International Journal of Innovations in Science & Technology
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A Deep Learning Approach to Semantic Clarity in Urdu Translations of the Holy Quran
Full text linkThe Holy Quran holds profound significance from both religious and linguistic perspectives yet its Urdu translations face difficulties in preserving the original meaning because of ambiguous words which create interpretation challenges for speakers and listeners. This research tackles translation ambiguity in the Urdu translations of the Holy Quran authored by Maulana Abul A’ala Maududi and Fateh Muhammad Jalandhry by applying Word Sense Disambiguation methods with deep learning algorithms. A model based on multilingual BERT identifies ambiguous word senses for Surah Al-Baqarah in particular. The dataset features Surah Al-Baqarah\u27s complete Urdu translation together with a Sense Inventory that contains 3 to 8 senses for 50 frequently used Urdu ambiguous words which are collected from GitHub repository. Sequence classification frameworks within BERT receive contextual embeddings during fine-tuning. The evaluation framework includes the determination of F1 scores alongside confusion matrix analysis and classification report assessment. The model achieved an F1-score of 0.82 when identifying the most frequent sense while reaching an average F1-score of 0.62 across eight predefined sense labels. A sense prediction system functions to improve word sense matching thereby leading to more precise translations. The proposed research makes significant contributions to computational linguistics and Quranic studies by delivering an expandable method that solves word sense ambiguity while offering important insights to help translators and scholars improve their understanding of how context affects meaning within translated texts
Evaluating the Meteorological Pattern of District Swat Using Different SSP Scenarios
Full text linkThis study investigates the observed and projected impacts of climate change in District Swat, Pakistan, using meteorological records and CMIP6-based projections under SSP2-4.5 and SSP5-8.5 scenarios. Metrological variables, such as temperature and precipitation, were examined for long-term trends, anomalies, and extremes. Machine learning techniques (XGBoost and SHAP) were used to identify the most relevant online datasets and climate models. ERA5 emerged as the most reliable online source, and INM-CM5-0, CNRM-CM6-1, and CMCC-ESM2 were selected as the best-performing GCMs. The Mann-Kendall test showed a significant rise in minimum and maximum temperatures based on future conditions. For instance, the maximum temperature under SSP5-8.5 had a significant increasing trend with a Kendall Tau value of 0.1517, a Sen Slope of 0.00018, and a p-value less than 0.001. In the meantime, the trend of precipitation under SSP2-4.5 was decreasing significantly, which indicated the likelihood of an even more arid future. Under SSP5-8.5, temperature anomalies might be as high as 6.5°C, and precipitation anomalies could be as low as -1.5 mm or as high as +2 mm. Furthermore, Intensity-Duration-Frequency (IDF) analysis indicated that extreme rainfall events are projected to intensify, with rainfall intensities for the 100-year return period increasing from an observed value of 340 mm/hr to 360 mm/hr under SSP5-8.5. These outcomes show a potential rising trend of warmer and possibly drier conditions in the Swat District, and higher vulnerability to severe weather conditions. The results show that we need infrastructure that can handle climate change, flexible water management plans, and aggressive planning to lessen the effects of future extreme weather events
Spatio-Temporal Estimation of Glacier Dynamics under Climate Change Scenarios Using Machine Learning Techniques
Full text linkGlaciers of the Upper Indus Basin (UIB) play a vital role in providing water resources, hydropower generation, and livelihood, but they are very vulnerable and sensitive to continuous climate change impacts. This research presents a novel approach for accurate mapping of glacier extent, clean ice, debris cover, seasonal snow, and glacier melt across the Hunza Basin. We have used Grey Level Co-occurrence Matrix (GLCM), Machine Learning (ML) techniques of Random Forest (RF), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) to conduct the purposeful research. ML models were trained on multispectral (Landsat, Sentinel-1 & 2, MODIS, and SPOT-5 from the last 35 years) and textural datasets. Overall, 6628 samples for training and 988 samples for testing were used to maintain a 70/ 30 ratio to evaluate overall accuracy (OA) and kappa coefficient (k̂). RF ensured the best results (OA = 95.4 %, k̂ = 0.965) in comparison of ANN (OA = 94%, k̂ = 0.92) and SVM (OA = 92 %, k̂ = 0.89). The accuracy of clean ice and seasonal snow remained consistent (producer accuracy and user accuracy >93%) compared to that of debris cover and glacier melt. Glacier retreat, increased ablation, formation of clean ice loss, and frequency of supraglacial melt due to expansion of debris cover up to 23.31% were witnessed spatially in the basin. Proposed approaches prove that ML techniques are very useful for the estimation of risk assessment in the climate-prone mountain basins and offer a robust way forward for hydrological modelling, glacier change monitoring, and water resource management
Artificial Intelligence Meets Endocrinology: A Machine Learning-Based Approach to Thyroid Disease Diagnosis Using Feature Selection Methods
