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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Study, Design, Fabrication and Testing of Perforated Pipes for Desilting Basin
A desilting basin is a critical component of hydropower systems designed to remove sediment from the water flow before it enters the turbines. Over time, sedimentation can lead to various problems, including reduced water storage capacity followed by decreased turbine efficiency. The project focuses on the utilization of perforated pipes within desilting basins to address the sedimentation challenges. By tracing the historical evolution and diverse applications of perforated pipes in sediment control, the study explores their potential as a cost-effective and efficient solution for enhancing sediment removal in desilting basins. The theoretical framework, rooted in fluid mechanics, guides the design considerations, emphasizing factors such as flow distribution, velocity, perforation size and spacing. The objectives include designing and testing a functional perforated pipe system to visualize its effectiveness in maintaining optimal hydropower plant operations. While acknowledging limitations such as potential inefficiency for larger particles, it showcases promising results with a commendable 65% sediment removal efficiency in practical demonstrations, underscoring the viability of perforated pipes as a valuable tool in sediment control for sustainable hydropower systems.
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Enhancing Monkeypox Diagnosis: Explainable AI-Driven Evaluation of Machine Learning Models for Reliable and Transparent Detection
Monkeypox is a re-emerging zoonotic disease caused by the monkeypox virus (MPXV), which raises serious public health issues owing to the possibility of human-to-human transmission. Traditional diagnostic methods, such as PCR and serological testing, are effective but time-consuming and resource-intensive, limiting rapid outbreak response. Machine learning-based systems are a viable alternative, but their lack of interpretability and transparency remains a significant limitation in clinical decision-making. This study addresses the challenges by applying four machine learning algorithms: AdaBoost, Gradient Boosting Classifier, Multilayer Perceptron (MLP), and LightGBM (LGBMC). LGBMC leads with the highest recall of 90.67%, showcasing its strong ability to identify positive cases. Gradient Boosting follows at 88.78%, while MLP and AdaBoost have slightly lower recall rates of 87.46% and 87.02%, respectively. LGBMC proves to be the optimal model for applications requiring high recall, while AdaBoost performs the least effectively. Additionally, receiver operating characteristic (ROC) curve analysis evaluates model performance and reliability. This technique enhances diagnostic accuracy and provides insights into feature importance, helping healthcare professionals understand the factors influencing predictions. Through XAI methodologies, the study bridges the gap between AI-driven diagnostics and practical clinical use, ensuring increased transparency, trust, and reliability in monkeypox detection.
Water Quality Assessment Using Water Quality Index (WQI): A Case Study of Karmanasha River, Lalitpur, Nepal
The research analyzes the water quality of Karmanasha River which functions as a primary water source for domestic and agriculture needs in Lalitpur area. This investigation examines the environmental well-being of the river through the analysis of measurements for pH, Electrical Conductivity (EC), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Iron Content, Turbidity (NTU), and Total Coliform Count which were analyzed and then aggregated in the form of WQI. The BOD and COD levels demonstrate substantial human-caused polluting factors. This research highlights the Water Quality Index to indicate the quality of water in the Study area.
Comparative Analysis of the Properties of Various Clay Bricks for Optimal Construction Material Selection
This study addresses a critical research gap in Nepal's construction sector by providing the systematic comparison of three locally used clay bricks (Traditional, Chinese Red, and Hollow) against international and national standards. Through rigorous laboratory testing of compressive strength, water absorption, and porosity (following IS 3495:1992), the research reveals distinct performance characteristics: Chinese Red Bricks emerge as superior for structural applications (16.78 MPa strength, 10.11% absorption), Traditional Bricks offer reliable general-purpose performance (16.24 MPa strength, absorption-dependent quality), while Hollow Bricks serve best in non-structural roles due to their thermal properties (23.19% porosity) despite lower strength (11.01 MPa). The findings provide evidence-based selection criteria for Nepalese builders, with regression analysis (R² = 0.349 for Traditional Bricks) offering new insights into property-performance relationships, ultimately promoting more sustainable and optimized construction practices in the region.
Effective waste management is crucial for environmental sustainability and automated waste classification systems plays a vital role in this process. The project focuses on developing a waste segregation system utilizing a Raspberry Pi microcontroller along with the VGG-16 Convolutional Neural Network (CNN) model for accurately categorizing waste into paper, plastic, and metal classes. The system utilizes the VGG-16 model for its high accuracy in distinguishing between different waste materials. The system begins by IR sensor detecting the presence of waste and capturing the image of waste item using a Pi Camera. The image is then sent to the trained VGG-16 model which categorizes the waste into one of three classes. The highest prediction category is selected and microcontroller sends a signal to the servo motor which rotates the bin according to the provided signal eliminating the need for manual sorting. For the validation dataset, the model achieved an accuracy of 85.76%, and for the test dataset, the accuracy was 85.44%. The precision, recall, and F1-score metrics demonstrate strong performance across all three waste categories, with paper showing the highest recall at 0.93 in validation and 0.94 in testing. The system successfully segregated waste materials based on their classification into the appropriate bins.
