Computer Vision and Abnormal Patient Gait: A Comparison of Methods

Abnormal gait, falls and its associated complications have high morbidity and mortality. Computer vision detects, predicts gait abnormalities, assesses fall risk, and serves as a clinical decision support tool for physicians. This paper performs a systematic review of computer vision, machine learning techniques to analyse abnormal gait. This literature outlines the use of different machine learning and poses estimation algorithms in gait analysis that includes partial affinity fields, pictorial structures model, hierarchical models, sequential-prediction-framework-based approaches, convolutional pose machines, gait energy image, 2-Directional 2-dimensional principles component analysis ((2D) 2PCA) and 2G (2D) 2PCA) Enhanced Gait Energy Image (EGEI), SVM, ANN, K-Star, Random Forest, KNN, to perform the image classification of the features extracted inpatient gait abnormalities.

A Hybrid Teaching Mode Based on Machine Learning Algorithm

Background: Hybrid teaching mode is a new trend under the Education Informatization environment, which combines the advantages of educators’ supervision offline and learners’ self-regulated learning online. Capturing learners’ learning behavior data becomes easy both from the traditional classroom and online platform. Methods: If machine learning algorithms can be applied to mine valuable information underneath those behavior data, it will provide scientific evidence and contribute to wise decision making as well as effective teaching process designing by educators. Results: This paper proposed a hybrid teaching mode utilizing machine learning algorithms, which uses clustering analysis to analyze the learner’s characteristics and introduces a support vector machine to predict future learning performance. The hybrid mode matches the predicted results to carry out the offline teaching process. Conclusion: Simulation results on about 356 students’ data on one specific course in a certain semester demonstrate that the proposed hybrid teaching mode performs very well by analyzing and predicting the learners’ performance with high accuracies.

An Efficient Approach for Vehicle Number Plate Recognition in Pakistan

Introduction: Recognition of Vehicle License Number Plates (VLNP) is an important task. It is valuable in numerous applications, such as entrance admission, security, parking control, road traffic control, and speed control. An ANPR (Automatic Number Plate Recognition) is a system in which the image of the vehicle is captured through high definition cameras. The image is then used to detect vehicles of any type (car, van, bus, truck, and bike, etc.), its’ color (white, black, blue, etc.), and its’ model (Toyota Corolla, Honda Civic etc.). Furthermore, this image is processed using segmentation and OCR techniques to get the vehicle registration number in form of characters. Once the required information is extracted from VLNP, this information is sent to the control center for further processing. Aim: ANPR is a challenging problem, especially when the number plates have varying sizes, the number of lines, fonts, background diversity, etc. Different ANPR systems have been suggested for different countries, including Iran, Malaysia, and France. However, only a limited work exists for Pakistan vehicles. Therefore, in this study, we aim to propose a novel ANPR framework for Pakistan VLNP recognition. Methods: The proposed ANPR system functions in three different steps: (i) - Number Plate Localization (NPL); (ii)- Character Segmentation (CS); and (iii)- Optical Character Recognition (OCR), involving template-matching mechanism. The proposed ANPR approach scans the number plate and instantly checks against database records of vehicles of interest. It can further extract the real=time information of driver and vehicle, for instance, license of the driver and token taxes of vehicles are paid or not, etc. Results: Finally, the proposed ANPR system has been evaluated on several real-time images from various formats of number plates practiced in Pakistan territory. In addition to this, the proposed ANPR system has been compared with the existing ANPR systems proposed specifically for Pakistani licensed number plates. Conclusion: The proposed ANPR Model has both time and money-saving profit for law enforcement agencies and private organizations for improving homeland security. There is a need to expand the types of vehicles that can be detected: trucks, buses, scooters, bikes. This technology can be further improved to detect the crashed vehicle’s number plate in an accident and alert the closest hospital and police station about the accident, thus saving lives.

