International Journal of Innovations in Science & Technology
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Game Brains: NPCs Intelligence Using Neural Network Brains
Get PDFThis paper aims to develop the foundational knowledge about the Unity game development engine embedded with AI for the development of a hyper-casual game that has intelligent NPCs, which operate strategically in the environment. The targeted audience comes in the class of those who are pursuing their career in the niche of AI game development and enhancing the gaming experience for single-player game users. Using Unity Engine and Python, Curriculum learning and self-learning experiments were conducted to test the AI game. Moreover, in this paper, different reinforcement learning methods have been discussed, which have been implemented in the game that produces the optimal results for the behavior of NPCs. Hence, this paper tends to represent a glimpse into the future perspective of the gaming industry in hyper-casual gaming platforms
Analyzing Changes in Land Use and Cover (LULC) in the Quetta Valley Over Four Decades (1990-2020) Using Geospatial Techniques
Get PDFLULC changes have profoundly impacted environmental conditions, socio-economic development, and resource management in various regions. This study examines LULC changes in Quetta Valley, Baluchistan, Pakistan, over the past four decades (1990-2020) using geospatial techniques. The observed transformations primarily result from rapid urbanization, agricultural expansion, and population growth. LULC changes were analyzed using geospatial data from Landsat satellites, employing GIS and remote sensing (RS) methods. The findings indicate a steady increase in urban areas, with built-up land rising from 3.4% in 2000 to 7.17% in 2020, reflecting ongoing urbanization trends. This urban expansion has been accompanied by an increase in agricultural land, especially between 2010 and 2020, driven by the need to enhance food security. Vegetative cover has shown fluctuations, influenced by climatic variations and changing land management practices. Although barren land remains the predominant land cover type, its proportion has decreased slightly over time. These trends highlight the growing need for sustainable urban planning, effective agricultural management, conservation efforts, and integrated land management strategies. Policymakers should consider these LULC changes and their underlying causes when developing policies to ensure careful land use development and resource management, thereby promoting long-term environmental and socio-economic stability in Quetta Valley. This investigation provides insights into the dynamics of LULC changes and offers recommendations for sustainable land use planning in the region
Validation of Satellite-Based Gridded Rainfall Products with Station Data Over Major Cities in Punjab
Get PDFA Critical evaluation of newly developed gridded rainfall datasets is essential for their effective application. Over the past two decades, the availability of gridded rainfall measurements has increased; however, finding suitable proxies for traditional station-based measurements remains challenging. This study conducted a comparative assessment of rainfall estimates from IMERG, CHIRPS, ERA-5, and APHRODITE against meteorological station data from five cities in Pakistan: Lahore, Faisalabad, Multan, Islamabad, and Murree. The assessment covered multiple temporal scales (daily, monthly, and yearly) using daily data recorded from 2001 to 2022. Analytical metrics applied included Bias, Mean Error (ME), Root Mean Square Error (RMSE), Correlation Coefficient (CC), and Coefficient of Determination (R²). The results revealed notable spatial and temporal patterns of agreement among the datasets. Correlations for daily data were generally weak across all gridded datasets, with APHRODITE performing the best. Monthly aggregates showed that IMERG had the highest association with ground data, followed by CHIRPS. Yearly accumulated rainfall records indicated that IMERG had the highest correlation, followed by CHIRPS. Overall, IMERG demonstrated higher consistency across stations at both monthly and yearly scales. CHIRPS exhibited lower errors (RMSE and bias) at most locations, especially Lahore, but showed higher errors in Murree at the monthly scale. The study concludes that a single satellite dataset alone may not provide sufficient accuracy over large areas; a combination of products may be required for better estimation
Appraisal the Impact of Urban Evolution and Change on Land Use and Land Cover: A Case Study of Abbottabad District
Get PDFIntroduction/Importance of Study: Abbottabad district has gone through urban evolution drastically. The evident changes have observed in LULC of the district using RS/GIS techniques, which contributes to various environmental changes and put stress on the resources of the city. It is important to highlight areas at higher risk of urban expansion in the district to tackle the future growth of the city.
Novelty Statement: Our Research contributes to identify the increasing urban growth of the Abbottabad District from 1985-2023 using RS/GIS techniques that had not done previously. District’s LULC change and problems affiliated with it, has been addressed with solutions and recommendations, which will lead to the sustainable development of the city.
