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The purpose of this paper is to examine the relationship between electronic journal downloads and citations and whether online electronic resource usage can be adopted as an alternative to citation for evaluation of scholarly discourse.

A consolidated 16 publishers’ COUNTER usage data of UGC-Infonet members was collected from INFLIBNET Centre. The usage was meticulously filtered from UGC-subscribed journals and institutional subscriptions. The quantitative data were analysed to establish the relationship between download, impact factor (IF) and price. Multiple regression analysis was used to assess the influence of price and IF on usage and to predict the usage when they are known and the threshold for significance was set at p < 0.05.

There exists a relationship between IF and downloads of journals in UGC-Infonet. Journal IF and price significantly influence usage, where journal IF plays an important role in the intensity of the use. Also, the top 25 hottest downloaded papers were journals with IF; hence, no journal without IF featured in the top 25 most downloaded journals in the consortia. The relationship between the top 25 IF journals in the consortia and download is strong (r = 0.368537).

The only account that reports on the relationship between journal IFs and downloads for UGC-Infonet consortia. Also, the influence of usage behaviour with respect to citation and price of a journal.

A laser‐based system has been developed to automatically measure complete inner profiles of various structures. The system uses a pointed laser source passing through a rotating optical device to obtain a series of distance measurements. To enhance the portability of the system, a hand‐held computer is selected to control the laser source and the rotating optical device. In addition to system control, the software associated with the handheld computer has also been developed to use the series distance measurements to construct a complete profile of the measured structure as well as to estimate the crosssectional area of the profile. The system can provide critical information for the construction industry and a variety of other commercial applications. The evaluation and analysis of measurement indicated that on accuracy within 4 per cent, for typical window designs and floor patterns, could be achieved. Recommendations for improvement of the system have also been included.

The open access movement has become the center of discussion during the last decade. Open access publishing facilitates researchers' and scientists' access to research literature through the internet free of cost. The purpose of this paper is to analyze the awareness of open access publishing among researchers and faculty members of Indian institutions, and to evaluate the development of open access initiatives in India.

This study is based on both primary and secondary sources of data. For studying awareness of open access publishing, a survey was conducted among the researchers of IITs and IIMs in July‐August 2012 by using a closed ended questionnaire. The growth of open access initiatives in India is analyzed through data collected from secondary sources, i.e. the websites of Ulrich's, DOAJ, ROAR, and OpenDOAR.

The results showed that India's contribution has increased in the last few years. It was found that the awareness about such open access information sources and initiatives among the research community is increasing.

This study will assist in understanding the practices of open access publishing in India.

Digital libraries promote and accelerate scientific research in academic institutions. The subscribed database resources of digital libraries have become an increasingly valuable asset for researchers. Database resources help generate new ideas, determine research directions and promote productive academic interaction between teachers and students in the information age. The purpose of this study is to examine the use of electronic resources by students in various databases, the research productivity of the faculty in the science network and the number of students who graduate each year.

This study uses a quantitative method to collect secondary data from the central database of the Higher Education Commission (HEC) for the population of 26 universities for 2 years (2015–2016). In addition to the HEC digital library, data was also collected from the Web of Science to determine the quality academic performance of faculty and researchers. Moreover, in the study, the total strength of teaching staff and doctoral faculty was extracted from the HEC website for investigation. The authors applied the Spearman’s correlation test to the secondary data using Statistical Package for Social Sciences version 25.

The correlation results of the enrolled students and the downloaded papers from various databases were statistically insignificant (p > 0.05). However, the result showed a positive correlation (p < 0.05) between the use of selected/known databases from a number of databases accessed by the HEC. More importantly, it turns out that the faculty’s productivity in the scientific network and the number of students who graduated from public and private universities are found to be insignificant (p > 0.05). However, the authors found a positive correlation (p < 0.05) between doctoral and non-doctoral faculties, which show that a significant number of non-doctoral faculties are still actively involved in teaching and research.

Research based on academic activities by faculties and students, performed for the first time on the basis of secondary data, will help the HEC and university management to determine the right direction and develop plans to improve academic performance and research quality.

This study aims to develop a trust mechanism in a Vehicular ad hoc Network (VANET) based on an optimized deep learning for selfish node detection.

The authors built a deep learning-based optimized trust mechanism that removes malicious content generated by selfish VANET nodes. This deep learning-based optimized trust framework is the combination of the Deep Belief Network-based Red Fox Optimization algorithm. A novel deep learning-based optimized model is developed to identify the type of vehicle in the non-line of sight (nLoS) condition. This authentication scheme satisfies both the security and privacy goals of the VANET environment. The message authenticity and integrity are verified using the vehicle location to determine the trust level. The location is verified via distance and time. It identifies whether the sender is in its actual location based on the time and distance.

A deep learning-based optimized Trust model is used to detect the obstacles that are present in both the line of sight and nLoS conditions to reduce the accident rate. While compared to the previous methods, the experimental results outperform better prediction results in terms of accuracy, precision, recall, computational cost and communication overhead.

The experiments are conducted using the Network Simulator Version 2 simulator and evaluated using different performance metrics including computational cost, accuracy, precision, recall and communication overhead with simple attack and opinion tampering attack. However, the proposed method provided better prediction results in terms of computational cost, accuracy, precision, recall, and communication overhead than other existing methods, such as K-nearest neighbor and Artificial Neural Network. Hence, the proposed method highly against the simple attack and opinion tampering attacks.

This paper proposed a deep learning-based optimized Trust framework for trust prediction in VANET. A deep learning-based optimized Trust model is used to evaluate both event message senders and event message integrity and accuracy.