Prostate Cancer Detection in Screening Using Magnetic Resonance Imaging and Artificial Intelligence

Background: Prostate cancer is a leading cause of death among men who do not participate in a screening programme. MRI forms a possible alternative for prostate analysis of a higher level of sensitivity than the PSA test or biopsy. Magnetic resonance is a non-invasive method and magnetic resonance tomography produces a large amount of data. If a screening programme were implemented, a dramatic increase in radiologist workload and patient waiting time will follow. Computer Aided-Diagnose (CAD) could assist radiologists to decrease reading times and cost, and increase diagnostic effectiveness. CAD mimics radiologist and imaging guidelines to detect prostate cancer. Aim: The purpose of this study was to analyse and describe current research in MRI prostate examination with the aid of CAD. The aim was to determine if CAD systems form a reliable method for use in prostate screening. Methods: This study was conducted as a systematic literature review of current scientific articles. Selection of articles was carried out using the “Preferred Reporting Items for Systematic Reviews and for Meta-Analysis” (PRISMA). Summaries were created from reviewed articles and were then categorised into relevant data for results. Results: CAD has shown that its capability concerning sensitivity or specificity is higher than a radiologist. A CAD system can reach a peak sensitivity of 100% and two CAD systems showed a specificity of 100%. CAD systems are highly specialised and chiefly focus on the peripheral zone, which could mean missing cancer in the transition zone. CAD systems can segment the prostate with the same effectiveness as a radiologist. Conclusion: When CAD analysed clinically-significant tumours with a Gleason score greater than 6, CAD outperformed radiologists. However, their focus on the peripheral zone would require the use of more than one CAD system to analyse the entire prostate.

Knowledge-Based Programming for the Cybersecurity Solution

Introduction:The problem of cyberattacks reduces to the unwanted infiltration of software through latent vulnerable access points. There are several approaches to protection here. First, unknown or improper system states can be detected through their characterization (using neural nets and/or symbolic codes), then interrupting the execution to run benchmarks and observe if they produce the states they should. If not, the execution can be rewound to the last successful benchmark, all states restored, and rerun.Methods:This will only work for cyber-physical systems that can be rewound. Benchmarks will often include sensory information. The second approach is termed, “semantic randomization”. This is similar to the well-known compiler technique known as “syntactic randomization”. The significant difference is that different variants of the algorithm itself are being automatically programmed. Cyberattacks will generally not be successful at more than one variant. This means that cybersecurity is moving us towards automatic programming as a desirable consequence. Knowledge-Based Software Engineering (KBSE) is the way to achieve automatic programming in practice.Discussion:There is non-determinism in the execution of such systems, which provides cybersecurity. Knowledge-based algorithmic compilers are the ultimate solution for scalable cost-effective cybersecurity. However, unlike the case for the less-secure syntactic randomization, the cost-effectiveness of semantic randomization is a function of scale. A simple randomization-based automatic programming method is illustrated and discussed.Conclusion:Semantic randomization is overviewed and compared against other technologies used to protect against cyberattack. Not only does semantic randomization itself, or in combination with other methodologies, offer improved protection; but, it serves as the basis for a methodology for automatic programming, which in turn makes the semantic randomization methodology for cybersecurity cost-effective.

Influence of Probability of Variation Operator on the Performance of Quantum-Inspired Evolutionary Algorithm for 0/1 Knapsack Problem

Quantum-Inspired Evolutionary Algorithm (QEA) has been shown to be better performing than classical Genetic Algorithm based evolutionary techniques for combinatorial optimization problems like 0/1 knapsack problem. QEA uses quantum computing-inspired representation of solution called Q-bit individual consisting of Q-bits. The probability amplitudes of the Q-bits are changed by application of Q-gate operator, which is classical analogous of quantum rotation operator. The Q-gate operator is the only variation operator used in QEA, which along with some problem specific heuristic provides exploitation of the properties of the best solutions. In this paper, we analyzed the characteristics of the QEA for 0/1 knapsack problem and showed that a probability in the range 0.3 to 0.4 for the application of the Q-gate variation operator has the greatest likelihood of making a good balance between exploration and exploitation. Experimental results agree with the analytical finding.

Authentication of JPEG Images Based on Genetic Algorithms

This paper presents an efficient authentication method for JPEG images based on Genetic Algorithms (GA). The current authentication methods for JPEG images require the receivers to know the quantization table beforehand in order to authenticate the images. Moreover, the quantization tables used in the JPEG compression are different for different quality factors, thus increasing the burden on the receivers to maintain several quantization tables. We propose a novel GA-based method which possesses three advantages. First, the computation at the receiver end is simplified. Second, it is no more required for the receivers to maintain quantization tables. Third, the method is resistant against Vector Quantization (VQ) and Copy-Paste (CP) attacks by generating the authentication information which is unique with respect to each block and each image. Furthermore, we develop a two- level detection strategy to reduce the false acceptance ratio of invalid blocks. Experimental results show that the proposed GA-based method can successfully authenticate JPEG images under variant attacks.