Chanda Raj Kumar

The stock market has seen an influx of capital as a result of the rapid economic expansion over the last few years. Therefore, trying to foretell how the stock market would behave is seen as an essential and, so far, more successful activity. If the investments are profitable, the stock values will go up. The ever-changing and unpredictable character of stock values, however, makes forecasting by investors quite challenging. This study presents the Stacked Ensemble Model for Trend Analysis (SEMP-TA), a novel approach to trend prediction in the stock market. The first step of SEMP-TA is data preprocessing, which involves selecting features based on clusters to find the most critical indicators quickly. The second step is trend prediction. The initial step in cleaning data is preprocessing, which includes tasks such as normalising data, identifying and discarding missing values, and selecting important features using cluster-based feature selection. The second step involves feeding the cleaned data into the prediction model. To anticipate future stock market patterns, trend prediction uses a mix of XGBoost, LightGBM, and Support Vector Regression (SVR). With an impressive Mean Absolute Error (MAE) of 1.60, a Mean Squared Error (MSE) of 7.896, and an R² score of 0.9997, the findings demonstrate that SEMP-TA outperforms other current approaches. In addition, the method’s efficacy and resilience have been shown by its application to the NIFTY-50 dataset.

Because there is more demand for freshwater around the world and the world’s population is growing at the same time, there is a severe lack of freshwater resources in the central part of the planet. The world’s current population of 7.2 billion people is expected to grow to over 9 billion by the year 2050. The vast majority of freshwater is used for things like cooking, cleaning, and farming. Most industrialised countries are in desperate need of smart irrigation systems, which are now a must-have because of how quickly technology is improving. In article presents IoT based Sensor integrated intelligent irrigation system for agriculture industry. IoT based humidity and soil sensors are used to collect soil related data. This data is stored in a centralized cloud. Features are selected by CFS algorithm. This will help in discarding irrelevant data. Clustering of data is performed by K means algorithm. This will help in keeping similar data together. Then classification model is build using the SVM, Random Forest and Naïve Bayes algorithm. Model is trained, validated and tested using the acquired data. Historical soil and humidity related data is also used in training the model. K-means SVM hybrid classifier is achieving better results for classification, prediction of water demand and saving fresh water by intelligent irrigation. K-means SVM hybrid classifier has achieved accuracy rate of 98.5 percent. Specificity, recall and precision of K-means SVM hybrid classifier is also higher than random forest and naïve bayes classifier.

RNA can no longer be regarded as a single component of the transfer. Conversely, a series of RNA molecules are identified, which revolutionizes our understanding of how cells were controlled. Inside neurons, huge and short ARNs, now make a vast repertoire of biochemical properties. RNA plays an important role in the basic management of vital biologic functions. NRAs, like enzymes, require three-dimensional models to produce them. Despite advances in our understanding of RNA folding and deployment, we still have a limited comprehension of such atomic processes through which RNA molecules become biologically important. Furthermore, because of volatile RNA molecules, it is difficult to quantitatively check RNA structures using X-ray crystallographic or NMR. As a result, numerical techniques to predict ARNs' 3D structure have become increasingly important in the research of RNA performance spectroscopy pathways. The basic principles of the RNA structure are first described, along with descriptions of data sets and methodologies to evaluate individual RNA subframeworks, as well as 3D frameworks.