Abstract
Introduction: This study offers a thorough analysis of deep learning and artificial intelligence from 1961 to 2018, providing information about the underlying mechanics, industrial applications, and future developments. The study aims to help researchers and practitioners understand the evolution, challenges, and opportunities associated with AI-driven innovations. Research Significance: This study's importance stems from its comprehensive exploration of the impact of AI on various industrial domains. AI-driven data analytics, predictive maintenance, and decision-making systems are reshaping industrial landscapes by improving operational efficiency and reducing costs. By analyzing past trends and current developments, this study offers insightful information for upcoming AI applications. It also draws attention to difficulties associated with to AI adoption, such as data security, algorithmic transparency, and ethical considerations, ensuring a holistic perspective for stakeholders in academia and industry. Methodology: SPSS Statistics is a powerful software tool utilized for to analyze data in a number of domains, like as social sciences, healthcare, marketing, and education. It offers an extensive collection of statistical tools for organizing, evaluating, and interpreting data. SPSS allows users to perform a wide range of analyses, such as descriptive statistics, regression, ANOVA, factor analysis, and hypothesis testing. Its sophisticated data manipulation features and user-friendly interface make it popular among researchers and analysts. SPSS also supports the creation of charts and reports, aiding in the presentation of data-driven insights. Input Parameters: Industry Sector, Data Management Approach, AI Integration Level, Primary AI Use Case, Data Privacy Strategy, Adoption of AI Ethics Framework. Evaluation Parameters: Data Processing Efficiency, AI Model Accuracy, Scalability & Flexibility, Regulatory Compliance, Business Value Impact. Reliability Statistics measure the consistency of a dataset or survey instrument. One important Cronbach's Alpha is a measure of internal reliability. The Alpha of Cronbach's value of 0.967 in this instance indicates a high degree of dependability, indicating that the dataset's items consistently yield reliable findings. This high The standardized Cronbach's Alpha further supports the degree of intrinsic coherence of 0.974. The data can be regarded as extremely dependable for research purposes after five items have been examined.