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Glaucoma is a leading cause of irreversible visual field degradation, significantly impacting ocular health. Timely identification and diagnosis of this condition are critical to prevent vision loss. A range of diagnostic techniques is employed to achieve this, from traditional methods reliant on expert interpretation to modern, fully computerized diagnostic approaches. The integration of computerized systems designed for the early detection and classification of clinical indicators of glaucoma holds immense potential to enhance the accuracy of disease diagnosis. Pupillary Light Reflex (PLR) analysis emerges as a promising avenue for glaucoma screening, mainly due to its cost-effectiveness compared to exams such as Optical Coherence Tomography (OCT), Humphrey Field Analyzer (HFA), and fundoscopic examinations. The noninvasive nature of PLR testing obviates the need for disposable components and agents for pupil dilation. This facilitates multiple successive administrations of the test and enables the possibility of remote execution. This study aimed to improve the automated diagnosis of glaucoma using PLR data, conducting an extensive comparative analysis incorporating neural networks and machine learning techniques. It also compared the performance of different data processing methods, including filtering techniques, feature extraction, data balancing, feature selection, and their effects on classification. The findings offer insights and guidelines for future methodologies in glaucoma screening utilizing pupillary light response signals.

This paper explores the challenges and potential solutions related to data collection, integration, processing, and utilization in defense manufacturing within the context of Industry 4.0. While Industry 4.0 envisions the integration of various technologies to achieve seamless operations in industries, the unique characteristics of defense manufacturing, such as stringent data limitations and security requirements, make direct translation challenging. Through a comprehensive review of academic literature, key themes were identified, including quality control, digitalization, cyber–physical aspects, sustainability, risk management, ownership of information, and security. Drawing from the reviewed publications, potential solutions were distilled into related approaches, such as data governance frameworks, data exchange standards, blockchain, additive manufacturing, transparent digital supply chains, and smart factories. These solutions present opportunities for the Australian defense manufacturing industry to overcome the identified challenges and leverage the benefits of Industry 4.0, including improved quality control, increased efficiency, enhanced security, and optimized supply chains. By embracing these opportunities, the defense manufacturing sector can successfully navigate the complexities of Industry 4.0 and realize its vision of seamless integration for continued growth and success.

Over the past two decades, computer-aided detection and diagnosis have emerged as a field of research. The primary goal is to enhance the diagnostic and treatment procedures for radiologists and clinicians in medical image analysis. With the help of big data and advanced artificial intelligence (AI) technologies, such as machine learning and deep learning algorithms, the healthcare system can be made more convenient, active, efficient, and personalized. The primary goal of this literature survey was to present a thorough overview of the most important developments related to computer-aided diagnosis (CAD) systems in medical imaging. This survey is of considerable importance to researchers and professionals in both medical and computer sciences. Several reviews on the specific facets of CAD in medical imaging have been published.

Nevertheless, the main emphasis of this study was to cover the complete range of capabilities of CAD systems in medical imaging. This review article introduces background concepts used in typical CAD systems in medical imaging by outlining and comparing several methods frequently employed in recent studies. This article also presents a comprehensive and well-structured survey of CAD in medicine, drawing on a meticulous selection of relevant publications. Moreover, it describes the process of handling medical images and introduces state-of-the-art AI-based CAD technologies in medical imaging, along with future directions of CAD. This study indicates that deep learning algorithms are the most effective method to diagnose and detect diseases.

