International Journal of Innovations in Science & Technology
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Position Prediction and Talent Discovery in Football Leagues Using Performance Data
Get PDFFootball has always been dependent on the subjective evaluation of scouts and coaches to find and hire players. Although these methods work to some extent, they usually have restrictions due to human biases, irregularity, and the huge volume of football data. As more data on player performance is made available, data analytics and machine learning represent a chance to introduce objectivity, consistency, and scalability in the recruitment process. This research study suggests a machine learning-based classification model along with a clustering model to classify football players in their main positional roles using statistical performance features. The research is based on the development of models that would help to differentiate among defenders, midfielders, and attackers based on their passing efficiency, contributions to defense, won duels, and attacking indicators. For data extraction, Fbref has been used as the source of data. The player-level data of the 2023-24 season of the Top 5 European Leagues has been extracted using the Python programming language. The data involved various statistical categories addressing all the areas of performance. Position labels were merged with the scraped tables to ensure accurate role mapping. This combination resulted in the creation of an entire dataset with both performance and position features. The dataset was cleaned and prepared using data preprocessing techniques, and selected features were then used in the training process. K-Means Clustering was applied to the PCA-transformed data to cluster similar players based on their playing profile. Different supervised learning algorithms have also been applied, such as Logistic Regression, Decision Tree, K-Nearest Neighbors (KNN), Random Forest, and Voting Classifier. The standard evaluation parameters are used to provide a detailed evaluation of the predictive performance. It was found that ensemble algorithms, in particular Random Forest and the Voting Classifier, performed better than single baseline models and were stronger and more reliable in positional classification. The results suggest the potential of machine learning models when recruiting players in football teams and to facilitate and aid expert judgment. This research sets up a systematic, data-driven framework that helps clubs to screen the enormous number of players effectively in a non-subjective manner
Growth and Characterization of Multilayer NiO/Ag and NiO/Al Structures for Energy Saving Heat Mirrors
Get PDFOptically Transparent multilayer NiO/Ag and NiO/Al heat mirrors were prepared which allow the visible radiation to pass through it and reflect the Infrared radiations. NiO thin film were deposited by sol-gel spin coating technique, Ag and Al thin films were prepared by thermal evaporation. XRD analysis showed that the formation of cubic structure of NiO. The chemical analysis reveals that Ni2O3 phase also present along with the NiO phase. The transmittance spectra of NiO/Ag and NiO/Al coatings showed good transmittance in visible region while highly reflective in infrared region.XRD analysis of the film showed the formation of cubic structure of NiO. The chemical analysis showed that some peaks are belongs to other phase of the NiO. The transmittance spectra of multilayer of NiO/Ag and NiO/Al films are good transparent in visible region and reflect well in infrared region
Computer-Aided System for the Detection of Rheumatoid Arthritis
Get PDFRheumatoid Arthritis (RA) is a chronic disease that causes disability in movement. RA classification is critical for effective diagnosis and treatment planning. This work explores the application of the EfficientNetB6 architecture using transfer learning to classify RA severity into three categories: Healthy, Moderate and Severe. Medical imaging dataset containing X-Ray images, enhanced with contrast limited adaptive histogram equalization (CLAHE), data augmentation techniques and fine-tuning of hyper-parameters were applied in this work. We compared EfficientNetB6 with all the models of EfficientNet family and all other state of the art models. When we combined EfficientNetB6 with CLAHE, we achieved the highest accuracy of 96.06%. Without using CLAHE accuracy dropped by 4% to 5% for all the models. For healthy class model, we achieved precision, recall and F1-score of 99.36%,97.81%,98.58% respectively, showing robustness in identifying healthy cases. Moderate class yielded precision, recall and F1-score of 89.45%,95.07%,92.17% respectively, demonstrating the model’s effectiveness in identifying moderate cases with minimal false negatives. The Severe class presented more challenges with a precision, recall and F1-score of 85.11%,78.43%, 81.63% highlighting the need for improved recall value. To further improve results we suggest enhancements such as advanced data augmentation and synthetic data generation, particularly for the Severe class consequently aiding clinicians for identification of RA
Forecasting the Impacts of Climate Variability on Cotton Production in South Punjab, Pakistan
