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The proliferation of advanced printing and scanning technologies has worsened the challenge of counterfeit currency, posing a significant threat to national economies. Effective detection of counterfeit banknotes is crucial for maintaining the monetary system's integrity. This study aims to evaluate the effectiveness of two prominent Python libraries, Keras and PyTorch, in counterfeit detection using Convolutional Neural Network (CNN) image classification. We repeat our experiments over 2 data sets, one dataset depicting the 1000 denomination of the Colombian peso under UV light and the second dataset of Bangladeshi Taka notes. The comparative analysis focuses on the libraries' performance in terms of accuracy, training time, computational efficiency, and the model behavior towards datasets. The findings reveal distinct differences between Keras and PyTorch in handling CNN-based image classification, with notable implications for accuracy and training efficiency. The study underscores the importance of choosing an appropriate Python library for counterfeit detection applications, contributing to the broader field of financial security and fraud prevention. 

The stock market is as volatile as it is unpredictable, the unstable nature of the stock market results in fluctuations in stock prices and invariably, the market performance of stocks. Understanding the underlying factors that contribute to the volatility of the stock market, which has its consequences on stock prices, has become important to researchers and investors alike. Some of the methods that researchers have used in the past as a gauge for understanding market performance include analyzing economic conditions, understanding company performance, following geopolitical events and market trends. To contribute to the vast research field of stock price predictions and the challenge of understanding stock price fluctuations, this study will aim to find a relationship between human sentiments on the social media platform, Reddit, and the S&P 500 stock index. In this study, we will analyze posts from five subreddits that typically discuss the stock market and stock price fluctuations. This will form the first part of our dataset. Historical stock prices for the S&P 500 index will be obtained from Yahoo Finance. This will form our final dataset. Using VADER (Valence aware dictionary and sentiment reasoner), we will extract the sentiments within the five subreddits and categorize them into positive and negative sentiments. The historical stock prices from Yahoo finance will be matched with the aggregate sentiments for each day and this data passed through the LSTM model for training. Our findings provide strong evidence of social media’s impact on stock price predictions. 

Against the backdrop of the ever-evolving IT industry, this comparative study explores the differences among various project management methods, highlighting key distinctions between Agile and traditional approaches by evaluating the benefits of Agile and the drawbacks of not adopting agile methods. Agile practices have gained recognition for their adaptability and efficiency, in addressing dynamic industry demands. Our multifaceted approach, which examines the pros and cons of Agile methodologies across various industries employs different machine learning algorithms—logistic regression, linear regression, and decision tree regressor. The study quantitatively measures Agile’s impact compared to other methodologies using prediction probabilities, classifications, confusion metrics, R-squared, and Mean Squared Error (MSE) for performance analysis. Results highlight that linear regression outperforms other models with 71% accuracy and 82% precision. These findings offer valuable insights into understanding Agile’s impact on IT industries, encouraging further exploration and refinements to make informed decisions on project management strategies and fostering future research to enhance IT project success rates. 

As the digital world gets increasingly ingrained in our daily lives, cyberattacks—especially those involving malware—are growing more complex and common, which calls for developing innovative safeguards. Keylogger spyware, which combines keylogging and spyware functionalities, is one of the most insidious types of cyberattacks. This malicious software stealthily monitors and records user keystrokes, amassing sensitive data, such as passwords and confidential personal information, which can then be exploited. This research introduces a novel browser extension designed to effectively thwart keylogger spyware attacks. The extension is underpinned by a cutting-edge algorithm that meticulously analyzes input-related processes, promptly identifying and flagging any malicious activities. Upon detection, the extension empowers users with the immediate choice to terminate the suspicious process or validate its authenticity, thereby placing crucial real-time control in the hands of the end user. The methodology used guarantees the extension's mobility and adaptability across various platforms and devices. This paper extensively details the development of the browser extension, from its first conceptual design to its rigorous performance evaluation. The results show that the extension considerably strengthens end-user protection against cyber risks, resulting in a safer web browsing experience. The research substantiates the extension's efficacy and significant potential in reinforcing online security standards, demonstrating its ability to make web surfing safer through extensive analysis and testing.