Protein expression of Late Elongated Hypocotyl (LHY) homolog genes of teak in Escherichia coli

Expression of an isolated gene in a system that directly translates it into a protein is an important step to study the protein encoded by the gene. The isolated gene can be expressed in vivo by a heterologous system. In this study, a bacteria system was used to translate the Tectona grandis Late Elongated Hypocotyl (Tg-LHY) gene, which was isolated from flowering tissues of teak (Tectona grandis). The gene was cloned into the pET 14b vector (Novagen) and transformed into BL 21(DE3)/pLysS and Rosetta 2 expression host cells (Novagen). Rosetta 2 host cell has been found to be a good candidate to express the Tg-LHY protein from plant origin, as it recognizes the codon that was found in plant but rarely used in bacteria. The expressed protein was about an expected size, which was 90 kD. Western blot analysis using antibody against His-tag, which was fused to the Tg-LHY protein, proved that the expressed protein was Tg-LHY protein

A fuzzy logic approach to manage uncertainty and improve the prediction accuracy in student model design

The intelligent tutoring systems (ITSs) are special classes of e-learning systems developed using artificial intelligent (AI) techniques to provide adaptive and personalized tutoring based on the individuality of each student. For an intelligent tutoring system to provide an interactive and adaptive assistance to students, it is important that the system knows something about the current knowledge state of each student and what learning goal he/she is trying to achieve. In other words, the ITS needs to perform two important tasks, to investigate and find out what knowledge the student has and at the same time make a plan to identify what learning objective the student intends to achieve at the end of a learning session. Both of these processes are modeling tasks that involve high level of uncertainty especially in situations where students are made to follow different reasoning paths and are not allowed to express the outcome of those reasoning in an explicit manner. The main goal of this paper is to employ the use Fuzzy logic technique as an effective and sound computational intelligence formalism to handle reasoning under uncertainty which is one major issue of great concern in student model design

Improving anomalous rare attack detection rate for intrusion detection system using support vector machine and genetic programming

Commonly addressed problem in intrusion detection system (IDS) research works that employed NSL-KDD dataset is to improve the rare attacks detection rate. However, some of the rare attacks are hard to be recognized by the IDS model due to their patterns are totally missing from the training set, hence, reducing the rare attacks detection rate. This problem of missing rare attacks can be defined as anomalous rare attacks and hardly been solved in IDS literature. Hence, in this letter, we proposed a new classifier to improve the anomalous attacks detection rate based on support vector machine (SVM) and genetic programming (GP). Based on the experimental results, our classifier, GPSVM, managed to get higher detection rate on the anomalous rare attacks, without significant reduction on the overall accuracy. This is because, GPSVM optimization task is to ensure the accuracy is balanced between classes without reducing the generalization property of SVM

Students' Perceptions towards the Use of Quizziz as A Tool in Improving Reading Skills

The use of various teaching and learning applications is now significant during online distance learning in the course of diversifying teaching approaches to help retain students’ focus and attract students’ interest in English language learning. With increasingly rapid technological advancement, Quizizz, an interactive quiz application is one of the current teaching and learning tools that can be used in classrooms to improve students’ reading skills. Educators can easily integrate the use of this application in the process of teaching to help build students’ language skills. Specifically focusing on skimming and scanning skills, Quizizz allows students to read and simultaneously practice both skills in an interactive way. The use of both linear and non-linear text can also be embedded in the application thus providing a variety in the activity designed. This study aimed to investigate UiTM diploma students’ perceptions toward using Quizizz as a tool in improving students’ reading skills. Consequently, an innovative speed-reading contest was designed in testing the effectiveness of Quizizz in helping students to apply the reading skills namely skimming and scanning that have been taught in class. The outcomes of this study are expected to provide educators with insights on the usage of Quizizz as an alternate learning media that is good for both educators and students in aiding teaching and learning proces

Solving classification problem using ensemble binarization classifier

Binarization strategy is broadly applied in solving various multi-class classification problems. However, the classifier model learning complexity tends to increase when expanding the number of problems into several replicas. One-Versus-All (OVA) is one of the strategies which transforming the ordinal multi-class classification problems into a series of two-class classification problems. The final output from each classifier model is combined in order to produce the final prediction. This binarization strategy has been proven as superior performance in accuracy than ordinal multi-class classifier model. However, learning model complexity (eg. Random Forest-RF ensemble decision trees) tends to increase when employing a large number of trees. Even though a large number of trees might produce a decent accuracy, generating time of the learning model is significantly longer. Hence, self-tuning tree parameter is introduced to tackle this matter. In such circumstances, a number of trees in the RF classifier are defined according to the number of class problem. In this paper, the OVA with self-tuning is evaluated based on parameter initialization in the context of RF ensemble decision tree. At the same time, the performance has also been compared with two classifier models such J48 and boosting for several well-known datasets

Two-stage feature selection using ranking self-adaptive differential evolution algorithm for recognition of acceleration activity

