International Journal on Engineering Technology
The International Journal on Engineering Technology (InJET) is a multidisciplinary journal that endeavors to publish innovative and state-of-art research in the field of engineering and technology. The primary objective of the journal is to provide platform for researchers to share their valuable insights and contribute to the advancement of engineering and technology.
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Design and Analysis of Mechanism and Driving system for Manual Rice Transplantation Machine
Novel design and fabrication of manual rice transplantation machine based on linkage dimension from Thomas to suit the cici4d varying terrains of Nepal and to initiate mechanization from the small land holders farmers are proposed in this study. The two row manual rice planter from Chandragiri Machiniery Industry, Satungal was taken as reference to modify transplanting mechanisms and driving systems to ease the rice plantation process. The optimum adjustable spacing of the rice seedlings is 20 to 25 cm hill to hill spacing and 10 cm row to row spacing. The depth of penetration of rice seedlings is also adjustable in this design. The power transmission for the driving mechanism is achieved through the traction power of the rice planting wheel. The capacity of rice transplanter is 270 sq. meters per hour which is equivalent to 5 number of manual hand rice transplanter workers. By cici4d using the proposed rice transplanter machine the total cost of hand rice transplantation can be reduced by approximately 75%.
An Intersection under Stress: Diagnosing Layout and Control Issues - A Case Study at Srijana Chowk, Pokhara
Srijana Chowk, a major intersection in Pokhara, Nepal, connects four critical roadways and experiences chronic congestion due to geometric and signal control deficiencies. This research aims to uncover the underlying issues affecting the intersection's performance, focusing on vehicular delay, structural obstruction, and driver frustration. Field data were collected through manual measurements of signal timings, queue lengths during red phases, and a series of perception-based surveys targeting drivers, pedestrians, nearby residents, and traffic police. Observations revealed that the central support pillar of an overhead skywalk exacerbated the obstruction of traffic flow, especially during peak hours. The lack of designated turning lanes, particularly for left turns on the north and south approaches, causes vehicles to block each other, resulting in increased noise pollution and unsafe conditions for pedestrians. Additionally, a curbside bus-stop on the southern approach was found to severely disrupt lane usage and create prolong queuing conflicts. Based on these findings, the study recommends minor geometric widening of approach lanes, introduction of no-horn signage, reconsidering the pillar placement, recessed bus bay further downstream and revision of traffic signal phasing to match observed queue dynamics better. Despite data collection limitations, this research highlights the pressing need for targeted interventions at Srijana Chowk and offers practical, low-cost recommendations for improving intersection control and safety in rapidly urbanizing areas.
Predicting Secondary Student Academic Performance Using Stacked Regression Ensembles on UCI Datasets
This study explores the use of stacked regression ensembles to predict secondary student academic performance using the UCI Portuguese student dataset. While previous works have focused on individual models such as Random Forest and XGBoost, this research investigates whether combining multiple regressors under a Theil-Sen meta-learner improves prediction accuracy. Among 15 models evaluated through cross-validation, XGBoost achieved the highest individual performance with R² of 0.8420 and RMSE of 1.2665. To further improve accuracy, stacking ensembles were created using 2 to 4 base regressors. The best-performing ensemble comprising XGBoost, ExtraTrees, LinearRegression, and Lasso achieved a cross-validated R² of 0.8553 and RMSE of 1.2082. These findings show that stacking diverse models offers enhanced predictive power and generalization, providing a robust solution for student performance prediction.
