International Journal of Innovations in Science & Technology
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Quantitative Analysis of Image Enhancement Algorithms for Diverse Applications
Get PDFThis research paper introduces a comprehensive comparative analysis of prominent image enhancement algorithms, including Histogram Equalization, Adaptive Histogram Equalization, CLAHE, Gamma Correction, and Unsharp Masking. In the realm of digital image processing, image enhancement plays a crucial role in various applications such as medical imaging, remote sensing, surveillance, and computer vision. Addressing the significance of this research, we present an evaluation of these algorithms using key metrics: Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity Index (SSIM), Contrast Improvement, and Sharpness Improvement. Our methodology encompasses dataset collection, algorithm implementation in MATLAB, and systematic performance evaluation. The results highlight the unique strengths and trade-offs of each algorithm. Histogram Equalization demonstrates moderate improvement in image quality, while Adaptive Histogram Equalization excels in preserving image details despite introducing some distortion. Contrast Limited Adaptive Histogram Equalization strikes a balance between enhancement and computational efficiency. Gamma Correction proves effective for specific adjustments but may compromise overall image quality. Notably, Unsharp Masking stands out with superior sharpness improvement while maintaining image fidelity. In conclusion, the choice of algorithm should be aligned with specific task requirements and the desired balance between image quality and enhancement goals. Considering these outcomes, Unsharp Masking emerges as a promising choice, demonstrating exceptional performance across multiple metrics. This research provides valuable insights for practitioners and researchers seeking to optimize image enhancement algorithms for diverse applications
Exploring Learning Patterns: A Review of Clustering in Data-Driven Pedagogy
Get PDFEducational institutes amass and retain extensive amounts of data including records of student attendance, test scores, exam results, and performance statistics. Extracting insights from this data can provide valuable information to educators and policymakers. The rapid expansion of educational data underscores the need for sophisticated algorithms to process such vast quantities of information. This challenge led to the emergence of the field of educational data mining (EDM). Clustering is a popular approach within EDM that can find hidden patterns in data. Numerous studies in EDM have concentrated on applying diverse clustering algorithms to educational attributes. This paper presents a comprehensive literature review focusing on 43 papers spanning between 2013 to 2023 on the use of clustering algorithms and their effectiveness within the realm of EDM. The review indicates that K-means clustering has been utilized extensively in the reviewed literature with 29 of the 43 reviewed papers using K-means clustering in their analysis. It was also uncovered that cluster-based analysis majorly focuses on analyzing student performance in a course or in a degree program closely followed by clustering students based on class of learners. Insights are deduced from the reviewed literature highlighting the focus of current research and potential directions for the future
Detection of Bronchitis Virus through Web-Based Interface and Management Strategies for Effective Control
Get PDFRNA viruses are distinguished by their quick adaptation to novel surroundings made possible by their high mutation and recombination rates. These viruses are responsible for the majority of newly identified illnesses and host transitions. Even well-known infections can be difficult to control due to their propensity for rapid evolution, which can impede our understanding of molecular epidemiology, reduce the sensitivity of diagnostic assays, reduce the efficiency of vaccines, and promote instances of immune escape. This scenario is consistent with the infectious bronchitis virus\u27s (IBV) past. The chicken industry has been aware of it since the 1930s, but it continues to be a major source of sickness and economic losses. Over the years, several different approaches have been tried and mostly unsuccessfully implemented to lessen its effects. However, they are rarely subjected to a fair and impartial assessment. Therefore, the pros and cons of IBV detection and control measures, and the efficacy of their execution, still mainly depend on the perspective of the observer. The purpose of this publication is to summaries the key aspects of IBV biology and evolution with an eye toward their diagnostic and preventative utility. Python based script has been developed for detection of Bronchitis virus
