Determination Factors of Roadside Tree Species Selection Model for Sustainable Smart City

This study aimed to determine the factors that are essential in developing Malaysian Roadside tree selection species model for a sustainable smart city. Two objectives have been formulated; (i) to identify the current practices in selecting roadside trees and (ii) to explore the factors affecting in developing roadside tree species model. The methodology used in the study is in-depth interviews and collecting archival data. Thirty of landscape architects and related expertise will be sorted by random sampling at Klang Valley area. The study emphasised the consideration of landscape, arboriculture, forestry and academician practices that consider the long-term benefits and impacts of planting roadside trees. The findings of this study provide valuable insights into the factors that should be considered when selecting tree species for roadside planting in city areas. Fifteen (15) important factors has been identified that is size and growth habit, native and local species, adaptability, maintenance and requirements, wind resistance, non-invasive roots, canopy density, soil requirements, aesthetic value, wildlife support, cultural significance, stakeholder input, longevity, urban tolerance, pest and disease resistance. The results can be used to guide the related parties and promote sustainable development in cities

Patterns of streets connection for sustainable urban development in Kota Bharu, Kelantan, Malaysia

Street network attributes, including street connectivity, street integration, and pedestrian accessibility, are closely interrelated concepts that play an important role in achieving the sustainable approach challenges facing all large cities throughout the world. However, the lack of awareness of sustainable development in both the city center and villages in the suburban area results in a low value of street connectivity, less direct route to the destination, and discouraging pedestrians from moving. This research aims to examine street connectivity and street integration patterns that form from street connections at existing streets in Kota Bharu Kelantan. DepthmapX software was used to analyze street connectivity and integrations to identify and compare the existing street influencing people to move from one destination to another in Kota Bharu, Kelantan. The streets in the Kota Bharu, Kelantan city center area are chosen as the study site to investigate the street connectivity and integration values. Data analysis using DepthmapX software was performed after digitizing the map in AutoCAD software. Findings show that street connectivity and integration are of higher value when the streets are well connected to other streets in the main area and attraction area. The conclusions of this paper can help landscape architects and urban planners optimize the achievement of well-connected street networks that produce directness routes in short-distance destinations to develop a sustainable urban environment

Vision-Based Apple Classification for Smart Manufacturing

Smart manufacturing enables an efficient manufacturing process by optimizing production and product transaction. The optimization is performed through data analytics that requires reliable and informative data as input. Therefore, in this paper, an accurate data capture approach based on a vision sensor is proposed. Three image recognition methods are studied to determine the best vision-based classification technique, namely Bag of Words (BOW), Spatial Pyramid Matching (SPM) and Convolutional Neural Network (CNN). The vision-based classifiers categorize the apple as defective and non-defective that can be used for automatic inspection, sorting and further analytics. A total of 550 apple images are collected to test the classifiers. The images consist of 275 non-defective and 275 defective apples. The defective category includes various types of defect and severity. The vision-based classifiers are trained and evaluated according to the K-fold cross-validation. The performances of the classifiers from 2-fold, 3-fold, 4-fold, 5-fold and 10-fold are compared. From the evaluation, SPM with SVM classifier attained 98.15% classification accuracy for 10-fold and outperformed the others. In terms of computational time, CNN with SVM classifier is the fastest. However, minimal time difference is observed between the computational time of CNN and SPM, which were separated by only 0.05 s

Investigation of fusion features for apple classification in smart manufacturing

Smart manufacturing optimizes productivity with the integration of computer control and various high level adaptability technologies including the big data evolution. The evolution of big data offers optimization through data analytics as a predictive solution in future planning decision making. However, this requires accurate and reliable informative data as input for analytics. Therefore, in this paper, the fusion features for apple classification is investigated to classify between defective and non-defective apple for automatic inspection, sorting and further predictive analytics. The fusion features with Decision Tree classifier called CurveletWavelet-Gray Level Co-occurrence Matrix (CW-GLCM) is designed based on symmetrical pattern. The CW-GLCM is tested on two apple datasets namely NDDA and NDDAWwith a total of 1110 apple images. Each dataset consists of a binary class of apple which are defective and non-defective. The NDDAW consists more low-quality region images. Experimental results show that CW-GLCM successfully classify 98.15% of NDDA dataset and 89.11% of NDDAW dataset. A lower classification accuracy is observed in other five existing image recognition methods especially on NDDAW dataset. Finally, the results show that CW-GLCM is more accurate among all the methods with the difference of more than 10.54% of classification accuracy. © 2019 by the authors

