International Journal of Innovations in Science & Technology
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Deep Faces: Advancing Age and Gender Classification using Facial Images with Deep Features
Get PDFIn the realm of identity recognition and social interactions, human facial features play a pivotal role. Accurate age estimation and gender classification from facial images have practical implications across various fields, including biometrics, surveillance, and personalized services. This study presents a novel approach that harnesses deep features extracted by the VGG-19 architecture for age and gender prediction, employing a custom convolutional neural network (CNN) for classification. Leveraging the UTKFace dataset, encompassing a diverse collection of facial images with annotated age and gender labels spanning various ages, ethnicities, and gender representations, provides a robust foundation for model training and evaluation. Deep features extracted from the VGG-19 architecture serve as rich representations of facial patterns, enabling our model to discern discriminative cues for age and gender. These deep features are input to CNN model, which is fine-tuned specifically for age and gender classification. The model comprises input layer, Dense layers, incorporating dropout and batch normalization to mitigate overfitting, and Activation Functions Sigmoid for gender classification and SoftMax for Age group classification. The dataset is divided into training and validation sets (70% and 30%, respectively), enabling the model to learn to map VGG-19 features to age and gender labels. To evaluate the performance of the model, metrics like accuracy, precision, recall, and F1-score are employed. The proposed model achieves an impressive 78.67% accuracy in predicting age and 97.02% accuracy in gender classification on the UTKFace dataset, outperforming traditional methods despite challenges posed by variations in lighting, pose, and expression. The robustness of our approach is evidenced by its capability to handle diverse gender representations
Assessment of Rooftop Potential for Solar Energy and Rainwater Harvesting in Islamabad: A Geospatial Approach towards Sustainable Urban Development
Get PDFIntroduction/Importance of Study: Water and energy crises due to abnormal temperatures, precipitation patterns and urban growth leading imbalance in sustainable process so this study explores the potential of underused area of urban settings and their potential in sustainable urban development.
Novelty Statement: This study integrates advance geospatial techniques to access rooftop characteristics and calculate the potential of rooftops for solar energy and rainwater harvesting.
Material and Method: Firstly, the total area of all rooftops is estimated by utilizing freely accessible Open Buildings V3 Polygons data with a resolution of 50 cm. Rooftop current uses and characteristics were retrieved from openly accessible remote sensing data and by implementing Geographic Information System (GIS) methodologies. To estimate the potential of renewable electricity Global Solar Atlas data is used, and the potential for rainwater harvesting is calculated using precipitation data by the Pakistan Meteorological Department.
Result and Discussion: The results show that rooftop area of Islamabad has the potential to harvest 778.92 million gallons/annual of rainwater and about 16,504.29 MWh per year, which can address the capital city\u27s energy and water demands. The sectors of I8, I9, H9, H13, I14, blue area, Rawat industrial area, and DHA have demonstrated significant potential for solar energy and rainwater harvesting. Taking into account the findings of the current research and public feedback, we can propose recommendations for future energy policies, new society planning, sustainable use of rooftop space and Islamabad can lead the way towards a more sustainable future.
Concluding Remarks: The successful implementation of proposed systems can lead to a reduction in reliance on non-renewable energy sources and might reduce urban flooding risk in region
Identification of the Potential Areas/Sites for Rain Water Harvesting and Agriculture Development Using GIS and Remote Sensing in District Dera Ismail Khan (DIK) Khyber Pakhtunkhwa
No full textWater resources are rapidly depleting in both rural and urban areas of Pakistan due to increasing demands from agriculture and domestic use. This study aims to identify potential rainwater harvesting sites and evaluate the surface runoff potential for sustainable water resource management in the Dera Ismail Khan district of Khyber Pakhtunkhwa province, utilizing GIS and remote sensing (RS) techniques. The research involves both laboratory and field work. Results were validated through a field survey using handheld GPS, while laboratory analysis was performed using ARCGIS software with the Multi-Influencing Factor (MIF) Model. This model incorporates soil classes, slope, geology, and drainage density, analyzed through detailed maps and scales. Geospatial modeling techniques, combined with ground data, led to the identification of several potential rainwater harvesting sites, primarily in the northern and northwestern parts of the district. A total of 26 sites were selected for rainwater harvesting interventions, located on areas ranging from flat to gentle slopes with elevations below 300 meters. The findings of this study can assist the Soil and Water Conservation Department of KP, which is responsible for rainwater harvesting initiatives in the region. The maps produced using the MIF Approach are valuable tools for engineers, planners, and decision-makers in locating and developing dams, storage ponds, and check dams, and for integrating rainwater harvesting into national water policies
