News publishers have decreased disseminating news through conventional newspapers and have migrated to the use of digital means like websites and purpose-built mobile applications. It is observed that news recommendation systems can automatically process lengthy articles and identify similar articles for readers considering predefined criteria. The objectives of the current work are to identify and classify the challenges in news recommendation domain, to identify state-of-the-art approaches and classify on the application domain, to identify datasets used for evaluation and their sources, the evaluation approaches used and to highlight the challenges explicitly addressed. The literature is thoroughly studied over the time span of 2001-2019 and shortlisted 81 related studies, broadly classified into six categories and discussed. The analysis showed that 60% of news recommendation system adopted a hybrid approach, 66% studies little talk about datasets, and addresses a few challenges from a long list of challenges in the news domain. This article is the first in the field to draw a comprehensive big picture of news recommendation and explore different dimensions covered in the studies. The last section presents the future research opportunities that lead to improving the recommendation of news articles in the news domain.

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Blockchain is an emerging field of study in a number of applications and domains. Especially when combine with Internet of Things (IoT) this become truly transformative, opening up new plans of action, improving engagement and revolutionizing many sectors including agriculture. IoT devices are intelligent and have high critical capabilities but low-powered and have less storage, and face many challenges when used in isolation. Maintaining the network and consuming IoT energy by means of redundant or fabricated data transfer lead to consumption of high energy and reduce the life of IoT network. Therefore, an appropriate routing scheme should be in place to ensure consistency and energy efficiency in an IoT network. This research proposes an efficient routing scheme by integrating IoT with Blockchain for distributed nodes which work in a distributed manner to use the communicating links efficiently. The proposed protocol uses smart contracts within heterogeneous IoT networks to find a route to Base Station (BS). Each node can ensure route from an IoT node to sink then base station and permits IoT devices to collaborate during transmission. The proposed routing protocol removes redundant data and blocks IoT architecture attacks and leads to lower consumption of energy and improve the life of network. The performance of this scheme is compared with our existing scheme IoT-based Agriculture and LEACH in Agriculture. Simulation results show that integrating IoT with Blockchain scheme is more efficient, uses low energy, improves throughput and enhances network lifetime.

Context. The improvements made in the last couple of decades in the requirements engineering (RE) processes and methods have witnessed a rapid rise in effectively using diverse machine learning (ML) techniques to resolve several multifaceted RE issues. One such challenging issue is the effective identification and classification of the software requirements on Stack Overflow (SO) for building quality systems. The appropriateness of ML-based techniques to tackle this issue has revealed quite substantial results, much effective than those produced by the usual available natural language processing (NLP) techniques. Nonetheless, a complete, systematic, and detailed comprehension of these ML based techniques is considerably scarce. Objective. To identify or recognize and classify the kinds of ML algorithms used for software requirements identification primarily on SO. Method. This paper reports a systematic literature review (SLR) collecting empirical evidence published up to May 2020. Results. This SLR study found 2,484 published papers related to RE and SO. The data extraction process of the SLR showed that (1) Latent Dirichlet Allocation (LDA) topic modeling is among the widely used ML algorithm in the selected studies and (2) precision and recall are amongst the most commonly utilized evaluation methods for measuring the performance of these ML algorithms. Conclusion. Our SLR study revealed that while ML algorithms have phenomenal capabilities of identifying the software requirements on SO, they still are confronted with various open problems/issues that will eventually limit their practical applications and performances. Our SLR study calls for the need of close collaboration venture between the RE and ML communities/researchers to handle the open issues confronted in the development of some real world machine learning-based quality systems.