An Enhanced Wavelet Neural Network Model For Epileptic Seizure Detection And Prediction

Epilepsi merupakan suatu penyakit neurologi yang sangat lazim dan ditakuti orang ramai. Banyak kajian telah dibuat untuk membangunkan pengelas automatik yang dapat memberikan ketepatan yang lebih tinggi. Pengelas automatik ini dapat membantu doktor dalam mengenali pelbagai segmen isyarat electroencephalography (EEG) yang berbeza. Dalam kerja penyelidikan ini, suatu model rangkaian neural wavelet (RNW) telah dicadangkan bagi tujuan pengesanan dan ramalan serangan epilepsi. Arkitektur dan kon�gurasi RNW dapat ditambah baik menggunakan pendekatan metaheuristik. Khususnya, algoritma carian harmoni (CH) digunakan dan diterapkan dalam proses pembelajaran RNW. Tesis ini mengandungi tiga sumbangan utama. Pertama, algoritma CH digunakan dalam proses pemilihan �tur. Firstly, the HS algorithm is used in the feature selection stage. The HS algorithm, which is originally used for optimization problems involving real numbers, is modi�ed and employed in the task of feature selection, which involves binary values. Epilepsy is a very common and much-feared neurological disorder. Much research has been done in developing better automated classi�ers with higher accuracy that can help clinicians identify the di�erent segments of electroencephalography (EEG) signals. In this research work, an enhanced wavelet neural network (WNN) model is proposed for the purpose of epileptic seizure detection and prediction. The architecture and con�guration of WNNs can be further enhanced using metaheuristic strategies. Speci�cally, the harmony search (HS) algorithm is employed and incorporated in the learning of WNNs. The contribution of this thesis is threefold. Firstly, the HS algorithm is used in the feature selection stage. The HS algorithm, which is originally used for optimization problems involving real numbers, is modi�ed and employed in the task of feature selection, which involves binary values

Reliable epileptic seizure detection using an improved wavelet neural network

Electroencephalogram (EEG) signal analysis is indispensable in epilepsy diagnosis as it offers valuable insights for locating the abnormal distortions in the brain wave. However, visual interpretation of the massive amounts of EEG signals is time-consuming, and there is often inconsistent judgment between experts

A harmony search-based learning algorithm for epileptic seizure prediction

The learning phase of wavelet neural network entails the task of finding the optimal set of parameter, which includes wavelet activation function, translation centers, dilation parameter, synaptic weight values, and bias terms. Apart from the traditional gradient descent-based approach, metaheuristic algorithms can also be used to determine these parameters. In this work, the harmony search algorithm is employed to find the optimal solution for both synaptic weight values and bias terms in the learning of wavelet neural network. The standard harmony search algorithm is modified accordingly in the aspect of initialization of harmony memory, as well as during the improvisation stage. The proposed harmony search-based learning algorithm is used in the task of epileptic seizure prediction. Simulation results show that the proposed algorithm outperforms other metaheuristic algorithms in terms of sensitivity

Efficient gear fault feature selection based on moth‑flame optimisation in discrete wavelet packet analysis domain

Rotating machinery—a crucial component in modern industry, requires vigilant monitoring such that any potential malfunction of its electromechanical systems can be detected prior to a fatal breakdown. However, identifying faulty signals from a defective rotating machinery is challenging due to complex dynamical behaviour. Therefore, the search for features which best describe the characteristic of different fault conditions is often crucial for condition monitoring of rotating machinery. For this purpose, this study used the intensification and diversification properties of the recently proposed moth-flame optimisation (MFO) algorithm and utilised the algorithm in the proposed feature selection scheme. The proposed method consisted of three parts. First, the vibration signals of gear with different fault conditions were decomposed by a fourth-level discrete wavelet packet transform, and the statistical features at all constructed nodes were derived. Second, the MFO algorithm was utilised to select the optimal discriminative features. Lastly, the MFO-selected features were used as the input for a support vector machine (SVM) diagnostic model to identify fault patterns. To further demonstrate the superiority of the proposed method, other feature selection approaches were applied, including randomly selected features and complete features, and other diagnostic models, namely the multilayer perceptron neural network and k-nearest neighbour. Comparative experiments demonstrated that SVM with the MFO-selected features outperformed the others, with the classification accuracy of 99.60%, thus validating its effectiveness

