International Journal of Innovations in Science & Technology
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Comprehensive Assessment of Air Quality Dynamics Around Yosemite National Park Using Remote Sensing, GIS, and Computational Analysis During Wildfire Events
Get PDFIn recent years, Mariposa County has experienced several significant wildfires, including the catastrophic Rim Fire of 2013. On July 22, 2022, Yosemite National Park faced one of its most devastating wildfires, profoundly affecting Aerosol Optical Depth (AOD) and overall air quality. This study employs an integrated approach using remote sensing, GIS, and advanced computational tools to investigate the impact of these wildfires on air quality, focusing specifically on aerosol pollution dynamics and key atmospheric pollutants. The research leverages satellite data from TROPOMI, MODIS AQUA, and Suomi NPP/VIIRS, along with meteorological inputs from GDAS. Data processing and analysis were performed using Python, MATLAB, and R, with spatial mapping and visualization achieved through ArcMap and Google Earth Engine. The study utilized the MODIS MAIAC algorithm to conduct a detailed examination of AOD fluctuations in the Yosemite region, spanning from July 21 to August 1, 2022. Our comprehensive analysis reveals significant temporal and spatial variations in aerosol pollution during the wildfire. Initial findings indicate a marked increase in AOD with the onset of the wildfire, reflecting severe impacts on atmospheric composition. Pre-fire AOD levels were relatively low at 0.12, but surged to 0.20 at the wildfire\u27s peak, demonstrating a substantial rise in atmospheric aerosol loading. The average AOD throughout the study period was recorded at 0.16, highlighting the wildfire\u27s prolonged effect on air quality. Furthermore, the study identifies elevated concentrations of key pollutants, including NO₂, SO₂, CO, HCHO, and O₃, during the wildfire event. The integration of data from various satellite sources and the application of machine learning models provided a more nuanced understanding of pollution patterns. The HYSPLIT model was also employed to track the distribution of air masses and contaminants, revealing significant northwestward transport. This research advances our understanding of the intricate relationships between wildfires, aerosol pollution, and air quality in Yosemite National Park. The findings offer critical insights for public health preparedness, the development of resilience strategies against wildfires, and the formulation of effective mitigation measures in fire-prone regions like Yosemite
Impact of Internal Forces on Employee Behaviors: Role of Situational Factors
Get PDFThe current research investigated the effects of motivation, ability, and role perception (internal forces), also known as drivers on employee behaviors as well as to find out the moderating role of situational factors between drivers and employee behaviors. Data were collected from 800 in-service employees across various organizations and industries in Gujranwala using a convenience sampling technique. Work-related behaviors assessment battery was used to collect data from individuals which consists of 7 scales. Each scale consists of 10 items and the response rate varies from 1= strongly disagree to 5= strongly agree. Analysis indicates that motivation, ability, and role perception have a significant effect on employee behaviors. Moderation analysis results indicate that situational factors significantly moderate the relationship between drivers and behaviors. The current research sheds light on the significance of behaviors depending upon the four driving forces that need to be changed, or modified in regards to an increase in organizational performance
Analysis of MLP, CNN, and Transfer Learning Using VGG-16 for CIFAR-10 Dataset
Get PDFArtificial Neural Networks (ANN) are becoming the core domain of Artificial Intelligence. Generally, Machine learning and specifically, deep learning gained popularity in problem-solving by virtue of Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), and transfer learning approach. Transfer learning is becoming a powerful and successful technique for a variety of computer vision and image analysis applications due to its capability of reusing well-known proven architectures and their weights. Identification of optimum architecture and classifier along with pre-trained architectures is one of the challenging tasks in achieving optimum accuracy in various image analysis tasks. This paper investigates the performance of MLP, CNN, and transfer learning approaches using VGG-16 by tweaking hyperparameters and classifier architecture. The investigations and critical analysis revealed that MLP and CNN architectures have achieved about 55 % and 80 % validation accuracy on test data. Further experiments using VGG-16 architecture with MLP as a classifier have achieved more than 93 % accuracy on standard specification hardware for image classification on the CIFAR-10 dataset
An ANFIS-Based High Precision Error Iterative Analysis Method (HPEIAM) to Improve Existing Software Reliability Growth Models
Get PDFSoftware Reliability Growth Models (SRGMs) are statistical interpolations of software failures by mathematical modeling. Up till now, more than 200 SRGMs have been proposed to estimate failure occurrence. Research continues to develop more accurate, efficient, and robust models. To overcome the shortcomings of SRGMs and adapt to the current software development process characterized by increasing complexity, a high-precision error iterative analysis method (HPEIAM) is proposed in this paper. HPEIAM combines the parametric SRGMs (PSRGMs) predicted results with their residual errors, which are considered as another source of information that can be modeled with an adaptive neuro-fuzzy inference system (ANFIS). The predicted errors are used to correct the PSRGMs forecasted results repeatedly with the help of ANFIS, which is considered a powerful model to deal with non-linear data. The proposed technique combines the advantages of the neural network with a fuzzy inference system and PSRGMs, which helps to overcome the disadvantages of these models. The performance of the proposed technique is compared with six PSRGMs using three sets of real software failure datasets based on five criteria. Experimental results demonstrate that the HPEIAM can significantly improve the model fitting and predictive performance of every parametric SRGM
Internet of Things (IOT) in Developing the Smart Farming and Agricultural Technologies
No full textBackground: The Internet of Things (IoT) is streamlining processes in food and agriculture, especially in developing countries with agriculture-based economies. These countries stand to gain a lot from the IoT innovations that bring about mechanisms to track and control the risks experienced due to factors such as low productivity, wastage of resources, and food scarcity.
