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Sourced by local newspaper articles, discusses the impact on Goa of the increasing numbers of tourists. Increasing land prices, increasing levels of consumption, violation of building and land use regulations are all prevalent. Locals feel threatened and degraded by inappropriate behaviour of tourists. There has been an escalation in violence, child sex abuse and prostitution. Drug abuse and drug trafficking has increased. Local people feel exploited and few benefit from the tourist trade. What can be done?

This paper tactics to implement the attack detection in medical Internet of things (IoT) devices using improved deep learning architecture for accomplishing the concept bring your own device (BYOD). Here, a simulation-based hospital environment is modeled where many IoT devices or medical equipment are communicated with each other. The node or the device, which is creating the attack are recognized with the support of attribute collection. The dataset pertaining to the attack detection in medical IoT is gathered from each node that is considered as features. These features are subjected to a deep belief network (DBN), which is a part of deep learning algorithm. Despite the existing DBN, the number of hidden neurons of DBN is tuned or optimized correctly with the help of a hybrid meta-heuristic algorithm by merging grasshopper optimization algorithm (GOA) and spider monkey optimization (SMO) in order to enhance the accuracy of detection. The hybrid algorithm is termed as local leader phase-based GOA (LLP-GOA). The DBN is used to train the nodes by creating the data library with attack details, thus maintaining accurate detection during testing.

This paper has presented novel attack detection in medical IoT devices using improved deep learning architecture as BYOD. With this, this paper aims to show the high convergence and better performance in detecting attacks in the hospital network.

From the analysis, the overall performance analysis of the proposed LLP-GOA-based DBN in terms of accuracy was 0.25% better than particle swarm optimization (PSO)-DBN, 0.15% enhanced than grey wolf algorithm (GWO)-DBN, 0.26% enhanced than SMO-DBN and 0.43% enhanced than GOA-DBN. Similarly, the accuracy of the proposed LLP-GOA-DBN model was 13% better than support vector machine (SVM), 5.4% enhanced than k-nearest neighbor (KNN), 8.7% finer than neural network (NN) and 3.5% enhanced than DBN.

This paper adopts a hybrid algorithm termed as LLP-GOA for the accurate detection of attacks in medical IoT for improving the enhanced security in healthcare sector using the optimized deep learning. This is the first work which utilizes LLP-GOA algorithm for improving the performance of DBN for enhancing the security in the healthcare sector.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

An essential requirement for tourism planning and policymaking is to review past strategies. The development of future roadmaps depends on a robust strategy validation process. The research analyses the technology strategies in Goa from 1962–2017 and highlights the impact of technology on tourism in Goa.

The paper presents a unique methodology that integrates tourism area life cycle (TALC) and Mintzberg’s strategy analysis framework to evaluate strategies at a tourism destination.

The findings show that most technological advancements have taken place during the period 1989–2008. Both deliberate and emergent strategies were identified. Development of technology altered the type of tourism practiced in Goa. The use of internet facilitated the growth of tourism.

This study clarifies the styles of public planning for tourism. It provides the foundation for strategic planning process.

Effective and efficient use of policy through technology road mapping can aid in co-creating a sustainable tourism destination for the future.

The strategic evaluation framework can be used in any tourism destinations to identify past strategies and assess its impact on the future.

The paper presents a unique methodology that integrates TALC and Mintzberg’s strategy analysis framework to evaluate strategies at a tourism destination. The research proposes a strategic evaluation process that recognizes past strategies to validate the strategic analysis process.

India considers tourism a high‐potential sector in terms of its contribution to employment generation and economic growth. A positive destination image has a direct bearing on the tourist arrivals and hence economic growth of that region. The purpose of this paper is to explore the online destination image of Goa, an important tourist destination in India and draw lessons for successful image management in the age of the internet.

Qualitative information was downloaded from multiple travel agent web sites, newspaper articles and “threads” on one travel message board. The information is further categorized into suitable destination image attributes to study their impact on the destination image of Goa.

Managing the communication to create positive experiences is important. There is similarity in coverage on some aspects among the three sources such as beach destination, tourist infrastructure, and attractive night life. However, important differences emerge around the issues of culture, social factors, political factors and the overall atmosphere of Goa.

Government and industry should assess as to how a particular destination is viewed by people at large. There could be inefficiencies which could be dealt with proactively. The paper also has lessons for travellers seeking information about destinations and sites supplying such information.