Analyzing the Impact of Smog on Human Health in District Lahore, Pakistan
Get PDFSmog is a term used to describe pollution suspended in humid air, consisting of dust particles of various sizes, non-metal oxides, organic compounds, and heavy metals. Exposure to these toxic compounds, in addition to cigarette smoking, is a significant factor in the development of respiratory diseases. Smog is a visible form of air pollution that results from excessive emissions of primary pollutants such as volatile organic compounds (VOCs), hydrocarbons, sulfur dioxide (SO₂), nitrogen oxides (NOₓ), and nitrogen dioxide (NO₂). These pollutants react in the atmosphere to form harmful and carcinogenic secondary smog components. Airborne chemicals that adversely impact public health include ozone and particulate matter (PM) of various sizes—PM2.5, PM2.5–10, and PM10—as well as nitrogen oxides. Special attention is given to lead, carbon dioxide (CO₂), sulfur dioxide (SO₂), and carbon monoxide (CO), with a focus on smaller dust particles (PM10 and PM2.5) because they can penetrate the lower respiratory tract. This page explores the effects of atmospheric pollutants on the onset and exacerbation of respiratory diseases, including asthma, chronic obstructive pulmonary disease (COPD), and lung cancer. It also discusses legislative measures implemented in various countries to mitigate exposure to harmful air pollution. Based on the survey responses, it appears that the individual may be experiencing symptoms related to respiratory, skin, and cardiac conditions, such as coughing, wheezing, and shortness of breath. They have been diagnosed with several health issues, including asthma, chronic bronchitis, pneumonia, and ischemic heart disease. Diagnostic tests such as chest X-rays and arterial blood gas (ABG) tests are likely to have been performed during their hospital stay. The individual has reported experiencing symptoms and health effects associated with air pollution or smog during their hospitalization
Spatial Assessment of Atmospheric Contamination and Urban Heat Phenomenon in Urban Centers of Sindh, Pakistan
Get PDFRapid urbanization and industrialization in Sindh, Pakistan, particularly in urban centers like Karachi, have led to significant challenges related to air pollution and climate change. This study uses Geographic Information Systems (GIS) to analyze air quality indices (AQI) in Sindh, focusing on particulate matter such as PM2.5. It offers a novel approach by visually mapping the spatial patterns and potential correlations between air pollution and temperature, a topic not extensively covered before. Through interpolation methods and temporal graphing of AQI values, the study identifies areas with high air pollution and examines their spatial distribution throughout the year. The comparison of PM2.5 concentrations with land surface temperature (LST) maps reveals patterns where higher pollution levels often align with urban centers, intensifying the urban heat island effect due to excess heat generated by human activities. Beyond impacting human health, air pollution affects ecosystems, soil, water, and biodiversity. The study highlights how areas with significant air pollution tend to have higher surface temperatures, indicating a direct link between pollution and temperature increases. However, the relationship is complex, as the effects of air pollution on climate are influenced by factors like geographic location, meteorological conditions, and pollutant composition. This research provides valuable insights into the spatial dynamics of atmospheric contamination and its implications for urban heat formation in Sindh, enhancing the understanding of how human activities, air quality, and climate change interact
Thematic Analysis of Tourism Downfall and Economic Consequences During Covid-19: Evidence from A Rural Mountain Community in Pakistan
Get PDFThe COVID-19 pandemic had a profound impact on the tourism industry in Gilgit-Baltistan, leading to a significant reduction in tourist arrivals and severely affecting the local economy. This study demonstrated that the livelihoods of many residents, particularly those involved in tourism-related businesses, were adversely affected. The analysis revealed substantial monthly losses in tourist arrivals in 2020 compared to 2019, with a marked decline in the summer months, typically the peak season for tourism in the region. The thematic analysis highlighted the socio-economic challenges faced by the local population, including loss of income, business closures, and a decline in living standards. The study also emphasized the need for strategic interventions to support the tourism industry in Gilgit-Baltistan, including the development of policies to enhance resilience against future disruptions. The integration of advanced data analysis techniques and Geographic Information Systems (GIS) provided a comprehensive understanding of the pandemic\u27s impact, contributing valuable insights for policymakers and stakeholders in the tourism sector
The Emergence of the Internet of Things in Military Defense: A Comprehensive Review
