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All Journal International Journal of Electrical and Computer Engineering Perfecting a Video Game with Game Metrics Indonesian Journal of Electrical Engineering and Computer Science
Rozniza Ali
Universiti Malaysia Terengganu
Computer Science & IT Electrical & Electronics Engineering

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The use of Least Significant Bit (LSB) and Knight Tour Algorithm for image steganography of cover image Soo Ann Nie; Ghazali Sulong; Rozniza Ali; Andrew Abel
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 6: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (578.452 KB) | DOI: 10.11591/ijece.v9i6.pp5218-5226

Abstract

Steganography is one of the method to communicate in a hidden way. In another word, steganography literally means the practice of hiding messages or information within another data. Previous studies have proposed various steganography techniques using different approaches including Least Significant Bit (LSB), Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). However, different approaches still have its own weaknesses. Therefore image stenography using Knight Tour Algorithm with Least Significant Bit (LSB) technique is presented. The main objective is to improve the security factor in the stego image. Basically, the proposed technique is divided into two parts which are the sender and receiver side. Then, steganalysis which is a type of attack on stenography algorithm is used to detect the secret message in the cover image by the statistical analysis of pixel values. Chi Square Statistical Attach which is one of the type of steganalysis is used to detect these near-equal Po Vs in images and bases the probability of embedding on how close to equal the even pixel values and their corresponding odd pixel values are in the test image. The Knight Tour Algorithm is applied due to the common Least Significant Bit technique that is weak in security and easily decoded by outsider.
Machine learning with multistage classifiers for identification of of ectoparasite infected mud crab genus Scylla Rozniza Ali; Muhamad Munawarar Yusro; Muhammad Suzuri Hitam; Mhd Ikhwanuddin Abdullah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 2: April 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i2.16724

Abstract

Recently, the mud-crab farming can help the rural population economically. However, the existing parasite in the mud-crabs could interfere the long live of the mud-crabs. Unfortunately, the parasite has been identified to live in hundreds of mud-crabs, particularly it happened in Terengganu Coastal Water, Malaysia. This study investigates the initial identification of the parasite features based on their classes by using machine learning techniques. In this case, we employed five classifiers i.e logistic regression (LR), k-nearest neighbors (kNN), Gaussian Naive Bayes (GNB), support vector machine (SVM), and linear discriminant analysis (LDA). We compared these five classfiers to best performance of classification of the parasites. The classification process involving three stages. First, classify the parasites into two classes (normal and abnormal) regardless of their ventral types. Second, classified sexuality (female or male) and maturity (mature or immature). Finally, we compared the five classifiers to identify the species of the parasite. The experimental results showed that GNB and LDA are the most effective classifiers for carrying out the initial classification of the rhizocephalan parasite within the mud crab genus Scylla.
Feature extraction and classification for multiple species of Gyrodactylus ectoparasite Rozniza Ali; Amir Hussain; Mustafa Man
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 3: March 2015
Publisher : Institute of Advanced Engineering and Science

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Abstract

Active Shape Models (ASM) are applied to the attachment hooks of several species of Gyrodactylus, including the notifiable pathogen G. salaris, to assign each species to its truespecies type. Linear (i.e. LDA and K-NN) andnon-linear (i.e. MLP and SVM) models are used to classify Gyrodactylus species. Speciesof Gyrodactylus, ectoparasitic monogenetic flukes of fish, are difficult to discriminate andidentify according to morphology alone and their speciation currently requires taxonomicexpertise. The current exercise sets out to confidently classify species, which in this example includes a species which is a notifiable pathogen of Atlantic salmon, to their true classwith a high degree of accuracy. The findings from the current exercise demonstrates thatimport of ASM data into a MLP classifier, outperforms several other methods of classification (i.e. LDA, K-NN and SVM) that were assessed, with an average classification accuracyof 98.72%. DOI: http://dx.doi.org/10.11591/telkomnika.v13i3.7096
A new modification CNN using VGG19 and ResNet50V2 for classification of COVID-19 from X-ray radiograph images Usman Haruna; Rozniza Ali; Mustafa Man
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp369-377

Abstract

Coronavirus often called COVID-19 is a deadly viral disease that causes as a result of severe acute respiratory syndrome coronavirus-2 that needs to be identified especially at its early stages, and failure of which can lead to the further spread of the virus. Despite with the huge success recorded towards the use of the original convolutional neural networks (CNN) of deep learning models. However, their architecture needs to be modified to design their modified versions that can have more powerful feature layer extractors to improve their classification performance. This research is aimed at designing a modified CNN of a deep learning model that can be applied to interpret X-rays to classify COVID-19 cases with improved performance. Therefore, we proposed a modified convolutional neural network (shortened as modification CNN) approach that uses X-rays to classify a COVID-19 case by combining VGG19 and ResNet50V2 along with putting additional dense layers to the combined feature layer extractors. The proposed modified CNN achieved 99.24%, 98.89%, 98.90%, 99.58%, and 99.23% of the overall accuracy, precision, specificity, sensitivity, and F1-Score, respectively. This demonstrates that the results of the proposed approach show a promising classification performance in the classification of COVID-19 cases.
Co-Authors Amir Hussain Andrew Abel Ghazali Sulong Mhd Ikhwanuddin Abdullah Muhamad Munawarar Yusro Muhammad Suzuri Hitam Mustafa Man Soo Ann Nie Usman Haruna