Search

Computational thinking (CT) has been promoted worldwide by educational systems and is an essential skill for technological citizens. Various strategies have been planned and developed to help in introducing, improving, and delivering CT. One of the strategies is by creating and developing the supporting tools for CT learning. In this article, educational robotics (ER) is chosen as the focus tool to support CT learning. Each CT and ER has a massive field of study. There are various available reports determining the suitability of CT subject integrated with ER for students' learning. However, all students do not develop similar style of learning and thinking. There is difference in their personal traits. There is a lack of research that designed CT learning through ER specifically based on student's preferences. Besides, it resulted in a challenge to determine the suitability of CT and ER for different kind of preferences. Therefore, this study aimed to develop an adaptive learning (AL) framework for students to deliver learning of CT through ER. The framework consists of three submodels: domain model, student model, and adaptation model. One case study is defined, which is learning the introductory level of CT through ER (CTER). At the end of the study, it can be observed that the AL framework produced positive results in performance and perception for various student categories. It was noted that students utilizing the AL framework had superior understanding of CTER. Individually or collaboratively, all students who applied or did not apply the AL framework in studying the CTER introduction had positive learning outcomes. [ABSTRACT FROM AUTHOR]

The purpose of this study is to determine the optimal eye aspect ratio (EAR) for a prototype capable of using computer vision techniques to detect driver sleepiness based on eyelid size changes. The prototype, developed with Raspberry Pi and OpenCV, provides a real-time evaluation of the driver's level of alertness. The prototype can accurately determine the onset of sleepiness by monitoring and detecting instances of prolonged eyelid closure. Due to the fact that the eye aspect ratios of different individuals vary in size, the system's accuracy may be compromised. For the first experiment, the research focuses on determining the optimal EAR threshold of the proposed prototype using a sample of 20 participants ranging in age from 20 to 30, 31 to 40, and 41 to 50 years old. The study also examines the effects of various environmental conditions, such as dark or nighttime settings and the use of spectacle. The optimal EAR threshold value, as dedicated by the first experiment, is 0.225 after testing 20 participants with and without eyeglasses in low and bright lighting and 7 participants with a 0.225 EAR threshold in dark and bright lighting environments. The result shows 100% precision. [ABSTRACT FROM AUTHOR]

Automatic text summarization is one of the most promising solutions to the ever-growing challenges of textual data as it produces a shorter version of the original document with fewer bytes, but the same information as the original document. Despite the advancements in automatic text summarization research, research involving the development of automatic text summarization methods for documents written in Hausa, a Chadic language widely spoken in West Africa by approximately 150,000,000 people as either their first or second language, is still in early stages of development. This study proposes a novel graph-based extractive single-document summarization method for Hausa text by modifying the existing PageRank algorithm using the normalized common bigrams count between adjacent sentences as the initial vertex score. The proposed method is evaluated using a primarily collected Hausa summarization evaluation dataset comprising of 113 Hausa news articles on ROUGE evaluation toolkits. The proposed approach outperformed the standard methods using the same datasets. It outperformed the TextRank method by 2.1%, LexRank by 12.3%, centroid-based method by 19.5%, and BM25 method by 17.4%. [ABSTRACT FROM AUTHOR]

The integration of microarray technologies and machine learning methods has become popular in predicting the pathological condition of diseases and discovering risk genes. Traditional microarray analysis considers pathways as a simple gene set, treating all genes in the pathway identically while ignoring the pathway network's structure information. This study proposed an entropy-based directed random walk (e-DRW) method to infer pathway activities. Two enhancements from the conventional DRW were conducted, which are (1) to increase the coverage of human pathway information by constructing two inputting networks for pathway activity inference, and (2) to enhance the gene-weighting method in DRW by incorporating correlation coefficient values and t-test statistic scores. To test the objectives, gene expression datasets were used as input datasets while the pathway datasets were used as reference datasets to build two directed graphs. The within-dataset experiments indicated that e-DRW method demonstrated robust and superior performance in terms of classification accuracy and robustness of the predicted risk-active pathways compared to the other methods. In conclusion, the results revealed that e-DRW not only improved the prediction performance, but also effectively extracted topologically important pathways and genes that were specifically related to the corresponding cancer types. [ABSTRACT FROM AUTHOR]

