Journal of Science & Technology
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The Crucial Role of Inspection in Software Quality Assurance
No full textThis article explores the pivotal role of inspection in Software Quality Assurance (SQA) and its significance in enhancing software development processes. Inspection, a formalized evaluation technique, involves a collaborative examination of software artifacts to identify defects and inconsistencies early in the development life cycle. The article discusses key components of the inspection process, including team collaboration, a formalized approach, checklists, and metrics. Emphasizing the benefits of inspection, it highlights improved software quality, cost-efficiency, knowledge transfer, and process improvement. As a fundamental practice in SQA, inspection contributes to early defect detection, thereby minimizing costs and fostering a culture of continuous improvement in software development projects. Software ecosystems are an increasingly popular model for software development and the associated business aspects
Unveiling the Future: Cybernetic Dynamics in Quality Assurance and Testing for Software Development
No full textThis research paper delves into the cybernetic dynamics of quality assurance (QA) and testing processes in software development, with a specific focus on the utilization of feedback loops and adaptive testing strategies. The objective is to investigate how these mechanisms contribute to enhancing the reliability and performance of software systems. The study employs a mixed-methods approach, combining quantitative analysis of real-world case studies and qualitative insights gathered from industry experts
AI and Software Engineering: Rapid Process Improvement through Advanced Techniques
No full textIn recent years, a variety of research have effectively applied machine learning approaches across a broad range of industries. This led to the creation of a large range of deep learning models, each adapted to a specific purpose in software development. There are various ways in which the software development business may benefit from employing deep learning models. Nowadays, nothing is more vital than consistently testing and maintaining software. Software engineers are responsible for a broad variety of duties during the lifespan of a software system, from original design to final delivery to clients via cloud-based platforms. It is evident from this list that all jobs involve meticulous planning and the availability of a range of materials. A developer may study a range of resources, including internal corporate resources, external websites with important programming material, and code repositories, before creating and testing a solution to the current issue. Finding out what went into building the recommended is what this inquiry is all about. Based on user feedback, this system examines the recommended\u27s effectiveness and proposes methods to enhance the programme
Integration of Robotic Process Automation with Low-Code Development for Enhanced Productivity
No full textIn the dynamic landscape of business process automation, organizations are increasingly leveraging the synergies between Robotic Process Automation (RPA) and Low-Code Development to achieve heightened levels of efficiency and productivity. This study explores the integration of RPA with low-code platforms, aiming to provide a comprehensive understanding of the collaborative impact on workflow automation and overall business productivity. The research delves into the unique strengths of RPA in automating rule-based, repetitive tasks and low-code development\u27s ability to empower users with diverse technical backgrounds to contribute to application development. The research investigates the intersection of RPA and Low-Code Development, elucidating how the automation capabilities of RPA and the rapid application development features of Low-Code platforms can complement each other
Leveraging AI for Advanced Troubleshooting in Telecommunications: Enhancing Network Reliability, Customer Satisfaction, and Social Equity
No full textThe integration of Artificial Intelligence (AI) in telecommunications is poised to revolutionize the industry\u27s approach to troubleshooting, offering a transformative solution to the persistent challenges of network reliability, customer satisfaction, and social equity. This paper delves into the application of AI-driven methodologies in proactively predicting, identifying, and resolving network issues, thereby significantly enhancing the performance and dependability of telecommunication networks. The research begins with a thorough examination of the current landscape in telecommunications, highlighting the technical and operational challenges associated with traditional troubleshooting methods, which are often reactive, time-consuming, and prone to human error. These conventional approaches are increasingly inadequate in addressing the complexities of modern, large-scale networks, where the rapid proliferation of connected devices and the demand for uninterrupted services necessitate more sophisticated and efficient solutions.
