International Journal of Innovations in Science & Technology
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Python-Based Land Suitability Analysis for Wheat Cultivation Using MCE and Google Earth Engine in Punjab-Pakistan
Get PDFThe present study aims to examine the suitability of wheat crops in the four districts of Sheikhupura, Gujranwala, Hafizabad, and Nankana Sahib by conducting a thorough examination of various environmental parameters. The study utilizes the Google Earth Engine and advanced mapping techniques to employ a comprehensive Land Use and Land Cover (LULC) categorization, effectively capturing the prevailing terrain characteristics. The integration of temperature-based and soil-based suitability maps provides a comprehensive understanding of the intricate geographical patterns governing the growth circumstances of wheat. The study highlights a significant finding regarding the identification of very appropriate zones, which encompass around 28% of the total land area (4243 square kilometers) out of complete study site. These zones are particularly noteworthy as they emphasize places that are best for the growing of wheat. Approximately 45% (6819 square kilometers) of the overall land area is classified as moderately suitable, while 15% (2273 square kilometers) of the land area is categorized as less suitable. Furthermore, 16% of the total land area, encompassing 2444 square kilometers, is deemed unsuitable. The rigorous examination of soil parameters, such as pH, drainage, electrical conductivity, and soil type, contributes to a comprehensive comprehension of the soil-related elements that influence the adaptability of wheat crops. The study utilizes a Classification and Regression Tree (CART) methodology to classify crops, resulting in accurate outcomes with a ground truthing accuracy rate of 82%. This study employs a comprehensive approach by integrating temperature and soil-based data to provide a suitability map that enhances the identification of places suitable for wheat growing. Notwithstanding the accuracy of the findings, the research acknowledges certain constraints, including the necessity for heightened farmer consciousness and the incorporation of climate change ramifications. This study offers a comprehensive framework for sustainable agricultural planning, focusing on identifying certain regions that are most suitable for wheat growth. The findings of this research will serve as a valuable resource for guiding future initiatives and decision-making processes related to agricultural development in the studied area
Hybrid Approach to Solve Thermal Power Plants Fuel Cost Optimization Using Ant Lion Optimizer with Newton-Based Local Search Technique
Get PDFIntroduction/Importance of Study: The optimization of the power system is a complicated problem that is extremely non-convex, nonlinear, and important for reducing the cost of production.
Novelty Statement: Despite the fact that several metaheuristic algorithms are proposed for solving power system optimization problems, the strength of hybridized global search-based techniques has not commonly been applied to power system optimization.
Material and Method: Deterministic power system optimization strategies are unable to yield global optimal outcomes because of the entrapment in local optimum zones. Stochastic approaches like those in which Ant-Lion Optimizer is used and hybridization algorithms with local search methods SQP, IPA, and active set give better results.
Result and Discussion: Hybridized global search-based techniques have been successfully applied to power system optimization with economic load dispatch in particular. Results from findings hybridized-ALO outperforms modern optimization methods.
Concluding Remarks: Results from findings show 3 and 13 generator systems that hybridized-ALO outperforms modern optimization methods
Breast Masses Detection Using YOLOv8
Get PDFBreast cancer stands as a formidable global health challenge, necessitating swift and precise diagnostic measures to combat its devastating impact. In this study, we delve into the efficacy of YOLOv8, a cutting-edge artificial intelligence model, for the precise detection and localizing of breast masses in digital mammography images. YOLOv8’s inherent capability to simultaneously detect and localize masses showcases accurate pinpointing of the exact locations of abnormalities within mammographic scans. Our comprehensive evaluation reveals compelling performance metrics, including an F1 score of 0.91 and a mean Average Precision (mAP) of 0.942. These results depict the robustness of the YOLOv8 in mass detection but also show better results than the conventional clinical methods, offering higher accuracy and efficiency in the diagnostic process. This study explains the transformative potential of YOLOv8 in revolutionizing breast cancer detection paradigms, presenting a promising pathway toward enhancing early detection rates and ultimately improving patient outcomes.
abnormalities within mammographic scans. Our comprehensive evaluation reveals compelling performance metrics,
including an F1 score of 0.91 and a mean Average Precision (mAP) of 0.942. These results depict the robustness of the
YOLOv8 in mass detection but also show better results than the conventional clinical methods, offering higher accuracy
and efficiency in the diagnostic process.