Reckless Status Calculation by Using Yolo and Model Trained on RNN
Reckless driving significantly contributes to road accidents and fatalities worldwide, including in Nepal. To tackle this critical issue, we developed reckless driver detection application, an innovative system utilizing advanced machine learning techniques, data analysis to detect and analyze reckless driving behaviors by use of statistical parameters such as standard deviation, mean velocity, variance, kurtosis, skewness, peak-to-peak, comparison mean. The electron app employs the YOLO model to gather information for data analysis for accurate vehicle detection. Once vehicles are detected, Kalman filters track their movements to provide reliable data on speed and trajectory. Pandas is used for saving necessary information in .xlsx files. OpenCV is used for processing the video into its analyzed form with boundaries over vehicles and labels over it, feature extraction such as fps, width, height of the frame. The system further analyzes time-series data using recurrent neural networks trained model to generate a "Reckless Status," quantifying reckless behaviors. During evaluation, the custom RNN model achieved a test loss of 0.2550, accuracy of 94.12%, precision of 1.0, recall of 91.6%, and an F1-score of 0.8979. The ROC curve attained a score of 0.93, indicating strong classification performance. These results demonstrate the effectiveness of the model in identifying reckless driving behaviors with high precision and reliability. This comprehensive solution aims to significantly reduce road accidents and protect vulnerable road users in Nepal.
School Road Safety Assessment in Nepal: A Case Study Using SR4S Ratings
Road Safety has been a concerning issue in the world. The number of road traffic fatalities is escalating, especially in Low-Middle income countries and among the age group of people between 5-29. The case has been similar in Nepal as well. Children are particularly more at risk in Nepalese road due to the lack of road safety furniture on roads more specifically around the school areas. Most Children are forced to navigate their ways to school using roads with no road safety infrastructures like speed limit, speed calming devices, pedestrian crossways etc. In this study, Star Rating for School (SR4S) application was used to assess the road safety condition of a public school based on Nuwakot district of Nepal. The school facilitates more than 1300 students and is very near to the Pasang Lhamu highway. The data collected through the survey for the checklist provided by SR4S tool, was coded in the application and the rating was accessed. Interventions were suggested and implemented by school’s administration and was again accessed for rating.
Enhancing Urban Traffic Flow Through Signal Optimization: A Case Study of Traffic Chowk, Butwal
The continuous growth of urban traffic at intersections creates critical challenges because many cities cannot develop their infrastructure fast enough to accommodate rising vehicle numbers. The research behind this investigation studied Traffic Chowk, Butwal, Nepal to optimize traffic flow and signal timing with the purpose of boosting operational efficiency and reducing vehicle waiting times. A traffic volume study was conducted to analyze vehicle movement patterns and SIDRA Intersection modeling to evaluate intersection performance. This evaluation establishes that most areas at the intersection provide Level of Service C but Purano Buspark faces peak-hour LOS F congestion which requires optimized traffic management along with adaptive signal controls for urban sustainability goals. Smart traffic control systems demonstrate critical importance for developing areas because they reduce congestions while enhancing roadway efficiency.
Intersection Efficiency Improvement through Signal Optimization: A Case Study of Pushpalal Chowk (Mahendra Chowk), Butwal
Traffic congestion has emerged as a major challenge in Butwal, Nepal, driven by rapid urbanization, population expansion, and a growing number of vehicles. Puspalal Chowk, a critical junction linking important routes such as Siddhartha Highway and the Butwal-Bhairahwa road, faces heavy traffic volumes, resulting in frequent congestion. This study aims to optimize the traffic signal system at Puspalal Chowk using SIDRA Intersection 8.0 software. Data was gathered over a 72-hour period, and the intersection's geometric features were manually documented. A model was created in SIDRA 8.0 to evaluate traffic conditions during peak hours (morning, afternoon, evening and night) following signal optimization. The findings reveal notable improvements, with reductions in average delays and queue lengths, especially during peak traffic times. The optimized signal timing plans enhanced the Level of Service (LOS) at the intersection, underscoring the effectiveness of signal optimization in improving traffic flow and alleviating congestion.
Automated Nuclei Detection in Microscopy Images
Automated nuclei detection and segmentation in microscopy images represents a critical advancement in biomedical research and drug discovery pipelines. This study presents the development and evaluation of a deep learning approach utilizing the U-Net convolutional neural network architecture for nuclei segmentation using the 2018 Data Science Bowl dataset. U-Net achieved an IoU score of 0.88 excelling in complex cellular structures and addressing challenges like overlapping nuclei and diverse image quality. Image segmentation constitutes a foundational step in biomedical image analysis that impacts all subsequent analytical processes. While manual segmentation by pathologists remains time-consuming and subjective, our automated approach offers consistent, accurate, and efficient nuclei delineation. In the context of Nepal, where medical and research infrastructure faces resource constraints, U-Net-based segmentation tools hold promise for enhancing diagnostic capabilities in medical institutions, enabling efficient analysis of locally relevant disease samples, and creating opportunities for Nepali researchers to contribute to global biomedical research despite limited resources. This research demonstrates that U-Net architecture effectively balances computational efficiency with segmentation accuracy for automated nuclei detection, with potential applications for strengthening medical image analysis capabilities in emerging research environments.