Reliable epileptic seizure detection using an improved wavelet neural network

BackgroundElectroencephalogram (EEG) signal analysis is indispensable in epilepsy diagnosis as it offers valuable insights for locating the abnormal distortions in the brain wave. However, visual interpretation of the massive amounts of EEG signals is time-consuming, and there is often inconsistent judgment between experts. AimsThis study proposes a novel and reliable seizure detection system, where the statistical features extracted from the discrete wavelet transform are used in conjunction with an improved wavelet neural network (WNN) to identify the occurrence of seizures. Method  Experimental simulations were carried out on a well-known publicly available dataset, which was kindly provided by the Epilepsy Center, University of Bonn, Germany. The normal and epileptic EEG signals were first pre-processed using the discrete wavelet transform. Subsequently, a set of statistical features was extracted to train a WNNs-based classifier. ResultsThe study has two key findings. First, simulation results showed that the proposed improved WNNs-based classifier gave excellent predictive ability, where an overall classification accuracy of 98.87% was obtained. Second, by using the 10th and 90th percentiles of the absolute values of the wavelet coefficients, a better set of EEG features can be identified from the data, as the outliers are removed before any further downstream analysis.ConclusionThe obtained high prediction accuracy demonstrated the feasibility of the proposed seizure detection scheme. It suggested the prospective implementation of the proposed method in developing a real time automated epileptic diagnostic system with fast and accurate response that could assist neurologists in the decision making process

Incorporating environmental elements in property marketing strategy in Kuala Lumpur

Half of the world population all over the countries reside in the cities. By 2050, the world proportion is likely to reach 75%. Malaysia is an urban society with majority people of the country approximately 70% living in the cities. The high demand of accommodation in the cities, and many developers supply the housing unit through condominium complex to fulfil the requirement of accommodation. Every day the number of condominium is increasing in Kuala Lumpur city. The natural green environment is decreasing with destructive impact on physical, mental illness and many problems among the people reside in the city compare to the rural. The modern developers in Kuala Lumpur facing difficulties to influence the target customers due to the lack of green environmental elements in a housing project and marketing strategy are one of the great problems to achieve the high performance of sales. Therefore, incorporate of important environmental elements in a housing project and marketing strategy to achieve the high performance of sales. The level of importance evaluates through quantitative research method with five (5) points Likert types scale. The data collected from Kuala Lumpur city area among condominium users, tenant, owner, management team and developers employees including marketing staff, managers, sales staff, and sales agents altogether 509 respondent. More than 85% respondents are agreed the environmental elements are very important at the condominium complex to have a healthy city life, and it strongly influences customers to buy or rent the apartment units. The green marketing is acting as a mediation to contribute the high performance of sales. As a result, less or no difficulty to reach the high performance of sales. In conclusion, those project has the most demanding environmental elements are more successful projects, compare to less or non-existing environmental facilities projects in Kuala Lumpur, Malaysia

A novel selection of optimal statistical features in the DWPT domain for discrimination of ictal and seizure-free electroencephalography signals

Properly determining the discriminative features which characterize the inherent behaviors of electroencephalography (EEG) signals remains a great challenge for epileptic seizure detection. In this present study, a novel feature selection scheme based on the discrete wavelet packet decomposition and cuckoo search algorithm (CSA) was proposed. The normal as well as epileptic EEG recordings were frst decomposed into various frequency bands by means of wavelet packet decomposition, and subsequently, statistical features at all developed nodes in the wavelet packet decomposition tree were derived. Instead of using the complete set of the extracted features to construct a wavelet neural networks-based classifer, an optimal feature subset that maximizes the predictive competence of the classifer was selected by using the CSA. Experimental results on the publicly available benchmarks demonstrated that the proposed feature subset selection scheme achieved promising recognition accuracies of 98.43–100%, and the results were statistically signifcant using z-test with p value <0.0001

Multivariate visualization of the global COVID-19 pandemic: A comparison of 161 countries.

