The emerging technology breakthrough of the Internet of Things (IoT) is expected to offer promising solutions for indoor/outdoor healthcare, which may contribute significantly to human health and well-being. In this paper, we investigated the technologies of healthcare service applications in telemedicine architecture. We aimed to resolve a series of healthcare problems on the frequent failures in telemedicine architecture through IoT solutions, particularly the failures of wearable body sensors (Tier 1) and a medical center server (Tier 3). For improved generalisability, we demonstrated an effective research approach, the fault-tolerant framework on mHealth or the so-called FTF-mHealth-IoT in the context of IoT, to resolve essential problems in current investigations on healthcare services. First, we propose a risk local triage algorithm known as the risk-level localization triage (RLLT), which can exclude the control process of patient triage from the medical center by using mHealth and can warn about failures related to wearable sensors. RLLT performs this initial step towards detecting a patient's emergency case and then identifying the healthcare service package of the risk-level. Second, according to the risk-level package, our framework can aid decision makers in hospital selection through multi-criteria decision making (MCDM). Accordingly, mHealth can connect directly with the servers of distributed hospitals to ascertain available healthcare services for the risk-level package in those hospitals. The time of arrival of the patient at the hospital (TAH) is considered for each hospital to reach a final decision and select the appropriate institution in case of medical center failure. This paper used two datasets. The first dataset involved 572 patients with chronic heart disease. Their triage levels were evaluated using our RLLT algorithm. The second dataset included hospital healthcare services with two levels of availability within distributed hospitals to show variety when testing the proposed framework. The former dataset is an actual dataset of services collected from 12 hospitals located in the capital Baghdad, which represents the maximum level of availability. The latter is an assumption dataset of the services within the 12 hospitals located in the capital Kuala Lumpur, which represents the minimum level of availability. Subsequently, the hospitals were prioritized using a unique MCDM method for estimating small power consumption, namely, the analytic hierarchy process (AHP), based on a crossover between the ''healthcare services package/TAH'' of each hospital and the ''hospital list''. The results showed that the AHP is effective for solving hospital selection problems within mHealth. The implications of this study support the patients, organizations, and medical staff in a modern lifestyle.INDEX TERMS Telemedicine, Internet of thing, healthcare, triage, hospital selection, medical centre failure.

In this study, pre-service teaching refers to teaching English as a second language (TESL) to Malaysian students whose first language is not English. TESL prepares English-language learners to become future teachers of English as a second language. To date, no multi-criteria framework has been developed to evaluate and select the skills of pre-service teachers. This study presents a new framework to assess and rank the English skills of pre-service teachers on the basis of fuzzy Delphi and multi-criteria analysis. Three experiments were conducted. Firstly, criteria were identified from the literature review and the opinions of representative experts via the Delphi method. Secondly, 31 pre-service teachers were evaluated to determine the skills of pre-service teachers on the basis of Delphi criteria outcomes. English proficiency was tested through the English Language Testing Service and four language skill examinations. Each examination was evaluated by experts with vast experience in English teaching. Thirdly, pre-service teachers were ranked on the basis of a set of evaluated Delphi criteria outcomes through the technique for the order of preference by similarity to ideal solution (TOPSIS) method. Thereafter, the mean and standard deviation were utilized to ensure the identical systematic ranking of pre-service teachers. Findings are as follows. Twenty-five criteria from previous studies are representative as evaluated by the opinions of experts, which were gathered through interviews and a structured questionnaire. The validity of content was verified using a five-point Likert scale. With Delphi method outcomes, 14 criteria were selected and included in the final framework. The results of the proposed evaluation framework were tested on Malaysian pre-service teachers. TOPSIS is effective for solving the selection problems of pre-service teachers. In the final experiment, significant differences were recognized between the scores of groups, indicating identical ranking results.

