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
ENERGY EFFICIENCY ASSESMENT OF CENTRIFUGAL GAS COMPRESSOR
­Centrifugal gas compressors are critical for energy consumption in petrochemical industry. However, continuous operations under varying load due to supply and demand mismatch cause energy performance inefficiency. Existing approaches lacks integration of operational data to assess actual energy efficiency of the centrifugal gas compressor. Therefore, this study monitors a real time performance parameter to assess energy efficiency of the centrifugal gas compressor (CC). Furthermore, energy consumption & load in terms of shaft speed as function of capacity were used as a key indicator to verify the actual operation and energy consumption of CC. To determine the condition and operation of centrifugal compressors, a detailed performance analysis was conducted. However, it was observed that drop in performance was due to the internal component degradation, mechanical wear, and fouling, resulted higher energy consumption, reduce reliability, costly unplanned shutdowns. The results showed that the lower and upper limits for compressing production gas for stage 1 are 71.52 and 88.20MMSCFD, which consuming power 4772.89 and 5239.54 kW respectively. Moreover, for stage 2 the lower and upper limits for compressing production gas are 181.3 and 196.9 MMSCFD, consuming power with 9813.10 and 10995.70 kW respectively. Moreover, results showed that on most working days, the energy consumption is less 1995.60 kWh/MMSCFD in the First stage due to the production of low-pressure gas and it will increase in the second stage up to 5175.89 kWh/MMSCFD due to the production of high-pressure gas and performance drop.
MMSCFD (Million standard cubic gas per day)
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DESIGN & RELIABILITY OF SOLAR PV SYSTEM AS SUSTAINABLE ENERGY SOURCE FOR THE UNIVERSITY OF LARKANO
Pakistan is facing an acute energy crisis due to the increasing energy demand in various sectors, and the load shedding of electricity has become a prevalent concern. Therefore, this study was conducted at the University of Larkano, Sindh, Pakistan, to investigate and propose a solar photovoltaic (PV) system for supplying electricity to fulfill the energy needs during load shedding while also lowering the operating cost of installed diesel generators that are used for backup during load shedding. Likewise, this study aimed to ascertain the reliability of a solar PV system in terms of shading risk to examine losses of PV modules, saving of carbon dioxide (CO2) emissions, actual energy production during the lifespan, payback period, levelized cost of energy (LCOE), solar PV system life cycle cost, and average unit cost. PVsyst software version 6.6.3 was employed for the design and reliability of the proposed photovoltaic (PV) system. The total energy requirement of the light appliances for replacement with solar PV system was 717.94 kWh/day. Moreover, the results showed that all the buildings of the engineering campus required a 178.8kWp PV system, with 596 modules that will produce 8573.25MWh during a 30 yrs lifespan. Similarly, when all types of energy losses were considered, the total energy supplied to the end user was obtained 201.32MWh/yr., with an average unit cost of 0.06 USD$/kWh and a performance ratio of 55.51%, which is satisfactory. In addition, the proposed solar PV system electrification will save approximately 2,968.9 tons of carbon dioxide CO2 throughout its lifetime, with a payback period of 8.5 yrs., LCOE of 0.032 US$/kWh, life cycle cost (LCC) of 275,191 US$. Likewise, electricity purchased from SEPCO is 2.5 times costly than the suggested solar PV system unit cost, while electricity generated from diesel generator sets is 3.33 times more expensive.
DEVELOPMENT OF AN IOT-BASED REAL-TIME PATIENT HEALTH MONITORING SYSTEM
Accurate and timely monitoring of cardiac patients can be difficult, especially in remote or marginalized communities. This study proposes a remote real-time cardiac monitoring system that leverages Internet of Things (IoT) technologies to overcome these challenges. It combines wireless communication and IoT technologies to allow seamless transfer of vital Electrocardiographic (ECG) data, facilitating timely monitoring and intervention. The system also features a smartphone app, making it easier for clinicians to access and monitor patients' data remotely. Accurate placement of electrodes ensures precise ECG data, offering insights into the patient's heart activity. Additionally, the system's robust data storage features securely maintain and manage patient data, allowing for long-term monitoring and analysis for a holistic view of the patient's condition and informed treatment strategies. With its cutting-edge features, this remote real-time cardiac monitoring system transforms the monitoring and management of cardiac patients, offering a convenient, accurate and holistic approach to patient data management for improved healthcare.
