Financial Fraud Detection using Improved Artificial Humming Bird Algorithm with Modified Extreme Learning Machine

More and more industries, including the financial sector, are moving their operations online as internet usage continues to rise at an exponential rate. As a result, financial fraud is on the rise in all its guises and in all parts of the world, causing enormous economic damage. The purpose of financial fraud detection systems is to identify potential dangers, such as unauthorised access or unusual attacks. In recent years, this problem has been attacked using a variety of machine learning and data mining methods. Aalgorithms, on the other hand, are better able to deal with only a small quantity of labelled data and a large amount of unlabeled data, making them useful in situations where it would be impractical to rely solely on supervised learning algorithms to train a good-performing classifier. In this research, we propose a Semi-supervised Extreme Learning Machine (SKELM) built on top of the weighted kernel, which we call SELMWK. For the purpose of detecting financial fraud, this research proposes an enhanced artificial hummingbird algorithm (IAHA). The algorithm combines two essential techniques to enhance its capacity for optimisation. To begin, the Chebyshev chaotic map is used to seed the first population of artificial hummingbirds, which boosts the population's overall ability to do global searches. Second, the guided foraging phase incorporates the Levy flight to enlarge the search field and forestall early convergence. The experimental results demonstration that the suggested technique recovers the Internet monetary fraud detections

A Dense Network Model for Outlier Prediction Using Learning Approaches

There are various sub-categories in outlier prediction and the investigators show less attention to related domains like outliers in audio recognition, video recognition, music recognition, etc. However, this research is specific to medical data analysis. It specifically concentrates on predicting the outliers from the medical database. Here, feature mapping and representation are achieved by adopting stacked LSTM-based CNN. The extracted features are fed as an input to the Linear Support Vector Machine () is used for classification purposes. Based on the analysis, it is known that there is a strong correlation between the features related to an individual's emotions. It can be analyzed in both a static and dynamic manner. Adopting both learning approaches is done to boost the drawbacks of one another. The statistical analysis is done with MATLAB 2016a environment where metrics like ROC, MCC, AUC, correlation co-efficiency, and prediction accuracy are evaluated and compared to existing approaches like standard CNN, standard SVM, logistic regression, multi-layer perceptrons, and so on. The anticipated learning model shows superior outcomes, and more concentration is provided to select an emotion recognition dataset connected with all the sub-domains

A review on triterpenoids from plant sources as potential antiviral agents

Viral infections are considered as leading a health issue globally. Numerous numbers of biologically active anti-viral agents have been identified from plants and other organisms. Particularly, terpenoids are a major component of the plant secondary metabolites and a complexity of these structures is accompanied by the potency of their biological activities. It is believed that most of the terpenoids possess the bioactivity against viral infections and cancer diseases. Hence, affected by the pressing a need elevated by the spreading of seriously life-threaten viruses, this review highlights the importance of terpenoids and their activity as antiviral agents that can be employed to treat current lethal diseases such as HIV, H1N1, SARS-CoV and HSV

A NOVEL CLOSED LOOP CONTROL OF SERIES/PARALLEL MULTILEVEL TOPOLOGY FOR RESIDENTIAL APPLICATIONS

Using parallel-series converter in as DC Power supply appliance gives a good opportunity to maintain stable supply while the load is changing. A novel multilevel inverter with a small number of switching devices is proposed. It consists of an H-bridge and an inverter which outputs multilevel voltage by switching the dc voltage sources in series and in parallel. The total harmonic of the output waveform is also reduced. The proposed inverter outputs larger voltage than the input voltage by switching the capacitors in series and in parallel. The maximum output voltage is determined by the number of the capacitors. In this paper a new topology called reversing voltage is implemented to improve the multilevel performance by compensating the disadvantages just mentioned. This topology requires fewer components compared to available multilevel inverters (especially in higher levels) and requires less carrier signals and does not need separate mechanism for balancing of the capacitor voltages. This inverter consists of a one H-bridge topology and a conditioning inverter which outputs of multilevel voltage by switching the DC voltage sources in series and in parallel. Unlike traditional multilevel inverters, this topology does not require an external voltage balancing circuit; a complicated control scheme is used to maintain its voltage levels while delivering sustained real power. The simulation results based on Matlab/Simulink are discussed in detail in this paper

Biologically active orcinol-based secondary metabolites originated from lichens

Lichens have attracted considerable interest since ancient time due to their medicinal properties. Lichen produce a variety of orcinol-based compounds such as xanthones, anthraquinones, dibenzofurans, depsides, and depsidones. Several related compounds have shown potent bioactivities as antiviral, antioxidant, anti-herbivore, insecticidal, antifungal, and anticancer. Lichens have been employed as traditional medicines, and these are continuing to be of great interest for their biotechnological potential. The purpose of this review was to systematically evaluate the literature on the orcinol based biologically active secondary metabolites of lichen

A review on triterpenoids from plant sources as potential antiviral agents

761-770Viral infections are considered as leading a health issue globally. Numerous numbers of biologically active anti-viral agents have been identified from plants and other organisms. Particularly, terpenoids are a major component of the plant secondary metabolites and a complexity of these structures is accompanied by the potency of their biological activities. It is believed that most of the terpenoids possess the bioactivity against viral infections and cancer diseases. Hence, affected by the pressing a need elevated by the spreading of seriously life-threaten viruses, this review highlights the importance of terpenoids and their activity as antiviral agents that can be employed to treat current lethal diseases such as HIV, H1N1, SARS-CoV and HSV

A review on triterpenoids from plant sources as potential antiviral agents

Viral infections are considered as leading a health issue globally. Numerous numbers of biologically active anti-viral agents have been identified from plants and other organisms. Particularly, terpenoids are a major component of the plant secondary metabolites and a complexity of these structures is accompanied by the potency of their biological activities. It is believed that most of the terpenoids possess the bioactivity against viral infections and cancer diseases. Hence, affected by the pressing a need elevated by the spreading of seriously life-threaten viruses, this review highlights the importance of terpenoids and their activity as antiviral agents that can be employed to treat current lethal diseases such as HIV, H1N1, SARS-CoV and HSV

Synthesis of some novel orsellinates and lecanoric acid related depsides as <i>α</i>-glucosidase inhibitors

<p>Sixteen novel orsellinic esters (<b>6a-l, 7a-d</b>) along with four lecanoric acid related depsides (3<b>a-c, 4</b>) were synthesized and confirmed their structures by spectroscopic data (¹H, ¹³C & HRMS). The synthesized compounds were evaluated for their <i>in vitro α</i>-glucosidase (<i>Saccharomyces cerevisiae</i>) inhibitory potential. Among the tested compounds, <b>3c</b> (IC₅₀: 140.9 μM) and <b>6c</b> (IC₅₀: 203.9 μM) displayed potent α-glucosidase inhibitory activity and found more active than the standard drug acarbose (IC₅₀: 686.6 μM). Both the test compounds were subjected to <i>in vivo</i> antihyperglycemic activity using sucrose loaded model in Wistar rats and found compound <b>3c</b> exhibited significant reduction in glucose levels.</p