A Scenario of the Canadian Generation Y Consumers’ Behavioral Usage on the Internet

The main purpose of this research is to explore how Canadian Generation Y consumers’ behavioral use of the Internet. This research explores to determine the factors and variables that can increase the number of Canadian Generation Y consumers’ behavior using Canadian small businesses’ retail websites. This provides information on how small business retailers are able to plan strategies and tasks to engage them. The research question is to gather qualitative data from Canadian Generation Y consumers’ on their daily Internet and online shopping activities, and security and privacy on the Internet. The rationale behind the research objectives is to better understand Canadian Generation Y’s behavioral usage of Canadian small businesses’ retail websites. As it is imperative for retail websites to be an intrinsic part of the Internet, it is therefore important to understand Canadian Generation Y’s behavioral usage of the Internet as well. This research makes a distinct contribution to the knowledge of the subject matters in the areas of Generation Y, small businesses, and the activities on the internet that are applicable to academicians and practitioners. This is the first time that research has been conducted specifically based on Canadian Generation Y consumers’ behavioral usage and activities of Consumers on the Internet

Adaptive route optimization for mobile robot navigation using evolutionary algorithm

As technologies are advancing, demand for an intelligent mobile robot also increases. In autonomous robot design, the main problem faced by researchers is the path planning of mobile robot. Various kind of path planning algorithm was introduced in the past, but no algorithm has absolute superior towards the others algorithm. Classical methods like artificial potential field, grid search, and visual method have been easily overtaken by artificial intelligence due to its adaptability and ability to learn from the past mistakes or experience. For example, Ant Colony Optimization (ACO) is an optimization algorithm based on swarm intelligence which is widely used to solve path planning problem. However, the performance of ACO is highly dependent on the selection of its parameters. In this paper, the proposed adaptive ACO introduced two different ants, namely abnormal ant and random ant into the normal ACO to increase its global search ability and reduce the high convergence rate of ACO. Conventional ACO and adaptive ACO are compared in this paper and the results showed that adaptive ACO has better performance than conventional ACO in path planning

A genetic algorithm for management of coding resources in VANET

This project aims to improve the throughput, energy consumption and overhead of vehicular ad hoc network (VANET) by optimising the network coding (NC) using Genetic Algorithm (GA). VANET shows a promising technology as it could enhance the traffic efficiency and promote traffic safety on the road systems. The conventional store-and-forward transmission protocol used in the intermediate node(s) simply stores the received packet and then send at a later time to the destination. However, the rapid changing in VANET topology has made the conventional store-and-forward approach inefficient to meet the throughput and reliability demand posed by VANET. Hence, NC is proposed to perform additional functions on the packet in the source or intermediate node(s). However, the chances to perform NC in wireless network is highly unlikely if the packet is not transmit to the potential NC node. Therefore, GA based network routing (GANeR) is embedded into network to search for shortest path from the source to the destination. It showed that the developed GANER in this work provides a better route with coding opportunities and reduces energy consumption in the network. The total energy consumed by GANER is 5.6% fewer than NC in wireless network transmission and forwarding structure (COPE)

SUMO ENHANCEMENT FOR VEHICULAR COMMUNICATION DEVELOPMENT

It is normal that every family is having at least one vehicle at their home as vehicles have become a daily needs for all of us. However, this also leads to the increased of road accidents where major causes are related to human errors which can be prevented. To tackle with this problem, vehicular ad hoc network (VANET) is introduced with the aim to make vehicles intelligent. In order to study the algorithm in VANET, a mobility simulator is needed for simulation purpose. In this case, SUMO is proved to be a good simulation tool in generating VANET environment while MATLAB is good for algorithm development. Yet, to develop a good simulation platform, modification on SUMO files are necessary. This paper discusses on the procedures in creating a left-hand traffic (LHT) simulation file that is suitable to be used in Malaysia. LHT simulation is not easy to achieve as modification on the road connection and traffic light files are required. This paper also showed the results of the simulation after SUMO files modification. Apart from that, this paper also showed the simulation of VANET environment using SUMO and MATLAB through a third party interfacing named TraCI4Matlab, which allows communication between MATLAB and SUMO simulator

Exploration of genetic algorithm in network coding for wireless sensor networks

Wireless network comprises of multiples nodes that work together to form a network. Each node in a wireless network communicates with one another by disseminating information packet among them. Source node and destination node are often far apart from each other, thus the information packet has to be transmitted to intermediate node(s) before it is able to be relayed to its destination. Network coding is introduced to combine several packets from different sources and broadcast the combined packet to several destinations in single transmission time slot. Each destination is capable to extract the intended information by decoding from a common packet. In short, network coding improves the throughput for wireless and wired networks but also causes side effects such as complexity of packets management and increases delay for coding opportunity. Hence, genetic algorithm is used to optimize the resources for network coding. Genetic algorithm will search for optimum routes to the destination according to the desired throughput with a desired multicast rate. In this paper, genetic algorithm is further enhanced in searching of optimum route for a packet. The simulation results show the enhanced genetic algorithm can adapt to various situations with different topologies with a better throughput and energy consumption compared to the store-and-forward method used in conventional wireless sensor network

HYBRID SIMULATION NETWORK FOR VEHICULAR AD HOC NETWORK (VANET)

Intelligent Transportation Systems (ITS) plays a vital role in providing different means of traffic management and enables users to be better informed of traffic condition, promoting safer, coordinated and efficient use of transport network. Vehicular Ad Hoc Network (VANET) shows promising reliability and validity in ITS. But, it poses challenges to researchers in designing protocol specifically for VANET as the deployment of VANET in real world will incur high cost. Therefore, simulation and non-physical testbed implementation have been widely adopted by the VANET research community in the development and assessment of the new or improved system and protocol of VANET. This paper presents a viable simulation platform for network development. Besides, a code cast or better known as network coding, a data packet transmission method has been developed and introduced into VANET protocol using the presented platform to assess and determine the potential of the introduced simulation platform

Functional characterization of D9, a novel deazaneplanocin a (DZNep) analog, in targeting acute myeloid leukemia (AML)

10.1371/journal.pone.0122983PLoS ONE104e012298

A risk- and complexity-rating framework for investment products

Sim Kee Boon Institute for Financial Economics SKB

Advanced fault detection in DC microgrid system using reinforcement learning

As technologies are expanding, the demand for power supply also increases. This causes the demand for power is difficult to be fulfilled as non-renewable sources are reducing. Therefore, the microgrid concept is introduced, where it is constructed with renewable energy sources, energy storage devices and loads. There are two types of microgrid, which are alternating current (AC) microgrid and direct current (DC) microgrid. Various research show that DC microgrid has more advantages over AC microgrid. However, DC microgrid is not widely used due to the lack of studies on it compared to AC microgrid. Besides, DC microgrid has one significant problem not fixed, which is the fault in the DC microgrid. Whenever a fault occurs, the whole DC microgrid will be affected rapidly. Therefore, this project aims to design a fault detector based on artificial intelligence to detect the fault and isolate the fault effectively. A fault detector based artificial intelligence should be implemented into the DC microgrid system to protect it. Two techniques in Artificial Immune System are being compared. The results showed that the improved Negative Selection Algorithm with variable sized detector has better performance than the general Negative Selection Algorithm with constant sized radius in detecting fault in DC microgrid system