Widespread research on activity recognition is becoming an imperative topic for improving the quality of human health. The fast development of sensing technology has become a fundamental platform for researchers to implement a system that could fulfill human needs. Due to privacy interests and low cost, wearable sensing technology is used in numerous physical activity monitoring and recognition systems. While these systems have proved to be successful, it is crucial to pay attention to the less relevant features to be classified. In such circumstances, it might happen that some features are less meaningful for describing the activity. Less complex and easy to understand, feature ranking is gaining a lot of attention in most feature dimension problems such as in bioinformatics and hyperspectral images. However, the improvement of ranking features in activity recognition has not yet been achieved. On the other hand, an evolutionary algorithm has proven its effectiveness in searching the best feature subsets. An exhaustive searching process of finding an optimal parameter value is another challenge. Consequently, this paper proposes a ranking self-adaptive differential evolution (rsaDE) feature selection algorithm. The proposed algorithm is capable of selecting the optimal feature subsets while improving the recognition of acceleration activity using a minimum number of features. The experiments employed real-world physical acceleration data sets: WISDM and PAMAP2. As a result, rsaDE performed better than the current methods in terms of model performance and its efficiency in the context of random forest ensemble classifiers

Database workload management through CBR and fuzzy based characterization

Database Management System (DBMS) is used as a data source with financial, educational, web and other applications from last many years. Users are connected with the DBMS to update existing records and retrieving reports by executing workloads that consist of complex queries. In order to get the sufficient level of performance, arrangement of workloads is necessary. Rapid growth in data, maximum functionality and changing behavior tends the database workload to be more complex and tricky. Each DBMS experiences complex workloads that are difficult to manage by the humans; human experts take much time to manage database workload efficiently; even in some cases it may become impossible and leads toward malnourishment. This problem leads database practitioners, vendors and researchers toward new challenges. To achieve a satisfactory level of performance, either Database Administrator (DBA) or DBMSs must have the knowledge about the workload shifts. Efficient execution and resource allocation of workload is dependent on the workload type that may be either On Line Transaction Processing (OLTP) or Decision Support System (DSS). The research introduces a way to manage the workload in DBMSs on the basis of the workload type. The main goal of the research is to manage the workload in DBMSs through characterization, scheduler and idleness detection modules. The database workload management is performed by using the case based reasoning characterization; Fuzzy logic based scheduling and finally detection of CPU Idleness. Results are validated through experiments that are performed on real time and benchmark workload to reveal effectiveness and efficiency

Estimation of outcrossing rates in Koompassia malaccensts from an open-pollinated population in Peninsular Malaysia using microsatellite markers

Koompassia malaccensis (Leguminosae), locally known as kempas, is an important tropical timber species in South-East Asia. Although studies have shown that most tropical tree species are predominantly outcrossing, there is no empirical support for this species prior to this study, with regard to its mating system. Information on its reproductive biology is also scanty. We report the estimation of the outcrossing rates of K. malaccensis using microsatellite markers, based on a fruiting season at the Semangkok Forest Reserve, Selangor. Microsatellite analysis was performed for an average of 46 seeds each from nine adult K. malaccensis trees, using four polymorphic microsatellite loci (Kma050, Kma067, Kma147 and Kma180). Single and multilocus population outcrossing estimates (ts and tm respectively) were determined using the software MLTR version 3.0. Results showed that this timber species was predominantly outcrossing (tm = 0.890). Biparental mating (tm – ts) was very low, only 0.026, suggesting low tendency of mating between relatives. Outcrossing estimates obtained for individual mother trees were in the range of 0.637 to 0.994. The relatively lower outcrossing rates exhibited by a few progeny arrays indicated that K. malaccensis was not completely self-incompatible

A new classification model for a class imbalanced data set using genetic programming and support vector machines: case study for wilt disease classification

Class imbalanced data set is a state where each class of the given data set is not evenly distributed. When such case happens, most standard classifiers fail to recognize examples that belong to a minority class. Hence, several methods have been proposed to solve this problem such as resampling, modification on classifier optimization problem or introducing a new optimization task on top of the classifier. This work proposes a new optimization task based on genetic programming, built on top of support vector machine, in order to improve the classification rate for minority class without significant reduction on accuracy metric. The experimentation carried out on wilt disease data set shows the new classifier, support vector based on genetic programming machine, gives a more balanced accuracy between classes compared to various classification techniques in solving the imbalanced classification problem

Activity recognition based on accelerometer sensor using combinational classifiers

In recent years, people nowadays easily to contact each other by using smartphone. Most of the smartphone now embedded with inertial sensors such accelerometer, gyroscope, magnetic sensors, GPS and vision sensors. Furthermore, various researchers now dealing with this kind of sensors to recognize human activities incorporate with machine learning algorithm not only in the field of medical diagnosis, forecasting, security and for better live being as well. Activity recognition using various smartphone sensors can be considered as a one of the crucial tasks that needs to be studied. In this paper, we proposed various combination classifiers models consists of J48, Multi-layer Perceptron and Logistic Regression to capture the smoothest activity with higher frequency of the result using vote algorithm. The aim of this study is to evaluate the performance of recognition the six activities using ensemble approach. Publicly accelerometer dataset obtained from Wireless Sensor Data Mining (WISDM) lab has been used in this study. The result of classification was validated using 10-fold cross validation algorithm in order to make sure all the experiments perform well