Efficiency of LSB steganography on medical information

The development of the medical field had led to the transformation of communication from paper information into the digital form. Medical information security had become a great concern as the medical field is moving towards the digital world and hence patient information, disease diagnosis and so on are all being stored in the digital image. Therefore, to improve the medical information security, securing of patient information and the increasing requirements for communication to be transferred between patients, client, medical practitioners, and sponsors is essential to be secured. The core aim of this research is to make available a complete knowledge about the research trends on LSB Steganography Technique, which are applied to securing medical information such as text, image, audio, video and graphics and also discuss the efficiency of the LSB technique. The survey findings show that LSB steganography technique is efficient in securing medical information from intruder

USABILITY EVALUATON OF USERS’ EXPERIENCE ON SOME EXISTING E-COMMERCE PLATFORMS

Internet has become increasingly popular nowadays. Several million of websites have been built and used for electronic buying and selling. Many designers have begun to focus their attention on whether these platforms can really be used to the satisfaction of users. Hence, the need to evaluate users’ experience on E-Commerce platforms. This research compares five platforms (Jumia, Ali-Express, Konga, Amazon and Jiji) based on users’ review through the use of online questionnaires for evaluating the platforms. From the data retrieved, Jumia, Konga and Ali Express recorded a total number of 105, 67 and 45 respondents representing 47.29%, 31.08% and 20.27% of the used sample population respectively. Amazon and Jiji recorded 2 and 3 respondents respectively accounting for 0.9% and 1.35% of the total population size. Attention should be given to attractive and easy-to-Navigate E-Commerce platform designs for users to have good user experience

Ethnicity and Biometric Uniqueness: Iris Pattern Individuality in a West African Database

We conducted more than 1.3 million comparisons of iris patterns encoded from images collected at two Nigerian universities, which constitute the newly available African Human Iris (AFHIRIS) database. The purpose was to discover whether ethnic differences in iris structure and appearance such as the textural feature size, as contrasted with an all-Chinese image database or an American database in which only 1.53% were of African-American heritage, made a material difference for iris discrimination. We measured a reduction in entropy for the AFHIRIS database due to the coarser iris features created by the thick anterior layer of melanocytes, and we found stochastic parameters that accurately model the relevant empirical distributions. Quantile-Quantile analysis revealed that a very small change in operational decision thresholds for the African database would compensate for the reduced entropy and generate the same performance in terms of resistance to False Matches. We conclude that despite demographic difference, individuality can be robustly discerned by comparison of iris patterns in this West African population.Comment: 8 pages, 8 Figure

Modified Advanced Encryption Standard Algorithm for Information Security

The wide acceptability of Advanced Encryption Standard (AES) as the most efficient of all of the symmetric cryptographic techniques has further opened it up to more attacks. Efforts that were aimed at securing information while using AES is still being undermined by the activities of attackers This has further necessitated the need for researchers to come up with ways of enhancing the strength of AES. This article presents an enhanced AES algorithm that was achieved by modifying its SubBytes and ShiftRows transformations. The SubBytes transformation is modified to be round key dependent, while the ShiftRows transformation is randomized. The rationale behind the modification is to make the two transformations round key dependent, so that a single bit change in the key will produce a significant change in the cipher text. The conventional and modified AES algorithms are both implemented and evaluated in terms avalanche effect and execution time. The modified AES algorithm achieved an avalanche effect of 57.81% as compared to 50.78 recorded with the conventional AES. However, with 16, 32, 64, and 128 plain text bytes, the modified AES recorded an execution time of 0.18, 0.31, 0.46, and 0.59 ms, respectively. This is slightly higher than the results obtained with the conventional AES. Though a slightly higher execution time in milliseconds was recorded with the modified AES, the improved encryption and decryption strength via the avalanche effects measured is a desirable feat

A Safe and Secured Medical Textual Information Using an Improved LSB Image Steganography

Safe conveyance of medical data across unsecured networks nowadays is an essential issue in telemedicine. With the exponential growth of multimedia technologies and connected networks, modern healthcare is a huge step ahead. Authentication of a diagnostic image obtained from a specialist at a remote location which is from the sender is one of the most challenging tasks in an automated healthcare setup. Intruders were found to be able to efficiently exploit securely transmitted messages from previous literature since the algorithms were not efficient enough leading to distortion of information. Therefore, this study proposed a modified least significant bit (LSB) technique capable of protecting and hiding medical data to solve the crucial authentication issue. The application was executed and established by utilizing MATLAB 2018a, and it used a logical bit shift operation for execution. The investigational outcomes established that the proposed technique can entrench medical information without leaving a perceptible falsification in the stego image. The result of this implementation shows that the modified LSB image steganography outperformed the standard LSB technique with a higher PSNR value and lower MSE value when compared with previous research works. The number of shifts was added as a new performance metric for the proposed system. The study concluded that the proposed secured medical information system was evidenced to be proficient in secreting medical information and creating undetectable stego images with slight entrenching falsifications when likened to other prevailing approaches

