Stock market is a well-known option for investments and has impact in today’s economy. Due to high returns and losses, it will be an investor’s dream to predict the rise and fall accurately. But future price is very unpredictable. Although there are different analysis present for years like fundamental analysis, technical analysis etc. Now a days different algorithms are used to forecast the future price. Stock price prediction is very important as it can be seen as the guideline used by the traders and investors about the future trend of the market. So, it will help them to take entry or exit in the market in right time. In this paper, we have predicted the stock price of five NIFTY 50 shares and then compared it with the actual value to analyze the accuracy of price prediction. Here Long-Short-Term Memory, a deep learning (DL) model is used. It is found that the DL model performed very well in predicting the price of required stock. Quantification of the performance of the model is done using mean absolute percentage error.

In recent years, wireless communications have evolved significantly, and many mobile devices have reduced in size. The antennas used in mobile terminals must be lowered in size to fulfil the downsizing standards. Planar antennas, like microstrip and printed antennas, have a low profile, compact size, and conformability to mounting hosts, making them particularly desirable candidates for achieving these needs. Additionally, planar antennas are also being used in communication devices for 2.4 GHz (2400 – 2484 MHz) and 5.2 GHz wireless local area network (WLAN) systems (5150). In wireless applications, an antenna is a crucial component. At the transmitter, it transforms electrical signals into RF signals, and at the receiver, it converts RF signals to electrical signals. The patch inside the antenna is made of a conducting material such as Cu (Copper) or Au (Gold), and it can be rectangular, round, triangular, or elliptical. Two unique designs of microstrip planar antennas with an operating frequency of 5.2 GHz having S11 parameters as -16.0 dB and -15.7 dB have been offered and their performance has been studied in this research article.

One of the biggest risks facing women in the twenty-first century is breast cancer. Invasion lobular carcinoma and invasion ductal carcinoma are the two main categories into which it is divided. Omics data is used to identify predictive biomarker signatures for clinical applications to detect breast cancer. Predictive performance has significantly improved because of recent advancements in machine learning techniques. Here, we are using an approach built on symbolic regression called the QLattice on a variety of clinical omics data sets. Through the identification of potential regulatory interactions between biomolecules, this method creates efficient, high-performing models that can forecast and explain the results of a specific omics experiment. The models have the potential to make it easier to find new biomarker signatures due to their clarity and obvious functional shape, which make them simple and easy to comprehend. A comprehensive experimental investigation was conducted to assess the machine learning model's efficacy in terms of the Area under the Curve (AUC) for breast cancer. The outcomes, which were contrasted with other approaches, demonstrate the suggested framework's efficacy and capacity to beat the alternative algorithms in terms of AUC, which is 0.66. Here, we profiled breast tumors in detail, including ductal carcinoma, mixed carcinoma, and invasive lobular carcinoma, by using the Gaussian method and TNXB gene.