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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.

Recent academic work on leadership has focused largely on organizational leadership. This study takes a close look at political leadership, especially that given to popular movements, and delineates a new model of transformational leadership.

The current study borrows models from organizational leadership research and applies them to a specific case study to reveal critical concepts underlying transformational leadership. Application of these models to Bangladesh's founding father, Bangabandhu Sheikh Mujibur Rahman, during the two decades of the 1950 and 1960s, shows potential for a new flexible framework for transformational leadership with added significance on leader–follower relatedness, socio-historical context and charisma.

This study presents clear evidence on the nature of leadership in popular movements and using a specific case study elucidates that movements pick leaders who meet distinct criteria specific to the movement, including a vision that resounds with key follower-groups and prototypicality.

This study presents a new lens under which political and popular leadership can be studied, focusing away from person, political party or rational choice and voting behavior-based ideas of political leadership.

The findings reveal the importance of seeking new ways to fit leadership theory with burgeoning social phenomenon.

Lack of understanding around mental illness and stigma are an overwhelming barrier in help-seeking behaviour for mental health concerns. The purpose of this paper is to examine mental health literacy and social attitudes as instrumental factors in building capacity of the demand-side to support and access mental health care at the community level in India.

Knowledge, Attitude and Practice surveys were administered to 521 persons from the general population, distributed equally in the age range of 15-60 years. The study included 52 respondents per district from ten districts across five states in India, namely Andhra Pradesh, Assam, Delhi, Gujarat and Uttar Pradesh. The responses were collected and analysed thematically, keeping in mind the relevance of these findings as contributors to knowledge of mental health and to the construct of stigma.

Pervasive socio-cultural factors, especially stigma inhibit access to basic mental health information and care, despite knowledge that mental illness is treatable. Degrading treatment, loss of personal liberty and social exclusion, i.e. compromised human rights at the community level are widespread. Self-reported attitudes when encountering a person with mental illness show that respondents act out of fear and are guided by misinformation and myths. Extant knowledge on mental health is attributed predominantly to informal networks, as a potential resource to be strengthened.

Realising mental health care, including help-seeking behaviour calls for greater knowledge-sharing, sensitisation and community engagement.

This paper fulfils an identified need to study current levels of mental health literacy and underlying perceptions that contribute to the persistent treatment gap.