International Journal of Innovations in Science & Technology
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Performance Evaluation of Fake News Detection Using Artificial Intelligence Techniques
Get PDFIntroduction/Importance of Study: As the proliferation of fake news poses significant challenges to traditional fact-checking methods, there is a growing need for robust and automated approaches to combat misinformation.
Novelty statement: This study presents a comprehensive evaluation of artificial models for fake news detection, offering insights into their effectiveness and applicability in addressing the contemporary issue of misinformation.
Material and Method: The research employs various artificial algorithms, including logistic regression, gradient boosting, decision trees, random forest, AdaBoost, passive aggressive classification, XGBoost, naive Bayes, and support vector machines (SVM), to train datasets and evaluate the performance of each model.
Result and Discussion: Through rigorous evaluation, the study finds that XGBoost and AdaBoost classifiers exhibit the highest accuracy rates of 99.83% and 99.77%, respectively, in detecting fake news. Decision Tree, Support Vector Machine, and Gradient Boosting classifiers also demonstrate commendable performance. Conversely, the Naive Bayes classifier exhibits the lowest accuracy, suggesting its limitations in fake news detection.
Concluding Remarks: This research underscores the significance of ensemble methods such as XGBoost and AdaBoost in effectively identifying fake news, laying the groundwork for future advancements in combatting misinformation
Gemstones Supply Chain Management through Blockchain Mechanism
Get PDFThe provenance of gemstones significantly enhances their value. However, both conventional supply chain management and digital systems are susceptible to counterfeiting, loss, and theft. Blockchain has emerged as a suitable technology to store tamper-proof records of gemstones allowing the storage of immutable journey of gemstones. This research article shows how the blockchain-based Ethereum network can be used for managing the supply chain of gemstones. Mining details, cutter information, digital certificates, proof of ownership, quality, and sales history of gemstones can be arranged in a two-tiered blockchain network to allow multiple organizations to securely share specific information within the organization and publicly. We cover the major supply chain exchanges for gemstones and end users with Ethereum smart contracts. We present that our suggested decentralized architecture-based solution can overcome many limitations in terms of immutability, traceability, verifiability, and security which exist in both conventional and digital supply chain management systems. Test scripts or smart contracts are publicly deployed on the Ethereum network
Assessment of Groundwater Potential Zones Using Electrical Resistivity in Muzaffargarh
Get PDFThe study integrates Earth observation and geospatial data to evaluate groundwater potential and conditions in Muzaffargarh, South Punjab, Pakistan, a region grappling with freshwater scarcity due to high sediment concentrations in subsurface water. The developed approach aims to enhance sustainable water resource management in areas affected by such sediment challenges. An electrical resistivity survey was conducted at 40 locations within the study area, incorporating Vertical Electrical Sounding (VES) and spatial analysis with hydrogeological parameters to analyze and visualize the spatial distribution of freshwater. A weighted overlay analysis was employed to map freshwater and saline water zones, supported by 2D resistivity maps. The study generated several thematic layers, including data on geology, rainfall, lineaments, land use/land cover (LULC), drainage density, soil type, and slope. A groundwater potential (GWP) zone map was created, categorizing the area into four zones: very good, good, moderate, and poor. Additionally, resistivity maps were produced at depths of 2m, 10m, 50m, 80m, 200m, and 300m to analyze resistivity variations in the Ghazi Ghat and Qasba Gujrat areas of Muzaffargarh district. The study\u27s findings include curves indicating potential groundwater zones and a comprehensive understanding of subsurface characteristics through resistivity curve comparisons. These results provide valuable insights for the sustainable management of groundwater resources in the region, particularly in addressing freshwater scarcity
Classifying Text in Citation Context as Relevant or Irrelevant to the Cited Paper
Get PDFCitation contexts, whether in the form of full citing sentences or text within a fixed window around the citation, have been widely used in various citation analysis applications. However, the absence of precise techniques to identify the exact span of text describing citations forces these applications to rely on extended texts as citation contexts. In this paper, we introduced new features combined with baseline features to accurately identify text that characterizes citations. Specifically, we utilized a Conditional Random Field (CRF) sequence classifier to categorize the surrounding text of citations as relevant or irrelevant. The integration of these features enhances the precision, recall, and F-measure scores for the Relevant (R) class. Although the average values of all measures are similar to those obtained with baseline features alone. Our approach significantly improves the extraction of relevant text
