Pakistan Journal of Scientific Research (PJOSR) is a peer-reviewed, scientific and technical journal owned and published by the Pakistan Association for the Advancement of Science, Lahore, Pakistan. PJOSR publishes high-quality original scientific articles dealing with the use of analytic and quantitative tools for modelling, analysis, design and engineering management in Engineering and Technology.
Pakistan Association for the Advancement of Science
INTEGRATED GEOTECHNICAL AND MINERALOGICAL CHARACTERIZATION OF INDIGENOUS ROCKS FOR SUSTAINABLE CONSTRUCTION MATERIALS IN THE NORTH WESTREN HIMALAYAS — DISTRICT NEELUM , AZAD JAMMU & KASHMIR, PAKISTAN
In the construction industry, the quality of raw materials like aggregates is of paramount importance. The use of non-durable or weak materials in a project's foundation can lead to structural failures, posing significant risks to both property and human safety. Geotechnical laboratory testing is essential to determine the strength and durability of these materials. Standard tests include Uniaxial Compressive Strength (UCS), Los Angeles Abrasion Value (LAAV), Aggregate Impact Value (AIV), Ultrasonic Pulse Velocity (UPV), specific gravity, water absorption, unit weight, and Schmidt hammer rebound tests. While these tests are crucial for assessing mechanical properties, it is equally important to consider the rock's mineralogical composition. A rock may exhibit high strength, but the presence of certain reactive minerals can initiate deleterious reactions over time. This study evaluated rocks from the Tithwal to Kel area in the Neelum District of Azad Kashmir for their suitability as construction material. The Uniaxial Compressive Strength values range from 52.66 MPa to 121.20 MPa. The Aggregate Impact Value ranges from 7.33% to 22.44%. The Los Angeles Abrasion Value ranges from 22.3% to 37.2%. Schmidt Hammer rebound values range from 40.33 to 50.33 Nm. Unit Weight values range from 1.73 to 2.48. Ultrasonic Pulse Velocity values range from 22.56 to 47.07 Hz. Specific Gravity values range from 2.45 to 5.82.The results indicated that, from a geotechnical perspective, all sampled rocks met the necessary strength criteria. However, it is recommended that some of these rocks be used with caution due to the presence of quartz minerals, which can participate in Alkali-Silica Reaction (ASR) when in contact with cement, potentially causing long-term durability issues.
Copyright (c) 2025 https://paas-pk.org/index.php/pjosr/cr https://creativecommons.org/licenses/by-sa/4.0
Inclusive and Accessible HRM Strategies for Talent Retention in the Socio-Technical Era: Insights from AI, Remote Work, and Urban Infrastructure
The objective of the study is to examine inclusive and accessible HRM strategies for talent retention in the socio-technical age that include AI, remote working practices, urban infrastructure and more. Organizations are not able to increase their productivity as the HRM approach mentioned above is not useful in today’s world. Through qualitative design, the data were collected by means of interviews, focus groups, and secondary sources. The thematic analysis through NVivo generated numerous views, as well as autonomy, trust, fairness, and digital-fatigue. Cross-case analysis revealed that transparent AI particularly governance, flexible working arrangements, and equitable access to digital significantly impact employee commitment. Urban infrastructure like digital connectivity and remote work hubs moderated retention outcomes by affecting accessibility and inclusiveness. The findings revealed that retention is determined less by monetary compensation and more by the overall experience that combines technology systems human needs. The study adds to the theory by extending socio-technical perspective in HRM and Practical contribution would be useful to HR professionals and policy makers. Integrating inclusivity, accessibility and trust into HRM strategies will lead to sustainable retention in the digitally mediated and urbanized workplaces.
Urban Infrastructure and Workforce Accessibility
EXPLAINABLE ENSEMBLE MACHINE LEARNING FRAMEWORK FOR WATER QUALITY ASSESSMENT USING PHYSICOCHEMICAL INDICATORS
Water quality monitoring is one of crucial activity for sustainable water resource management and global environmental protection. The conventional assessment techniques mostly rely on the threshold value based analysis of physicochemical data. Such methods may fail to capture complex and nonlinear relationships in the water quality parameters. In our study, a machine learning–based framework has proposed for classification of water quality by using physicochemical parameters. The parameters in dataset includes pH, hardness, solids, conductivity, organic carbon, and turbidity. The proposed methodology for this work includes an enhance data preprocessing strategy for imputation of adaptive missing-values using Mean, KNN and Iterative (MICE). Then evaluation of multiple ML models, logistic regression, support vector machines, random forests, and gradient boosting were performed for optimization of model. The stacking ensemble model combining heterogeneous base learners is developed for the enhancement of model classification and its performance. The efficiency of Model is assessed on evaluation metrics, including accuracies, precision, recall, F1-score, and ROC curves. The results demonstrated an enhanced performance for ensemble-based models compared to individual classifiers. Moreover, explainable artificial intelligence based on Shapley Additive Explanations, known as SHAP, have been adopted to interpret model’s prediction. The results show the effectiveness of ensemble machine learning and explainable AI for robust and interpretable water quality assessment. This can be useful offering for a data-driven based decision-support framework for environmental monitoring applications.
