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Systematic Analysis of COVID-19 Ontologies
No full textThis comprehensive study conducts an in-depth analysis of existing COVID-19 ontologies, scrutinizing their objectives, classifications, design methodologies, and domain focal points. The study is conducted through a dual-stage approach, commencing with a systematic review of relevant literature and followed by an ontological assessment utilizing a parametric methodology. Through this meticulous process, twenty-four COVID-19 Ontologies (CovOs) are selected and examined. The findings highlight the scope, intended purpose, granularity of ontology, modularity, formalism, vocabulary reuse, and extent of domain coverage. The analysis reveals varying levels of formality in ontology development, a prevalent preference for utilizing OWL as the representational language, and diverse approaches to constructing class hierarchies within the models. Noteworthy is the recurrent reuse of ontologies like OBO models (CIDO, GO, etc.) alongside CODO. The METHONTOLOGY approach emerges as a favored design methodology, often coupled with application-based or data-centric evaluation methods. Our study provides valuable insights for the scientific community and COVID-19 ontology developers, supplemented by comprehensive ontology metrics. By meticulously evaluating and documenting COVID-19 information-driven ontological models, this research offers a comparative cross-domain perspective, shedding light on knowledge representation variations. The present study significantly enhances understanding of CovOs, serving as a consolidated resource for comparative analysis and future development, while also pinpointing research gaps and domain emphases, thereby guiding the trajectory of future ontological advancements
Tight Security Bound of 2k-LightMAC_Plus
No full textIn ASIACRYPT’17, Naito proposed a beyond-birthday-bound variant of the LightMAC construction, called LightMAC_Plus, which is built on three independently keyed n-bit block ciphers, and showed that the construction achieves 2n/3-bits PRF security. Later, Kim et al. claimed (without giving any formal proof) its security bound to 23n/4. In FSE’18, Datta et al. have proposed a two-keyed variant of the LightMAC_Plus construction, called 2k-LightMAC_Plus, which is built on two independently keyed n-bit block ciphers, and showed that the construction achieves 2n/3-bits PRF security. In this paper, we show a tight security bound on the 2k-LightMAC_Plus construction. In particular, we show that it provably achieves security up to 23n/4 queries. We also exhibit a matching attack on the construction with the same query complexity and hence establishing the tightness of the security bound. To the best of our knowledge, this is the first work that provably shows a message length independent 3n/4-bit tight security bound on a block cipher based variable input length PRF with two block cipher keys
Explainable AI in medical image processing for health care
No full textThe integration of artificial intelligence (AI) techniques in medical image processing has revolutionized the field of healthcare. However, the lack of interpretability and transparency in traditional AI models has hindered their widespread adoption in clinical practice. Explainable AI (XAI) offers a solution to this problem by providing interpretable explanations for AI-based decisions. It is very difficult to interpret predictions made by classifiers on image data. Therefore, Explainable artificial intelligence (XAI) has gained significant attention in the field of healthcare, particularly in medical image processing. This chapter investigates the explainable AI methods in healthcare, focusing on their role in medical image processing. It uses XAI approaches such as LIME, and Grad-Cam for analyzing a Biopsy dataset. The random forest classifier achieved 96.54% accuracy better than Naive Bayes on the Biopsy data set. The GradCam approach is the fastest one and the slowest one is LIME on VGG-16. The VGG-16 is faster than ResNet-50 for image classification. The results of XAI models in medical image processing enable healthcare professionals to understand the decision-making process of AI models, leading to increased diagnostic accuracy, improved decision-making and enhanced trust in AI systems. The chapter concludes by outlining future research directions and recommendations for the integration of explainable AI in health care, emphasizing the need for interdisciplinary collaborations, standardized evaluation metrics, and regulatory frameworks to ensure the responsible and effective use of XAI in medical image processing
Rough Set, ELM Classifier and Deep Architecture for Remote Sensing Images
No full textThe progress in Remote Sensing (RS) technology provides high- definition images of land cover. The precise classification of RS images lies in leveraging its rich feature spectrum. In this context, Convolutional Neural Network (CNN) is a prominent tool for feature extraction, producing a high number of extracted features. With more number of input features the network architecture and its training becomes complex and time consuming, respectively. Our study addressed these issues by using rough set concepts to select the most informative features for classification with extreme learning machine (ELM). Thether, the ELM network connection is partially established through a random rule matrix, effectively reducing network complexity and computational requirements without compromising model performance. The designed model is evaluated on two data sets i.e., UC Merced and RSSCN7. The model’s superior classification performance is compared with Support Vector Machine (SVM) and similar methods on the ground of overall accuracy, precision, F1 score etc. measures
A p-centered coloring for the grid using O(p) colors
