Image enlargement using multiple sensors

Image sensing is generally performed with multiple spectral sensors. For example, combination of three sensors (red, green, and blue) is used for color image reproduction, and electrooptical and infrared sensors are used for surveillance and satellite imaging, respectively. The resolution of each sensor can be intensified by taking the other sensors into account and applying correlations between different sensors. There are various successful applications of image enlargement using multiple sensors and even multimodal sensors. However, there still are several open issues in sensor processing which can be explained by signal processing-based image enlargement using redundancy among the sensors

A GPT-Based Approach for Scientometric Analysis: Exploring the Landscape of Artificial Intelligence Research

This study presents a comprehensive approach that addresses the challenges of scientometric analysis in the rapidly evolving field of Artificial Intelligence (AI). By combining search terms related to AI with the advanced language processing capabilities of generative pre-trained transformers (GPT), we developed a highly accurate method for identifying and analyzing AI-related articles in the Web of Science (WoS) database. Our multi-step approach included filtering articles based on WoS citation topics, category, keyword screening, and GPT classification. We evaluated the effectiveness of our method through precision and recall calculations, finding that our combined approach captured around 94% of AI-related articles in the entire WoS corpus with a precision of 90%. Following this, we analyzed the publication volume trends, revealing a continuous growth pattern from 2013 to 2022 and an increasing degree of interdisciplinarity. We conducted citation analysis on the top countries and institutions and identified common research themes using keyword analysis and GPT. This study demonstrates the potential of our approach to facilitate accurate scientometric analysis, by providing insights into the growth, interdisciplinary nature, and key players in the field.Comment: 29 pages, 10 figures, 5 table

Brain Differences Between Men and Women: Evidence From Deep Learning

Do men and women have different brains? Previous neuroimage studies sought to answer this question based on morphological difference between specific brain regions, reporting unfortunately conflicting results. In the present study, we aim to use a deep learning technique to address this challenge based on a large open-access, diffusion MRI database recorded from 1,065 young healthy subjects, including 490 men and 575 women healthy subjects. Different from commonly used 2D Convolutional Neural Network (CNN), we proposed a 3D CNN method with a newly designed structure including three hidden layers in cascade with a linear layer and a terminal Softmax layer. The proposed 3D CNN was applied to the maps of factional anisotropy (FA) in the whole-brain as well as specific brain regions. The entropy measure was applied to the lowest-level image features extracted from the first hidden layer to examine the difference of brain structure complexity between men and women. The obtained results compared with the results from using the Support Vector Machine (SVM) and Tract-Based Spatial Statistics (TBSS). The proposed 3D CNN yielded a better classification result (93.3%) than the SVM (78.2%) on the whole-brain FA images, indicating gender-related differences likely exist in the whole-brain range. Moreover, high classification accuracies are also shown in several specific brain regions including the left precuneus, the left postcentral gyrus, the left cingulate gyrus, the right orbital gyrus of frontal lobe, and the left occipital thalamus in the gray matter, and middle cerebellum peduncle, genu of corpus callosum, the right anterior corona radiata, the right superior corona radiata and the left anterior limb of internal capsule in the while matter. This study provides a new insight into the structure difference between men and women, which highlights the importance of considering sex as a biological variable in brain research

Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases

Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”, we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI

Image processing for plastic surgery planning

This thesis presents some image processing tools for plastic surgery planning. In particular, it presents a novel method that combines local and global context in a probabilistic relaxation framework to identify cephalometric landmarks used in Maxillofacial plastic surgery. It also uses a method that utilises global and local symmetry to identify abnormalities in CT frontal images of the human body. The proposed methodologies are evaluated with the help of several clinical data supplied by collaborating plastic surgeons

Smart attendance monitoring system using computer vision.