The volume of digital text data is continuously increasing both online and offline storage, which makes it difficult to read across documents on a particular topic and find the desired information within a possible available time. This necessitates the use of technique such as automatic text summarization. Many approaches and algorithms have been proposed for automatic text summarization including; supervised machine learning, clustering, graph-based and lexical chain, among others. This paper presents a novel systematic review of various graph-based automatic text summarization models. [ABSTRACT FROM AUTHOR]

Computational thinking (CT) is useful in the daily problemsolving process while educational robotic (ER) is well known as a pedagogical tool in attracting students to learn more in problem-solving activities. Both subjects are widely used for formal learning and informal learning regardless of the age and gender of the learners. However, there is a lack of studies in integrating CT into ER and both have big dimensions in learning and teaching. Thus, nurturing CT through ER remains a challenge. This study focuses on designing a conceptual model of the integration between CT and ER. Qualitative analysis is done for this research where grounded theory analysis (GTA) is used to analyze CT and ER from various sources such as literature, book, and survey to make the model more relevant and fit to the education. The finding represents a final conceptual model which has been evaluated by the expert. The final conceptual model detailed the relationship between CT and ER besides giving benefits to the community who are planning, designing, or revising a new framework or platform in nurturing CT through ER. [ABSTRACT FROM AUTHOR]

Early diagnosis methods in cancer diagnosis studies are making great challenge as they require the involvement of different fields. Deoxyribonucleic acid (DNA) microarray analysis is one of the modern cancer diagnosis techniques used by scientists to measure the gene expression level changes in gene expression data. From the perspective of computing, an algorithm can be developed to identify more difficult cases. Numerous cancer studies have combined different machine learning techniques for the cancer diagnosis. This study is conducted to improve the cancer diagnosis technique, directed random walk (DRW) from the direction of framework. Improved directed random walk framework is proposed with the new introduced sub-algorithms, a larger directed graph and a different classifier. It is named as significant directed walk (SDW). In this study, six gene expression datasets are applied to study the effectiveness of the sub-algorithm, directed graph and classifier in SDW in terms of cancer prediction and cancer classification. Sub-algorithms of SDW can be further divided into data pre-processing phase, specific tuning parameter selection, weight as additional variable, and exclusion of unwanted adjacency matrix. Besides that, SDW also incorporated four directed graphs to study the usability of the directed graph. The best directed graph among the four is chosen to be part of the structure in SDW. The experimental results showed that the combination of SDW with walker network and linear regression is the best among all. SDW is achieves accuracy of 95.03% in average which is higher by 8.97% compare to conventional DRW for all cancer datasets. This study provides a foundation for further studies and research on early diagnosis of cancer with machine learning technique. It is found that these findings would improve the early diagnosis methods of cancer classification. [ABSTRACT FROM AUTHOR]

The selection and classification of genes is essential for the identification of related genes to a specific disease. Developing a user-friendly application with combined statistical rigor and machine learning functionality to help the biomedical researchers and end users is of great importance. In this work, a novel stand-alone application, which is based on graphical user interface (GUI), is developed to perform the full functionality of gene selection and classification in high dimensional datasets. The so-called HDG-select application is validated on eleven high dimensional datasets of the format CSV and GEO soft. The proposed tool uses the efficient algorithm of combined filter-GBPSO-SVM and it was made freely available to users. It was found that the proposed HDG-select outperformed other tools reported in literature and presented a competitive performance, accessibility, and functionality. [ABSTRACT FROM AUTHOR]

