In NLP research, unsupervised or semi-supervised learning techniques are increasingly getting more attention. These learning techniques are capable of learning from data that has not been manually annotated with the necessary answers or by combining non-annotated and annotated data. This essay presents a survey of various natural language processing methods. The discipline of natural language processing, which integrates linguistics, artificial intelligence, and computer science, was established to make it easier for computers and human language to communicate with one another. It is, as we can say, relevant psychopathology for the study of computer-human interaction. The understanding of natural language, which entails enabling machines to naturally interpret human language, is one of the many challenges this area faces. Discourse analysis, morphological separation, machine translation, production and understanding of NLP, part-of-speech tagging, recognition of optical characters, speech recognition, and sentiment analysis are some of the most frequent NLP tasks. As opposed to learning, which is supervised and typically yields few correct results for a given amount of input data, this job is typically quite difficult. However, there is a sizable amount of data available that is unannotated in nature, i.e. the entire contents are available on the internet, and it typically yields less accurate findings.

The ongoing development of business and the most recent advances in artificial intelligence (AI) allow for the many business practices to be improved by the capacity to establish new forms of collaboration, which is a significant competitive advantage. This rapidly developing technology enables to offer brand services and even some new forms of business interactions with consumers and personnel. The digitalization of AI concurrently emphasized for businesses that they need concentrate on their present strategies while also routinely and early pursuing new chances in the market. Not only in business but also in different industry sectors, Al techniques are being used and revolutionized different industry sectors. This review focuses on the application of AI techniques in business and different industries.

In the computer world, data science is the force behind the recent dramatic changes in cybersecurity's operations and technologies. The secret to making a security system automated and intelligent is to extract patterns or insights related to security incidents from cybersecurity data and construct appropriate data-driven models. Data science, also known as diverse scientific approaches, machine learning techniques, processes, and systems, is the study of actual occurrences via the use of data. Due to its distinctive qualities, such as flexibility, scalability, and the capability to quickly adapt to new and unknowable obstacles, machine learning techniques have been used in many scientific fields. Due to notable advancements in social networks, cloud and web technologies, online banking, mobile environments, smart grids, etc., cyber security is a rapidly expanding sector that requires a lot of attention. Such a broad range of computer security issues have been effectively addressed by various machine learning techniques. This article covers several machine-learning applications in cyber security. Phishing detection, network intrusion detection, keystroke dynamics authentication, cryptography, human interaction proofs, spam detection in social networks, smart meter energy consumption profiling, and security concerns with machine learning techniques themselves are all covered in this study. The methodology involves collecting a large dataset of phishing and legitimate instances, extracting relevant features such as email headers, content, and URLs, and training a machine-learning model using supervised learning algorithms. Machine learning models can effectively identify phishing emails and websites with high accuracy and low false positive rates. To enhance phishing detection, it is recommended to continuously update the training dataset to include new phishing techniques and to employ ensemble methods that combine multiple machine learning models for better performance.

Machine Learning (ML) has emerged as a transformative force in the field of Business Intelligence (BI), revolutionizing the way organizations extract insights from vast amounts of data. This abstract explores the role of ML in transforming BI and its impact on decision-making processes. ML enables efficient data collection and preparation through integration, cleaning, and feature engineering. Predictive analytics powered by ML facilitates forecasting, customer segmentation, demand prediction, and churn analysis. ML's anomaly detection capabilities identify outliers, fraud, and operational anomalies. Natural Language Processing (NLP) empowers sentiment analysis, text mining, and chatbots for enhanced customer support. Recommendation systems provide personalized suggestions using ML techniques like collaborative and content-based filtering. Once the data is prepared, it is subjected to analysis using various techniques and algorithms. ML-driven data visualization and reporting enable interactive dashboards and real-time monitoring. The benefits of ML in BI include improved accuracy, faster decision-making, enhanced customer experience, cost reduction, and competitive advantage. However, challenges such as data quality, ethics, interpretability, and skill gaps need to be addressed. Future trends include advanced ML techniques, augmented analytics, edge computing, and ethical AI practices. ML's role in transforming BI is pivotal, urging businesses to embrace ML to unlock its full potential and gain a competitive edge.

Purpose: The purpose of the research is to investigate the role of machine learning (ML) and artificial intelligence (AI) in the growth of smart cities. It aims to understand how these technologies are being used to manage expanding metropolitan areas, boost economies, reduce energy consumption, and improve the living standards of residents. The study also aims to analyze the information flow associated with ICT in smart cities. Methodology: The methodology involves conducting a survey to identify the typical technologies used to support communication in smart cities. It also involves a systematic evaluation of current patterns in publications related to ICT in smart cities. The research utilizes ML and AI techniques to analyze and interpret the collected data. Findings: The findings of the study indicate that ML and AI play a significant role in various aspects of smart cities, particularly in the field of intelligent transportation systems. These technologies are utilized for tasks such as modeling and simulation, dynamic routing and congestion management, and intelligent traffic control. The research also reveals the application of ML and AI in other forms of transportation like air, rail, and road travel. Recommendations: Based on the findings, the study suggests that the agent computing paradigm is a powerful technology for the development of large-scale distributed systems, particularly in the context of geographically dispersed and dynamic transport systems. The research emphasizes the interoperability, flexibility, and extendibility of agent-based traffic control and management systems. It concludes by suggesting potential future research directions to effectively integrate agent technology into traffic and transportation systems.