Search results for: Skandia navigator

Authors: Edward C. W. Chan, Benny C. F. Cheung

Abstract:

In the knowledge economy, the financial indicator is not the unique instrument to gauge the performance of a company. The role of intellectual capital contributing to the company performance is increasing. To measure the company performance due to intellectual capital, the value-added intellectual capital (VAIC) model is adopted to measure the intellectual capital utilisation efficiency of the subject companies. The purpose of this study is to review the readiness of measuring intellectual capital for the Hong Kong listed companies in the property development and property investment industry by using VAIC model. This study covers the financial reports from the representative Hong Kong listed property development companies and property investment companies in the period 2014-2019. The findings from this study indicated the industry is ready for IC measurement employing VAIC framework but not yet ready for using the extended VAIC model.

Keywords: intellectual capital, intellectual capital measurement, property development, property investment, Skandia navigator, VAIC

Procedia PDF Downloads 86

Authors: Neda Navidi, Rene Jr. Landry

Abstract:

Vehicle tracking and accident recognizing are considered by many industries like insurance and vehicle rental companies. The main goal of this paper is to detect the location of a car accident by combining different methods. The methods, which are considered in this paper, are Global Navigation Satellite Systems/Inertial Measurement Units (GNSS/IMU)-based navigation and vehicle accident detection algorithms. They are expressed by a set of raw measurements, which are obtained from a designed integrator black box using GNSS and inertial sensors. Another concern of this paper is the definition of accident detection algorithm based on its jerk to identify the position of that accident. In fact, the results convinced us that, even in GNSS blockage areas, the position of the accident could be detected by GNSS/INS integration with 50% improvement compared to GNSS stand alone.

Keywords: driver behavior monitoring, integration, IMU, GNSS, monitoring, tracking

Procedia PDF Downloads 195

Authors: Akiko Ohta, Masahiro Minami, Yusra Qadir, Jennifer York

Abstract:

The Home Instruction for Parents of Preschool Youngsters (HIPPY) is a home instruction program for mothers of children 3 to 5 years old. Using role-play as a method of teaching, the participating mothers work with their home visitors and learn how to deliver the HIPPY curriculum to their children. Applying HIPPY, Reviving Hope and Home for High-risk Refugee Mothers Program (RHH) was created to provide more personalized peer support and to respond to ongoing settlement challenges for isolated and vulnerable Government Assisted Refugee (GAR) mothers. GARs often have greater needs and vulnerabilities than other refugee groups. While the support is available, they often face various challenges and barriers in starting their new lives in Canada, such as inadequate housing, low first-language literacy levels, low competency in English or French, and social isolation. The pilot project was operated by Mothers Matter Centre (MMC) from January 2019 to March 2021 in partnership with the Immigrant Services Society of BC (ISSofBC). The formative evaluation was conducted by a research team at Simon Fraser University. In order to provide more suitable support for GAR mothers, RHH intended to offer more flexibility in HIPPY delivery, supported by a home visitor, to meet the need of refugee mothers facing various conditions and challenges; to have a pool of financial resources to be used for the RHH families when necessitated during the program period; to have another designated staff member, called a community navigator, assigned to facilitate the support system for the RHH families in their settlement; to have a portable device available for each RHH mother to navigate settlement support resources; and to provide other variations of the HIPPY curriculum as an option for the RHH mothers, including a curriculum targeting pre-HIPPY age children. Reflections on each program component was collected from RHH mothers and staff members of MMC and ISSofBC, including frontline workers and management staff, through individual interviews and focus group discussions. Each of the RHH program components was analyzed and evaluated by applying Moore’s four domains framework to identify key information and generate new knowledge (data). To capture RHH mothers’ program experience more in depth based on their own reflections, the photovoice method was used. Some photos taken by the mothers will be shared to illustrate their RHH experience as part of their life stories. Over the period of the program, this evaluation observed how RHH mothers became more confident in various domains, such as communicating with others, taking public transportations alone, and teaching their own child(ren). One of the major factors behind the success was their home visitors’ flexibility and creativity to create a more meaningful and tailored approach for each mother, depending on her background and personal situation. The role of the community navigator was tested out and improved during the program period. The community navigators took the key role to assess the needs of the RHH families and connect them with community resources. Both the home visitors and community navigators were immigrant mothers themselves and owing to their dedicated care for the RHH mothers; they were able to gain trust and work closely and efficiently with RHH mothers.