Material and Method: The study utilized the images of Landsat 5 (TM) and Landsat 8 (TIRS/OLI) collected from Earth Explorer of year 2000-2023. MLC classification has been carried on to identify the LULC classes in study area. The study employed the weighted overlay method, which provided the Urban Expansion Risk Model of the study area by analyzing parameters i.e. LULC, NDVI, LST, NDBI, Elevation and Population.
Result and Discussion: Our Findings represented the LULC changes of 2000-2020 through Maximum Likelihood Classification (MLC), which indicated the increase in built-up land that is 19.1% in last two decades and decrease in vegetation cover that has cleared for the construction and agricultural purposes. The LULC change brought problems in all spheres- housing, health, education, sanitation, transport, security, jobs etc. The NDVI values also showed the decrease in vegetation cover. To analyze Land Surface Temperature LST tool was used. LST calculations have represented the increase of 3o C in temperature. The study highlighted the consequences of NDVI and LST changes along with the environmental problems, such as pollution, Loss of biodiversity, land sliding, topographical changes and flooding. The field visit contributed towards understanding the ground realities.
Concluding Remarks: The study will help government to pay proper attention towards eradicating the problems of urban growth, pollution, urban flooding, land sliding and deforestation in the district. The better planning for the urban growth and LULC can prevent the further degradation of the district. It will also contribute towards sustainable growth of the city in future
A Comparative Assessment of Orchards Distribution in Urban and Peri-Urban Agriculture Zones in Karachi Through RS/GIS Techniques: (2005-2023)
Get PDFOrchard farming plays a crucial role in the urban ecosystem, contributing to biodiversity and offering opportunities for organic waste reprocessing and natural resource conservation. It holds significant economic and cultural value, enhancing the urban environment. This research on orchard distribution in Karachi provides insights that can guide sustainable development, infrastructure planning, environmental protection, and policy-making. The study employs advanced remote sensing and GIS techniques to monitor and analyze changes in orchards over time. Satellite images from Google Earth Pro were utilized to detect temporal changes, with a comparative assessment of agricultural land use between 2005 and 2023. ArcGIS software was used for digitization, revealing that most orchards in Karachi are concentrated in the peripheral areas along the Malir River, predominantly consisting of sapodilla, papaya, and guava trees. These orchards contribute to sustenance, employment, and environmental improvement. However, digitization has uncovered significant changes, including the transformation of some orchard lands into industrial and built-up areas. The data generated from this process can inform sustainable land management decisions for urban agricultural areas in developing nations
A Spatio-Temporal Assessment Of Land use Land Cover Change on Agriculture Productivity in Punjab, Pakistan
Get PDFIntroduction/Importance of Study: The agricultural sector is crucial to the development of any nation, particularly where food security is a concern. In Punjab province, urban settlements are increasingly encroaching on established agricultural lands, posing a significant threat to agriculture in the region. This issue is compounded by the continuous urban expansion and encroachment on fertile lands. The primary aim of this study is to assess the impact of Land Use and Land Cover (LULC) changes on agricultural productivity in Punjab. Utilizing the Earth Engine, this research performs LULC classification and estimates wheat crop yields in the province.
Novelty Statement: This study presents an innovative application of Earth Engine analytics to monitor and analyze the effects of LULC changes on agricultural productivity in Punjab province.
Material and Method: The research employs 20 years of Land Use and Land Cover data from the MODIS dataset, accessed via Google Earth Engine (GEE). In addition, wheat crop production is estimated using the capabilities of GEE.
Result and Discussion: The findings indicate a substantial shift in land cover in Punjab, which has significantly affected wheat crop production. The study emphasizes the importance of public awareness campaigns and the adoption of advanced agricultural technologies. Continuous monitoring of LULC changes using GEE can enable timely interventions to mitigate negative impacts.
Concluding Remarks: By integrating urban growth management strategies with the preservation of agricultural lands, long-term agricultural sustainability and development can be achieved. This research highlights the urgent need for comprehensive policies and collaborative efforts to counteract the adverse effects of urban expansion on agricultural productivity
A Computational Study of Ichthyofaunal Diversity of River Kabul
No full textMcClelland initiated the scientific study of the fish species of the River Kabul in 1842, and many researchers have continued this work since then. The primary goal of these studies has been to review the fully characterized fish fauna of the River Kabul and its major tributaries. The fish in the river are all members of the superclass Gnathostomata, including the Actinopterygii, subclass Neopterygii, division Teleostei, and superorder Ostaryophysi. Seventy-five fish species have been described from Pakistan and Afghanistan, belonging to four orders, ten families, and thirty-nine genera. Research indicates that Cypriniformes is the largest order and Cyprinidae is the largest family of fish in the River Kabul. Of the thirty-nine genera, twenty-seven are monospecific, and twelve are polyspecific. Notably, 27% of these fish are large and edible, highlighting the river\u27s significant economic potential for the region. It is concluded that the ichthyofauna of this river is diverse and holds great economic value for the local population. However, pollution from industrial zones and anthropogenic settlements poses a significant threat to the aquatic fauna. To preserve the fish and aquatic resources in this river, proper management, law enforcement, and public education are highly recommended
Analyzing the Contribution of Transportation in Formation of Smog in District Lahore, Pakistan
Get PDFIntroduction: Globally, smog has become a growing environmental issue, largely due to rising pollution from both anthropogenic and natural sources. In Lahore, a densely populated city in Pakistan, this problem is intensified by rapid urbanization, transportation emissions, and activities related to industry and agriculture.
