Problem Solving

It is the process of working through details of problem to reach a solution. Problem solving may include mathematical or systematic operations and can be a gauge of an individual's critical thinking skill. Problem solving refers to a state of desire for reaching a definite 'goal' from a present condition either is not directly moving toward the goal, or needs more complex logic for finding a missing description of conditions or steps toward the goal

V.V.S. Aiyar, Pudhumaipithan, Nanjil Nadan, Subrabharathimanian’s in Short stories from a theoretical point of view

Theory refers to the thought that is based on in-depth research and based on knowledge. One has to explore any one theory in many cultural contexts and see if that theory applies to many cultures. Contemporary literary forms such as novels, short stories, revival poetry and plays express the community life of millions of people. Modern writers, with their broad outlook on society, use a variety of techniques to bring about tremendous change in the creative field. Some of the theoretical ideas mentioned in the stories of some of the short story personalities need to be explored. Creators create works with a story in mind. When stories are created with a societal perspective it transforms into a success for society

Unveiling Future Trends for Predicting Online Smart Market Stock Prices using Ensemble Neural Network

Predicting stock prices in the online smart market is a complex task, and leveraging advanced data mining techniques has become essential for accurate forecasting. This study proposes a novel approach utilizing an ensemble neural network combined with swarm optimization for enhanced predictive accuracy. The ensemble neural network, a robust machine learning approach, is adept at capturing complex patterns in stock market data. Concurrently, swarm optimization further refines the model's predictive capabilities, optimizing parameters for superior performance. By incorporating these techniques, the study unveils future trends in predicting online smart market stock prices, providing investors and traders with invaluable insights for informed decision-making. Existing algorithms are limited. The ensemble neural network integrates diverse models to capture intricate patterns in financial data, while swarm optimization refines the model parameters for optimal performance. The experimental results showcase an impressive accuracy of 92.5%, highlighting the efficacy of the proposed methodology. This research not only contributes to the field of stock price prediction but also provides valuable insights into future trends in the online smart market

Machine Learning Algorithms for Detection: A Survey and Classification

There is an enormous amount of data being dealt with by the medical field on a daily basis. Using a conventional method for handling data can affect the accuracy of the results. Early recognition of the disease is crucial for the analysis of patient medicines and specialists. The objective of this paper is to provide a comprehensive review of the techniques used in disease detection. Machine learning algorithms can be used to find out facts in medical research, particularly disease prediction. Machine learning algorithms such as Support vector machine [SVM], Decision trees, Bayes classifiers, K-Nearest Neighbours [KNN] Ensemble classifier techniques, etc. are used to determine different ailments. The use of machine learning algorithms can lead to fast and high accuracy prediction of diseases. This research paper analyses how machine learning techniques and algorithms are used to predict different diseases and their types. This paper provides an extensive survey of the machine learning techniques used for the prediction of chronic kidney disease, liver disease, haematological diseases, Alzheimer’s disease, and urinary tract infections

A statistical approach to spectrum sensing using bayes factor and p-Values

The sensing methods with multiple receive antennas  in the Cognitive Radio (CR) device, provide a promising solution for reducing the error rates in the detection of the Primary User (PU) signal. The received Signal to Noise Ratio at the CR receiver is enhanced using the diversity combiners. This paper proposes a statistical approach based on minimum Bayes factors and p-Values as diversity combiners in the spectrum sensing scenario. The effect of these statistical measures in sensing the spectrum in a CR environment is investigated. Through extensive Monte Carlo simulations it is shown that this novel statistical approach based on Bayes factors provides a promising solution to combine the test statistics from multiple receiver antennas and can be used as an alternative to the conventional hypothesis testing methods for spectrum sensing. The Bayesian results provide more accurate results when measuring the strength of the evidence against the hypothesis

(1,2) - Domination in the Total Graphs of Cn , Pn and K1,n

In this paper, we discuss the (1,2) - domination in the total graphs of Cn ,  Pnand K1,n &nbsp

Biofilm inhibitory potential of Oscillatoria tenuis against Candida albicans

Prokaryotic autotrophs have a key role in maintaining the sustainability of nature. Their secondary metabolites and stored chemicals have wide utility in human life. Cyanophytes, the primitive producers, can become a necessity of the modern world as they have enormous unexplored features. Candida albicans, an opportunistic pathogen having multidrug resistance, fallout health concerns in human and animal hosts. This study focused on the antibiofilm potential of Oscillatoria tenuis NTAPD 02, isolated from a hydrocarbon-polluted area against the hyphal switching of Candida albicans. Ethanolic extract of the algal sample, OEE, was taken to perform the biofilm quantification test and CLSM studies to determine the antibiofilm potential of Oscillatoria tenuis against Candida albicans. The MBIC for OEE was found to be 30 µg/mL against C. albicans and also shows a 70.8% reduction of fungal biofilm. The GC-MS and FTIR analysis illustrates the presence of potent phenolic hydrocarbons having an anti-proliferative effect. OEE was also found stress generative in C. elegans (500 µg/mL). The ROS generation in the worms intensified by increased concentration of OEE. The study proves that Oscillatoria tenuis, NTAPD 02, can be considered an anti-proliferative alga against C. albicans invasions

A Study about the Intension to Purchase Electric Two-Wheelers in the State of Tamil Nadu

Although the market for electric vehicles (EVs) has grown dramatically in recent years, they still make up a very small portion of every new vehicle sold worldwide. Over one percent of all two- wheelers sold in 2023 were electric. Thus, it is necessary to research the adoption of electric two- wheelers (E2W). A behavioral model of electric two-wheeler adoption intention is developed in this study. The goal of the current study was to determine the variables that affect customers' preferences to purchase electric two-wheelers. Using the questionnaire approach, 182 valid answers were gathered. The study hypothesis was tested using partial least squares structural equation modelling (PLS-SEM). The research findings show that customers' opinions towards electric two-wheelers are strongly influenced by social influence, perceived economic gain, charging infrastructure, and environmental concern. The consumer's intention to buy an electric two-wheeler is also highly influenced by their attitude. It was discovered that the primary factor driving people to buy electric two-wheelers was perceived economic benefits. The results of this study also indicate that women are more likely than males to buy electric two-wheelers. Governments and manufacturers of electric two-wheelers can better understand customer behaviour towards electric two-wheeler purchases with the help of these data