Forecasting Stock Market Indices Using Gated Recurrent Unit (GRU) Based Ensemble Models: LSTM-GRU

A time sequence analysis is a particular method for looking at a group of data points gathered over a long period of time. Instead of merely randomly or infrequently, time series analyzers gather information from data points over a predetermined length of time at scheduled times. But this kind of research requires more than just accumulating data over time. Data in time series may be analyzed to illustrate how variables change over time, which makes them different from other types of data. To put it another way, time is a crucial element since it demonstrates how the data changes over the period of the information and the outcomes. It offers a predetermined architecture of data dependencies as well as an extra data source. Time Series forecasting is a crucial field in deep learning because many forecasting issues have a temporal component. A time series is a collection of observations that are made sequentially across time. In this study, we examine distinct machine learning, deep learning and ensemble model algorithms to predict Nike stock price. We are going to use the Nike stock price data from January 2006 to January 2018 and make predictions accordingly. The outcome demonstrates that the hybrid LSTM-GRU model outperformed the other models in terms of performance

Deep Learning-based Gated Recurrent Unit Approach to Stock Market Forecasting: An Analysis of Intel\u27s Stock Data

The stock price index prediction is a very challenging task that\u27s because the market has a very complicated nonlinear movement system. This fluctuation is influenced by many different factors. Multiple examples demonstrate the suitability of Machine Learning (ML) models like Neural Network algorithms (NN) and Long Short-Term Memory (LSTM) for such time series predictions, as well as how frequently they produce satisfactory outcomes. However, relatively few studies have employed robust feature engineering sequence models to forecast future prices. In this paper, we propose a cutting-edge stock price prediction model based on a Deep Learning (DL) technique. We chose the stock data for Intel, the firm with one of the quickest growths in the past ten years. The experimental results demonstrate that, for predicting this particular stock time series, our suggested model outperforms the current Gated Recurrent Unit (GRU) model. Our prediction approach reduces inaccuracy by taking into account the random nature of data on a big scale

Credit Card Fraud Detection Using Logistic Regression and Synthetic Minority Oversampling Technique (SMOTE) Approach

Financial fraud is a serious threat that is expanding effects on the financial sector. The use of credit cards is growing as digitization and internet transactions advance daily. The most common issues in today\u27s culture are credit card scams. This kind of fraud typically happens when someone uses someone else\u27s credit card details. Credit card fraud detection uses transaction data attributes to identify credit card fraud, which can save significant financial losses and affluence the burden on the police. The detection of credit card fraud has three difficulties: uneven data, an abundance of unseen variables, and the selection of an appropriate threshold to improve the models\u27 reliability. This study employs a modified Logistic Regression (LR) model to detect credit card fraud in order to get over the preceding difficulties. The dataset sampling strategy, variable choice, and detection methods employed all have a significant impact on the effectiveness of fraud detection in credit card transactions. The effectiveness of naive bayes, k-nearest neighbour, and logistic regression on highly skewed credit card fraud data is examined in this research. The accuracy of the logistic regression technique will be closer to 0.98%; with this accuracy, frauds may be easily detected. The fact that LR receives the highest classifier score illustrates how well LR predicts credit card theft

Factors associated with poor self-rated health among chronic kidney disease patients and their health care utilization: Insights from LASI wave-1, 2017-18

BackgroundChronic kidney disease (CKD), associated with other chronic conditions affects the physical, behavioral, and psychological aspects of an individual, leading to poor self-rated health. Hence, we aimed to assess the factors associated with poor self-rated health (SRH) in CKD patients. Additionally, we assessed their health care utilization.MethodsThis is an observational study consisting of 527 CKD patients from Longitudinal Aging Study in India (LASI), 2017-2018. A descriptive statistic computed prevalence. Regression analysis assessed the association between poor SRH and socio-demographic variables presented as adjusted odds ratio with a confidence interval of 95%. Health care utilization among CKD patients was graphically presented.ResultsAround 64% of CKD patients had poor SRH. Aged 75 years and above (AOR=1.8, 95% CI= 0.5-6.8), rural residents (AOR= AOR 1.8, 95% CI =1.0 -3.1) and those with other chronic conditions (AOR=5.1, 95% CI= 2.3-11.0) were associated with poor SRH. Overall 79% of the CKD patients availed health care facility, most (44.8%) of those visit private facility.ConclusionWe observed older adults, females, rural residents, and having other chronic conditions were associated with poor SRH among CKD patients which highlights the need for equitable and strengthened health care system. There is an urgent need to provide accessible, affordable and quality healthcare services for these individuals so as to maintain continuity of care

