A motor imagery (MI)-based brain-computer interface (BCI) has performed successfully as a control mechanism with multiple electroencephalogram (EEG) channels. For practicality, fewer EEG channels are preferable. This paper investigates a single-channel EEG signal for MI. However, there are insufficient features that can be extracted due to a single-channel EEG signal being used in one region of the brain. An effective feature extraction technique plays a critical role in overcoming this limitation. Therefore, this study proposes a fusion of discrete wavelet transform (DWT)-based and time-domain feature extraction to provide more relevant information for classification. The highest accuracy obtained on the BCI Competition III (IVa) dataset is 87.5% with logistic regression (LR) while the OpenBMI dataset attained the highest accuracy of 93% with support vector machine (SVM) as the classifier. Addressing the potential of enhancing the performance of a single EEG channel located on the forehead, the achieved result is relatively promising.

In this research paper, we present and compare the randomness analysis conducted towards LBlock block cipher and its modified version namely Modified LBlock. Among the important requirement when designing a block cipher algorithm is that the algorithm can act as a random number generator. Therefore, the aim of performing modification towards LBlock algorithm is to enhance its randomness results. Modification were made by replacing the eight 4 X 4 S-boxes with four different 16 X 16 S-boxes which has the same security strength as S-box of AES. During experimentation, this research project considers a full rounds of LBlock and Modified LBlock algorithms which both algorithms accepts a 64-bit plaintext, utilizes an 80-bit key, executes in 32 rounds and produces a 64-bit ciphertext. Nine different data categories were used to generate inputs (plaintext and key), with each having 100 samples. Blocks of ciphertext were generated from these algorithms and were concatenated to construct a binary sequence. NIST Statistical Test Suite consisting of sixteen tests was used to conduct testing and analysis, and the significance level was set to 1%. From the comparative analysis done, it is concluded that the randomness analysis results for Modified LBlock block cipher has 42.96% improvement compared to its original design when tested under 1% significance level and using the same samples.

Within the region of India, monsoon prediction was reported to use the concentration of 7Be cosmogenic radionuclide that contains crucial information such as atmospheric behaviour change. This review aims to identify and analyse the existing research on the usage of 7Be Cosmogenic radionuclide in assisting weather forecasts, especially rain onset and withdrawal in the monsoon season and the correlations of rainfall and monsoonal variation in Malaysia. A comprehensive search of major scientific databases was conducted, and relevant studies were selected based on inclusion and exclusion criteria between January 2017 and August 2022. The review found that the method of utilising 7Be as a parameter in monsoon forecasting for India is newly developed and only be assessed using the trans-equatorial method. The review also highlighted the expectation that the 7Be concentration analysis throughout the years could provide new insight into understanding monsoon behaviour and can be further utilised in forecasting the rainfall intensity during a wet season in Malaysia.

In Malaysia, the pandemic coronavirus disease 2019 (COVID-19) was first detected on 25th January and has been spreading massively and reported to have reached more than 20,000 new cases per day from July to August 2021. COVID-19 data is voluminous describing the pandemic trend around the globe. How does Big Data help decision-makers understand the pandemic behaviour which is very crucial in responding to the situation? How do data analytics on the COVID-19 spreading pattern which is time-series in nature may provide insight into the situation that may lead to a better response through forecasting future trends? This paper aims to explain the concept of Big Data and its applications that demonstrates its potential for responding to the pandemic. COVID-19 data analytic is proposed using sliding window time-series forecasting method and demonstrated using data from 25th January until 10th October 2020 obtained from the Malaysian Ministry of Health and Department of Statistics Malaysia website. The data analytics demonstrated the value gain for useful insights.

The losses of developing countries from landmines accidents are very large. Thus, the need for new techniques to improve the efficiency of Landmines tracking systems is evident. In the recent years, many of research efforts have been directed to develop new and improved landmine detection methods. However, the increased costs of improving these methods led to drive up their prices. Thus they will not be available to the general public. The aim of this paper is to find a cheap and an effective method to help people for protecting and warning them from landmines risk during practiced their daily lives. In this context, this paper presents the design and development of framework for a Wireless Intelligent Landmines Tracking System (IWLTS) using mobile phone based on GPS and fuzzy logic. Proposed framework is really very helpful for the users who living near mine affected areas to track their children and themselves through Smart phones from landmines risk.