Copyright (c) 2025 https://paas-pk.org/index.php/pjosr/cr https://creativecommons.org/licenses/by-sa/4.0
Assessing Hydroclimatic Drivers of Groundwater Fluctuations through IoT-based Real-Time Monitoring
To better understand recharge processes, groundwater depletion in semi-arid regions experiencing increasing development needs ongoing monitoring. This study examines the role of hydroclimatic factors in regulating groundwater level variations on a short timescale and assesses the efficacy of a low-cost Internet of Things (IoT) network for groundwater level monitoring in a managed aquifer recharge (MAR) well in Islamabad, Pakistan. Strong agreement (R2 = 0.998) was found when the system was calibrated and validated in the lab against a commercial water level data recorder. In addition to satellite-based meteorological data, daily data from December 2023 to April 2024 contained temperature, total dissolved solids (TDS), and groundwater level. The analysis of the data reveals that atmospheric demand primarily regulates the groundwater system, particularly during the dry season. This is demonstrated by the positive correlations between groundwater level and air temperature (r = 0.68) and ET0 (r = 0.70), whereas precipitation is a response with a lag. Simplified groundwater balance models are inadequate, as evidenced by the weekly water balance's only 2% variability. The need for thorough water quality monitoring is highlighted by the high total dissolved solids (TDS) concentration values (2,300–3,900 mg/L). The study proves the effectiveness of IoT-based systems for real-time MAR performance evaluation in data-poor urban areas.
Groundwater monitoring; Managed aquifer recharge (MAR); Internet of Things; Precipitation; Evapotranspiration; Urban groundwater; TDS; Water table fluctuation
A Robust Adaptive Temporal Attention Mixture Network for Multi-Object Tracking and Segmentation
Traditional video object tracking and segmentation methods including Gaussian Mixture Models (GMM) and fuzzy morphological filtering produce unstable results when operating under conditions of changing illumination, camera movement and object blocking. The current deep learning and attention-based methods have enhanced temporal stability but most existing pipelines continue to operate in separate stages for segmentation and denoising and tracking which results in progressive error accumulation and decreased real-world performance consistency. The research introduces ATAM-Net (Adaptive Temporal Attention Mixture Network) as an end-to-end framework which unifies adaptive Gaussian mixture modeling with temporal attention-based denoising and differentiable object association into one system. The ATAM-Net system learns particular illumination parameters for each pixel through its adaptation process and temporal attention minimizes frame-to-frame noise and appearance-aware embeddings maintain identity during fast motion and occlusion. The SportsMOT dataset serves as the exclusive testing ground for ATAM-Net to prove its ability to produce stable visual segmentation and smooth temporal coherence and precise multi-player tracking in active sports environments. The qualitative visual results show clear object edges and minimal flicker. These results indicate that ATAM-Net provides high-motion environments such as sports analytics, reliable and interpretable approach for multi-object tracking and segmentation in complex and real-time video understanding.
