Proceedings of International Conference on Innovation in Computing, Science, Engineering and Technology
Hate Speech Detection in Nepali Social Media: A Comparative Analysis of Machine Learning and Transformer-based Approaches
The growth of hate speech on social media presents serious problems for low-resource languages like Nepali, as automated detection systems for the Nepali language are underdeveloped. This paper presents a comprehensive approach for detecting hate speech in Nepali social media by combining lexicon-based feature engineering with machine learning and transformer-based models. The NEHATE dataset, which included 13,505 annotated tweets from Nepal’s municipal election discourse in 2022, was used for this study along with a curated lexicon of 1,077 offensive terms divided into classes such as Politics (115 terms), Race (77), Vulgar (50), Disability (37), and Gender (15). The lexicon includes 159 taboo terms and 158 severely offensive terms, with ratings ranging from 1 (mild) to 5 (severe). The methodology uses the lexicon for feature engineering rather than training data, extracting offensive term counts, maximum offensiveness scores, taboo presence, and category-specific indicators. Two traditional machine learning models – Naive Bayes and Gradient Boosting were applied using character-level TF-IDF (n-grams 2–5) with lexical characteristics. Additionally, three multilingual transformers—mBERT, XLM-RoBERTa, and MuRIL were fine-tuned. The pre-processing pipeline handles both Devanagari script and Romanized Nepali text common on social media. Experimental results on an 80-20 train-test split demonstrate that Random Forest Classifier achieves the best performance among traditional machine learning models (F1-score: 0.847, AUC-ROC: 0.912), while MuRIL outperforms other transformer models (F1-score: 0.863). Also for the ablation study lexicon enhanced features improve F1-score over TF-IDF alone. This study demonstrates that lexicon-enhanced feature engineering significantly improves hate speech detection in lowresource languages and provides practical recommendations for developing content moderation systems for Nepali-speaking communities.
Copyright (c) 2025 Proceedings of International Conference on Innovation in Computing, Science, Engineering and Technology
Powerlifting Game Digitalization and Management System
This work relies on the fact that organizing powerlifting game is still carried out traditionally in Nepal. We have analyzed various steps throughout the game that are being handled by different fragmented tools and software. It was observed that various fragmented tools delayed the game, decreasing performance efficiency of players and organizers which needed to be solved using a single software platform with all integrated features. By incorporating crucial feature like IPF-GL calculation, leaderboard display, lift submission and validations etc., Powerlifting Game Digitalization and Management System (PGDMS) has been designed on a mobile platform to be used specifically in Nepal by aiming at digitalizing and simplifying the traditional and hectic manual process of organizing and executing powerlifting events.
A Hybrid Attention-Driven Recurrent Neural Network Model for Sentiment Classification of Social Media Texts
With the rapid expansion of user-generated content on social media platforms like Twitter, Facebook, and Reddit, accurately identifying sentiment from textual data has become an essential yet challenging task due to the informal, noisy, and contextually diverse nature of these platforms. To address this, we propose a Hybrid Attention-Driven Recurrent Neural Network (HA-RNN) model that effectively combines Bidirectional Gated Recurrent Units (Bi-GRU) with a sophisticated attention mechanism for sentiment classification. The model utilizes pre-trained GloVe embeddings (300 dimensions) to capture rich semantic features from raw text, enhancing the initial representation of social media data. The Bi-GRU layers are employed to model sequential dependencies Safeyah Tawil Department of Computer Science and Engineering, Faculty of Information Technology, Zarqa University, Zarqa, Jordan. University of Business and Technology, Jeddah, Saudi Arabia stawil@zu.edu.jo I. INTRODUCTION Social media platforms have become primary channels for individuals to express opinions, emotions, and sentiments on a wide range of topics, including politics, products, services, and global events. The explosive growth of platforms such as Twitter, Facebook, and Instagram has led to an overwhelming amount of unstructured textual data that offers valuable insights into public sentiment [1]. Analyzing this vast content can support businesses, governments, and researchers in understanding user perceptions, improving services, and detecting social trends. in both forward and backward directions, ensuring a comprehensive understanding of context within a sentence. The integrated attention layer enables the model to dynamically focus on sentiment-bearing words, thereby improving classification accuracy and interpretability. We evaluated the proposed model on two widely recognized datasets: the Twitter US Airline Sentiment Dataset and the Sentiment140 Dataset. The HA-RNN achieved an accuracy of 90.8% on the Twitter US Airline dataset and 88.5% on Sentiment140, outperforming traditional models such as CNN (84.3% accuracy), LSTM (86.7%), and Bi-GRU without attention (87.1%). Furthermore, the attention mechanism provided insightful visualization, highlighting the critical words influencing sentiment predictions. The model demonstrated a balanced performance with high precision, recall, and F1-scores, validating its robustness across different sentiment classes. Overall, the HA- RNN model presents an effective and interpretable solution for sentiment analysis on noisy and diverse social media texts, supporting applications in social monitoring, brand analysis, and opinion mining.