This study explains the transformative potential of YOLOv8 in revolutionizing breast cancer detection paradigms, presenting a promising pathway toward enhancing early detection rates and ultimately improving patient outcomes
A Conceptual Framework for Reducing Requirement Engineering Challenges in Industrial-Scale Software Projects
Get PDFIntroduction/Importance of Study: Industrial-scale software development tends to create more business value and effective strategic capabilities in software industries. IT organizations are spending about 50% of the budget on software development to build faster software programs at minimal cost to achieve success in industrial-scale projects. The crucial part of developing industrial-scale software is deciding ‘what is intended to be built’. If the problem is not tackled properly, this can result in serious errors that impact the entire Software Development Life Cycle (SDLC) and make it difficult and costly to repair in later stages. Similarly, challenges in industrial-scale development related to Requirements are complex including Requirement scope, elicitation, specification, validation, and management. The Requirement engineering challenges become bigger and harder to overcome in industrial-scale projects due to time and cost factors. The money spent on Requirement change may affect the overall development time of the project. The complexity of industrial-scale projects does not increase linearly, thus, impacting the development process.
Novelty Statement: Therefore, the need to address challenges in large IT projects comes with the reason of their economic value in local and international markets. Researchers have come up with the identification of challenges, but their studies lack the overall Requirement engineering process. There is a need to design a comprehensive solution to overcome the Requirement engineering challenges that contribute to project failure.
Material and Method: Therefore, the research is divided into three phases: “The Identification Phase”, where the project challenges would be identified; “The Implementation Phase”, where these factors would be shortlisted to design a framework; and “The Validation Phase”, in which validation of the framework would be done using triangulation technique.
Result and Discussion: The outcomes will focus on facilitating the software development industry for addressing the Requirement of engineering challenges in industrial-scale projects to reduce the chances of failure
Stock Market Analysis and Prediction Using Deep Learning
Get PDFThe stock market is a complex system influenced by various factors, including economic indicators, geopolitical events, and investor sentiments. Traditional methods of stock market analysis often rely on statistical models and technical indicators, which may struggle to capture the intricate patterns and non-linear relationships present in financial data. This paper is about an innovative application which is designed to fill the gap between traditional stock market analysis and cutting-edge predictive modeling. The paper not only addresses the challenges associated with fragmented data and delayed analysis but also opens avenues for continuous monitoring and optimization of predictive models in response to dynamic market conditions. These models are seamlessly integrated into the application developed in the Analysis Phase, providing users with real-time predictions and valuable insights. Many machines learning (ML) and deep learning (DL) techniques have demonstrated to perform well in stock price prediction by prior research, and most people regard DL techniques them as one of the most accurate prediction methods, particularly when used for longer prediction ranges. In this research, after performing pre-processing steps like data normalization, we have employed an LSTM and GRU based models. Through training and testing, we determined the ideal settings for the optimizer, dropout, batch size, epochs, and other parameters. The outcome of comparing the LSTM network model with GRU we concluded that LSTM it is not suitable for short-term forecasting, and performs well for long-term forecasting whereas GRU performs well in both cases
Assessment of Long-Term Relationship of Tropospheric NO2 with Meteorological Parameters for Sustainability in Pakistan
Get PDFIntroduction: Assessing atmospheric changes is crucial as population density increases and countries industrialize to meet growing demands. Pakistan is listed among the countries with the most deteriorating air quality globally.
Novelty Statement: This research investigates tropospheric NO₂ patterns in Pakistan from 2005 to 2022 using OMI data. It reveals seasonal variations and anthropogenic impacts, offering valuable insights for air quality policies in developing regions.
Material and Methods: This study analyzed tropospheric nitrogen dioxide (NO₂) patterns using data from the Ozone Monitoring Instrument (OMI) and examined their relationship with meteorological parameters such as rainfall, wind speed, and temperature. The analysis focused on NO₂ pollution patterns at the district level in Pakistan from 2005 to 2022, including major urban centers like Lahore, Faisalabad, and Peshawar.
Results and Discussion: An increasing trend in NO₂ concentrations was observed, with a rise of 9.028 x 10¹⁵ molecules/cm² in winter. Summer values were lower, around 1.9 x 10¹⁵ molecules/cm². A notable decrease in NO₂ concentrations occurred in the pre-monsoon months, except in Peshawar, where concentrations fell during spring. The study revealed varied patterns in NO₂ levels in relation to temperature, wind speed, and rainfall over the years. Industrial cities with heavy traffic, large populations, agricultural fires, and fossil fuel combustion exhibited high anthropogenic emission levels in the lower atmosphere.