SOFTWARE DEFECT PREDICTION USING MACHINE LEARNING TECHNIQUES
Software flaws can result in large financial losses, decreased user’ happiness, and a general decrease in the dependability of software systems. Therefore, early in the software development lifecycle defect, detection and mitigation are critical. This paper provides an overview of machine learning-based software defect prediction. Exploring the use of machine learning algorithms to forecast and categorize software problems based on historical data and other software metrics is the goal. In addition to discussing the difficulties in defect prediction, this study provides a thorough analysis of the most recent machine learning methods used to this problem. Additionally, it sheds light on the feature selection, evaluation criteria, and data preprocessing methods frequently used in software defect prediction studies. The article concludes with a comparison of various machines learning methods and how well they perform in forecasting software flaws, highlighting the advantages and disadvantages of each method. The results of this study help to advance our understanding of software defect prediction using machine learning and give researchers and practitioner’s advice on how to select the best tools for their individual needs.
ELECTROMAGNETIC ANALYSIS OF THE SKEWED IPM MOTOR USING MULTI-SLICED 2D FINITE ELEMENT METHOD
<span style="font-size: 10.0pt; mso-bidi-font-size: 12.0pt; font-family: 'Calibri','sans-serif'; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA; mso-bidi-font-weight: bold; mso-bidi-font-style: italic;">This research paper investigates the adoptability of the multi-sliced 2D finite element method (FEM) on the skewed interior permanent magnet (IPM) motor. Conventional IPM motors are without skewing, and analyzed by using the 2D FEM, however, skewed IPM motors require 3D FEM, thus huge computation time and memory are needed. The multi-sliced 2D FEM consumes less computational time compared to a traditional 3D FEM. Therefore, a multi-sliced 2D FEM is presented and adopted to analyze the skewed-stator IPM motor for the hybrid electric vehicle (HEV) application. To realize the computational accuracy of the multi-sliced 2D FEM, the back EMF of a conventional un-skewed IPM motor computed by a conventional 2D FEM is compared with the back EMF results of the multi-sliced 2D FEM. The electromagnetic characteristics, such as cogging torque, torque ripples, back EMF and electromagnetic torque of the skewed-stator IPM motor are computed using the multi-sliced 2D FEM, and results are discussed.
permanent magnet machines, skewing effect
ELECTROMAGNETIC ANALYSIS OF THE INTERIOR PERMANENT MAGNET MOTOR FOR THE HYBRID ELECTRIC VEHICLE
This research paper deals with the electromagnetic analysis of the interior permanent magnet (IPM) motor for the hybrid electric vehicle (HEV). Like Toyota prius III traction motor, a V-typed single-layered IPM motor is investigated. A two-dimensional (2D) finite element analysis (FEA) methodology is discussed and adopted to compute the electromagnetic characteristics of the IPM motor, at no load and rated load with constant speed conditions. At no load, the cogging torque and back EMF of the IPM motor are computed, whereas at rated load and constant speed, the electromagnetic torque, magnet and iron core losses, and input electrical and output mechanical powers are computed using the 2D FEA. The cogging torque periodic cycle is determined using the analytical approach, and the current control angle is determined for the subsequent control strategies for the IMP motor. The electromagnetic output results are discussed in detail to understand their implications on the performance and efficiency of the IPM motor for the HEV traction application.
OPTIMIZATION OF DEEP LEARNING BASED LIGHT WEIGHT SKIN DISEASE CLASSIFICATION MODEL USING TRANSFER LEARNING AND MODEL QUANTIZATION FOR RESOURCE-CONSTRAINED ENVIRONMENTS
Skin disease classification using dermoscopic images is one of challenging diagnostic process because of its visual similarities among various disease conditions, imbalance classes of diseased images, and the unavailability of annotated data. Many of Deep learning models have demonstrated encouraging detection performance. However, the most of Deep Learning Models requires high computational and memory requirements, that makes their deployment difficult in resource-constrained environments remains. In this study, we investigate an efficient convolutional neural network-based framework for multi-class skin disease classification using MobileNetV2. This technique is specially used for the modelling of systems for resource-constrained environments. The proposed technique will progressively enhance the performance of the model in three stages. The modelling will start from the baseline in initial stage and then considering data augmentation and unfreezing the layers. In middle stage partial fine-tuning, and class weighting to address class imbalance is considered for further improvement. Additionally, post-training quantization is used to reduce model size and enhance deployment potential with a little trade-off in performance. Experimental results show the improvements are moderately enhanced for performance macros across our three training stages. Quantization were achieved for a significant memory reduction while maintaining competitive performance of the model. This study highlights the suitability of the proposed approach for mobile based clinical applications
A HYBRID MODEL USING CLUSTERING AND REINFORCEMENT LEARNING FOR TEST CASE PRIORITIZATION IN AGILE ENVIRONMENTS
Agile development involves rapid and dynamic regression testing to ensure that it keeps pace with the frequent code changes and continuous integration. The traditional methods of test case prioritization (TCP) rely on static historical data and thus cannot handle noisy logs, changing test behavior, and inconsistent failure modes typical of Agile systems. In this paper, the authors describe a hybrid approach to prioritization based on the combination of DBSCAN clustering and Q-learning as the means of overcoming the two challenges, namely the quality of data and adaptability. DBSCAN groups similar test cases and removes noise and Q-learning is used to train an execution order in each cluster using a reward function balancing early fault detection and cost of execution. A mixed dataset of approximately 15,000 test executions of both Defects4J and real CI pipeline are used to test the model. Findings indicate that the given strategy attains an accuracy of 90.5 percent, APFD of 0.87, and a decrease in the overall testing time of 28 percent, which outperform the Random, Greedy, and DeepOrder baselines. It is lightweight and scalable with an easy deployment into CI/CD pipelines, which makes it highly applicable in the contemporary Agile testing process.
