·         Table of Contents (Volume #7, Issue #4)

·         Cover Page (Volume #7, Issue #4)

·         Editorial (Volume #7, Issue #4)

Paper #1

A Comparative Analysis of Community Detection Agglomerative Technique Algorithms and Metrics on Citation Network

Sandeep Kumar Rachamadugu and Pushphavathi Thotadara Parameshwarappa

Abstract: Social Network Analysis is a discipline that represents social relationships as a network of nodes and edges. The construction of social network with clusters will contribute in sharing the common characteristics or behaviour of a group. Partitioning the graph into modules is said to be a community. Communities are meant to symbolize actual social groups that share common characteristics. Citation network is one of the social networks with directed graphs where one paper will cite another paper and so on. Citation networks will assist the researcher in choosing research directions and evaluating research impacts. By constructing the citation networks with communities will direct the user to identify the similarity of documents which are interrelated to one or more domains. This paper introduces the agglomerative technique algorithms and metrics to a directed graph which determines the most influential nodes and group of similar nodes. The two stages required to construct the communities are how to generate network with communities and how to quantify the network performance. The strength and a quality of a network is quantified in terms of metrics like modularity, normalized mutual information (NMI), betweenness centrality, and F-Measure. The suitable community detection techniques and metrics for a citation graph were introduced in this paper. In the field of community detection, it is common practice to categorize algorithms according to the mathematical techniques they employ, and then compare them on benchmark graphs featuring a particular type of assortative community structure. The algorithms are applied for a sample citation sub data is extracted from DBLP, ACM, MAG and some additional sources which is taken from and consists of 101 nodes (nc) with 621 edges € and formed 64 communities. The key attributes in dataset are id, title, abstract, references SLM uses local optimisation and scalability to improve community detection in complicated networks. Unlike traditional methods, the proposed LS-SLM algorithm is identified that the modularity is increased by 12.65%, NMI increased by 2.31%, betweenness centrality by 3.18% and F-Score by 4.05%. The SLM algorithm outperforms existing methods in finding significant and well-defined communities, making it a promising community detection breakthrough.

Keywords: Citation Network; Community Detection; Directed Graph; Modularity; SLM.

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Paper #2

A Comparative Analysis of Design Principles for Integration in Wearable Persuasive Multimedia

Umi Hanim Mazlan and Siti Mahfuzah Sarif

Abstract: Many studies, to varying degrees, have confirmed the importance of persuasive approaches in wearable technology. Meanwhile, there are also a growing number of studies in persuasive multimedia, particularly in promoting awareness. Also, many studies reported on wearable multimedia, especially in game-based and VR/AR applications. Given the increasing emergence of these technologies, there is a need to integrate existing diverse research endeavours and consolidate them for improved planned effects on human attitude and behaviour, including one's awareness. However, a similar attempt to incorporate a triad of persuasive, multimedia and wearable design principles toward improved controllability awareness lacks empirical evidence. Here, this study explores the design principles of persuasive, multimedia and wearable technologies that can be leveraged into an integrated design model, especially in promoting controllability awareness of mental health issues. Moreover, this study believes exploring the potential integration of the design principles would significantly impact the application's effectiveness. Therefore, this study conducted a comparative analysis which involved 20 relevant studies pertinent to wearable design principles, persuasive design principles, and multimedia design principles. Furthermore, all identified studies were reviewed regarding the domain, the technology used, target outcomes, and utilisation of the design principles. As a result, this study discovered that many studies were on integrating persuasive and multimedia design principles and persuasive and wearable technologies. Therefore, the outcome of this study could be leveraged to incorporate all three design principles (i.e., wearable, persuasive technology, multimedia) into a conceptual model. The conceptual model is expected to produce a more effective result, especially in enhancing controllability awareness in the mental health domain.

Keywords: Multimedia Technology; Persuasive Technology; Wearable Persuasive Multimedia; Wearable Technology.

