Geofence alerts application with gps tracking for children monitoring

Infamous cases of Malaysia child victim. 2004, Nurul huda, 11 years, raped multiple time, sodomized and strangled. 2008, Burin jazlin. 8 years, dead body stuffed in bag.Cucumber and eggplant inserted into private part. 2013, Nurul nadirah, 4 years, strangled to death. Dead body burned. 2018. Siti masitah, 11 years, head severed. Organs removed

Real-time fault diagnostic in rotating shaft using IoT-based architecture and fuzzy logic analysis

The rotating shaft, commonly known as an axle, plays a crucial role in enabling rotational motion and power transmission within industrial rotating machines. However, assessing the condition of a rotating shaft presents a significant challenge due to its concealed nature. Traditionally, manual inspections by technicians have been relied upon to detect potential damage, resulting in time-consuming processes and potential delays in fault diagnostic. To address this issue, this paper proposes an IoT-based architecture integrated with fuzzy logic to enable real-time fault diagnostic in rotating shaft. By employing fuzzy logic classification based on vibration frequency and noise analysis, the system accurately determines the condition of the rotating shaft. Experimental results confirm the successful implementation of the proposed system, providing valuable insights into the current condition of the rotating shaft. This real-time approach enables proactive maintenance strategies and mitigates the risk of unexpected industrial machine failures

Machine learning in dam water research: an overview of applications and approaches

Dam plays a crucial role in water security. A sustainable dam intends to balance a range of resources involves within a dam operation. Among the factors to maintain sustainability is to maintain and manage the water assets in dams. Water asset management in dams includes a process to ensure the planned maintenance can be conducted and assets such as pipes, pumps and motors can be mended, substituted, or upgraded when needed within the allocated budgetary. Nowadays, most water asset management systems collect and process data for data analysis and decision-making. Machine learning (ML) is an emerging concept applied to fulfill the requirement in engineering applications such as dam water researches. ML can analyze vast volumes of data and through an ML model built from algorithms, ML can learn, recognize and produce accurate results and analysis. The result brings meaningful insights for water asset management specifically to strategize the optimal solution based on the forecast or prediction. For example, a preventive maintenance for replacing water assets according to the prediction from the ML model. We will discuss the approaches of machine learning in recent dam water research and review the emerging issues to manage water assets in dams in this paper

Enhancing chiller plant modelling performance through NARX-based feature optimization

The research focuses on the modelling chiller plants in air cooling systems of large buildings. The existing evaluation of prediction efficiency and identification of efficient components in chiller plants has been limited. The goal of this research is to develop a methodology for modeling chiller plants by utilizing key parameters from their components. The resulting model accurately simulates the actual chiller plant system and can be used by organizations to predict future events, aiding in preventative maintenance and reducing maintenance costs, especially in critical buildings like hospitals. The research process include compiling the chiller plant's history, simulating the machinery using a regression technique called NARX, selecting crucial parameters using an optimization technique (BPSO), and validating the model. This study enhances our understanding and management capabilities of these important cooling systems by addressing the challenges of efficient modeling and prediction accuracy in chiller plant systems

Dynamic ransomware detection for windows platform using machine learning classifiers

In this world of growing technological advancements, ransomware attacks are also on the rise. This threat often affects the finance of individuals, organizations, and financial sectors. In order to effectively detect and block these ransomware threats, the dynamic analysis strategy was proposed and carried out as the approach of this research. This paper aims to detect ransomware attacks with dynamic analysis and classify the attacks using various machine learning classifiers namely: Random Forest, Naïve Bayes, J48, Decision Table and Hoeffding Tree. The TON IoT Datasets from the University of New South Wales' (UNSW) were used to capture ransomware attack features on Windows 7. During the experiment, a testbed was configured with numerous virtual Windows 7 machines and a single attacker host to carry out the ransomware attack. A total of 77 classification features are selected based on the changes before and after the attack. Random Forest and J48 classifiers outperformed other classifiers with the highest accuracy results of 99.74%. The confusion matrix highlights that both Random Forest and J48 classifiers are able to accurately classify the ransomware attacks with the AUC value of 0.997 respectively. Our experimental result also suggests that dynamic analysis with machine learning classifier is an effective solution to detect ransomware with the accuracy percentage exceeds 98%

A review of data quality research in achieving high data quality within organization