USABILITY EVALUATON OF USERS’ EXPERIENCE ON SOME EXISTING E-COMMERCE PLATFORMS

Internet has become increasingly popular nowadays. Several million of websites have been built and used for electronic buying and selling. Many designers have begun to focus their attention on whether these platforms can really be used to the satisfaction of users. Hence, the need to evaluate users’ experience on E-Commerce platforms. This research compares five platforms (Jumia, Ali-Express, Konga, Amazon and Jiji) based on users’ review through the use of online questionnaires for evaluating the platforms. From the data retrieved, Jumia, Konga and Ali Express recorded a total number of 105, 67 and 45 respondents representing 47.29%, 31.08% and 20.27% of the used sample population respectively. Amazon and Jiji recorded 2 and 3 respondents respectively accounting for 0.9% and 1.35% of the total population size. Attention should be given to attractive and easy-to-Navigate E-Commerce platform designs for users to have good user experience

Android Malware Detection through Machine Learning Techniques: A Review

The open source nature of Android Operating System has attracted wider adoption of the system by multiple types of developers. This phenomenon has further fostered an exponential proliferation of devices running the Android OS into different sectors of the economy. Although this development has brought about great technological advancements and ease of doing businesses (e-commerce) and social interactions, they have however become strong mediums for the uncontrolled rising cyberattacks and espionage against business infrastructures and the individual users of these mobile devices. Different cyberattacks techniques exist but attacks through malicious applications have taken the lead aside other attack methods like social engineering. Android malware have evolved in sophistications and intelligence that they have become highly resistant to existing detection systems especially those that are signature-based. Machine learning techniques have risen to become a more competent choice for combating the kind of sophistications and novelty deployed by emerging Android malwares. The models created via machine learning methods work by first learning the existing patterns of malware behaviour and then use this knowledge to separate or identify any such similar behaviour from unknown attacks. This paper provided a comprehensive review of machine learning techniques and their applications in Android malware detection as found in contemporary literature

Implementation of a Framework for Healthy and Diabetic Retinopathy Retinal Image Recognition

The feature extraction stage remains a major component of every biometric recognition system. In most instances, the eventual accuracy of a recognition system is dependent on the features extracted from the biometric trait and the feature extraction technique adopted. The widely adopted technique employs features extracted from healthy retinal images in training retina recognition system. However, literature has shown that certain eye diseases such as diabetic retinopathy (DR), hypertensive retinopathy, glaucoma, and cataract could alter the recognition accuracy of the retina recognition system. This connotes that a robust retina recognition system should be designed to accommodate healthy and diseased retinal images. A framework with two different approaches for retina image recognition is presented in this study. The first approach employed structural features for healthy retinal image recognition while the second employed vascular and lesion-based features for DR retinal image recognition. Any input retinal image was first examined for the presence of DR symptoms before the appropriate feature extraction technique was adopted. Recognition rates of 100% and 97.23% were achieved for the healthy and DR retinal images, respectively, and a false acceptance rate of 0.0444 and a false rejection rate of 0.0133 were also achieved

Healthcare Diagnosis Support System for Detection of Heart Disease in a Patient using Machine Leaming Methods

One of the most considerable investigative areas has remained the applications area of medical advancement. The early warning method for heart  disease (HD) is one of these medical technologies. The goal of a healthcare diagnosis support system (HDSS) is to diagnose HD at an early stage  such that the diagnosis can be streamlined, advanced cases stopped, and care costs can be minimized. A machine learning (ML) HDSS for heart  disease identification is obtainable in this study, and it is capable of obtaining and learning information from each patient's experimental data  automatically. The authors employed a dimensionality reduction technique autoencoder (AE) with three ML classifiers detection of HD. The HD  dataset employed for the HDSS was collected from the National Health Service (NHS) database. The result was evaluated using the confusion matrix  performance measures such as accuracy, specificity, detection rate, Fl score, and precision. The result shows that NB+Autoencoder outperformed  the other two classifiers with an accuracy of 57.2% and 55.4 precision.&nbsp