An Efficient Machine Learning Framework for Roman Urdu Sentiment Analysis Using Hybrid Word–Character TF-IDF Features
The problem this study focuses on is sentiment analysis on Roman Urdu text due to its low resource status coupled with high informality and inconsistencies associated with the language. Specifically, Roman Urdu lacks standardized spellings and exhibits significant use of code mixing with English. Therefore, the main goal of the study is developing a computationally affordable machine learning model for performing sentiment analysis on Roman Urdu text. The research suggests applying a hybrid strategy of feature extraction based on term frequency-inverse document frequency (TF-IDF) word-level and character-level vectors to overcome issues related to spelling inconsistency and capture semantic context simultaneously. For the purpose of building different classifiers, the data set with more than 22,000 Roman Urdu reviews was employed. Four classifiers were selected for analysis – logistic regression, linear support vector machine, naïve Bayes, and random forest. Besides, a hard voting ensemble classifier was built to utilize advantages of individual classifiers. The results revealed that logistic regression had the highest individual accuracy (78.40%) whereas the ensemble classifier had the best overall result (79.19%).
Term Frequency-Inverse Document Frequency (TF-IDF)
Wastes Generated during Requirement Engineering in AI Era
Requirement Engineering is a core software engineering activity that acts as the foundation of quality software. In this modern AI Era, it becomes increasingly critical and is prone to generating various forms of waste. For instance, partially cognitive load, analysis paralysis, partially done work, and gold plating. It can impact not only the requirement engineer’s mental well-being but also decrease client satisfaction. Waste identification and elimination have been explored in the literature; however, there is a gap in identifying waste generated during requirement engineering. There is a dire need to identify waste generated during RE in this modern AI era. To achieve this, the current study presents a systematic literature review protocol to identify waste generated during RE. This study will be beneficial to requirements engineers in managing the identified wastes and improving their productivity. It will also provide research opportunities for researchers to provide solutions to the identified waste. The researchers can extend their research to other software engineering activities that have not yet been explored.
Distributed Machine Learning with Spark MLlib: A Comprehensive Performance Evaluation
The growing demand for big data processing and distributed machine learning is necessary to apply predictive models to millions, or even billions, of records. Apache Spark MLlib is one of the most well-known Spark libraries, providing machine learning algorithms for processing large-scale data both in memory and in a distributed manner. Researchers have explored the distributed and in-memory processing capabilities of the Spark framework, little research has been conducted regarding empirical comparison of predictive performance and system efficiency using the Spark framework. These pipelines, classified as classification, regression, and clustering, are implemented in Spark MLlib. The experimental datasets consist of 540,000 to 12 M records with unified feature encoding and normalisation, and partitioning techniques. The evaluation metrics of the developed models include F1-Score, accuracy, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Silhouette Score, Time spent on training, Memory Usage, and Computational Speedup. The study results show that the ensemble models (Random Forest and Gradient-Boosted Trees) outperform the others in classification. Linear Regression has lower regression errors, K-Means clustering is stable across different data sizes, and distributed computation speeds up computation while also providing better accuracy when applied to more complex models. These results provide insights into design decisions for both algorithms and partitioning strategies that can improve performance and accuracy in the production of Spark MLlib workflows.
From Evidence to Practice: Quantitative Synthesis and Thematic Analysis of Inclusive, Accessible, and Disability-Sensitive Urban Planning
Rapid urbanization and the rising rates of disability in all parts of the globe have enhanced the necessity of inclusive, accessible and disability sensitive urban planning. Although international frameworks, including the UN Convention on the Rights of Persons with Disabilities (CRPD) and the Sustainable Development Goal 11, have normative commitments to accessibility, their application in city contexts is uneven. The paper aims to synthesize quantitative findings and qualitative data to guide practical action in urban planning to ensure equity and the involvement of people with disabilities. It involved a systematic review using PRISMA, which combined the meta- summary measures to combine the quantitative data and thematic analysis to combine the qualitative evidence of the empirical studies published between 2019 and 2026. The results have shown increased accessibility and mobility of infrastructure, which are not evenly distributed in the context of urban settings. Chronic obstacles involve disjointed governance systems, inadequate participatory action, and intersectional issues of individuals with mixed or concealed disabilities. Based on the synthesis, the proposed paper advocates an evidence-to-practice model that can guide planners and policymakers in integrating the concepts of inclusive design, improving participation-based planning, and creating monitoring systems to guarantee equitable access. The framework highlights the focus on integrating the technical, social and governance aspects as a key to developing urban places that are not only accessible but also socially right.