Restrictions, Challenges and Opportunities for AI and ML
Get PDFArtificial intelligence (AI) refers to a collection of techniques that are being developed to address a wide variety of practical problems. Machine learning (ML) is the backbone of artificial intelligence (AI), comprising a suite of algorithms and techniques designed to solve the issues of categorization, clustering, and prediction. There are bright prospects for putting AI and ML to use in the real world. As a result, there is a lot of study being done in this field. However, mainstream adoption of AI in industry and its widespread use in society are still in their infancy. For understanding the obstacles involved with mainstream AI implementations, both the AI (internal problems) and societal (external problems) viewpoints are required. With this in mind, we can determine what has to happen first to get AI technology into the hands of industry and the public. This article identifies and discusses some of the obstacles to using artificial intelligence in resource-based economies and societies. Publications in the field form the basis for the systematic application of AI&ML technology. This methodical approach makes it possible to define institutional, human resource, societal, and technological constraints. This paper provides a roadmap for future research in artificial intelligence and machine learning that will help us overcome current obstacles and broaden the range of these technologies\u27 potential uses
Smart Homes and AI Based Models in Future
Get PDFA new era of architectural ideas is likely to be ushered in by the natural progression of "smart buildings," which necessitates the integration of sensors, rich data, and artificial intelligence (AI) simulation models. Better control, enhanced reliability, and automation are just a few ways in which AI simulation models can make homes more convenient, more comfortable, and more energy efficient. This article discusses the ways in which AI models can be used to enhance the development of smart homes, particularly in the realm of interior design. This case study demonstrates how AI may be integrated into smart homes to enhance the user experience and reduce energy consumption. After that, the essay will delve into the study of current research on the application of AI technology in smart houses, utilizing a wide range of novel concepts such as smart interior design and a Smart Building System Framework based on digital twins (DT). The benefits of employing AI models in smart homes, with an emphasis on living areas, are discussed at length before the paper concludes. The theme\u27s case study is meant to inspire new ways of thinking about how artificial intelligence (AI) might be practically implemented in smart homes to enhance their utility, comfort, and environmental friendliness. The ultimate objective is to maximize the benefits of AI in order to revolutionize domestic life and enhance the quality of human existence. Unanswered questions and promising directions for future AI research in the realm of smart homes are addressed in the article\u27s last section. Smart houses that incorporate AI technology are beneficial to homeowners because they improve security, convenience, and energy economy
Efficient Optimization of Adaptive Transmission Range in MANET - Maximizing Packet Delivery Ratio
No full textMobile Ad-hoc Network (MANET) is a self-systematized network, hasn’t fixed infrastructure and centralized administration system. Due to the frequent changes in network topology, MANET nodes are free to change locations anywhere they like.
Novelty statement: Typically, mobile devices in MANET are configured identically to have same transmission ranges, homogeneously. Previous research proves the optimum homogeneous transmission range that maximizes Packet Delivery Ratio (PDR). Mostly, it has been shown inversely proportional to the node density, and transmission range itself along with its PDR is not being studied. This study aims to show that instead of using an optimum homogenous transmission range for all mobile nodes, a non-homogenous scheme, where optimum transmission range for each node is computed separately.
Material and Method: In order to validate the study, simulations were performed on the network simulator NS3 with node ranges of 25, 50, and 100 over an area of 500 m2. Destination Sequences Distance Vector (DSDV) Protocol was selected to perform simulations in which each scenario was executed for 300 seconds (5 minutes).
Result and Discussion: The evaluation of results show that the maximum PDR can be achieved by computing a separate transmission range for each node as compared to the homogenous transmission ranges.
