A New Multi-threaded and Interleaving Approach to Enhance String Matching for Intrusion Detection Systems

String matching algorithms are computationally intensive operations in computer science. The algorithms find the occurrences of one or more strings patterns in a larger string or text. String matching algorithms are important for network security, biomedical applications, Web search, and social networks. Nowadays, the high network speeds and large storage capacity put a high requirement on string matching methods to perform the task in a short time. Traditionally, Aho-Corasick algorithm, which is used to find the string matches, is executed sequentially. In this paper, a new multi-threaded and interleaving approach of Aho-Corasick using graphics processing units (GPUs) is designed and implemented to achieve high-speed string matching. Compute Unified Device Architecture (CUDA) programming language is used to implement the proposed parallel version. Experimental results show that our approach achieves more than 5X speedup over the sequential and other parallel implementations. Hence, a wide range of applications can benefit from our solution to perform string matching faster than ever before

Eye Tracking-Based Diagnosis and Early Detection of Autism Spectrum Disorder Using Machine Learning and Deep Learning Techniques

Eye tracking is a useful technique for detecting autism spectrum disorder (ASD). One of the most important aspects of good learning is the ability to have atypical visual attention. The eye-tracking technique provides useful information about children’s visual behaviour for early and accurate diagnosis. It works by scanning the paths of the eyes to extract a sequence of eye projection points on the image to analyse the behaviour of children with autism. In this study, three artificial-intelligence techniques were developed, namely, machine learning, deep learning, and a hybrid technique between them, for early diagnosis of autism. The first technique, neural networks [feedforward neural networks (FFNNs) and artificial neural networks (ANNs)], is based on feature classification extracted by a hybrid method between local binary pattern (LBP) and grey level co-occurrence matrix (GLCM) algorithms. This technique achieved a high accuracy of 99.8% for FFNNs and ANNs. The second technique used a pre-trained convolutional neural network (CNN) model, such as GoogleNet and ResNet-18, on the basis of deep feature map extraction. The GoogleNet and ResNet-18 models achieved high performances of 93.6% and 97.6%, respectively. The third technique used the hybrid method between deep learning (GoogleNet and ResNet-18) and machine learning (SVM), called GoogleNet + SVM and ResNet-18 + SVM. This technique depends on two blocks. The first block used CNN to extract deep feature maps, whilst the second block used SVM to classify the features extracted from the first block. This technique proved its high diagnostic ability, achieving accuracies of 95.5% and 94.5% for GoogleNet + SVM and ResNet-18 + SVM, respectively

Intelligent traffic light flow control system using wireless sensors networks

Vehicular traffic is continuously increasing around the world, especially in large urban areas. The resulting congestion has become a major concern to transportation specialists and decision makers. The existing methods for traffic management, surveillance and control are not adequately efficient in terms of performance, cost, maintenance, and support. In this paper, the design of a system that utilizes and efficiently manages traffic light controllers is presented. In particular, we present an adaptive traffic control system based on a new traffic infrastructure using Wireless Sensor Network (WSN) and using new techniques for controlling the traffic flow sequences. These techniques are dynamically adaptive to traffic conditions on both single and multiple intersections. A WSN is used as a tool to instrument and control traffic signals roadways, while an intelligent traffic controller is developed to control the operation of the traffic infrastructure supported by the WSN. The controller embodies traffic system communication algorithm (TSCA) and the traffic signals time manipulation algorithm (TSTMA). Both algorithms are able to provide the system with adaptive and efficient traffic estimation represented by the dynamic change in the traffic signals ' flow sequence and traffic variation. Simulation results show the efficiency of the proposed scheme in solving traffic congestion in terms of the average waiting time and average queue length on the isolated (single) intersection and efficient global traffic flow control on multiple intersections. A test bed was also developed and deployed for real measurements. The paper concludes with some future highlights and useful remarks

How to co-create a multicultural dementia education initiative

Background: Culturally and linguistically diverse (CALD) people affected by dementia have poorer health outcomes and experience greater social exclusion than their non-CALD counterparts. These disparities are worsened by: linguistic barriers; inaccessible and culturally inappropriate education about dementia and relevant support services; and stigma. This is especially prominent in the multicultural South Western Sydney region, where over 12,500 residents are already living with dementia, and dementia prevalence is forecast to increase at the highest rate in NSW by 2050: up to 460 % in some local government areas. Culturally sensitive education about dementia is needed, but no such programs exist here. Objective: We aimed to co-create a dementia education initiative that was accessible, culturally sensitive, and tailored to meet the needs of English, Arabic, Vietnamese, Chinese, and Greek-speaking communities. Methods: We established a Dementia Alliance comprising representatives from the local dementia support group, council, university, and multicultural service providers. Through a series of co-creation workshops with English, Arabic, Vietnamese, Chinese, and Greek-speaking alliance members, we mapped out the education program’s content, structure, format, and evaluation methods to suit all cultural groups. This research discusses the barriers and enablers of disseminating useable and accessible dementia information. Findings: The Dementia Alliance adapted the global Dementia Friends initiative for multicultural delivery. The co-creation workshops revealed the following barriers to uptake of information: the stigmatised translations of ‘dementia’; lengthy duration (>2 hrs); online delivery; and long, high-literacy evaluation surveys. The key enablers were: advertising the education program as a ‘memory information session’; using trained bilingual educators along with an academic co-facilitator; acknowledging stigma; durations <2 hrs; in-person, oral delivery; and using plain language paper-based evaluation surveys with <30 items. Conclusion: Co-creating a multicultural dementia education program with information that is useable and accessible by CALD communities and service providers is feasible through partnerships. This work offers practical insights into knowledge mobilisation in multicultural settings and can be applied to other areas of health where disparities exist