Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023

AI-Powered Threat Intelligence: Revolutionizing Cybersecurity with Proactive Risk Management for Critical Sectors

2024-12-19T04:58:42+00:00

The rapid evolution of cyber threats has necessitated a paradigm shift in cybersecurity strategies, particularly in critical sectors such as healthcare, finance, energy, and transportation. This paper explores the transformative role of AI-powered threat intelligence in revolutionizing cybersecurity practices. By leveraging advanced machine learning algorithms, natural language processing, and predictive analytics, AI-driven systems can detect, analyze, and mitigate threats with unprecedented speed and accuracy. This research highlights the integration of real-time data processing, threat intelligence platforms, and adaptive security frameworks to enable proactive risk management. Case studies and experimental results underscore the effectiveness of AI-powered approaches in anticipating cyberattacks, reducing response times, and minimizing operational disruptions. The findings demonstrate that AI is not merely a tool but a pivotal enabler of robust, adaptive, and scalable cybersecurity strategies in the face of an ever-evolving threat landscape.

Transforming User Stories into Java Scripts: Advancing Qa Automation in The Us Market With Natural Language Processing

2025-01-14T05:42:04+00:00

With constant updates in software development, it is paramount that higher reliability of the software is achieved by having sound testing procedures for the software. The tradition ways of creating test script are manual and time-consuming and can accommodate a lot human error as well as do not adapt to Agile and DevOps environments properly. This research presents an alternative solution that can be used to address the problem: an apparatus based on Natural Language Processing technologies that enables the transition from user stories to test scripts written in Java. The advantage of the proposed framework is that it can support the interpretation of user stories written in natural language and transform these into strictly structured test cases that are compatible with Selenium, JUnit, or Cucumber. As such, a fundamental objective of this framework is to minimize the time needed to write test script and at the same time be accurate and consistent. It covers problems typical to many projects like vagueness in requirements description, increased size of systems under test, and specific terminology in the domain area, making the generated test scripts covering both typical and extraordinary situations. Besides, it meets specifications that are particular to particular sectors like H-HIPAA for health facilities and H-PCI-DSS for facilities that deal with finances. The outcome of leveraging the exaction of the conceived framework into prototypes/practical applications from industries such as financial, healthcare, and e-commerce illustrate the raise in efficacy and scalability in QA line functions. By increasing the time to perform manual test by 80%, detecting defects at a higher percentage compared to the manual method and test coverage of the application, the framework provides more accurate results than the other methods. Additionally, incorporating the framework into CI/CD pipelines means that developers can TEST their codes quickly and have an almost real-time feedback based on the software that has been DEVOPed for implementation, without having to slow down the processes by running a lot of test more than once.

AI-Driven Identity and Financial Fraud Detection for National Security

2024-12-21T06:58:19+00:00

In the digital age, financial systems and personal identities are increasingly targeted for fraud by sophisticated actors, including criminal organizations, terrorist groups, and rogue states. The U.S., as a global financial hub, faces unique challenges in mitigating these threats, which have direct implications for national security. The rise of cloud-native AI-based systems offers a powerful solution for detecting and preventing identity and financial fraud at scale. Leveraging artificial intelligence (AI) in a cloud-native environment enables federal agencies and private-sector institutions to uncover fraudulent transactions, trace illicit funds, and disrupt organized networks with unprecedented speed and accuracy.

Intent Prediction in AR Shopping Experiences Using Multimodal Interactions of Voice, Gesture, and Eye Tracking: A Machine Learning Perspective

2024-12-22T09:43:06+00:00

Augmented Reality (AR) is revolutionizing the shopping experience by allowing consumers to interact with virtual products in real-time. Intent prediction – the mechanism of predicting a consumer’s intention based on their behavioral patterns and actions – is crucial for enhancing the personalization of AR shopping environments. This paper explores how multimodal interactions, including voice commands, gesture recognition, and eye tracking, can be integrated into AR shopping experiences to predict user intent more effectively. We review current advancements in multimodal interaction systems, discuss the importance of intent prediction in AR, and assess the impact of combining multiple input modalities on prediction accuracy. Our research identifies the challenges and future directions for intent prediction in AR shopping landscapes, aiming to improve user engagement, personalization, and the overall shopping experience.

