International Journal of Innovations in Science & Technology
Not a member yet
813 research outputs found
Sort by
Review of Peer Feedback in Collaborative Tutoring Systems
Get PDFIntroduction/Importance of Study:
Collaborative tutoring systems (CTSs) allow students to communicate from different geographical areas to learn, share, and explain ideas related to a particular problem.
Novelty statement:
Many CTSs employ peer tutor evaluation to offer feedback to students as they solve scenarios. When they receive similar questions, the students utilize the feedback to enhance their thinking. The accuracy of peer feedback is important because it helps students to enhance their learning skills. If the student serving as a peer tutor is unfamiliar with the topic, he or she may suggest incorrect feedback. Considering peer feedback’s importance in learning systems, this study\u27s primary goal is to critically examine various collaborative tutoring systems and evaluate the strategies they have created to enhance group learning. Numerous reviews have been published in the past, but none of them have taken into account the methods by which these systems deliver or assess peer feedback.
Material and Method:
This article critically reviews different CTSs based on the proposed evaluation scheme to investigate their design and methods that support peer collaboration.
Result and Discussion:
Through this study, it was found that there are few attempts in which the feedback sent from one student to another student is evaluated by CTS. The peer feedback accuracy is important, because a student who gets inaccurate feedback may reach the wrong conclusions, which would affect the learner\u27s knowledge.
Concluding Remarks:
It is concluded that all of the CTSs provide chances to boost student\u27s learning gains. Fortunately, the entire degree to which these advantages can be realized is subject to further investigation
AI Based Predictive Tool-Life Computation in Manufacturing Industry
Get PDFFor maximum productivity and optimal utilization of tools, predictive maintenance serves as a standard operation procedure in the manufacturing industry. However, unnecessary or delayed maintenance both causes increased downtime and loss of revenue which should be optimized. Accordingly, this paper presents a method for predicting the maintenance requirement to ensure the optimal utilization of the tools. The experimental data for this research has been collected from a CNC lathe machine in a manufacturing plant for multiple days. The CNC machine equipped with three sensors leads to a detailed log for parameters related to tool wear including current, voltage, acceleration in 3D, motor rpm, and tool temperature respectively. Detailed experimentation has been performed to investigate the importance of different parameters. A direct relationship between current and tool temperature was observed leading to an immediate halt of machine operations. In the subsequent step, maintenance prediction was performed using Logistic regression and Random Forest technique respectively to validate the machine behavior. The retrospective data validated the performance with precise accuracy equal to 98% and 95% for both of methods respectively. The promising results predicting the maintenance schedule of the Lathe machine signify the effectiveness of Machine Learning towards advance scheduling for maintenance. The proactive maintenance strategy helps in potential benefits such as avoiding further costs, avoidance of disruptions, and increased efficiency productivity, thereby enhancing tool life cycles
Spatial Investigation of Soil Erosion Risk in the High Rainfall Zone of Pakistan by Using Rusle Model
Get PDFThe degradation of soil quality and agricultural sustainability is threatened by soil erosion, which poses a serious threat to livelihoods and food security. Maintaining soil fertility and reducing the danger of erosion require efficient evaluation and management strategies. This research presents an innovative approach to assessing soil erosion in Nowshera District, leveraging remote sensing technology coupled with Geographic Information System (GIS) tools. The study intends to offer a more thorough and accurate understanding of erosion patterns and drivers in the area by incorporating these cutting-edge approaches. Cloud-free LANDSAT 8 multispectral images, characterized by minimal vegetation cover, serve as the primary dataset for this analysis. The integration of the RUSLE model with GIS and remote sensing techniques enables the calculation of soil erosion rates throughout the research region. The study demonstrates variation in soil erosion parameters across different locations, as indicated by the range of R factor values, which range from 603.43 to 696.43 MJ mm/ha/h/year. The southeastern portion demonstrates significantly lower erosion rates than the northwestern part, which can be linked to variations in topography and land use patterns. This study highlights the significance of using remote sensing techniques to evaluate soil erosion changes over time and provide valuable information for land management plans in Nowshera District, Pakistan. This information may be used to make better decisions regarding conservation planning and agricultural sustainability