Objectives: The purpose of this research paper is to demonstrate how different IoT solutions can be effective in food and agriculture technology in less developed countries. It focuses on the potential of IoT solutions to improve productivity, reduce wastage of resources, and encourage sustainable agro practices. Furthermore, the paper examines the reasons behind the slow adoption of IoT technologies, and strategies to surmount such factors are proposed.
Methodology: The study employed both literature and analytical as well; however, a majority of it was on the primary data concerning IoT solutions that were smart irrigation and precision agriculture. Data was collected from farmers, and technology neglected mostly the politicians of developing countries whose focus was to understand or rather assess the uptake, challenges, and impacts of IoT technology.
Results: The results show that IoT can cause a drastic enhancement of agricultural productivity by efficient water irrigation, keeping a check on soil health and lowering post-harvest waste. MFIs IoT made adoption of the system and use of resources more efficient, increases profits and lowers expenses. However, they also revealed obstacles for the process such as the cost of implementation, expertise in both technical and operational levels and internet services.
Conclusion: Smart agriculture and agricultural systems everywhere will undergo a revolution owing to IoT technologies because it enhances the practices and innovations. However, potential benefits cannot, be maximized Secure fencing of these barriers will not be straightforward since a significant amount of time will have to be devoted to understanding each of the suggestions made by the officers present
Complex Human Activities Recognition Using Smartphone Sensors: A Deep Learning Approach
Get PDFHuman Activity Recognition (HAR) plays a critical role in understanding human behavior, with mobile phone sensors offering a promising approach for practical applications. This research uniquely addresses the challenge of Complex Human Activity Recognition (CHAR) using Long Short-Term Memory (LSTM) networks, advancing beyond basic activity recognition. LSTM was applied to three publicly available datasets—PAMAP2, Complex Human Activities, and WISDM—using accelerometer, gyroscope, and magnetometer sensor data. The research evaluated the effectiveness of both single-sensor (accelerometer) and multi-sensor combinations for recognizing complex activities. The study achieved 94-98% accuracy across datasets, showing that a single accelerometer sensor provides reasonable accuracy, while adding more sensors, like gyroscope and magnetometer, further boosts performance at a resource cost. The LSTM-based approach consistently outperformed traditional methods, including CNNs, in complex activity recognition, demonstrating its robustness in simplifying sensor requirements without compromising accuracy. LSTM networks offer an efficient and accurate solution for complex human activity recognition, balancing performance and resource optimization
Low Aperture High Gain Antenna for Wi-Fi Applications
Get PDFThis article proposes a design for a low-aperture, high-gain antenna specifically tailored for Wi-Fi applications. This project aims to enhance the performance of Wi-Fi networks by increasing signal strength and coverage area. Conventional dielectric rod antennas face three main challenges: first, they typically have low gain and excessive length; second, they exhibit high side lobe levels; and third, as the antenna length increases, side lobe levels also rise alongside gain. The proposed antenna structure effectively addresses these issues. The novelty of the proposed antenna lies in its ability to achieve greater gain at the same length compared to conventional antennas while maintaining low side lobe levels that do not increase with antenna length. The proposed design features a Yagi-Uda configuration on a printed circuit board (PCB) made from FR4 epoxy with a dielectric constant of 4.4, combined with a tapering dielectric Teflon rod with a dielectric constant of 2.1. The antenna was simulated using HFSS software, fabricated, and then tested to compare simulated and experimental results, which indicate that the proposed structure primarily operates at a frequency of 5 GHz. It achieves performance within the frequency band of 4.8–5.3 GHz, with a fractional bandwidth of 10%. At these frequencies, the structure provides a directivity of 16.4 dBi. A comparison of results demonstrates that the presented antenna outperforms traditional antennas in the same class, making it suitable for Wi-Fi, WLAN, and satellite applications. This revision enhances clarity, coherence, and overall readability while preserving the original intent and details