ARTIFICIAL NEURAL NETWORK MODEL FOR INTEGRATING ELECTRICAL RESISTIVITY AND PENETRATION RESISTANCE OF ALLUVIAL SOILS USING TENSOR FLOW-KERAS-DRIVEN BACKPROPAGATION FEEDFORWARD ANALYSIS & HYPERPARAMETER TUNING
This study presents an integrated approach combining Electrical Resistivity Survey (ERS) and Standard Penetration Test (SPT-N) data to improve subsurface characterization in alluvial soils, overcoming limitations of conventional drilling methods including logistical difficulties in mobilizing rotary drilling rigs measurement errors, high costs, and terrain constraints in difficult low-accessibility areas. A novel multivariate Artificial Neural Network (ANN) framework was developed using TensorFlow-Kera’s, implementing feedforward architecture with backpropagation learning. The model incorporates an innovative hyperparameter tuning protocol that systematically evaluates network depth (1-5 hidden layers) and complexity (2-10 neurons/hidden layer), identifying a 4-layer configuration provide optimal predictive accuracy (adjusted R2 = 0.99, RMSE = 6.36, MAPE = 1.1%, MSLE =0.01) with effectively balancing between both underfitting and overfitting tendencies. The finalized model transforms ERS and SPT-N inputs into predictive multivariate regression equations for key geotechnical parameters estimations & foundation design analyses by applying backpropagations feedforward analysis on well trained & tested modular weight-bias matrixes of each hidden layer. This methodology advances Sustainable Development Goal 9 (SDG 9) by enabling efficient, non-invasive subsurface investigations in challenging environments (floodplains, remote areas). Specifically, it addresses Target 9.1 (resilient infrastructure development) and Target 9.4 (sustainable industrialization) through its reduced reliance on conventional drilling, demonstrating how machine learning can enhance geotechnical practice while supporting sustainable infrastructure planning.
A DATA SCIENCE APPROACH TO APPLIED ECONOMICS: PREDICTING GDPGROWTH USING MACHINE LEARNING TECHNIQUES
The convergence of data science and applied economics has ushered in a transformative era for macroeconomic forecasting, particularly in predicting gross domestic product (GDP) growth—a cornerstone metric for assessing national economic vitality, guiding fiscal and monetary policy, and informing global investment strategies. This comprehensive research paper presents a rigorous, data-driven framework for forecasting annual GDP growth rates using advanced machine learning techniques applied to a hybrid panel dataset comprising six major economies: the United States, China, India, Germany, Brazil, and Japan, over the period 2000–2023. The dataset integrates realistic economic trends extracted from World Bank Development Indicators with carefully simulated data to address common empirical challenges such as missing observations, short time series, and the underrepresentation of extreme economic events. Realistic components are calibrated to historical averages—for instance, the United States exhibits a mean GDP growth of 2.5% with a standard deviation of 1.5%, while China averages 8.0% ± 2.5%. Simulated values are generated via multivariate normal distributions with country-specific parameters and overlaid with structural shocks mimicking the 2008 Global Financial Crisis (GDP drop of 4–6%, unemployment spike of 3– 5%) and the 2020 COVID-19 pandemic (GDP contraction of 5–8%, unemployment surge of 4–7%). Three machine learning models are rigorously evaluated: 1. Linear Regression – a classical econometric baseline grounded in ordinary least squares (OLS); 2. Random Forest Regression – an ensemble method leveraging bagging and feature randomness to capture nonlinear interactions; 3. Long Short-Term Memory (LSTM) Networks – a deep recurrent neural network designed to model temporal dependencies in sequential economic data. Predictive features include lagged GDP growth, inflation (CPI annual %), unemployment rate (% of labor force), and exports as % of GDP, selected based on established macroeconomic theory (e.g., Okun’s Law, Phillips Curve, export-led growth hypothesis). Empirical results demonstrate the random forest model’s superiority, achieving a Mean Absolute Error (MAE) of 1.85 and Root Mean Squared Error (RMSE) of 2.45 on the test set—representing a 37% improvement in MAE over linear regression (MAE: 2.95, RMSE: 3.82) and a 12% edge over LSTM (MAE: 2.10, RMSE: 2.68). Feature importance analysis reveals lagged GDP growth as the dominant predictor (importance: 0.52), followed by unemployment (0.21), inflation (0.15), and exports (0.12), reinforcing the autoregressive nature of economic momentum and the critical role of labor market conditions. The study’s contributions are threefold:  Methodological: Introduces a reproducible hybrid data construction pipeline for economic forecasting under data constraints.  Empirical: Provides cross-country comparative evidence of machine learning’s efficacy across developed and emerging markets.  Policy-Relevant: Offers actionable insights for real-time nowcasting and scenario-based policymaking. Limitations include reliance on simulated shocks, exclusion of fiscal policy variables, and the annual frequency of data. Future research should incorporate high-frequency indicators (e.g., PMI, satellite night lights), geopolitical risk indices, and hybrid neuro-econometric models. This work advances the field of econoinformatics, demonstrating that machine learning, when grounded in economic theory and robust data practices, can significantly enhance predictive accuracy and support evidence-based economic governance in an era of uncertainty.