The aim of this review is to highlight issues in data quality research and to discuss potential research opportunity to achieve high data quality within an organization. The review adopted systematic literature review method based on research articles published in journals and conference proceedings. We developed a review strategy based on specific themes such as current research area in data quality, critical dimensions in data quality, data quality management model and methodologies and data quality assessment methods. Based on the review strategy, we select relevant research articles, extract and synthesis the information to answer our research questions. The review highlights the advancement of data quality research to resemble its real world application and discuss the available gap for future research. Research area such as organizations management, data quality impact towards the organization and database related technical solutions for data quality dominated the early years of data quality research. However, since the Internet is now taking place as the new information source, the emerging of new research areas such as data quality assessment for web and big data is inevitable. This review also identifies and discusses critical data quality dimensions in organization such as data completeness, consistency, accuracy and timeliness. We also compare and highlight gaps in data quality management model and methodologies. Existing model and methodologies capabilities are restricted to the structured data type and limit its ability to assess data quality in web and big data. Finally, we uncover available methods in data quality assessment and highlight its limitation for future research. This review is important to highlight and analyse limitation of existing data quality research related to the recent needs in data quality such as unstructured data type and big data

Trojan Detection System Using Machine Learning Approach

Malware attack cases continue to rise in our current day. The Trojan attack, which may be extremely destructive by unlawfully controlling other users' computers in order to steal their data. As a result, Trojan horse detection is essential to identify the Trojan and limit Trojan attacks. In this study, we proposed a Trojan detection system that employed machine learning algorithms to detect Trojan horses within the system. A public dataset of Trojan horses that contain 2001 samples comprises of 1041 Trojan horses and 960 of benign is used to train the machine learning classification. In this paper, the Trojan detection system is trained using four types of classifiers which are Random Forest, J48, Decision Table and Naïve Bayes. WEKA is used for the execution of the classification process and performance analysis. The results indicated that the detection system trained with the Random Forest and Decision Table algorithms obtained the maximum level of accuracy

SAISMS : Transforming ammunition management through loT-enabled inventory and safety monitoring system

Ammunition plays a crucial role in military and defense operations, requiring significant investments to arm military forces adequately. However, ammunition is susceptible to environmental factors that can degrade its quality, leading to defects or even accidental explosions. To ensure constant combat readiness, it is vital to maintain secure storage facilities with sufficient ammunition supplies. This project aims to enhance ammunition inventory and safety management procedures by leveraging loT technologies. This project proposed the implementation of an loT-powered web application dashboard that utilizes weight measurements to provide real-time inventory tracking and monitors environmental conditions such as temperature and humidity for quality control. Additionally, the system can predict ammunition condition outcomes. By adopting this loT-based solution, ammunition management processes will be streamlined, resulting in improved efficiency and effectiveness

Geofence alerts application with GPS tracking for children monitoring (CTS)

Geofence Alerts Application with GPS Tracking for Children Monitoring (CTS) is a mobile application that helps parents to track the location of their child. It provides the parents with the route and real-time location of the children. Parents often face difficulties in getting hold of the whereabouts of their children when they are not in sight. This situation increases the insecurity of parents toward the safety of their children. The first objective of this paper is to obtain a latitude, longitude, and time information of a child’s location in real-time using GPS tracker. The second objective is to develop a smartphone application that capable to track the location of children in real-time. The third objective is to evaluate the functionality of the developed smartphone application in tracking children’s location. Features, advantages, and disadvantages of three commercialized application are compared to collect requirements for the CTS application. The requirements are then used to design and develop the interface of CTS application using Rapid Application Design (RAD) framework. Three main modules, which are the View Current Location module, View History Route module and Setup Geofence module are proposed for the application. Additionally, a GPS tracker based on Arduino Uno board is developed to provide the longitude and latitude of children’s current location. The functionality of the CTS application and the GPS tracker is then evaluated to determined bugs and its usability. It was discovered that CTS is in helping parents to track the location of their child in real-time, view the past route taken by the child, set up geofence area, and receive notification when their child enters or leave the geofence area within the scheduled time

Non-Linear Autoregressive with exogenous input (Narx) chiller plant prediction model

A chiller plant is a centralized system used for air cooling systems, commonly, for covering a large area of building with various components such as chillers, cooling towers, pumps, and chilled water storage tanks. Each component has several sensors or indicators with status information. Users can use the information to plan for maintenance and as guidance during troubleshot if an event occurs. It is crucial to ensure the chiller plant is operating efficiently without any faulty especially in critical buildings such as a hospital. The main problem of the chiller plant is to conduct preventive maintenance for avoiding the chiller plant failure and breakdown unexpectedly. Based on the literature, approximately 80 components in the chiller plant has found as the possible reason for the chiller plant faulty. In the current research, modeling chiller plants has been done by several researchers, objectively for preventative maintenance purposes. Study case for this project is for a chiller plant at Hospital Raja Permaisuri Bainun, Ipoh, Perak, Malaysia. A model for the proposed chiller plant system is to be designed using System Identification (SI) technique based on Nonlinear Autoregressive with Exogenous Inputs (NARX). Validation result shows, the proposed chiller plant system can be modelled and to be used as One Step Ahead prediction tool with residual Mean Square Error (MSE) of 1.018E-3 for training set and 1.017E-3 for testing set