Social and biological evaluation of antimicrobial resistance (SOBEAR) in rural India: a study protocol

BackgroundAntimicrobial resistance (AMR) has been one of the biggest global health threats in recent years, mostly in low- and middle-income countries, which requires urgent research using a multidisciplinary research approach. The use of large quantities of antimicrobial drugs inappropriately for humans, poultry and agriculture has been recognized as a leading cause of antibiotic resistance and the predominance of drug-resistance pathogens in the environment. This protocol aims to describe the use/misuse of antibiotics (ABs) in the community and evaluate clinical samples from healthcare settings to detect genes associated with antimicrobial resistance.MethodsWe will conduct a community-level survey in different villages of the Tigiria block to assess knowledge and awareness on ABs and AMR. We will conduct in-depth interviews (IDIs) with doctors, pharmacists, nurses and drug sellers, as well as focus group discussions (FGDs) with ASHA and ANM workers who are involved in antibiotic supplies to the community. Quantitative data from the community survey and qualitative data of IDIs and FGDs will be linked and analyzed using statistical modeling and iterative thematic content analysis. Specimens (stool, urine, blood and wound/pus) will be collected from clinically diagnosed patients of different healthcare centers of Tigiria block. The samples will be cultured for bacterial isolation and antibiotic sensitivity testing. Genomic DNA will be isolated from positive bacterial cultures and sequenced using PCR to evaluate high-threat multi-drug resistance organisms (MDROs), screening of plasmid-mediated quinolone resistance (PMQR) genes, antimicrobial genes responsible for MDR and quinolone resistance-determining regions (QRDRs).ConclusionThis is the community-based protocol to evaluate the knowledge, attitudes, awareness and practices regarding ABs and AMR. The study protocol establishes a foundation for evaluating population-based prevalence and risk factors for AMR and MDROs in rural areas of the Odisha state, India

Immunization of Chickens with Newcastle Disease Virus Expressing H5 Hemagglutinin Protects against Highly Pathogenic H5N1 Avian Influenza Viruses

Highly-pathogenic avian influenza virus (HPAIV) and Newcastle disease virus (NDV) are the two most important poultry viruses in the world. Natural low-virulence NDV strains have been used as vaccines over the past 70 years with proven track records. We have previously developed a reverse genetics system to produce low-virulent NDV vaccine strain LaSota from cloned cDNA. This system allows us to use NDV as a vaccine vector for other avian pathogens.Here, we constructed two recombinant NDVs (rNDVs) each of which expresses the hemagglutinin (HA) gene of HPAIV H5N1 strain A/Vietnam/1203/2004 from an added gene. In one, rNDV (rNDV-HA), the open reading frame (ORF) of HA gene was expressed without modification. In the second, rNDV (rNDV-HAF), the ORF was modified so that the transmembrane and cytoplasmic domains of the encoded HA gene were replaced with those of the NDV F protein. The insertion of either version of the HA ORF did not increase the virulence of the rNDV vector. The HA protein was found to be incorporated into the envelopes of both rNDV-HA and rNDV-HAF. However, there was an enhanced incorporation of the HA protein in rNDV-HAF. Chickens immunized with a single dose of either rNDV-HA or rNDV-HAF induced a high titer of HPAIV H5-specific antibodies and were completely protected against challenge with NDV as well as lethal challenges of both homologous and heterologous HPAIV H5N1.Our results suggest that these chimeric viruses have potential as safe and effective bivalent vaccines against NDV and. HPAIV. These vaccines will be convenient and affordable, which will be highly beneficial to the poultry industry. Furthermore, immunization with these vaccines will permit serological differentiation of vaccinated and avian influenza field virus infected animals

Multi-objective clustering: a kernel based approach using Differential Evolution

A multi-objective algorithm is always favoured over a single objective algorithm as it considers different aspects of a dataset in the form of various objectives. In this article, a multi-objective clustering algorithm has been proposed based on Differential Evolution. Here, three objectives have been considered to handle different complex datasets. In addition to this, a kernel function is hybridised with the objectives to evaluate the data on a hyperspace for reducing the impact of nonlinearity on cluster formation. Moreover, to get the best compromised solution from the Pareto front an effective fuzzy concept has been followed. Five metaheuristic approaches have been taken into consideration for performance comparison. These methodologies have been applied to twelve datasets and the result reveals the efficacy of the proposed model in data clustering