Enhancing Cardiovascular Disease Risk Prediction Using Resampling and Machine Learning
Get PDFCardiovascular Disease (CVD) remains a critical health concern around the globe, requiring precise risk prediction approaches for timely intervention. The primary motive of this study is to enhance CVD risk prediction through innovative techniques, just like resampling the imbalanced datasets using random oversampling and employing advanced Machine Learning (ML). In this study, different robust ML algorithms such as Random Forest Classifier, Decision Tree Classifier, XGBoost Classifier and Logistic Regression were trained on a diverse dataset encompassing demographic, clinical and lifestyle factors related to CVD. By addressing class imbalance through oversampling, the models showed significant performance improvements, showcasing the effectiveness of our ML algorithms in accurately forecasting CVD risks. Specifically, the Random Forest model with an accuracy score of 96% and AUC-ROC score of 99%. This study emphasizes the potential of modern approaches to improve CVD risk assessment by leveraging cutting-edge technologies for enhanced healthcare outcomes. Enfolding these approaches and tools, it becomes easy to pave the way for more personalized risk assessment and early intervention strategies, eventually aiming to alleviate the global burden of CVD
Skin Scan: Cutting-edge AI-Powered Skin Cancer Classification App for Early Diagnosis and Prevention
Get PDFMobile health applications (mHealth) use machine learning (AI)-based algorithms to classify skin lesions; nevertheless, the influence on healthcare systems is unknown. In 2019, a large Dutch health insurance provider provided 2.2 million people with free mHealth software for skin cancer screening. To evaluate the effects on dermatological care consumption, the research conducted a practical transitional and population-based study. To evaluate dermatological needs between the two groups throughout the first year of free access, the research compared 18,960 mHealth users who completed at least one successful evaluation with the app to 56,880 controls who did not use the app. The odds ratios (OR) were then computed. A cost-effectiveness analysis was conducted in the near term to find out the expense for each extra-diagnosed premalignancy. Here, results indicate that mHealth users had a three-fold greater incidence of requests for benign tumors on the skin and the nevi (5.9% vs 1.7%, OR 3.7 (95% CI 3.4–4.1)), and they had greater numbers of claims for (pre)malignant skin cancers as groups (6.0% vs 4.6%, OR 1.3 (95% CI 1.2– 1.4)). Compared to the existing standard of care, the expenses associated with using the app to detect one additional (pre) malignant skin lesion were €2567. These results suggest that AI in m Health may help identify more dermatological (pre)malignancies, but this could be weighed against the current greater rise in the need for care for benign tumors of the skin and nevi
Relevance Classification of Flood-Related Tweets Using XLNET Deep Learning Model
Get PDFFloods, being among nature\u27s most significant and recurring phenomena, profoundly impact the lives and properties of tens of millions of people worldwide. As a result of such events, social media structures like Twitter often emerge as the most essential channels for real-time information sharing. However, the total volume of tweets makes it hard to manually distinguish between those relating to floods and those that are not. This poses a large obstacle for responsible government officials who need to make timely and well-knowledgeable decisions. This study attempts to overcome this challenge by utilizing advanced techniques in natural language processing to effectively sort through the extensive volume of tweets. The outcome we obtained from this process is promising, as the XLNET model achieved an extraordinary F1 rating of 0.96. This high degree of overall performance illustrates the model’s usefulness in classifying flood-related tweets. By leveraging the abilities of the XLNET model, we aim to provide a valuable guide for responsible governance, aiding in making timely and well-informed choices during flood situations. This, in turn, will assist reduce the impact of floods on the lives and property-affected communities around the world
Potential Challenges and Solutions for Implementing NOMA in Smart Grid
Get PDFEfficient two-way communication is crucial for Smart Grid (SG) networks, enabling real-time monitoring, data collection, and control. This study introduces the novel integration of Non-Orthogonal Multiple Access (NOMA) into SG systems to enhance spectral efficiency and support numerous smart devices, addressing the limitations of traditional communication methods. A comprehensive survey of existing wired and wireless communication technologies was conducted, followed by the implementation of a NOMA scheme tailored for SG environments. Results demonstrate that NOMA significantly improves spectral efficiency, enables access to a large number of smart meters, and enhances the system\u27s resilience to electromagnetic interference. Additionally, the study addresses challenges such as impulse noise, optimizing spectral and energy efficiency tradeoffs, and power consumption in interference cancellation. These findings underscore the potential of NOMA to revolutionize SG communication infrastructure. Conclusively, integrating NOMA in SG networks offers a robust solution for future smart grid communication needs