Enhanced Emotion Recognition on the FER-2013 Dataset by Training VGG from Scratch
Get PDFRecognizing facial emotions is still a major obstacle in computer vision, particularly when dealing with complex datasets such as FER-2013. Advancements in deep learning have simplified the process of achieving high accuracy, yet obtaining high accuracy on the FER-2013 dataset with traditional methods remains challenging. The aim of this research is to analyze the effectiveness of Convolutional Neural Networks (CNNs) utilizing VGG16 and ResNet50 architectures through three different training methods: training from scratch, with transfer learning, and fine-tuning. Our research demonstrates that while training VGG16 from scratch achieved a validation accuracy of 67.23%, fine-tuning produced a slight reduction in performance at 64.80%. Conversely, ResNet50 struggled across all approaches, with the highest validation accuracy being only 54.69% when trained from scratch. We offer an in-depth analysis of these methodologies by utilizing confusion matrices, training durations, and accuracy measures to showcase the balance between computational expenses and model effectiveness. Our results indicate that, although transfer learning and fine-tuning offer rapid convergence, training from scratch may still be necessary for specialized feature learning in complex FER tasks. These results help in the continuous work of improving emotion recognition systems by maintaining a balance between accuracy and computational efficiency
Intelligent license Plate Recognition System
Get PDFSince the 19th century, the number of vehicles has been increasing rapidly with the growth of the human population. To supervise vehicles, license plates are used all over the world. The license plate is the unique identity for vehicles; that’s why it is always used to monitor and keep records of vehicles by law enforcement, border monitoring, parking control, and many other applications. Monitoring a huge number of vehicles is a difficult task using traditional (manual) methods. The Intelligent License Plate Recognition (ILPR) system overcomes these problems by recognizing plate identities without human involvement through artificial intelligence and machine learning processes. This system extracts the identity number allocated to each vehicle from the license plate and can provide information about a specific vehicle. It can be further applied in regulated zones such as military areas, parking control, toll collection, and for identifying non-tax-paid vehicles. For developing the ILPR system, text extraction and deep learning techniques must be combined.
The ILPR system, developed by integrating Deep Learning (DL), Image Processing (IP), and image-to-text extraction approaches, is used to detect plate identity. YOLOv8 is used for object detection and the OCR engine for text extraction. The system will be capable of detecting live license plates with high accuracy, which will help in regulated zones and traffic system applications
Classification of Medical Images Through Convolutional Neural Network Modification Method
Get PDFThe COVID-19 positive, tuberculosis and pneumonia, share the trait of being able to be identified using radiological investigations, such as Chest X-ray (CXR) images. This paper aims to distinguish between four classes, including tuberculosis (TB), COVID-19 positive, healthy, and pneumonia using CXR images. Many deep-learning models such as a Convolutional Neural Network (CNN) have been developed for the Classification of CXR images. Deep learning-based models such as CNN offer significant advantages over traditional methods in the classification of diseases like TB, COVID-19, pneumonia, and healthy states. They provide higher accuracy, automation, early detection, reduced subjectivity, and resource efficiency, ultimately leading to improved patient care and outcomes. However, well-liked CNNs are massive models that require a lot of data to achieve optimal accuracy. In this paper, we propose a new CNN model that can be used to distinguish between different classes of CXR images. This model proves to be effective in classifying different diseases such as pneumonia, COVID-19, and tuberculosis. This study has used 6326 CXR images dataset containing COVID-19 positive, tuberculosis, and pneumonia and has normal images. In this dataset, 80% of the CXR images are taken for the training purpose and 20% are taken for the validation purpose, of the proposed CNN model. The proposed CNN modified model with parameter adjustment as well as using categorical cross-entropy as a loss function obtains the highest classification accuracy of 98.51% with a precision, recall, and F1 score of 0.98, 0.985, and 0.98 respectively