Revolutionizing BA-QA Team Dynamics: AI-Driven Collaboration Platforms for Accelerated Software Quality in the US Market

2024-12-24T04:33:07+00:00

In today’s fast-paced software development environment, the collaboration between Business Analysts (BAs) and Quality Assurance (QA) teams is essential for delivering high-quality products efficiently. However, traditional methods often lead to inefficiencies due to silos and misalignment between these teams. This article explores how Artificial Intelligence (AI)-driven collaboration platforms are transforming BA-QA dynamics, offering a more integrated, data-driven approach to software development. By leveraging AI technologies such as predictive analytics, automated test case generation, and real-time collaboration tools, businesses can enhance decision-making, improve communication, and optimize testing strategies. This paper discusses the key benefits of AI in accelerating software quality, highlights real-world case studies of AI applications, and examines the future potential of AI in revolutionizing BA-QA collaboration, particularly in the US market. It also addresses the emerging trends and challenges that come with adopting AI, emphasizing the importance of continuous learning, training, and integration of AI tools with other technologies like IoT and blockchain. As AI continues to evolve, its role in streamlining BA-QA collaboration will become increasingly critical, offering organizations a competitive edge in delivering high-quality software at an accelerated pace.

Transforming QA Efficiency: Leveraging Predictive Analytics to Minimize Costs in Business-Critical Software Testing for the US Market

2024-12-24T07:41:34+00:00

In the context of information assurance, specifically software testing, predictive analytics has rapidly become the ‘go-to’ solution for application QA. In this article, the author discusses the adaptation of this technology in the QA processes and its aim to optimize the processes, decrease costs and increase the quality of the software product in the USA. The study shows that through analysis of data, testing cycles can be managed effectively and defects detected before the time and resource is spent on developing and testing the unnecessary features. Main milestones are described in the paper, including data gathering, machine learning algorithms, and feedback, which show how they shifted traditional approaches to QA. Moreover, it goes a step further and discusses the application of the solution such as cost saving, efficiency and ways of decision making. This article also looks at the difficulties organizations encounter while implementing these tools such as technical issues as well as resistance from the organization and ways which can be used to ensure a proper implementation of the predictive analytics. Finally, the paper defines tendencies for the nearest future like future uses of AI in QA processes and interaction with DevOps, accentuating on their capability to contribute in the continuous advancement of software testing. The article provides practical examples of using predictive analytics in QA and demonstrates how companies can obtain tangible enhancements in product quality and reduce expenses. Therefore, the work’s conclusions could be summarized as a call to adapt and adopt predictive analytics due to the current fast pace of market evolution in software.

Enhancing Cloud Security with Automated Service Mesh Implementations in DevOps Pipelines

2024-12-29T06:13:24+00:00

This paper explores the integration of automated service mesh tools, such as Istio, into DevOps pipelines to enhance cloud security. It discusses methods to implement mTLS and define ingress/egress traffic controls, reducing vulnerabilities in microservice communication. The research evaluates case studies to measure improvements in security and operational efficiency, laying a foundation for scalable, secure cloud-native environments.

Multi-Cloud DevOps Strategies: A Framework for Agility and Cost Optimization

2024-12-29T06:56:02+00:00

This paper investigates the challenges and benefits of adopting multi-cloud strategies within DevOps environments. It highlights automation tools such as Terraform and Kubernetes to balance agility, performance, and cost, providing actionable insights for enterprises navigating complex cloud ecosystems.

