A Stock Market Trading System Using Deep Neural Network

The stock market prediction is a lucrativefield of interest withpromising profit and covered with landmines for the unprecedented. The mar-kets are complex, non-linear and chaotic in nature which poses huge difficultiesto predict the prices accurately. In this paper, a stock trading system utilizingfeed-forward deep neural network (DNN) to forecast index price of Singaporestock market using the FTSE Straits Time Index (STI) in t days ahead is pro-posed and tested through market simulations on historical daily prices. There are40 input nodes of DNN which are the past 10 days’opening, closing, minimumand maximum prices and consist of 3 hidden layers with 10 neurons per layer.The training algorithm used is stochastic gradient descent with back-propagationand is accelerated with multi-core processing. A trading system is proposedwhich utilizes the DNN forecasting results with defined entry and exit rules toenter a trade. DNN performance is evaluated using RMSE and MAPE. Theoverall trading system shows promising results with a profit factor of 18.67,70.83% profitable trades and Sharpe ratio of 5.34 based on market simulation ontest data

A genetic-algorithm-based approach for audio steganography

In this paper, we present a novel, principled approach to resolve the remained problems of substitution technique of audio steganography. Using the proposed genetic algorithm, message bits are embedded into multiple, vague and higher LSB layers, resulting in increased robustness. The robustness specially would be increased against those intentional attacks which try to reveal the hidden message and also some unintentional attacks like noise addition as well

Understanding user participation in information security risk management

Risk management is the continuing process to control and manage the risk in organisation for identifying, accessing and controlling threats to an organisation’s capital and earning. The implementation of information security risk management (ISRM) helps to address the risks to information processed by an organisation that may help the organisation to manage the risk effectively. Involving the user throughout the process of ISRM is important to ensure that it provides an effective security risk management (SRM). There are limited evidence shows that user participation is important in ISRM. Therefore, the aim of this paper to investigate user participation in ISRM from user participation and access control constructs. A quantitative method is implemented by distributing a questionnaire to two different organisational backgrounds to 20 respondents. This paper presents the initial findings that user participation play a significant role towards ISRM by presenting the results from the two constructs. The findings contribute to the body of knowledge that understanding user participation in ISRM shows that the process of risk management is different between two organisational backgrounds

UTMInDualSymFi: A Dual-Band Wi-Fi Dataset for Fingerprinting Positioning in Symmetric Indoor Environments

Recent studies on indoor positioning using Wi-Fi fingerprinting are motivated by the ubiquity of Wi-Fi networks and their promising positioning accuracy. Machine learning algorithms are commonly leveraged in indoor positioning works. The performance of machine learning based solutions are dependent on the availability, volume, quality, and diversity of related data. Several public datasets have been published in order to foster advancements in Wi-Fi based fingerprinting indoor positioning solutions. These datasets, however, lack dual-band Wi-Fi data within symmetric indoor environments. To fill this gap, this research work presents the UTMInDualSymFi dataset, as a source of dual-band Wi-Fi data, acquired within multiple residential buildings with symmetric deployment of access points. UTMInDualSymFi comprises the recorded dual-band raw data, training and test datasets, radio maps and supporting metadata. Additionally, a statistical radio map construction algorithm is presented. Benchmark performance was evaluated by implementing a machine-learning-based positioning algorithm on the dataset. In general, higher accuracy was observed, on the 5 GHz data scenarios. This systematically collected dataset enables the development and validation of future comprehensive solutions, inclusive of novel preprocessing, radio map construction, and positioning algorithms

A comparison of Mesua ferrea L. and Hura crepitans L. for shade creation and radiation modification in improving thermal comfort

Open spaces in tropical climates are highly exposed to solar radiation. These conditions will influence the outdoor energy budget, leading to an increased heat island effect and reduced human thermal comfort. Trees, however, can influence the microclimate through radiation control that indirectly reduces direct radiation uptake and glare by humans and buildings. This condition affects building energy budget and human thermal comfort. This study compares the effectiveness of Mesua ferrea L. and Hura crepitans L. in shade creation and radiation modification in improving human thermal comfort. The study employed two methods: (i) a field measurement procedure and (ii) a computer-based sun-shading analysis using ECO-TECT. The results from this study indicate that both M. ferrea L. and H. crepitans L. contribute significantly to direct thermal radiation modification below their canopies. The average solar filtration under the tree canopy for M. ferrea L. was 93%, with 5% canopy transmissivity, 6.1% of leaf area index (LAI) and 35% of shade area. For H. crepitans L. the average heat filtration under the canopy was 79%, with transmissivity of 22%, LAI of 1.5 and 52% of shade area. Thus, the study found that M. ferrea L. was more significant as a thermal radiation filter than H. crepitans L., due to the former's denser foliage cover and branching habit. This significant filtration capability contributes to reduce more terrestrial radiation, cooling the ground surfaces by promoting more latent heat, reducing air temperature by promoting more evapotranspiration and effectively improves outdoor thermal comfort in tropical open spaces

Abstracts of the International Halal Science Conference 2023

This book presents the extended abstracts of the selected contributions to the International Halal Science Conference, held on 22-23 August 2023 by the International Institute for Halal Research and Training (INHART), IIUM, Malaysia in collaboration with Halalan Thayyiban Research Centre, University Islam Sultan Sharif (UNISSA), Brunei Darussalam. With the increasing global interest in halal products and services, this conference is timely. Conference Title:  International Halal Science ConferenceConference Acronym: IHASC23Conference Theme: Halal Industry Sustainability Through ScienceConference Date: 22-23 August 2023Conference Venue: International Islamic University (IIUM), MalaysiaConference Organizer: International Institute for Halal Research and Training (INHART), International Islamic University (IIUM), Malaysi