Copyright (c) 2023 https://paas-pk.org/index.php/pjosr/cr https://creativecommons.org/licenses/by-sa/4.0
THE ECONOMICS OF DIGITAL SCIENCE: EVALUATING THE TECHNICAL ANDSTRATEGIC DETERMINANTS OF FIRM PERFORMANCE IN THE AI ERA
The rapid proliferation of artificial intelligence (AI) and digital technologies has ushered in a transformative era for firm competitiveness, where digital science—defined as the systematic integration of advanced computational tools, machine learning algorithms, data analytics, and digital infrastructure—serves as a foundational mechanism for value creation, operational efficiency, and strategic differentiation. Despite substantial investments in AI and digital platforms, empirical evidence reveals significant variance in performance outcomes across firms, suggesting that technological adoption alone is insufficient for sustained competitive advantage. This study addresses this gap by systematically evaluating the technical and strategic determinants of firm performance in the AI-driven digital economy. Grounded in the Resource-Based View (RBV), Knowledge-Based View (KBV), and Dynamic Capabilities Theory, the research develops a comprehensive conceptual framework that links core technical determinants—namely, AI capability, data infrastructure quality, and digitalization maturity—to firm performance through the mediating roles of strategic determinants, including digital transformation strategy, organizational agility, ecosystem participation, and innovation culture. The model posits that technical excellence establishes a foundation of digital science maturity, but superior performance emerges only when these capabilities are dynamically orchestrated through adaptive strategies and organizational learning mechanisms. Empirical validation is drawn from a synthesis of global studies spanning diverse contexts: ecommerce platforms in China, manufacturing firms in Taiwan, SMEs in emerging markets, and large enterprises in the United States. Key findings confirm that:  AI capability significantly enhances innovation and decision quality, with indirect effects mediated by creativity and strategic alignment;  High-quality data infrastructure reduces operational costs and enables scalable analytics, but its impact is contingent on robust governance and interoperability;  Digitalization (e.g., cloud computing, IoT) drives productivity gains, yet returns diminish without complementary organizational agility;  Strategic factors—particularly digital transformation strategy and innovation culture—amplify the performance impact of technical investments, while ecosystem participation extends market reach and accelerates learning. The study further introduces a hypothetical performance curve illustrating diminishing marginal returns to AI capability in the absence of strategic complements, underscoring the necessity of co-evolution between technical and strategic domains. A proposed integrated research model offers testable hypotheses for future empirical work, emphasizing mediation and moderation effects. Theoretical contributions include an extension of RBV and KBV into digital contexts by conceptualizing digital science as a higher-order dynamic capability, and the identification of interaction effects among technical, strategic, and environmental factors. Practical implications urge managers to prioritize strategic coherence alongside technological investment, while policymakers are encouraged to support digital infrastructure, workforce reskilling, and regulatory frameworks that foster ethical AI deployment and ecosystem collaboration. Keywords: Artificial Intelligence, Digital Science, Firm Performance, Resource-Based View, Organizational Agility, Digital Transformation, Data Quality, Strategic Determinants
Sentiment Analysis of Amazon Customer Reviews Using Machine Learning Models
Sentiment analysis of Amazon customer reviews has become more important in today's digital marketplace, where understanding user mood directly impacts business strategies, product improvements, and customer satisfaction. Millions of reviews are created by everyday manual analysis, and there is an increasing demand for appropriate, automatic, and accurate ML solutions. This study addresses this need by implementing and comparing five ML models, which are D.T., which has 82.34% accuracy, Random Forest, which has 89.53% accuracy, Logistic regression, which has 91.52% accuracy, AdaBoost has 83.43% accuracy and XGboost, with 90.1% accuracy, to classify reviews as positive or negative. For the imbalanced dataset, the SMOTE technique was applied to balance sentiments. To address uneven distribution in mood analysis, SMOTE was used in this study. These discoveries provide businesses with actionable insights to automate review analysis, identify customer complaints, and make data-driven choices to boost products and services. This aims to classify user feedback into positive or negative categories. We trained our models on a dataset of 30,847 Amazon customer reviews covering various products and genres. This study shows the scalability of ML in actual-world mood categorization tasks and adds to the expanding body of work on applications. We also discuss the importance of striking a balance between computational effectiveness and model interpretability, particularly for parts that rely on illegal insights from massive amounts of unstructured feedback.
AI Based Smart Energy Saving and Consumption Management for Industries
This paper introduces an intelligent energy management system (EMS) designed to enhance energy efficiency in office, academic, and industrial environments through real-time human presence detection and IoT-based monitoring. Unlike conventional motion sensors, the proposed system employs image processing and machine learning algorithms implemented on a Raspberry Pi platform to accurately and contactless identify occupants. Based on detected occupancy, the system dynamically controls connected electrical loads via Arduino-based relays, minimizing unnecessary power usage and improving operational efficiency. In parallel, a real-time three-phase monitoring module was developed using PZEM-004T sensors and ESP8266 microcontrollers to measure voltage, current, and power factor across industrial networks. The data is processed locally and uploaded to a cloud dashboard for visualization, fault detection, and maintenance planning. Experimental validation demonstrates that both systems operate reliably under varying load conditions and significantly reduce energy wastage. The proposed dual-module approach—combining intelligent occupancy-based control with IoT-enabled monitoring—provides a cost-effective, scalable, and practical solution for modern energy management. It not only improves efficiency and safety but also establishes a foundation for future integration with renewable energy systems, predictive analytics, and privacy-aware smart building architectures aimed at promoting sustainability in industrial and institutional settings.