Transparent Prediction of Diabetic Retinopathy Using Machine Learning and Fuzzy Logic
Diabetic retinopathy (DR) is a major cause of preventable blindness, and with its global prevalence rising rapidly, automated grading of fundus images has become an integral part of screening programs to enhance timeliness and coverage. However, the clinical implementation of machine learning (ML) and deep learning (DL) systems is often limited by insufficient transparency, as it is crucial for ophthalmologists and healthcare stakeholders to have an understandable rationale linked to retinal findings before they can trust algorithmic predictions in safety-critical situations. The objective of the present review is to summarize and organize the state of the art in transparent prediction of DR from fundus photographs, pursuing two complementary pathways: (i) explainable artificial intelligence (XAI) applied to ML/DL models, and (ii) fuzzy logic (including fuzzy inference and hybrid neuro-fuzzy schemes) enabling rule-based linguistic decision-making. The novelty of this survey lies in bringing together these different strands under a common transparency umbrella and deriving a practical taxonomy for fundus-based DR detection/grading. A systematic literature review methodology based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was adopted. Relevant studies were obtained from major scholarly databases, screened using predefined inclusion/exclusion criteria (fundus modality, DR grading/detection focus, transparency via XAI or fuzzy rules), and synthesized using qualitative data extraction fields covering dataset usage, modeling choices, explainability mechanisms, and reported results. The reviewed evidence suggests a prevailing tendency towards post-hoc explanations for high-performing DL models (e.g., heatmap and attribution-based methods), alongside lesion-centered and rule-extraction strategies that align better with clinical reasoning. Fuzzy and hybrid fuzzy-DL approaches offer inherently interpretable rule bases but face challenges in feature design, scalability, and standardized benchmarking. Key gaps include inconsistent reporting, limited clinical validation, and inadequate integration of domain knowledge into transparent predictive pipelines.
Attention-Guided Face Mask Classification Using YOLOv8 with CBAM on MaskedFace-Net Dataset
The COVID-19 pandemic has highlighted the critical role of face mask classification in safeguarding public health. While existing computer vision approaches primarily focus on binary mask detection, limited attention has been given to fine-grained multi-class face mask classification, which is more representative of real-world scenarios. In this work, we propose a robust and efficient YOLOv8-CBAM based deep learning framework for classifying three mask-wearing conditions: with mask, without mask, and incorrectly worn mask. Experiments on the MaskedFace-Net dataset demonstrate that the proposed YOLOv8-CBAM model achieves a macro F1-score of 99.71%, with ablation and comparative studies confirming the effectiveness of attention mechanisms, enabling accurate real-time face mask classification for surveillance and edge applications.
Multi-Scale Dual-Stream CNN–Mamba Hybrid Framework for Accurate Nuclei Segmentation in H&E Images
Accurate nuclei segmentation in H&E-stained histopathology images is critical for quantitative tissue analysis and downstream computational pathology tasks. We propose MS-DS Mamba-Seg, a multi-scale dual-stream framework that integrates CNN-based local feature extraction with Mamba-based global context modeling to enhance nuclei segmentation. The Local Stream captures fine-grained morphological details such as nuclear boundaries and texture, while the Global Stream models long-range spatial dependencies across tissue regions. A novel Fusion Gate adaptively combines these complementary features, producing high-resolution and morphologically precise segmentation masks. Evaluated on the NuInsSeg dataset, MS-DS Mamba-Seg achieves a Dice score of 95.41%, outperforming baseline methods including Attention U-Net, UNETR, and SegMamba, while maintaining structural consistency and boundary accuracy. These results demonstrate the framework’s effectiveness in leveraging complementary local and global features for robust nuclei segmentation.
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.
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.
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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.