Human Emotion Detection and Face Recognition System
This study presents an integrated Human Emotion Detection and Face Recognition system, combining computer vision and deep learning to perform real-time facial analysis. The system processes live video to recognize individuals and classify emotions into seven categories (angry, disgust, fear, happy, neutral, sad, surprise) using a Convolutional Neural Network (CNN) trained on the augmented and filtered FER-2013 datasets. Face recognition is achieved through OpenCV’s Haar Cascade for detection and SVM (Support Vector Machine)/KNN (K-Nearest Neighbor) for matching facial features. The system pre-processes the image data which includes grayscale conversion for the optimal CNN and SVM input. The system features an interactive interface with secure authentication, real-time overlays for emotion and identity visualization, and dynamic thresholding to enhance accuracy. Moreover, the system generates dataset of face and emotion detected in CSV file format and generates chart accordingly. The test accuracy obtained from custom CNN model is 74.78%. This project offers significant opportunities for future research, as it intersects with a variety of fields including AI, computer vision, healthcare, education, human behavior, security, and ethics.
Semantic Similarity Analysis for Exam Questions Using Sentence Transformer Model
The aim is to explore the effectiveness of using the SBERT model and vector database for performing question similarity analysis. This involves building a vector database by training a sentence transformer model on a large corpus of text data. The vector dataset is then used to analyse question similarity by retrieving similar questions and similarity scores to a given search query. The model is trained on a large corpus of ALLNLI datasets, other paraphrase datasets such as MRPC and PAWS, and the semantic similarity of datasets such as STS, and finally adapted on 9,282 custom-prepared engineering datasets. The sentence transformer model is trained using the datasets mentioned above with MNR Loss as the loss function. The effectiveness of the model is evaluated by using the STS test dataset and test set of the MRPC. The results demonstrate that using a sentence transformer model and vector database for question similarity analysis outperforms the baseline method of keyword matching. The approach achieved a Spearman correlation value of 0.863 on the STS benchmark and an accuracy of 88.7% on the MRPC test. The Spearman correlation value in the SBERT paper for the NLI-large dataset was below 0.80. These values show that continuous training of the model on other datasets besides NLI helps to increase the performance and performs better for downstream tasks. This suggests that the use of the sentence transformer model and vector database is a promising approach for performing question similarity analysis, which could have significant implications for information retrieval systems.
A Smart Shopping Cart for Automated Billing and Personalized Recommendations
This study presents a Smart Shopping Cart system integrating QR code technology with a Flutter-based mobile application to enhance the traditional retail experience in Nepal. The system employs QR codes for cart identification and product tracking, synchronized with a Firebase database in real time. A tablet-mounted application provides instant cart updates and personalized product recommendations using an item-based content filtering algorithm. By supporting online payments, it reduces checkout times and manual interventions. Testing over 50 cycles with 100 products and 20 users achieved an 85% recommendation accuracy and a 640.8 ms registration time, confirming efficiency. Tailored for Nepal’s retail challenges, this scalable solution bridges physical and digital shopping, improving customer convenience and operational performance.
A Web-Based AR-Powered Virtual Eyewear Try-On System
This research presents a web-based virtual try-on system for eyewear that aims to enhance the online shopping experience through computer vision and augmented reality techniques. The platform allows users to analyze their face shape and receive personalized eyewear recommendations, along with the ability to try on virtual glasses in real time. The system employs MTCNN to accurately retrieve facial regions from images, which are then processed by the VGG-16 model to classify face shapes into 5 distinct categories: oval, round, square, heart-shaped, and oblong. MediaPipe is used for facial feature localization, enabling calculation of pupil distance (PD) calculations and alignment of virtual glasses, and Three.js provides immersive 3D visualization for realistic try-on experiences. Performance evaluation proves a face shape classification accuracy of 92%, with occasional misclassifications for similar facial types. The augmented reality module achieves an average Intersection over Union (IoU) of 81% and a width error margin of approximately 5%, ensuring correct and visually appealing overlay alignment. By integrating face shape analysis and virtual try-on capabilities, this research contributes to advancing interactive and personalized solutions in the e-commerce domain, bridging the gap between traditional in-store and digital shopping experiences.
Enhancing Traffic Efficiency of Unsignalized Intersections Through Signal Coordination: A Case Study of Pulchowk and Sahidchowk in Bharatpur
The aim of this study is to improve the traffic flow at Pulchowk and Sahidchowk intersections in Bharatpur, Nepal through signal coordination and two alternatives were adopted optimizing the cycle time and optimised signal timing with left-turn control. Traffic counts during peak hours and geometric data were measured, and intersection performance was evaluated on the basis of Level of Service (LOS), average delay, and Back of Queue (BOQ) length. Among the two alternatives analyzed, left turn control showed the highest improvement. The average delays during the morning peak hour were cut from 113.8 seconds to 54.2 seconds in Pulchowk and from 76.6 seconds to 35.6 seconds in Sahidchowk, while the back-of-queue lengths shortened from 821.5 meters to 414.5 meters and from 364.9 meters to 178.5 meters, respectively. During the peak evening times, delays decreased from 507.2 seconds to 62.4 seconds in Pulchowk and from 318.1 seconds to 32.7 seconds in Sahidchowk, with corresponding drops in BOQ from 1345.9 meters to 328.3 meters and from 861.3 meters to 277 meters, respectively. These findings showcase the efficiency of the integrated approach in averting delays and congestion in the study intersections.