Calorie Burn Prediction using XGBoost with Feature Selection and SHAP Analysis
Calorie burn prediction plays a crucial role in fitness assessment and personalized exercise guidance. This study applies XGBoost regressor with feature selection and SHAP (SHapley Additive exPlanations) analysis to predict calories burned in an exercise session, using physiological and activity-based features for training model. Feature selection was conducted using correlation analysis and Variance Inflation Factor (VIF) screening to reduce redundancy and improve interpretability, resulting in a simplified 5-feature model that balances performance and model complexity.While the full-feature XGBoost regressor achieves the highest predictive accuracy, the proposed five-feature XGBoost regressor demonstrates similar performance with an MAE of 2.19, an MSE of 9.98, and an R2 of 0.9972, while reducing input dimensionality. Model interpretability is further enhanced through global and local SHAP analysis, revealing the significant influence of heart rate and duration on predictions. These results indicate the potential of gradient boosting models for session calorie burn prediction while suggesting their applicability in fitness tracking systems and personalized exercise guidance system.
AI-Powered Roadmap Generator Prompt Engineering
One of the most common issues in AI is discussed in this paper the problem of making Large Language Models (LLMs) produce the correct, clean and structured output in the form of a JSON. This is of significance in a majority of real world applications employing AI. The researchers examined the solution of this issue by an app named “Roadmint”. They searched the key working formula. The trick is simple like - Make the artificial intelligence know what to do, Clearly give the JSON structure inside the prompt, Give a few good examples, Create some strict rules - such as what’s the AI is not allowed to do. The most appropriate is that everyone can use this approach as a template. In case you are developing an AI project and you want your model to provide the same and correct output, such a strategy can make your system more stable and easy to manage.
Dual Conditional Diffusion Models and Generative Diffusion Models for Sequential Recommendations
Sequential recommendation systems are designed to forecast the subsequent item a user is expected to engage with, based on their past interactions. Both Generative Diffusion Models for Sequential Recommendations (known as DiffuRecSys) and Dual Conditional Diffusion Models for Sequential Recommendation (referred to as DCRec) utilize diffusion models to enhance the accuracy of recommendations. While DiffuRecSys emphasizes improving robustness and understanding user-item interactions via cross-attention and offset noise, DCRec adopts a dual conditional strategy that combines both implicit and explicit conditioning to boost recommendation accuracy and computational efficiency. This paper presents a comparative evaluation of the two methods, emphasizing their approaches, significant contributions, and findings. Both models show remarkable advances compared to leading baseline methods, with DiffuRecSys particularly adept at understanding varied user preferences, while DCRec stands out in terms of both accuracy and efficiency. The overview wraps up with an examination of their individual advantages, drawbacks, and possible paths for future development.