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Paper #3

Text Clustering of Tafseer Translations by Using k-means Algorithm: An Al-Baqarah Chapter View

Mohammed A. Ahmed, Hanif Baharin and Puteri NE. Nohuddin

Abstract: Al-Quran is Muslims’ main book of belief and behaviour. The Al-Quran is used as a reference book by millions of Muslims worldwide, and as such, it is useful for Muslims in general and Muslim academics to gain knowledge from it. Many translators have worked on the Quran’s translation into many different languages around the world, including English. Thus, every translator has his/her own perspectives, statements, and opinions when translating verses acquired from the (Tafseer) of the Quran. However, this work aims to cluster these variations among translations of the Tafseer by utilising text clustering. As a part of the text mining approach, text clustering includes clustering documents according to how similar they are. This study adapted the (k-means) clustering technique algorithm (unsupervised learning) to illustrate and discover the relationships between keywords called features or concepts for five different translators on the 286 verses of the Al-Baqarah chapter. The datasets have been preprocessed, and features extracted by applying TF-IDF (Term Frequency-Inverse Document Frequency). The findings show two/three-dimensional clustering plotting for the first two/three most frequent features assigned to seven cluster categories (k=7) for each of five translated Tafseer. The features ‘allah/god’, ‘believ’, and ‘said’ are the three most features shared by the five Tafseer.

Keywords: Al-Baqarah chapter; k-means algorithm; Text clustering; Text mining; Tafseer translation.

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Paper #4

SkCanNet: A Deep Learning based Skin Cancer Classification Approach

J.Andrew Onesimu, Varun Unnikrishnan Nair, Martin K. Sagayam, Jennifer Eunice, Mohd Helmy abd Wahab and Nor’Aisah Sudin

Abstract: Skin Cancer classification has been one of the most challenging problems for dermatologists; it is a tremendously tedious process to detect the kind of lesion/cancer form it is for just the human eye. Deep learning has become popular due to its potential to learn complex traits from the huge dataset. A prominent deep learning model for image categorization is the convolutional neural network (CNN). Many researchers have been conducted on the efficiency of CNN’s use to classify skin cancer forms. In this paper, the efficiency of VGG bottleneck features and transfer learning have been used on 3 kinds of skin cancers namely, (a) squamous cell carcinoma, (b) basal cell carcinoma and (c) melanoma. The proposed model comprises of VGG-16 NET and Transfer Learning with 2 fully-connected layers. The proposed model is experimented on 1077 dermoscopy images in total (MSK-1, UDA -1, UDA-2, HAM10000). The experimental analysis proves that the proposed model achieves higher values for accuracy, specificity and sensitivity.

Keywords: Classification; CNN; Deep learning; pretrained model; Skin cancer; Transfer learning.

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Paper #5

Remote Augmented Reality Application: A Study on Cues and Behavioural Dimension

Nur Intan Adhani Muhamad Nazri and Dayang Rohaya Awang Rambli

Abstract: Remote augmented reality (AR) collaboration promotes an interactive way to present information to the user by conveying a message and instruction to the local and remote participants. Despite its advantages, it is found that due to the limited use of sensory modalities during the remote collaboration process, it can interrupt the transmission of information and interaction cues, by not conveying the right information in remote AR collaboration in which can affect focus, and responses between local and remote users. This study is intended to investigate the behavioural dimension of collaboration (collaborator’s behaviour) and cues involved between local and remote user for physical task. Six participants performed as local participants where they need to build a LEGO, while another 6 participants performed as remote participants that have a complete manual instruction. Participants were given maximum 60 minutes to complete the given task. The results shown that most of the time participants used gesture and speech cues to interact with each other. There are certain signals and keywords established by both participants to have mutual understanding in achieving desired goal. Moreover, it was shown that the task completed by using handsfree produce faster response.

Keywords: Augmented Reality; Behavioural dimension; Communication cues; Remote collaboration.

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