Concluding Remarks: In the end, it can be concluded that adaptive transmission ranges are optimally effective as compared to homogenous transmission range
Action Recognition of Human Skeletal Data Using CNN and LSTM
Get PDFHuman action recognition recognizes an action performed by human beings in order to witness the type of action being performed. A lot of technologies have been developed in order to perform this task like GRN, KNN, SVM, depth maps, and two-stream maps. We have used 3 different methods in our research first method is a 2D CNN model, the second method uses an LSTM model and the third method is a combination of CNN+LSTM. With the help of ReLu as an activation function for hidden and input layers. Softmax is an activation function for output training of a neural network. After performing some epochs the results of the recognition of activity are declared. Our dataset is WISDM which recognizes 6 activities e.g., Running, Walking, Sitting, Standing, Downstairs, and Upstairs. After the model is done training the accuracy and loss of recognition of action are described. We achieved to increase in the accuracy of our LSTM model by tuning the hyperparameter by 1.5%. The accuracy of recognition of action is now 98.5% with a decrease in a loss that is 0.09% on the LSTM model, the accuracy of 0.92% and loss of 0.24% is achieved on our 2D CNN model while the CNN+LSTM model gave us an accuracy of 0.90% with the loss of 0.46% that is a stupendous achievement in the path of recognizing actions of a human. Then we introduced autocorrelation for our models. After that, the features of our models and their correlations with each other are also introduced in our research
AI-Based Stoichiometric Engineering of Zinc Cobaltite
Get PDFThis study investigates the impact of stoichiometric variations and defect engineering on the structural, electrical, and electrochemical properties of zinc cobaltite (Zn1-xCo(1+x)-O4) synthesized via a modified sol-gel method. By systematically varying the Zn: Co ratio, an optimal composition, Zn0.75Co2.25O4, was identified, demonstrating superior performance metrics. SEM images confirmed the morphological changes of spinel phase, with lattice parameter variations correlating to Zn content. EIS analysis revealed that moderate oxygen vacancies significantly enhanced conductivity, with Zn0.75Co2.25O4 exhibiting the highest electrical and electrochemical performance. The optimized material achieved a specific capacity of 290 mAh/g at 1 A g-1 and retained ~90% capacity after 500 cycles, surpassing prior benchmarks. This study provides a detailed understanding of the structure-property-performance relationship, highlighting the potential of defect-engineered zinc cobaltite for advanced energy storage applications
Detection And Quantification of Lung Nodules Using 3D CT images
No full textIn computer vision image detection and quantification play an important role. Image Detection and quantification is the process of identifying nodule position and the amount of covered area. The dataset which we have used for this research contains 3D CT lung images. In our proposed work we have taken 3D images and those are high-resolution images. We have compared the accuracy of the existing mask and our segmented images. The segmentation method that we have applied to these images is Sparse Field Method localized region-based segmentation and for Nodule detection, I have used ray projection. The ray projection method is efficient for making the point more visible by its x, y, and z components. like a parametric equation where the line crossing through a targeted point by that nodule is more dominated. The Frangi filter was to give a geometric shape to the nodule and we got 90% accurate detection. The high mortality rate associated with lung cancer makes it imperative that it be detected at an early stage. The application of computerized image processing methods has the potential to improve both the efficiency and reliability of lung cancer screening. Computerized tomography (CT) pictures are frequently used in medical image processing because of their excellent resolution and low noise. Computer-aided detection systems, including preprocessing and segmentation methods, as well as data analysis approaches, have been investigated in this research for their potential use in the detection and diagnosis of lung cancer. The primary objective was to research cutting-edge methods for creating computational diagnostic tools to aid in the collection, processing, and interpretation of medical imaging data. Nonetheless, there are still areas that need more work, such as improving sensitivity, decreasing false positives, and optimizing the identification of each type of nodule, even those of varying size and form
Comparative Analysis of Urban Sprawl through KNN and Random Forest Classification (RFC) ML Techniques
Get PDFThe majority of optimization strategies fail to take into account the dynamic impact of urban sprawl on the spatial criteria that underlie decision-making processes. Furthermore, the integration of the existing simulation methodology with land use optimization techniques to arrive at a sustainable judgment regarding the appropriate site involves intricate procedures. The urban heat island phenomenon is a prominent consequence of urban expansion and human activities, leading to elevated temperatures within cities compared to their rural surroundings. The extent of sprawl was estimated through ML algorithms and it was revealed that RFC provided promising results that were near to statistics by various administrative authorities. Urban vegetation plays a crucial role in countering the urban heating effect by providing cooling mechanisms through evaporation and shading. In this context, a study was conducted in Allama Iqbal Town, Lahore, focusing on the assessment of land use changes, as well as the analysis of Normalized Difference Vegetation Index and Land Surface Temperature data for the years 2000, 2010, and 2023, obtained from Landsat 5 and Landsat 8 satellite imagery. The findings reveal significant land use changes of 7.52% (36.2 km2) in the study area. The built-up areas expanded by 50.76%, while smart green spaces decreased by 48.30%. The relationships between NDVI and LST demonstrate a robust negative relationship (R² = 0.99). This research underscores the potential of utilizing GIS and remote sensing techniques to inform urban planning, decision-making, and policy formulation, ultimately contributing to the creation of sustainable urban environments in Allama Iqbal Town