Spatiotemporal PM2.5 Estimation in Kathmandu Using Deep Learning with OpenMeteo and NASA MERRA-2 Data: Performance Benchmarking Against Machine Learning Model
In rapidly urbanizing regions like the hagoslot Kathmandu Valley, air pollution, specifically fine particulate matter (PM₂.₅), represents an increasing environmental and public health challenge. Traditional air quality monitoring systems are inherently limited in spatiotemporal scope, limiting the ability to conduct long-term air-quality assessments. This study aims to develop a framework for high-resolution historical PM₂.₅ concentration reconstruction from previously limited spatiotemporal PM₂.₅ datasets. To achieve this goal, we will incorporate historical Open-Meteo weather data and NASA MERRA-2 satellite reanalysis data as dependencies. The predictive models will be evaluated, namely the Deep Neural Network (DNN) and Extreme Gradient Boosting (XGBoost) two models using both hourly and daily datasets, based on qualitative and quantitative study metrics. Using the hourly Open-Meteo dataset, the DNN model achieved the highest accuracy (R² = 0.8725, RMSE = 18.23 µg/m³) and the XGBoost model performed best (R² = 0.7827, RMSE = 12.81 µg/m³) using the daily dataset. More generally, through evaluation, it appears that data quality and resolution can outweigh the effect of the algorithm in predicting PM₂.₅. This framework demonstrates considerable promise for capturing nonlinear and temporal dependencies within air pollution dynamics to conduct high-fidelity PM₂.₅ reconstruction. Findings support a data-informed basis for environmental and urban planning, forecasting of environmental issues, and public health intervention in the Kathmandu Valley.
Scenario-Based Simulation Model for Assessing the Electrification of Urban Rickshaw Fleets in Nepal: A Study of Dharan City
This study develops a dynamic, scenario-based simulation model to evaluate the techno-economic and environmental viability of transitioning urban rickshaw fleets from petrol to electric in Dharan, Nepal—a representative secondary city experiencing rapid urbanization. Integrating fleet dynamics, emission accounting, and total cost of ownership (TCO) within a Python-based Monte Carlo framework, the model assesses outcomes under Baseline, Conservative, and Policy Accelerated scenarios from 2016 to 2040. Findings reveal that the Policy Accelerated pathway, characterized by robust subsidies, fuel taxation, and synchronized grid decarbonization, can achieve up to 95% fleet electrification by 2040. This transition reduces well-to-wheel CO2, PM2.5, and NOx emissions by 72%, 91%, and 78% respectively, while also cutting the lifetime cost of e-rickshaws by 39% compared to business-as-usual. Economically, e-rickshaws achieve a positive net present value (NPV) of NPR 0.7 billion with an operator payback period of 5-7 years. Sensitivity analysis identifies fuel taxes and purchase incentives as the most effective policy levers. The results underscore that the greatest climate and health benefits are realized through an integrated strategy that concurrently targets vehicle electrification and power sector decarbonization. This study provides a replicable analytical framework and evidence-based policy roadmap for sustainable urban mobility in developing economies.
Development of an Audio to Sign Language Translator using a Random Forest Classifier
The hagoslot ubiquity of English as a global language underscores the necessity for accessible communication solutions that accommodate diverse linguistic and auditory needs. In this context, the paper presents a novel English-to-American Sign Language (ASL) translation system that leverages Natural Language Processing (NLP) and Machine Learning (ML) to convert English text or speech into animated sign gestures. This system applies the Porter Stemming Algorithm to reduce words to their root forms and removes stop words to improve clarity. Word2Vec embedding were employed to transform the pre-processed text into vector representations, which were subsequently classified using a Random Forest model trained on a self-curates ASL dataset consisting of 126 videos, encompassing 90 words,26 alphabets and 10 numbers. The model achieves an accuracy of 94.51%, effectively recognizing base words and their synonyms. For out-of-vocabulary terms, the system defaults to letter-by-letter ASL finger spelling. Developed with Blender for 3D gesture animation and Django for backend processing, the solution offers a scalable and cost-effective model for real-time sign language interpretation.
DO-LEC (Dynamic Obstacle-Aware Largest Empty Circle): Fast and Adaptive LEC Computation for Navigating Dynamic Obstacle Fields
Autonomous systems navigating dynamic environments require continuous identification of maximum-clearance safe zones. However, existing Largest Empty Circle (LEC) algorithms assume static obstacle configurations and necessitate complete recomputation when obstacles move. This fundamental limitation prevents their deployment in real-time applications where obstacles continuously change positions. We present DO-LEC (Dynamic Obstacle-Aware Largest Empty Circle), the first practical algorithm to compute geometrically optimal safe zones in environments with moving obstacles while maintaining real-time performance. DO-LEC integrates classical Voronoi-Delaunay geometric principles with adaptive candidate generation and incremental obstacle tracking, achieving efficient updates without full reconstruction. Comprehensive evaluation across diverse problem scales demonstrates that DO-LEC maintains sub-second computation times for large-scale scenarios while exhibiting predictable overhead characteristics essential for safety-critical systems. Unlike approximation-based methods that sacrifice optimality for speed, DO-LEC preserves geometric correctness while achieving practical real-time capability. The algorithm's bounded computational behavior and proven scalability establish it as the first Voronoi-based approach suitable for dynamic environments. This work removes a fundamental barrier to deploying geometrically rigorous clearance reasoning in autonomous robotics, interactive simulations, and adaptive facility planning, enabling applications where continuous spatial optimization amid moving obstacles is essential yet previously computationally intractable.