Use of Wireless LoRa Technology in Flood Early Warning Systems
Wireless communication is paramount for effective environmental monitoring and disaster preparedness, particularly in remote and flood-prone regions. This paper investigates the application of Long Range (LoRa) technology as a robust and efficient solution for Flood Early Warning Systems (EWS). We delve into LoRa’s fundamental characteristics, including its extended range, low power consumption, and resilience to interference, which make it uniquely suited for deploying sensor networks in challenging geographical areas. The paper details the typical architecture of a LoRa-based EWS, encompassing remote sensing nodes, repeaters, and a central receiver, and discusses key operational parameters such as carrier frequency, transmit power, and data packet configurations. Furthermore, we explore essential security measures, including AES-128 encryption and authentication mechanisms, to ensure data integrity and confidentiality. Through a generalized examination of its deployment, this paper demonstrates LoRa’s efficacy in facilitating realtime data collection for hydrological parameters, enabling timely data access and enhancing community resilience against natural disasters.
Generating Sanskrit Captions for Images using Transformers and Long Short-Term Memory
This study addresses the challenge of automated image captioning for Sanskrit, a low-resource language that lacks dedicated models and datasets for vision-language tasks. We propose a encoder-decoder architecture that integrates a Vision Transformer (ViT) for visual feature extraction with a Long Short-Term Memory (LSTM) for generating syntactically coherent Sanskrit captions. We curated and utilized a dataset of 40,000 image-caption pairs, with English captions from Flickr manually translated into Sanskrit. In validation data, trained model achieved BLEU-1, BELU-2, BELU-3, BELU-4, ROUGE L scores of 0.3082, 0.1843, 0.1115, 0.0639, and 0.3472 respectively. This work represents a significant advancement in the processing of Sanskrit language within computer vision, with applications in multimedia retrieval for digital archives, automated content analysis of cultural heritage materials, and the development of assistive accessibility tools.
Vision Language Model for Clinical Decision Support System
In the field of assisted reproductive technology, the differentiation of competent embryos ready to transfer is also founded on the Gardner grading system, which is an assessment of blastocysts that primarily focuses on morphology, but is also largely subject to inter- and intra-observer variability. This variability can decrease the integrity and continuity of the invitro fertilization (IVF) process based on embryo classification to determine viability and an improved probability of pregnancy. To solve this problem, we present the application of Bootstrapping Language Image Pre-training (BLIP), for blastocyst grading along Gardner, and from that grading proposes an easy classification scheme. Of 249 day-5 human blastocyst images and Gardner grades, 204 images were used to fine-tune BLIP where BLIP frames the grading task as medical image captioning. Select the number of unique grades to determine that the number of model outputs was greater than 10. The average training loss was 0.1010 and the fine-tuned model achieved a Recall-Oriented Understudy for Gisting Evaluation score of 0.7391, Hamming accuracy of 0.8913 and a Metric for Evaluation of Translation with Explicit ORdering of 0.3696 for the test set. These results demonstrate that a fine-tuned vision language model can accurately approximate complex morphological features in the blastocyst image and predict their assigned grade classification.
Design and Implementation of a Customized Door Lock System for Local Needs
This paper presents a design and implementation of a customized door lock system utilizing Internet of Things (IoT) technology. The proposed system focuses on accessing the door at the ground floor from the top floor of a three- to five story building, which is not supported by most of the prevailing door lock systems. The idea emerged from a local survey conducted among the 72 houses in the vicinity of Bhaktapur, Nepal, which highlighted the local problem among the elderly population facing difficulty in opening the door at the ground floor while they are residing at the top floor. This research aims to provide a low-cost, affordable door lock system to solve the local issue of remotely accessing the door. The paper employs an ESP32-CAM microcontroller including a camera module and a Wi-Fi module, integrated with an RFID reader, keypad, and a relay-controlled solenoid lock providing the mechanical actuation for door locking and unlocking operations.
