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Dementia directly influences the quality of life of a person suffering from this chronic illness. The caregivers or carers of dementia people provide critical support to them but are subject to negative health outcomes because of burden and stress. The intervention of mobile health (mHealth) has become a fast-growing assistive technology (AT) in therapeutic treatment of individuals with chronic illness. The purpose of this comprehensive study is to identify, appraise, and synthesize the existing evidence on the use of mHealth applications (apps) as a healthcare resource for people with dementia and their caregivers. A review of both peer-reviewed and full-text literature was undertaken across five (05) electronic databases for checking the articles published during the last five years (between 2014 and 2018). Out of 6195 searches yielded articles, 17 were quantified according to inclusion and exclusion criteria. The included studies distinguish between five categories, viz., (1) cognitive training and daily living, (2) screening, (3) health and safety monitoring, (4) leisure and socialization, and (5) navigation. Furthermore, two most popular commercial app stores, i.e., Google Play Store and Apple App Store, were searched for finding mHealth based dementia apps for PwD and their caregivers. Initial search generated 356 apps with thirty-five (35) meeting the defined inclusion and exclusion criteria. After shortlisting of mobile applications, it is observed that these existing apps generally addressed different dementia specific aspects overlying with the identified categories in research articles. The comprehensive study concluded that mobile health apps appear as feasible AT intervention for PwD and their carers irrespective of limited available research, but these apps have potential to provide different resources and strategies to help this community.

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A variety of three-factor smart-card based schemes, specifically designed for telecare medicine information systems (TMIS) are available for remote user authentication. Most of the existing schemes for TMIS are customarily proposed for the single server-based environments and in a single-server environment. Therefore, there is a need for patients to distinctly register and login with each server to employ distinct services, so it escalates the overhead of keeping the cards and memorizing the passwords for the users. Whereas, in a multi-server environment, users only need to register once to resort various services for exploiting the benefits of a multi-server environment. Recently, Barman et al. proposed an authentication scheme for e-healthcare by employing a fuzzy commitment and asserted that the scheme can endure many known attacks. Nevertheless, after careful analysis, this paper presents the shortcoming related to its design as well as it is to prove in this paper that the scheme of Barman et al. is prone to many attacks including: server impersonation, session-key leakage, user impersonation, secret temporary parameter leakage attacks as well as its lacks user anonymity. Moreover, their scheme has the scalability issue. In order to mitigate the aforementioned issues, this work proposes an amended three-factor symmetric-key based secure authentication and key agreement scheme for multi-server environments (ITSSAKA-MS). The security of ITSSAKA-MS is proved formally under automated tool AVISPA along with a security feature discussion. Although, the proposed scheme requisites additional communication and computation costs, but the informal and automated formal security analysis indicate that only proposed scheme withstands several known attacks as compared with recent schemes. INDEX TERMS Authentication and key-agreement (AKA), AVISPA tool, E-Healthcare, Fuzzy commitment scheme, Multi-server authentication, Telecare medicine information system (TMIS).

One of the main concerns for online shopping websites is to provide efficient and customized recommendations to a very large number of users based on their preferences. Collaborative filtering (CF) is the most famous type of recommender system method to provide personalized recommendations to users. CF generates recommendations by identifying clusters of similar users or items from the user-item rating matrix. This cluster of similar users or items is generally identified by using some similarity measurement method. Among numerous proposed similarity measure methods by researchers, the Pearson correlation coefficient (PCC) is a commonly used similarity measure method for CF-based recommender systems. The standard PCC suffers some inherent limitations and ignores user rating preference behavior (RPB). Typically, users have different RPB, where some users may give the same rating to various items without liking the items and some users may tend to give average rating albeit liking the items. Traditional similarity measure methods (including PCC) do not consider this rating pattern of users. In this article, we present a novel similarity measure method to consider user RPB while calculating similarity among users. The proposed similarity measure method state user RPB as a function of user average rating value, and variance or standard deviation. The user RPB is then combined with an improved model of standard PCC to form an improved similarity measure method for CF-based recommender systems. The proposed similarity measure is named as improved PCC weighted with RPB (IPWR). The qualitative and quantitative analysis of the IPWR similarity measure method is performed using five state-of-the-art datasets (i.e. Epinions, MovieLens-100K, MovieLens-1M, CiaoDVD, and MovieTweetings). The IPWR similarity measure method performs better than state-of-the-art similarity measure methods in terms of mean absolute error (MAE), root mean square error (RMSE), precision, recall, and F-measure.