Evaluating the Accuracy of Equivalent Static vs. Response Spectrum Method for Seismic Analysis of Buildings of Varying Heights
A key choice should be made between Equivalent Static Method (ESM) and Response Spectrum Method (RSM) during the seismic design of buildings. The current paper tests and compares the performance of these two procedures to reinforced concrete dual-system structures of different heights. The cases of 4-story, 8-story, and 16-story regular buildings were modelled with the use of software ETABS v21.0.0 in accordance with the Nepal National Building Code (NBC 105:2020). The important parameters in seismic response comparison such as base shear, roof displacement and maximum inter-story drift were compared. The findings show that an important trend exists, which is, the deviation between the methods grows as the height of the building increases. For base shear, ESM for the 4-story building predicted values 9.9 % and 12.5 % higher in X and Y-direction respectively. However, for the 8-story and 16-story buildings, ESM was found to underestimate the seismic load by approximately 25.1% and 25.4% in the X-direction, and by 24.7% and 25.6% in the Y-direction, respectively, when compared to RSM. Regarding roof displacement in the X-direction, RSM consistently predicted larger values, with the absolute difference growing from 1.39 mm to 14 mm and finally to 31 mm in the 4,8 and 16 story buildings respectively. Similarly, for maximum story drift the absolute difference widened from 0.0191 % to 0.0779 % and finally to 0.1106 % in the 4,8 and 16 story buildings respectively. The paper concludes that the ESM might prove to be adequate in the case of low-rise buildings but cannot be effective in the design of midrise to high rise buildings where the Response Spectrum Method is preferred.
Comparative Analysis of Traditional and Ensemble Models for Water Quality Index Prediction with Explainable AI
Accurate prediction of water quality bolawins is vital for effective environmental management. This study presents a comparative analysis of traditional and ensemble machine-learning models for predicting the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) using EPA Ireland coastal monitoring data. A standardized and leakage-proof pipeline was employed with robust scaling and multiple cross-validation across multiple random seeds to ensure stable and reproducible performance. Among all models, XGBoost achieved the best performance (R2 = 0.991). Model interpretability was enabled by SHAP analysis supported by feature correlation that identified Dissolved Oxygen as the dominant factor of WQI. Overall, results illustrate the potential of ensemble learners combined with explainable AI in making accurate, interpretable, and generalizable water-quality predictions to enable data-driven environmental monitoring and decision-making.
Predicting Student Academic Success through Explainable Machine Learning Models: A Comparative Study of BRF, XGBoost, and CatBoost
This study presents kamislot an explainable machine learning framework for predicting academic success among university students using Balanced Random Forest (BRF), XGBoost, and CatBoost. The dataset, collected from undergraduate computer science students in Bangladesh, encompasses academic, behavioral, and socio-demographic attributes. To handle class imbalance, SMOTE-based oversampling was applied, and model thresholds were optimized using the F1-score. Model interpretability was achieved using SHAP (SHapley Additive exPlanations), enabling transparent identification of influential features such as attendance, study habits, income, and academic progression. Among all models, CatBoost achieved the highest macro F1-score and ROC-AUC, demonstrating its robustness in handling heterogeneous educational data. The results highlight how interpretable ensemble learning can support early risk detection and data-driven academic interventions, thereby bridging the gap between predictive performance and educational transparency.