Detection and Classification of Corn Leaf Diseases Using Resnet-18
Crop disease is a major problem for farmers, especially in Nepal, where many people depend on agriculture for their livelihood. Traditional methods of disease detection are often time-consuming, labor-intensive, and prone to human error. This study proposes an automated system for detecting and classifying corn leaf diseases using deep learning techniques. The ResNet-18 model is employed to detect and validate corn leaves and to classify diseases such as Common Rust, Blight, and grey Leaf Spot. The system achieved an accuracy of 91%, with a precision of 91.91% and a recall of 91.44%. By leveraging image processing and deep learning, this research provides a scalable and cost-effective solution for early disease detection in corn leaves, aiding farmers in timely intervention and crop management.
TechBot: A Computer Science Focused QA System
This paper presents Tech Bot: A Computer Science Focused QA System designed for the computer science domain. Since there is a rapid increase in the field of computer science, a system that can provide accurate, context-aware responses specific to computer science is desperately needed. TechBot addresses this need by combining the advanced capabilities of Natural Language Processing techniques and Deep Learning Models. It includes a strong preprocessing pipeline, a query comprehension module that is capable of decoding technical terms and an answer retrieval framework optimized for computer science content. It performs better in the computer science domain than in general-purpose systems. Using the Hugging Face Dataset Library with extra web scraping, 20,076 question-answer pairs were collected. Secondly, the QA pairs were split into training and validation sets. Two state-of-the-art transformer-based sequence-to-sequence models, T5 and BART, which are designed for natural language generation, were employed. After fine-tuning on the domain-specific dataset, BART outperformed T5 based on the ROUGE score evaluated on the validation set. Initially, the BART model with 140 million parameters was trained for 21 epochs, achieving a ROUGE score of 0.2867. Finally, an average ROUGE score of 0.2890 was obtained on the test dataset.
Image Synthesis using U-net: Sketch 2 Image
Image synthesis has been an important part of digital art, fashion design, and law enforcement, among others. In this paper, we introduce Sketch2Image, an automatic system for converting hand-drawn sketches to realistic images based on Conditional Generative Adversarial Networks (cGANs). The model employs a U-Net-based encoder and decoder to produce high-quality images with detailed finesse. The feasibility study takes into consideration technical, operational, economic, and scheduling factors, guaranteeing practicability and effectiveness. The incremental development approach is followed in the project, guaranteeing iterative improvement and performance boost. The assessment is done based on metrics like Mean Squared Error (MSE), Structural Similarity Index (SSIM), and adversarial loss, guaranteeing model efficacy. Experimental results confirm the system's capacity for creating visually realistic and contextually relevant images, with potential applications in creative and investigative fields.
Surakshit Web: An AI-Powered Phishing URL Detection System
Phishing attacks have become a significant cybersecurity threat, exploiting users' trust to steal sensitive information through deceptive URLs. Traditional detection methods often fail to keep up with evolving phishing techniques. This research proposes Surakshit Web, an AI-powered phishing URL detection system leveraging deep learning and optimization techniques. The system integrates Convolutional Neural Networks (CNNs) and a Genetic Algorithm (GA)-optimized Multi-Layer Perceptron (MLP) to enhance detection accuracy. A comprehensive dataset of malicious and benign URLs was used, incorporating advanced feature extraction and data pre-processing techniques to improve model performance. The results indicate that the GA-optimized MLP outperforms CNN, achieving a 96.12% accuracy compared to CNN’s 93.78%, demonstrating its effectiveness in identifying phishing URLs. This research highlights the potential of evolutionary algorithms in optimizing deep learning models for cybersecurity applications.
CNN-Based Bird Sound Detection: A Comparative Performance Study
The use of automated systems for biodiversity monitoring has positioned bird sound detection as a crucial technology for modern ecological research. This study introduces a deep learning framework for bird sound detection, demonstrating strong performance in challenging acoustic environments. By leveraging feature extraction techniques such as Mel spectrograms and comparing multiple convolutional neural network architectures, this approach achieves competitive results. In our experiments, the best-performing model ResNet50 achieved an accuracy of 90.75%, with a recall of 0.89, a precision of 0.91, and an F1-score of 0.90 on the test set. These results highlight the method’s potential for real-time biodiversity monitoring, providing a reliable tool for ecological research, citizen science initiatives, and conservation efforts.
Modeling Precipitation: A Statistical and Machine Learning Approach
Precipitation modeling is indispensable for understanding hydrological processes, optimizing water resource management, and mitigating precipitation-induced geophysical mass flows such as debris and mud flow risks. This study employed both statistical and machine learning techniques to model precipitation patterns in Kathmandu, Nepal. Specifically, it compares the performance of the autoregressive integrated moving average (ARIMA) model and the Support Vector Regressor (SVR), using five years of historical meteorological data from the Department of Hydrology and Meteorology, Babarmahal. Stationarity tests confirmed the dataset's stationarity, allowing the use of ARIMA without differencing. Initial ARIMA parameters were selected using ACF and PACF plots and refined through grid search with AIC scores. Performance evaluation demonstrated that SVR outperformed ARIMA in both in-sample and out-of-sample predictions, as indicated by lower RMSE and MAE and higher Nash-Sutcliffe Efficiency (NSE) values, suggesting a better data fit and generalization. Furthermore, the Durbin-Watson statistic confirmed SVR's superior handling of autocorrelation, reinforcing its effectiveness in precipitation forecasting. However, both models faced challenges in accurately predicting extreme precipitation events. Future research should explore a hybrid approach integrating statistical models, machine learning, and deep learning to enhance predictive performance. Additionally, further consideration of meteorological and geographical parameters may improve model accuracy. These findings can support meteorologists, urban planners, disaster management authorities, and policymakers in strengthening early warning systems, improving water resource management, and enhancing resilience against climate-related challenges.
