In the rapidly evolving field of artificial intelligence (AI), the scalability of AI models has emerged as a critical factor determining their efficacy and applicability across various domains. This paper explores the integral role of cloud infrastructure in addressing the scalability challenges faced by contemporary AI models.

Testing software is essential in the development phase because it enables the developers to produce robust, error-free software that is usable, acceptable, and functional. The research reviews software testing and explicitly testing techniques and levels utilised in all testing of software products.

This article explores the impact of the advancements of artificial intelligence (AI) and machine learning in the field of information operations. The article begins by examining how artificial intelligence can be harnessed to address false information on the web.

In recent years, the Internet's popularity has led to a significant rise in the spread of information through social media platforms. Given the strong interest from various sectors of society in analyzing this data, it is crucial to study improved techniques for handling and understanding this data to efficiently and accurately interpret this massive amount of information.

This paper explores the pivotal role of semiconductor technologies in accelerating artificial intelligence (AI) and machine learning (ML) applications within cloud computing environments. As the demand for advanced AI capabilities continues to surge, the computational, energy, and efficiency requirements of AI operations have become increasingly critical challenges.

This abstract explores a unique synergy among three pivotal components within the domain of algorithmic trading: Algorithmic Trading, Price Prediction Models, and News Web Scraping. Each element plays a vital role in shaping the landscape of modern trading strategies and financial decision-making. Algo Trading, the cornerstone of automated trading, leverages complex mathematical algorithms to execute high-frequency, data-driven trading strategies with precision and speed.

The research aims to explore the multi-layer application of machine learning techniques in the field of cybersecurity, with a particular focus on anomaly detection as a pivotal aspect of cyber defense systems. It investigates how machine learning algorithms can enhance cybersecurity practices by enabling the detection and prevention of various types of cyber threats through predictive and monitoring services.

Large-scale processing power, enabled by machine learning, has become a pivotal device of choice for exploring previously unexplored domains. This article explores the frameworks, obstacles, and improvements in scaling machine learning systems, focusing on solutions that allow to achieve both the throughput and the reliability of the system when working with large amounts of data.

The rapid development of information technology allows users to work in the networked environment of the Internet, and a large amount of information and data is created every day, which indicates the transition to the data era. Networks and information systems have become indispensable elements in the economic development of society. Network attacks are encountered at all layers of computer networks and pose risks to society, economy, and public interest.

The development of a chatbot consultant for depressed patients is an exciting area of research that can help improve access to mental health services. This work outlined the design and implementation of an intelligent chatbot consultant for depressed patients. Chatbots are automated computer programs that are able to hold, e.g., a script-based conversation with a human being. Chatbots could contribute to the extension of health care services.

The application of RPA in business operations is the augmentation of rote and repetitive tasks to machines and smart software. This paper critically analyzes the influence of RPA on the contemporary business environment and argues that this technology has great potential for improved efficiency, cost savings, and accuracy. We discussed the essential elements of RPA, how it impacts trade facilitation, and the challenges that organizations face in implementing RPA.

FPGA are slowly becoming an indispensable part of the future mobile devices having flexible, high-performance modes and the capacity to speed up a lot of procedures. However, one of the main obstacles in integrating mobile devices into an application context is power management. This paper aims to identify and analyze several low-power design techniques for FPGAs in mobile devices in relation to power savings enhancement on various levels: architectural, circuit, and algorithmic.

Financial security is a critical concern for individuals, businesses, and governments alike. The increasing reliance on digital transactions and the interconnectedness of global financial systems have amplified the risks associated with fraud and financial crimes. In this context, the role of data science in enhancing financial security has become paramount. This paper explores the applications of data science in risk management and fraud detection within the financial sector.

Deep learning, a branch of Artificial Intelligence (AI) and Machine Learning (ML), is right now enjoying enormous success in the regaining process of the last ten years. The following article focuses on an all-encompassing subject of deep learning, where its role in essential sectors such as healthcare, finance, automotive and entertainment has been highlighted.

Cloud computing has changed the way that businesses operate, with Salesforce CRM being one of the leading tools for digital innovation across markets. In this article, we review how the Salesforce CRM Cloud helps to advance engineering and integrates a value proposition across key functional areas of operations, customer engagement, and growth strategy. As a cloud-based tool for sales, marketing, customer service and business analytics Salesforce Customer Relationship Management (CRM) system is critical for any organization to have, especially for organizations that are keen on succeeding in the digital economy.

In a world where data grows exponentially, enterprises often struggle with managing disparate data sources, leading to confusion over which sources to trust, inefficiencies in analysis, and time-consuming decision-making processes. This paper introduces the development of a unified data pipeline designed to standardize and integrate data from multiple sources into a single source of truth. By implementing this solution, organizations can support tech-based ad-hoc solutions, deliver consistent and accurate data to stakeholders, and foster collaboration across tech, product, and process teams.

The increase in claim sophistication in both the insurance and legal domains is a result of an increase in stokes and heterogeneity of data needed to assess the claim validity. Originally, this task was performed by some sort of subjectivity assessments and graphical rule sets, which is very slow and may be inherently erroneous due to its purely manual nature.

Cloud computing has dislocated many aspects of technological construction by providing flexible and inexpensive access to solutions. Nevertheless, increased and accelerated cloud adoption with the help of technological developments has become a higher risk of complex cyber threats. This article aims to analyze the use of neural networks in improving cloud computing security, with special reference to threat identification and the capacity for self-organization and response.