BrightSpaces: Interactive 3D Learning for Children on the Spectrum
This research offers a specific 3d learning environment using the most recent technological hardware and software. The learning environment’s hardware includes a projector and a specially designed room structure, as well as a Kinect device for gesture recognition. The application Autism Spectrum was developed on Filter and Dart and incorporates activities for varying learning levels, such as matching and challenging puzzles. Integration strategies for software and hardware incorporate Kinect for gesture interaction, synergetic fluid learning, and sensor-based requirements. Children with autism offer great insights for the continuous evolution of the design and development of new learning tools. The research investigates how well environments enable the development of cognitive and social-emotional skills and how adaptive they are in learning styles. Addressing the challenges of technology supports the development of new initiatives. The autism study results emphasize research and development projects for new educational games, collaboration activities, and 3d learning environments for children with autism.
HybridSVG: Ensemble Framework for Detecting Spatially Variable Genes in Spatial Transcriptomics using Fusion of Global and Local Autocorrelation
Spatial transcriptomics enables the study of gene expression within intact tissue architecture, providing insights into cellular organization and tissue-specific molecular processes. Accurately identifying spatially variable genes is critical for understanding how gene expression patterns correspond to anatomical structures and functional domains. Here, we present HybridSVG, an ensemble framework that integrates global and local spatial autocorrelation measures to detect genes with diverse spatial expression patterns. We applied HybridSVG to publicly available mouse brain and human dorsolateral prefrontal cortex datasets generated using the 10x Genomics Visium platform. Our results demonstrate that HybridSVG captures both broad tissue-wide gradients and fine-grained spatial niches with greater precision than existing methods, including SpatialDE, scGCO, and SPARK-X. Genes identified by HybridSVG show stronger spatial autocorrelation and clearer local clustering, reflecting biologically meaningful structures such as cortical layers and subcortical domains. These findings highlight the ability of HybridSVG to robustly identify spatially variable genes across species and tissue types, providing a versatile tool for the analysis of complex spatial transcriptomics data.
Waste Generated in Distributed Data Processing Systems: Strategies and Future Directions
Distributed Data Processing (DDP) is integral to modern cloud and edge computing environments. Additionally, it serves as a fundamental component of big data analytics, particularly for real-time analytics. Various frameworks such as Spark FSpark, spark flow, and Storm are designed for effective distributed data processing. However, DDP generates significant waste in terms of energy consumption, resource allocation, and data transmission. Efficiently managing these wastes is crucial for improving system performance and minimizing environmental impact. This paper explores various waste and their reduction strategies employed in distributed systems, focusing on energy- efficient scheduling, network optimization, and resource management techniques. The result indicates various wastes such as energy waste, improper or inefficient utilization or distribution of computational resources, carbon footprint waste, inefficient execution time, storage of unnecessary or duplicate data, and inefficientdata transmission. Moreover, the study result also signifies that carbon- and energy-aware scheduling, task offloading, and data deduplication strategies show promising results in minimizing waste. The contribution of this paper lies in identifying waste, key approaches to waste reduction and offering insights into their practical applications in distributed data systems. The study will be useful for the researcher to extend the research by providing additional effective solutions or verifying the reported solutions in terms of waste minimization.
MASKYLO: Hybrid Deep Learning Framework for Detection and Segmentation of HE Stained Histology Image
The analysis of pathology images is a crucial step of modern healthcare that helps in diagnosing tumours, determining the grade and type of tumours, planning to treat them and performing surgeries. In this study, we present MASKYLO, a novel hybrid deep learning framework that integrates YOLOv11 for real-time nuclei detection and using YOLOv8 and Mask R-CNN for precise pixel-level segmentation. MASKYLO ensures the speed and accuracy of YOLOv11 for detecting regions of interest, while the fused segmentation networks refine mask predictions to achieve high spatial fidelity. We evaluate our approach on the NuInsSeg dataset, demonstrating superior performance compared to existing methods. Our YOLOv11 detection branch achieves a precision of 0.89, a recall of 0.85, and an mAP@50 of 0.88, whereas the segmentation branch attains an IoU of 0.82 and a Dice score of 0.85, surpassing previously reported YOLOv8 and Mask R-CNN benchmarks with a 0.96 Dice Score and an IoU on the same dataset. These results highlight MASKYLO’s ability to provide accurate, efficient, and reliable detection and segmentation of histopathological images, making it a promising tool for automated pathology workflows.
Ensemble-based Machine Learning Model for Online Fake Reviews Detection
Online shopping/e-commerce sites are usually unknown to most customers, and they do not know the seller or the goods and services. They will purchase and make any decisions without consulting the reviews done by customers. It does not matter whether they are true or not, but product reviews can play a huge role in the bottom line of a company. Some e-commerce sites have a section where one can confirm the authenticity of the seller, but the majority of buyers would rather read reviews done by real people who have bought the target product and used it. Due to the possible number of reviews regarding a particular product being hundreds or even thousands, it is dubious as to which ones are authentic. Machine learning (ML) has, in recent years, enabled machines to perform tricky tasks with close human levels of expertise. It is possible to find different ways. Conventional means of fake reviews are time-consuming and usually unproductive due to the vast number of reviews produced. Moreover, there is no accuracy or robustness. So, we require a powerful ML-based solution that will be able to automatically evaluate the reviews, distinguish between the authentic and fake ones and then, in a very small time period, choose the most valuable comments of others. To achieve this. In that regard, we propose a modern fake reviews detecting model using ML. The evolution of this study assumes the combination of baseline learning, deep learning and ensemble learning algorithms for fake reviews detection. Therefore, Naive Bayes, Random Forest, Decision Tree, SVM, and K-N Neighbour have been paired together to train and test our proposed model. The proposed model of voting consists of a strict pre-processing procedure and feature extraction. The functions that were carried out before preprocessing are tokenization, removal of stop words, punctuation, and even deletion of rare words. We availed the step of feature engineering, which enhances data prior to entering the next stage, which is the advanced bi-grams, whose name is the N-gram and TFIDF. We have done several experiments and compared the future model and the state-of-the-art models with reference to one another. The obtained data yields that our proposed model is superior to the received data regarding the Uni-Bi-Gram TFIDF-features and effectively classifies the reviews into two classes, real and fake, with 93Percent success precision.