Deep Fake Audio Detection Using a Hybrid CNN-BiLSTM Model with Attention Mechanism
The increasing sophistication of deepfake technologies has raised significant concerns regarding the authenticity of audio files. To address this challenge, we propose a deepfake audio detection system that employs a CNN-BiLSTM hybrid model for identifying synthetic speech. The system processes audio files by converting them into Mel-Spectrograms, which effectively capture the unique spectral features distinguishing real voices from fake ones. The processed data is then fed into the CNN-BiLSTM model, which leverages the strengths of Convolutional Neural Networks (CNNs) for spatial pattern recognition and Bidirectional Long Short-Term Memory Networks (BiLSTMs) for capturing long-term dependencies in the temporal sequence of the audio data. The model, trained on dataset, achieves an accuracy of 95%, effectively detecting subtle irregularities indicative of deepfake audio. The system provides users with a comprehensive analysis, including a confidence score and detailed insights into the authenticity of the audio, offering an effective tool for distinguishing real from fake audio. Our system combines cutting-edge machine learning technology with a user-friendly interface, making it both highly effective and accessible for practical applications in deepfake detection. Unlike existing systems that rely solely on either CNN or RNN architectures, our approach integrates both to enhance detection accuracy, particularly for complex and subtle manipulations. Additionally, we introduce a confidence scoring mechanism and insight analysis that provides users with transparent reasoning behind each detection.
This Paper demonstrates an automated system to streamline the KYC (Know Your Customer) process using machine learning. It integrates document validation, image pre-processing, text extraction, automated form filling, face detection, liveliness detection, and verification. Tesseract, utilizing LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network), extracts text from smart driving licenses to auto-fill digital KYC forms, reducing manual entry errors. MTCNN (Multi-task Cascaded Convolutional Networks) handles face detection, OpenCV ensures liveliness, and FaceNet (Inception-ResNet-v1) verifies the selfie against the ID photo for security. Successful verification grants confirmation; otherwise, users resolve issues. The system achieved 86.43% training and 86.25% validation accuracy, improving efficiency, accuracy, and user experience.
Network Intrusion Detection System using Machine Learning
In modern network environments, security threats such as Denial of Service (DoS) and Distributed Denial of Service (DDoS attacks pose significant risks to network availability and performance. These attacks aim to overwhelm network resources, disrupt services, and make systems inaccessible to legitimate users. A Network Intrusion Detection System (NIDS) plays a crucial role in identifying and mitigating these threats by continuously monitoring network traffic and detecting malicious activities. This project presents a Network Intrusion Detection System (NIDS) utilizing the Fast k-Nearest Neighbors (Fast k-NN) algorithm for detecting Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks in a network environment. The CICIDS2017 dataset is used for model training, involving data preprocessing steps such as cleaning, trans formation, feature selection, extraction, and labeling. The dataset is split into training, validation, and testing sets, where the Fast k-NN algorithm is applied to train the NIDS model. The experimental setup includes a Mininet-based custom topology controlled by the POX SDN controller. DoS and DDoS attacks are simulated using hping3, and network traffic is captured using Wireshark. Features are extracted using NTL FlowLyzer and processed for intrusion detection. The trained model predicts attack patterns, logging detected intrusions for analysis. This approach provides an efficient and scalable intrusion detection system that enhances network security by leveraging machine learning for accurate attack detection.
Study on Loan Default Pattern and Auction Cases with Special Reference to Nabil Bank Limited, Global IME Bank Limited, and NIC Asia Bank Limited, Nepal
Loan in banks are highly susceptible of being default during the loan period due to factors such as political instability, poor financial management and others. Among these factors, loan default not only influence the financial situation of the bank itself but also the country as a whole. This study aims to investigate the causes, consequences, and potential remedies for loan default and its process of auction in Nepal, with Global IME Bank Limited, NIC Asia Bank Limited and Nabil Bank Limited as case studies. The research employed structured questionnaires as the primary data collection tool, with responses from 103 participants and the period of study is carried over a span of a year Analysis revealed that the real estate has the highest number of loan defaults among banks and poor business management among borrowers is the primary reason for loan default. Also during the auction process, the respondents claimed that valuation disputes is the major issue. The respondents also suggested that quick legal guidelines should be followed in a step-by-step process in order to deal with auction after loan default and also they suggested that ensuring that the loan is used for the proper purpose could also be an effective method to mitigate the defaulted. The study further identified that the most significant consequence of loan default is impact to country’s economy, apart from the bank financial situation.