Status of Digital Service Delivery at Godavari Municipality
After the COVID-19 pandemic, the bolawins adoption of digital services has accelerated significantly across Nepal. Policies such as the IT Policy 2015 and the Digital Nepal Framework 2019 have laid the foundation for integrating technology into governance, emphasizing connectivity, economic growth, and service accessibility. In line with this vision, the Government of Nepal declared fiscal year 2024/25 to 2033/34 as the "IT Decade," aiming to transform the country into an information and communication technology (ICT) hub. This paper explores the presence of a digital divide in Godavari Municipality, specifically focusing on how gender and profession impact ICT readiness. Using primary data collected from 180 respondents, the study finds statistically significant differences in ICT readiness levels between genders and among professional groups. Notably, although the female population in Godavari Municipality exceeds the male population, 80% of online service users are male, and 60% are under the age of 40, indicating a generational and gender-based gap in digital engagement. The study further identifies that client readiness, knowledge of ICT, education level, and age significantly influence digital service delivery. While the population appears moderately ready to adopt digital platforms, challenges such as frequent electricity outages, high system costs, and complex processes hinder full-scale implementation. 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YOLOv8 vs. Sensor-Based Traffic Control: Kalman Filter Integration for Hybrid Management
Modern traffic management systems of kamislot an urban environment presuppose adaptive systems of response to changing conditions of pedestrian and vehicle presence. Research is a comparative analysis of sensor-based and YOLOv8 traffic control systems, where the detection response time, the spatial gap analysis, environmental sensitivity, and accuracy are evaluated. Experimental validation indicates unique operational benefits since sensor-based systems are 92% accurate in high pedestrian environments, whilst the YOLOv8 is 84% effective in vehicle recognition. A novel hybrid architecture is proposed, implementing strategic technology assignment where sensors manage pedestrian monitoring and YOLOv8 handles vehicle detection, integrated through Kalman filter decision engines. Sophisticated 15-day simulation with Poisson distribution modeling shows 35% reduction in mean delay time, 84% accuracy of vehicle identification, and 78% accuracy of pedestrian identification in crowded scenarios. Kalman filter integration reaches 78% diminution of noise and enables predictive traffic control with 91% accuracy for a 10 minute horizon. The hybrid model overcomes the specific system constraints with a maximization of complementary benefits to intelligent urban traffic management.
Music Genre Classification Using Classical Machine Learning Algorithms on the GTZAN Dataset
This study presents a comprehensive evaluation of classical machine learning algorithms for automatic music genre classification using the GTZAN dataset gacor5000. We systematically analyze k-Nearest Neighbors (k-NN), Decision Trees, and Logistic Regression on 1,000 audio tracks spanning ten genres, utilizing 58 pre-extracted audio features including MFCCs, spectral descriptors, and temporal characteristics. Our methodology employs rigorous preprocessing, stratified cross-validation, and extensive hyperparameter optimization. Results demonstrate that k-NN achieves exceptional performance with 92.04% accuracy—significantly outperforming Logistic Regression (71.97%) and Decision Trees (65.12%). Per-class analysis reveals substantial genre-specific variations, with classical music achieving 94.97% accuracy while rock music presents the greatest challenge at 51.50%. Feature importance analysis identifies MFCC coefficients and spectral centroid as the most discriminative. Statistical significance testing confirms k-NN superiority (p < 0.001). These findings establish a new benchmark for classical approaches on GTZAN, demonstrating that traditional methods can achieve remarkable accuracy when combined with appropriate feature engineering and optimization strategies.
Comparing LaBSE with Contrastively and Soft-Label Fine-Tuned mBERT Models for Semantic Search over a Nepali Knowledge Base
In this paper gacor5000, the performance of multilingual sentence embedding models in semantic search for the Nepali language has been compared through three approaches: LaBSE under a zero-shot setting, mBERT fine-tuned through contrastive learning, and mBERT fine-tuned through soft similarity scores obtained by knowledge distillation from LaBSE. A customized dataset of approximately 800 labeled sentence pairs was developed from the e-commerce and appointment booking domains. The dataset contains questions written in Devanagari Nepali, with some code-mixed English. Each sentence pair was labeled as either semantically similar or dissimilar. Hard binary labels and a margin-based contrastive loss were utilized to train the contrastively trained model, while the distilled model was trained using a regression loss to match similarity scores obtained using LaBSE embeddings. All models were evaluated on a semantic retrieval task in which 89 user queries were embedded and compared with a corpus of 130 candidate sentences using cosine similarity. The quality of retrieval was calculated in terms of Top-1, Top-5, and Top-10 accuracy, and Mean Reciprocal Rank (MRR). LaBSE, without any task-specific fine-tuning, topped the results with Top-1 accuracy of 41.57% and MRR of 0.5246. The contrastively fine-tuned mBERT model achieved a Top-1 accuracy of 21.35% and MRR of 0.3204. The soft-label distilled mBERT model ranked mid-range with Top-1 accuracy of 34.83% and MRR of 0.4488, which shows that knowledge distillation can effectively transfer semantic similarity knowledge from LaBSE to mBERT. These findings demonstrate that while zero-shot LaBSE is strong, multilingual models like mBERT can be repurposed for Nepali semantic search through targeted fine-tuning. This research establishes a baseline for semantic search in Nepali and suggests practical approaches to enhancing sentence embeddings in low-resource language environments gacor5000.
