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Fulltext Classification of Partial Discharge Measured under Different Levels of Noise Contamination

Jee Keen Raymond W, Illias HA, Abu Bakar AH

PLoS One, 2017;12(1):e0170111.
PMID: 28085953 DOI: 10.1371/journal.pone.0170111

Cable joint insulation breakdown may cause a huge loss to power companies. Therefore, it is vital to diagnose the insulation quality to detect early signs of insulation failure. It is well known that there is a correlation between Partial discharge (PD) and the insulation quality. Although many works have been done on PD pattern recognition, it is usually performed in a noise free environment. Also, works on PD pattern recognition in actual cable joint are less likely to be found in literature. Therefore, in this work, classifications of actual cable joint defect types from partial discharge data contaminated by noise were performed. Five cross-linked polyethylene (XLPE) cable joints with artificially created defects were prepared based on the defects commonly encountered on site. Three different types of input feature were extracted from the PD pattern under artificially created noisy environment. These include statistical features, fractal features and principal component analysis (PCA) features. These input features were used to train the classifiers to classify each PD defect types. Classifications were performed using three different artificial intelligence classifiers, which include Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM). It was found that the classification accuracy decreases with higher noise level but PCA features used in SVM and ANN showed the strongest tolerance against noise contamination.

Matched MeSH terms: Fractals
Fulltext Fractal-Fractional Mathematical Model Addressing the Situation of Corona Virus in Pakistan

Shah K, Arfan M, Mahariq I, Ahmadian A, Salahshour S, Ferrara M

Results Phys, 2020 Dec;19:103560.
PMID: 33200064 DOI: 10.1016/j.rinp.2020.103560

This work is the consideration of a fractal fractional mathematical model on the transmission and control of corona virus (COVID-19), in which the total population of an infected area is divided into susceptible, infected and recovered classes. We consider a fractal-fractional order

SIR

type model for investigation of Covid-19. To realize the transmission and control of corona virus in a much better way, first we study the stability of the corresponding deterministic model using next generation matrix along with basic reproduction number. After this, we study the qualitative analysis using "fixed point theory" approach. Next, we use fractional Adams-Bashforth approach for investigation of approximate solution to the considered model. At the end numerical simulation are been given by matlab to provide the validity of mathematical system having the arbitrary order and fractal dimension.

Matched MeSH terms: Fractals
Ion conductive polymer electrolyte membranes and simulation of their fractal growth patterns

Amir S, Mohamed N, Hashim Ali S

Sains Malaysiana, 2011;40:1123-1127.

Due to their high ionic conductivity, solid polymer electrolyte (SPE) systems have attracted wide spread attention as the most appropriate choice to fabricate all-solid-state electrochemical devices, namely batteries, sensors and fuel cells. In this work, ion conductive polymer electrolyte membranes have been prepared for battery fabrication. However, fractals were found to grow in these polymer electrolyte membranes weeks after they were prepared. It was believed that the formation of fractal aggregates in these membranes were due to ionic movement. The discovery of fractal growth pattern can be used to understand the effects of such phenomenon in the polymer electrolyte membranes. Digital images of the fractal growth patterns were taken and a simulation model was developed based on the Brownian motion theory and a fractal dialect known as L-system. A computer coding has been designed to simulate and visualize the fractal growth.

Matched MeSH terms: Fractals
Malaysian household income distribution: a fractal point of view

Fatimah Abdul Razak, Faridatulazna Ahmad Shahabuddin

Sains Malaysiana, 2018;47:2187-2194.

Malaysian Household Income Survey data provided by the Malaysian Department of Statistics is used to provide evidence
that the upper tails of the household income distribution follows a fractal based distribution known as power-law.
Inequality measures are then applied to ascertain the levels of inequality based on this distribution. In addition to that,
we analyzed the data in terms of different classes of occupation, obtained power-law exponents for each class and then
highlighted the inequality between these classes.

Matched MeSH terms: Fractals
Fulltext Mathematical Modelling and Prediction of the Effect of Chemotherapy on Cancer Cells

Namazi H, Kulish VV, Wong A

Sci Rep, 2015;5:13583.
PMID: 26316014 DOI: 10.1038/srep13583

Cancer is a class of diseases characterized by out-of-control cells' growth which affect DNAs and make them damaged. Many treatment options for cancer exist, with the primary ones including surgery, chemotherapy, radiation therapy, hormonal therapy, targeted therapy and palliative care. Which treatments are used depends on the type, location, and grade of the cancer as well as the person's health and wishes. Chemotherapy is the use of medication (chemicals) to treat disease. More specifically, chemotherapy typically refers to the destruction of cancer cells. Considering the diffusion of drugs in cancer cells and fractality of DNA walks, in this research we worked on modelling and prediction of the effect of chemotherapy on cancer cells using Fractional Diffusion Equation (FDE). The employed methodology is useful not only for analysis of the effect of special drug and cancer considered in this research but can be expanded in case of different drugs and cancers.

Matched MeSH terms: Fractals
Complexity-based classification of EEG signal in normal subjects and patients with epilepsy

Namazi H, Aghasian E, Ala TS

Technol Health Care, 2020;28(1):57-66.
PMID: 31104032 DOI: 10.3233/THC-181579

Analysis of human brain activity is an important topic in human neuroscience. Human brain activity can be studied by analyzing the electroencephalography (EEG) signal. In this way, scientists have employed several techniques that investigate nonlinear dynamics of EEG signals. Fractal theory as a promising technique has shown its capabilities to analyze the nonlinear dynamics of time series. Since EEG signals have fractal patterns, in this research we analyze the variations of fractal dynamics of EEG signals between four datasets that were collected from healthy subjects with open-eyes and close-eyes conditions, patients with epilepsy who did and patients who did not face seizures. The obtained results showed that EEG signal during seizure has greatest complexity and the EEG signal during the seizure-free interval has lowest complexity. In order to verify the obtained results in case of fractal analysis, we employ approximate entropy, which indicates the randomness of time series. The obtained results in case of approximate entropy certified the fractal analysis results. The obtained results in this research show the effectiveness of fractal theory to investigate the nonlinear structure of EEG signal between different conditions.

