Asian Journal of Research in Computer Science

Internet of Things (IoT) plays a vital role in the modern life. The authors see that offering IoT services by telecom operators better than others, especially in developing countries, where the concern of the community related to security issues is the most prominent obstacles. This paper addressed a future vision of IoT services in developing countries by telecom operators. This paper identified the possible opportunities, and obstacles for telecom operators to offer these services. This paper also presented a case study for telecom operators in Yemen. The case study has been taken from MTN and Yemen Mobile operators. The study's samples includes 73 engineers and SPSS has been used to analyse data. The results showed that (91.35%) from MTN's respondents and (81.14%) from Yemen Mobile's respondents agree that offering IoT services by the operator create new opportunities for profit. Also, results concerning to present services that can be provided are eleven services by MTN and seven services by Yemen Mobile operator.

Aims: To provide a baseline for the configuration of Automated Fingerprint Verification System (AFVS) in the face of changing weather and environmental conditions in order to ensure performance accuracy. 

Study Design:  Statistical and theoretical research approaches.

Place and Duration of Study: Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria, between July 2017 and July 2018.

Methodology: Data set were collected in the South-South geopolitical zone of Nigeria. We use 10,000 minutiae points defined by location and orientation features extracted from fingerprint samples obtained at 9 various physical and environmental conditions over 12 months period. These data were used to formulate linear regression models that were used as constraints to the verification objective function derived as constrained linear least squares. The effects of the changing weather and environmental conditions were incorporated into the optimised point-set matching model in order to minimise the total relative error on location and orientation differences between pairs of minutiae. The model was implemented using interior-point convex quadratic programming was implemented in Matlab.

Results: The results obtained from the optimisation function by adjusting the thresholds of the effects of weather and environmental conditions to 0.0, 0.0 for location and orientation properties of minutiae, respectively, showed minimal total relative errors on the corresponding pairs of matched minutiae, when compared with using the default threshold values of the selected conditions.

Conclusion: The optimisation of point-set based model could provide a computational basis for accurate fingerprint verification for low and high-security AFVS in unfavourable conditions if they are incorporated into the matching model. However, further validation and evaluation of the model with data sets from regions with similar weather and environmental conditions is needed to further validate its robustness in terms of performance accuracy

One of the most promising commercial applications of Human Computer Interface is the vision based Human fatigue detection systems. Most methods and algorithms currently rely heavily on movement of the head and the colorization of the eye ball. In this paper, a new algorithm for detecting human fatigue by relying primarily on eyelid movements as a facial feature is proposed. The features of the eye region and eyelid movement which are geometric in nature are processed alongside each other to determine the level of fatigue of a person. Haar classifiers are employed to detect the eye region and eyelid features. The eye region is, however processed to ascertain attributes of eyelid movement of each individual of interest. The eyelids are then detected as either opened, closed or in transition state. The movement or velocity of the eyelid is tracked using a Kalman filtered velocity function. This algorithm calculates a human blink cycle for each individual, and estimates the associated errors of the eye movement due to friction using the Kalman filter. The study has established human blink cycle calculation as a new classifier to characterize human fatigue and the calculation of the movement of eyelid using the Kalman filter in determining the level of fatigue.

The increase in popularity of electronic transactions has created a necessity to develop and adopt information security systems. As the popularity of e-services has grown, so has the need for effective information security. As such, information needs to be well defined, stored, integrated, transmitted and made available whenever needed in a safe and secure manner. The main goal of the information security process is to protect information confidentiality, integrity and availability. This paper highlights essential and common e-service architectures, who and what is involved in an online transaction, challenges related to online transactions and the role of both individuals and organizations towards successful and secure transactions. A general framework for establishing, assessing, and maintaining a reliable security management system for e-services is suggested. The proposed multilayer framework helps to determine how useful, comprehensive, and adaptive an information security management system actually is. It focuses on determining the critical processes of an information security system and how they can be identified and implemented in real-world situations in order to provide better and more secure protection.

This paper is set out to evaluate the performance of feature extraction techniques that can determine ethnicity of an individual using fingerprint biometric technique and deep learning approach. Hence, fingerprint images of one thousand and fifty-four (1054) persons of three different ethnic groups (Yoruba, Igbo and Middle-Belt) in Nigeria were captured. Kernel Principal Component Analysis (K-PCA) and Kernel Linear Discriminant Analysis (KLDA) were used independently for feature extraction while Convolutional Neural Network (CNN) was used for supervised learning of the features and classification.

The results showed that out of sixty (60) individual fingerprints tested, eight (8) were classified as Yoruba, forty-eight (48) as Igbo and four (4) as Hausa. The Recognition Accuracy for K-PCA was 93.97% and KLDA was 97.26%. For Average Recognition time, K-PCA used 9.98seconds while KLDA used 10.02seconds. The memory space utilized by K-PCA was 94.57KB while KLDA utilized 52.17KB.

T-Test paired sample statistics was carried out on the result obtained; the outcome presented reveal that KLDA outperformed the K-PCA technique in terms of Recognition Accuracy. The relationship between the average recognition time () and threshold value () was found to be polynomial of order four (4) with a high correlation coefficient for KPCA and polynomial of order three (3) with a high correlation coefficient for KLDA. In terms of computation time analysis, KLDA is computationally more expensive than KPCA by reason of processing speed.