International Journal of Innovations in Science & Technology
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A Sustainable Growth Meta-Mask Consulting Application for Agriculture Sector Using Ethereum and Blockchain Technology
Get PDFPakistan\u27s economy depends heavily on the agricultural sector, yet a large number of farmers encounter financial constrains, including debt, loan repayment, a lack of loan security, and crowdfunding scams, which are the primary reasons for converting their lands into real estate. Crowdfunding for agriculture on the blockchain will cut out the middlemen and connect customers and producers directly. Blockchain technology provides a way to share a database or ledger that will guarantee an unalterable and consistent version of the truth even amongst untrustworthy players. Therefore, this study establishes a peer-to-peer network and a marketplace where community members can fund agricultural endeavors in exchange for food items. The novelty of this research is that the blockchain-based crowdfunding system for agriculture that enable investors to connect with farmers consistently and directly. The methodology includes the integration of AI-powered consultation tool, like ChatGPT into a web application to increase its efficacy. With the use of this instrument, enables farmers to solve issues pertaining to saline lands and obtain information regarding land productivity. This tool provides farmers quick, accurate, and easily available information to assist them in making better decisions. Therefore, this research aims to provide a comprehensive approach to aid impoverished farmers, encourage agricultural expansion, and ensure equitable profit sharing among all parties involved. Through the integration of blockchain technology, cooperative investment, and AI-powered consulting, this study aims to promote the agriculture sector\u27s sustainable growth
An Aggregated Approach Towards NILM on ACS-F2 Using Machine Learning
Get PDFThe Energy Sector across the globe is experiencing rapid growth, driven by Internet of Things (IoT) integration technologies and advanced algorithms. This evolution is particularly evident in the ongoing competition among tech companies in the development of smart metering solutions. Despite these advancements, a critical challenge persists— the lack of definitive technical protocols for monitoring the total usage or power signatures of individual appliances, referred to as non-intrusive load monitoring (NILM) in aggregate. While intrusive load monitoring (ILM) provides very accurate and thorough insights, non-intrusive methods are essential to address losses specially in residential areas. In this research a groundbreaking approach is proposed towards handling NILM problems by analyzing and aggregating the load patterns of four key appliances of daily use, namely the Coffee Machine, Fridge, Kettle, and Laptop from the ACS-F2 dataset. The generated aggregated dataset, is systematically combined using electrical formulations to yield the desired data which reflects the simultaneous operation of multiple appliances, this has been explored for the first time in the known literature. The proposed dataset contains around 6750 aggregated appliance load patterns for both training and testing. Furthermore, multiple Time Series Classifiers (TSC) were gauged using a suite of evaluation metrics, on the proposed dataset and an accuracy of 92.1% was achieved by the CATCH22 classifier
Particle Filter Based Multi-sensor Fusion for Remaining Service Life Estimation of Energized LV-Aerial Bundled Cables
Get PDFAerial Bundled Cables (ABC) consist of several wires that contain numerous layers of thermal insulation, which reduces the risk of theft. Nonetheless, there have been regular reports of rapid degeneration of such cables in coastal areas, resulting in multiple unplanned breakdowns. This study employs the data, collected from field-based nondestructive assessment techniques such as ultrasonic listening and thermal imaging. There is a pressing need for advanced tools to estimate the remaining lifespan of ABCs deployed along coastlines. This paper presents a novel approach using a particle filter-based fusion of multiple sensors framework for estimating the Remaining Useful Life (RUL) of in-service ABCs in a severe coastal atmosphere. The use of multi-sensor measurement data improves the accuracy and reliability of the RUL estimation. This will allow electric power distribution companies to plan maintenance and replacement activities well in time. In the reported research work, the f-step prediction scheme under the framework of the Particle filter algorithm is implemented to predict the posterior density function of degradation growth in the cable insulation. The Particle Filter (PF) method performs effectively with nonlinear state transitions and measurement functions, even when addressing non-Gaussian or multidimensional noise variations. The technique also contains a step error calculation approach for determining forecast accuracy when measurement data is missing. The encouraging outcomes of this strategy illustrate its efficacy
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
LULC-NEAT: Land Use Land Cover Classification Using Neuroevolutionary of Augmenting Topologies
Get PDFIntroduction/Importance of Study: In this paper, a novel application of NeuroEvolution of Augmenting Topologies for Land Use Land Cover Classification, which remains a perennial activity in environmental monitoring and management, is considered.