Comprehensive Assessment of Air Quality Dynamics Around Yosemite National Park Using Remote Sensing, GIS, and Computational Analysis During Wildfire Events
Get PDFIn recent years, Mariposa County has experienced several significant wildfires, including the catastrophic Rim Fire of 2013. On July 22, 2022, Yosemite National Park faced one of its most devastating wildfires, profoundly affecting Aerosol Optical Depth (AOD) and overall air quality. This study employs an integrated approach using remote sensing, GIS, and advanced computational tools to investigate the impact of these wildfires on air quality, focusing specifically on aerosol pollution dynamics and key atmospheric pollutants. The research leverages satellite data from TROPOMI, MODIS AQUA, and Suomi NPP/VIIRS, along with meteorological inputs from GDAS. Data processing and analysis were performed using Python, MATLAB, and R, with spatial mapping and visualization achieved through ArcMap and Google Earth Engine. The study utilized the MODIS MAIAC algorithm to conduct a detailed examination of AOD fluctuations in the Yosemite region, spanning from July 21 to August 1, 2022. Our comprehensive analysis reveals significant temporal and spatial variations in aerosol pollution during the wildfire. Initial findings indicate a marked increase in AOD with the onset of the wildfire, reflecting severe impacts on atmospheric composition. Pre-fire AOD levels were relatively low at 0.12, but surged to 0.20 at the wildfire\u27s peak, demonstrating a substantial rise in atmospheric aerosol loading. The average AOD throughout the study period was recorded at 0.16, highlighting the wildfire\u27s prolonged effect on air quality. Furthermore, the study identifies elevated concentrations of key pollutants, including NO₂, SO₂, CO, HCHO, and O₃, during the wildfire event. The integration of data from various satellite sources and the application of machine learning models provided a more nuanced understanding of pollution patterns. The HYSPLIT model was also employed to track the distribution of air masses and contaminants, revealing significant northwestward transport. This research advances our understanding of the intricate relationships between wildfires, aerosol pollution, and air quality in Yosemite National Park. The findings offer critical insights for public health preparedness, the development of resilience strategies against wildfires, and the formulation of effective mitigation measures in fire-prone regions like Yosemite
The Analysis of Drinking Water Quality and Associated Human Health Risks. A Case Study of Rawalpindi Pakistan
No full textWater is essential for the survival of all living beings, but the rapid increase in population is causing a significant decline in water quality. Access to safe and hygienic drinking water is crucial for human health, yet approximately 44% of Pakistan\u27s population lacks access to clean drinking water. In Rawalpindi, a densely populated area, the challenges associated with drinking water are further exacerbated by industrialization and rapid population growth. This study aims to analyze the spread of waterborne diseases, identify sources of water pollution, and propose preventive measures specifically for the Mareer Hassan Saddar area within the Rawalpindi cantonment. The study assessed various water quality parameters, including aesthetic factors such as taste, odor, and appearance; chemical factors like pH, Total Dissolved Solids (TDS), hardness, nitrates, and turbidity; as well as heavy metals such as zinc, chromium, lead, and arsenic. Biological parameters, including the presence of total coliform bacteria, were also evaluated. Water samples were collected from different endpoints within the Rawalpindi district and compared against the drinking water quality standards established by the World Health Organization (WHO). The findings revealed that while the chemical quality of the water was within acceptable ranges according to WHO and national standards, the biological content was highly problematic. The presence of total coliform and fecal coliform bacteria in the water samples was particularly concerning, as these bacteria are known to cause various diseases in humans. This highlights the urgent need for improved water quality management in the study area to protect public health
Gender-Based Analysis of Employee Attrition Prediction Using Machine Learning
Get PDFEmployee turnover is a significant problem in organizations because it comes with productivity and cost implications. This paper focuses on predicting employee turnover using machine learning techniques that incorporate gender aspects. We used strong Random Forest classifiers to predict attrition based on a wide cross-section of the employees’ activities and the feature importance assessment. The procedure involved data cleaning, splitting the dataset for males and females, creating models for them, and using assessment tests with different measures. When we separated the data base by gender, our analysis identified unique factors that predisposed the two groups to dropping out. The importance of features, the ROC curve, and the SHAP map showed how variables such as "job role," "monthly income," and "work-life balance" affected attrition differently between males and females. For female employees, job satisfaction and time directly influenced attrition, whereas for male employees, previous companies and distance from home had a greater impact. The results of the research therefore imply the need for gender-sensitive HR practices that can inform the development of gender-sensitive accommodation policies as a way of responding to the challenges facing each gender. This approach aids in the explanation of attrition tendencies and the provision of better organizational practices