Extracting the usability requirements are crucial during requirements review and requirements validation for different purposes. Usability requirements are hard to be determined until the real user has experienced the software. It is even more challenging when these usability requirements are documented in natural language, which has an inconsistent and unrestricted structure. Automated requirements extraction has been widely studied to facilitate the process of requirements checking. Nevertheless, the accuracy of requirements extraction method still can be improved. Thus, this paper has presented the usability keywords repository that followed the ISO 9126 and ISO 25010 usability category and has gone through the expert validation process. The usability requirement extraction method is moreover enhanced with extra procedures in conforming the usability requirement statement. First, each statement in the requirement document is checked if there is a keyword usability, whereby the keywords used must match with the usability keyword repository. In order to ensure that the selected statement is a usability requirement, the corresponding usability keyword position should be after the fixed auxiliary verbs. The performance of the improved usability requirements extraction method is then evaluated using precision, recall and accuracy. [ABSTRACT FROM AUTHOR]

Numerous cancer studies have combined different datasets for the prognosis of patients. This study incorporated four networks for significant directed random walk (sDRW) to predict cancerous genes and risk pathways. The study investigated the feasibility of cancer prediction via different networks. In this study, multiple micro array data were analysed and used in the experiment. Six gene expression datasets were applied in four networks to study the effectiveness of the networks in sDRW in terms of cancer prediction. The experimental results showed that one of the proposed networks is outstanding compared to other networks. The network is then proposed to be implemented in sDRW as a walker network. This study provides a foundation for further studies and research on other networks. We hope these finding will improve the prognostic methods of cancer patients. [ABSTRACT FROM AUTHOR]

In this work, gene expression in autism spectrum disorder (ASD) is analyzed with the goal of selecting the most attributed genes and performing classification. The objective was achieved by utilizing a combination of various statistical filters and a wrapper-based geometric binary particle swarm optimization-support vector machine (GBPSO-SVM) algorithm. The utilization of different filters was accentuated by incorporating a mean and median ratio criterion to remove very similar genes. The results showed that the most discriminative genes that were identified in the first and last selection steps included the presence of a repetitive gene (CAPS2), which was assigned as the gene most highly related to ASD risk. The merged gene subset that was selected by the GBPSO-SVM algorithm was able to enhance the classification accuracy. [ABSTRACT FROM AUTHOR]

As a context of this research, combinatorial testing (CT) in simplifications of the parameter interactions strategy is to grade or rank the best algorithm in order to eliminated the parameter interaction on various crucial issue. These will become the most interesting research finding so as to enhance the efficiency of the test cases generations. The objective of this research is to identify and analyze existing combinatorial testing technique in the context of the formulated research questions. Search terms with relevant keywords were used to identify primary research that was related to combinatorial testing technique classified under journal articles, conference papers, workshops, symposiums, book chapters and IEEE bulletins. In the end of this research, we provided a primarily result of comparison on efficiency of the simplifications strategy in order to enhance the combinatorial testing technique. [ABSTRACT FROM AUTHOR]

Poverty in Malaysia is a controversial economic issue. Although poverty alleviation strategies in Malaysia have been acclaimed success by United Nation Development Programme (UNDP, 2013), new form of poverty emerged in urban area as a result of rapid economic growth and development. Poverty is a multifaceted phenomenon and different societies have different perceptions of poverty. These will led to uncertainty in determining of poverty. Most of welfare institutions in Malaysia measure poverty from the monetary perspective using monthly income or expenditure. In practice, conventional institutions such as Jabatan Kebajikan Masyarakat Malaysia (JKMM) use monetary approach in determining poverty through the Poverty Line Income (PLI). While, Islamic institutions adopt the monetary approach in determining poverty using Had Al-Kifayah (HAK). The objective of this paper is to explain the concept and analyze the uncertainty factors that have contributed to the incidence of poverty in urban area using PLI and HAK method. This study would highlight the similarities and differences of both the methods. A survey aided by a structured questionnaire was carried out on 300 selected households in the state of Kuala Lumpur and 150 household datasets are obtained from Department of Zakat, Islamic Center, UTM, Johor. This empirical study will able to use in designing case representation for case-based reasoning that will be implemented in future work. [ABSTRACT FROM AUTHOR]

