The Level of Information Security Awareness among Academic Staff in IHL

IS security awareness plays a significant role in the process of the overall information security of any organisation. Based on an empirical study of 368 academic staff in three institutions of higher learning (IHL), we found that the level of information security awareness can be considered good, but it can certainly be improved further. Employees need further training in this area mainly at institutions which only recently received the ISO/IEC 27001:2013 certification. Our sample seems to suggest that demographics such as the age of the respondents contributed to their information security risk tolerance and adherence behaviour

Deep Learning Based Gait Recognition Using Convolutional Neural Network in the COVID-19 Pandemic

Gait recognition is the behavioral biometric trait that tracks humans based on their walking motion. It has gained attention because of its non-invasive and unobtrusive behaviors and applicable to the different application area. In this paper, we target model-free gait recognition with the deep learning approach for the Muslim community in the COVID-19 pandemic. The different convolutional neural network architectures (CNN) are examined by using the spatio-temporal gait representation called Gait Energy Images (GEI). We explored both the identification and verification problems to determine the suitability of the proposed CNN frameworks. In gait recognition, the intraclass variation is larger than the inter-class variation because of the shooting view, the walking speed, the wearing condition, and so on. To tackle this challenge, the verification framework is more suitable for the 1:1 association of gait recognition. As for the verification problem, we implemented the Siamese network with the parallel CNN architecture. All the proposed methods are tested against the public gait datasets called OUISIR-LP and OUISIR-MVLP to determine the identification and verification performance in terms of recognition accuracy and error rate

The Impact of Marketing Innovation on Economic Development in Nigeria: A Literature Review

Marketing innovation is one of the keys to increasing a nation’s economy. It is said that economic growth is an increase in national output that leads to a rise in the average per capita GDP. (GNP) additionally, increasing the average per capita GNP is insufficient to convey the implied or predicted value of economic growth. (World situation) Marketing innovation is becoming more intense towards the economic development in Nigeria. It is to be noted that there is a distinct lack of literature on the effect of marketing innovations on Nigeria's economic growth. While the country's economic growth has improved, it has not been evenly distributed. Since business activity appears to decide the degree of economic growth in each society, marketing innovation becomes an indicator of economic development. (Nigeria situation) The article review aimed to look at previous research on the effect of marketing innovation on Nigeria’s economic growth. Natural resources aren't enough for Nigeria to compete, as shown by the pattern and descriptions provided. As a result, this study suggests that marketing innovation be viewed as a primary driver of Nigeria's economic growth. Government and private sector efforts can therefore be made to invest in human capital creation and R&D to generate the ground power for exponential growth. In addition, this study also focused on the main findings of each study conducted. This study also examines the methodology of the study used. The findings of this study are intended to help future researchers research the impact of marketing innovation on economic development in Nigeria. In addition, this analysis indicates several research topics for future study

IoT Based Indoor Object Location Tracking Solution

Internet of Things (IoT) is enhancing the pleasant of present-day life. IoT-based objects tracking is a crying need for a smart indoor environment. In the age of smart cities, there are many applications in which indoor localization can be used for monitoring and tracking objects inside smart buildings. This research study is based on the development of a robust real-time system capable of localizing and tracking objects accurately. Global Positioning Systems (GPS) are typically used for outdoor localization because of their ease of implementation and accuracy of up to five meters. Because of the limited space and the many obstacles in indoor environments, GPS is not an appropriate option for overcoming those obstacles. Thus, tracking objects in an indoor environment is a major challenge, both in terms of accuracy and efficiency. The main objective of this research is to design and develop an IoT-based effective solution for tracking the location of objects indoors using the fingerprinting technique. There are some existing applications for tracking objects in indoor localization. Those existing indoor location tracking technologies' reported pitfalls are expensive infrastructure, high connectivity, and less accuracy. Therefore, we have come up with this proposed algorithm to solve those problems. The proposed approach has the potential to estimate the position and track objects very accurately indoors. The proposed algorithm is applied in two different indoor location simulations. The proposed method has been implemented and experiments have been conducted. Experiment results demonstrate that the proposed approach works very well with wi-fi/LTE collected data

A comprehensive survey on deep-learning based gait recognition for humans in the COVID-19 pandemic

Human gait recognition is a biometric technique that has been utilized for security purposes for the last decade. Gait recognition is an appealing biometric modality that aims to identify individuals based on the way they walk. The outbreak of the novel coronavirus (COVID-19), has spread across the world. The number of people infected with COVID-19 is rising rapidly throughout the world. Even though some vaccines for this pandemic have been developed to minimize the effects of COVID-19, deep learning-based gait recognition techniques have shown themselves to be an effective tool for identifying the individuals wearing face mask in COVID-19 pandemic. These techniques play an important part in reducing the rate of COVID-19 spreading throughout the world in the context of the COVID-19 pandemic. Deep learning methods are currently dominating the state-of-the-art in gait recognition and have fostered real-world applications. The main objective of this paper is to provide a comprehensive overview of recent advancements in gait recognition with deep learning, including datasets, test protocols, stateof-the-art solutions, challenges, and future research directions. The purpose of this discussion is to identify current challenges that need to be addressed as well as to suggest some directions for future research that could be explore