Masters Degree. University of KwaZulu-Natal, Durban.Monitoring of student’s attendance remains the fundamental and vital part of any educational institution. The attendance of students to classes can have an impact on their academic performance. With the gradual increase in the number of students, it becomes a challenge for institutions to manage their attendance. The traditional attendance monitoring system requires considerable amount of time due to manual recording of names and circulation of the paper-based attendance sheet for students to sign their names. The paper-based attendance recording method and some existing automated systems such as mobile applications, Radio Frequency Identification (RFID), Bluetooth, and fingerprint attendance models are prone to fake results and time wasting. The limitations of the traditional attendance monitoring system stimulated the adoption of computer vision to stand in the gap. Student’s attendance can be monitored with biometric candidate’s systems such as iris recognition system and face recognition system. Among these, face recognition have a greater potential because of its non-intrusive nature. Although some automated attendance monitoring systems have been proposed, poor system modelling negatively affects the systems. In order to improve success of the automated systems, this research proposes the smart attendance monitoring system that uses facial recognition to monitor student’s attendance in a classroom. A time integrated model is provided to monitor student’s attendance throughout the lecture period by registering the attendance information at regular time intervals. Multi-camera system is also proposed to guarantee an accurate capturing of students. The proposed multi-camera based system is tested using a real-time database in an experimental class from the University of KwaZulu-Natal (UKZN). The results show that the proposed smart attendance monitoring System is reliable, with the average accuracy rate of 98%.Examiner's copy of thesis

A Review on MAS-Based Sentiment and Stress Analysis User-Guiding and Risk-Prevention Systems in Social Network Analysis

[EN] In the current world we live immersed in online applications, being one of the most present of them Social Network Sites (SNSs), and different issues arise from this interaction. Therefore, there is a need for research that addresses the potential issues born from the increasing user interaction when navigating. For this reason, in this survey we explore works in the line of prevention of risks that can arise from social interaction in online environments, focusing on works using Multi-Agent System (MAS) technologies. For being able to assess what techniques are available for prevention, works in the detection of sentiment polarity and stress levels of users in SNSs will be reviewed. We review with special attention works using MAS technologies for user recommendation and guiding. Through the analysis of previous approaches on detection of the user state and risk prevention in SNSs we elaborate potential future lines of work that might lead to future applications where users can navigate and interact between each other in a more safe way.This work was funded by the project TIN2017-89156-R of the Spanish government.Aguado-Sarrió, G.; Julian Inglada, VJ.; García-Fornes, A.; Espinosa Minguet, AR. (2020). A Review on MAS-Based Sentiment and Stress Analysis User-Guiding and Risk-Prevention Systems in Social Network Analysis. Applied Sciences. 10(19):1-29. https://doi.org/10.3390/app10196746S1291019Vanderhoven, E., Schellens, T., Vanderlinde, R., & Valcke, M. (2015). Developing educational materials about risks on social network sites: a design based research approach. Educational Technology Research and Development, 64(3), 459-480. doi:10.1007/s11423-015-9415-4Teens and ICT: Risks and Opportunities. 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IEEE Latin America Transactions, 14(11), 4560-4566. doi:10.1109/tla.2016.7795829Vizer, L. M., Zhou, L., & Sears, A. (2009). Automated stress detection using keystroke and linguistic features: An exploratory study. International Journal of Human-Computer Studies, 67(10), 870-886. doi:10.1016/j.ijhcs.2009.07.005Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82-89. doi:10.1145/2436256.2436274Schouten, K., & Frasincar, F. (2016). Survey on Aspect-Level Sentiment Analysis. IEEE Transactions on Knowledge and Data Engineering, 28(3), 813-830. doi:10.1109/tkde.2015.2485209Ji, R., Cao, D., Zhou, Y., & Chen, F. (2016). Survey of visual sentiment prediction for social media analysis. Frontiers of Computer Science, 10(4), 602-611. doi:10.1007/s11704-016-5453-2Li, L., Cao, D., Li, S., & Ji, R. (2015). 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Evaluation and analysis of hybrid intelligent pattern recognition techniques for speaker identification