Occult hepatitis B virus infection in chronic liver disease: full-length genome and analysis of mutant surface promoter

Background and Aims: Genome sequence of hepatitis B virus (HBV) from occult chronic infection is scarce. Fifty-six (9.4%) of 591 patients seronegative for hepatitis B surface antigen (HBsAg) with chronic liver disease were positive for HBV DNA. The complete HBV genome from 9 of these patients (S1-S9) and 5 controls positive for HBsAg (SWT.1-SWT.5) were analyzed. Methods: Overlapping genome fragment amplification, cloning, and sequencing was performed on these cases. Functional analysis of surface promoter was conducted using fusion construct. Results: All patients with occult infection except one (S8) had a low viral titer. Eight patients had infection with genotype A (S1-S5, SWT.1-2, SWT.5) and 6 had infection with genotype D (S6-S9, SWT.3-4). S4 and S5.1 of genotype A had the characteristic nucleotide deletions in core and pre-S1 region seen in genotype D. The major observations in patients with occult HBV infection were as follows: frequent quasispecies variation, deletions in pre-S2/S region affecting the surface promoters (nt 3025-54) and pre-S protein (S3, S5, S6, S8), truncated precore (S6, S8, S7.1) and core (S9) owing to stop signal, alternate start codon for the Polymerase gene (S3, S9), and YMDD mutation (S1, S4, S9) in patients not on antiviral therapy. HBsAg and core proteins could be shown immunohistochemically in 3 of 5 liver biopsy specimens available. The mutant surface promoters (pre-S2 and S) on functional analysis showed alterations in HBsAg expression. Conclusions: These changes in the regulatory region with possible alterations in the ratio of large and small surface proteins along with other mutations in the genome may decrease the circulating HBsAg level synergistically, making the immunodetection in serum negative

Proposed Method for Shoot-Through in Three Phase ZSI and Comparison of Different Control Techniques

<span style="color: black; font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; font-size: 9pt;" lang="FR"><span style="color: black; font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; font-size: 9pt;" lang="FR">This paper presented the new methodology for different control techniques applied to three phase Z-source inverter for minimisation of switching losses. The procedure for proposed control techniques and its effects on the performance of operation of three phase Z-source inverter are analyzed. The graphs for </span><span style="font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; font-size: 9pt;" lang="IN">voltage gain and voltage stress are drawn for different control methods. </span><span style="color: black; font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; font-size: 9pt;" lang="FR">The flow-chart for the symmetrical and unsymmetrical </span><span style="color: black; font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; font-size: 9pt;" lang="IN">control techniques for creating pulse signals for switches of three phase inverter are shown</span><span style="color: black; font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; font-size: 9pt;" lang="FR">. All the methods are studied and compared with each other. The Total harmonic distortion (THD) of output voltage of both the control methods has been analyzed using FFT analysis. The </span><span style="color: black; font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; font-size: 9pt;" lang="IN">experiments</span><span style="color: black; font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; font-size: 9pt;" lang="FR"> done and the results shown for capacitor voltage, load current and load line voltage for simple boost and constant boost control techniques are presented using MATLAB/ Simulink.</span></span

Elitism-based multi-objective differential evolution with extreme learning machine for feature selection: a novel searching technique

The features related to the real world data may be redundant and erroneous in nature. The vital role of feature selection (FS) in handling such type of features cannot be ignored in the area of computational learning. The two most commonly used objectives for FS are the maximisation of the accuracy and minimisation of the number of features. This paper presents an Elitism-based Multi-objective Differential Evolution algorithm for FS and the novelty lies in the searching process which uses Minkowski Score (MS) and simultaneously optimises three objectives. The MS is considered as the third objective to keep track of the feature subset which is capable enough to produce a good classification result even if the average accuracy is poor. Extreme Learning Machine because of its fast learning speed and high efficiency has been considered with this multi-objective approach as a classifier for FS. Twenty-one benchmark datasets have been considered for performance evaluation. Moreover, the selected feature subsets are tested using 10-fold cross-validation. A comparative analysis of the proposed approach with two classical models, three single objective algorithms, and four multi-objective algorithms has been carried out to test the efficacy of the model