There is a wide scope of interdisciplinary crossing points between Artificial Intelligence (AI) and Cybersecurity. On one hand, AI advancements, for example, deep learning, can be introduced into cybersecurity to develop smart models for executing malware classification and intrusion detection and threatening intelligent detecting. Then again, AI models will confront different cyber threats, which will affect their sample, learning, and decision making. Along these lines, AI models need specific cybersecurity defense and assurance advances to battle ill-disposed machine learning, preserve protection in AI, secure united learning, and so forth. Because of the above two angles, we audit the crossing point of AI and Cybersecurity. To begin with, we sum up existing research methodologies regarding fighting cyber threats utilizing artificial intelligence, including receiving customary AI techniques and existing deep learning solutions. At that point, we analyze the counterattacks from which AI itself may endure, divide their qualities, and characterize the relating protection techniques. And finally, from the aspects of developing encrypted neural networks and understanding safe deep learning, we expand the current analysis on the most proficient method to develop a secure AI framework. This paper centers mainly around a central question: "By what means can artificial intelligence applications be utilized to upgrade cybersecurity?" From this question rises the accompanying set of sub-questions: What is the idea of artificial intelligence and what are its fields? What are the main areas of artificial intelligence that can uphold cybersecurity? What is the idea of data mining and how might it be utilized to upgrade cybersecurity? Hence, this paper is planned to reveal insight into the idea of artificial intelligence and its fields, and how it can profit by applications of AI brainpower to upgrade and improve cybersecurity. Using an analytical distinct approach of past writing on the matter, the significance of the need to utilize AI strategies to improve cybersecurity was featured and the main fields of application of artificial intelligence that upgrade cybersecurity, for example, machine learning, data mining, deep learning, and expert systems.

Artificial intelligence and machine learning are the future of every field. These can be applied in any field for better or efficient performance. Both these can be used in retail pharmacy as a solution to different problems. The machine learning prediction model can help in predicting the disease of patients and it can also be used to predict the medicine for the patient. AI systems can be used to automate the tasks that will help in saving time and also the tasks will be performed by using fewer resources.

In this competitive business world satisfied employee is the prime asset of any business organization as an employee’s satisfaction can ensure continuous growth. The purpose of this study was to find out, is there any significant relationship between socio-demographic characteristics and job satisfaction of private bank employees in Bangladesh? By using Yamane's (1967) formula the study consisted of 56 (male 89.3% and female 10.7%) respondents and it used semi-structured questionnaires containing pre-coded and open-ended questions. All questions were rated with the Likert 5-point scale. As all the variables used in this study (both dependent and independent) were categorical, the Chi-square test was used to assess the relationship. In this study, significant relations were found between some demographic characteristics, such as, sex, age, salary, and family income with job satisfaction indicators which were participation in decision making, training facilities, and increase knowledge and capacity. Education and geographic location did not show any significant relationship with job satisfaction indicators. Around 92.5% of male employees reported that the current organization helped to increase their knowledge and working capacity (p<0.028). More than half of the employees (55.3 %) of the 30-35 age group could not take part in decision making (p<0.013). In addition, family income and salary also exerted significant associations with participation in decision-making and proper training facilities respectively. Several stakeholders and concern authorities should give top priority in these demographic areas while developing strategies to improve the job satisfaction level of employees.

Machine Learning and Artificial Intelligence applications in the financial sector have been thriving in the recent past. Their immense power has been harnessed in these institutions to offer business solutions in front end and back end processes to create efficiency and improve customer experience. This article will lay bare the applications of Machine Learning and Artificial Intelligence and evaluate their utility in different banking industry functional areas and frame how these institutions effectively use computational intelligence to improve their business. While traditional banking institutions are quickly catching up with the computational intelligence technologies with products like Chatbot, fintech companies, which seem to have embrace A.I. a long time ago, plays a critical role through its innovation and contribute substantially to financial intelligence. In conclusion, we can aptly say that Machine Learning and Artificial Intelligence technologies are taking over the banking sector, and it seems like there's nothing we can do about it.

Voice search technology is not new. It's been there since Audrey came out. But now it is increasing. People are using voice assistants, and so far 420 million voice assistants have been sold. This is growing rapidly because artificial intelligence integrated with voice search technology is more accurate and faster than text search. According to Google, its accuracy has improved to 95%. In short, it's an old technology that's gaining popularity with the rise of mobile phone voice assistants. In this study, the researcher has introduced different aspects and practical implications of voice search technology.