Get PDFCotton, an important crop in Pakistan, contributes 0.8% of GDP and 4.1% of total agricultural value added. However, it faces challenges such as a reduction in area under cultivation, unfavorable weather conditions, whitefly infestation, crop stunting, bollworms, and other insect pests. The objective of this study is to identify the factors contributing to the decline in cotton production in selected districts of southern Punjab in Pakistan during the period 2004-2020. The second objective of this study was an over-time analysis of secondary data to calculate compound growth and forecast area production and average yield in Punjab, Pakistan. Data was collected from secondary sources and analyzed using SPSS and Microsoft Excel. The results showed that Rahim Yar Khan and Khanewal had no impact on production, while Lodhran had a significant impact due to the minimum temperature. A decrease in minimum temperature by one unit led to a decrease in cotton production by 21947. Pearson\u27s correlation showed a weak relationship between humidity and cotton yield in the study area. The time series analysis revealed that cotton production in Khanewal and Multan districts will increase in the future, while in Jhang, Sahiwal, Pakpattan, Vehari, and Rahim Yar Khan districts will experience a declining trend. Previous studies suggest that Pakistan’s crop production could be significantly affected by a reduction of rainfall, a 0.5-degree rise in temperature over the last three decades, and changes in the frequency of droughts and floods. This study aims to develop a policy framework involving suitable cotton varieties to improve cotton production and raise the country\u27s GDP
Eco-Friendly Nano Catalyst Preparation for Biodiesel Production from Melia azedarach Seeds: A Step Toward Climate Mitigation
Get PDFBiodiesel is a renewable and sustainable alternative to fossil fuels, offering a cleaner source of energy with significantly reduced greenhouse gas emissions. This study explores the production of biodiesel from non-edible Melia azedarach seed oil using green synthesis through TiO₂ nanocatalysts. Oil was extracted using n-hexane, and transesterification was performed under different conditions of the molar ratio of oil-to-methanol (1:3, 1:6, 1:9, 1:12, and 1:15), temperatures (70, 80, 90, 120, and 140 °C), concentration of TiO₂ catalyst (0.3, 0.5, 0.7, 0.9, and 1 g), and reaction times (1, 2, 3, 4, and 5 hours). Maximum biodiesel yield (93%) was achieved when the molar ratio was 1:12, the temperature was 80 °C, the weight of the TiO₂ catalyst was 0.7 g, and the reaction time was 3 hours. Fourier Transform Infrared Spectroscopy, X-ray Diffraction (XRD), and Scanning Electron Microscopy (SEM) were used to characterize the TiO₂ nanocatalyst and verified its catalytic activity and structure. The FTIR characterization of the produced biodiesel verified the presence of methyl esters. The use of non-edible feedstock like Melia azedarach is eco-friendly since it is not in food-vs-fuel competition and can be grown on marginal lands. Secondly, the method combats global climate change by minimizing the use of fossil fuels and carbon emissions. Through this research, it is proven that biodiesel synthesis using non-edible feedstock (Melia azedarach seed oil) is a sustainable method of climate-resilient large-scale biodiesel production in accordance with renewable energy and climate resilience criteria
Irrational Beliefs and Psychological Violence Among Adolescents
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Assessing the connection between teenagers\u27 psychological violence and illogical ideas was the aim of the research study. A proportionate stratified sampling technique was used to collect data from selected colleges of the Gujrat District. A sample of 1000 adolescents with an age range of 15-20 was approached for data collection. Irrational beliefs were measured with the help of 4 indicators (demandingness, awfulizing, limited frustration tolerance and global evaluation/ self-downing) and psychological abuse was measured with the help of seven indicators (verbal abuse, control and coercion, isolation, gas lighting, emotional manipulation, blaming and scapegoat and degradation and humiliation) on a 5-point Likert scale. The data analysis included descriptive statistics, correlation analysis, and multivariate regression analysis. Results indicate all factors were positively correlated with each other
An Integrated Geospatial Approach of GHG Emission Impact on Air Quality Due To Above-Ground Biomass In Rawalpindi Division, Punjab, Pakistan
Get PDFThis study investigates the influence of above-ground biomass (AGB) on greenhouse gas (GHG) emissions and air quality in the Rawalpindi Division, Punjab, Pakistan, from 2018 to 2024. An integrated geospatial approach was applied using Sentinel-2 for vegetation indices, Sentinel-3 for land surface temperature (LST), Sentinel-5P for atmospheric pollutants, and MODIS for active fire detection. Results indicate that while high AGB zones expanded, moderate and low biomass areas declined, suggesting biomass redistribution due to vegetation change. Fire radiative power (FRP) was strongly correlated with AGB (R² = 0.9888), indicating that biomass burning significantly contributed to pollutant concentrations. Linear regression showed strong positive correlations between AGB and NDVI (R² = 0.89), LST (R² = 0.96), and GHGs, including CO₂, CO, NO₂, SO₂, aerosols, and ozone. Notably, LST and pollutant levels peaked during dry seasons. The findings emphasize the dual role of biomass as a carbon sink and emission source, highlighting the utility of remote sensing for environmental monitoring and climate planning
