Guest Editorial Special Issue: “Emerging Trends in Advanced Computing and Next-Generation Technologies”

This special issue explores the latest developments in computing, including advancements in artificial intelligence, quantum computing, edge computing, and blockchains. It highlights how these technologies are shaping the future of computing, driving innovation, and addressing complex challenges in various industries. It also delves into the integration of next-generation technologies, their impact on society, and the potential for creating transformative solutions.

This special issue brings researchers and industrial experts in computer science, engineering, and technology to present their research findings and practical applications. The main objective is to emphasize hardware and software advances, their applications in innovative engineering systems, and advanced technology for the benefit of humanity and discuss the next generation of high-performance computing, the internet of things, software engineering, quantum computing, and related disciplines.

The paper “Enhancing Android Ransomware Detection Using an Ensemble Machine Learning Classifier” enhances ransomware detection by combining static features (permissions, intents, API calls) with dynamic network traffic features using machine learning classifiers. The proposed model, implemented in Python, achieved recall scores of 99.2% and 97% before and after cross-validation, identifying key features critical for accurate detection.

The article “Manycore Parallel Simulations of Fishing Nets” discusses the implementation of fishing net simulations using parallel computing on manycore infrastructure. Applying the TBB library, the simulations were developed for Xeon Phi architectures, achieving a maximum speedup of 161.81 with correct scalability and efficient resource use, aiding in sustainable fishing practices.

The study “Representation of Traffic Congestion: A Multicriteria Analysis Approach based on Distances” proposes a mathematical model for representing traffic congestion by considering distances between essential city services and traffic incidents, enabling characterization and mitigation of congestion. The model supports informed decision-making to improve urban mobility.

The study “Identifying Suicide Ideation in Social Media Posts: A Study on Word Embeddings and ML Classifiers” compares various word embedding techniques and classifiers to detect suicide ideation in social media posts. It concludes that GPT-3 with Multilayer Perceptron provides the most accurate results, achieving over 90% in evaluation metrics.

The paper “Class Balancing Approaches to Improve Software Defect Prediction Estimations: A Comparative Study” assesses the impact of class balancing methods on software defect prediction using machine learning. It concludes that balancing classes improves prediction performance, recommending class balancing as a pre-processing step.

The study “Comparison Prediction Models Using Time Series in COVID-19 Infection in Mexico” compares six predictive models for COVID-19 infection in Mexico using time series data. ARIMA and ANN MLP models are found to be most effective for daily data, improving prediction accuracy.

The paper “Intuitionistic Fuzzy Recurrence Plots for Classifying Cardiac Arrhythmias Using Deep Learning” combines recurrence plots generated by intuitionistic fuzzy clustering with deep learning models to classify cardiac arrhythmias. The approach achieves superior performance, with an accuracy of 98%.

The paper “Merging Distinct Sources Databases to Improve Software Estimation Models” proposes using the Kruskal-Wallis test to merge distinct source databases, enhancing software estimation models. The case study showed improved estimation accuracy, data point number, and standard deviation.

A systematic review, “Learning Analytics in Higher Education Institutions in the Last Decade: A Systematic Literature Review,” highlights learning analytics (LA) in higher education, its evolution, and its application in improving student outcomes. The study identifies gaps in LA’s use for tutoring and academic advising, suggesting future research in these areas.

The paper “Analysis of Behavior-Driven Development: A Thematic Synthesis” synthesizes the application of Behavior-Driven Development (BDD), analyzing its benefits and challenges. It emphasizes BDD’s role in aligning software development with business objectives and suggests future research to improve its adoption.

The study “Empirical-Theoretical Approaches for Conceptualizing Cognitive Solutions in Complex Domains” explores three approaches to conceptualizing cognitive solutions in complex informal structured domains. It highlights the need for a methodological model to support cognitive solution development in these domains, focusing on organizational decision-making.

A literature review, “A Systematic Literature Review of 10 Years of Research on Program Synthesis and NLP,” examines program synthesis from natural language specifications over the past decade. The study identifies trends in simplifying programming tasks and suggests areas for future research, particularly in broadening software development access through natural language processing.

The paper “Domain-Driven Design in Microservices-Based Systems Development” reviews the application of Domain-Driven Design (DDD) in microservices development, identifying its role in microservice identification and addressing challenges. It calls for further research on practical DDD implementation in this architectural style.