Novelty statement: We introduce NEAT for evolving feed-forward neural networks (FFNNs) tailored for LULC classification, offering a unique solution that addresses the challenge of optimal neural network architecture design.
Material and Method: The EuroSAT RGB benchmark satellite dataset was preprocessed using Numpy, Keras, and TensorFlow, and then evaluated using the NEAT algorithm to create diverse FFNNs with varying hidden layers.
Result and Discussion: The NEAT-evolved FFNN architecture with two hidden layers showed excellent and high accuracy percentages during the training and testing, respectively. Although high training accuracy implies successful feature learning, it also indicates probable overfitting. However, the high accuracy obtained in testing, 99.83%, shows the excellent generalization ability of the model toward unseen data and thus does not overfit. The results were cross-validated with the state-of-the-art CNN models, and the experiments prove that NEAT can be effectively used for LULC classification.
Concluding Remarks: The study confirms that NEAT can effectively evolve neural networks for high-accuracy LULC classification, offering a robust alternative to traditional CNN models
Deep Learning for Viral Detection: Affordable Camera Technology in Public Health
Get PDFViral infections like chickenpox, measles, and monkeypox pose significant global health challenges, affecting millions with varying severity. This study presents a novel deep learning approach using widely available low-cost RGB camera technology to accurately identify these infections based on skin manifestations. Our aim is to enhance diagnostic capabilities and enable timely interventions, thus improving public health outcomes and individual well-being. Using MobileNetV3 for data classification, our model achieved a precision of 95% for positive cases, an overall accuracy of 95.73%, a recall of 88.37%, and an F1-score of 91.56%, indicating balanced performance between precision and recall. Notably, the model demonstrated exceptionally high specificity at 98.34%, effectively identifying negative cases. This deep learning approach holds promise for improving diagnostic accuracy and efficiency, especially in resource-limited settings with limited access to specialized medical expertise. By leveraging low-cost RGB camera technology, our method enables broad deployment, facilitating early detection and treatment of viral infections. We focus on the potential of deep learning in public health by emphasizing the critical role of early detection and intervention in mitigating the impact of viral infections. Our findings contribute to advancing healthcare technology and lay the groundwork for future innovations in disease detection and management
Comprehensive Multi-Criteria Evaluation for Landfill Site Selection in Faisalabad, Pakistan
Get PDFIntroduction/Importance of Study: Solid waste management (SWM) has become a critical issue in urban planning due to population growth and urban migration, particularly in developing countries. In Pakistan, there are no standardized regulations for landfill site selection. Faisalabad, often referred to as the "Manchester of Pakistan" due to its industrial base and growing population, faces significant challenges in this regard. Identifying a suitable landfill site is essential to minimize health and environmental risks and ensure the long-term sustainability of both urban and peri-urban areas.
Novelty Statement: This study aims to propose an optimized landfill site in Faisalabad, combining the Analytical Hierarchy Process (AHP), Multi-Criteria Decision Analysis (MCDA), and Geographic Information Systems (GIS) to guide sustainable solid waste management practices.
Material and Method: The study utilized raster, vector, and attribute data based on eight key criteria: proximity to settlements, groundwater depth, roads, airport, surface water, power stations, railway infrastructure, and population density. Using AHP within the MCDA framework and GIS modeling with weighted overlay operations, we identified potential landfill sites for Faisalabad. Population data was incorporated to validate site suitability.