This study proposes an evaluation and benchmarking decision matrix (DM) on the basis of multi-criteria decision making (MCDM) for young learners' English mobile applications (E-apps) in terms of listening, speaking, reading and writing (LSRW) skills. Benchmarking E-apps for young learners is challenging due to (a) multiple criteria, (b) criteria importance and (c) data variation. The DM was constructed on the basis of the intersection amongst evaluation criteria in terms of LSRW and E-apps for young learners. The criteria were adopted from a preschool education curriculum standard. The DM data included six E-apps as alternatives and 17 skills as criteria. Thereafter, the six E-apps were evaluated by distributing a checklist form amongst six English learning experts. These apps were subsequently benchmarked by utilising MCDM methods, namely, best-worst method (BWM) and technique for order of preference by similarity to ideal solution (TOPSIS). BWM was used for criterion weighting, whereas TOPSIS was employed to benchmark and rank the apps. TOPSIS was utilised in two contexts, namely, individual and group. In the group context, internal and external aggregations are applied. Mean was computed to ensure that the E-apps undergo a systematic ranking for objective validation. This study provides scenarios and a benchmarking checklist to evaluate and compare the proposed work with six relative studies. Results indicated that (1) BWM is suitable for criteria weighting. (2) TOPSIS is suitable for benchmarking and ranking E-apps. Moreover, the internal and external TOPSIS group decision making exhibited similar findings, with the best app being 'Montessori' and the worst app being 'FunWithFlupe.' (3) For objective validation, remarkable differences were observed amongst the group scores, which indicate that the internal and external ranking results are identical. (4) In the evaluation, the proposed DM revealed advantages over the six relative studies by 40.00%, 53.33%, 40.00%, 46.67%, 46.67% and 46.67%. INDEX TERMS Language learning app evaluation, language learning app assessment, language teaching/learning strategies. B. B. ZAIDAN received the B.Sc. degree in applied mathematics from Al-Nahrain University, Baghdad, Iraq, in 2004, and the M.Sc. degree in data communications and information security from the University of Malaya, Malaysia, in 2009. He is currently working as a Senior Lecturer with the Department of Computing, University Pendidikan Sultan Idris. He led or has been a member for many funded research projects, and has published more than 150 articles at various international conferences and journals. His research areas are artificial intelligence, decision theory, information security and network, and multicriteria evaluation and benchmarking. O. S. ALBAHRI received the B.Sc. degree in computer science from Al Turath University College, Baghdad, Iraq, in 2011, the M.Sc. degree in computer science and communication from Arts,

Steganography is a form of technology utilised to safeguard secret data during communication in addition to data repository. Numerous researchers have endeavoured to enhance the performance of steganography techniques through the development of an effective algorithm for the selection of the optimal pixel location within the host image for the concealment of secret bits, for the enhancement of the embedding capacity of the secret data, and for maintaining the visual quality of the host image (stego image) in an accepted rate after the concealment of the secret data. Therefore, steganography is perceived as a challenging task. Thus, the current study proposes a new technique for image steganography based on particle swarm optimisation (PSO) algorithm by using pixel selection for the concealment of a secret image in spatial domain, for the purpose of high embedment capacity. The stego possesses a high level of resistance against a steganalytic attack due to the security provided via image steganography. The function of PSO algorithm is to choose an optimal pixel in grey scale host image for the concealment of secret bits, as the PSO has the ability to achieve an efficient fitness calculation that depends on the cost matrix by dividing the host and secret images into four parts. First of all, the secret bits are modified, which are then embedded within the host image. Several locations in the host image are determined through the order of scanning the host pixels and starting point of the scanning for better least significant bits LSBs of each pixel. The PSO algorithm was utilised to ascertain the ideal initiating point and scanning order. Experimental results show that (1) the average peak signal to noise ratio PSNR value in the benchmark technique based on genetic algorithm for five standard stego images is 45.13%, whereas the result obtained from the recommended technique is 56.60%. (2) The proposed technique has an advantage over the benchmark with a percentage of 33.34%, which encompasses all associated issues within the checklist scenario. Therefore, the performance of the recommended technique is superior over existing techniques.

A review is conducted to deeply analyse and map the research landscape of current technologies in finger vein (FV) biometric authentication in medical systems into a coherent taxonomy. This research focuses on articles related to the keywords 'biometrics', 'finger veins' and 'verification' and their variations in three major databases, namely, Web of Science, ScienceDirect and IEEE Xplore. The final set of collected articles related to FV biometric authentication systems is divided into software-and hardware-based systems. In the first category, software development attempts are described. The experiment results, frameworks, algorithms and methods that perform satisfactorily are presented. Moreover, the experiences obtained from conducting these studies are discussed. In the second category, hardware development attempts are described. The final articles are discussed from three aspects, namely, (1) number of publications, (2) problem type, proposed solutions, best results and evaluation methods in the included studies and (3) available databases containing different scientific work collected from volunteers, such as staff and students. The basic characteristics of this emerging field are identified from the following aspects: motivations of using FV biometric technology in authentication systems, open challenges that impede the technology's utility, authors' recommendations and future research prospects. A new solution is proposed to address several issues, such as leakage of biometrics that leads to serious risks due to the use of stolen FV templates and various spoofing and brute-force attacks in decentralised network architectures in medical systems, including access points and various database nodes without a central point. This work contributes to literature by providing a detailed review of feasible alternatives and research gaps, thereby enabling researchers and developers to develop FV biometric authentication medical systems further. Insights into the importance of such a technology and its integration into different medical applications and fields are also provided.