https://nova.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:46421 Wed 23 Nov 2022 08:20:39 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:33906 x_a), signal energy (Ω^x), Fuzzy entropy (E^x_f), Kolmogorov Sinai entropy (E^x_ks), permutation entropy (E^x_p), Renyi entropy (E^x_r), sample entropy (E^x_s), Shannon entropy (E^x_sh), Tsallis entropy (E^x_t), wavelet entropy (E^x_w), fractal dimension (F^x_D), Kolmogorov complexity (C^x_k), largest Lyapunov exponent (E^x_LLE), recurrence quantification analysis (RQA) parameters (RQ^x_i), Hurst exponent (H^x), activity entropy (E^x_ac), Hjorth complexity (H^x_c), Hjorth mobility (H^x_m), modified multi scale entropy (E^x_mmsy) and higher order statistics (HOS) bispectrum (Bi^x_i) are obtained from the DWT coefficients. Later, these features are subjected to sequential forward feature selection (SFS) method and selected features are then ranked using seven ranking methods namely, Bhattacharyya distance, entropy, Fuzzy maximum relevancy and minimum redundancy (mRMR), receiver operating characteristic (ROC), Student’s t-test, Wilcoxon and ReliefF. These ranked features are supplied independently into the k-Nearest Neighbor (kNN) classifier. Our proposed system achieved maximum accuracy, sensitivity and specificity of (i) 97.72%, 94.79% and 98.74% for 5s, (ii) 98.34%, 95.49% and 99.14% for 8s and (iii) 98.32%, 95.16% and 99.20% for 10s of ECG segments using only ten features. The integration of the proposed algorithm with ECG acquisition systems in the intensive care units (ICUs) can help the clinicians to decipher the shockable and nonshockable life-threatening arrhythmias accurately. Hence, doctors can use the CPR or AED immediately and increase the chance of survival during shockable life-threatening arrhythmia intervals.]]> Wed 23 Jan 2019 10:40:16 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:42058 Wed 17 Aug 2022 14:40:18 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:9978 Wed 11 Apr 2018 17:01:17 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:4333 Wed 11 Apr 2018 14:51:48 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:14052 Nb = ba^N〉, which we show to be D-finite. It follows that their cogrowth rates are algebraic numbers.]]> Wed 11 Apr 2018 14:21:37 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:21682 Wed 11 Apr 2018 14:14:29 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:10973 Wed 11 Apr 2018 13:55:17 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:11143 Wed 11 Apr 2018 12:41:07 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:24664 Wed 11 Apr 2018 10:32:23 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:50699 Wed 02 Aug 2023 11:58:45 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:46751 Tue 29 Nov 2022 15:25:35 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:33891 four levels of wavelet packet decomposition (WPD) to obtain various coefficients. Using the fourth-level coefficients obtained for each lead ECG signal beat, new 2s. ECG signal beats are reconstructed. Later, the reconstructed signals are split into two-fold data sets, in which one set is used for acquiring common spatial pattern (CSP) filter and the other for obtaining features vector (vice versa). The obtained features are one by one fed into k-nearest neighbors (KNN) classifier for automated classification. The proposed system yielded maximum average classification results of 99.65% accuracy, 99.64% sensitivity and 99.7% specificity using 10 features. Our proposed algorithm is highly efficient and can be used by the clinicians as an aiding system in their CAD diagnosis, thus, assisting in faster treatment and avoiding the progression of CAD condition.]]> Tue 22 Jan 2019 14:22:16 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:52549 Tue 17 Oct 2023 15:26:34 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:32665 Tue 10 Jul 2018 10:51:11 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:54136 Tue 06 Feb 2024 11:50:12 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:34633 Tue 03 Sep 2019 18:26:48 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:34009 Thu 31 Jan 2019 10:14:09 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:33827 six levels of Wavelet Packet Decomposition (WPD) to obtain seven wavebands (delta (d), theta (t), lower alpha (la), upper alpha (ua), lower beta (lb), upper beta (ub), lower gamma (lg)). From each wavebands (activity bands), 19 non-linear features such as Recurrence Quantification Analysis (RQA) (^x_y), Approximate Entropy (^x_ap), Energy (Ω^x), Fractal Dimension (FD) (F^x_D), Permutation Entropy (E^x_p), Detrended Fluctuation Analysis (α^x_y), Hurst Exponent (E^x_H), Largest Lyapunov Exponent (E^x_LLLE), Sample Entropy (E^x_s), Shannon's Entropy (E^x_sh), Renyi's entropy (E^x_r), Tsalli's entropy (E^x_ts), Fuzzy entropy (E^x_f), Wavelet entropy (E^x_w), Kolmogorov-Sinai entropy (E^x_ks), Modified Multiscale Entropy (E^x_mmsy), Hjorth's parameters (activity (S^x_a), mobility (H^x_m), and complexity (H^x_c)) are extracted. The extracted features are then ranked using Bhattacharyya, Entropy, Fuzzy entropy-based Max-Relevancy and Min-Redundancy (mRMR), Receiver Operating Characteristic (ROC), -test, and Wilcoxon. These ranked features are given to train Support Vector Machine (SVM) classifier. The SVM classifier with radial basis function (RBF) achieved 95.41% accuracy, 93.33% sensitivity and 97.50% specificity using four non-linear features ranked by Wilcoxon method. In addition, an integrated index called Alcoholic Index ( ALCOHOLI) is developed using highly ranked two features for identification of normal and alcoholic EEG signals using a single number. This system is rapid, efficient, and inexpensive and can be employed as an EEG analysis assisting system by clinicians in the detection of alcoholism. In addition, the proposed system can be used in rehabilitation centers to evaluate person with alcoholism over time and observe the outcome of treatment provided for reducing or reversing the impact of the condition on the brain.]]