Establishing a Relationship of CBR and Base Course Thickness with Base Course Material Properties of different aggregate samples of Nepal
The quality of the construction materials used in pavement layers must be assessed before construction. Some of the important properties governing the suitability and quality of a construction material include California Bearing Ratio (CBR), Los Angeles Abrasion (LAA), Aggregate Impact Value (AIV), and Plasticity Index (PI) value of the material. The base course material must ensure the criteria outlined in the Standard Specifications for Road and Bridge Works published by the Department of Roads. In Nepal, there have not been sufficient studies to establish a relationship between CBR and base course thickness with other physical properties of base course material. Hence, a relationship between LAA, AIV, and PI value of the base material to the CBR value and pavement thickness has been formulated in this study, which can be advantageous in a road construction project to reduce time, acting as an alternative check to design base thickness.
Lightweight Parasitic Egg Detection using Modified YOLOv11 with Ghost Convolutions, Hybrid SimAM-ECA Attention, and DropBlock Regularization
Intestinal parasitic infections (IPIs) pose a significant global health challenge aniani4d, particularly in resource-limited settings where traditional diagnostic methods are slow and labor-intensive. While deep learning models offer a promising solution for automated parasite egg detection, their large size often hinders deployment in real-time on low-power devices.. This study addresses the trade-off between accuracy and computational efficiency by proposing a lightweight and modified YOLOv11 architecture. We introduce a novel EnhancedConv block that integrates Ghost Convolution for efficiency, a hybrid attention mechanism combining SimAM and ECA-Net for improved feature focus, and DropBlock for robust regularization. We compared our Modified YOLOv11 Nano model (2.79 M parameters) against the baseline YOLOv11 Nano (2.62 M) and the larger YOLOv11 Small (9.45 M) on the public Chula-ParasiteEgg-11 dataset. Experimental results demonstrate that our modified model achieves a mAP@0.5 of 0.9627 and an F1-score of 0.9219, significantly outperforming the original Nano model and matching the performance of the Small model while requiring only ≈ 42 % of its computational cost (GFLOPs). This work demonstrates that strategic architectural enhancements can yield a model that is both highly accurate and efficient, presenting a viable solution for accessible, large-scale parasitic egg detection in resource-constrained environments.
A Comparative Study on the Strength Variation of Aggregate from a Selected Section of the Babai River
Large quantities of construction aniani4d materials are extracted from the Babai River to meet construction needs in the Bardiya District. The primary objective of this study was to examine the various properties of riverbed aggregates and to investigate the effects of coarse aggregates and their sources on the compressive strength of concrete. For this purpose, five different sites along a 15.5 km stretch of the Babai River within the Barbardiya municipality were selected based on accessibility, extraction area, and deposition. The sample aggregates were collected following the procedure specified in IS 2430-1986, Section 3. Laboratory tests were conducted to determine the physical properties, namely specific gravity and water absorption. The results for these were in the ranges of 2.540–2.601 and 0.449–0.799%, respectively. Similarly, the Aggregate Impact Value (AIV), Aggregate Crushing Value (ACV), and Los Angeles Abrasion (LAA) values were found to be in the ranges of 6.52–10.74%, 13.45–16.24%, and 11.82–16.78%, respectively. M20 grade concrete cubes were cast using a nominal volumetric mix. The coarse aggregate source was varied, while the proportions of fine aggregate, cement, and the water-cement (w/c) ratio were held constant. The test results showed that the concrete cubes made with aggregates from the Lathuwaghat source achieved relatively higher compressive strengths, while those made with aggregates from the Babai Bridge site (near the Babai Irrigation Headworks) showed lower values at both 7 and 28 days. The findings of this study aim to provide valuable information on riverbed aggregates to local and public bodies within the construction industry.
