A hybrid supervised ANN for classification and data visualization

Supervised ANNs such as Learning Vector Quantization (LVQs) and Multi-Layer Perceptrons (MLPs) usually do not support data visualization beside classification. Unsupervised visualization focused ANNs such as Self-organizing Maps (SOM) and its variants such as Visualization induced SOM (ViSOM) on the other hand, usually do not optimize data classification as compared with supervised ANNs such as LVQ. Thus to provide supervised classification and data visualization simultaneously, this work is motivated to propose a novel hybrid supervised ANN of LVQwithAC by hybridizing LVQ and modified Adaptive Coordinate (AC) approach. Empirical studies on benchmark data sets proven that, LVQwithAC was able to provide superior classification accuracy than SOM and ViSOM. Beside LVQwithAC was able to provide data topology, data structure, and inter-neuron distance preserve visualization. LVQwithAC was also proven able to perform promising classification among other supervised classifiers besides its additional data visualization ability over them. Thus, for applications requiring data visualization and classification LVQwithAC demonstrated its potential if supervised learning is all possible

AC-ViSOM: Hybridising the Modified Adaptive Coordinate (AC) and ViSOM for Data Visualization

ViSOM’s (Visualization induced SOM) final map can be seen as a smooth net embedded in the input space, where the distances among neurons are controlled by a regularization control parameter which is usually heuristically chosen on a trial and error basis. Empirical studies shown that ViSOM suffers from dead neuron problem, since a big number of neurons fall outside of the data region due to the regularization effect, even though the regularization control parameter is properly chosen. In this paper, a modified Adaptive Coordinate (AC) approach that is able to preserve data structure is hybridised with ViSOM is proposed. Experimental studies on benchmark datasets shown that the proposed method was able to eliminate the selection of regularization control parameter and minimizing the dead neuron for better data representation

Hybridization of Learning Vector Quantization (LVQ) and Adaptive Coordinates (AC) for data classification and visualization

Most of the artificial neural network (ANN) methods do not support data classification and visualization simultaneously. Some ANN methods such as learning vector quantization (LVQ), multi-layer perceptrons (MLP) and radial basis function (RBF) perform classification without any visualization. Excellent data visualization on the other hand has been prominently supported by various unsupervised methods such as self-organizing maps (SOM) and its recent variants of visualization induced SOM (ViSOM) and probabilistic regularized SOM (PRSOM). However, being unsupervised these methods do not optimize classification accuracy compared with the supervised classification methods such as LVQ. Thus, the scope of a novel supervised method is felt necessary to facilitate applications requiring good data visualization and intensive classification. LVQ demonstrates classification performance at least as high as other supervised ANN classifiers. Adaptive coordinate (AC) on the other hand, has demonstrated the ability of mirroring weight vectorspsila movements in N-dimensional input space to low dimensional output space to reveal the clustering tendency of data learned by SOM. This mirroring concept motivates this work to hybridize a modified AC with LVQ (LVQwihAC) to support data visualization and classification simultaneously. Empirical studies on benchmark data sets demonstrated that, the LVQwihAC method provides better classification accuracy than the unsupervised methods of SOM, ViSOM and PRSOM besides its promising data visualization with higher computational efficiency. The classification performance is also found at least as good as other supervised classifiers with additional data visualization abilities over them

A novel hybrid supervised artificial neural network (ANN) for data visualization and classification to the application of mental disorder diagnosis

The objective of this research was set to propose a supervised ANN method able to perform data classification and data structure, inter-neuron distances and data topology preserved visualization simultaneously. A real world application of mental disorder diagnosis in counseling domain was then investigated and LVQ with AC was employed to facilitate classification and visualization in designing and development of an intelligent decision support system to assist counselors in diagnosis of mental disorders

Protective Effects of Black Cumin (Nigella sativa) and Its Bioactive Constituent, Thymoquinone against Kidney Injury: An Aspect on Pharmacological Insights

The prevalence of chronic kidney disease (CKD) is increasing worldwide, and a close association between acute kidney injury (AKI) and CKD has recently been identified. Black cumin (Nigella sativa) has been shown to be effective in treating various kidney diseases. Accumulating evidence shows that black cumin and its vital compound, thymoquinone (TQ), can protect against kidney injury caused by various xenobiotics, namely chemotherapeutic agents, heavy metals, pesticides, and other environmental chemicals. Black cumin can also protect the kidneys from ischemic shock. The mechanisms underlying the kidney protective potential of black cumin and TQ include antioxidation, anti-inflammation, anti-apoptosis, and antifibrosis which are manifested in their regulatory role in the antioxidant defense system, NF-κB signaling, caspase pathways, and TGF-β signaling. In clinical trials, black seed oil was shown to normalize blood and urine parameters and improve disease outcomes in advanced CKD patients. While black cumin and its products have shown promising kidney protective effects, information on nanoparticle-guided targeted delivery into kidney is still lacking. Moreover, the clinical evidence on this natural product is not sufficient to recommend it to CKD patients. This review provides insightful information on the pharmacological benefits of black cumin and TQ against kidney damage

A Systematic Review on Marine Algae-Derived Fucoxanthin: An Update of Pharmacological Insights

Fucoxanthin, belonging to the xanthophyll class of carotenoids, is a natural antioxidant pigment of marine algae, including brown macroalgae and diatoms. It represents 10% of the total carotenoids in nature. The plethora of scientific evidence supports the potential benefits of nutraceutical and pharmaceutical uses of fucoxanthin for boosting human health and disease management. Due to its unique chemical structure and action as a single compound with multi-targets of health effects, it has attracted mounting attention from the scientific community, resulting in an escalated number of scientific publications from January 2017 to February 2022. Fucoxanthin has remained the most popular option for anti-cancer and anti-tumor activity, followed by protection against inflammatory, oxidative stress-related, nervous system, obesity, hepatic, diabetic, kidney, cardiac, skin, respiratory and microbial diseases, in a variety of model systems. Despite much pharmacological evidence from in vitro and in vivo findings, fucoxanthin in clinical research is still not satisfactory, because only one clinical study on obesity management was reported in the last five years. Additionally, pharmacokinetics, safety, toxicity, functional stability, and clinical perspective of fucoxanthin are substantially addressed. Nevertheless, fucoxanthin and its derivatives are shown to be safe, non-toxic, and readily available upon administration. This review will provide pharmacological insights into fucoxanthin, underlying the diverse molecular mechanisms of health benefits. However, it requires more activity-oriented translational research in humans before it can be used as a multi-target drug

Potentials of curcumin against polycystic ovary syndrome: Pharmacological insights and therapeutic promises

Polycystic ovary syndrome (PCOS) is a common hormonal disorder among women (4%–20%) when the ovaries create abnormally high levels of androgens, the male sex hormones that are typically present in women in trace amounts. The primary characteristics of PCOS include oxidative stress, inflammation, hyperglycemia, hyperlipidemia, hyperandrogenism, and insulin resistance. Generally, metformin, spironolactone, eflornithine and oral contraceptives are used to treat PCOS, despite their several side effects. Therefore, finding a potential candidate for treating PCOS is necessary. Curcumin is a major active natural polyphenolic compound derived from turmeric (Curcuma longa). A substantial number of studies have shown that curcumin has anti-inflammatory, anti-oxidative stress, antibacterial, and anti-apoptotic activities. In addition, curcumin reduces hyperglycemia, hyperlipidemia, hyperandrogenism, and insulin resistance in various conditions, including PCOS. The review highlighted the therapeutic aspects of curcumin against the pathophysiology of PCOS. We also offer a hypothesis to improve the development of medicines based on curcumin against PCOS