> Thu 28 Oct 2021 13:05:10 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:41169 Thu 28 Jul 2022 10:02:32 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:33829 Thu 17 Jan 2019 13:58:10 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:33828 Thu 17 Jan 2019 13:58:03 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:48535 Diagnostic and Statistical Manuals of Mental Disorders “DSM-IV” and the most recent addition, DSM-5 allow us to introspect, using the solution provided by modern algorithms, if there exists a consensus between the clusters obtained via a data-driven approach, with the current classifications. In the case of mental disorders, the availability of a follow-up consensus collection (e.g. in this case the DSM-5), potentially allows investigating if the classification of disorders has moved closer (or away) to what a data-driven analytic approach would have unveiled by objectively inferring it from the data of DSM-IV. In this contribution, we present a new type of mathematical approach based on a global cohesion score which we introduce for the first time for the identification of communities of symptoms. Different from other approaches, this combinatorial optimization method is based on the identification of “triangles” in the network; these triads are the building block of feedback loops that can exist between groups of symptoms. We used a memetic algorithm to obtain a collection of highly connected-cohesive sets of symptoms and we compare the resulting community structure with the classification of disorders present in the DSM-IV.]]> Thu 02 May 2024 14:32:58 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:13088 Sat 24 Mar 2018 10:35:18 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:31599 Sat 24 Mar 2018 08:45:02 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:8361 Sat 24 Mar 2018 08:39:50 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:8612 Sat 24 Mar 2018 08:35:42 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:14729 Sat 24 Mar 2018 08:23:56 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:13061 Sat 24 Mar 2018 08:15:37 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:11307 Sat 24 Mar 2018 08:12:36 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:11316 Sat 24 Mar 2018 08:12:36 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:11039 Sat 24 Mar 2018 08:11:46 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:17533 Sat 24 Mar 2018 08:03:54 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:21495 Sat 24 Mar 2018 08:03:39 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:21298 Sat 24 Mar 2018 07:54:38 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:6773 Sat 24 Mar 2018 07:48:20 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:6774 Sat 24 Mar 2018 07:48:20 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:26252 Sat 24 Mar 2018 07:40:19 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:29729 G = (V,E) and for a pair of vertices u, v ∈ V , we say that a vertex w ∈ V resolves u and v if the shortest path from w to u is of a different length than the shortest path from w to v. A set of vertices R ⊆ V is a resolving set if for every pair of vertices u and v in G, there exists a vertex w ∈ R that resolves u and v. The minimum weight resolving set problem is to find a resolving set M for a weighted graph G such that Σ_{v∈ M} w(v) is minimum, where w(v) is the weight of vertex v. In this paper, we explore the possible solutions of this problem for grid graphs P_n⎕P_m where 3 ≤ n ≤ m. We give a complete characterization of solutions whose cardinalities are 2 or 3, and show that the maximum cardinality of a solution is 2n − 2. We show that the grid has the property that given a landmark set, we only need to investigate whether or not all pairs of vertices that share common neighbors are resolved to determine if the whole graph is resolved. We use this result to provide a characterization of a class of minimals whose cardinalities range from 4 to 2n−2 and show that the number of such minimals is Ω(2ⁿ).]]> Sat 24 Mar 2018 07:37:31 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:30905 k-regular sequence based on information from its k-kernel. In order to provide such a classification, we introduce the notion of a growth exponent for k-regular sequences and show that this exponent is equal to the base-k logarithm of the joint spectral radius of any set of a special class of matrices determined by the k-kernel.]]> Sat 24 Mar 2018 07:30:39 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:3440 Sat 24 Mar 2018 07:20:37 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:48744 Mon 17 Apr 2023 13:56:08 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:34519 Fri 22 Mar 2019 13:04:10 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:43361 CFₙ is 3n−3/2 (3n−3/2) for odd n and 3n−4/2(3n−4/2) for even n; (2) CFₙ is super edge-connected; (3) the matching preclusion number of CFₙ is 3n−3/2 for odd n and 3n−4/2 for even n; (4) the conditional matching preclusion number of CFₙ is 3n – 5 for odd n and n ≥ 3, and 3n – 6 for even n and n ≥ 4.]]> Fri 16 Sep 2022 08:32:44 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:49355 Fri 12 May 2023 12:28:02 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:41852 Fri 12 Aug 2022 16:22:24 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:51334 Fri 01 Sep 2023 10:18:08 AEST ]]>