AI, with its ability to process vast amounts of data in real time, offers a paradigm shift in troubleshooting by enabling predictive maintenance, anomaly detection, and automated resolution processes. This paper explores the various AI techniques, including machine learning algorithms, deep learning models, and natural language processing, that are being integrated into telecom networks to facilitate advanced troubleshooting. By analyzing historical data, identifying patterns, and learning from past incidents, AI systems can preemptively address potential network failures before they impact users, thus reducing downtime and ensuring a more resilient network infrastructure. The research also addresses the technical challenges of implementing AI in telecommunications, such as the integration of AI with existing network management systems, the scalability of AI solutions in large networks, and the need for continuous learning and adaptation of AI models to cope with evolving network dynamics.
The paper provides a detailed analysis of case studies where AI-driven troubleshooting has been successfully implemented in real-world telecom scenarios. These case studies demonstrate the practical benefits of AI, including significant reductions in mean time to repair (MTTR), cost savings through optimized resource allocation, and enhanced customer satisfaction due to fewer service disruptions and faster issue resolution. Moreover, the paper emphasizes the social implications of leveraging AI in telecommunications, particularly in promoting social equity. Improved network reliability and performance, driven by AI, can enhance access to critical communication services in underserved and rural communities, bridging the digital divide and fostering greater inclusion in the digital economy. The research highlights how AI can enable telecom providers to offer more equitable services, ensuring that all segments of society benefit from reliable and high-quality telecommunications.
This paper asserts that the integration of AI into telecommunications is not only a technical necessity for improving network reliability and customer satisfaction but also a crucial step toward achieving broader social equity in access to communication technologies. The findings underscore the potential of AI to transform the telecommunications industry by enabling proactive and efficient troubleshooting, ultimately leading to a more resilient, customer-centric, and socially responsible telecom infrastructure. The research contributes to the ongoing discourse on the future of telecommunications by providing insights into the practical applications of AI, the challenges that need to be addressed, and the potential social benefits that can be realized through the widespread adoption of AI-driven solutions in the industry
AI-Powered Fraud Detection and Prevention Mechanisms in Online Banking
No full textIn the digital age, the proliferation of online banking services has necessitated sophisticated fraud detection and prevention mechanisms to safeguard financial transactions from malicious activities. This paper investigates the application of artificial intelligence (AI) technologies in the realm of online banking, with a particular focus on the development and deployment of AI-powered systems for detecting and preventing fraudulent transactions. The primary aim is to elucidate how AI methodologies, such as machine learning (ML) and deep learning (DL), are employed to enhance the accuracy and efficiency of fraud detection processes, emphasizing real-time monitoring and anomaly detection techniques.
The paper begins by providing an overview of the current landscape of online banking fraud, outlining prevalent types of fraud and the limitations of traditional detection methods. It then introduces various AI technologies and their relevance to fraud detection. A comprehensive analysis is conducted on how AI algorithms, including supervised and unsupervised learning models, are utilized to identify patterns indicative of fraudulent behavior. Special attention is given to the mechanisms through which AI systems continuously learn from new data, thereby improving their predictive accuracy and reducing false positives.
Real-time monitoring is a critical component of effective fraud prevention, and the paper explores how AI-driven solutions facilitate immediate analysis of transactions. The discussion highlights how AI systems leverage vast amounts of transaction data to detect anomalies that deviate from established patterns of normal behavior. The integration of real-time data feeds into AI models allows for the swift identification of potential threats, enabling proactive measures to prevent financial losses.
The paper also examines the role of anomaly detection in AI-powered fraud prevention. Anomaly detection algorithms are designed to recognize unusual patterns and deviations in transactional data that may indicate fraudulent activities. The study delves into the various approaches to anomaly detection, including statistical methods, clustering techniques, and neural network-based models. It discusses the advantages and limitations of each approach, emphasizing how combining multiple techniques can enhance overall detection capabilities.
Furthermore, the paper addresses the challenges associated with implementing AI-powered fraud detection systems in online banking. These challenges include data privacy concerns, the need for high-quality and representative datasets, and the complexity of balancing detection accuracy with operational efficiency. The discussion includes strategies for overcoming these challenges, such as employing privacy-preserving techniques and optimizing model performance to ensure timely and accurate fraud detection.