In today's dynamic IT landscape, multi-cloud environments have become the cornerstone of enterprise strategies, enabling organizations to leverage the unique strengths of various cloud providers. This paper presents a comprehensive framework for implementing Multi-Cloud DevOps strategies aimed at enhancing operational agility and cost optimization. The proposed framework integrates best practices for seamless deployment, monitoring, and scaling of applications across diverse cloud platforms. By employing tools for orchestration, automation, and continuous integration/continuous delivery (CI/CD), the framework ensures rapid adaptability to changing business needs while maintaining cost efficiency. This study underscores the importance of aligning DevOps principles with multi-cloud architectures, thereby empowering businesses to maximize resource utilization and achieve competitive advantages in a rapidly evolving market.

Enhancing Healthcare Analytics and Accelerating Personalized Treatment through Comparative Studies of High-Throughput Database Architectures

2024-12-30T04:09:01+00:00

This study looks at the transformative role of high-throughput database architectures in advancing healthcare analytics and accelerating personalized treatment. The study explores various database frameworks, including distributed SQL, NoSQL, and specialized analytical platforms, to find the best option for handling medical data that is growing very fast. The paper discusses, through a comparative analysis, the speed of ingestion, query performance, scalability, fault tolerance, and data integration for applicability in modern healthcare needs. These insights are expected to help healthcare organizations in the selection and deployment of database systems that enhance data-driven decision-making and, in turn, improve the outcomes of patient care. The findings contribute to the broader discourse on integrating advanced technologies in personalized medicine, with efficient database systems playing a pivotal role.

Product Management in Fintech

2025-01-07T08:12:45+00:00

Fintech represents the financial technologies that have revolutionized banking and other related financial services to improve clients' experiences. In the context of Fintech, product management is a critical function that enables firms to tackle the challenges of this highly dynamic and competitive market environment governed by some of the most demanding regulatory standards. Thus, the present paper explores the theoretical background, practical strategies, and trends in the management of Fintech products. It emphasizes that to create long-lasting and customer-oriented solutions, it is necessary to focus on such aspects as an agile approach, data science, and cross-functional teamwork. Issues include addressing regulations, privacy, competition, and how to incorporate compliance into products and use emerging technologies like blockchain and AI. It also discusses some of the limitations of previous studies and provides insights into globalization, Fintech, sustainability, and Fintech and financial inclusion. This paper employs theoretical analysis, research reviews, and Earnest's precision pricing model as a case study to offer a clear research framework for researchers, practitioners, and policymakers interested in promoting innovation and sustainability in Fintech product management.

AI at the Crossroads of Health and Society: Emerging Paradigms

2025-01-08T04:50:41+00:00

Artificial Intelligence (AI) is rapidly reshaping the landscape of healthcare and societal development, offering transformative solutions to longstanding challenges. This article explores the emerging paradigms where AI intersects health and society, highlighting its applications in personalized medicine, disease prediction, public health surveillance, and healthcare accessibility. The discussion underscores the potential of AI to revolutionize medical diagnostics, enhance patient outcomes, and bridge gaps in healthcare systems globally. Concurrently, the societal implications of these advancements are critically analyzed, including ethical concerns, data privacy, and the impact on workforce dynamics. By examining case studies and the latest technological innovations, the article provides a comprehensive overview of the opportunities and challenges at this intersection. It concludes with recommendations for fostering responsible AI development to ensure equitable benefits for all sectors of society.

Intelligent Cloud Solutions Bridging Technology Gaps for Small and Medium-Sized Enterprises

2025-01-10T05:36:34+00:00

Introduction: Cloud computing has become a revolutionary technology with many features especially in terms of cost reduction, availability of space and time for strategic enterprise evolution for any company, no matter the size of the company. SMEs have adopted cloud solutions at a slow pace because of several factors including security, lack of expertise and resource constrains among others. This review aims to explore the potential of intelligent cloud solutions in bridging the technology gap for SMEs and facilitating their digital transformation.