To protect systems from malicious activities, it is important to differentiate between valid and harmful commands. One way to achieve this is by learning the syntax of the commands, which is a complex task because of the expansive and evolving nature of command syntax. To address this, we harnessed the power of a language model. Our methodology involved constructing a specialized vocabulary from our commands dataset, and training a custom tokenizer with a Masked Language Model head, resulting in the development of a BERT-like language model. This model exhibits proficiency in learning command syntax by predicting masked tokens. In comparative analyses, our language model outperformed the Markov Model in categorizing commands using clustering algorithms (DBSCAN, HDBSCAN, OPTICS). The language model achieved higher Silhouette scores (0.72, 0.88, 0.85) compared to the Markov Model (0.53, 0.25, 0.06) and demonstrated significantly lower noise levels (2.63%, 5.39%, 8.49%) versus the Markov Model’s higher noise rates (9.31%, 29.85%, 50.35%). Further validation with manually crafted syntax and BERTScore assessments consistently produced metrics above 0.90 for precision, recall, and F1-score. Our language model excels at learning command syntax, enhancing protective measures against malicious activities.

Privacy Impact Assessments (PIAs) offer a process for assessing the privacy impacts of a project or system. As a privacy engineering strategy, they are one of the main approaches to privacy by design, supporting the early identification of threats and controls. However, there is still a shortage of empirical evidence on their use and proven effectiveness in practice. To better understand the current literature and research, this paper provides a comprehensive Scoping Review (ScR) on the topic of PIAs “in the wild,” following the well-established Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. This ScR includes 45 studies, providing an extensive synthesis of the existing body of knowledge, classifying types of research and publications, appraising the methodological quality of primary research, and summarising the positive and negative aspects of PIAs in practice, as reported by those studies. This ScR also identifies significant research gaps (e.g., evidence gaps from contradictory results and methodological gaps from research design deficiencies), future research pathways, and implications for researchers, practitioners, and policymakers developing and using PIA frameworks. As we conclude, there is still a significant need for more primary research on the topic, both qualitative and quantitative. A critical appraisal of qualitative studies revealed deficiencies in the methodological quality, and only four quantitative studies were identified, suggesting that current primary research remains incipient. Nonetheless, PIAs can be regarded as a prominent sub-area in the broader field of empirical privacy engineering, in which further scientific research to support existing practices is needed.

The task of next basket recommendation is pivotal for recommender systems. It involves predicting user actions, such as the next product purchase or movie selection, by exploring sequential purchase behavior and integrating users’ general preferences. These elements may converge and influence users’ subsequent choices. The challenge intensifies with the presence of varied user purchase sequences in the training set, as indiscriminate incorporation of these sequences can introduce superfluous noise. In response to these challenges, we propose an innovative approach: the Selective Hierarchical Representation Model (SHRM). This model effectively integrates transactional data and user profiles to discern both sequential purchase transactions and general user preferences. The SHRM’s adaptability, particularly in employing nonlinear aggregation operations on user representations, enables it to model complex interactions among various influencing factors. Notably, the SHRM employs a Recurrent Neural Network (RNN) to capture extended dependencies in recent purchasing activities. Moreover, it incorporates an innovative sequence similarity task, grounded in a k-plet sampling strategy. This strategy clusters similar sequences, significantly mitigating the learning process’s noise impact. Through empirical validation on three diverse real-world datasets, we demonstrate that our model consistently surpasses leading benchmarks across various evaluation metrics, establishing a new standard in next-basket recommendation.

Steganalysis, a field devoted to detecting concealed information in various forms of digital media, including text, images, audio, and video files, has evolved significantly over time. This evolution aims to improve the accuracy of revealing potential hidden data. Traditional machine learning approaches, such as support vector machines (SVM) and ensemble classifiers (ECs), were previously employed in steganalysis. However, they demonstrated ineffective against contemporary and prevalent steganographic methods. The field of steganalysis has experienced noteworthy advancements by transitioning from traditional machine learning methods to deep learning techniques, resulting in superior outcomes. More specifically, deep learning-based steganalysis approaches exhibit rapid detection of steganographic payloads and demonstrate remarkable accuracy and efficiency across a spectrum of modern steganographic algorithms. This paper is dedicated to investigating recent developments in deep learning-based steganalysis schemes, exploring their evolution, and conducting a thorough analysis of the techniques incorporated in these schemes. Furthermore, the research aims to delve into the current trends in steganalysis, explicitly focusing on digital image steganography. By examining the latest techniques and methodologies, this work contributes to an enhanced understanding of the evolving landscape of steganalysis, shedding light on the advancements achieved through deep learning-based approaches.

