents and nearly 30 pieces of literature in the past ten years. Results: This paper reviews the research progress of robot visual SLAM in the last 10 years, summarizes its typical features, especially describes the front part of the visual SLAM system in detail, describes the main advantages and disadvantages of each method, analyses the main problems in the development of robot visual SLAM, prospects its development trend, and finally discusses the related products and patents research status and future of robot visual SLAM technology. Conclusion: The Robot Vision SLAM can compare the texture information of the environment and identify the difference between the two environments, thus improving accuracy. However, the current SLAM algorithm is easy to fail in fast motion and highly dynamic environments, most SLAM action plans are inefficient, and the image features of VSLAM are too distinguishable. Furthermore, more patents on the Robot Vision SLAM should also be invented.

Background: A research concern revolves around as to what can make the representation of entities and relationships fully integrate the structural information of the knowledge atlas to solve the entity modeling capability in complex relationships. World knowledge can be organized into a structured knowledge network by mining entity and relationship information in real texts. In order to apply the rich structured information in the knowledge map to downstream applications, it is particularly important to express and learn the knowledge map. In the knowledge atlas with expanding scale and more diversified knowledge sources, there are many types of relationships with complex types. The frequency of a single relationship in all triples is further reduced, which increases the difficulty of relational reasoning. Thus, this study aimed to improve the accuracy of relational reasoning and entity reasoning in complex relational models. Methods: For the multi-relational knowledge map, CTransR based on the TransE model and TransR model adopts the idea of piecewise linear regression to cluster the potential relationships between head and tail entities, and establishes a vector representation for each cluster separately, so that the same relationship represented by different clusters still has a certain degree of similarity. Results: The CTransR model carried out knowledge reasoning experiments in the open dataset, and achieved good performance. Conclusion: The CTransR model is highly effective and progressive for complex relationships. In this experiment, we have evaluated the model, including link prediction, triad classification, and text relationship extraction. The results show that the CTransR model has achieved significant improvement.

Digital security is one of the important aspects of today’s era. Digital content is being grown every day on the internet; therefore, it is essential to guard the copyright of digital content using various techniques. Watermarking has emerged as an important field of study aiming at securing digital content and copyright protection. None of the watermarking techniques can provide well robustness against all the attacks, and algorithms are designed based on required specifications, which means there is a lot of opportunity in this field. Image watermarking is a vast area of research, starting from spatial-based methods to deep learning-based methods, and it has recently gained a lot of popularity due to the involvement of deep learning technology for ensuring the security of digital content. This study aims at exploring important highlights from spatial to deep learning methods of watermarking, which will be helpful for the researchers. In order to accomplish this study, the standard research papers of the last ten years have been obtained from various databases and reviewed to answer the five research questions. Open issues and challenges are identified and listed after reviewing various kinds of literature. Our study reveals that hybrid watermarking performs better in terms of balancing the trade-off between imperceptibility and robustness. Current research trends and future direction is also discussed.

Aims: A Comprehensive Model Incorporating Multiple Spatial Relations in 3D Space. Background: At present, the research on two-dimensional spatial relation expression and inference models is relatively mature, but these models cannot be used to deal with three-dimensional spatial orientation relations. With the application of spatial orientation relations, threedimensional spatial orientation relations are involved in many fields such as urban architectural design, robotics, image processing, etc. Two-dimensional spatial orientation relations models cannot satisfy the needs of three-dimensional spatial applications, so there is an urgent need to research three-dimensional spatial object orientation relations expression and inference models. Objective: This work aims to provide a comprehensive model incorporating multiple spatial relations in 3D space. The joint representation of direction, distance, and topological relations describes more complex spatial position relations among objects. Method: Based on this comprehensive model, the computational properties of interval algebra are used to combine the directional and topological relations. Result: The study lays a good foundation for the formal representation and reasoning of spatial relations between regions, enhances the analyzability of spatial relations between objects, and improves the accuracy of spatial analysis. Conclusion: The main novel contribution of this paper is that we propose a comprehensive orientation relation model, called 3D-TRD, which considers three spatial orientation relations simultaneously. The paper gives examples to represent the position relations of two spatial objects by comparing the RCC8 model, the 3DR46 model, and the comprehensive model to highlight the advantages of our proposed model. Based on the model, the bidirectional mapping association method is also used to represent the location of the spatial objects. The first advantage of the 3DTRD model is that it represents spatial location relations more accurately than 3DR46, RCC8, and five qualitative distances. The second advantage of the 3D-TRD model is that it proposes a bidirectional mapping representation in three-dimensional space. The third advantage of the 3DTRD model is that it provides a good basis for the formal representation and inference study of the spatial relations between regions.

Background: A significant problem in economics is stock market prediction. Due to the noise and volatility, however, timely prediction is typically regarded as one of the most difficult challenges. A sentiment-based stock price prediction that takes investors' emotional trends into account to overcome these difficulties is essential. Objective: This study aims to enhance the ELM's generalization performance and prediction accuracy. Methods: This article presents a new sentiment analysis based-stock prediction method using a modified extreme learning machine (ELM) with deterministic weight modification (DWM) called S-DELM. First, investor sentiment is used in stock prediction, which can considerably increase the model's predictive power. Hence, a convolutional neural network (CNN) is used to classify the user comments. Second, DWM is applied to optimize the weights and biases of ELM. Results: The results of the experiments demonstrate that the S-DELM may not only increase prediction accuracy but also shorten prediction time, and investors' emotional tendencies are proven to help them achieve the expected results. Conclusion: The performance of S-DELM is compared with different variants of ELM and some conventional method.

This paper aims to provide a comprehensive study of the underlying buffer management issues and challenges in developing an efficient DTN routing protocol. Our aim is to begin with the discussion of buffer management schemes in DTNs in full generality and then dive in-depth, covering aspects of buffer management. Buffer strategies are used to determine which packets need to be forwarded or dropped. This paper will focus on the variety of buffer management strategies available, providing a comprehensive survey and analysis. We have also conducted an empirical analysis using simulator ONE to analyze the buffering time in various primary routing protocols such as Epidemic, Spary-and-wait (SNW), Prophet, Encounter based Routing (EBR) and Inter-Contact Delay and Location Information based Routing (ICDLIR). For these algorithms, it is also observed how varying the buffer size effect the delivery probability and overhead.