A Comprehensive Review: Hyperspectral and Modern Spectroscopy Identification of Yogurt Adulteration
Yogurt is a valued dairy product known for its taste and texture, but these qualities also make it prone to adulteration. Common practices include adding starch, milk powders, vegetable oils, sweeteners, preservatives, and, in some cases, even harmful heavy metals, which can reduce quality and harm consumer health. Conventional analysis can be selective but is often destructive, labor-intensive, and slow for routine screening. The rapid, non-destructive alternatives provided by modern image-based spectroscopy can probe both composition and spatial heterogeneity in yogurt. This review summarizes the developments of hyperspectral imaging (HSI) and new spectroscopic methods of detecting yogurt adulterants, including Near-Infrared (NIR), Terahertz spectroscopy, Raman, Laser-Induced Breakdown Spectroscopy (LIBS), Nuclear Magnetic Resonance (NMR), vibrational mid-IR/FTIR, and UV-V are prominent methods. Spectroscopy gives robust chemical fingerprints (NIR/Raman/FTIR), and HSI gives space (LIBS/THz), as well as elemental and bulk properties; the combination of the two with ML enhances the precision and generalization. Key challenges include product variability, fermentation effects, calibration transfer, and the lack of publicly available datasets. To address these, future priorities should focus on developing portable instruments, applying multimodal data fusion and transfer learning, ensuring interpretable AI models, and establishing standardized protocols for routine, non-destructive yogurt authenticity testing.
Non-Destructive Quality Grading of Apples and Oranges using Hyperspectral Imaging and Machine Learning
Hyperspectral Imaging (HSI) offers a powerful non-destructive technique for fruit quality evaluation by simultaneously capturing both spatial and spectral information. In this research, apples and oranges were analyzed using a 224-band visible–near-infrared (400–1000 nm) imaging system with a controlled illumination setup and a standardized region-of-interest (ROI) extraction protocol. To enhance signature quality, preprocessing methods such as Savitzky–Golay smoothing and median filtering were applied, effectively reducing noise while retaining meaningful spectral characteristics. The dataset comprised 34,394 spectra (224 bands each), including 19,595 fresh and 14,799 spoiled samples. Class balance was preserved during training and testing to avoid bias. Several supervised learning algorithms—k-nearest neighbours (KNN), support vector machines (SVM), random forests (RF), decision trees (DT), linear discriminant analysis (LDA), and logistic regression (LR)—were employed to classify fruit freshness both within individual species and across a four-class scenario (apple-fresh, apple-spoiled, orange-fresh, orange-spoiled). Model performance was assessed using accuracy, precision, recall, and the F1-score. The top-performing classifiers achieved nearly 99% accuracy with balanced error rates across all categories. These findings demonstrate that the integration of HSI with supervised machine learning (ML) provides a reliable and automated solution for grading apples and oranges. The approach is highly applicable to real-time sorting lines, supporting improved quality assurance, food safety, and waste reduction. Furthermore, the methodology is adaptable to other agricultural commodities and can be optimized for industrial deployment through band selection and lightweight models that reduce computational requirements while maintaining classification accuracy.
Real-Time Crop Health Monitoring Using AI-Based Drone Surveillance and YOLOv12
Early crop disease detection remains challenging for precision agriculture. This research presents an AI-drone surveillance system using YOLOv12 deep learning model to automatically identify diseases for real-time monitoring in potato, banana, and cotton crops. The complete pipeline includes automated image acquisition, intelligent preprocessing, and real-time analysis. Compared to traditional manual inspection, this approach reduces diagnosis time from days to minutes while improving reliability. Key innovations include optimized model architectures for resource-limited environments and multi-spectral disease pattern recognition. Field tests confirm the system's robustness across varying weather conditions and growth stages. Proposed method processes the drone-captured images through Raspberry Pi edge computing, achieving 99.5%, 98.1%, and 89.7% detection accuracy of potato, banana, and cotton crops respectively. The lightweight YOLO-Nano variants enable efficient field deployment while maintaining precision. A merged dataset across 28 disease classes demonstrates 91.8% overall accuracy through comprehensive validation metrics. Farmers receive immediate alerts for targeted treatment, reducing pesticide use by 30-45% in trial implementations. This scalable solution outperforms existing methods in both speed (4.2ms per image) and accuracy. Results demonstrate practical potential for transforming global agricultural monitoring through accessible AI technology.