The “Systematic Literature Review of Project Management Maturity Models” identifies and classifies project management maturity models, highlighting their use in evaluating and improving project management processes. The study categorizes models into four areas, aiding project managers in selecting appropriate models for their needs.

The study “Is it Possible to Use ChatGPT to Perform Measurements Using the COSMIC Method?” investigates ChatGPT’s capability to perform software functional size measurements using the COSMIC method. It concludes that ChatGPT falls short in accuracy and reproducibility, particularly in extracting key elements needed for reliable measurement.

The paper “Exploring the Frontier of Software Engineering Education with Chatbots” explores the use of chatbots like ChatGPT in teaching object-oriented programming (OOP) and software engineering. It presents survey results showing positive student experiences but also highlights limitations in problem interpretation and best practice adherence, suggesting the need for improved tools and training.

The study “AMI/IoT Data Model for Public Lighting in Mexico Using Lz4 Compression, IPFS, and Blockchain” proposes a model to improve public lighting management in Mexico using AMI and IoT with LZ4 and IPFS compression, ensuring data efficiency and security with blockchain technology. The model reduces costs and enhances energy resource management.

The paper “Application of Design Science Methodology to the Design of a Process Model for V Gene Annotation” applies Design Science methodology to model the antibody V gene annotation process, aiming to standardize and improve the annotation process. The model was developed and validated by experts, enhancing the efficiency and accuracy of molecular biology procedures.

The paper “Skills Required for Quantum Computing: A Comprehensive Review of Recent Studies” reviews the core competencies needed, aiding academic institutions in designing curricula to prepare the workforce for the quantum computing field. It discusses quantum computing’s complexity, rooted in quantum mechanics, demands of strong technical skills in quantum topics, mathematics, and related fields, and soft skills for multidisciplinary teamwork.

The study “SPAM: An Enhanced Performance of Security and Privacy-Aware Model Over Split Learning” presents a security and privacy-aware model that improves split learning in consumer electronics. It offers enhanced protection against data leakage and better performance in speed and latency, making it a preferred choice for data security in this domain.

Guest Editors
	Prof. Dr. Andrei Tchernykh, CICESE Research Center, carretera Tijuana-Ensenada 3918, 22860, Ensenada, BC, Mexico. Ivannikov Institute for System Programming, Russian Academy of Sciences, Moscow, Aleksandr Solzhenitsyn ul. 25, 109004, Moscow, Russia e-mail: [email protected], http://usuario.cicese.mx/~chernykh/ Interests: resource optimization, adaptive resource provisioning, multi-objective optimization, computational intelligence, incomplete information processing, cloud computing, and security
	Prof. Reyes Juárez Ramírez, Universidad Autónoma de Baja California, Avenida Universidad 14418, Parque Industrial Internacional Tijuana, 22427, Tijuana, Baja California, México e-mail: [email protected], https://conisoft.org/redmis/ Interests: software engineering, human-computer interaction, usability engineering, video games, knowledge engineering
	Dr. Carlos A. Fernández y Fernández Universidad Tecnológica de la Mixteca, Av. Doctor Modesto Seara Vázquez No.1, Huajuapan de León, Oaxaca, México. e-mail: [email protected] Interests: software engineering, agile and traditional development methods, visual modeling, andformal software specification.
	Dr. Esteban Mocskos, Universidad de Buenos Aires, Pabellón 0+infinito, C1428EGA, Ciudad Autónoma de Buenos Aires, Argentina. Centro de Simulación Computacional p/Aplic. Tecnológicas (CSC-CONICET), Godoy Cruz 2398, C1425FQD, Ciudad Autónoma de Buenos Aires, Argentina e-mail: [email protected] Interests: distributed systems, computer networks and protocols, parallel programming, and applications
	Prof. Sergio Nesmachnow, Universidad de la República, Montevideo 11300, Uruguay e-mail: [email protected] Interests: optimization, high performance computing, simulation, metaheuristics, smart-cities, scientific computing, scheduling, artificial intelligence

The paper “Framework for Development and Execution of Scientific Workflows: Designing Service-oriented Applications” proposes a new framework for service-oriented, workflow-based scientific applications, addressing issues in modularity, standardization, and interdisciplinary research, leading to reduced labor costs in experiment preparation and execution.

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