Reconstruction of Binary Image Using Techniques of Discrete Tomography

Discrete tomography deals with the reconstruction of images, in particular binary images, from their projections. A number of binary image reconstruction methods have been considered in the literature, using different projection models or additional constraints. Here, we will consider reconstruction of a binary image with some prescribed numerical information on the rows of the binary image treated as a binary matrix of 0's and 1's. The problem involves information, referred to as row projection, on the number of 1's and the number of subword 01's in the rows of the binary image to be constructed. The algorithm proposed constructs one among the many binary images having the same numerical information on the number of 1's and the number of subword 01. This proposed algorithm will also construct the image uniquely for a special kind of a binary image with its rows in some specific form

Variants Of Array-Rewriting P Systems For Generating Picture Arrays

Bidang pengkomputeran membran dimulakan sekitar tahun 2000, berinspirasikan struktur dan fungsi sel-sel hidup. Model teori pengkomputeran membran ini dipanggil sistem P dan variannya dan penggunaan model ini dalam pelbagai masalah telah disiasat secara intensif sejak itu. Sistem P tatasusunan menghubungkan tatabahasa tatasusunan bahasa formal dengan sistem P. Dalam teori bahasa formal, salah satu kajian utama adalah terhadap keupayaan tatabahasa untuk menjana bahasa, yang disebut sebagai keupayaan generatif, yang bergantung kepada jenis-jenis peraturan yang digunakan. Kami menyiasat keupayaan generatif sistem P tatasusunan dengan memperkenalkan dalam peraturan sistem ciri-ciri benar, tatabahasa dengan penulisan semula selari dan kaedah mengumpul peraturan. Di sini dengan mengaitkan simbol benar dalam kaedah sistem P tatasusunan, kami memperkenalkan varian baru, yang dinamakan sebagai sistem P tatasusunan dengan ciri-ciri benar. Kami membuktikan bahawa jumlah membran yang digunakan dalam pembinaan itu dapat dikurangkan berbanding sistem P tatasusunan. Kami menggabungkan penulisan semula selari dalam sistem P rentetan di dalam sistem P tatasusuan, dengan itu memperkenalkan satu lagi varian baru dalam sistem P tatasusunan dan dinamakan sebagai sistem P tatasusunan selari. Inspired by the structure and functioning of the living cells, the field of membrane computing was initiated around the year 2000. Since then the theoretical model introduced in this area, called P system has been intensively investigated for properties and applications. One such P system known as array-rewriting P systems provides a link between two dimensional formal language theory and membrane computing. In formal language theory, one of the main studies is on the language generating capability of the grammars, referred to as the generative capacity, which depends on the types of rules. Also a standard technique to increase the generative capacity is to endow the rules with additional features. Here the array-rewriting P system is investigated by endowing the grammatical rules of the system with three such features, namely, permitting symbols, parallel rewriting and grouping of rules. Thus this thesis introduces and develops three such variants of the array-rewriting P system and brings out their advantages

Predicting dengue transmission rates by comparing different machine learning models with vector indices and meteorological data

Machine learning algorithms (ML) are receiving a lot of attention in the development of predictive models for monitoring dengue transmission rates. Previous work has focused only on specific weather variables and algorithms, and there is still a need for a model that uses more variables and algorithms that have higher performance. In this study, we use vector indices and meteorological data as predictors to develop the ML models. We trained and validated seven ML algorithms, including an ensemble ML method, and compared their performance using the receiver operating characteristic (ROC) with the area under the curve (AUC), accuracy and F1 score. Our results show that an ensemble ML such as XG Boost, AdaBoost and Random Forest perform better than the logistics regression, Naïve Bayens, decision tree, and support vector machine (SVM), with XGBoost having the highest AUC, accuracy and F1 score. Analysis of the importance of the variables showed that the container index was the least important. By removing this variable, the ML models improved their performance by at least 6% in AUC and F1 score. Our result provides a framework for future studies on the use of predictive models in the development of an early warning system

Regression study for thyroid disease prediction Comparison of crossing-over approaches and multivariate analysis

Regression analysis is one of the common machine learning method to model the relationship between dependent and independent variables. In this study, we aim to tackle two crucial elements that affect the performance of regression models, which are the type of crossing-over method used for model evaluation and multivariate analysis with the number of predictors. We used the classic thyroid disease dataset from the UCI machine learning repository and compare the crossing-over approaches of k-fold with different folds, bootstrap, Leave One Out Cross-Validation (LOOCV), and repeated k-fold on linear and logistics regression. For multivariate analysis, we compare the performance of the models by using the different combinations of bi-predictors and multi-predictors. Our result shows that models that use kfold cross-validation have greater performance, and a higher number of k does not improve the model performance. For the multivariate analysis, we found that the number of variable is not the key element to determine the performance of a model, rather than a suitable combination of strong predictors. Future studies could explore the effects of cross-validation and multivariate analysis on other machine learning algorithms

A protocol for developing a classification system of mosquitoes using transfer learning

Mosquito identification and classification are the most important steps in a surveillance program of mosquito-borne diseases. With conventional approach of data collection, the process of sorting and classification are laborious and time-consuming. The advancement of computer vision with transfer learning provides excellent alternative to the challenge. Transfer learning is a type of machine learning that is viable and durable in image classification with limited training images. This protocol aims to develop step-by-step procedure in developing a classification system with transfer learning algorithm for mosquito, we demonstrate the protocol to classify two species of Aedes mosquito - Aedes aegypti L. and Aedes albopitus L, but user can adopt the protocol for higher number of species classification. We demonstrated the way of start from the scratch, fine-tuning two pre-trained model performance by using different combination of hyperparameters – batch size and learning rate, and explain the terminology in the Appendix. This protocol target on the domain expert such as entomologist and public health practices to develop their own model to solve the task of mosquito/insect classification

The Preliminary Investigation on Micro-Credentials Practices in Malaysia

This COVID-19 has lately changed the way individuals study and teach by making it available at any time, from any location, and at a low cost. Traditional face-to-face teaching and learning are losing popularity as more students opt for hybrid or all-online learning. In today's fast-paced business, the advent of the gig economy needs the development of individuals and experts with specialised skill sets to fill increasingly specialised positions. Higher education providers needed a more dynamic and quick style of learning to match these demands, which Micro-Credential, a well-known player in 21st-century training and education, delivered. Micro-credentials are nanodegrees, also known as small qualifications, that demonstrate a person's talents, knowledge, and/or experience in a certain subject area or ability. After completing a micro-credentials course, the learner will receive a digital badge. The adoption of Micro-Credential courses in Malaysia was investigated using desktop research and a survey questionnaire in this study. This study examines how Micro-Credentials are used in Malaysia's top three public universities, as well as a poll of Malaysian students' Micro-Credentials habits. Micro-credentialing appears to be gaining popularity at Malaysian universities. The research will be broadened to collect and analyse data for the preliminary study, which will focus on learners' attainment of Digital Badges for Micro Credential Computing Courses using a quantitative research technique