Satellite images taken in hazy conditions tend to have low brightness and contrast, and to address this problem, a simple and optimised method using near-infrared (NIR) histogram equalisation has been proposed, in order to improve the brightness and contrast of the SPOT-5 images taken by satellite. This helps to improve the detection of shoreline with a higher efficiency in edge detection, and produces results with a high success rate of more than 90%. In order to remove haze from SPOT-5 images, a false colour composite is used to prevent misclassified pixels of the shoreline, and ensure minimal error in image classification. The presence of an NIR channel is suitable for the detection of vegetation and waterbodies. The NIR channel is important for this study, in ensuring that contrast between vegetated areas and sea can be clarified, due to the high reflectance of vegetation leaves in the NIR wavelength region. This research used three areas of interest to show different results in shoreline detection. The results showed that the proposed method performed better than existing methods, when dealing with the low brightness or contrast ratio of SPOT-5 images. [ABSTRACT FROM PUBLISHER]

Highlights: [•] To analyze the exact pattern of motion-blurred iris. [•] Fusion of homomorphic filtering and multiscale retinex enhanced iris pattern. [•] The processed image will be free of shadows and the iris pattern enhanced. [•] Shadow-free image allow to achieve good recognition rate. [Copyright &y& Elsevier]

Objectives: To design and develop a classifier, named Sewing Driving Training based Optimization-Deep Residual Network (SDTO_DRN) for hand gesture recognition., Methods: The electrical activity of forearm muscles generates the signals that can be captured with Surface Electromyography (sEMG) sensors and includes meaningful data for decoding both muscle actions and hand movement. This research develops an efficacious scheme for hand gesture recognition using SDTO_DRN. Here, signal pre-processing is done through Gaussian filtering. Thereafter, desired and appropriate features are extracted. Following that, effective features are chosen using SDTO. At last, hand gesture identification is accomplished based on DRN and this network is effectively fine-tuned by SDTO, which is a combination of Sewing Training Based Optimization (STBO) and Driving Training Based Optimization (DTBO). The datasets employed for the implementation of this work are MyoUP Dataset and putEMG: sEMG Gesture and Force Recognition Dataset., Results: The designed SDTO_DRN model has gained superior performance with magnificent results by delivering a maximum accuracy of 0.943, True Positive Rate (TPR) of 0.929, True Negative Rate (TNR) of 0.919, Positive Predictive Value (PPV) of 0.924, and Negative Predictive Value (NPV) of 0.924., Conclusions: The hand gesture recognition using the proposed model is accurate and improves the effectiveness of the recognition., (© 2023 Walter de Gruyter GmbH, Berlin/Boston.)

Numerous cancer studies have combined different datasets for the prognosis of patients. This study incorporated four networks for significant directed random walk (sDRW) to predict cancerous genes and risk pathways. The study investigated the feasibility of cancer prediction via different networks. In this study, multiple micro array data were analysed and used in the experiment. Six gene expression datasets were applied in four networks to study the effectiveness of the networks in sDRW in terms of cancer prediction. The experimental results showed that one of the proposed networks is outstanding compared to other networks. The network is then proposed to be implemented in sDRW as a walker network. This study provides a foundation for further studies and research on other networks. We hope these finding will improve the prognostic methods of cancer patients.

To date, classification of structural class using local protein structure rather than the whole structure has been gaining widespread attention. It is noted that the structural class lies in local composition or arrangement of secondary structure, while the threshold-based classification method has restricted rules in determining these structural classes. As a consequence, some of the structures are unknown. In order to determine these unknown structural classes, we propose a fusion algorithm, abbreviated as GSVM-SigLpsSCPred (Granular Support Vector Machine--with Significant Local protein structure for Structural Class Prediction), which consists of two major components, which are: optimal local protein structure to represent the feature vector and granular support vector machine to predict the unknown structural classes. The results highlight the performance of GSVM-SigLpsSCPred as an alternative computational method for low-identity sequences.