Smart Multiplug: IoT-Based Energy Monitoring and Remote-Control System for Sustainable Power Management
This paper presents an IoT-based smart multiplug system designed for energy conservation and device protection. The system leverages an ESP32 microcontroller with integrated Wi-Fi connectivity to enable remote monitoring and control of electrical appliances through the Arduino IoT Cloud platform. Key features include real-time current and temperature monitoring, remote switching capability, and customizable threshold alerts for device protection. Evaluation results demonstrate the system's effectiveness in monitoring power consumption with a current threshold of 10A and temperature threshold of 70°C for safety. The total implementation cost of approximately $25 makes it an economically viable alternative to commercial solutions. The proposed system successfully addresses the limitations of traditional electrical outlets by providing seamless integration of monitoring and control capabilities, thereby enhancing energy efficiency and device protection in both residential and commercial settings.
Comparison of In-situ and Numerical Shear strength analysis of Sandstone at Nalagad Hydropower Site
The shear strength of rock is a critical factor influencing the stability and safety of hydropower structures, particularly in underground powerhouse sites. This study investigates the shear strength behavior of sandstone at the underground powerhouse site of the Nalagad Hydropower Project through a comparative analysis of in-situ direct shear tests and numerical simulations performed in Abaqus/CAE 2020. A dynamic explicit approach with quasi-static conditions is employed to simulate stress distribution. Additionally, a mesh sensitivity analysis is conducted to evaluate the influence of element size on numerical shear strength predictions. The results indicate that the numerical model converges at a 20 mm mesh size, ensuring accuracy without excessive computational cost. In in-situ testing, the shear stress of sandstone was measured as 1.15 MPa and 1.80 MPa under normal stresses of 0.25 MPa and 0.65 MPa, respectively. Corresponding numerical modeling results yielded shear stresses of 1.10 MPa and 1.47 MPa under the same normal stress conditions. This indicates that the numerical model underpredicted shear stress values by approximately 4.3% at 0.25 MPa and 18.3% at 0.65 MPa compared to in-situ measurements. The observed discrepancies highlight the influence of material heterogeneity, boundary conditions, and constitutive model assumptions in numerical simulations. This research paper helps to understand the shear behavior of sandstone under specific loading condition and contribute in rock engineering. The paper can be useful for model enhancement and future research. The paper only focuses on shear behavior of sandstone but future research can be extended to other rock masses.
Assessment of Prevalent Practice for Detection and Prevention of Unbalanced Bids in Nepal
There are various factors causing delay in developmental activities of the country, among which low bidding and unbalanced bid is the major one. Contractors tend to unbalance their bids in order to win contracts and maximize profits, causing disadvantage to the balanced least bidder and indirect economic loss to the client. The purpose of this study was to compare and analyze prevalent practice with theoretical developed models and develop the necessity of further study. Five projects of above 20 million (Single Stage – Two Envelope) from Road Division Surkhet and three models were considered to evaluate whether the bidder to whom the contract was awarded, is the most balanced bidder and also determine whether the prevalent practice is the most beneficial to the client. This study concluded that 3 out of 5 contracts were awarded to the unbalanced bidder, which caused systematic disadvantage to the balanced least bidder and In 4 projects, the use of rebalancing model could have been economic to the Client, saving the public fund
Impact of Modernization of Rani Jamara Kulariya Irrigation System in Kailali District
The modernization of the Rani Jamara Kulariya Irrigation System (RJKIS) in Tikapur, Kailali district, Nepal, has significantly impacted agricultural productivity, water management, and socio-economic conditions in the command area. The study examines the impacts of the modernization of the irrigation system after the completion of the first phase of modernization and the ongoing second phase of modernization which is close to the completion phase. A questionnaire, interviews, and field observation were used to collect data about the pre-modernization position and the recent status of the system. The study reveals an increase in cropping intensity from 193% to 253%, a shift toward diversified crops, and an increase in the annual crop value of NPR 5,660.27 million. Institutional strengthening of the Water User Association (WUA) improved governance and dispute management in the farmer community with affirmative ratings of modernization showing better operational efficiency. Labour shifts created employment opportunities, contributing NPR of 1.47 billion annually. The financial assessment indicates a pay-back period of 8 years, with an Economic Rate of Return (ERR) of 12.96% and a Benefit Cost (B/C) Ratio of 4.63 illustrating the system's economic sustainability and financial viability. For sustaining the system, farmer training, policy revision, agro-industry promotion, and community participation enhancement are the primary recommendations.
Assessment of Problem Associated with the Ilam Urban Water Supply System of Nepal
The Ilam urban water supply system in Nepal faces serious challenges that affect its efficiency and reliability. Many of the pipes and infrastructure are old and damaged, leading to frequent leaks and water wastage. Some areas do not get enough water, while others have too much, causing distribution problems. The Water Users' Committee (WUC) also struggles financially because it does not receive enough support from the municipality. Due to this, it cannot afford proper maintenance or pay staff well, making it harder to keep the system running smoothly. Additionally, the recent shift to a new management system has created further issues, as poor knowledge transfer and resistance to change have disrupted daily operations and led to financial losses. To fix these problems, a comprehensive plan is needed. First, the infrastructure must be improved by using better materials, fixing leaks regularly, and adopting modern leak detection technology. Strengthening financial planning, creating a formal water management board, and ensuring proper training for staff will also help improve management and reduce disruptions. Proper planning for water demand and supply, along with risk assessment, is necessary to keep the system stable in the long run. By taking these steps, the Ilam water supply system can become more reliable, ensuring a steady and efficient water supply for the growing community.