Matched MeSH terms: Fractals
Decoding of the relationship between human brain activity and walking paths

Kamal SM, Sim S, Tee R, Nathan V, Aghasian E, Namazi H

Technol Health Care, 2020;28(4):381-390.
PMID: 31796717 DOI: 10.3233/THC-191965

BACKGROUND: The human brain controls all actions of the body. Walking is one of the most important actions that deals with the movement of the body. In fact, the brain controls and regulates human walking based on different conditions. One of the conditions that affects human walking is the complexity of path of movement. Therefore, the brain activity should change when a person walks on a path with different complexities.
OBJECTIVE: In this research we benefit from fractal analysis to study the effect of complexity of path of movement on the complexity of human brain reaction.
METHODS: For this purpose we calculate the fractal dimension of the electroencephalography (EEG) signal when subjects walk on different paths with different fractal dimensions (complexity).
RESULTS: The results of the analysis show that the complexity of brain activity increases with the increment of complexity of path of movement.
CONCLUSION: The method of analysis employed in this research can also be employed to analyse the reaction of the human heart and respiration when subjects move on paths with different complexities.

Matched MeSH terms: Fractals
Fractal-based classification of electroencephalography (EEG) signals in healthy adolescents and adolescents with symptoms of schizophrenia

Namazi H, Aghasian E, Ala TS

Technol Health Care, 2019;27(3):233-241.
PMID: 30829625 DOI: 10.3233/THC-181497

Brain activity analysis is an important research area in the field of human neuroscience. Moreover, a subcategory in this field is the classification of brain activity in terms of different brain disorders. Since the Electroencephalography (EEG) signal is, in fact, a non-linear time series, employing techniques to investigate its non-linear structure is rather crucial. In this study, we evaluate the non-linear structure of the EEG signal between healthy and schizophrenic adolescents using fractal theory. The results of our analysis revealed that in terms of all recording channels, the EEG signal of healthy subjects is more complex compared to the ones suffering from schizophrenia. The statistical analysis also indicated that there is a significant difference in the complex structure of the EEG signal between these two groups of subjects. We also utilized approximate entropy in our analysis in order to verify the obtained results of the fractal analysis. The result of the entropy analysis suggested that EEG signal for healthy subjects is less random compared to the EEG signal in schizophrenic individuals. In addition, the employed methodology in this research can be further investigated in order to classify the brain activity in terms of other brain disorders, where one can explore how the complex structure of the EEG signal alters between them.

Matched MeSH terms: Fractals*
Fulltext Zone specific fractal dimension of retinal images as predictor of stroke incidence

Aliahmad B, Kumar DK, Hao H, Unnikrishnan P, Che Azemin MZ, Kawasaki R, et al.

ScientificWorldJournal, 2014;2014:467462.
PMID: 25485298 DOI: 10.1155/2014/467462

Fractal dimensions (FDs) are frequently used for summarizing the complexity of retinal vascular. However, previous techniques on this topic were not zone specific. A new methodology to measure FD of a specific zone in retinal images has been developed and tested as a marker for stroke prediction. Higuchi's fractal dimension was measured in circumferential direction (FDC) with respect to optic disk (OD), in three concentric regions between OD boundary and 1.5 OD diameter from its margin. The significance of its association with future episode of stroke event was tested using the Blue Mountain Eye Study (BMES) database and compared against spectrum fractal dimension (SFD) and box-counting (BC) dimension. Kruskal-Wallis analysis revealed FDC as a better predictor of stroke (H = 5.80, P = 0.016, α = 0.05) compared with SFD (H = 0.51, P = 0.475, α = 0.05) and BC (H = 0.41, P = 0.520, α = 0.05) with overall lower median value for the cases compared to the control group. This work has shown that there is a significant association between zone specific FDC of eye fundus images with future episode of stroke while this difference is not significant when other FD methods are employed.

Matched MeSH terms: Fractals*
Fusion of spatial gray level dependency and fractal texture features for the characterization of thyroid lesions

Raghavendra U, Rajendra Acharya U, Gudigar A, Hong Tan J, Fujita H, Hagiwara Y, et al.

Ultrasonics, 2017 05;77:110-120.
PMID: 28219805 DOI: 10.1016/j.ultras.2017.02.003

Thyroid is a small gland situated at the anterior side of the neck and one of the largest glands of the endocrine system. The abrupt cell growth or malignancy in the thyroid gland may cause thyroid cancer. Ultrasound images distinctly represent benign and malignant lesions, but accuracy may be poor due to subjective interpretation. Computer Aided Diagnosis (CAD) can minimize the errors created due to subjective interpretation and assists to make fast accurate diagnosis. In this work, fusion of Spatial Gray Level Dependence Features (SGLDF) and fractal textures are used to decipher the intrinsic structure of benign and malignant thyroid lesions. These features are subjected to graph based Marginal Fisher Analysis (MFA) to reduce the number of features. The reduced features are subjected to various ranking methods and classifiers. We have achieved an average accuracy, sensitivity and specificity of 97.52%, 90.32% and 98.57% respectively using Support Vector Machine (SVM) classifier. The achieved maximum Area Under Curve (AUC) is 0.9445. Finally, Thyroid Clinical Risk Index (TCRI) a single number is developed using two MFA features to discriminate the two classes. This prototype system is ready to be tested with huge diverse database.

Matched MeSH terms: Fractals