Energy efficiency (EE) is crucial in addressing environmental issues and organisational performance. Energy awareness and knowledge is needed in promoting EE practices. This paper aims to review Maqasid Syariah (MS) knowledge in enhancing good EE practices, and the objectives are to investigate the EE awareness, knowledge and practices in Malaysia, plus to explore the importance of MS knowledge. Qualitative method was employed through literature review and narrative study. A conceptual model of the incorporation of MS knowledge into EE awareness and knowledge is introduced. This conceptual model could be implemented to develop an enhanced good EE practices among occupants.

Throughout the covid-19 pandemic, technology in education has been important. This is a new norm in learning patterns. The study first investigates how user experience factors (useful, desire, accessible, usable) influence secondary school students' open distance learning (ODL). The second aim is to measure the emotion associated with their profiles. The method used is a questionnaire involving 161 respondents aged 13 to 17 years old. Findings show that user experience factors are significant to ODL, and the emotions associated with the likelihood ratio of gender and type of school are pleasant and unpleasant. Future work expands different target respondents, expands other user experience factors such as findable and credible, and expands different types of emotions.

Reservoir operation involves many critical decisions due to unpredicted circumstances, such as drought or flood. The reservoir water release decision is one of the challenging tasks for the reservoir operator since the decision to deal with many complicated decision variables and multipurpose operation. Typically, the water that flow into the reservoir comes from the upstream river network. The gauging stations are located at different locations and the distance between the gauging stations and the reservoir are varied. This information indicates that there are spatial relationships between gauging stations and reservoir. This paper aims to investigate the spatial relationship between the upstream gauging stations and the reservoir by mapping temporal rainfall information on each gauging station with the reservoir water level. The mapping was established using Backpropagation Neural Network. The findings show that different location and distance of each gauging station with the Timah Tasoh reservoir does affect the water travel time from gauging stations to the reservoir.

We are pleased to present this issue to readers as a special issue on Healthy Prophetic Lifestyle presented at the Prophetic Science International Conference (Al-FURSAN18) which was held on the 4th November to 5th November 2018 at Universiti Sains Islam Malaysia. The conference was jointly organized by NRGS Research Group on Prophetic Food and Islamic Science Institute. Rasulullah SAW is a perfect and exemplary model not only in spiritual, but also physical and mental well being through healthy nutritional diet. Thus this conference aims to gather scholars from various disciplines such as science and technology, Islamic studies and social sciences to discuss and share research findings on topics related to prophetic science on healthy and sustainable living. The discussion starts with the article by Mohd Rahman, Ummi Affah and Hana Maizuliana who presented on scientific evidences of complementary and prophetic medicine in managing dengue infection.

Al-Quran is the holy book of the Muslims and the most important scripture containing knowledge on many domains. The recent advent of smart technologies like smart phones, digital devices and tablets has connected the daily life routines under a single touch adopted by many, these new tools with an exponential growth. This paper presented a Digital Quran Model (DQM) using hexadecimal representa-tion using Unicode Hexadecimal and UTF-8 for character encoding, which is backward compatible with ASCII code. DQM target to handle all duplicated words or verses in Al-Quran using sparse matrix with double offset indexing to handle memory optimization. Three approaches were discussed: indexing and representation of the digital Quran to optimize storage, organize verses structure using sparse matrix to handle repetition with double offset indexing to efficiently use the space. The algorithms were implemented using Visual studio and Java server and the solution quality was measured by the size of a file before and after applying DQM model. For surah Al-Baqarah, the longest chapter in the Al-Quran, the reduction of the storage size was 25.00% whereas surah Al-Fatihah was 47.89%. The proposed DQM model is able to optimize the memory space and can be extended to other non-Roman characters used for information retrieval such as Hindi, Chinese and Japanese that are categorized in unicode standards.