Consumers are increasingly going to balance out the overly marketing‐oriented information coming from more traditional sources with more realistic travel accounts. Feedback for a destination is easily available online – particularly through consumer generated data. If harnessed properly, it can provide a valuable guide to the better management of a destination and its continued economic success.

Online destination image research is still in its nascent stage. The paper contributes to a better understanding of online sources of information of a destination. Government and the industry are no longer the only providers of information about a destination. Consumer viewpoints can provide low‐cost feedback about the challenges a destination faces and possible solutions to it.

Wireless Body Area Network (WBAN) technology envisions a network in which sensors continuously operate on and obtained critical physical and physiological readings. Sensors deployed in WBANs have restricted resources such as battery energy, computing power and bandwidth. We can utilize these resources efficiently. By devising a mechanism that is energy efficient with following characteristics, i.e. computational complexity is less, routing overhead is minimized, and throughput will be maximum. A lot of work has been done in this area but still WBAN faces some challenges like mobility, network lifetime, transmission range, heterogeneous environment, and limited resources. In the present years well, contemplative studies have been made through a large body to reach some holistic points pertaining to the energy consumption in WBAN. Thus we/put forward appropriate algorithm for energy efficiency which can vividly corroborate the advances in this specific domain. We have also focused on various aspects and phases of the studies like study computational complexity, routing overhead and throughput type of characteristics. There is still a room for improvement to get the desired energy optimization in WBAN. The network performance mainly relies upon the algorithm used for optimization process. In this work, we intended to develop an energy optimization algorithm for energy consumption in WBAN which is based on evolutionary algorithms for inter-BAN communications using cluster-based routing protocol.

In this paper we propose a meta heuristics algorithm Goa to solve the optimization problem in WBAN. Grasshopper is an insect. Generally, this insect is viewed individually and creating large swarm in nature. Figure 5 shows the individual grasshoppers' primitive patterns in swarm. Figure 7 depicts the pseudo code of Goa. In Goa, experiments are done to view the behavior of grasshoppers in swarm. How they gradually move towards the stationary and mobile target. Through experimentation it is conceived that swarm gradually converge towards their target. Another interesting pattern related to convergence of grasshopper is that it slowly towards its target. This shows that grasshopper does not trapped in local optima. In starting iterations of exploration process Goa, search globally and in last iterations it searches local optima. Goa makes the exploration and exploitation process balanced while solving challenging optimization problems.

Energy efficiency is achieved in the optimization process of cluster formation process. As the use of proposed algorithm Goa creates the optimal number of clusters. Shorter cluster lifetime means more times clustering procedure is called. It increases the network computational cost and the communication overhead. Experimentation results show that proposed Goa algorithm performs well. We compare the results of Goa with existing optimization Algorithms ACO and MFO. Results are generated using MATLAB.

A lot of work has done for the sake of energy optimization in WBAN. Many algorithms are proposed in past for energy optimization of WBAN. All of them have some strengths and weaknesses. In this paper we propose a nature inspired algorithm Goa. We use the Goa algorithm for the sake of energy optimization in WBAN.

Tourist destinations are constantly changing products, evolving as per the controls exerted by the stakeholders. The study aims to map the pattern of tourism development and identify the strategies formed at the destination over a seven-decade period for a state as a unit of analysis.

The paper evaluates tourism development through the tourism area life cycle (TALC) model and uses Mintzberg's strategy analysis process to identify strategies. The study involves time series analysis, pattern matching and explanation-building techniques. The TALC is plotted for the number of tourist arrivals from 1947 to 2019, and strategies are mapped for each stage.

The TALC shows a cycle-recycle pattern of tourism development. The research revealed several strategies at different stages. Both the central and state governments and entrepreneurs, distinctively and in conjunction, have formed strategies. The pattern shows the period of piecemeal and global strategic changes contributing to tourism development.

The research unearths the strategies that drive the development curves of TALC, emphasising the integration of TALC with other theories. The research also assesses the strategy formed in the pre-tourism stage.

The research brings to light the use of TALC as a strategic road-mapping tool. In addition, the study emphasises the significance of global and piecemeal strategic periods and stakeholder's regulatory and operational roles.

The research uses a unique methodology that maps the strategies, periods of strategic changes and incremental strategies for each stage of TALC, along with identifying the stakeholders.

