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
Operational Model Based Regional Estimation using Remote Sensing Data
Get PDFWater serves as the vital hub for sustaining life. There is indisputable evidence that the progress of agriculture, which relies directly on water resources, bears direct responsibility for the current global human population. While undeniably invaluable, our planet\u27s freshwater reserves face a mounting challenge in keeping up with the ever-expanding global population. This is primarily due to inefficiencies prevalent in various residential water applications, with irrigation practices in developing nations standing out as a significant contributor to this issue. As our communities continue to grow, it becomes increasingly imperative to address these inefficiencies to ensure sustainable access to this precious resource for generations to come. This dilemma is particularly concerning given the projection of continued population expansion. Concerning irrigation, it is widely acknowledged that more than 60% of water allocated for agricultural purposes is presently being administered in excess, leading to substantial annual wastage. To obtain a precise estimation of the water needed for crop production, it is imperative to devise, develop, and implement a practical and effective method. Employing manual techniques, such as utilizing a lysimeter, for gauging a structure\u27s water requirements is both subjective and financially demanding. This research has been designed to provide a comprehensive measurement of daily ET over a wide geographical area, offering detailed field-specific information. This research work is carried out by utilizing the European Space Agency satellites i.e., Sentinel 2 and 3, and ECMWF meteorological data. The Sentinel-2 data was processed to calculate the biophysical variables, structural parameters, fraction of green vegetation, and aerodynamic roughness. Sentinel 3 data was used to get the land surface temperature. The whole data is then processed to estimate the ET of the chosen area which is discussed in the materials and methods section. Actual water requirement and the water provided to the tobacco crops were compared. The results of the study reveal that estimated ET values were inline with the average surveyed tobacco field values that represents the consistency. However, a significant discrepancy arises due to irregular irrigation practices, indicating a lack of consideration for ET values among farmers. This oversight, coupled with unadjusted irrigation timing and methods, contributes to variance between computed and required ET values, attributed to factors such as human error, insufficient rainfall, and improper practices
Modeling of Post-Myocardial Infarction and Its Solution Through Artificial Neural Network
Get PDFCardiovascular diseases, particularly myocardial infarction (MI) constitute a significant health concern globally. A myocardial infarction, which is commonly known as a heart attack, happens when a part of the heart muscle doesn’t get enough blood because of a blockage. Studying MI is complex and it requires looking at it from different angles. In recent years the fusion of mathematical modeling and artificial intelligence (AI) techniques has emerged as a promising avenue for understanding the complexities associated with MI. The primary goal of this study is to provide an AI-based solution for a new nonlinear mathematical model related to myocardial infarction phenomena. To obtain the solution we will use a well-known deep learning technique, known as artificial neural networks (ANNs) with the combination of the optimization technique Levenberg-Marquardt back propagation (LMB). This combined method is referred to as ANNs-LMB. The results obtained from the model using ANNs-LMB are compared with a reference dataset constructed through the adaptive MATLAB solver ode45. The numerical performance is validated through a reduction in mean square error (MSE). The MSE is around and the obtained results by ANNs-LMB almost overlapped with the reference dataset, which shows the accuracy and efficiency of the proposed methodology
Machine Learning-Based Estimation of End Effector Position in Three-Dimension Robotic Workspace
Get PDFIntroduction/Importance of Study: The Workspace is the area around the robot where a robot can freely move with possible input variations of different joint angles.
Novelty statement: Conventionally iterative simulation methods are used to find robotic workspace. Which are computationally slow and difficult to model. Our approach utilizes machine-learning algorithms to predict the workspace and position of an end effector.
Material and Method: Multiple Linear Regression (MLR), Decision-Tree Regression, and Artificial Neural Network (ANN) algorithms trained for prediction. The dataset, which is collected and used as train and test data, is further for the validation step.
