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The purpose of this paper is to examine the association of gender and travel decision-making style of generation Y (Gen Y) – recreational, price conscious, impulsive, perfectionist, high quality, novelty seeker, habitual, confused by over-choice – to the use of social networking sites (SNS) for travel information share. The study focuses on purchase decision pattern of young travelers by examining the travel decision-making styles.

The author has adopted the quantitative approach for the fulfillment of the objectives of the study. The exploratory method is used to get a better understanding of different concepts used in the study, Social Networking Sites and Decision-Making Style and Gen Y, and the primary information is collected from the structured questionnaire. Using travel decision-making style and gender as grouping variables, different tests were performed to test the hypothesis and to understand the influence of different travel decision-making style and gender on travel information share on SNS.

The present study identifies the existing seven travel decision-making styles of Gen Y, which are Confused by over-choice, Habitual, Novelty seeker, Price conscious, Perfectionist high-quality conscious, Recreational and Impulsive. The results of the study reflect that confused by over-choice, habitual, perfectionist, price conscious travel dimensions have an association with SNS for travel information share. In addition, gender also has an association with usages of SNS for travel information share.

This study has only used consumer typology approach to study decision-making styles, whereas other variables like personality and attitude can be further studied. Second, the study is limited by the spatiotemporal limitation, as the study is just restricted to one geographical area and time, and generalizations can vary with the change in spatiotemporal features in the study.

The findings of the study imply that Gen Y is an important travel market segment, and to cater this segment, SNS can be used as an effective marketing tool. The study of various segments in different groups will help in understanding the market more clearly and using SNS more effectively. In addition, finding association of travel decision-making style with SNS helps in forming effective and innovative marketing strategies.

India is a developing country where many market segments are still unexplored and Gen Y is being one of them. The study was conducted, keeping in mind the need of tourism industry. The study explores the tourism market segment of Gen Y by identifying the decision-making style and also identifies the association of different decision-making style with Gen Y information-sharing behavior on social networking site.

The purpose of this study is to identify the factors affecting the quality of technical education in a developing nation, India.

Participants were 465 students and 310 faculty members who were randomly chosen from a total of 31 institutions/colleges/universities providing engineering education in Punjab state of India. The factor structures were obtained by applying factor analysis.

The result of this research reveals ten factors determining the quality of technical education, such as teaching practices, infrastructural facilities, industry–institute linkage, faculty's qualification, reputation of the institute, procedural simplification, administrative staff services, access and equity, financial burden of the course and work culture. Moreover, the research results also reveal eight factors affecting the quality of technical education, namely, institutional standards, institutional support, teaching environment, teaching practices, performance-linked promotion, work culture, academic freedom and administrative services in this order of preference are essential for the delivery of quality in technical education.

This study is the first attempt to examine the factor structure of technical education quality from both the perspectives of students and faculty. The implications of this study are expected to help the management of technical education institutes, regulatory agencies and the government in devising strategies to enhance the quality of technical education in India.

This study aims to present a novel system for detection and prevention of black hole and wormhole attacks in wireless sensor network (WSN) based on deep learning model. Here, different phases are included such as assigning the nodes, data collection, detecting black hole and wormhole attacks and preventing black hole and wormhole attacks by optimal path communication. Initially, a set of nodes is assumed for carrying out the communication in WSN. Further, the black hole attacks are detected by the Bait process, and wormhole attacks are detected by the round trip time (RTT) validation process. The data collection procedure is done with the Bait and RTT validation process with attribute information. The gathered data attributes are given for the training in which long short-term memory (LSTM) is used that includes the attack details. This is used for attack detection process. Once they are detected, those attacks are removed from the network using the optimal path selection process. Here, the optimal shortest path is determined by the improvement in the whale optimization algorithm (WOA) that is called as fitness rate-based whale optimization algorithm (FR-WOA). This shortest path communication is carried out based on the multi-objective function using energy, distance, delay and packet delivery ratio as constraints.

This paper implements a detection and prevention of attacks model based on FR-WOA algorithm for the prevention of attacks in the WSNs. With this, this paper aims to accomplish the desired optimization of multi-objective functions.

From the analysis, it is found that the accuracy of the optimized LSTM is better than conventional LSTM. The energy consumption of the proposed FR-WOA with 35 nodes is 7.14% superior to WOA and FireFly, 5.7% superior to grey wolf optimization and 10.3% superior to particle swarm optimization.

This paper develops the FR-WOA with optimized LSTM detecting and preventing black hole and wormhole attacks from WSN. To the best of the authors’ knowledge, this is the first work that uses FR-WOA with optimized LSTM detecting and preventing black hole and wormhole attacks from WSN.