Nano Silica-Modified Asphalt: Enhancing Resistance to Rutting, Cracking, and Moisture Damage
Road infrastructure durability with extended lifespan through asphalt pavement determines its potential for sustainable development. The asphalt material experiences different types of damage throughout its existence. However, fatigue cracking is combined with rutting and moisture damage, which create major pavement failures that demand ongoing maintenance efforts. The exploration of Nano Silica (NS) nanomaterial modification of asphalt represents an emerging technique to bolster the mechanical and rheological capabilities. Nano Silica shows promise as an improving agent for asphalt binders because of its tiny particles and expansive surface area, which enhances stiffness and aging resistance while boosting rutting resistance. This study investigates the effect of Nano Silica on the physical properties of asphalt mixtures. Laboratory tests such as penetration and softening point while also evaluating ductility and Marshall Stability were conducted on traditional mixtures and mixtures that included NS modification. The evaluation of Asphalt performance enhancement required the addition of Nano Silica from 1% to 5% to identify the most effective amount. Lab results confirm that Nano Silica addition results in asphalt mixtures with higher stiffness levels, better thermal stability, and stronger permanent deformation resistance. Adding NS enhances the adhesive qualities between aggregate particles and asphalt binders, thus minimizing their susceptibility to moisture damage. Research determined the best NS addition level by weighing the mixture's performance outcomes against its ability to be easily worked. Research results confirm that Nano Silica is a successful modifier for improving asphalt pavement mechanical strength and service life capabilities. This research proves that nanotechnology investigations in pavement engineering can be valuable by examining asphalt mixture development for long-term usage in busy road networks.
Hair-Based Concrete Reinforcement: A Novel Approach to Sustainable and Cost-Effective Pavement Construction
The increasing demand for sustainable and cost-effective construction materials has led to innovative approaches in concrete reinforcement. This study investigates the impact of human hair fibre as a reinforcement material in concrete pavements, focusing on variations in mechanical properties such as compressive strength, flexural strength, skid resistance, and workability. Human hair, an abundant and biodegradable waste material, possesses high tensile strength comparable to that of copper wire of similar diameter. Its integration into concrete provides potential benefits in enhancing structural integrity while addressing environmental concerns associated with waste disposal. The research involved experimental testing of concrete specimens with different percentages of human hair fibre reinforcement, ranging from 0% to 3% by weight of cement. The findings indicate that the addition of hair fibres up to a certain threshold improves the flexural and compressive strength of concrete while enhancing skid resistance. However, excessive fibre content reduces workability and introduces challenges in uniform mixing. This study underscores the viability of human hair as an eco-friendly and cost-efficient reinforcement alternative in concrete pavement construction, contributing to sustainable engineering practices.
A Framework for Statistical Analysis of Student Engagement in OBE-based Classrooms Environment Using YOLO Model
The most important aspect of effective learning in an Outcome-Based Education (OBE) classroom environment is student involvement and attentiveness during the lecture. This study examines various student behaviours and evaluates the attention of students during lectures using deep learning techniques, YOLO v8. The classroom representative visuals were perceived from an online available dataset containing students’ images that have been recorded during the lecture. These photos are subjected to YOLO, which classifies different student activities into those that contribute positively or negatively to attention in the classroom. Positive markers of attention include actions like raising hands, concentrating on the front, reading, writing, and interacting with the teacher. On the other hand, distractions like eating, drinking, using a phone, or seeming drowsy have a detrimental impact. The results assist teachers in enhancing their teaching methodologies and offer insights into patterns of classroom involvement.
A Deep Learning Approach for Hand and Arm Bone Fracture Detection based on ResNet18 and EfficientNetB0 using X-rays images
Quick diagnosis and successful treatment depend on the accurate identification of bone fractures. Despite their value, traditional diagnostic techniques like MRI and CT scans are frequently costly, less accessible, and may not be able to identify small fractures. Critical clinical decisions may be delayed due to the time-consuming and human error-prone process of manual X-ray imagine assessment. This paper offers an automated deep learning-based method for detecting and classifying bone fractures utilising two advanced convolutional neural network architectures: ResNet18 and EfficientNetB0. The study's dataset includes X-ray images of hands and arms of both normal (4383 images) and fractured (4480 images) patients. To improve image quality and avoid overfitting, preprocessing and data augmentation approaches were applied. With an accuracy of 99%, precision ranging from 0.97 to 1.00, and an F1-score of 0.99, EfficientNetB0 outperformed the other evaluated model, demonstrating remarkable classification competencies. Reliability was also demonstrated by the ResNet18 model, which identified fracture patterns with excellent accuracy and robustness of 98%. These findings demonstrate how deep learning may be used to quickly and accurately automate fracture identification. Additionally, even on systems with restricted resources, EfficientNetB0's lightweight design makes it appropriate for real-time clinical applications. This method can help doctors identify fractures early and accurately, decrease errors, and greatly increase diagnostic efficiency.
DEEP LEARNING-DRIVEN WILDFIRE DETECTION: A HYBRID FRAMEWORK INTEGRATING MULTI-SOURCE DATA AND ENSEMBLE LEARNING
This paper presents a state-of-the-art framework for detection and prediction of forest fires with the use of advanced ensemble learning techniques in combination with CNNs. Traditional systems of forest-fire detection suffer from problems of poor accuracy, slow speeds, and scalability issues. On the contrary, this paper uses a hybrid approach which integrates DL and DIP specifically for handling multi-source datasets comprising satellite imagery, drone videos, and environmental variables like temperature, wind velocity, and humidity. The data preprocessing techniques that will be included for improving feature extraction effectiveness will involve color space conversion, image enhancement, and spatial segmentation. The CNN architecture had a number of convolutional and pooling layers that were set up to extract high-dimensional features pertinent to fire detection. Transfer learning using pre-trained models significantly improved performance even with limited labeled data. This was complemented by ensemble learning where the outputs of CNN models were combined with probabilistic models such as Random Forests and Gradient Boosting for enhancement of classification robustness and avoidance of overfitting. The system was trained on different datasets collected from the Kaggle repositories, satellite feeds, and field observations of the Margalla Hills. A k-fold cross-validation technique ensured the generalization of the model, and it achieved 92% detection accuracy with a precision of 89% and an F1-score of 90%. Complex scenarios such as smoke fog differentiation and small-scale fire hotspots in dense vegetation were successfully addressed. Experimental results show that the model has better adaptability in a variety of environmental conditions, and its real-world evaluation proves its validity. Predictive capabilities further enabled identification of fire-prone zones before ignition while triggering its utility in early intervention systems.