A Conceptual Framework for Reducing Requirement Engineering Challenges in Industrial-Scale Software Projects
Get PDFIntroduction/Importance of Study: Industrial-scale software development tends to create more business value and effective strategic capabilities in software industries. IT organizations are spending about 50% of the budget on software development to build faster software programs at minimal cost to achieve success in industrial-scale projects. The crucial part of developing industrial-scale software is deciding ‘what is intended to be built’. If the problem is not tackled properly, this can result in serious errors that impact the entire Software Development Life Cycle (SDLC) and make it difficult and costly to repair in later stages. Similarly, challenges in industrial-scale development related to Requirements are complex including Requirement scope, elicitation, specification, validation, and management. The Requirement engineering challenges become bigger and harder to overcome in industrial-scale projects due to time and cost factors. The money spent on Requirement change may affect the overall development time of the project. The complexity of industrial-scale projects does not increase linearly, thus, impacting the development process.
Novelty Statement: Therefore, the need to address challenges in large IT projects comes with the reason of their economic value in local and international markets. Researchers have come up with the identification of challenges, but their studies lack the overall Requirement engineering process. There is a need to design a comprehensive solution to overcome the Requirement engineering challenges that contribute to project failure.
Material and Method: Therefore, the research is divided into three phases: “The Identification Phase”, where the project challenges would be identified; “The Implementation Phase”, where these factors would be shortlisted to design a framework; and “The Validation Phase”, in which validation of the framework would be done using triangulation technique.
Result and Discussion: The outcomes will focus on facilitating the software development industry for addressing the Requirement of engineering challenges in industrial-scale projects to reduce the chances of failure
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
Integrating Multiple Datasets in Google Earth Engine for Advanced Hydrological Modeling Using the Soil Conservation Service Curve Number Method
Get PDFThis research explores the feasibility of using cloud computing and open data sources for hydrological modeling, specifically leveraging Google Earth Engine (GEE) and the Soil Conservation Service Curve Number (SCS CN) method to estimate runoff. The SCS CN approach is commonly applied in simulating rainfall-runoff processes and is effective for estimating water inflow into rivers, lakes, and streams. Google Earth Engine provides a range of functionalities, including algorithms for rapid data manipulation and visualization, and access to extensive global remote sensing and geographic information system (GIS) datasets. The study introduces an algorithm developed in GEE to analyze precipitation data and generate antecedent moisture condition (AMC) maps. This algorithm integrates MODIS land use/land cover (LULC) data with USDA soil texture data to classify hydrological soil groups. Runoff estimation utilizes three datasets: CHIRPS, GPM, and TRMM. A thorough analysis of the rainfall-runoff relationship in the Mangla watershed from 2005 to 2015 is conducted. The study quantifies runoff estimates from each dataset and performs comparative analysis to validate the accuracy and reliability of the hydrological modeling. Over the ten-year period (2005-2015), significant fluctuations in average rainfall and runoff levels are observed, with notable seasonal patterns. The highest average precipitation of 1412.194 mm occurred in 2015, resulting in an average runoff of 215.021 mm. Conversely, 2009 recorded the lowest average precipitation of 672.808 mm and an average runoff of 78.476 mm. The accuracy of the modeled runoff observations is validated using meteorological data from the Pakistan Meteorological Department (PMD), Water and Power Development Authority (WAPDA), and Climate Forecast System Reanalysis (CFSR). In 2008, 2009, and 2010, CHIRPS consistently demonstrated better accuracy compared to GPM and TRMM, with accuracies of 90%, 79%, and 86% respectively. Additionally, a sensitivity analysis of the SCS CN model parameters reveals the effects of initial abstraction and Curve Number values on runoff estimation. In conclusion, this research enhances the understanding of hydrological processes in monsoon-affected regions and offers valuable recommendations for implementing sustainable water resource management practices