In today's modern era, with the significant increase in the number of vehicles on the roads, there is a pressing need for an advanced and efficient system to monitor them effectively. Such a system not only helps minimize the chances of any faults but also facilitates human intervention when required. Our proposed method focuses on detecting vehicles through background subtraction, which leverages the benefits of various techniques to create a comprehensive vehicle monitoring solution. In general, when it comes to surveillance and monitoring moving objects, the initial step involves detecting and tracking these objects. For vehicle segmentation, we employ background subtraction, a technique that distinguishes foreground objects from the background. To target the most prominent regions in video sequences, our method utilizes a combination of morphological techniques. The advancements in vision-related technologies have proven to be instrumental in object detection and image classification, making them valuable tools for monitoring moving vehicles. Methods based on moving object detection play a vital role in real-time extraction of vehicles from surveillance videos captured by street cameras. These methods also involve the removal of background information while filtering out noisy data. In our study, we employ background subtraction-based techniques that continuously update the background image to ensure efficient output. By adopting this approach, we enhance the overall performance of vehicle detection and monitoring.

The use of virtual keyboards in mobile devices such as smartphones and tablets has become an essential tool for inputting information. The sound of keystrokes has been observed in previous studies to be recorded along with ambient noises, such as those produced by uncontrolled student noise, fans, doors and windows, moving cars, and similar sources. The presence of such noises negatively affects the quality of the keystrokes signal, which in turn affects keystroke analysis. The traditional FFT-based denoising methods are vital but they are often limited by their inability to adapt to the varying characteristics of real-world audio and noises. This paper proposes an enhanced Fast Fourier Transform (FFT) with an adaptive threshold technique that reduces ambient noises. The adaptive threshold technique is developed to identify frequency bins that contain noise and set their sizes to zero or attenuate them to reduce the noise. The paper evaluates the performance of the enhanced FFT with adaptive threshold on keystrokes recorded audio and validates it through extensive experimentation. The results show that the enhanced FFT outperforms the traditional FFT in terms of speed and the amount of noise removed from the recorded audio signal, indicating a significant improvement.

Reading the words can be confusing, and it may be hard to picture what is happening. There are some circumstances where words can be misunderstood. It's much simpler to recognize text if it's displayed as an image. The use of visuals is proven to increase viewership and retention.
Synthesizing realistic images automatically is a challenging undertaking, and even the most advanced artificial intelligence and machine learning algorithm has trouble meeting this standard. GANs (Generative Adversarial Networks) are just one example of a powerful neural network architecture that has shown promising results in recent years. Existing text-to-image methods can generate examples that generally reflect the meaning of the provided descriptions, but they often lack the necessary details and colorful object elements.
The primary objective of our research was to explore diverse architectural methodologies with the intention of facilitating the generation of visual representations from textual descriptions. By delving into this investigation, we aimed to discover and examine various approaches that could effectively support the creation of visuals that accurately depict the content and context provided within written narratives. Our aim was to unlock new possibilities in the realm of visual storytelling by establishing a strong connection between language and imagery through innovative architectural techniques.

This paper investigates the effect of forming techniques on the mechanical and dielectric properties of porcelain insulators. Insulators are used in electrical equipment to separate conductors and prevent the flow of electrical charges. Slip casting is commonly used because it is cheaper and easier, but its inferior strength properties have been reported. However, other forming techniques like press casting, which can compact particle sizes by applying pressure, may result in better mechanical strength and dielectric properties. The study examined slip-casting and press-casting methods to determine which method produces better-quality electrical porcelain insulators. Using both techniques, locally available raw materials such as kaolin, feldspar, silica, and ball clay were processed before being used to create samples. The mechanical and dielectric strength of the electrical porcelain insulators produced through slip and press cast methods were analyzed using an independent t-test to compare the mean value between the two variables (Slip and press cast). The study found that slip casting produces insulators with slightly lower bulk density than press casting, but the difference is not significant enough to affect the insulators' mechanical and dielectric properties. The results showed no significant difference in bulk densities between the two forming techniques, implying that both methods are equally viable for producing shackle-type electrical insulators. These findings provide valuable insights for manufacturers, allowing them to select the most suitable forming technique based on their specific production needs and constraints.

Predicting the liquid flow rate in the process industry has proved to be a critical problem to solve. To develop a mathematical, in-depth of physics-based prognostics understanding is often required. However, in a complex process control system, sometimes proper knowledge of system behaviour is unavailable, in such cases, the complement model-based prognostics transform into a smart process control system with the help of Artificial Intelligence. In previous research a number of prognostic methods, based on classical intelligence techniques, such as artificial neural networks (ANNs), Fuzzy logic controller, Adaptive Fuzzy inference system (ANFIS) etc., utilized in a liquid flow process model to predict the effectiveness. Due to system complexity, Computational time &over fitting the performance of the AI has been limited. In this work we proposed three machine learning regression model: Random Forest (RF), decision Tree (DT) & linear Regression (LR) to predict the flow rate of a process control system. The effectiveness of the model is evaluated in terms of training time, RMSE, MAE & accuracy. Overall, this study suggested that the Decision Tree outperformed than other two models RF & LR by achieving the maximum accuracy, least RMSE & Computational time is 98.6%, 0.0859 & 0.115 Seconds respectively.