Materials and Methods: In this research study, the use of literature review data collection was adopted as the method of collecting information on cloud computing trends and adoptions, challenges that faces SMEs and the contribution of intelligent cloud solutions in responding to these challenges from published research articles and industry reports from credible academic databases. This process involved formulation of the problem under study, search for material relevant to the problem, selection of material, collection of the material and organization and summary of the findings of the material collected.

Results: The study exposed that SMEs encounter following challenges with cloud adoption; They have small budgets; They lack adequate technical skills; They are doubtful of security and privacy; and They have resistance to change. Though, superior intelligent clouds like cloud-ERP, BI, and cloud-secured solutions appear as potential opportunities to solve these challenges. These solutions tap on next-generation technologies such as ML/AI and data analysis to introduce SMEs to affordable, scalable, and secure business technologies.

Discussion: The review also underscores what intelligent clouds mean to SMEs and their opportunity to adopt the latest technologies and innovation in order to transform and gain a competitive edge. With cloud-based ERP and BI system integration, SMEs are able to introduce better values attached to decision making, resources and the flow of business. Also, the use of cloud security solutions is possible to reduce the potential threats, and ensure the safety of strictly confidential data, which is an essential issue for SMEs. This review also investigates the involvement of cloud service providers in meeting SMEs’ special needs, user training and change management, as well as the interfacing of cloud solutions with current systems. Further, the review addresses the role of new technologies including Internet of Things (IoT) and artificial intelligence (AI) in partnership with cloud computing to increase the performance of SMEs.

Conclusion: Intelligent cloud solutions can help SMEs to apply advanced technologies to compete in digital environment after analysing the abovementioned factors. However, for the CPO to be successful, there are some key factors that should be considered or probably managed; change management, skill management and system integration among others. More empirical studies are required to examine ideal models, frameworks and examine the effects of intelligent cloud solutions on performance and growth of SMEs in the long-run.

Optimizing Software Performance in Distributed Cloud Systems: Challenges and Solutions

2025-01-12T07:07:17+00:00

The rapid proliferation of distributed cloud systems has revolutionized software deployment and scalability, enabling organizations to meet dynamic demands efficiently. However, optimizing software performance in such environments presents unique challenges, including resource heterogeneity, network latency, and dynamic workload variations. This paper explores the critical factors influencing software performance in distributed cloud systems, highlighting common bottlenecks and their root causes. It also presents a comprehensive analysis of state-of-the-art solutions, such as advanced resource allocation algorithms, load balancing techniques, and containerized microservices. Additionally, the study introduces a framework for evaluating performance metrics and proposes strategies to mitigate inefficiencies while ensuring scalability and cost-effectiveness. By addressing these challenges, this research aims to provide actionable insights and foster innovation in the design and optimization of software systems for distributed clouds.

Applications Analyzing E-commerce Reviews with Large Language Models (LLMs): A Methodological Exploration and Application Insight

2025-01-17T02:38:03+00:00

The ubiquity of online shopping has transformed our daily lives, offering unparalleled convenience and enriching our purchasing experiences. It has become an indispensable part of our existence, allowing us to acquire everything from basic essentials to high-end luxury items with ease. Amazon, a leading e-commerce platform [1], employs two primary customer feedback mechanisms: the Star Rate (1-5) and detailed reviews. The Star Rate is a quick, convenient, and visually intuitive method for customers to score products, while reviews provide a more comprehensive description of the product and their shopping experience. These feedback mechanisms not only influence other users' purchasing decisions but also serve as a guide for businesses to adjust their offerings based on customer opinions, establishing a negative feedback adjustment mechanism.[2,3,4,5,6]. We introduce the innovative LLM model, commonly used in computer vision, into our NLP text analysis. Utilizing WORD2vec, we pass word vectors through classification functions to analyze pessimistic and optimistic sentiments. We then correlate these emotions with Star Rates, discovering a higher-order functional relationship between them.