Travel has always been innate desire of human beings. People wanted to travel irrespective of any hurdles like geographical barrier, age, gender, or colour with different motivations. Nowadays travel and adventure became the most trending entertainment as well. Planning a trip is a time-consuming and herculean task for inexperienced travelers. Here comes the possibility of expert opinion for scheduling a perfect travel plan. With the development of information technology and social media, there are numerous possibilities and opportunities in fetching suitable information which can turn out to set up an appropriate travel plan and hence enhance the quality of travel. The significance of a Recommender System (RS) comes in the picture which can address travel-related queries. Personalized Travel RS will add more customization and user-specific features than Automatic Travel RS. In this paper, we conducted a detailed review and chronological evolutions of various methods and techniques used in the travel and tourism sector and compared their efficiency in Recommendations.

An incredible amount of research has been conducted in speech recognition and accent-based speech recognition during recent decades. Automatic Speech Recognition in various dialects in any natural language is examined as one among the most complicated domains in Automatic Speech Recognition (ASR). The increasing significance of speech recognition in any dialect is attributable to the ever-developing interest for applications that handle humanmachine interaction through geographically influenced natural languages. The objective of this paper is to provide an overview of recent developments in dialect or accent-based speech processing. This paper concentrates on the study of accent-based speech recognition techniques in various languages and the technologies used for the same.

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Phishing is a deceptive technique to steal confidential information like user credentials and bank account details of web users. Employing technical and social engineering skills phishers make huge financial loss to web users and large organizations alike, and it has become one of the serious cybercrime today. This paper discusses different types of phishing techniques, their impacts, common indicators of phishing attacks, and analyses various anti-phishing solutions from conventional methods implementing blacklist, white list, heuristics, fuzzy logic, visual similarity, etc. to machine learning methods. The study provides gap analysis of conventional anti-phishing techniques, and points out the challenges facing machine learning based approaches including proper feature selection, diversity in data sets, imbalanced scenarios, and differences in evaluation metrics. This investigation outlines the need for serious researches in this area since there is no foolproof solution to phishing as phishers change their tactics very often to bypass anti-phishing detection systems.

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