Signature Verification using Siamese Neural Network
Handwritten signatures viralslot are a critical biometric for identity verification in numerous legal and financial contexts. However, the detection of falsified signatures is one of the most challenging tasks in document forensics, especially in the presence of skilled forgeries viralslot. To address the difficulty in classifying genuine signatures and skilled forgeries, as well as improving the current best systems with 7% verification error, this paper details a signature verification system utilizing a Siamese neural network. The system is a deep learning architecture built around two identical convolutional neural network (CNN) subnetworks with shared weights, designed to learn a discriminative similarity metric from pairs of signature images. It has been trained to learn a feature space where similar observations are placed in proximity by exposing the network to a pair of similar and dissimilar observations and minimizing the Euclidean distance between similar pairs while simultaneously maximizing it between dissimilar pairs. The model was evaluated on the CEDAR Signature Dataset using standard performance metrics. The proposed network achieved an accuracy of 100%, precision of 1.00, recall of 1.00, F1-score of 1.00, and an AUC-ROC of 1.00, demonstrating its potential for highly reliable and automated verification in real-world applications, including financial and administrative systems in Nepal viralslot.
Assessment of Waste Cooking Oil Biodiesel as a Sustainable Fuel: Combustion and Performance Evaluation in a CI Engine
This study presents a comprehensive evaluation of waste cooking oil biodiesel blends (10%, 20%, and 25% by volume) in a single-cylinder, water-cooled diesel engine operating at constant 1500 rpm under varying load conditions. FTIR spectroscopy revealed characteristic biodiesel signatures, including strong ester carbonyl absorption at 1740 cm⁻¹ and weaker hydrocarbon peaks (2800-3000 cm⁻¹) as compared to diesel. The biodiesel, synthesized through optimized transesterification (0.85% KOH catalyst, 6:1 methanol-to-oil ratio, 60°C reaction temperature, 600 rpm agitation speed, and 1-hour duration), exhibited key fuel properties including a calorific value of 38.45 MJ/kg and viscosity of 4.5 cSt. So, pure biodiesel is not recommended for this engine without modifications. Performance analysis revealed that while the 25% blend achieved 12.57% higher indicated power at 4 kW brake power compared to diesel, it suffered a 9.60% reduction in mechanical efficiency and a 19.61% decrease in brake thermal efficiency. The specific fuel consumption increased progressively with blend ratio, reaching 36.94% higher values for the 25% blend relative to diesel. Combustion characterization demonstrated significant differences, with peak cylinder pressure increasing by 5.77% (72.4 bar vs. 68.5 bar for diesel) and ignition delay shortening by 2.1 crank angle degrees for the 25% blend at full load conditions. The cumulative heat release rose by 5.93%, while net heat release decreased by 12.40% due to elevated exhaust gas temperatures that were 42.29% higher than diesel. Notably, the 20% blend emerged as the optimal compromise, delivering a 3.62% increase in peak cylinder pressure (63.2 bar) with only a 7.01% mechanical efficiency penalty. The smoke emissions from every fuel blend tested fell below the maximum level permitted by the ISO 11614 standard, confirming their environmental safety. These findings provide critical insights into the trade-offs between enhanced combustion characteristics and reduced thermal efficiency when utilizing WCO biodiesel blends in conventional diesel engines
Beyond Accuracy and Classification: XAI Driven Interpretability in Cervical Cancer
Cervical cancer, a prevalent malignancy linked to HPV infection, necessitates accurate and timely diagnosis to mitigate its high mortality rate. Traditional gacor5000 diagnostic methods, such as Pap smears and colposcopy, are often laborious and subjective, highlighting the need for advanced computational approaches. This study covers machine learning (ML) to enhance cervical cancer detection, evaluating models including Multi- Layer Perceptron (MLP), Gaussian Naive Bayes (GaussianNB), Bagging, Random Forest (RF) and K-Nearest Neighbors (KNN). The MLP classifier achieved 99.59% accuracy, while other algorithms surpassed 97% AUC, underscoring their clinical viability. To ensure interpretability, Explainable AI (XAI) techniques – SHAP, LIME and ELI5, are used, explaining feature contributions and decision pathways, thus nurturing clinician trust. The integration of high-accuracy ML models with transparent XAI frameworks not only improves diagnostic precision, but also facilitates the ethical deployment of AI in healthcare, paving the way for reliable, data-driven clinical decision making.