BackgroundThe aim of the study was to visualize the global spread of the COVID-19 pandemic over the first 90 days, through the principal component analysis approach of dimensionality reduction.MethodsThis study used data from the Global COVID-19 Index provided by PEMANDU Associates. The sample, representing 161 countries, comprised the number of confirmed cases, deaths, stringency indices, population density and GNI per capita (USD). Correlation matrices were computed to reveal the association between the variables at three time points: day-30, day-60 and day-90. Three separate principal component analyses were computed for similar time points, and several standardized plots were produced.ResultsConfirmed cases and deaths due to COVID-19 showed positive but weak correlation with stringency and GNI per capita. Through principal component analysis, the first two principal components captured close to 70% of the variance of the data. The first component can be viewed as the severity of the COVID-19 surge in countries, whereas the second component largely corresponded to population density, followed by GNI per capita of countries. Multivariate visualization of the two dominating principal components provided a standardized comparison of the situation in the161 countries, performed on day-30, day-60 and day-90 since the first confirmed cases in countries worldwide.ConclusionVisualization of the global spread of COVID-19 showed the unequal severity of the pandemic across continents and over time. Distinct patterns in clusters of countries, which separated many European countries from those in Africa, suggested a contrast in terms of stringency measures and wealth of a country. The African continent appeared to fare better in terms of the COVID-19 pandemic and the burden of mortality in the first 90 days. A noticeable worsening trend was observed in several countries in the same relative time frame of the disease's first 90 days, especially in the United States of America

Search for Scalar Diphoton Resonances in the Mass Range 65−60065-600 GeV with the ATLAS Detector in pppp Collision Data at s\sqrt{s} = 8 TeVTeV

A search for scalar particles decaying via narrow resonances into two photons in the mass range 65–600 GeV is performed using $20.3\text{}\text{}{\mathrm{fb}}^{-1}$ of $\sqrt{s}=8\text{}\text{}\mathrm{TeV}$ $pp$ collision data collected with the ATLAS detector at the Large Hadron Collider. The recently discovered Higgs boson is treated as a background. No significant evidence for an additional signal is observed. The results are presented as limits at the 95% confidence level on the production cross section of a scalar boson times branching ratio into two photons, in a fiducial volume where the reconstruction efficiency is approximately independent of the event topology. The upper limits set extend over a considerably wider mass range than previous searches

Search for Higgs and ZZ Boson Decays to J/ψγJ/\psi\gamma and Υ(nS)γ\Upsilon(nS)\gamma with the ATLAS Detector

A search for the decays of the Higgs and $Z$ bosons to $J/\psi\gamma$ and $\Upsilon(nS)\gamma$ ($n=1,2,3$) is performed with $pp$ collision data samples corresponding to integrated luminosities of up to $20.3\mathrm{fb}^{-1}$ collected at $\sqrt{s}=8\mathrm{TeV}$ with the ATLAS detector at the CERN Large Hadron Collider. No significant excess of events is observed above expected backgrounds and 95% CL upper limits are placed on the branching fractions. In the $J/\psi\gamma$ final state the limits are $1.5\times10^{-3}$ and $2.6\times10^{-6}$ for the Higgs and $Z$ bosons, respectively, while in the $\Upsilon(1S,2S,3S)\,\gamma$ final states the limits are $(1.3,1.9,1.3)\times10^{-3}$ and $(3.4,6.5,5.4)\times10^{-6}$, respectively