10 research outputs found
A Rational and Efficient Algorithm for View Revision in Databases
The dynamics of belief and knowledge is one of the major components of any
autonomous system that should be able to incorporate new pieces of information.
In this paper, we argue that to apply rationality result of belief dynamics
theory to various practical problems, it should be generalized in two respects:
first of all, it should allow a certain part of belief to be declared as
immutable; and second, the belief state need not be deductively closed. Such a
generalization of belief dynamics, referred to as base dynamics, is presented,
along with the concept of a generalized revision algorithm for Horn knowledge
bases. We show that Horn knowledge base dynamics has interesting connection
with kernel change and abduction. Finally, we also show that both variants are
rational in the sense that they satisfy certain rationality postulates stemming
from philosophical works on belief dynamics
A New Rational Algorithm for View Updating in Relational Databases
The dynamics of belief and knowledge is one of the major components of any
autonomous system that should be able to incorporate new pieces of information.
In order to apply the rationality result of belief dynamics theory to various
practical problems, it should be generalized in two respects: first it should
allow a certain part of belief to be declared as immutable; and second, the
belief state need not be deductively closed. Such a generalization of belief
dynamics, referred to as base dynamics, is presented in this paper, along with
the concept of a generalized revision algorithm for knowledge bases (Horn or
Horn logic with stratified negation). We show that knowledge base dynamics has
an interesting connection with kernel change via hitting set and abduction. In
this paper, we show how techniques from disjunctive logic programming can be
used for efficient (deductive) database updates. The key idea is to transform
the given database together with the update request into a disjunctive
(datalog) logic program and apply disjunctive techniques (such as minimal model
reasoning) to solve the original update problem. The approach extends and
integrates standard techniques for efficient query answering and integrity
checking. The generation of a hitting set is carried out through a hyper
tableaux calculus and magic set that is focused on the goal of minimality.Comment: arXiv admin note: substantial text overlap with arXiv:1301.515
A Quantum Convolutional Neural Network Approach for Object Detection and Classification
This paper presents a comprehensive evaluation of the potential of Quantum
Convolutional Neural Networks (QCNNs) in comparison to classical Convolutional
Neural Networks (CNNs) and Artificial / Classical Neural Network (ANN) models.
With the increasing amount of data, utilizing computing methods like CNN in
real-time has become challenging. QCNNs overcome this challenge by utilizing
qubits to represent data in a quantum environment and applying CNN structures
to quantum computers. The time and accuracy of QCNNs are compared with
classical CNNs and ANN models under different conditions such as batch size and
input size. The maximum complexity level that QCNNs can handle in terms of
these parameters is also investigated. The analysis shows that QCNNs have the
potential to outperform both classical CNNs and ANN models in terms of accuracy
and efficiency for certain applications, demonstrating their promise as a
powerful tool in the field of machine learning
Noise removal methods on ambulatory EEG: A Survey
Over many decades, research is being attempted for the removal of noise in
the ambulatory EEG. In this respect, an enormous number of research papers is
published for identification of noise removal, It is difficult to present a
detailed review of all these literature. Therefore, in this paper, an attempt
has been made to review the detection and removal of an noise. More than 100
research papers have been discussed to discern the techniques for detecting and
removal the ambulatory EEG. Further, the literature survey shows that the
pattern recognition required to detect ambulatory method, eye open and close,
varies with different conditions of EEG datasets. This is mainly due to the
fact that EEG detected under different conditions has different
characteristics. This is, in turn, necessitates the identification of pattern
recognition technique to effectively distinguish EEG noise data from a various
condition of EEG data
Glioblastoma Multiforme Classification On High Resolution Histology Image Using Deep Spatial Fusion Network
Brain tumor is a growth of abnormal cells in brain, which canbe cancerous or non-cancerous. The Brain tumor have scarce symptomsso it is very difficult to classify. Diagnosing brain tumor with histologyimages will efficiently helps us to classify brain tumor types. Sometimes,histology based image analysis is not accepted due to its variations inmorphological features. Deep learning CNN models helps to overcomethis problem by feature extraction and classification. Here proposed amethod to classify high resolution histology image. InceptionResNetV2is an CNN model, which is adopted to extract hierarchical features with-out any loss of data. Next generated deep spatial fusion network to ex-tract spatial features found in between patches and to predict correct fea-tures from unpredictable discriminative features. 10-fold cross-validationis performed on the histology image. This achieves 95.6 percent accu-racy on 4-class classification (benign, malignant, Glioblastoma, Oligo-dendroglioma). Also obtained 99.1 percent accuracy and 99.6 percentAUC on 2-way classification (necrosis and non-necrosis)
Glioblastoma Multiforme Classification On High Resolution Histology Image Using Deep Spatial Fusion Network
Brain tumor is a growth of abnormal cells in brain, which canbe cancerous or non-cancerous. The Brain tumor have scarce symptomsso it is very difficult to classify. Diagnosing brain tumor with histologyimages will efficiently helps us to classify brain tumor types. Sometimes,histology based image analysis is not accepted due to its variations inmorphological features. Deep learning CNN models helps to overcomethis problem by feature extraction and classification. Here proposed amethod to classify high resolution histology image. InceptionResNetV2is an CNN model, which is adopted to extract hierarchical features with-out any loss of data. Next generated deep spatial fusion network to ex-tract spatial features found in between patches and to predict correct fea-tures from unpredictable discriminative features. 10-fold cross-validationis performed on the histology image. This achieves 95.6 percent accu-racy on 4-class classification (benign, malignant, Glioblastoma, Oligo-dendroglioma). Also obtained 99.1 percent accuracy and 99.6 percentAUC on 2-way classification (necrosis and non-necrosis)
Searching for S-boxes with better Diffusion using Evolutionary Algorithm
Over the years, a large number of attacks have been proposed against substitution boxes used in symmetric ciphers such as differential attacks, linear attacks, algebraic attacks, etc. In the Advanced Encryption Standard (AES) Block cipher, the substitution box is the only nonlinear component and thus it holds the weight of the cipher. This basically means that if an attacker is able to mount a successful attack on the substitution box of AES, the cipher is compromised. This research work aims to provide a solution for increasing cryptographic immunity of S-boxes against such attacks. A genetic algorithm based approach has been proposed to search for 8 × 8 balanced and bijective S-boxes that exhibit values of differential branch number, non-linearity, differential uniformity, count and length of cycles present and distance from strict avalanche criterion that are similar to or better than the AES S-box. An S-Box evaluation tool is also implemented to evaluate any S-boxes generated. S-box of AES is resistant to the crypt-analytic attacks. S-boxes constructed by the proposed algorithm have better cryptographic properties so they are also resistant to the crypt-analytic attacks. The strict avalanche criterion[11], which is based on completeness[22] and diffusion[5], is an essential property for any 8 × 8 S-box. Good diffusion means that a small change in the plaintext may influence the complete block after a small number of rounds. Therefore, a lower DSAC value is desirable to prevent vulnerabilities to attacks such as differential attacks. The DSAC is therefore used as the primary fitness criterion in this research work to search for S-boxes with better diffusion