Papers by International Journal of Scientific Research in Computer Science, Engineering and Information Technology IJSRCSEIT
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2018
Innovative and long-lasting testing techniques are required as the difficulties of testing softwa... more Innovative and long-lasting testing techniques are required as the difficulties of testing software programs rise in tandem with their complexity and scope. —A crucial step in the software development process is software testing. Unfortunately, despite testing efforts, defects still plague many projects, and testing still consumes a large amount of time and money. Software testing offers a way to lower the system's total cost and mistake rate. To improve software quality, a variety of software testing approaches, strategies, and tools are available. Given its importance in both the earlier and later creation phases, software validation is an essential component of the life cycle of software development. Should be supported by improved and effective processes and procedures. This article offers a brief overview of software testing, including its goals and fundamentals. Additionally it also responds to inquiries concerning the fundamental abilities needed for software testers, or those who want to pursue a career in testing. Focuses on the fundamentals that are considered while creating test cases and planning. Writing effective test cases is another topic covered in this article. This is among the crucial elements in testing.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023
The healthcare industry is undergoing a significant transformation in data management, spurred by... more The healthcare industry is undergoing a significant transformation in data management, spurred by the integration of artificial intelligence (AI) and cloud technologies in data warehousing. This paper investigates the transformative potential of AI-driven Extract, Transform, Load (ETL) processes and cloud integration within healthcare data warehouses. We explore how these technologies address key challenges such as data integration, real-time processing, and scalability, which are critical in healthcare environments. By examining various applications and proposing an implementation framework, this study provides a roadmap for optimizing healthcare data warehouses to support enhanced patient care, operational efficiency, and advanced analytics capabilities.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2020
This paper highlights various data integration techniques that I applied when implementing cross ... more This paper highlights various data integration techniques that I applied when implementing cross Platform analysis including ETL integration techniques, API integration and real-time integration techniques. This looks at examples from Netflix, Amazon and Uber how these enable organisations to aggregate information from various sources for decisions, customisation and business operations. The consequence of concerning working discussed in this paper is an evaluation of the potentials of effective data integration and presenting it as a crucial factor towards the realization of competitiveness and innovation.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2022
Generative AI is thus a game-changer in the creative industries, especially in music and Art, sin... more Generative AI is thus a game-changer in the creative industries, especially in music and Art, since using machines to produce content by themselves has become a reality. This paper aims to review the use of generative AI in these fields, particularly emphasizing the techniques and models conducive to innovation. By examining state-of-the-art methods, such as GANs and RNNs, the paper explains how these technologies are leveraged to generate music and artwork. This paper provides case studies to show AI's potential in developing new music and artworks. Furthermore, the difficulties of incorporating AI into creative workflows, including ethical questions and overcoming the uncanny valley, are discussed. Moreover, the study indicates that while Generative AI can produce colossal returns, efforts should be made to minimize its weaknesses and address the future balance between utilizing artificial intelligence as a creative tool and a scripted one.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2022
Generative AI has become a change-maker in many fields, using different text, image, and voice ge... more Generative AI has become a change-maker in many fields, using different text, image, and voice generation modes. One of the profound sub-areas within this domain is the optimum utilization of learning systems with minimal information using zero- and few-shot learning. Zero-shot learning lets models operate on novel classes or tasks for which it has no training sample, while few-shot learning allows models to learn with initial samples. Such approaches are useful when it is difficult or expensive to obtain information, which suggests a technique for providing a direction for developing accurate AI models when data are lacking. This paper explains the background, application, and challenges of generative AI models that use zero-shot/one-shot learning, outlining how these techniques help set new paradigms and raise innovative horizons for AI systems.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024
