Steganography Detection and Analysis of Hidden Data in Images

Steganography is the art of hiding the information that is going to be sent from one party to another. Information can be hidden into image, text, audio or video. Steganography allowed communication to happen without other people notice there is transmission of message except the intended party. This project is to develop a program that can detect the presence or absence of the steganography in an image. The program is developed using Matlab. It is developed based on the existing algorithms which is Pairs Analysis, introduced by Fridrich et a!. We tried to write a simple embedding Matlab code to embed random message in the tested images. From the result that we got for each of the image with embedded message, we find the distribution graph for cover image and stegogramme. Threshold can be set up by calculating the overlapped area between the two distribution graphs. We plot the Receiver Operating Characteristic (ROC) graph in order to calculate the best and most accurate point to be set as threshold. Once we have set up the threshold for the cover image and stegogramme, the program will be tested on the images produce by the chosen steganography software, which is S-Tools and InfoStego. Both steganographic softwares are for spatial domain, which hide the secret message in the least significant bit (LSB) of image bits. For the output, our program is expected to produce histogram or graph that will detect the presence of the secret message in the cover image. This detection algorithm also produces the number of bitflips in the embedded image. The program should be able to estimate the length of the embedded message in percentage. The type of images used in this project are bmp,gif and jpeg format. They are comprised of natural images such as people, flowers, building, landscape and so forth

The implementation of information strategies to support sustainable procurement

In our research context, sustainable procurement can be seen as a process to reduce damage to the environment by integrating certain aspects into making procurement decisions, such as value for money throughout the whole life cycle and being of benefit to society and the economy. This research has found more than one way of interpreting the ‘sustainable system’, for example, ‘green-friendly’ versus remaining effective in the long term. Sustainable procurement requires specific information to support the procurement process. The study reported in this thesis aimed to investigate the type of information needed in order for organisations to make correct sustainable procurement decisions. From these findings, information architecture for sustainable procurement in UK universities has been derived. While the initial focus has been on the information needed to make informed decisions in purchasing sustainable information technology (IT) equipment, it is believed that the framework would also be more widely applicable to other types of purchases. To ensure that these findings would support the university aspiration in terms of sustainability practices, a goal-context modelling technique called VMOST/B-SCP was chosen to analyse the sustainable procurement strategy in order to evaluate the alignment of IT strategy and its business strategy. A goal-context model using VMOST/B-SCP was produced to evaluate the procurement strategy, with this validated by procurement staff. This research helps to improve the way that goals and context are identified by integrating another technique, namely, social network analysis (SNA) to produce actor network diagrams. The VMOST/B-SCP technique is transferrable to the mapping of action strategies. The findings from goal-context modelling show that a goal-context model is not static: it changes as external circumstances and organisational priorities change. Most changes to the strategy occurred where external entities on which the change programme depended did not act as planned. The actor networks produced in our version of VMOST/B-SCP can be used to identify such risks. This research was pioneering in its use of VMOST/B-SCP in examining a business change while it was actually taking place rather than after it had been completed (and thus needed to accommodate changes in objectives and strategies). In addition, the research analysed a system with some IT support but where human-operated procedures predominated. The original B-SCP framework used Jackson’s problem frames which focus on possible software components: in our scenario, SNA-inspired actor diagrams were found to be more appropriate

A Case Study to Explore IoT Readiness in Outbound Logistics

Most of the logistics companies are facing problems with tracking and tracing in their logistics networks that led to poor last-mile service quality. This problem can be solved by improving technology such as the Internet of Things (IoT). Midway through the second decade of the millennium, the rapid development of IoT has influenced the company’s outbound logistics operations such as last-mile delivery. Implementation of IoT will help courier companies to optimize their delivery process. Despite its popular perceived benefit in assisting last-mile delivery, IoT remains a new technology and its adoption rate is still low in Malaysia. Thus, the main purpose of this research is to explore the status of IoT readiness among logistics companies in Malaysia. Apart from that, this research also intends to propose the best practice for courier companies to implement IoT. The finding of this research will indicate the factors affecting the readiness of the organizations to adopt IoT and the best practice for the implementation of IoT for the last-mile of a parcel delivery service in Malaysia will be proposed. This research is carried out by making use of qualitative methods with a number of courier companies in Malaysia as case studies. The case study provided is related to a courier company in Malaysia. In the preliminary study phase, results show that IoT helps to improve productivity and enhance the efficiency of the company

Electronic document management system

Electronic Document Management System (EDMS) is needed by many organizations to properly managed large volumes of physical documents. One of them is Supply Chain Management (SCM) department of Oil and Gas (O&G) company in Malaysia. The objective of this research is to develop a framework of EDMS that is tailor-made to the SCM department, which is believed could help the department to manage their physical documents which are now located in their rented external storage due to the lack of space in the department itself. The requirements gathering is conducted by using interview method. The current business flow which involved the usage of the documents from one unit to another needed to be fully understood before developing the EDMS framework. This research has successfully developed an EDMS framework of SCM department and it could be used in developing the EDMS for future research