An Optimized Graph Based Implementation for Efficient Journey Planning with Public Transport in Kathmandu Valley
Kathmandu Valley’s public transport system, serving 28% of the population with only 3% of registered vehicles, lacks centralized route information, causing inefficiencies in commuting. This paper proposes an optimized graph-based journey planning system tailored for the valley’s semi-formal transit network. The system integrates multi-modal routing algorithms, optimizing routes based on travel time, cost, transfers, and walking distance. A combination of data collection methods, including user surveys and on-site validation, informs the route recommendations. Evaluation results show that the system reduces average journey time by 18%, with 73% of users discovering more efficient routes. The findings highlight the potential for data-driven optimization in urban mobility without major infrastructure changes, offering insights applicable to other developing cities.
Shallow Groundwater Level Monitoring in the Kathmandu Valley: Investigating Citizen Scientist Performance and Groundwater Level Data Fluctuation
Shallow groundwater level monitoring is crucial for the sustainable management of water, particularly in rapidly growing cities like the Kathmandu Valley (Valley). Engaging local communities as citizen scientists (CS) offers cost-effective data collection and creates awareness about groundwater status and management practices. This study investigates the performance of citizen scientists (regularity and data submission error) in groundwater level monitoring and evaluating the groundwater fluctuation trend in the Valley. During 2020, 68% of the groundwater CS was categorized as regular. In 2021 and 2022, regularity increased to 91% compared to the previous years, and regularity decreased in 2023 and increased in 2024. Meanwhile, a rise in the percentage of CS sending incorrect data was observed. Despite these fluctuations, the overall error percentage was very low (<8%), highlighting the reliability of the CS-collected data. The monthly groundwater level was recorded from 2020 to 2024 by citizen scientists mobilized across the Valley. Groundwater levels were shallower during the monsoon and post-monsoon months, while in pre-monsoon and winter, the levels were observed to be deeper. The study demonstrates the reliability of CS-collected data and highlights the seasonal trends in the shallow groundwater wells in the Valley, while also demonstrating the power of citizen science in improving the monitoring and sustainable management of groundwater.
A Contextualized Groundwater Governance Framework for Sarlahi, Nepal
Effective groundwater governance is vital for sustainable resource management. This study presents a contextualized groundwater governance assessment framework applied to Barahathawa Municipality, Sarlahi, Nepal. The framework evaluates governance across four dimensions—technical, legal/institutional, cross-sector policy coordination, and operational—using 32 indicators to derive a composite Groundwater Governance Index (GGI). Barahathawa’s GGI of 1.03 reflects an early acceptable governance state. The technical dimension (midway between non-existent and basic) highlights gaps in groundwater data collection and dissemination. Legal and institutional mechanisms are approaching a basic level but are limited by the absence of comprehensive regulatory frameworks. Cross-sector policy coordination has reached an acceptable state, reflecting efforts to collaborate with local units/departments and sectoral entities, although these efforts are often informal and ad hoc. The operational dimension demonstrates initial progress toward an acceptable state, with advancements in transparency, conflict resolution, and community engagement in groundwater management discussions. However, further efforts are needed to enhance inclusivity and to establish a community-focused groundwater management action plan. This assessment framework thus provides a holistic and context-specific approach to identifying strengths and gaps in groundwater governance. By integrating insights from both experts and community stakeholders, it serves as an effective tool for understanding governance dynamics and guiding targeted improvements.
Evaluation of Mechanical and Physical Properties of Timber Available in Nepal using Indian Standard
In Nepal's construction sector, timber remains an indispensable material, forming the backbone of residential buildings, commercial structures, and cultural heritage sites. Yet the absence of standardized quality assessment protocols has resulted in unpredictable material performance, compromising structural integrity and safety. This study bridges this critical gap by systematically evaluating four predominant Nepalese timber species - Sal (Shorea robusta), Sisau (Dalbergia sissoo), Chir Pine (Pinus roxburghii), and Utis (Alnus nepalensis) - employing arduous testing methodologies outlined in Indian Standard IS 1708:1986. Through comprehensive laboratory analysis, we quantified key mechanical properties including modulus of rupture (MOR), modulus of elasticity (MOE), compressive and tensile strengths, alongside physical characteristics like density. Our results demonstrate Sal's superior structural capacity (MOR: 129.9 N/mm²; MOE: 13,959.7 N/mm²), validating its traditional use in critical load-bearing elements. Sisau exhibited exceptional surface hardness, confirming its suitability for high-traffic flooring, while the lighter Chir Pine and Utis showed promising characteristics for cost-effective, non-structural applications. This research provides the first standardized dataset for Nepalese timber species, offering a scientific basis for material selection that harmonizes traditional building knowledge with modern engineering requirements. The implementation of IS-based evaluation protocols presents a practical pathway for Nepal to establish much-needed quality benchmarks, enhance construction safety, and optimize the use of its valuable forest resources in an era of increasing seismic awareness and sustainability concerns.