The paper concludes with a review of case studies that demonstrate the successful application of AI technologies in fraud detection and prevention within online banking environments. These case studies provide insights into practical implementations, highlighting the impact of AI systems on reducing fraudulent activities and improving security measures. The analysis of these case studies offers valuable lessons and best practices for financial institutions looking to adopt AI-driven solutions.
This paper provides a thorough exploration of AI-powered fraud detection and prevention mechanisms in online banking. By examining the integration of machine learning and deep learning technologies, real-time monitoring capabilities, and anomaly detection approaches, the paper underscores the significant advancements AI brings to the field of fraud prevention. The insights gained from this study aim to contribute to the ongoing development of more robust and effective fraud detection systems in the ever-evolving landscape of online banking
Role of Middleware, Integration Platforms, and API Solutions in Driving Digital Transformation for Enterprises
No full textOrganizations in today\u27s digital environment must use advanced technological solutions that enable their operational functions, strategic initiatives, and customer engagement processes. Strong businesses that aim to stay competitive in digital worldwide systems need digital transformation as their key survival requirement (Trakadas et al., 2019). Interoperable communication depends on middleware technologies that use integration platforms and APIs to enable better data administration and flexible business procedures (Forcadell et al., 2019). This paper investigates the elements that support digital transformation and their impact on enterprise accomplishments
Scalable NLP in the Enterprise: Training Transformer Models on Distributed Cloud GPUs
No full textThis paper explores the large-scale deployment of transformer-based models, specifically BERT and its variants, for enterprise applications in customer service automation and legal document processing. It presents an in-depth analysis of strategies for training such models on distributed cloud-based GPU infrastructures, highlighting optimizations in data parallelism, model parallelism, and input pipeline design. Leveraging frameworks such as TensorFlow and PyTorch, along with orchestration via Kubernetes and Horovod, the paper examines techniques to achieve scalability, fault tolerance, and efficient resource utilization. Additionally, it discusses domain-specific pretraining, fine-tuning pipelines, and inference acceleration for real-time enterprise workloads. Empirical results demonstrate the feasibility and performance trade-offs of scaling transformer architectures in production environments. The findings underscore the practical implications of marrying cutting-edge NLP with robust cloud-native infrastructure to drive operational efficiency in data-intensive domains
Quality and Metrics in Software Quality Engineering
No full textComplexity metrics play a crucial role in assessing essential information related to the reliability and maintainability of software systems through regular source code analysis. These metrics offer continuous feedback throughout a software project, aiding in the control of the development process. Various models are employed to categorize the quality of a software product, each incorporating distinct measures to evaluate the extent to which the product meets specific quality attributes. The highest level of classification in each model may consist of a different set of attributes, and these attributes might be defined uniquely at every level. Given the increasing reliance on computer programs for various activities, the importance of their quality becomes paramount. While consensus exists on the significance of quality, defining precisely what quality entails remains a point of contention. Software reliability is highly affected by software quality attributes and measurements. Faults, bugs, and errors are shown not only in the development process but also in end-user period hereby it is required to detect these issues earlier. This paper aims to introduce key models and standards for assessing software quality. Additionally, it will present metrics for software complexity, elucidating the connection between complexity and quality
Emerging Technological Trends for Enhancing Healthcare Access in Remote Areas
No full textAccess to quality healthcare is a fundamental human right, yet many remote areas around the world lack adequate healthcare services. Emerging technologies offer promising solutions to bridge this gap and improve healthcare access in remote areas. This paper explores the latest technological trends and innovations that are transforming healthcare delivery in remote areas. We discuss the role of telemedicine, mobile health (mHealth) applications, wearable devices, Internet of Things (IoT) devices, and artificial intelligence (AI) in enhancing healthcare access. Additionally, we highlight the challenges and opportunities associated with implementing these technologies in remote healthcare settings. By understanding these emerging trends, policymakers, healthcare providers, and technology developers can collaborate to create more accessible and efficient healthcare systems for remote populations