Academic literature on the topic '220403 Artificial intelligence'

Identification of eye considering biometric traits is an essential field to recognize persons. Biometrics using iris images seems to be an effective identification of individuals. Various Iris Recognition at-Distance (IAAD) systems are used for extracting features of iris and improve image quality using the biometric model. Even though the quality of the iris is better, accuracy is a challenging question for the research community. Thus, an effective IAAD, namely Chronological Monarch Butterfly Optimization-Deep Belief Network (Chronological MBO-DBN) is devised to detect iris. The detection of iris using DBN is trained with Chronological MBO, which is the integration of Chronological theory and Monarch Butterfly Optimization (MBO). The features of iris are extracted with ScatT-Loop descriptor and Local Gradient Pattern (LGP) and subjected to Chronological MBO-DBN for the recognition of iris which improved accuracy. The implementation of proposed Chronological MBO-based DBN is performed using the dataset, CASIA Iris, and efficiency is evaluated by the accuracy of 96.078%, False Rejection Rate (FRR) of 0.4745% False Acceptance Rate (FAR) of 0.4847%, and F-Measure of 98.658%.

A set S ⊆ V in a graph G is a MED-set if every vertex in V - S has a monophonic eccentric vertex in S. The MED-number γ me (G) is the cardinality of a minimum MED-set of G. A set S ⊆ V in a graph G is a CMED-set if S is a MED-set and the induced subgraph is connected. The CMED-number γ cme (G) is the cardinality of a minimum CMED-set of G. We investigate some properties of the CMED-sets. Also, we determine the bounds of the CMED-number and find the same for some standard graphs. The CMED-number has applications in security based communication networks in real life situations. This motivated us to introduce and investigate CMED-set in a graph.

Dealing with the explosive growth of web sources on the Internet requires the use of efficient systems. Automatic text summarization is capable of addressing this issue. Recent years have seen remarkable success in the use of graph theory on text extractive summarization. However, the understanding of why and how they perform so well is still not clear. In this paper, we intend to seek a better understanding of graph models, which can benefit from graph extractive summarization. Additionally, analysis has been performed qualitatively with the graph models in the design of recent graph extractive summarization. Based on the knowledge acquired from the survey, our work could provide more clues for future research on extractive summarization.

EMF has a variety of biological impacts and has an impact on the metabolic process in the human body. Antenna towers, anechoic chambers, and other sources can all produce this. Some of the human populations live very close to the EMF-emitting antenna towers. We can make humans aware of the EMF radiation and protect from diseases if there is a proper method to anticipate the EMF radiation of antennas installed in different places. For the study of telecom data and EMF emission, many machine learning and deep learning techniques have been developed in recent years. Predictive analytics played a bigger part in this. For prediction, it comprises advanced statistics, modeling and more machine learning methodologies. However, the appropriate hyper parameters must be established for the model’s effective prediction, but this cannot be guaranteed in a dynamic environment where the data is always changing. The learning model’s performance improves when these parameters are optimized. The goal of this study is to use the Telecom dataset to create a novel hybrid deep learning model for forecasting the trend of EMF radiations. The patterns were first discovered using Artificial Neural Networks (ANN) and Multilayer Perceptron (MLP) combined with the Particle Swarm Optimization method (PSO). Later to boost its performance the hybrid approach (MLP-RFD-PSO) was developed and 98.8% accuracy was achieved.

Why are natural theories pre-well-ordered by consistency strength? In previous work, an approach to this question was proposed. This approach was inspired by Martin’s Conjecture, one of the most prominent conjectures in recursion theory. Fixing a reasonable subsystem T of arithmetic, the goal was to classify the recursive functions that are monotone with respect to the Lindenbaum algebra of T. According to an optimistic conjecture, roughly, every such function must be equivalent to an iterate Con T α of the consistency operator “in the limit” within the ultrafilter of sentences that are true in the standard model. In previous work the author established the first case of this optimistic conjecture; roughly, every recursive monotone function is either as weak as the identity operator in the limit or as strong as Con T in the limit. Yet in this note we prove that this optimistic conjecture fails already at the next step; there are recursive monotone functions that are neither as weak as Con T in the limit nor as strong as Con T 2 in the limit. In fact, for every α, we produce a function that is cofinally equivalent to Con T α yet cofinally equivalent to Con T .

This arts-based, practice-led, qualitative theoretical work amplifies young people’s ethical opinions of what it means to be human both now and in the future through three cumulative technological lenses. These are artificial intelligence (AI), and the anticipated superintelligence which has already commenced (Andrews, 2017), which enables the third lens of posthumanism, defined by the Oxford dictionary as ‘The idea that humanity can be transformed, transcended, or eliminated either by technological advances or the evolutionary process; artistic, scientific, or philosophical practice which reflects this belief (Dictionary, 1970).' Posthumanism auspices Transhumanism, which is defined as using technology to further enhance the skills, abilities, and lifespans of human beings who ultimately become posthuman (H+). This research renders a superintelligent AI which might orchestrate the qualities that future humans / posthumans will have, considering eugenics, gene modification, and the ultimate designing of entities and/or sentient beings, has already happened and is expressed via art and narrative experience as research. Transcending our biology to coexist with future world(s), including inter-dimensionally, is suggested as being inevitable when technological acceleration is viewed not linearly, but exponentially, especially considering quantum superintelligence(s). This research examines both the ethical lives of young people (extrapolated from recorded interviews) and of the author (through retrospective art and memory). It splices across generational divides, as well as past analogue and current/ future digitisation, compares young people today and in years gone by, also splicing human or posthuman survival as framed by the Anthropocene, (‘Anthrop’ meaning human and ‘cene’ meaning a geological period of time, in simple language, ‘The Human Era’), stemming out of compassion for nature and the living world, while conversing with young people about global catastrophic risks. An unexpected existential creative artefact(s) emerges through the methodologies of A/r/ tography, Arts-Based Research, Narrative Inquiry, otherwise referred to throughout as Living inquiry. This metaphorically mirrors ‘The Event Horizon’ of the technological singularity discussed within the data collection section, in which outcomes are and were impossible to predict. The metaphor of me talking to students about events unknowable past a technological singularity, or event horizon, is precisely what happened as my memories and art revealed themselves with the help of healers, and detailed in the creative component ‘A Splice of My Life.’ I have since learned that this idea can be attributed to Deleuze.