Spatial Analysis of Land Use and Land Cover of Gujranwala District Using Remotely Sensed Data
Get PDFLand use and land cover change a major problem in most metropolitan areas in the world, where a natural land surface is changed by commercial land. Gujranwala is the 5th most populous city of Pakistan. The present population is 2,290,000. This study is an effort to assess the land use changes in Gujranwala District from the years 1990 to 2020. Land use Land cover (LULC) is the spatial change in land use and land cover from 1990 to 2020. The whole research is categorized into four classes (i.e., Vegetation, Uncultivable Land, Built-up Area, and Waterbody). The objectives revolve around the detection and assessment of Land use and Land cover in the district. The land cover is directly proportional to the expansion of the population of the district. The reasons for the changes are the development of residential and commercial buildings. Two types of analysis are being used in the methodology. The temporal analysis is done using Spatial techniques, including Geographic Information System (GIS) and Remote Sensing. Furthermore, the statistical analysis was also performed using the statistical data of the built-up area. The findings depicted that the alterations in land cover were due to an increase in built-up area and population in the city
Analyzing the Predictors of Mortality Among Asphyxiated Neonates
Get PDFBirth asphyxia refers to the inability to initiate and sustain breathing at birth, leading to inadequate oxygen supply to vital organs. It is one of the most common causes of perinatal brain injury, contributing to high rates of morbidity and mortality. Neonatal asphyxia is a major cause of early neonatal death, accounting for an estimated 900,000 deaths annually. It results from impaired respiratory gas exchange in the fetus or newborn, causing hypoxia, hypercapnia, and, in some cases, ischemia. This condition can affect multiple organs, leading to biochemical and functional changes, such as lactic acidosis, which may result in death or severe neurological impairment. Neonatal asphyxia is frequently associated with multiple organ failure, primarily impacting the brain, heart, and kidneys. It can lead to complications affecting motor, sensory, cognitive, and psychological development. Several factors contribute to birth asphyxia, including maternal anemia, diabetes, and placental abruption. Other significant predictors of mortality among asphyxiated neonates include neonatal sepsis, preterm birth, lack of Kangaroo Mother Care (KMC), low birth weight, seizures, need for resuscitation at birth, stage III asphyxia, hypoxic-ischemic encephalopathy (stages II and III), seizures and thrombocytopenia. This systematic review aims to identify the pooled predictors of mortality among asphyxiated neonates. Various online databases, including PubMed, MEDLINE, Google Scholar, and WHO websites, were searched for relevant studies. The review included cross-sectional, case-control, and cohort studies conducted in Pakistan and Ethiopia. Data entry and statistical analysis were performed using Excel and SPSS (version 27). The pooled mortality rate of birth asphyxia was found to be 64.0%. Among asphyxiated neonates, 27.1% who were delivered via spontaneous vaginal delivery (SVD) did not survive. Mortality rates were 39.4% for neonates born after prolonged labor, 42.6% for those delivered following premature rupture of membranes, and 50% for those weighing less than 2500g at birth. Additionally, 60.2% of asphyxiated neonates with seizures and 35.7% requiring resuscitation at birth did not survive. The highest mortality rate (81.1%) was observed in neonates with stage III asphyxia. It is concluded that asphyxiated neonates exhibit a high mortality rate. Key predictors of mortality include neonatal sepsis, vaginal delivery, lack of Kangaroo Mother Care (KMC), low birth weight, seizures, need for resuscitation at birth, stage III asphyxia, advanced maternal age, delivery complications, and prolonged rupture of membranes
Cybersecurity Legislature Challenges and Remedies
Get PDFThe use of the Internet is growing rapidly in every field of life, such as in business, education, entertainment, information technology, the government sector, and sports. The majority of people are using internet services for online businesses and other online activities. Therefore, it is the need of the hour that the online system should be secure enough, and everyone is fully assured about privacy and the protection of their information. A country needs to have an efficient plan to secure its digital information. Different countries have established legislatures to manage cybercrime activities and cyber threats. In this paper, we analyzed the challenges faced by cybersecurity concerning legislation along with their probable solutions. The purpose of this paper is to provide an extensive review of the literature on cybersecurity, including its loopholes, and present the findings of a survey conducted in various organizations regarding cybersecurity. This study also highlights the improvements and the need for future work in the field of cybersecurity. In addition, the mandatory procedures and mitigation techniques to reduce the occurrence of cybercrime have also been discussed