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INVESTIGATION ON THE MECHANICAL PROPERTIES OF CONCRETE WITH PARTIAL REPLACEMENT OF FINE AGGREGATE BY SAWDUST ASH FOR SUSTAINABLE CONSTRUCTION
The growing demand for sustainable construction materials has led to the exploration of alternative solutions to reduce environmental impact and conserve natural resources. This study investigates the feasibility of using sawdust ash (SDA) as a partial replacement for fine aggregate (sand) in concrete, focusing on its effects on compressive strength, tensile strength, and workability. Sawdust ash, a waste byproduct from wood processing industries, was incorporated into concrete mixes at varying replacement levels (0%, 5%, 10%, and 15% by weight of sand). The experimental program involved casting and testing concrete specimens at different curing periods (7, 14, 21, and 28 days) to evaluate mechanical performance. Results indicate that the inclusion of sawdust ash influences concrete properties in distinct ways. Compressive strength exhibited a gradual decline with increasing SDA content, with reductions of 2%, 7.36%, and 12% observed at 5%, 10%, and 15% replacement levels, respectively, after 28 days of curing. Conversely, tensile strength showed significant improvement, with increases of 13.04%, 22.36%, and 26.96% for the same replacement levels at 28 days. Workability, assessed via slump tests, decreased as SDA content increased, highlighting the need for mix adjustments in practical applications. The study concludes that while higher SDA content reduces compressive strength, it enhances tensile properties, making it suitable for applications requiring improved crack resistance. The optimal replacement level was identified as 5– 10% for balancing strength and sustainability. These findings contribute to the development of eco-friendly concrete by repurposing industrial waste, aligning with global sustainability goals. Further research is recommended to address long-term durability and large-scale implementation challenges.
Optimizing Malicious Website Detection Through Comparative Analysis of Machine Learning Techniques
The improvement of malware data exploitation risks, which appeared due to malicious websites, as well as an increase in their frequency, is results of modern threats. Modern methods for malicious website detection display a bad performance, producing multiple incorrect alarms, but fail to identify contemporary security threats correctly. More advanced malware website identification techniques are based on XGBoost systems combined with AdaBoost and Random Forest. The framework is composed of four phases: (1) Data Acquisition and Preliminary Analysis, utilizing a Kaggle dataset to discern key patterns; (2) Data Preprocessing and Model Implementation, which consists of data cleaning, normalization, and segmentation to train the model effectively; (3) Detection and Classification Evaluation, which computes performance metrics like precision, recall, F1-score, and accuracy; and (4) Comparative Analysis, where XGBoost outperforms traditional methods. The XGBoost model had a detection accuracy of 86.60% in its practice run since it generated less wrong outputs to show its capability in malware URL detection. Cybersecurity research needs machine learning in threat detection in order to eradicate human-based new threat evaluation processes and to demonstrate the need for sophisticated machine learning frameworks. The development of proven modern theoretical algorithms in malicious website detection should be researched upon because these algorithms show better effectiveness in research work.
OPTIMIZATION PRODUCTION FLOOR LAYOUT IN AN ELECTRONIC INDUSTRY USING THE PRODUCT LAYOUT APPROACH
This report aims to present a study on redesigning the production floor layout in an electronic appliance manufacturing company using a product layout approach. The study was initiated to improve the overall efficiency of the production process and reduce production costs. The report provides an in-depth analysis of the existing layout of the production floor, highlighting the key strengths and weaknesses of the current design. The study identifies the areas of the production floor that are causing bottlenecks and delays and recommends potential changes to the layout that could reduce these issues. The report describes the proposed layout design and its potential benefits, including improved flow of materials, reduced handling and transportation time, and increased production process efficiency. The study concludes with a discussion of the potential challenges that may arise while implementing the new layout and recommends strategies for overcoming them. Overall, this study is expected to result in significant improvements in production efficiency, reduced cycle times, and increased throughput, thereby enhancing the company's competitiveness in the market. This study found that batch size changes from 20% to 10%, and 33.3% of space is utilized for external cabinets.
Cultivating Innovation: An In-Depth Exploration of IoT and AI in Smart Agriculture
The integration of smart agriculture technologies, especially through the Internet of Things (IoT), machine learning (ML), and artificial intelligence (AI), has significantly transformed modern agriculture. Smart agriculture has enabled precision farming by optimizing irrigation, monitoring soil health, predicting yields, and efficiently managing resources. This article discusses recent advances in smart agriculture technologies, especially in rice production and drought forecasting. A systematic analysis of 08 research papers high lights the role of communication systems, predictive analytics, and deep learning in optimizing agricultural productivity. Results indicate that there are significant gaps in the assessment of socio-economic impacts, real-world field validation, and integration of multifunctional smart agriculture technologies. Furthermore, challenges such as high implementation costs, lack of farmer training, and limited access to the technology hinder widespread adoption. The paper concludes with recommendations for future research to improve the sustainability, efficiency, and adoption of smart agriculture worldwide.