Full text linkThyroid Disease (TD) arises when the thyroid gland either grows abnormally or does not generate enough thyroid hormones, and might cause serious health issues and consequences. Early and efficient identification of thyroid disease is important for improved clinical intervention and disease management. By combining sophisticated and advanced machine learning models with a range of advanced feature selection strategies, this research study aims to enhance the classification of thyroid disease based on a machine learning based diagnostic system. The preprocessed dataset used in this study and the trials were taken from the machine learning repository at the University of California, Irvine (UCI). We employ two popular feature selection techniques- Chi-Square, and Recursive Feature Elimination, and a dimensionality reduction technique Linear Discriminant Analysis (LDA), and to choose the best features from the dataset for experiments. After selecting the most suitable features, they were then used to train and test the machine learning models: Multi-Layer Perceptron (MLP), Gradient Boost (GB), and Recurrent Neural Network (RNN). Evaluation matrices, accuracy, precision, recall, and F1-score were used to assess models\u27 performance. The experimental results show that the machine learning model Gradient Boost (GB) outperformed the other models and yielded an accuracy of 99%, indicating its ability to classify the Thyroid Disease (TD) accurately. The proposed research work helps to create an intelligent decision-support system for medical diagnostics by offering an understandable and reliable framework for Thyroid Detection
Understanding the Role Of Emotional Intelligence in Agile Teams in Context of Requirement Change Management
Full text linkRequirement changes are inevitable in Agile software development, wherein flexibility is the key. Although Scrum offers defined change management procedures, but they tend to overlook the emotional aspects involved in Requirement Change Management (RCM) success. This systematic literature survey investigates the application of Emotional Intelligence (EI) in Agile RCM, drawing conclusions from 27 studies. Results emphasize recurring issues like ineffective change planning, uncertain prioritization, inadequate stakeholder involvement, resistance to changes, and affective barriers in the form of fear, lack of trust, and low motivation. The review finds shortcomings in disciplined RCM practices, role-based EI integration, and alignment for performance. To fill these gaps, the research recommends a role-centric RCM framework incorporating EI concepts to enhance communication, trust, and flexibility with Agile adaptability
Maximum Value Attribute based Decision Tree and Random Forest for COVID-19 Prediction
Full text linkThe COVID-19 pandemic emerged as one of the most disruptive global health crises of the century, affecting social and economic systems worldwide. The rapid rise in infections placed immense pressure on healthcare infrastructures, demanding fast and reliable diagnostic tools. In recent years, Machine Learning (ML) has gained considerable importance in the medical field, supporting the diagnosis of conditions such as heart failure, pneumonia, dengue, breast cancer, and diabetes. In a similar way, clinical symptoms related to COVID-19 can be utilized to support early prediction, helping limit transmission. Although ensemble learning techniques such as Decision Trees and Random Forests have shown strong predictive performance for COVID-19, they often require more time and a larger number of iterations, which can be challenging when rapid detection is needed.
This study focuses on improving the efficiency of COVID-19 prediction by integrating Rough Set Theory (RST) through the Maximum Value Attribute (MVA) approach with classical Decision Tree (DT) and Random Forest (RF) models. The objective is to reduce computation time and iterations while maintaining reliable diagnostic accuracy. The proposed method classifies patients as COVID-19 positive or negative based on eight key clinical symptoms. A dataset containing clinical records of 136,294 patients, collected from an open-source GitHub repository, was used for evaluation. Four models—DT, RF, MVA-DT, and MVA-RF—were implemented in Python using Jupyter Notebook. Standard evaluation metrics were applied to assess performance. Overall, the MVA-DT model achieved the most efficient execution, while the MVA-RF model demonstrated strong predictive capability with an accuracy of 95.82%, precision of 81.90%, recall of 59.28%, and an F1 score of 68.77%.
 
Microbial-Plant-Biochar System for the Removal of Pollutants from Effluents and Contaminated Soil
Full text linkIndustrial activities released wastewater containing heavy metals and synthetic dyes that remain in the environment for a longer period. These pollutants disrupt ecosystems, pose risks to human health, and continue to accumulate if they are not treated properly. Many conventional remediation methods are costly and often generate secondary waste, which limits their practical use. As a result, researchers are increasingly exploring sustainable and environmentally friendly alternatives. This review discusses an integrated-microbial-biochar system as a promising approach for wastewater and soil remediation. Biochar produced from materials such as sewage sludge, oil-field drilling mud, and agricultural residues offers a highly porous and chemically active surface that can effectively bind heavy metals (Cd, Cu, and Zn) as well as a wide range of industrial dyes. In addition, microbial strains such as Acinetobacter sp. and Bacillus subtilis play an important role in degrading organic pollutants, restoring enzymatic activity and contaminated soils, and improving nutrient cycling. Recent developments, including biochar-microbe beads and composite bioreactor systems, have shown better performance than biochar or microbes used alone. These combined systems enhance microbial survival, reduce toxicity, and significantly improve pollutant removal efficiency. By summarizing recent findings on pyrolysis conditions, microbial immobilization techniques, and pollutant removal behavior, this review highlights the potential of hybrid remediation strategies. Emerging modifications, such as magnetic chitosan-modified biochar, are also discuss future directions to further strengthen integrated remediation systems.