Result and Discussion: By simulating the robot with the Denavit-Hartenberg (D-H) approach in MATLAB. The results findings show the accuracy of Machine learning algorithms specifically Artificial Neural Networks (ANN) perform better than conventional mathematical methods
Concluding Remarks: Artificial Neural Network (ANN) outperformed other machine learning methods
Visually: Assisting the Visually Impaired People Through AI-Assisted Mobility
Get PDFThis research introduces “Visually”, a revolutionary mobile application that aims to address the complications that visually impaired people come across in their daily lives. By deploying advanced deep learning models for real-time object detection, facial recognition, and currency identification with voice outputs for each feature, the “Visually” application strives to enhance the autonomy, independence, and mobility of visually impaired people. The system undergoes thorough training on a diverse dataset, incorporating augmentation techniques to enhance the robustness of the models. The project\u27s multifaceted objectives include a user-friendly interface, real-time object detection, multi-modal recognition, Text-to-Speech audio output, and an overarching aim of enriching the lives of visually impaired individuals. Driven by the global prevalence of visual impairment and the demand for cost-effective solutions, “Visually” is aligned with international efforts for accessibility and inclusivity. For cross-platform compatibility, the machine learning models have been integrated whilst being deployed with TensorFlow Lite. With Offline availability, the application ensures accessibility even in rural areas with limited network connectivity. To make a substantial societal impact "Visually" aims to contribute to a more inclusive and equitable society, by transforming the way visually impaired individuals navigate around the environment. Positioned at the intersection of technology, accessibility, and empowerment, the “Visually” project is poised to bring about positive change for a community that frequently encounters unique challenges in their daily lives
Epidemiological Insights and Statistical Analysis of a Recent Conjunctivitis Outbreak in Lahore, Pakistan
Get PDFThis study presents a comprehensive epidemiological analysis of a recent outbreak of conjunctivitis, known as pink eye disease, in Lahore, Pakistan. Conjunctivitis is a highly contagious eye infection that poses a significant public health concern, particularly in social environments. The research focuses on understanding the prevalence and influencing factors of this ailment through a statistical analysis of patient data. The gender distribution among patients revealed a slightly higher prevalence among males (52.5%) as compared to females (47.5%). Young adults (age 18-25) comprised the highest affected group (89%), emphasizing the higher infection\u27s prevalence among this demographic. Symptom analysis highlights moderate to severe manifestations as predominant, significantly impacting patients\u27 daily routines. Males exhibit a higher severity, potentially associated with increased social engagement compared to females. Notably, the infection commonly affects both eyes (86%), and individuals with a history of prior eye infections demonstrate a reduced likelihood of contracting conjunctivitis (11%). The onset of symptoms is typically sudden (85%), with a gradual presentation in some cases (15%). Despite the contagious nature of the infection, its spread to family members’ remains relatively limited (36.8%). Remarkably, although symptoms are severe, the duration of the infection is brief, with most patients recovering within 2-5 days, even without medical consultation. Moreover, the spatial distribution showed that redness and itchiness were very severe in location 1(latitude 31.4972, and longitude 74.2735) and severe in location 4 (latitude 31.508, and longitude 74.327). In conclusion, this study is the first to report on the rapid yet severe nature of a conjunctivitis outbreak in Lahore. Key trends, including gender disparities, previous eye infection history, sudden onset of symptoms, and limited familial transmission, have emerged. Understanding these dynamics is crucial for implementing targeted preventive measures and developing effective management strategies for this contagious eye infection. The findings contribute valuable epidemiological insights that can guide public health interventions in similar scenarios
Exploring the Dynamics of Urban Sprawl Using GIS & RS Techniques and By Modeling Using Ca-Markov Model in District Peshawar
Get PDFUrban Sprawl is described by the unplanned and uneven growth pattern in the built-up areas, determined by several processes and leading to ineffective resource utilization. Pakistan, a developing country, is struggling with extreme population growth and currently ranks fifth globally in terms of population size. Peshawar, the provincial capital of Khyber Pakhtunkhwa, has undergone significant urbanization in recent decades for various reasons, necessitating a comprehensive analysis to inform urban planning. In the present work the urban sprawl of the Peshawar district has been studied from 2010-2020, and future predictions for the year 2030 are evaluated. This research uniquely utilizes the CA-Markov model to predict urban sprawl for the year 2030, a method not previously applied in the earlier studies in the study area. This research is carried out to examine the land use pattern, to find out the urban sprawl from 2010 to 2020 using remotely sensed satellite data for three periods (2010, 2015 and 2020). The object-Based Image Analysis (OBIA) approach was used to examine the land use patterns. The LULC prediction till 2030 is done by using the CA-Markov Model in a GIS environment. The pattern of development of urban sprawl in Peshawar is