No full textA p-centered coloring of a graph G, where p is a positive integer, is a coloring of the vertices of G in such a way that every connected subgraph of G either contains a vertex with a unique color or contains more than p different colors. We give an explicit construction of a p-centered coloring using O(p) colors for the planar grid
A possible late-time transition of M B inferred via neural networks
No full textThe strengthening of tensions in the cosmological parameters has led to reconsidering the fundamental aspects of standard cosmology. The tension in the Hubble constant can also be viewed as a tension between local and early Universe constraints on the absolute magnitude M B of Type Ia supernova. In this work, we reconsider the possibility of a variation of this parameter in a model-independent way. We employ neural networks to agnostically constrain the value of the absolute magnitude as well as assess the impact and statistical significance of a variation in M B with redshift from the Pantheon+ compilation, together with a thorough analysis of the neural network architecture. We find an indication for a possible transition redshift at the z ≈ 1 region
Access Structure Hiding Verifiable Tensor Designs
No full textThe field of verifiable secret sharing schemes was introduced by Verheul et al. and has evolved over time, including well-known examples by Feldman and Pedersen. Stinson made advancements in combinatorial design-based secret sharing schemes in 2004. Desmedt et al. introduced the concept of frameproofness in 2021, while recent research by Sehrawat et al. in 2021 focuses on LWE-based access structure hiding verifiable secret sharing with malicious-majority settings. Furthermore, Roy et al. combined the concepts of reparable threshold schemes by Stinson et al. and frameproofness by Desmedt et al. in 2023, to develop extendable tensor designs built from balanced incomplete block designs, and also presented a frameproof version of their design. This paper explores ramp-type verifiable secret sharing schemes, and the application of hidden access structures in such cryptographic protocols. Inspired by Sehrawat et al.’s access structure hiding scheme, we develop an ϵ-almost access structure hiding scheme, which is verifiable as well as frameproof. We detail how the concept ϵ-almost hiding is important for incorporating ramp schemes, thus making a fundamental generalisation of this concept
Algebraic properties of binomial edge ideals of Levi graphs associated with curve arrangements
No full textIn this article, we study algebraic properties of binomial edge ideals of Levi graphs associated with certain plane curve arrangements. Using combinatorial properties of Levi graphs, we discuss the Cohen-Macaulayness of binomial edge ideals of Levi graphs associated to some curve arrangements in the complex projective plane, like the d-arrangement of curves and the conic-line arrangements. We also discuss the existence of certain induced cycles in the Levi graphs of these arrangements and obtain lower bounds for the regularity of powers of the corresponding binomial edge ideals
Application of Taguchi design of experiments in the food industry: a systematic literature review
No full textThe optimal utilization of available resources is critical for industrial experimentation. In addition, a structured approach with reliable and reproducible results and robust predictability is paramount to achieving profitability, waste reduction, and improvements in the food industry. The Taguchi design of experiments (DOE) is a practical approach that optimizes the critical characteristics using minimum time and resources–reviewing its application in the food sector assists in determining research gaps that could provide sustainable solutions to academia, industry, and policymakers. This research provides a systematic review of the literature on the Taguchi DOE application for the past two decades in the food industry. The systematic review considered articles published in peer-reviewed journals indexed in Scopus, Web of Science, and PubMed databases. The research categorized the relevant articles into three core themes and 31 sub-themes. This study identified only 31 relevant articles despite the significant advantages of applying the Taguchi DOE in the food industry. Most applications did not explore the true potential of the Taguchi DOE approach to obtain robust and scalable solutions. Also, the analysis determined that a structured approach is missing in most studies, with a lack of utilization of essential tools during the research
Assessing predator–prey interactions during the Late Triassic of India from bite marks on Hyperodapedon (Archosauromorpha, Rhynchosauria)
No full textMultiple cranial and mandibular remains of Hyperodapedon huxleyi known from the lower part of the Upper Triassic Maleri Formation were examined to reveal distinct bite marks. These were identified as punctures based on their circular or oval or elliptical outline and deep penetration into the cortical bone, and were found associated with several bone-damaging features such as radiating fractures, serrated boundaries, and collapsed bony surface. In addition, bite traces or drag marks in the form of parallel grooves with U-shaped cross sections are identified. The bite marks are compared with dental morphology of varied Late Triassic carnivores of India, and the probable producers are suggested to be phytosaurs and dinosauriforms. The Maleri trophic structure or food web is reconstructed to reveal interconnectedness between different animal groups, unconfined feeding habits of the animals where the predators were inclined towards opportunistic feeding rather than a niche-based dietary habit. The study highlights the significance of bite marks in the reconstruction of ancient paleoecosystems