Roles of artificial intelligence in construction engineering and management: A critical review and future trends

Hand gesture for communication has proven effective for humans, and active research is ongoing in replicating the same success in computer vision systems. Human–computer interaction can be significantly improved from advances in systems that are capable of recognizing different hand gestures. In contrast to many earlier works, which consider the recognition of significantly differentiable hand gestures, and therefore often selecting a few gestures from the American Sign Language (ASL) for recognition, we propose applying deep learning to the problem of hand gesture recognition for the whole 24 hand gestures obtained from the Thomas Moeslund’s gesture recognition database. We show that more biologically inspired and deep neural networks such as convolutional neural network and stacked denoising autoencoder are capable of learning the complex hand gesture classification task with lower error rates. The considered networks are trained and tested on data obtained from the above-mentioned public database; results comparison is then made against earlier works in which only small subsets of the ASL hand gestures are considered for recognition.

Deep learning in vision-based static hand gesture recognition

A new Approach for Classifying Coronavirus COVID-19 based on its Manifestation on Chest X-Rays using Texture Features and Neural Networks

For a productive and a good life, education is a necessity and it improves individuals' life with value and excellence. Also, education is considered a vital need for motivating self-assurance as well as providing the things are needed to partake in today's World. Throughout the years, education faced a number of challenges. Different methods of teaching and learning are suggested to increase the learning quality. In today's world, computers and portable devices are employed in every phase of daily life and many materials are available online anytime, anywhere. Technologies like Artificial Intelligence had a surprising evolution in many fields especially in educational teaching and learning processes. Higher education institutions have started to adopt the use of technology into their traditional teaching mechanisms for enhancing learning and teaching. In this paper, two datasets have been considered for the prediction and classification of student performance respectively using five machine learning algorithms. Eighteen experiments have been performed and preliminary results suggest that performances of students might be predictable and classification of these performances can be increased by applying pre-processing to the raw data before implementing machine learning algorithms.

Student Performance Prediction and Classification Using Machine Learning Algorithms

The cases identified with Brain tumor have increased with respect to time owing to various reasons. One of the major challenging issues can be defined by incorporating image processing along with data mining models as classification approach. There are various procedures as of now exhibited for segmentation of brain tumor effectively. In any case, it is as yet unequivocal to distinguish the brain tumor from MR images. In this new tumor classifying, considering two significant models, such as Feature Selection (FS) and Machine Learning classification techniques, are extremely valuable for distinguishing and visualizing the tumor in the MRI brain images; it is classified using Adaptive Neuro‐Fuzzy Interface System (ANFIS). For better classification of image, Optimal Feature Level Fusion (OFLF) is considered to fuse low and high‐level feature of brain image; from this analysis, the images are classifying as Benign or Malignant. From this implementation of medical images, the experiment results are evaluating performance metrics are compared existing classifiers. From the proposed MRI image classification process the accuracy as 96.23%, sensitivity as 92.3%, and specificity as 94.52%, compared to existing classifier. It is in the working platform of MATLAB that this proposed methodology is implemented.

Optimal feature level fusion based ANFIS classifier for brain MRI image classification

The fundamental goal of image data compression is to set an optimal compression ratio while maintaining an acceptable reproduction quality. This study describes the principles of design of image compression system that automatically sets an optimal compression ratio for particular image content by identifying the image compression method while maintaining a tolerable reproduction quality. The proposed image compression system employs subjective and objective criteria for an assessment of the quality of the image compression system. The supervised artificial neural network is used to identify a compression method among different compression techniques by using subjective criterion. An objective criterion is used to determine an optimal compression ratio, which is calculated by using linear regression analysis that establishes analytical expression between a compression ratio, a property of an image and a reconstructed quality of an image. It was proved that the system is an effective tool for medical image compression applications.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2019.0114

Image compression system with an optimisation of compression ratio

This paper presents a new pedestrian detection algorithm used in Advanced Driver-Assistance System with only one camera aiming to improving traffic safety. The new pedestrian detection algorithm differs from traditional pedestrian detection algorithm, which only focuses on pedestrian detection rate or pedestrian detection accuracy. Conversely, the proposed algorithm focuses on both the accuracy and the rate. Some new features are proposed to improve pedestrian detection rate of the system. Also color difference was used to decrease the false detecting rate. The experimental results show that the pedestrian detection rate can be around 90% and the false detecting rate is 3%.