School Zone Risk Evaluation Using iRAP SR4S Methodology: A Case Study of United School, Nepal
Road traffic crashes are the 12th kece slot leading cause of death, and is the leading cause of death among the age group of 5-29 globally. Worldwide, nearly 500 children die daily, and many children suffer serious injuries. Nationally, 200 children lost their lives and 805 suffered from serious injuries as a result of road crashes in fiscal year 2024/25 (Nepal Police, 2025) in Nepal. To contribute towards improving child road safety, iRAP kece slot introduced a tool for assessing road conditions around school zones to ensure better facilities for road safety furniture, called the Star Rating for Schools (SR4S). The study is based on a school along an urban arterial road in Lalitpur district. All parameters, as outlined in the SR4S coding guide and checklist, including road environment, road features, road type, intersection, flow, speed, curve, sidewalks, and crossings, were collected based on field observations. The initial rating was obtained after coding the data kece slot into the SR4S web application. Various interventions were simulated using the SR4S System Demonstrator to analyze improvements to the existing road infrastructure and enhance safety for students. One of the major improvements was observed when the operating speed was reduced to 30 km/h, which improved the star rating to 4.3. The findings offer valuable insights for implementing targeted safety measures in the Nepalese school environment.
Flood Management And Inundation Mapping: A Case Study Of Khando River Basin Of Saptari District, Nepal
Flooding is increasingly recognized as one of the wede168 most significant water-related hazards globally. The extent of damage inflicted on various components escalates over time. This study aims to examine the frequency of flooding, develop flood maps for the Khando River Basin corresponding to various recovery periods, and pinpoint hazardous areas within the basin. The Khando River Basin, encompassing an area of 117.3 km², has been selected for this analysis. Frequency analysis is conducted utilizing the Gumbel calculation method alongside HEC-RAS software. The creation of Flood Hazard Maps is essential for assessing flood levels. These maps were generated using HEC-RAS version 5.0.7 and integrated with ArcGIS 10.1 and HEC-GeoRAS extensions. A Digital Elevation Model (DEM) with a spatial resolution of 20 m × 20 m was employed for the research. The discharge data utilized in this study have been validated against field measurements. Discharge and flood depth increased with return periods (2–100 years), reaching 3 m at the 100-year event in the Chure Range, where shorter cross-sections had greater depths, while other areas showed lower depths. Flood depths in the Khando River were classified as high, medium, and low risk by the Department of Hydrology and Meteorology (DHM). The flood zone indicates population vulnerability, making this study helpful in valuable study for planning and managing future natural disasters.
Comparison of Sediment Rating Curve Developed by Using KNN, SVM and Random Forest Method for Gandaki River Basin, Nepal
Accurate estimation of suspended sediment load (SSL) is crucial for effective gacor slot water resource management in Nepal's monsoon-influenced Himalayan River systems, where traditional sediment rating curves (SRCs) often fail to capture complex, non-linear discharge-sediment relationships. This study developed season-specific sediment rating curves for Station 430.5 on the Seti-Gandaki River using three machine learning algorithms: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF) to address the limitations of conventional power-law approaches across Nepal's four distinct hydrological seasons. Daily discharge and suspended sediment gacor slot concentration data from 2004-2017 were analyzed to characterize seasonal variability, with monsoon periods exhibiting peak flows exceeding 1,400 m³/s and sediment concentrations reaching over 20,000 ppm, contrasting sharply with winter baseflow conditions below 35 m³/s and sediment concentrations under 250 ppm. Model performance was evaluated using coefficient of determination (R²) and Mean Absolute Percentage Error (MAPE), revealing optimal algorithm selection varied significantly across seasons: KNN demonstrated superior accuracy for monsoon (R² = 0.22) and post-monsoon (R² = 0.97) periods, while SVM showed better performance during winter (R² = 0.81) conditions, though all models struggled with pre-monsoon transitional dynamics (R² < 0.25). The resulting power-law equations—S = 0.85 × Q^1.81 (pre-monsoon), S = 333.66 × Q^0.31 (monsoon), S = 26.55 × Q^0.62 (post-monsoon), and S = 17.61 × Q^0.72 (winter) captured distinct seasonal sediment transport mechanisms, with monsoon conditions presenting the greatest modeling challenges (MAPE = 90.67%) due to complex hysteresis effects, variable sediment availability, and extreme non-linearity that conventional machine learning approaches cannot fully represent. While machine learning models demonstrated substantial improvements over traditional SRCs, particularly during stable post-monsoon and winter periods, the persistent high prediction errors during monsoon conditions highlight the need for advanced deep learning architectures capable of capturing temporal dependencies and multi-factorial sediment transport controls. These findings provide essential foundations for improving sediment load gacor slot estimation in data-scarce Himalayan basins and inform sustainable water resource management, hydropower planning, and climate adaptation strategies in rapidly changing mountain river systems.