Federated learning (FL) on edge devices has emerged as a promising approach for decentralized mod... more Federated learning (FL) on edge devices has emerged as a promising approach for decentralized model training, enabling data privacy and efficiency in distributed networks. However, the complexity of these models presents significant challenges in terms of transparency and interpretability, which are critical for trust and accountability in real-world applications. This paper explores the integration of explainable AI techniques to enhance model interpretability within federated learning systems. By incorporating computational geometry, we aim to optimize model structure and decision-making processes, providing clearer insights into how models generate predictions. Additionally, we examine the role of advanced database architectures in managing the complexity of federated learning models on edge devices, ensuring efficient data handling and storage. Together, these approaches contribute to a more transparent, efficient, and scalable framework for federated learning on edge networks, addressing key challenges in both model explainability and performance optimization. This review highlights recent advancements and suggests future directions for research at the intersection of federated learning (FL), edge computing, explainability, and computational techniques.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, Aug 30, 2021
AI and ML in practice are discussed in this paper to analyse how these technologies affect busine... more AI and ML in practice are discussed in this paper to analyse how these technologies affect business operations in various industries. Artificial Intelligence and Machine Learning, concepts which for a long time were a part of theory only, are now essential to develop organizational performance and minimize the burden of various tasks as well as optimize decision making. In the present paper, reviewing a vast amount of the relevant literature, the author explains the advantages of AI and ML, including higher productivity, lower costs, and higher customer satisfaction; at the same time, the listed disadvantages, including poor data quality, adaptation of employees, and ethical issues, are also mentioned. It covers industries such as finance, healthcare, retail, manufacturing, and many others to demonstrate how AI & ML disrupt conventional approaches and unveil innovative sources of competitiveness. The paper also covers the future of AI and focuses on existing hurdles that inhibit the utilization of these technologies in businesses.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023
This comprehensive research paper explores cutting-edge debugging techniques for multi-processor ... more This comprehensive research paper explores cutting-edge debugging techniques for multi-processor communication in 5G systems. As 5G networks continue to evolve and expand, the complexity of multi-processor communication introduces unique challenges in system debugging and optimization. This study examines various advanced debugging methodologies, including distributed tracing, time-travel debugging, AI-assisted anomaly detection, and hardwareassisted techniques. The research also delves into real-time debugging protocols, security considerations, and performance analysis of these debugging solutions. By synthesizing current literature and industry practices, this paper provides valuable insights into the state-of-the-art debugging approaches for 5G systems and outlines future research directions in this critical field.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2020
As this research paper will demonstrate, integrating cloud related architectures in to SAP landsc... more As this research paper will demonstrate, integrating cloud related architectures in to SAP landscapes has revolutionized the approach to optimization. The old approaches to SAP landscape optimization are compared with the new approaches which are based upon cloud solutions such as scalability, cost effectiveness and flexibility. Some of the important new concepts like virtualization, containerization and serverless architecture are discussed with regards to the performance characteristics and operational improvements. It also identifies the various issues of implementation strategies and integration and gives recommendations regarding organisations sustainability of the transition. Projections that would allow users to have a glimpse of the developments in the features and features of cloud-based SAP environments are considered to present users with directions in the development of trends and technologies. Thus, the paper has concluded that implementing cloudbased strategies prepares organizations to take adv
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2020
In the development of high-volume data processing systems, effective monitoring of Service Level ... more In the development of high-volume data processing systems, effective monitoring of Service Level Objective (SLO) turns out to be a crucial topic. The needs and importance of critical operations require large-scale data processing. Large-scale data processing necessitates maintaining the performance, reliability, and efficiency of such organizations. This research work focuses on the foundational principles of SLO monitoring, architectural considerations for high-volume data processing systems, and advanced techniques for implementing and scaling SLO monitoring solutions. The research includes areas like metric selection, instrumentation techniques, data collection strategies, statistical analysis, and emerging trends in the field. It is a synthesis of current literature and industry practices that presents an organized guide for organizations that want to implement robust SLO monitoring in their data processing infrastructure.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024