Affective computing in education: A systematic review and future research

It is becoming a trend to apply an emotional lens and to position emotions as central to educational interactions. Recently, affective computing has been one of the most actively research topics in education, attracting much attention from both academics and practitioners. However, despite the increasing number of papers published, there still are deficiencies and gaps in the comprehensive literature review in the specific area of affective computing in education. Therefore, this study presents a review of the literature on affective computing in education by selecting articles published from 2010 to 2017. A review protocol consisting of both automatic and manual searches is used to ensure the retrieval of all relevant studies. The final 94 selected papers are reviewed and relevant information extracted based on a set of research questions. This study classifies selected articles according to the research purposes, learning domains, channels and methods of affective recognition and expression, and emotion theories/models as well as the emotional states. The findings show the increased number and importance of affective computing studies in education domain in recent years. The research purposes of most affective computing studies are found to be designing emotion recognition and expression systems/methods/instruments as well as examining the relationships among emotion, motivation, learning style, and cognition. Affective measurement channels are classified into textual, visual, vocal, physiological, and multimodal channels, while the textual channel is recognized as the most widely-used affective measurement channel. Meanwhile, integration of textual and visual channels is the most widely-used multimodal channel in affective computing studies. Dimensional theories/models are the most preferred models for description of emotional states. Boredom, anger, anxiety, enjoyment, surprise, sadness, frustration, pride, hopefulness, hopelessness, shame, confusion, happiness, natural emotion, fear, joy, disgust, interest, relief, and excitement are reported as the top 20 emotional states in education domain. Finally, this study provides recommendations for future research directions to help researchers, policymakers and practitioners in the education sector to apply affective computing technology more effectively and to expand educational practices. © 2019 Elsevier Lt

Water-body segmentation in satellite imagery applying modified Kernel K-means

The main purpose of k-Means clustering is partitioning patterns into various homogeneous clusters by minimizing cluster errors, but the modified solution of k-Means can be recovered with the guidance of Principal Component Analysis (PCA). In this paper, the linear Kernel PCA guides k-Means procedure using filter to modify images in situations where some parts are missing by k-Means classification. The proposed method consists of three steps: 1) transformation of the color space and using PCA to solve the eigenvalue problem pertaining to the covariance matrices of satellite image; 2) feature extraction from selected eigenvectors and are rearranged by applying the training map to extract the useful information as a set of new orthogonal variables called principal components; and 3) classification of the images based on the extracted features using k-Means clustering. The quantitative results obtained using the proposed method were compared with k-Means and k-Means PCA techniques in terms of accuracy in extraction. The contribution of this approach is the modification of PCA selection to achieve more accurate extraction of the water-body segmentation in satellite images

Cloud-assisted gamification for education and learning – Recent advances and challenges

Gamification has gained considerable interest in education circles due to its capability of enhancing the learning process among students. In the future, it is expected that gamification will overtake the traditional way of learning resulting in issues such as scalability, upgradation of learning modules. To address these issues, merging gamification with cloud computing seems a viable solution. However, the employability of gamification through cloud computing is still in its infant stage. Hence, this article investigates the applicability of gamification through cloud computing and presents a comprehensive survey of state-of-the-art gamification in education and learning. We also identify the subject areas that can be gamified and taught using the cloud service. The critical elements and minimum requirements necessary to gamify education are also identified. Moreover, a specific cloud-assisted gamification architecture is proposed and discussed together with its possible applications. The article is concluded with the research challenges and suggestions for future work

The relationship between stingless bee and native plants studies

In the past decade, the benefits of stingless bee honey as an anticancer agent has increased in demand in Malaysia. Despite the increasing demand, the quality produced highly depends on a certain plant, Ixora spp. The practice of planting design in landscape development only by considering the aesthetic values leads to the poor productivity of stingless bee honey. Therefore, this review focuses on the suitability of native plants used in landscapes designed for stingless bees. This study employed thematic analysis related to the issues between native plants, stingless bee and landscape development. Based on the assessment, literature on stingless bees discussed issues related to its habitat in the tropical rain forest, the behaviour of finding food, characteristics of its honey and the benefits of consuming the honey. Meanwhile, studies on native plant demonstrated the use of plants in providing food and habitat to the local insects which contribute to the continuity of the species. As a conclusion, the quality of honey is dependent on the relationship between stingless bees and native plants. Hence, the native plants can be potentially used in planting design for the improvement of stingless bee honey production