To reviews whether healthcare-monitoring systems for lymphatic treatment of leg pain fits any requirement related on its selected classification. Some reviewed articles will be asses to provide a relatable explanation regarding computational intelligence, double-loop feedback theory, healthcare system and healthcare monitoring system. Each criteria provide a basic strength to fit in healthcare monitoring system for lymphatic treatment of leg pain issues. The article comparison will produce profound results with proper justification to be embed in healthcare monitoring system for lymphatic treatment of leg pain. If the comparisons fit certain requirement on healthcare system, it will be applied into the system in a meantime.

Nowadays, search engines tend to use the latest technologies in enhancing the personalization of web searches, which leads to a better understanding of user needs. One of these technologies is web search results clustering which returns meaningful labeled clusters from a set of Web snippets retrieved from any Web search engine for a given user’s query. Search result clustering aims to improve searching for information from the potentially huge amount of search results. These search results consist of URLs, titles, and snippets (descriptions or summaries) of web pages. Dealing with search results is considered as treating large-scale data, which indeed has a significant impact on effectiveness and efficiency. However, unlike traditional text mining, queries and snippets tend to be shorter which leads to more ambiguity. K-means tend to converge to local optima and depend on the initial value of cluster centers. In the past, many heuristic algorithms have been introduced to overcome this local optima problem. Nevertheless, these algorithms suffer several shortcomings. In this paper, we present an efficient hybrid web search results clustering algorithm referred to as G-K-M, whereby, we combine K-means with a modified genetic algorithm. The AOL standard dataset is used for evaluating web data log clustering. ODP-239 and MORESQUE are used as the main gold standards for the evaluation of search results clustering algorithms. The experimental results show that the proposed approach demonstrates its significant advantages over traditional clustering. Besides, results show that proposed methods are promising approaches that can make search results more understandable to the users and yield promising benefits in terms of personalization.

The current study investigates the Intelligent Water Drops (IWD) metaheuristic algorithm to construct and produce good quality solutions for the university examination timetabling problem (UETP). The IWD is a population-based metaheuristic that simulates the dynamic of the river systems. The main motivations for investigating IWD algorithm for examination timetabling problem is the ability to explore the search space effectively. The main drawback of IWD algorithm is like other population-based algorithm in exploitation process where it is very efficient scanning several search space niches, but it is unable to drilling down in each niche to which it navigates. In this paper we propose a hybrid approach based on IWD and locale search algorithm to improve the exploitation of IWD algorithm. The experimental results demonstrated that the proposed algorithm (i.e., Hybrid IWD) obtained best results in three datasets when comparing with the best-known results performed by the swarm intelligent approaches. Finally, the proposed algorithm achieved one best results in comparison with the other metaheuristic approaches.

As people are increasingly engaging in e-commerce, security is becoming a paramount issue. E-commerce involves a great deal of credit card numbers, fund transfers, web shopping, and other forms of private information that needs to remain secure. Although, the knowledge of how to develop a secure web application is known, the major concern is the insufficiency of the right developmental framework and practices. The use of inappropriate methodologies in developing an e-commerce application can seriously undermine organizations' confidentiality and integrity of data. Although different web application development frameworks have basic security considerations, most of the security concerns are not applied across the entire development lifecycle. This is unfortunate. This study proposes an Improved Extreme Programming Framework with security concerns across the entire developmental lifecycle by leveraging the successes of Extreme Programming Methodology. The major contribution is the building of a security framework that inculcates security considerations across the development lifecycle which can be adapted by security critical applications like e-commerce sites. The implementation and evaluation of this proposed framework, with previous web application security development frameworks, have shown that a 96 percent security level is achieved, despite the four percent of failures. The Failures are of information severity status, which are not typically critical to the security of the underlying application. This further point to inculcating security considerations at all the stages of the developmental lifecycle is significantly necessary for vulnerability mitigation. Previous frameworks do not address this problem due to the trade-off between security and functionality.

Reservoir operation involves many critical decisions due to unpredicted circumstances, such as drought or flood. The reservoir water release decision is one of the challenging tasks for the reservoir operator since the decision to deal with many complicated decision variables and multipurpose operation. Typically, the water that flow into the reservoir comes from the upstream river network. The gauging stations are located at different locations and the distance between the gauging stations and the reservoir are varied. This information indicates that there are spatial relationships between gauging stations and reservoir. This paper aims to investigate the spatial relationship between the upstream gauging stations and the reservoir by mapping temporal rainfall information on each gauging station with the reservoir water level. The mapping was established using Backpropagation Neural Network. The findings show that different location and distance of each gauging station with the Timah Tasoh reservoir does affect the water travel time from gauging stations to the reservoir.