Novelty: This research highlights these factors as primary contributors to smog and utilizes a quantitative approach to evaluate their impacts, particularly focusing on the role of transportation in smog formation.
Methodology: The study examines data on vehicular emissions, traffic patterns, and meteorological conditions to explore the relationship between transportation activities and smog. Data collection was performed via an online questionnaire survey. This data was then mapped onto traffic congestion areas and major industrial pollution sources, with charts created to depict public perceptions of the issue.
Results and Discussion: The results show that vehicles are a significant source of smog, emitting NOx, VOCs, and particulate matter (PM). Older vehicles, especially those lacking modern pollution control technologies, emit far more pollutants per kilometer than newer models that adhere to stringent emission standards. Traffic congestion during peak hours leads to extended vehicle idling, which increases fuel consumption and emissions. Additionally, stop-and-go traffic conditions further heighten fuel use and pollutant release.
Concluding Remarks: Tackling transportation-related smog is essential and necessitates effective management and policies at the local, regional, and national levels. Public-private partnerships can be instrumental in advancing cleaner technologies and practices within the transportation sector to mitigate smog
Analyzing the Shadows: Machine Learning Approaches for Depression Detection on Twitter
Get PDFDepression is a leading cause of disability worldwide, affecting approximately 4.4% of the global population. It can escalate from mild symptoms to severe outcomes, including suicide, if not treated early. Thus, developing systematic techniques for automatic detection is crucial. Social media platforms like Facebook, Twitter, TikTok, Snapchat, and Instagram provide users with the means to share personal feelings and daily activities, offering valuable insights into their thoughts and behaviors. This research aims to identify users who publicly disclosed their diagnosis and collect their data from Twitter. We created three different datasets, each varying in the number of tweets stored based on criteria discussed later. We selected six classifiers for analysis: Support Vector Machine (SVM), Logistic Regression, Random Forest, Max Vote Ensemble, Bagging, and Boosting. We conducted two analyses. In the first, textual data was converted into embeddings using the Bag of Words approach before analysis. In the second, a multivariate analysis, we trained algorithms on multi-dimensional data. Our findings revealed that Logistic Regression outperformed other techniques on smaller datasets. However, the Boosting algorithm yielded the best results on a dataset of 3,200 tweets, and the Bagging algorithm excelled when trained on 3,200 tweets of multivariate data. Overall, nearly all algorithms performed well on the 3,200-tweet datasets
Geo-visualization of Debris Flow Susceptibility in District Chitral, North-West of Pakistan
Get PDFDebris flows are a recurrent environmental hazard in hilly regions and significantly impact socioeconomic development in Pakistan. This study aims to conduct debris flow risk zonation using remote sensing data, including NASA\u27s Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) and Landsat-8 imagery. These data were combined with geographic indices to identify debris flow factors such as slope, aspect, elevation, vegetation cover, and land cover changes like NDWI and NDVI. The weighted overlay technique was employed to achieve the study\u27s objective in the target area. The classes were ranked from most to least favorable, with numerical weights assigned based on each factor\u27s importance in debris flow occurrence. A composite map was then developed using the weighted overlay analysis to represent the significance of each factor. The resulting debris flow risk zonation map categorized the area into four classes: very high-risk, high-risk, moderate-risk, and low-risk zones. The villages located in the very high-risk zone include Mulkoh, Mastuj, Reshun, Shegram, Terich Gol, Rogar, Asurat, Boni, Brep, and Rech Tockhow, which have been frequently affected by hazards over the past decade. While the results and landslide susceptibility maps provide valuable insights for understanding landslides and planning mitigation measures, field surveys are essential for more accurate predictions. Overall, the study offers important information for authorities to prioritize landslide mitigation efforts in the region