/pdf/static-and-dynamic-pedestrian-detection-algorithm-for-visual-3onl8qa8tx.pdf

Static and Dynamic Pedestrian Detection Algorithm for Visual Based Driver Assistive System

Several fruit recognition researches have been performed on so many fruits cutting across orange, banana, strawberry, apple, etc. But little or no consideration has been given to pawpaw. This might be due to its complex nature. Identification of ripe pawpaw from unripe species using colour-based feature extraction method and back propagation neural network (BPNN) model is a complex task since a green looking pawpaw could be ripped when analysed. In this paper, the ripeness of pawpaw was determined by a simple colour recognition algorithm using a BPNN model. RGB colour components of the captured images are extracted after the pawpaw images are resized. After the application of a simple heuristic method, the colour components of the resized images are rescaled. Finally, the rescaled images’ colour histogram is obtained and used as feature vector and the BPNN model uses the feature vector to classify the pawpaw species. The proposed model has an accuracy of 97\(\%\).

Automated Classification of Fruits: Pawpaw Fruit as a Case Study

With the emergence of COVID-19, our lifestyles have changed. Because of its high infectivity and high severity, fighting with this virus has become a global problem in no time. Minimizing the speed of spread and keeping the numbers under control is a way to save more people by taking care of them better and also have more time spent on research to find a cure such as medicine or vaccine which will put an end to this situation. During this time, personal protection like using face masks is very important since it protects people and others close to them and significantly reduces the risk of infection on correct usage. Unfortunately, some people can act careless or reckless which puts many people at risk. This leads us to use automated systems in crowded places to detect those who do not follow the rules. When it comes to automated systems, artificial intelligence-related applications are favorable for supporting humans. In this paper, at first, a dataset was obtained by adding face masks to the existing Labeled Faces in the Wild (LFW) dataset to detect three mask-wearing conditions; correct, wrong and no mask. Then NASNetMobile and ResNet50 networks were trained using the considered dataset. The ResNet50 outperformed the NASNetMobile by achieving 92% detection accuracy.

Face Mask Detection and Classification for COVID-19 using Deep Learning

Computers and portable devices are employed in all phases of todays world, and many large collections of materials are available electronically. These developments have created new ways for educators to communicate with learners. Higher education institutions have started to build blended learning approaches, which adopt the use of virtual learning environments (VLEs) into their traditional teaching mechanisms for enhancing learning and teaching, through housing both instructor-generated and learner-generated content. E-learning has been gaining more mainstream adoption in higher education and it is a powerful tool as a complement to the traditional resources on a module, but not a substitute. Findings suggest that students use e-learning materials for reviewing the concepts discussed in lectures they previously attended and they are more effective revision tools than their textbooks and more effcient than learners’ own notes. Technological innovations are providing new opportunities to alter the nature and delivery of teaching without necessarily increasing tutor workloads. Multi-disciplinary fields have been emerged to increase quality of higher education. Apart from VLEs, artificial neural networks are used to understand, differenciate and improve learning strategies of the students and provide detailed information to the educators for improving students’ learning strategies. The prediction of eye direction detection is the one of the popular research topic in human computer interaction area. This paper defines eye gaze detection system for higher education students using VLEs, that predicts students’ motivation through the online sessions. Actually, determining the position of eyes is difficult to estimate the location of gaze which is more challenging. The database of the suggested research is organized as gaze directions of right, left and centre. The database is created with varied ages of images. In this paper, image processing techniques have been applied to the captured images, then back propagation neural networks will be applied in order to categorize the gaze images, providing a detailed information to the educators for understanding the students’ interest on the subject, topics and provided notes and examples.

Kamil Dimililer

Papers

Image compression system with an optimisation of compression ratio

Static and Dynamic Pedestrian Detection Algorithm for Visual Based Driver Assistive System

Automated Classification of Fruits: Pawpaw Fruit as a Case Study

Face Mask Detection and Classification for COVID-19 using Deep Learning

The effectiveness of a new classification system in higher education as a new e-learning tool