Kubernetes Autoscaling Mechanism for Integration into Cloud Services to Achieve Cost Efficiency O... more Kubernetes Autoscaling Mechanism for Integration into Cloud Services to Achieve Cost Efficiency Organizations have turned towards containerized applications and microservices architecture. Optimizing and using resources appropriately as per the expected operational cost becomes the need of the hour. There are several autoscaling mechanisms within Kubernetes, that include Horizontal Pod Autoscaler, Vertical Pod Autoscaler, and Cluster Autoscaler, working towards cost optimization. We study predictive scaling algorithms, multi-dimensional autoscaling strategies, and machine learning-based approaches for resource allocation. Among the new challenges of implementing the solution are the methodologies followed in evaluating the research, which also involves complex advanced optimization techniques: from integrating serverless, towards multicloud autoscaling. Our findings will give an understanding of the status quo of Kubernetes autoscaling towards cost efficiency and recommendations for future research and industrial implementation.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024
The paper reviewed relevant articles on strategic pursuit management. Being a conceptual paper, I... more The paper reviewed relevant articles on strategic pursuit management. Being a conceptual paper, I have started by highlighting the overview / essence of strategic pursuit management and elaborating the role of Pursuit leader. This was followed by research and refereeing different proven strategies and factors that may lead to discussions on factors that affect strategy formulation, strategy implementation as well as factors bedeviling strategy implementation. Essentiality of strategic management practices as a radical improvement method was equally highlighted.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024
This article explores the transformative potential of Artificial Intelligence (AI) and Machine Le... more This article explores the transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing the integration of E-commerce platforms with Enterprise Resource Planning (ERP) systems. As E-commerce experiences explosive growth and ERP systems become increasingly complex, businesses face significant challenges in maintaining scalability and efficiency. We examine how AI and ML can optimize various aspects of these integrated systems, from intelligent automation and predictive analytics to anomaly detection and decision support. Through case studies and analysis of current trends, we demonstrate the tangible benefits of AI/ML implementation, including reduced costs, improved accuracy, and enhanced customer experiences. The article also addresses key challenges such as data quality, scalability, ethical considerations, and the skills gap. Finally, we explore future research directions in explainable AI, edge computing, blockchain integration, and natural language processing, highlighting their potential impacts on the E-commerce and ERP landscape.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024
This article explores the groundbreaking collaboration between Amazon Web Services (AWS) and SAP ... more This article explores the groundbreaking collaboration between Amazon Web Services (AWS) and SAP in developing secure and scalable generative AI solutions for businesses. It examines the critical need for robust AI implementations in the face of growing security and scalability challenges, highlighting the unique strengths that AWS and SAP bring to their partnership. The article delves into real-world applications across industries, discusses key benefits for businesses including operational efficiency and enhanced decision-making, and outlines implementation strategies and best practices. Finally, it looks ahead to emerging trends and the long-term vision for AI in business, emphasizing how the AWS-SAP collaboration is poised to shape the future of enterprise AI.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024
Sugarcane is a crucial crop for the global sugar industry, but its yield and quality can be signi... more Sugarcane is a crucial crop for the global sugar industry, but its yield and quality can be significantly impacted by various leaf defects. Accurate and timely identification of these defects is essential for effective pest management and crop improvement. This review paper explores recent advancements in sugarcane leaf defect identification, focusing on technological progress and methodological innovations. The study covers traditional techniques, such as visual inspections and manual identification, and examines how modern approaches, including machine learning, computer vision, and remote sensing, have transformed the field. Recent progress in image processing technologies and the development of automated systems have greatly enhanced the accuracy and efficiency of defect detection. Despite these advancements, challenges remain, including variability in defect appearance, the need for large annotated datasets, and the integration of detection systems into practical agricultural practices. The review also discusses the impact of these technologies on improving disease management, optimizing yield, and supporting sustainable farming practices. By highlighting current trends and future directions, this paper aims to provide a comprehensive understanding of the state-of-the-art methods in sugarcane leaf defect identification and their implications for the agricultural industry.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024