Study on Road Safety Audit of Existing Kanti Rajmarg: A Case Study of (Tika Bhairab- Baguwa Section) of Kanti Rajmarg at Lalitpur, Nepal
Ensuring road safety of existing road is crucial to preventing accidents and improving the long-term usability of roadways. This study focuses on a road safety audit (RSA) conducted during the existing phase of the Tika Bhairab-Baguwa section of Kanti Rajmarg in Lalitpur, Nepal. The main aim is to assess the safety performance of the road, identify potential hazards, and highlight design flaws that may lead to accidents in its operation level. Through field observations, surveys, and interviews with road users and experts, the research identified several key safety concerns. These include poor road alignment, narrow lanes, limited visibility, and insufficient signage or markings. Additionally, gaps in traffic law enforcement, coupled with inadequate training for traffic police, contribute to heightened road safety risks. Key findings highlight critical safety concerns such as poor visibility at curves, inadequate pedestrian crossings, lack of proper signage, and hazardous roadside encroachments. Based on questionnaire survey major problems associated with road safety of Kanti Rajmarga with highest RII value were narrow lane width, poor road signage and marking both with RII value 0.85. Similarly, poor road signage and marking, improper alignment both with RII value 0.8. The main cause of narrow lane width was found due to budget constraint, poor plaining and hilly terrain. Based on the results, practical recommendations are proposed, including improved road markings, installation of guardrails, enhanced street lighting, and better traffic management at conflict zones, increasing law enforcement efforts, and providing better training for traffic police and drivers are critical steps to making the road safer. A good drainage system should be created to prevent flooding and erosion, and strong barriers should be placed near steep drops. These recommendations aim to reduce risks and ensure the safety of all road users. Thus this study contributes to the broader discourse on road safety in Nepal by providing a case-specific assessment and actionable solutions. The findings can aid local authorities in implementing effective safety upgrades, ultimately reducing accidents and ensuring safer mobility on Kanti Rajmarg.
The "RFID-Based Quick Registration and Biometric Recognition System" is an advanced, technology-driven initiative designed to address critical challenges in patient registration, data management, and healthcare service delivery, particularly in resource-constrained regions like Nepal. This innovative system integrates RFID (Radio-Frequency Identification) technology with biometric authentication to create a seamless, secure, and efficient process for patient identification and data retrieval. When a patient’s RFID card is scanned, their complete medical history, personal details, and treatment records are instantly accessed from a centralized, cloud-based database, enabling healthcare providers across Nepal to retrieve critical information in real-time, regardless of geographic location. The inclusion of biometric recognition ensures robust security, safeguarding sensitive patient data against unauthorized access and ensuring compliance with privacy regulations. In rural areas of Nepal, where healthcare infrastructure is often limited, this system holds immense significance by reducing the dependency on manual record-keeping, minimizing errors, and significantly cutting down patient waiting times. It also facilitates better coordination among healthcare facilities, enabling timely diagnosis and treatment, especially for patients referred from remote areas to urban centers. Furthermore, the system supports telemedicine initiatives, allowing healthcare professionals in rural clinics to consult with specialists in urban hospitals by providing instant access to patient data. By streamlining administrative processes, improving data accuracy, and enhancing inter-hospital communication, this project aims to bridge the gap between urban and rural healthcare services, promote equitable access to medical care, and lay the foundation for a modern, interconnected healthcare ecosystem in Nepal. Its implementation has the potential to transform healthcare delivery, improve patient outcomes, and contribute to the overall development of the nation’s healthcare infrastructure.
24th April 2025 Dear Colleagues, Contributors, and Readers, I am proud to announce the release of the special edition of the International Journal on Engineering Technology (InJET), featuring the KEC Conference 2025 Proceedings, Volume VII. As the Editor-in-Chief of the KEC Conference 2025, I extend my warmest greetings to all participants contributing to this academic endeavor. The theme of the conference, "Dedicated to Enhancing Research Ability," highlights the importance of research in shaping the future of engineering and technology. We remain committed to fostering collaboration and advancing knowledge. In its seventh edition, the KEC Conference continues to attract students, academicians, and researchers from around the world. The conference focuses on key areas, including Civil Engineering, Computer Engineering/Science, Electronics Engineering, Mechanical Engineering, and Environmental Science. This volume includes carefully reviewed research papers written by passionate contributors who share our dedication to excellence. Each paper undergoes rigorous evaluation by national and international experts to ensure its quality and relevance. I express my sincere thanks to our reviewers for their critical insights and efforts in maintaining high academic standards. Their expertise plays a crucial role in elevating these proceedings to an international level. In addition, we hosted two symposiums during the conference. The first was the Symposium on Traffic and Transportation Engineering, organized in collaboration with the Society of Transport Engineers Nepal (SOTEN) and Safe & Sustainable Travel Nepal (SSTN). The second was the Symposium on Environment, Energy, and Water, held in collaboration with the Center for Research for Environment, Energy, and Water (CREEW). Finally, I would like to sincerely thank Kantipur Engineering College for trusting us with the responsibility of organizing this prestigious event. Their continuous support and commitment to academic excellence have been vital to our success. Best Regards, _______________ Er. Pralhad Chapagain Editor-in-Chief 7th Conference on Engineering and Technology KEC Conference 2025