Volume 4

ABSTRACT: Vegetation cover classification using mixed or low-resolution scalar images is challenging. Fortunately, recently deep learning object detection methods have emerged as a replacement to the conventional machine learning methods for the detection and classification of land use and land cover. This paper presents a deep learning object detection approach for land use and land cover detection using low/mixed resolution satellite images acquired from Google Earth satellite images. Google Earth images are accessible freely using the Google Earth Pro desktop application. Our dataset consists of two (02) classes (vegetation and non-vegetation) with a total of 450 labeled images captured from different parts of Pakistan. We present a comparison of the recent anchor-free object detection model YOLOX with the anchor-based object detection model YOLOR for solving real-time problems. The end-to-end differentiability, efficient GPU utilization, and absence of hand-crafted parameters make anchor-free models a compelling choice in object detection, and yet not been explored on Land cover classification using satellite images. Our experimental study shows that YOLOX delivers an overall accuracy of 83.50% on Vegetation and 86% on Non-Vegetation classes, which outperformed YOLOR by 30% on Vegetation classes and 34% on non-Vegetation classes for our dataset. We also show how an object detection system can be used for Vegetation and Non-Vegetation classification tasks, which can then be used for change monitoring and assisting in developing geographical maps using low/mixed resolution freely available satellite image.

ABSTRACT: This study uses Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks to predict COVID-19 in Pakistan. The pandemic epidemic that hit Wuhan, China, in December 2019 and affected millions of people worldwide. As of March 1, 2023, Pakistan had 296,149 confirmed cases, 6,298 deaths, and 280,970 recoveries. The predictive algorithms above will predict confirmed cases, deaths, and recoveries for 30 days. The approach collects time-series data on confirmed cases, deaths, and recoveries. The data is processed and analyzed using ARIMA and ANN models. These models were chosen because they can handle non-linear and complex time series data, making them excellent for pandemic prediction. The research hypothesis is that ARIMA and ANN models can accurately anticipate Pakistani COVID-19 case trends over the next 30 days. Correlation and MSE are used to compare models. Early results reveal that ARIMA and ANN models accurately estimate COVID-19 prevalence in Pakistan. An in-depth study of the methodology suggests adjustments to improve forecast accuracy. This study has significant ramifications. Accurate projections can help policymakers choose public health initiatives, saving lives and money. Successfully using these machine learning models could lead to their usage in epidemic prediction.

Abstract: Thermoelectric power generation is a renewable energy conversion process that directly converts heat to electricity. This research proposes a novel fluid-thermal-electric multi-physics numerical model for predicting the performance of a thermoelectric-producing system. On the ANSYS platform, numerical simulations are done along with the exhaust temperature and mass flow rate. The range of hot end temperatures is from 100 to 450 degrees Celsius. Similarly, the cold end temperature can reach a maximum of 25 degrees Celsius and a minimum of 10 degrees Celsius. When the temperature at the generator’s hot end rises, it means the temperature at the generator’s cold end must fall. It means both have an inverse relation to each other. The maximum heat absorbed at the hot junction is 32.762 W and the minimum heat absorbed at the hot junction is 4.6305 W. Hence the maximum and minimum current values produced in this paper by the thermoelectric generator are 72.156 A & 10.816 A respectively.  The hot side heat exchanger’s position of the thermoelectric modules has a significant impact on output. Combining the benefits of many models are advised to construct an inclusive thermoelectric generator system for use in real-world applications

Abstract: Al₂O₃-Water nanofluids have become popular for their significantly higher heat transfer rates, which are related to their increased thermal conductivity. These fluids are particularly useful in tiny pipelines, especially in situations where effective thermal control is required. Because of this, they are particularly beneficial to sectors like aerospace and automotive, where the need for lightweight and compact heat exchanger designs is crucial. This research investigates several input factors, such as temperature and velocity, using ANSYS Fluent. The investigation’s initial temperature was 290 K, and the pipe’s entry velocity was 3.78×10^-1 m/s. At 2.98×10² K, the exit temperature stabilized. However, a clear inverse link between the temperature at the pipe’s output side and the velocity at its entrance side was found. This unique development served as the main focus of our investigation on how to optimize heat flow and thermal conductivity inside the pipe. We used a two-phase approach, incorporating the nanofluid phases phi-0 and phi-4, to increase our comprehension. This methodological decision departs from traditional research, which frequently uses simulations in a single phase. Our new method was inspired by the necessity for a more thorough investigation. The results of this study demonstrate the effectiveness of the two-phase method, showing a significant rise in heat flow and thermal conductivity when compared to traditional one-phase simulations used in earlier research. This emphasizes how our approach was original and significant, bringing fresh perspectives to the field of nanofluid heat transfer research.

ABSTRACT: Finding a secret fact that is known to one person but kept from others is the aim of lie detection. When it comes to security and legal concerns, it is crucial to be able to analyze human deceptive behavior. Based on the idea that lying causes specific emotions, which in turn cause certain physiological reactions depending on a person’s level of stress, psychological lie detection is based on this premise. Modern lie detectors measure things like skin conductance, respiration, and blood pressure. The intended study presents a lie detection system that uses fuzzy analysis to integrate many factors, including skin conductance, facial expressions (eye blinking), and voice frequency, which is actually influenced by a person’s degree of stress. Fuzzy is an AI technique based on graphical representations and mathematical calculations of an output which is affected by input parameters. In comparison to employing a single indicator alone, the integration of skin analysis, voice level, and facial expression using fuzzy analysis may improve the accuracy of lie detection. This analysis provides a way to predict that a person is lying when all three parameters i.e., Skin conductance, voice frequency, and blinking of eye increases which effect on stress level of a body.