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The rapid momentum of the technology progress in the recent years has led to a tremendous rise in the use of biometric authentication systems. The objective of this research is to investigate the problem of identifying a speaker from its voice regardless of the content (i.e. text-independent), and to design efficient methods of combining face and voice in producing a robust authentication system. A novel approach towards speaker identification is developed using wavelet analysis, and multiple neural networks including Probabilistic Neural Network (PNN), General Regressive Neural Network (GRNN)and Radial Basis Function-Neural Network (RBF NN) with the AND voting scheme. This approach is tested on GRID and VidTIMIT cor-pora and comprehensive test results have been validated with state- of-the-art approaches. The system was found to be competitive and it improved the recognition rate by 15% as compared to the classical Mel-frequency Cepstral Coe±cients (MFCC), and reduced the recognition time by 40% compared to Back Propagation Neural Network (BPNN), Gaussian Mixture Models (GMM) and Principal Component Analysis (PCA). Another novel approach using vowel formant analysis is implemented using Linear Discriminant Analysis (LDA). Vowel formant based speaker identification is best suitable for real-time implementation and requires only a few bytes of information to be stored for each speaker, making it both storage and time efficient. Tested on GRID and Vid-TIMIT, the proposed scheme was found to be 85.05% accurate when Linear Predictive Coding (LPC) is used to extract the vowel formants, which is much higher than the accuracy of BPNN and GMM. Since the proposed scheme does not require any training time other than creating a small database of vowel formants, it is faster as well. Furthermore, an increasing number of speakers makes it di±cult for BPNN and GMM to sustain their accuracy, but the proposed score-based methodology stays almost linear. Finally, a novel audio-visual fusion based identification system is implemented using GMM and MFCC for speaker identi¯cation and PCA for face recognition. The results of speaker identification and face recognition are fused at different levels, namely the feature, score and decision levels. Both the score-level and decision-level (with OR voting) fusions were shown to outperform the feature-level fusion in terms of accuracy and error resilience. The result is in line with the distinct nature of the two modalities which lose themselves when combined at the feature-level. The GRID and VidTIMIT test results validate that the proposed scheme is one of the best candidates for the fusion of face and voice due to its low computational time and high recognition accuracy

Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH): A Novel Feature Extraction Technique

In this paper, we present a novel feature extraction technique, termed Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH), and exploit it to detect breast cancer in volumetric medical images. The technique is incorporated as part of an intelligent expert system that can aid medical practitioners making diagnostic decisions. Analysis of volumetric images, slice by slice, is cumbersome and inefficient. Hence, 3D-LESH is designed to compute a histogram-based feature set from a local energy map, calculated using a phase congruency (PC) measure of volumetric Magnetic Resonance Imaging (MRI) scans in 3D space. 3D-LESH features are invariant to contrast intensity variations within different slices of the MRI scan and are thus suitable for medical image analysis.The contribution of this article is manifold. First, we formulate a novel 3D-LESH feature extraction technique for 3D medical images to analyse volumetric images. Further, the proposed 3D-LESH algorithmis, for the first time, applied to medical MRI images. The final contribution is the design of an intelligent clinical decision support system (CDSS) as a multi-stage approach, combining novel 3D-LESH feature extraction with machine learning classifiers, to detect cancer from breast MRI scans. The proposed system applies contrast-limited adaptive histogram equalisation (CLAHE) to the MRI images before extracting 3D-LESH features. Furthermore, a selected subset of these features is fed into a machine-learning classifier, namely, a support vector machine (SVM), an extreme learning machine (ELM) or an echo state network (ESN) classifier, to detect abnormalities and distinguish between different stages of abnormality. We demonstrate the performance of the proposed technique by its application to benchmark breast cancer MRI images. The results indicate high-performance accuracy of the proposed system (98%±0.0050, with an area under a receiver operating charactertistic curve value of 0.9900 ± 0.0050) with multiple classifiers. When compared with the state-of-the-art wavelet-based feature extraction technique, statistical analysis provides conclusive evidence of the significance of our proposed 3D-LESH algorithm

A review on deep-learning-based cyberbullying detection

Bullying is described as an undesirable behavior by others that harms an individual physically, mentally, or socially. Cyberbullying is a virtual form (e.g., textual or image) of bullying or harassment, also known as online bullying. Cyberbullying detection is a pressing need in today’s world, as the prevalence of cyberbullying is continually growing, resulting in mental health issues. Conventional machine learning models were previously used to identify cyberbullying. However, current research demonstrates that deep learning surpasses traditional machine learning algorithms in identifying cyberbullying for several reasons, including handling extensive data, efficiently classifying text and images, extracting features automatically through hidden layers, and many others. This paper reviews the existing surveys and identifies the gaps in those studies. We also present a deep-learning-based defense ecosystem for cyberbullying detection, including data representation techniques and different deep-learning-based models and frameworks. We have critically analyzed the existing DL-based cyberbullying detection techniques and identified their significant contributions and the future research directions they have presented. We have also summarized the datasets being used, including the DL architecture being used and the tasks that are accomplished for each dataset. Finally, several challenges faced by the existing researchers and the open issues to be addressed in the future have been presented