typical of most Pakistani major cities, where ribbon sprawl is common along major roads, while leapfrog sprawl is dominant in the city’s outer edge. The LULC changes derived from the OBIA method show that urban area expanded from 23% to 39% of the whole area, while agriculture decreased from 44% to 35% over ten years. To grip land use changes better, the paper proposes a method for the simulation of spatial patterns. The simulating method can be divided into two parts: one is a quantitative forecast by using the Markov model and the other is simulating the spatial pattern changes by using the CA model. The above two models construct the simulative model of the spatial pattern of land use. CA–Markov is used to simulate the spatial pattern of land use in Peshawar for 2030, which indicates that the urban land will reach a total of 44% consuming areas from Barren Land and Vegetation land
The Exploring Political Emotions Sentiment Analysis of Urdu Tweets
No full textThis research is a multi-text categorization based on a collection of Pakistani political texts. The major goal of this research is to use Natural Language Processing (NLP) and Machine Learning classification models to categorize multi-text for Urdu. Political tweets from 13 different Pakistani famous leaders were collected for this research. These politicians make use of the platform to promote themselves and engage with their supporters. To analyze the model accuracy the desired dataset is divided into six categories which have been composed of their official Twitter account. We also collect top trends from Pakistan and around the world to examine current trends regularly. In the proposed research, the major political corpus data comprises 1300+ tweets in the Urdu language, encompassing political policies, campaigns, opinions, and so on. Sentiment analysis is an essential component of every deep learning approach. For that, we have used the deep learning approach i.e. sentiment analysis of the politician since it provides insight into their moods and views on a certain topic. Furthermore, text corpus pre-processing is conducted utilizing NLP techniques, such as data cleaning, data balancing, and stop word removal. TF-IDF is used as word filtering for feature extraction count vectors. Machine Learning classification algorithms such as SVM, Decision Tree, XGboost, and Random Forest, and for implementation of neural network we have used Word2vector
Impact Assessment of Monsoon Precipitation on Groundwater Level in Lahore District GEE Script
Get PDFIntroduction/Importance of Study: Precipitation is a crucial component of the global water cycle and a primary source of freshwater, with groundwater being vital for drinking water worldwide, especially in regions like Lahore, Pakistan, where it supports 60-70% of the population.
Novelty Statement: This study uniquely addresses the impact of monsoon precipitation on groundwater levels in Lahore, providing a comprehensive analysis that has not been previously undertaken.
Material and Method: Data from the Pakistan Meteorological Department and WASA’s hydrology branch (2018-2022) were analyzed using GIS-based Inverse Distance Weighted Interpolation and statistical methods to assess precipitation patterns and groundwater levels.
Result and Discussion: The findings indicate that monsoon rainfall significantly raises groundwater levels by 2-3 meters due to seepage and infiltration. Spatial and temporal analyses revealed that the monsoon period, especially July and August, contributes the most to groundwater recharge. Despite this, the overuse of groundwater during non-monsoon months and extensive urban infrastructure limit overall groundwater recharge. The study found a positive correlation between monsoon precipitation and groundwater levels, emphasizing the critical role of sustainable aquifer management to maintain groundwater resources.
Concluding Remarks: Sustainable management of aquifer recharge is essential to ensure the long-term availability of groundwater resources in Lahore
Advanced Blast Algorithm for Molecular Identification, Biodegradation and Decolorization of Synthetic Melanoidins Using Fungal Species Isolated from Soil and Spent Wash
Get PDFIntroduction/Importance of Study: Distillery spent wash contains a high organic load as Melanoidins. It is generated due to the Millard reaction, which produces sugar and amino acids, leading to extensive water and soil pollution. Anaerobic digestion removes 60-70% COD and color, so post treatment is required for degradation by using fungal species as biological process.
Objectives and Novelty statement for this study: The study aims to isolate and identify fungal species for the degradation of synthetic melanoidins from spent wash using a cost-effective, low-toxicity, and environmentally friendly fungal-based biological process.
Material and Method: Three mixed fungal culture inoculums (spent wash, wet, and dry soil) and seven isolated fungal strains were examined on solid media that degraded and decolorized melanoidins at controlled pH 5.5, 25oC, 160 rpm for 3-5 days.
Result and Discussion: The results showed that mixed culture of spent wash removed the highest COD 91.8 %, color removal was 75.7 %, F-S6 isolate identified as Penicillium showed maximum soluble COD removal was 96.7 %, and F-S5 isolate identified as Syncephalastrum showed a maximum color removal was 98.8 %.
Concluding Remarks: It was concluded that the microbial process using fungal species was successfully applied to enhance degradation and decolorization to remove melanoidins. Furthermore, Gompertz Modeling was done to check the fitting of the curve at 680 nm Optical Density (OD) analysis for seven fungal strains with the following five factors significantly estimating maximum specific growth rate µM, Asymptote A, coefficient of determination R2, lag time λ, and goodness of fit