Distributed Denial-of-Service (DDoS) attacks have emerged as a critical threat to network securit... more Distributed Denial-of-Service (DDoS) attacks have emerged as a critical threat to network security, causing significant disruptions by overwhelming systems with malicious traffic. The motivation behind this review is the growing sophistication and frequency of DDoS attacks, which demand more robust and scalable detection and mitigation techniques. While numerous methods have been proposed, limitations such as high false positive rates, resource constraints, and the evolving nature of attacks continue to challenge existing solutions. This review aims to analyze and evaluate various robust detection mechanisms, including machine learning, anomaly detection, and hybrid models, with a focus on scalability and adaptability in real-world applications. The objective is to identify key strengths and weaknesses in current approaches, highlighting future research directions for building more resilient DDoS defense systems capable of operating efficiently under high-traffic conditions.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024
The differential analysis of emergency vehicle detection in urban traffic is crucial for improvin... more The differential analysis of emergency vehicle detection in urban traffic is crucial for improving response times and reducing traffic-related delays during emergencies. This systematic review aims to analyze various methods and technologies, such as acoustic, visual, and sensor-based systems, used for detecting emergency vehicles in complex urban environments. The motivation behind this study is the growing need for more efficient traffic management systems that prioritize emergency vehicles, minimizing delays caused by congestion. However, limitations include the inconsistent performance of detection systems in varying weather, lighting, and noise conditions, as well as integration challenges with existing infrastructure. The objective is to evaluate current detection methods, identify their limitations, and propose potential improvements to enhance the accuracy and reliability of emergency vehicle detection in urban traffic systems.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024
Text spotting, the task of detecting and recognizing text within images, is vital in applications... more Text spotting, the task of detecting and recognizing text within images, is vital in applications like document analysis, autonomous navigation, and surveillance. The motivation for this review arises from the growing need for accurate automated text extraction methods, driven by the surge of visual data and the complexity of real-world environments. Despite advances in deep learning and computer vision, current text spotting techniques face significant challenges, including handling complex backgrounds, curved or distorted text, varied font styles, and low-resolution images. These limitations restrict their effectiveness in diverse, real-world settings. This systematic review aims to conduct a differential analysis of modern text spotting methods, highlighting their strengths, weaknesses, and performance in addressing such challenges. The objectives are to evaluate state-of-the-art techniques, identify gaps in the field, and propose future research directions. By critically synthesizing recent literature, this review provides insights that can help enhance the robustness and accuracy of text spotting systems, making them more adaptable to real-world conditions.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024
Advancements in ATM security have become critical due to rising incidents of theft and vandalism.... more Advancements in ATM security have become critical due to rising incidents of theft and vandalism. This review aims to evaluate the current state of movement and tampering detection technologies in ATMs, focusing on the integration of advanced machine learning (ML) and deep learning (DL) techniques. Motivated by the need for robust, real-time security solutions, the review addresses limitations in existing systems, such as inadequate anomaly detection and high false-positive rates. The objective is to synthesize advancements in ML and DL methods, including YOLO-based approaches, to enhance ATM security. By examining various methodologies, this review highlights the strengths and weaknesses of different detection systems and proposes directions for future improvements, particularly through the application of the latest YOLO models.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024
The rapid advancement in machine learning (ML) and deep learning (DL) techniques has significantl... more The rapid advancement in machine learning (ML) and deep learning (DL) techniques has significantly impacted the detection and diagnosis of ocular diseases, which are critical for preserving vision and overall eye health. This review aims to explore the various ML and DL methodologies applied to the detection of multiple ocular diseases, highlighting their effectiveness, limitations, and areas for improvement. The motivation behind this review stems from the increasing prevalence of ocular diseases and the need for efficient, accurate diagnostic tools. Despite the promising results of existing techniques, limitations such as data variability, the need for extensive training data, and computational resource requirements persist. The objective is to synthesize current methodologies and propose enhancements, particularly through the integration of attention mechanisms in convolutional neural networks (CNNs). This review identifies gaps in current research and suggests directions for future work to enhance diagnostic accuracy and clinical applicability.
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Papers by International Journal of Scientific Research in Computer Science, Engineering and Information Technology IJSRCSEIT