Conclusion: This study provides regulators with a deeper understanding of anthropogenic emission levels in major cities, helping to identify sources and develop effective air quality management strategies
Concrete Expansion: Urban Growth Estimation Through Geo informatics, A Case Study of Karachi
Get PDFKarachi, once the capital of Pakistan and currently the capital of Sindh province, is the country’s largest city and the 12th most populous city globally. It plays a crucial role in the national economy, contributing 60-70% of the country\u27s total revenue. However, the city faces significant challenges due to rapid urban sprawl—a prevalent issue in developing countries. The uncontrolled expansion of Karachi’s urban area, often described as the “concrete jungle,” presents considerable risks due to inadequate management and lack of long-term planning. This uncontrolled growth has led to increased population density, resource deficits, management challenges, and ecological pressures. Remote sensing and GIS technologies are now being employed for change detection and urban expansion analysis. Satellite data, including both current and historical images in various spatial resolutions, facilitate this analysis. In this study, change detection techniques are used to assess Karachi\u27s urban growth through historical maps, employing Landsat 7 ETM+ images from 2002 and Landsat 8 OLI images from 2022. This analysis, based on satellite images and measurements, reveals that Karachi\u27s population has grown at an annual rate of approximately 4%, driven by high natural increase and substantial migration from other regions. The population increased from 9.34 million in 1998 to 15 million in the 2017 census. Over the past 20 to 25 years, Karachi has expanded at a rate of about 15% annually, adding approximately 2 square kilometers per year to accommodate its growing population, resulting in a notably high population density
Ecotourism Potential Assessment for District Lower Chitral-Pakistan Using Integration of GIS and Remote Sensing
Get PDFEcotourism is a sustainable and responsible tourism approach that emphasizes the protection of natural ecosystems while offering visitors immersive experiences. This study evaluates the ecotourism potential of District Lower Chitral, Pakistan, using an integrated approach that combines Geographic Information Systems (GIS) and Remote Sensing technologies. Planning for ecotourism development is a multi-criteria process that often involves spatial analysis. A Multi-Criteria Decision Analysis (MCDA) model was employed to assess ecotourism suitability in District Lower Chitral. Eighteen variables, selected based on local knowledge and expert opinion, were considered, encompassing natural beauty, infrastructure, and physical parameters of the area. The study\u27s results indicate that the majority of the study area has a moderate potential for ecotourism, covering 3,141.026 km² (51.33%) of the total area. Additionally, 103.3733 km² (1.69%) was classified as "Very Highly" suitable for ecotourism, and 1,750 km² (26.61%) was deemed "Highly" suitable. Areas classified as having low suitability measured 1,118.666 km² (18.28%), while the very low suitability category covered the smallest proportion, with 5.645 km² (0.09%)
Driving Sustainable Growth: Eco-Innovation in Pakistan\u27s Chemical and Pharmaceutical Sector
Get PDFThis paper explores the state of eco-innovation in Pakistan\u27s chemical and pharmaceutical industries, focusing on advancements in process technology, product technology, and organizational eco-innovation. By analyzing survey data, the study evaluates the adoption of eco-friendly practices and identifies key drivers of eco-innovation, including environmental regulations, organizational initiatives, collaboration, environmental management systems, customer pressure, and cost barriers. The results show notable progress in adopting cleaner processes and pollution control measures, with over 65% of companies implementing these techniques. However, green energy technology adoption remains low, with only 18% of industries utilizing it. Product eco-innovation is more widely accepted, with more than 50% of industries responding positively. The study also highlights that around 60% of Pakistan\u27s chemical and pharmaceutical industries are export-oriented and have formal environmental management systems in place. These industries are committed to improving environmental performance and sustainability throughout their supply chains. While there is generally a neutral stance towards environmental regulations, the high cost of eco-innovation and the lack of collaboration between organizations and research institutions pose significant barriers. Overall, the findings indicate a growing environmental awareness among industries in Pakistan, but more efforts are needed to fully adopt green technologies and practices. Enhanced collaboration and coordination among stakeholders are essential for advancing sustainable development in the country’s industries
Designing an AI-Based Greenhouse Plant Monitoring System to Detect and Classify Plant Diseases from Leaf Images
Get PDFPlant diseases can significantly hinder food crop production, leading to substantial economic losses and posing a threat to global food security. Machine learning, particularly deep learning, plays a crucial role in object detection and classification. In this study, we present an AI-based plant monitoring system for detecting and classifying plant diseases using visual images. Our deep learning models are trained on plant images obtained from natural environments. Manual detection and classification are both challenging and labor-intensive, making accurate and timely diagnoses from an automatic system highly beneficial for treating plant diseases. Traditionally, plant disease detection using deep learning has relied on images taken in controlled environments, which do not support in-situ detection for remote monitoring. The Plantdoc dataset, a popular resource consisting of plant images from actual field conditions, is used in our study. We employ the YOLOv5 algorithm from the field of computer vision to the Plantdoc dataset, achieving results that surpass previous work on the same dataset. This success is attributed to our selected model and data augmentation techniques. Our model can classify and detect various diseased and healthy leaf classes with a mean Average Precision (mAP) of 92%. This capability enables farmers and researchers to remotely monitor plant health and diagnose plant diseases, thereby saving time, reducing costs, and minimizing crop loss