Keywords: refugee mothers, settlement support, program evaluation, Canada

Procedia PDF Downloads 146

Authors: P. G. Kaviya, B. Muthupandian, R. Ganesan

Abstract:

On-chip routers typically have buffers are used input or output ports for temporarily storing packets. The buffers are consuming some router area and power. The multiple queues in parallel as in VC router. While running a traffic trace, not all input ports have incoming packets needed to be transferred. Therefore large numbers of queues are empty and others are busy in the network. So the time consumption should be high for the high traffic. Therefore using a RoShaQ, minimize the buffer area and time The RoShaQ architecture was send the input packets are travel through the shared queues at low traffic. At high load traffic the input packets are bypasses the shared queues. So the power and area consumption was reduced. A parallel cross bar architecture is proposed in this project in order to reduce the power consumption. Also a new adaptive weighted routing algorithm for 8-port router architecture is proposed in order to decrease the delay of the network on chip router. The proposed system is simulated using Modelsim and synthesized using Xilinx Project Navigator.

Keywords: buffer, RoShaQ architecture, shared queue, VC router, weighted routing algorithm

Procedia PDF Downloads 521

Authors: Seun Mayowa Sunday

Abstract:

Loan prediction is one of the most significant and recognised fields of research in the banking, insurance, and the financial security industries. Some prediction systems on the market include the construction of static software. However, due to the fact that static software only operates with strictly regulated rules, they cannot aid customers beyond these limitations. Application of many machine learning (ML) techniques are required for loan prediction. Four separate machine learning models, random forest (RF), decision tree (DT), k-nearest neighbour (KNN), and logistic regression, are used to create the loan prediction model. Using the anaconda navigator and the required machine learning (ML) libraries, models are created and evaluated using the appropriate measuring metrics. From the finding, the random forest performs with the highest accuracy of 80.17% which was later implemented into the Django framework. For real-time testing, the web application is deployed on the Alibabacloud which is among the top 4 biggest cloud computing provider. Hence, to the best of our knowledge, this research will serve as the first academic paper which combines the model development and the Django framework, with the deployment into the Alibaba cloud computing application.

Keywords: k-nearest neighbor, random forest, logistic regression, decision tree, django, cloud computing, alibaba cloud

Procedia PDF Downloads 102

Authors: Agada N. Ihuoma, Nagata Yasunori

Abstract:

Artificial intelligence (AI) techniques play a crucial role in predicting the expected energy outcome and its performance, analysis, modeling, and control of renewable energy. Renewable energy is becoming more popular for economic and environmental reasons. In the face of global energy consumption and increased depletion of most fossil fuels, the world is faced with the challenges of meeting the ever-increasing energy demands. Therefore, incorporating artificial intelligence to predict solar radiation outcomes from the intermittent sunlight is crucial to enable a balance between supply and demand of energy on loads, predict the performance and outcome of solar energy, enhance production planning and energy management, and ensure proper sizing of parameters when generating clean energy. However, one of the major problems of forecasting is the algorithms used to control, model, and predict performances of the energy systems, which are complicated and involves large computer power, differential equations, and time series. Also, having unreliable data (poor quality) for solar radiation over a geographical location as well as insufficient long series can be a bottleneck to actualization. To overcome these problems, this study employs the anaconda Navigator (Jupyter Notebook) for machine learning which can combine larger amounts of data with fast, iterative processing and intelligent algorithms allowing the software to learn automatically from patterns or features to predict the performance and outcome of Solar Energy which in turns enables the balance of supply and demand on loads as well as enhance production planning and energy management.

Keywords: artificial Intelligence, backward elimination, linear regression, solar energy

Procedia PDF Downloads 137