The goal of this study is to determine the values of the process parameters that should be used during the machining of ceramic tile using the abrasive water jet (AWJ) process in order to achieve the lowest possible values for surface roughness and kerf taper angle.

In the present work, ceramic tile is processed by the AWJ process and experimental data were recorded using the RSM approach based Box–Behnken design matrix. The input process factors were water jet pressure, jet traverse speed, abrasive flow rate and standoff distance, to determine the surface roughness and kerf taper angle. ANOVA was used to check the adequacy of model and significance of process parameters. Further, the elite opposition-based learning grasshopper optimization (EOBL-GOA) algorithm was implemented to identify the simultaneous optimization of multiple responses of surface roughness and kerf taper angle in AWJ.

The suggested EOBL-GOA algorithm is suitable for AWJ of ceramic tile, as evidenced by the error rate of ±2 percent between experimental and predicted solutions. The surfaces were evaluated with an SEM to assess the quality of the surface generated with the optimal settings. As compared with initial setting of the SEM image, it was noticed that the bottom cut surface was nearly smooth, with less cracks, striations and pits in the improved optimal results of the SEM image. The results of the analysis can be used to control machining parameters and increase the accuracy of AWJed components.

The findings of this study present an innovative method for assessing the characteristics of the nontraditional machining processes that are most suited for use in industrial and commercial applications.

An important lesson that philosophy can learn from the Turing test and computer science more generally concerns the careful use of the method of levels of abstraction (LoAs). The purpose of this paper is to summarize the method and apply it to the paper, modelling and analysis of phenomenological and conceptual systems showing its principal features and main advantages.

The constituents of the method are “observables”, collected together and moderated by predicates restraining their “behaviour”. The resulting collection of sets of observables is called a “gradient of abstractions” (GoAs) and it formalises the minimum consistency conditions that the chosen abstractions must satisfy. Two useful kinds of GoA – disjoint and nested – are identified. It is then argued that in any discrete (as distinct from analogue) domain of discourse, a complex phenomenon may be explicated in terms of simple approximations organised together in a GoAs. Thus, the method replaces, for discrete disciplines, the differential and integral calculus, which form the basis for understanding the complex analogue phenomena of science and engineering.

The result formalises an approach that is rather common in computer science but has hitherto found little application in philosophy. So the philosophical value of the method is demonstrated by showing how making the LoA of discourse explicit can be fruitful for phenomenological and conceptual analysis. To this end, the method is applied to the Turing test, the concept of agenthood, the definition of emergence, the notion of artificial life, quantum observation and decidable observation.

This paper applies the method of abstraction to the paper, modelling and analysis of phenomenological and conceptual systems showing its principal features and main advantages. It is hoped that this treatment will promote the use of the method in certain areas of the humanities and especially in philosophy.

The purpose of this paper is to study the use of electronic (e)‐journals by doctoral research scholars of Goa University, Goa (India). The main aim is to know the use of e‐journals for research work. Further, the study aims to highlight the problems in accessing e‐journals, degree of utilization, and influence of e‐journals on research work.

A survey is conducted through a structured questionnaire circulated among 100 doctoral research scholars (PhD) in Goa University and the response rate is found to be 96 percent. The responses received from the doctoral research scholars to 16 questions is presented in the form of tables and figures and analyzed by using a simple method of calculation. Stratified accidental random method is used for selection of respondents.

The paper arrives at the following conclusions: the e‐journals have become vital part of information for research work. This study helps in knowing the importance and use of e‐journals in comparison to printed journals particularly for the research community. Most of the respondents are accessing e‐journals from the Department Computer Labs for their research work and e‐journals meet the instant desire of users to have an access to information. Springer & Kluwer is the leading publisher. Boolean Search is the most popular advance search technique. The study shows that there is need for user orientation for efficient searching of e‐journals. The most common problem faced by the respondents is that there is difficulty in accessing full text and many of the respondents are not satisfied with the Internet facilities available in Goa University and they provide some constructive suggestions for improving the accessing of e‐journals.

The paper is confined to the use of e‐journals by the doctoral research scholars of Goa University, Goa.

This paper is an outcome of the study made in Goa University, Goa about the use of University Grants Commission (UGC)‐Infonet Digital Library Consortium's e‐journals by doctoral research scholars during the study period, i.e. 1‐28 June 2008.