RFID-enabled presence aware system (RPAS)

Presence is a person’s availability and willingness for communication at the time responses were sought from him or her. This piece of information is vital to people in need of immediate communication. By knowing one’s presence, the person can explore on meeting opportunities and perform decision making instantaneously. SMS, e-mail, shared calendar or instant messaging (IM) are the common methods to seek presence, but they impose some limitations that do not remedy the situation completely. As such, RFID-enabled Presence Aware System (RPAS) was proposed. The objective of the paper is to design and implement RPAS to enable automatic presence detection using RFID and make presence information sharable via web application. The study was conducted using six-phased structural development method. The user acceptance testing and the formal tests carried out revealed that the system was easy to use and useful. This study implies that automatic presence detection and shareable presence status will contribute to effective time management for personnel that indirectly gain the competitive strength for an organization. Future work includes integrating context-aware computing in RPAS for more intelligent presence detection system

Cost implication analysis of concrete and Masonry waste in construction project

Concrete and masonry waste are the main types of waste typically generated at a construction project. There is a lack of studies in the country regarding the cost implication of managing these types of construction waste To address this need in Malaysia, the study is carried out to measure the disposal cost of concrete and masonry waste. The study was carried out by a site visit method using an indirect measurement approach to quantify the quantity of waste generated at the project. Based on the recorded number of trips for waste collection, the total expenditure to dispose the waste were derived in three construction stages. Data was collected four times a week for the period July 2014 to July 2015. The total waste generated at the study site was 762.51 m3 and the cost incurred for the 187 truck trips required to dispose the waste generated from the project site to the nearby landfill was RM22,440.00. The findings will be useful to both researchers and policy makers concerned with construction waste

Data Governance Practice for Outbound Supply Chain Management (SCM): A Case Study of Malaysian Textile Industry

The purpose of this paper is to implement a supply chain management (SCM) data governance of outbound logistics for Malaysian textile company. According to this paper, an apparel or textile company was once the top three export earners for manufactured goods of Malaysia. Hence, this research is looking forward to enhancing better performance and business value of Malaysian textile industry which is corresponding with the new application of technology known as “Industry 4.0â€. Without having any standards or guidelines, a company might abortive due to the absence of express semantics and substance of data and information towards integrating and exchanging information among various associates including in the supply chain of the organization. Indeed, changes in policy might also give the impression of instability which impacts the stock prices of a textile company. Hence, this paper focuses on the SCM data governance, whereby the main goal is to identify data governance that exists and must be implemented in compliance with the Malaysian outbound logistic. Throughout this paper, the term data governance is used to refer to the practice of managing all the information to identify and improve the business value of an organization

Event Detection and Information Extraction Strategies from Text: A Preliminary Study Using GENIA Corpus

In the world we live today, data is the new oil. Data can reveal hidden knowledge that gives us an advantage over our competitors. However, data that are present in an unstructured form such as text documents are difficult to be processed by conventional machine learning algorithms. Therefore, in this study, we attempted to perform information extraction from textual data using current and state-of-the-art models to understand their working mechanisms. To perform this study, we have chosen the GENIA corpus for evaluating the performance of each model. These selected event extraction models are evaluated based on specific measures which are precision, recall, and F-1 measure. The result of our study shows that the DeepEventMine model has scored the highest for trigger detection with a precision of 79.17%, recall at 82.93%, and F-1 measure at 81.01%. Similarly, for event detection, the DeepEventMine model has scored highest among other models with a precision of 65.24%, recall at 55.93%, and F-1 measure at 60.23% based on the selected corpus

Predictive Analytics for Oil and Gas Asset Maintenance Using XGBoost Algorithm

One of the most important aspects of the oil and gas industry is asset management at their respective platforms. Without proper asset management, it will lead to various unexpected scenarios including an increase in plant deterioration, increased chances of accidents and injuries, and breakdown of assets at unexpected times which will lead to poor and hurried maintenance. Given the significant economic contribution of the oil and gas sector to oil-producing countries like Malaysia, accurate asset maintenance prediction is essential to ensure that the oil and gas platform can manage its operations profitably. This research identifies the parameters affecting the asset failure on oil and platform that will be interpreted using the XGBoost gradient boosting model from machine learning libraries. The model is used to predict the asset's lifetime based on readings collected from the sensors of each machine. From result, our prediction method using XGBoost for asset maintenance has presented a 6.43% increase in classification accuracy as compared to the Random Forest algorithm