Trust Management for the LPWAN Devices in a Smart City
Full text linkThe Internet of Things (IoT) is used in several domains like health care, transportation, military, banking, and many more. These applications can lead to the realization of a smart city application. Recently, Low Power Wide Area Networks (LPWAN) have been getting attention to implement various IoT applications. However, LPWAN devices are deployed in an environment where they can face malicious cyber-attacks leading to compromised data. To make successful network communication, security is an important factor that must be taken into consideration. Previously, many solutions involving sophisticated data encryption and machine learning techniques have been proposed for this purpose. However, they require processing power which is mostly not available in the LPWAN devices. Here, we can apply lightweight trust management techniques to find the reliability of a node. In this article, we propose a trust management framework for securing LPWAN-based Smart City applications. Multiple Smart City case studies are considered for evaluating the proposed technique and results show better intruder detection
Photocatalytic Degradation of Deltamethrin in Drinking Water Under Visible Light by Using Zno and Tio2
Full text linkThe use of deltamethrin is increasing due to its high demand in agriculture. However, it is toxic to both surface and groundwater. Agriculture plays a crucial role in the economy of any major nation. This study aims to enhance pesticide degradation by using specially designed catalysts optimized for visible light exposure. The key innovation lies in the customized catalyst design, which improves photocatalytic efficiency while offering a cost-effective and environmentally friendly approach. Various factors affecting degradation, including adsorbent quantity, pH, contact time, and initial concentration, were analyzed. The reactor consists of a 6-watt (380 nm) visible light lamp and a stirrer to ensure uniform mixing of the sample. Photocatalysts ZnO and TiO₂, in concentrations ranging from 0.1 to 3.0 g/L, were used to generate oxidizing agents. Under visible light, the impact of these factors on the degradation of different pesticide solutions was examined. The optimal doses were found to be 1.5 g/L for ZnO and 0.1 g/L for TiO₂. ZnO achieved a degradation rate of 96.3%, while TiO₂ slightly outperformed it with a rate of 96.34%. The study also investigated the effect of pH variations on deltamethrin degradation, revealing stronger degradation in alkaline conditions. Additionally, TiO₂ effectively reduced the COD value, demonstrating its superior efficiency in pesticide breakdown
Investigate the Operating Temperature Effect on Fast Pyrolysis Products of Food Waste with Hydrogen
Full text linkEnergy crises and environmental pollution are the main issues of concern all over the world and the disposal of wastes by converting into gaseous products can reduce this to a level. Investigating how operating temperature affects the yield and makeup of bio-oil, bio-char, and bio-gas during the pyrolysis process in the presence of hydrogen is the goal of this study. By offering a novel method for enhancing the quality and yield of gaseous products through controlled thermal decomposition in a hydrogen-enriched environment, the findings improve sustainable technologies. In this research, the fast pyrolysis of food waste carried out by using a lab scale fixed bed reactor in the presence of different composition of Nitrogen and Hydrogen to investigate the effect of operating parameters high pyrolysis temperature 600, 650, 700, 750 and 800 °C and hydrogen gas 0 %, 10 % and 20 % with Nitrogen as a carrier gas. The gaseous products maximum yield i.e. 45.68 comes out at 750 °C temperature in the presence of 10 % hydrogen. The results indicate that increasing the pyrolysis temperature boosts decomposition reactions, encouraging the formation of gaseous products. Hydrogen plays a crucial role by facilitating cracking and stabilizing the reaction intermediates, minimizing the formation of heavier components. The results demonstrate that the fast pyrolysis of food waste give residue at high temperature and in the presence of hydrogen up to 10 % achieved a maximum the bio gas yield. Energy crises and environmental pollution are major global concerns. Converting waste into gaseous products can help address these issues. This study examines how operating temperature influences the yield and composition of bio-oil, bio-char, and bio-gas during pyrolysis in a hydrogen-rich environment. By introducing a novel approach to enhance the quality and yield of gaseous products through controlled thermal decomposition, the findings contribute to sustainable technologies. The research involves fast pyrolysis of food waste using a lab-scale fixed-bed reactor, with varying nitrogen and hydrogen compositions. The effects of different operating parameters were analyzed, including high pyrolysis temperatures (600, 650, 700, 750, and 800 °C) and hydrogen concentrations (0%, 10%, and 20%), with nitrogen as the carrier gas.
The highest gas yield (45.68%) was achieved at 750 °C in the presence of 10% hydrogen. The results show that increasing pyrolysis temperature enhances decomposition reactions, leading to higher gas production. Hydrogen plays a key role by promoting cracking reactions and stabilizing reaction intermediates, reducing the formation of heavier byproducts. The study demonstrates that fast pyrolysis of food waste at high temperatures, with up to 10% hydrogen, results in the highest bio-gas yield