Results and Discussion: Through geospatial analysis, we identified and prioritized three potential landfill sites. After a population analysis, we recommended Site-1, covering 147 acres, as the most sustainable option for the next 50 years. This site offers a balance between accessibility and environmental safety.
Concluding Remarks: The integration of AHP and GIS under MCDA proved to be an effective method for landfill site selection. These tools can significantly aid decision-makers in achieving environmentally sustainable outcomes. Future research incorporating real-time data and community feedback could enhance site selection and decision-making processes
Geodemographic assessment of tuberculosis patients using Principal Component Analysis (PCA) in Gujranwala city, Pakistan
Get PDFIntroduction/Importance of the Study: Tuberculosis (TB) is a highly contagious disease caused by the bacterium Mycobacterium tuberculosis. It has persisted for centuries and primarily affects the lungs, spreading through airborne droplets. First identified by Robert Koch in 1882, TB remains a global health challenge. The World Health Organization (WHO) has been actively working to reduce TB incidence worldwide, and their efforts have led to a decline in infection rates over time. TB is closely related to geodemographic factors, which influence its prevalence and distribution.
Objective: This study aims to investigate the risk factors, spatial distribution, and hotspot areas of TB in Gujranwala city.
Material and Methods: Primary data were collected through questionnaire surveys, and secondary data were obtained from TB center records. These data were analyzed using statistical Principal Component Analysis (PCA) and Geographic Information System (GIS) software.
Novelty Statement: This study provides a geographical analysis of TB patients, offering significant insights that could enhance TB treatment strategies.
Results and Discussion: The analysis revealed that socioeconomic status, diet, diagnostic practices, and ecological conditions are key risk factors for TB. High-incidence areas are often characterized by poor ecological and economic conditions, predominantly inhabited by low- to middle-income labor class populations. Specific areas such as Ladhewala Wraich, Chicherwali, Kachi Phatuman, and Loyawala face ongoing environmental and socioeconomic challenges.
Concluding Remarks: Addressing these adverse conditions is crucial for reducing TB spread. Strengthening the immune system is also vital in preventing the disease. The government has a critical role in implementing measures to eradicate TB in Pakistan and improve overall public health
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
Comprehensive Review on Postoperative Central Nervous System Infections (PCNSI): Causes, Prevention Strategies, and Therapeutic Approaches using Computer Based Electronic Health Record (EHR)
Get PDFThe central nervous system is susceptible to various infections. Over centuries, bacterial infections have proven lethal in various surgical procedures. Infections that occur after craniotomy are often due to the reopening of operating wounds and past contamination of the scalp. Electronic health record (EHR) although provides programs to support surveillance efforts for these infections. But the problem with these tools traditionally used is lack of accuracy. Till now, the EHR systems are giving data to monitor and plan for these infections but this system definitely needs more accuracy. The rate of postoperative infection in craniotomy ranges from 0.8% to 7% in patients who have received preoperative antibiotic prophylaxis. This rate increases significantly to about 10% in patients without antibiotic prophylaxis. Different types of bacteria manifest infections at different intervals after surgery. For instance, Streptococcus pyogenes infections typically appear within one or two days, Staphylococcal infections usually become evident after four to five days post-surgery, while gram-negative bacillary problems may arise within six or seven days. Resistance in bacteria contributes to the prevalence of postoperative infections, with examples such as Vancomycin Resistant Streptococcus aureus (VRSA), Vancomycin Resistant Enterococci (VRE), and Methicillin-Resistant Streptococcus aureus (MRSA). Given the high incidence of postoperative neurosurgical infections, there is a pressing need to manage such infections meticulously to reduce the risk of infections and associated fatalities. Treatment options include antibiotics and surgical practices aimed at minimizing pathogenic infections. Early and prompt recognition of bacterial infections after craniotomy is crucial, necessitating an understanding of both local and general infection symptoms. Additionally, cranioplasty can be considered as a means to address postoperative neurosurgical pathogenic infections