Digital Quran may be implemented as image-based, text-based, audio, or video based. In terms of storage, the compilation of Quranic verses in images, audio, or video will consume ample storage space. On the other hand, the digital Quran in text-based format benefitted the Hexadecimal representation due to the use of Unicode. However, space was not optimized due to the string concatenation approach of the Unicode of each letter that occurs in a particular Quranic word. Thus, with the Unicode representation, the storage size is directly proportional to the length of a word. This paper presents a new Digital Quran Model (DQM) that aims to reduce storage requirements through individual word conversion into Hexadecimal instead of combining letter sequences. Due to the repetitive nature of Quran words, space usage can be further improved by handling Quranic word duplication. DQM was implemented using Visual Studio and Java servers. The solution quality was measured by comparing the file size before and after applying the DQM model. For example, space reduction for surah Al-Fatihah is 46.91%. The outcome helps the researchers to develop a lightweight Digital Quran application that users can benefit from due to its reliability, validity, and storage management efficiency and thus can be used as a standard application.

An inquiry is a sort of student-centered learning that uses questions in a dialog format with the instructor and peers to examine and enhance student’s understanding in a subject matter. The purpose of the questions and answers session is to nurture constructive critical and analytical skills, progressing from a low-level to a higher-level question while encouraging deep thinking. This study investigated interrogative passages in the Qur`ān, focusing on hamzah istifhām in Surah al-Baqarah. An inquiry learning model will be presented from the question verses pattern extracted from the Qur`ān.

Social network is term used to refer to the social structure that is made up of a set of social actors. The social actors in this case include organizations or individuals. Social networks allow people to interact and socialize as they get to learn and know each other. Through social networking sites, people from different parts of a country or the world also get to meet and interact. However, there have been issues with regards to social network privacy for those who use the internet to use social network sites. This paper will look at some of the factors that affect trust of the users as well as the privacy issues related to social networks (Fernandez, 2009). Keywords:Social network;Privacy model;factors effecting trust

Social and political behaviour relies much on communication. However in Islamic teaching, these human to human behaviours need to comply with the al-Quran and Sunnah that will be realized as adab and akhlaq or in other words ethics. This human to human relationship is founded by human to Allah relationship that will give rise to ibadah and taqwa. In Islam , privacy of another person is held in high respect. One should not pry in another person personal life or space because it is considered unethical thus not a good sign of taqwa. In the digital world, with the advancement of Internet that gave birth to social network, ethics, privacy and security is important concern. This study looks at privacy and security from the Islamic perspectives. A survey was conducted with 200 social network users using snowball sampling to find put their awareness of privacy and security related issues on social network. The study showed that majority of users are not aware even some do not even bother on the privacy and security issues. Thus, a user oriented privacy model was proposed that will protect users information by default unless the user set it otherwise. It will give more control to users on what to share on the social media with the public.

A plethora of publications have shed light, particularly on the affordances of artificial intelligence (AI) in language education, garnering significant attention, promising transformative impacts on teaching and learning practices. However, the rapid adoption of AI tools has raised ethical concerns regarding data privacy, bias and academic integrity. in response to these concerns, this systematic review aims to explore the responsible and ethical use of AI in language education (REALE) by examining recent literature from 2020 to 2024. The structure of this research revolves around two key questions: What are the emerging patterns and practices in REALE? and What research methodologies have been utilized in studies examining REALE? The researchers selected 9 studies from 65 publications in the Web of Science (WoS) and Scopus databases, following a rigorous screening process based on predefined inclusion and exclusion criteria. These selected studies were analyzed using thematic codes: the objective of the study, methodologies applied, sample, country and the key outcomes reported. The findings reveal a growing trend towards implementing AI in language education, with an emphasis on ethical training and awareness. The review suggests the necessity for educators and policymakers to develop comprehensive guidelines for the responsible and ethical use of AI in language education. It also recommends further research into inclusive and ethical AI practices across different educational levels to foster a more equitable and responsible use of technology in language education.