The Assessment of Public Participation Modalities through Social Media Platforms for Approval of Private Housing Schemes: Case Studies under LDA Lahore, Pakistan: A Case of Lahore, Pakistan
No full textPublic participation through social media networks in Private Housing Scheme (PHS) projects is essential for fostering a feeling of community and avoiding resistance to the planning of housing scheme initiatives. It might help the private developers and government in identifying potential hurdles to any given landuse, allowing officials to work to eliminate them before making a final decision. This study will look at public participation in private housing scheme projects through online platforms in the metropolitan corporation Lahore. It emphasizes how the Government and Lahore Development Authority (LDA) encourage residents to participate more actively in PHS projects and the requirement of aligning tools with goals to enhance citizen engagement. To get a comparative understanding, the approaches and practices of public engagement in urban planning projects in selected industrialized and developing nations and Pakistan have been critically studied. On the other hand, Social media plays effective role in engaging public in concerned projects. It allows for cost-effective, efficient information sharing among public/stakeholders through various media types, including videos. It allows for the education of a broad audience about issues and encourages engagement. It can be used alongside other communication initiatives for wider public/stakeholder interaction. Moreover, participant\u27s education was greatly aided by public consultation. It is maintained that public engagement in PHS is steadily increasing in Lahore, Pakistan despite some obstacles. Applying a more proactive strategy throughout the PHS clearance process and prior to site selection for development projects is one suggestion made to improve PHS public engagement effectiveness in Pakistan
Enhancing Mobile Efficiency: A Cloud-Powered Paradigm for Extended Battery Life and Enhanced Processing Capabilities
Get PDFIn an interconnected world where mobile phones are essential to everyday operations, the constraints of these devices in terms of processing power, memory, storage, and energy efficiency are becoming increasingly apparent. This research introduces an innovative solution by integrating Mobile Cloud Computing (MCC) to address these challenges. The research focuses on the creation of an Android application called "ServiVerse" that efficiently drains the phone\u27s battery to imitate real-world conditions. The software is accompanied by a Firebase-connected battery optimizer, which provides users with complete insights into battery state, cleaning history, and graphical representations of performance. The system\u27s distinguishing feature is outsourcing power-intensive operations to a cloud server, resulting in increased energy efficiency and battery life. The study demonstrated successful battery optimization tactics adapted to individual users, such as the amount of cache and RAM deleted and storage space freed up on the mobile devices. This strategy has proven to be vital in addressing a key concern about background processing and the loss of power generation on mobiles, which is providing users with more efficient and longer-lasting battery life
An Advanced 2-Output DNN Model for Impulse Noise Mitigation in NOMA-Enabled Smart Energy Meters
Get PDFThe next-generation power grid enables information exchange between consumers and suppliers through advanced metering infrastructure. However, the performance of the smart meter degrades due to impulse noise present in the power system. Besides conventional thresholding techniques, deep learning has been proposed in the literature for detecting noise in NOMA-enabled smart energy meters. This research introduces a novel Deep Neural Network (DNN) capable of simultaneously detecting and classifying impulse noise as either high or low impulse. Combining the analysis of detected noise and its class has proven to be more effective in mitigating noise compared to previously proposed methods. The input feature vector to DNN is chosen based on its characteristics to detect impulse noise and its level in the data and includes ROAD characteristics, median differences, and probability of impulse arrival. The performance evaluation shows that the Bit Error Rate (BER) of the proposed DNN is lower than the BER of single output DNN which is proposed in the literature for mitigation only. It is also shown that besides simultaneous detection and mitigation, the second output of the proposed DNN i.e. classification of IN validates the first output which is IN identification