Sentimental analysis has gained popularity in recent years. Gathering massive volumes of data from the sources, utilizing the appropriate techniques or algorithms, and categorizing them are the key challenges in an emotional analysis. In the ever-expanding internet world of today, social media provides a platform for people to express their emotions.

Daily, numerous musical works are uploaded on social media platforms. The process of searching for content according to our preferences is time-consuming. One of the emerging research fields that is concerned with the process of extracting content from audio data is known as musical information retrieval.

The number of Internet users globally peaked at 4.7 billion in early 2020, representing a startling 1,187% surge injust 20 years. In addition, our growing reliance on Internet-based technology produces enormous amounts of data (1 followedby 21 zeros!). A large portion of this data needs to be encrypted because it contains "sensitive" information. To make sure that only authorized persons in possession of encryption keys may access this data, we utilize sophisticated encryption technologies.

Healthcare systems worldwide rely on health informatics to create electronic medical records (EMRs) to capture a wide range of information about patient health. Electronic medical records have the potential to transform the healthcare system by providing higher-quality care to patients.

The personnel management system, an application-based system, consists of two applications that have been developed. While one programme is used by companies to keep track of employee information, the other is used by employees to track their attendance. Information systems[2.] are used by every company, public or commercial, to maintain personnel data.

Advancements in a variety of artificial intelligence fields have spurred technological advancements not only in power consumption and efficiency but also in battery development, influencing electric vehicle advancement. By recognizing the great importance of batteries in electric vehicles, a platform that unites genetic algorithms with finite element analysis with neural networks in AI has been developed to consider various factors that affect battery design.

The complexity of computing problems has made it possible for researchers to seek different computational environments to achieve optimum performance in high-performance computing and artificial intelligence. In this context, Field Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs) are now seen as key technologies as each has its strengths.

The semiconductor industry has received the pressure of the need to develop techniques for higher efficiency and accuracy of wafer inspection processes. It has been a problem to inspect the complexity of the semiconductor wafers with traditional inspection systems and therefore sophisticated solution is required. This paper looks at the evaluation of Artificial Intelligent (AI) in semiconductor wafer inspection systems to improve the outcome.

The role of generative AI in digitization and automation has grown as many generative techniques, such as transformers, are increasingly able to create human-consistent and/or close-to-real media and content. These AI models are becoming quicker, more accessible, and more enhanced. We research the current generative AI abilities, specifically GPT, about private use quality control to see if it can provide value.

The economic and social costs of motor vehicle crashes and the strong media attention given to autonomous vehicle crashes are driving interest in solving the associated technical and policy-related issues. A key to alleviating these concerns is to enable autonomous vehicle control systems with advanced sensor technologies and data analytics. This work presents a machine learning and data analytics framework for driving assessment under partially observable Markov Decision Processes (MDPs).

Personalized medicine is a concept shifting from a conventional system of patient treatment to patient-specific genetic and phenotypic variations. Modern technologies in sophisticated artificial intelligence and machine learning in the healthcare industry have enhanced the possibility of making tailored treatments depending on a client’s genetic makeup. This paper is going to examine the roles of AI, ML and personal medication with a view to analyzing how these technologies are implemented to personalize medicine.

Data science solutions helped modernize the drug approval process and bring fresh approaches for regulation to solve the challenges across modern healthcare areas. As the pharma industry is growing and getting under pressure to cut costs, increase the transparency of the decision-making process and engage in constant innovation, data science provides the necessary tools for better management of the decision-making process, risk evaluation and the speeding up of the approval of safe drugs.

Database storage plays a very critical role in the Real world of Critical applications, In Today's Environment Infrastructure is very costly when we procure a server with high-level storage, to reduce cost, we must focus on multiple options line NetApp which is a very cost-effective solution. NetApp provides multiple features like cloning, compression, and snapshot management of the disk where databases reside. We will explore best practices for integrating NetApp storage with various Database management systems.

In today's complex global manufacturing and consumer service landscape, effective parts management is crucial for maintaining operational efficiency and competitive advantage. This paper presents an innovative approach to parts management that integrates both structured and unstructured data sources. I propose a hybrid system that combines traditional database management with advanced natural language processing, generative AI and machine learning techniques to extract valuable insights from diverse data types.

The proliferation of Internet of Things (IoT) devices in urban areas presents significant challenges for spectrum management due to limited bandwidth and increased interference. This paper explores various theoretical spectrum management strategies specifically designed for IoT applications in dense urban environments. We analyze existing research on these strategies, highlighting their potential to optimize spectrum utilization and improve network performance.

The rapid evolution of cloud platforms has really transformed the way in which AI and Big Data applications are being developed, deployed, and then scaled. This article looks at how innovation is happening with cloud platforms to support AI-driven analytics and machine learning at scale. The key improvements include real-time data processing capability, dynamic auto-scaling to optimize resources, and an increase in the capability of machine learning tools that enable organizations to derive actionable insights from massive datasets.

In today’s data-driven economy, Fortune 500 firms recognize the strategic importance of robust data architecture to sustain growth and innovation. This paper presents a multi-year roadmap designed to create a comprehensive data architecture strategy that aligns with business needs, enables data governance, and supports scalable analytics. By adopting a phased approach, this roadmap addresses challenges related to data silos, regulatory compliance, and the need for agile data systems.

This paper explores Jenkins, an open-source automation server widely used for continuous integration (CI) and continuous delivery (CD) in software development. The paper examines its architecture, core features, and implementation & best practices of using Jenkins. It discusses how Jenkins enhances the developer productivity and ensuring for a higher software quality. The paper concludes with an overview of best practices for implementing Jenkins in modern DevOps environments.

As modern enterprises scale their digital operations, data centers face increasing demands to provide reliable, high-performance networking solutions. The complexities of managing extensive networks—spanning critical primary links, underutilized backup paths, and dynamic traffic patterns—pose challenges such as performance degradation, delayed fault resolution, and operational inefficiencies. Traditional, manual approaches to network management are insufficient to address these issues on a scale, necessitating the adoption of network automation platforms.

Cloud computing has become one of the most important tools that have transformed business by providing solutions at the right price. Huge global popularity has led to the seemingly countless number of cloud services, and thus, security has become a critical issue. The technology known as Artificial Intelligence (AI) has turned out to become a useful weapon to fight against these challenges since threat detection, management of vulnerabilities, limiting threat proneness and formation of counterattacks can be availed through AI solutions.

Unit economics provides a critical lens through which the financial viability of individual components within a supply chain can be examined. This paper explores the concept of unit economics in the context of modern supply chains, emphasizing how granular cost and revenue analysis at the unit level can guide strategic decisions, improve profitability, and enhance operational resilience.

This paper explores the application of lean management principles, six sigma methodologies, and operational excellence strategies in accelerating innovation in enterprise software development. A comprehensive framework is introduced to optimize decision-making processes, reduce time-to-market, enhance agility through iterative development and rigorous A/B testing, and integrate robust customer feedback loops, particularly vital for products involving artificial intelligence (AI) and generative AI capabilities.