Get PDFThe Internet of Things (IoT) has emerged as a significant research field. The main concept of IoT technology is to connect millions of devices and facilitate interaction between these devices and the cloud. Recently this concept has been considered and applied in the design of systems intended for distributing data and information between heterogeneous devices. The goal is to enhance the performance of the business and decision-making process. IoT enables energy and supply chain monitoring, production coordination, equipment performance optimization, transportation, and public health, to improve, and enhance worker safety. It is revolutionizing military operations enhancing battlespace awareness operational efficiency, and command structures while enabling more intelligent and responsive security systems across diverse defense sectors and mission domains. This paper discusses how IoT technology shapes the future of military information and defense systems. The goal of this article is to present a comprehensive literature review on the application of IoT in military defense. This review also puts future recommendations for the further development of IoT technology in the defense sector
Enhancing Three-Phase Induction Motor Performance with Soft Ramp Control
Get PDFThree-phase induction motors experience high inrush currents during start-up, exceeding their rated capacity and potentially damaging stator windings. This paper explores the implementation of soft ramp control to address this challenge. Soft starters progressively increase the voltage applied to the motor, mitigating the current surge and associated electromagnetic torque. This reduces stress on the motor shaft, and connected equipment even though preventing disruptions in the power supply network. The proposed technique aims to start the motor at a lower speed and gradually climb to its maximum rated speed. This paper employed a gentle ramp control strategy using TRIAC-based voltage regulation. This technique prevents abrupt surges that could harm the motor and related equipment. The study illustrates the soft start process for a three-phase induction motor using a prototype setup and a simulation model. The soft start technique dramatically lowers starting current, mechanical stress, and electromagnetic disturbances, improving motor health and performance, according to experimental data. This method is a cost-effective alternative for industrial applications since it not only increases motor longevity but also lowers the related power losses
Physio-Mechanical and Petrographic Characteristics of Granitic Rocks from the Demote Valley, Gilgit, Pakistan: Implications for Strength and Bearing Capacity
Get PDFThe granitic rocks of the Damote Valley (Juglote Group, Kohistan Batholith) were evaluated for their physio-mechanical and petrographic properties to assess their suitability for construction, particularly as dimension stones. Detailed petrography and tests such as Uniaxial Compressive Strength (UCS), Brazilian Tensile Strength (BTS), Ultrasonic Pulse Velocity (UPV), Schmidt Hammer, Specific Gravity, Porosity, Water Absorption, and Slake Durability were conducted. The granitic rocks, medium to coarse-grained with no preferred orientation, consist mainly of plagioclase (19–35%), quartz (30–43%), and alkali feldspar (40–44%), along with biotite, muscovite, sericite, and minor opaque minerals. Based on geographic location, the granites are divided into three zones: Fulkin granite (Zone 1), Bargin (Zone 2), and Shing (Zone 3). The average Uniaxial Compressive Strength (UCS) values of the granite from the Demote area are 63 MPa for Fulkin granite, 66 MPa for Bargain granite, and 53 MPa for Shing granite, reflecting the granite’s suitability for engineering applications. BTS values range from 7.55 to 12.04 MPa. Schmidt hammer rebound values range from 43 to 47, while specific gravity averages from 2.5 to 2.98. Water absorption is low (0.34–0.60%), and porosity ranges from 1.19% to 1.28%. All results fall within ASTM specifications. The medium-grained granite is stronger and more durable than coarse-grained varieties due to its tighter grain packing and fewer microcracks. Based on these findings, Damote granites are suitable for construction in roads, bridges, constructions, and the dimension stone in the area
A Investigation of Feminism Trends Through Sentiment Analysis Using Machine Learning and Natural Language Processing
No full textIntroduction/Importance of Study:
One of the recent changes seen in Pakistan is the growing awareness among people, to end gender discrimination and bring equality, across various spheres. “Aurat March”, is a series of rallies that began in 2018 to mark International Women’s Day. People across the nation, comment on these rallies through social media.
Novelty Statement:
The response to the “Aurat March”, held annually since 2018, was mixed and no analysis of Twitter data had been previously done to investigate the polarity of comments, through Machine learning and Natural Language Large Language Models.
Material and Method:
For this, Sentiment analysis was performed, using Machine learning and NLP techniques, on the pre-processed data. Lexical rule-based VADER and transformer-based pre-trained Large Language Models were used to check the polarity of Twitter comments.
Results and Discussion:
The best results were achieved through RoBERTa-LARGE, which were closest to the Human Labelled Data, hence validating the accuracy of the LLM. On the other hand, VADER results were clearly far from the manually labeled results. The sentiment analysis that was applied the first time on “Aurat March” tweets, gave us satisfactory results, and we were further able to validate our research, by comparing the models’ accuracy with human-labeled results. Consequently, by analyzing the sentiments expressed on Twitter, we were able to discern the general mindset of users and gain insights into prevailing trends.
Conclusing Remarks:
This analysis provided us with a reasonably accurate gauge to assess the perception of feminism over the past few years, allowing us to evaluate whether it has garnered fame or faced defamation in the public discourse.