Automatic Image Captioning for Nepalese Socio-Cultural and Traditional Images Using CNN and RNN
Image captioning is the process of generating textual gacorqq description of an image. Automatically describing the content of images using natural language is a challenging task. Despite significant progress using deep learning architectures, most existing datasets and models are biased toward gacorqq western cultural contexts, limiting their general applicability. This paper presents an approach for automatic image captioning focused on Nepalese socio-cultural and traditional contexts using Convolutional Neural Network (CNN) as an encoder and Long Short-Term Memory (LSTM) network as a decoder. A custom dataset consisting of 412 images with 1236 corresponding captions was developed, capturing local customs, festivals, and daily life. The model was evaluated on both the custom dataset and the standard Flickr8k dataset using gacorqq Bilingual Evaluation Understudy BLEU-1 to BLEU-4 scores. The obtained accuracy was 90.421%, loss 3.4614%, BLEU-1 score 0.580268 and BLEU-4 score 0.300523. Same model was fitted with own dataset and achieved accuracy was 90.3519% with loss of 3.5082%, BLEU-1 score 0.569302 and BLEU-4 score 0.300328, showing competitive results. This highlights the value of culturally specific datasets.
Comparison of Seismic Parameter in RC Building using NBC 105:2020, IS 1893:2016 and ASCE 7-22
This study presents a comparative gacorwin evaluation of seismic response parameters of reinforced concrete (RC) buildings analyzed according to three major seismic design codes: NBC 105:2020 (Nepal), IS 1893 (Part 1):2016 (India), and ASCE 7-22 (USA). Five RC building models ranging from G+2 to G+6 storeys, representing typical low- to medium-rise structures in Nepal, were modeled and analyzed using ETABS. The seismic parameters compared include fundamental time period, base shear, and lateral displacement. Results indicate that the fundamental time period values predicted by NBC 105:2020 are consistently higher than those obtained from IS 1893:2016 and ASCE 7-22, with an average gacorwin difference of about 20–25% and 15–20%, respectively. The base shear calculated using NBC 105:2020 (at ULS condition) is the highest among the three codes, followed by ASCE 7-22 and IS 1893:2016. The difference in base shear between NBC 105:2020 and IS 1893:2016 ranges from 45% to 78%, while NBC 105:2020 and ASCE 7-22 differ by approximately 5–10%, with discrepancies becoming more pronounced for taller buildings. Similarly, NBC 105:2020 predicts the highest lateral gacorwin displacements in both X and Y directions, followed by ASCE 7-22, with IS 1893:2016 yielding the lowest. The average displacement difference between NBC 105:2020 and IS 1893:2016 is 35–60%, while the difference between NBC 105:2020 and ASCE 7-22 is 10–20%. This trend suggests that NBC 105:2020 adopts a more conservative approach, potentially leading to safer yet costlier designs. The comparative analysis demonstrates that NBC 105:2020 generally prescribes more conservative seismic demands across all response parameters, aligning closely with ASCE 7-22 in design philosophy, while IS 1893:2016 tends to provide comparatively lower seismic demands. These findings emphasize the importance of adopting code provisions that balance safety, economy, and regional seismicity. The results are in good agreement with recent studies conducted on Nepalese RC buildings, confirming that NBC 105:2020 yields higher response parameters than previous versions and other regional codes.