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

Performance analysis of gray code number system in image security

The encryption of digital images has become essential since it is vulnerable to interception while being transmitted or stored. A new image encryption algorithm to address the security challenges of traditional image encryption algorithms is presented in this research. The proposed scheme transforms the pixel information of an original image by taking into consideration the pixel location such that two neighboring pixels are processed via two separate algorithms. The proposed scheme utilized the Gray code number system. The experimental results and comparison shows the encrypted images were different from the original images. Also, pixel histogram revealed that the distribution of the plain images and their decrypted images have the same pixel histogram distributions, which means that there is a high correlation between the original images and decrypted images. The scheme also offers strong resistance to statistical attacks

Enhanced image security using residue number system and new Arnold transform

This paper aims to improve the image scrambling and encryption effect in traditional two-dimensional discrete Arnold transform by introducing a new Residue number system (RNS) with three moduli and the New Arnold Transform. The study focuses on improving the classical discrete Arnold transform with quasi-affine properties, applying image scrambling and encryption research. The design of the method is explicit to three moduli set {2n, 2n+1+1, 2n+1-1}. These moduli set includes equalized and shapely moduli leading to the effective execution of the residue to binary converter. The study employs an arithmetic residue to the binary converter and an improved Arnold transformation algorithm. The encryption process uses MATLAB to accept a digital image input and subsequently convert the image into an RNS representation. The images are connected as a group. The resulting encrypted image uses the Arnold transformation algorithm. The encrypted image is used as input at decryption using the anti-Arnold (Reverse Arnold) transformation algorithm to convert the picture to the original RNS (original pixel value). Then the RNS was used to retransform the original RNS to its binary form. Security analysis tests, like histogram analysis, keyspace, key sensitivity, and correlation coefficient analysis, were administered on the encrypted image. Results show that the hybrid system can use the improved Arnold transform algorithm with better security and no constraint on image width and size

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

Software Defect Prediction Using Dagging Meta-Learner-Based Classifiers

To guarantee that software does not fail, software quality assurance (SQA) teams play a critical part in the software development procedure. As a result, prioritizing SQA activities is a crucial stage in SQA. Software defect prediction (SDP) is a procedure for recognizing high-risk software components and determining the influence of software measurements on the likelihood of software modules failure. There is a continuous need for sophisticated and better SDP models. Therefore, this study proposed the use of dagging-based and baseline classifiers to predict software defects. The efficacy of the dagging-based SDP model for forecasting software defects was examined in this study. The models employed were naïve Bayes (NB), decision tree (DT), and k-nearest neighbor (kNN), and these models were used on nine NASA datasets. Findings from the experimental results indicated the superiority of SDP models based on dagging meta-learner. Dagging-based models significantly outperformed experimented baseline classifiers built on accuracy, the area under the curve (AUC), F-measure, and precision-recall curve (PRC) values. Specifically, dagging-based NB, DT, and kNN models had +6.62%, +3.26%, and +4.14% increments in average accuracy value over baseline NB, DT, and kNN models. Therefore, it can be concluded that the dagging meta-learner can advance the recognition performances of SDP methods and should be considered for SDP processes

Enhancing Skin Cancer Detection and Classification in Dermoscopic Images through Concatenated MobileNetV2 and Xception Models

One of the most promising research initiatives in the healthcare field is focused on the rising incidence of skin cancer worldwide and improving early discovery methods for the disease. The most significant factor in the fatalities caused by skin cancer is the late identification of the disease. The likelihood of human survival may be significantly improved by performing an early diagnosis followed by appropriate therapy. It is not a simple process to extract the elements from the photographs of the tumors that may be used for the prospective identification of skin cancer. Several deep learning models are widely used to extract efficient features for a skin cancer diagnosis; nevertheless, the literature demonstrates that there is still room for additional improvements in various performance metrics. This study proposes a hybrid deep convolutional neural network architecture for identifying skin cancer by adding two main heuristics. These include Xception and MobileNetV2 models. Data augmentation was introduced to balance the dataset, and the transfer learning technique was utilized to resolve the challenges of the absence of labeled datasets. It has been detected that the suggested method of employing Xception in conjunction with MobileNetV2 attains the most excellent performance, particularly concerning the dataset that was evaluated: specifically, it produced 97.56% accuracy, 97.00% area under the curve, 100% sensitivity, 93.33% precision, 96.55% F1 score, and 0.0370 false favorable rates. This research has implications for clinical practice and public health, offering a valuable tool for dermatologists and healthcare professionals in their fight against skin cancer