Examination timetabling using genetic algorithm case study: KUiTTHO

Genetic Algorithm (GA) is one of the most popular optimization solutions. It has been implemented in various applications such as scheduling. The flows of GA are using selection, crossover and mutation operators applied to populations of chromosomes. This paper reports the powerful techniques using GA in scheduling. Examination timetabling problem is one of the applications in scheduling. In one aspect, it deals with students such that it fulfils the process time slot. These aspects are important for the examination can be done in a smooth way and no students can sit more than one exam in a same time slot. The other constraint is the student workload should be arranged less than three exams in a row. The examination timetabling problem at Kolej Universiti Teknologi Tun Hussein Onn (KUiTTHO) is introduced and the prototype has been developed using Java language. The prototype suggested several feasible solutions to the user

Examination Timetabling Using Genetic Algorithm Case Study : KUiTTHO

Genetic Algorithm (GA) is one of the most popular optimization solutions. It has been implemented in various applications such as scheduling. The flows of GA are using selection, crossover and mutation operators applied to populations of chromosomes. This paper reports the powerful techniques using GA in scheduling. Examination timetabling problem is one of the applications in scheduling. In one aspect, it deals with students such that it fulfils the process time slot. These aspects are important for the examination can be done in a smooth way and no students can sit more than one exam in a same time slot. The other constraint is the student workload should be arranged less than three exams in a row. The examination timetabling problem at Kolej Universiti Teknologi Tun Hussein Onn (KUiTTHO) is introduced and the prototype has been developed using Java language. The prototype suggested several feasible solutions to the user

Workplace safety risk assessment model based on fuzzy regression

Regulating safety and health in a workplace is crucial for any industry. It makes measuring a level of risk to characterize hazards in a workplace is a necessary. A systematic risk assessment in a workplace is capable to evaluate the level of risk which might occur. The assessment of risk in workplace regularly is performed by several identified attributes. At present, quantitative risk assessment uses crisp value in its evaluation. However, risk assessment process is exposed to uncertain information, due to human evaluation which uses linguistic value and is difficult to translate into precise numerical value. It makes the risk assessment process in workplace is imprecise. Thus, a robust fuzzy regression is introduced in this paper to determine the fuzzy weights of assessment attribute and build a robust fuzzy assessment model. This is important to identify the relationship among attributes, and helps the examiners to conduct a proper assessment in uncertain environment. A triangular fuzzy number is used to present the fuzzy judgment. An explanatory example is included to show the working procedure. The result indicates that the proposed model is beneficial to facilitate the decision model in evaluating risk, and specify excellent choice under the presence of uncertainty

Comparative analysis on bayesian classification for breast cancer problem

The problem of imbalanced class distribution or small datasets is quite frequent in certain fields especially in medical domain. However, the classical Naive Bayes approach in dealing with uncertainties within medical datasets face with the difficulties in selecting prior distributions, whereby parameter estimation such as the maximum likelihood estimation (MLE) and maximum a posteriori (MAP) often hurt the accuracy of predictions. This paper presents the full Bayesian approach to assess the predictive distribution of all classes using three classifiers; naïve bayes (NB), bayesian networks (BN), and tree augmented naïve bayes (TAN) with three datasets; Breast cancer, breast cancer wisconsin, and breast tissue dataset. Next, the prediction accuracies of bayesian approaches are also compared with three standard machine learning algorithms from the literature; K-nearest neighbor (K-NN), support vector machine (SVM), and decision tree (DT). The results showed that the best performance was the bayesian networks (BN) algorithm with accuracy of 97.281%. The results are hoped to provide as base comparison for further research on breast cancer detection. All experiments are conducted in WEKA data mining tool

Plant Watering Management System using Fuzzy Logic Approach in Oil Palm Nursery

Plant watering is an important part of a nursery production. In oil palm plantation, the nursery is the basis to produce healthy seedlings. In nurseries, several plants of different stages of their growth are raised. Therefore, timely and right amount supply of water is essential. Thus, a proper arrangement should be made to meet the water requirement of a nursery. Nursery supervisor may face difficulties to acquire the exact amount of water requirement needed by plants due to factors like rainfall and watering time. The decision to determine the amount of required water is crucial to avoid excessive or inadequate water supply, which gives bad effects to the plant’s growth. Therefore, Water Management System based on fuzzy approach is introduced to help nursery supervisor to manage the watering system in the nursery appropriately. External factors of rainfall and watering time are used to determine sufficient amount of water based on fuzzy logic approach. Nursery supervisor can view watering simulation, which shows the watering status of each plant bed in the nursery. This system emphasizes its benefit to assist nursery supervisor to manage and monitor a watering task for a better nursery management

Combining Deep Learning Models for Enhancing the Detection of Botnet Attacks in Multiple Sensors Internet of Things Networks

Distributed-Denial-of-Service impacts are undeniably significant, and because of the development of IoT devices, they are expected to continue to rise in the future. Even though many solutions have been developed to identify and prevent this assault, which is mainly targeted at IoT devices, the danger continues to exist and is now larger than ever. It is common practice to launch denial of service attacks in order to prevent legitimate requests from being completed. This is accomplished by swamping the targeted machines or resources with false requests in an attempt to overpower systems and prevent many or all legitimate requests from being completed. There have been many efforts to use machine learning to tackle puzzle-like middle-box problems and other Artificial Intelligence (AI) problems in the last few years. The modern botnets are so sophisticated that they may evolve daily, as in the case of the Mirai botnet, for example. This research presents a deep learning method based on a real-world dataset gathered by infecting nine Internet of Things devices with two of the most destructive DDoS botnets, Mirai and Bashlite, and then analyzing the results. This paper proposes the BiLSTM-CNN model that combines Bidirectional Long-Short Term Memory Recurrent Neural Network and Convolutional Neural Network (CNN). This model employs CNN for data processing and feature optimization, and the BiLSTM is used for classification. This model is evaluated by comparing its results with three standard deep learning models of CNN, Recurrent Neural Network (RNN), and long-Short Term Memory Recurrent Neural Network (LSTM–RNN). There is a huge need for more realistic datasets to fully test such models' capabilities, and where N-BaIoT comes, it also includes multi-device IoT data. The N-BaIoT dataset contains DDoS attacks with the two of the most used types of botnets: Bashlite and Mirai. The 10-fold cross-validation technique tests the four models. The obtained results show that the BiLSTM-CNN outperforms all other individual classifiers in every aspect in which it achieves an accuracy of 89.79% and an error rate of 0.1546 with a very high precision of 93.92% with an f1-score and recall of 85.73% and 89.11%, respectively. The RNN achieves the highest accuracy among the three individual models, with an accuracy of 89.77%, followed by LSTM, which achieves the second-highest accuracy of 89.71%. CNN, on the other hand, achieves the lowest accuracy among all classifiers of 89.50%