Alzheimer’s disease, characterized by progressive and irreversible deterioration of cognitive functions, represents a significant health concern, particularly among older adults, as it stands as the foremost cause of dementia. Despite its debilitating nature, early detection of Alzheimer’s disease holds considerable advantages for affected individuals. This study investigates machine-learning methodologies for the early diagnosis of Alzheimer’s disease, utilizing datasets sourced from OASIS and ADNI. The initial classification methods consist of a 5-class ADNI classification and a 3-class OASIS classification. Three unique methodologies encompass binary-class inter-dataset models, which involve training on a single dataset and subsequently testing on another dataset for both ADNI and OASIS datasets. Additionally, a hybrid dataset model is also considered. The proposed methodology entails the concatenation of both datasets, followed by shuffling and subsequently conducting training and testing on the amalgamated dataset. The findings demonstrate impressive levels of accuracy, as Light Gradient Boosting Machine (LGBM) achieved a 99.63% accuracy rate for 5-class ADNI classification and a 95.75% accuracy rate by Multilayer Perceptron (MLP) for 3-class OASIS classification, both when hyperparameter tweaking was implemented. The K-nearest neighbor algorithm demonstrated exceptional performance, achieving an accuracy of 87.50% in ADNI-OASIS (2 Class) when utilizing the Select K Best method. The Gaussian Naive Bayes algorithm demonstrated exceptional performance in the OASIS-ADNI approach, attaining an accuracy of 77.97% using Chi-squared feature selection. The accuracy achieved by the Hybrid method, which utilized LGBM with hyperparameter optimization, was 99.21%. Furthermore, the utilization of Explainable AI approaches, particularly Lime, was implemented in order to augment the interpretability of the model.

This study delves into the comparative efficacy of YOLOv5 and YOLOv8 in corrosion segmentation tasks. We employed three unique datasets, comprising 4942, 5501, and 6136 images, aiming to thoroughly evaluate the models’ adaptability and robustness in diverse scenarios. The assessment metrics included precision, recall, F1-score, and mean average precision. Furthermore, graphical tests offered a visual perspective on the segmentation capabilities of each architecture. Our results highlight YOLOv8’s superior speed and segmentation accuracy across datasets, further corroborated by graphical evaluations. These visual assessments were instrumental in emphasizing YOLOv8’s proficiency in handling complex corroded surfaces. However, in the largest dataset, both models encountered challenges, particularly with overlapping bounding boxes. YOLOv5 notably lagged, struggling to achieve the performance standards set by YOLOv8, especially with irregular corroded surfaces. In conclusion, our findings underscore YOLOv8’s enhanced capabilities, establishing it as a preferable choice for real-world corrosion detection tasks. This research thus offers invaluable insights, poised to redefine corrosion management strategies and guide future explorations in corrosion identification.

Since early machine learning models, metrics such as accuracy and precision have been the de facto way to evaluate and compare trained models. However, a single metric number does not fully capture model similarities and differences, especially in the computer vision domain. A model with high accuracy on a certain dataset might provide a lower accuracy on another dataset without further insights. To address this problem, we build on a recent interpretability technique called Dissect to introduce inter-model interpretability, which determines how models relate or complement each other based on the visual concepts they have learned (such as objects and materials). Toward this goal, we project 13 top-performing self-supervised models into a Learned Concepts Embedding (LCE) space that reveals proximities among models from the perspective of learned concepts. We further crossed this information with the performance of these models on four computer vision tasks and 15 datasets. The experiment allowed us to categorize the models into three categories and revealed the type of visual concepts different tasks required for the first time. This is a step forward for designing cross-task learning algorithms.