A Review on IoT with Big Data Analytics

The Internet of Things (IoT) is a powerful and transformative force for the convergence of the physical and digital world of technology. The IoT is connecting things, businesses, and people in real-time and on a massive scale. The IoT is the network of interconnected devices that contains actuators, sensors, electronics, software and connectivity which lets these things connect, interact and transfer data. Connected devices and software work in ways that produce massive amounts of data where Big Data comes into the picture. The terminology of Big Data represents diverse sets of information that are both very large and complex in nature. Big data offers a better way of managing and using a large amount of data with the opportunity to conduct deeper and richer analysis. Although the extensive number of big data analytics and IoT studies has been performed, the overlapping of the two fields of study creates various possibilities for thriving data analysis in the IoT environment. This paper presents a thorough review of the recent advancement of IoT with big data and analytics. We also make a review of the relationship between these fields. We present a discussion on the application area of IoT and big data analytics as well as the opportunities created by enabling analytics in an IoT system

An Automated Driver’s Context Recognition Approach Using Smartphone Embedded Sensors

Context recognition plays an important role in connecting the space between high-level applications and low-level sensors. To recognize human context, various kinds of sensors have been adopted. Among the variety of exploited sensors, smartphone internal sensors such as accelerometer and gyroscope are widely used due to convenience, non-intrusiveness and low deployment cost. Automatic detection of driver’s context is a very crucial factor to determine the driver’s behaviors. This paper proposes an approach to recognize driver’s context which is a very specific research direction in the domain of human context recognition. The objective of this approach is to automatically detect the contexts of drivers using a smartphone’s internal sensors. The proposed algorithm explores the power of a smartphone’s built-in accelerometer and gyroscope sensors to automatically recognize the driver’s context. Supervised machine learning k-nearest neighbor is employed in the proposed algorithm. Empirical results validated the efficiency of the proposed algorithm

Smartphone-Based Drivers Context Recognition

Various embedded sensors such as accelerometer and gyroscope have opened a new horizon in the scientific studies. One of the most prevailing areas of research is context recognition which can be adopted for smartphone-based parking, road condition detection and sports. To the best of our knowledge, the existing context recognition research covers human’s basic contexts such as walking, jogging and are position dependent that require tightening sensors in fixed position of the body. Furthermore, none of the work has seen to be more specific to detect the contexts of driver. Therefore, to be more specific, in this study, we have constructed a position-independent approach to recognize driver’s contexts that occurs while a driver parks car or leaves from parking place. The support vector machine, random forest and decision tree are employed and the accuracies of 83.38, 93.71 and 98.41% are obtained, respectively

A Comparative Survey on Indoor Object Location Tracking Techniques and Technologies

Indoor object location tracking has newly vouched an increment in Eagerness, owing to the probable extensive length of services it can deliver by universal connectedness and Internet of Things (IoT). In outdoor location, GPS technology is mostly used that can be found in Geographical Map. But GPS technology a dicey technique for fixing positions of objects in indoor location owing to lack of line of sight (LoS). We discuss different approaches, technique and technologies which are used in indoor positioning system such as Radio Frequency Identification Device (RFID), Ultra-Wideband (UWB), Received Signal Strength (RSS); based on like WiFi technology, Angle of Arrival (AoA), Time of Flight (ToF), Return Time of Flight (RTOF) and also highlight their energy efficiency, cost, reception range, availability, latency, tracking exactness and efficiency

An Efficient Shortest Path Algorithm: Multi-Destinations in an Indoor Environment

The shortest path-searching with the minimal weight for multiple destinations is a crucial need in an indoor applications, especially in supermarkets, warehouses, libraries, etc. However, when it is used for multiple item searches, its weight becomes higher as it searches only the shortest path between the single sources to each destination item separately. If the conventional Dijkstra algorithm is modified to multi-destination mode then the weight is decreased, but the output path is not considered as the real shortest path among multiple destinations items. Our proposed algorithm is more efficient for finding the shortest path among multiple destination items with minimum weight, compared to the single source single destination and modified multi-destinations of Dijkstra’s algorithm. In this research, our proposed method has been validated by real-world data as well as by simulated random solutions. Our advancement is more applicable in indoor environment applications based on multiple items or destinations searching