Over the last few decades, the development in the field of navigation and routing devices has become a hindering task for the researchers to develop smart and intelligent guiding mechanism at indoor and outdoor locations for blind and visually impaired people (BVIPs). The existing research need to be analysed from a historical perception including early research on the first electronic travel aids to the use of modern artificial vision models for the navigation of BVIPs. Diverse approaches such as: e-cane or guide dog, infrared-based cane, laser based walker and many others are proposed for the navigation of BVIPs. But most of these techniques have limitations such as: infrared and ultrasonic based assistance has short range capacities for object detection. While laser based assistance can harm other people if it directly hit them on their eyes or any other part of the body. These trade-offs are critical to bring this technology in practice.To systematically assess, analyze, and identify the primary studies in this specialized field and provide an overview of the trends and empirical evidence in the proposed field. This systematic research work is performed by defining a set of relevant keywords, formulating four research questions, defining selection criteria for the articles, and synthesizing the empirical evidence in this area. Our pool of studies include 191 most relevant articles to the proposed field reported between 2011 and 2020 (a portion of 2020 is included). This systematic mapping will help the researchers, engineers, and practitioners to make more authentic decisions for finding gaps in the available navigation assistants and suggest a new and enhanced smart assistant application accordingly to ensure safety and accurate guidance of the BVIPs. This research work have several implications in particular the impact of reducing fatalities and major injuries of BVIPs.

Internet of Things (IoT) devices are operating in various domains like healthcare environment, smart cities, smart homes, transportation, and smart grid system. These devices transmit a bulk of data through various sensors, actuators, transceivers, or other wearable devices. Data in the IoT environment is susceptible to many threats, attacks, and risks. Therefore, a robust security mechanism is indispensable to cope with attacks, vulnerabilities, security, and privacy challenges related to IoT. In this research, a systematic literature review has been conducted to analyze the security of IoT devices and to provide the countermeasures in response to security problems and challenges by using mobile computing. A comprehensive and in-depth security analysis of IoT devices has been made in light of mobile computing, which is a novel approach. Mobile computing’s technological infrastructures such as smartphones, services, policies, strategies, and applications are employed to tackle and mitigate these potential security threats. In this paper, the security challenges and problems of IoT devices are identified by a systematic literature review. Then, mobile computing has been used to address these challenges by providing potential security measures and solutions. Hardware and software-based solutions furnished by mobile computing towards the IoT security challenges have been elaborated. To the best of our knowledge, this is the first attempt to analyze the security issues and challenges of IoT in light of mobile computing and it will open a gateway towards future research.

Security is one of the most critical aspects of software quality. Software security refers to the process of creating and developing software that assures the integrity, confidentiality, and availability of its code, data, and services. Software development organizations treat security as an afterthought issue, and as a result, they continue to face security threats. Incorporating security at any level of the Software Development Life Cycle (SDLC) has become an urgent requirement. Several methodologies, strategies, and models have been proposed and developed to address software security, but only a few of them give reliable evidence for creating secure software applications. Software security issues, on the other hand, have not been adequately addressed, and integrating security procedures into the SDLC remains a challenge. The major purpose of this paper is to learn about software security risks and practices so that secure software development methods can be better designed. A systematic literature review (SLR) was performed to classify important studies to achieve this goal. Based on the inclusion, exclusion, and quality assessment criteria, a total of 121 studies were chosen. This study identified 145 security risks and 424 best practices that help software development organizations to manage the security in each phase of the SDLC. To pursue secure SDLC, this study prescribed different security activities, which should be followed in each phase of the SDLC. Successful integration of these activities minimizing effort, time, and budget while delivering secure software applications. The findings of this study assist software development organizations in improving the security level of their software products and also enhancing their security efficiency. This will raise the developer’s awareness of secure development practices as well.

Healthcare systems are transformed digitally with the help of medical technology, information systems, electronic medical records, wearable and smart devices, and handheld devices. The advancement in the medical big data, along with the availability of new computational models in the field of healthcare, has enabled the caretakers and researchers to extract relevant information and visualize the healthcare big data in a new spectrum. The role of medical big data becomes a challenging task in the form of storage, required information retrieval within a limited time, cost efficient solutions in terms care, and many others. Early decision making based healthcare system has massive potential for dropping the cost of care, refining quality of care, and reducing waste and error. Scientific programming play a significant role to overcome the existing issues and future problems involved in the management of large scale data in healthcare, such as by assisting in the processing of huge data volumes, complex system modelling, and sourcing derivations from healthcare data and simulations. Therefore, to address this problem efficiently a detailed study and analysis of the available literature work is required to facilitate the doctors and practitioners for making the decisions in identifying the disease and suggest treatment accordingly. The peer reviewed reputed journals are selected for the accumulated of published research work during the period ranges from 2015 - 2019 (a portion of 2020 is also included). A total of 127 relevant articles (conference papers, journal papers, book section, and survey papers) are selected for the assessment and analysis purposes. The proposed research work organizes and summarizes the existing published research work based on the research questions defined and keywords identified for the search process. This analysis on the existence research work will help the doctors and practitioners to make more authentic decisions, which ultimately will help to use the study as evidence for treating patients and suggest medicines accordingly.

With the quick evolution of medical technology, the era of big data in medicine is quickly approaching. The analysis and mining of these data significantly influence the prediction, monitoring, diagnosis, and treatment of tumor disorders. Since it has a wide range of traits, a low survival rate, and an aggressive nature, brain tumor is regarded as the deadliest and most devastating disease. Misdiagnosed brain tumors lead to inadequate medical treatment, reducing the patient's life chances. Brain tumor detection is highly challenging due to the capacity to distinguish between aberrant and normal tissues. Effective therapy and long-term survival are made possible for the patient by a correct diagnosis. Despite extensive research, there are still certain limitations in detecting brain tumors because of the unusual distribution pattern of the lesions. Finding a region with a small number of lesions can be difficult because small areas tend to look healthy. It directly reduces the classification accuracy, and extracting and choosing informative features is challenging. A significant role is played by automatically classifying early-stage brain tumors utilizing deep and machine learning approaches. This paper proposes a hybrid deep learning model Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) for classifying and predicting brain tumors through Magnetic Resonance Images (MRI). We experiment on an MRI brain image dataset. First, the data is preprocessed efficiently, and then, the Convolutional Neural Network (CNN) is applied to extract the significant features from images. The proposed model predicts the brain tumor with a significant classification accuracy of 99.1%, a precision of 98.8%, recall of 98.9%, and F1-measure of 99.0%.

Ever since the emergence of big data concept, researchers have started applying the concept to various fields and tried to assess the level of acceptance of it with renown models like technology acceptance model (TAM) and it variations. In this regard, this paper tries to look at the factors that associated with the usage of big data analytics, by synchronizing TAM with organizational learning capabilities (OLC) framework. These models are applied on the construct, intended usage of big data and also the mediation effect of the OLC constructs is assessed. The data for the study is collected from the students pertaining to information technology disciplines at University of Liverpool, online programme. Though, invitation to participate e-mails are sent to 1035 students, only 359 members responded back with filled questionnaires. This study uses structural equation modelling and multivariate regression using ordinary least squares estimation to test the proposed hypotheses using the latest statistical software R. It is proved from the analysis that compared to other models, model 4 (which is constructed by using the constructs of OLC and TAM frameworks) is able to explain 44% variation in the usage pattern of big data. In addition to this, the mediation test performed revealed that the interaction between OLC dimensions and TAM dimensions on intended usage of big data has no mediation effect. Thus, this work provided inputs to the research community to look into the relation between the constructs of OLC framework and the selection of big data technology.