WeatherWave: A Machine Learning-Integrated Web Application for Localized Weather Forecasting in Nepal
Accurate and accessible weather information is critical for decision-making in regions characterized by complex terrain and climatic variability. This paper presents Weather Wave, a machine learning–enhanced web application for localized weather forecasting across Nepal. The system integrates real time meteorological data from external weather APIs with a Random Forest regression model trained on NASA POWER satellite-derived reanalysis data covering all 77 administrative districts of Nepal. Experimental evaluation on a held-out test set of 24,187 samples shows that the proposed Random Forest model achieves a Mean Absolute Error (MAE) of 0.424◦C, RMSE of 0.695◦C, and an R2 score of 0.9934. This high predictive performance is attributed in part to strong short-term temperature persistence characteristic of Nepal’s continental climate zones. Comparative and statistical analyses demonstrate that the model significantly outperforms standard API-based persistence forecasting across districts. The results highlight the feasibility of combining lightweight machine learning models with modern web architectures to deliver accurate, localized, and accessible weather information for geographically diverse regions.
Image Captioning using Transformer Model
This paper presents a deep learning approach for generating descriptive captions for images through the combi nation of deep learning driven image understanding and text generation. The rapid progress in deep learning has greatly enhanced machines’ capacity to interpret visual information and generate natural-language descriptions. Among these advance ments, Transformer architectures have proven especially effective due to their self-attention mechanisms, which enable models to better capture global image, text relationships. Traditional image captioning models that use CNNs for visual encoding and RNNs for sentence generation often struggle to model long-range dependencies and produce contextually rich descriptions. To address these challenges, this study introduces an intelli gent image captioning framework that utilizes InceptionV3 for visual feature extraction and a custom-built Transformer-based encoder decoder architecture for generating textual descriptions. Unlike conventional systems that depend on pre-trained language models, the decoder in this work is implemented and optimized exclusively for image captioning. Comprehensive training and evaluation are conducted using a large-scale benchmark dataset, MS COCO, to validate the system’s effectiveness across diverse image domains. This paper aims to integrate image understand ing and linguistic reasoning by providing a practical framework with applications in accessibility, digital asset management, and automated content generation.
Query Refinement using Latent Dirichlet Allocation
This study introduces a query refinement technique that leverages Latent Dirichlect Algorithm ( LDA ) and coher ence techniques. The proposed method falls under unsupervised learning, firstly preprocessing large text corpus, applying topic modelling via LDA and finding the optimal number of topics using coherence scores eventually leading to the topics which are meaningful and relevant to the text corpus. We made use of coherence measures to make sure the topics extracted from the corpus are relevant to each other and understandable. The results demonstrated that the method was successful in topic extraction and modelling of large text corpus. However, the results significantly vary on the quality of text corpus on which the method is being applied.
ViT-WoundNet: An Explainable Deep Learning Framework for Wound Type and Severity Classification
Deep learning advances have revolutionized automated wound assessment, yet convolutional neural networks (CNNs) struggle with inter-class similarities, illumination variations, and generalization across diverse datasets. This work introduces ViT-WoundNet, a Vision Transformer (ViT)-based framework leveraging self-attention to capture long-range spatial dependencies and global context, addressing these limitations effectively. ViT-WoundNet outperforms CNN baselines—ConvNeXt (85.4%), EfficientNet (86.7%), and ResNet50 (74.1%)—achieving 91.3% accuracy for wound-type classification and 88.4% for severity across 2,372 multi-class images from five public datasets. Confusion matrices and Grad-CAM visualizations confirm superior inter-class separability and clinically interpretable feature localization, positioning ViT-WoundNet as a robust, explainable solution for telemedicine applications.
Enhancing Text-based Spam Detection using Machine Learning
This research focuses on developing an intelligent and accurate system for text-based spam detection using advanced machine learning models. With the exponential growth of digital communication, spam messages have become a major issue. Core research problem include spam messages have become a major issue, often carrying misleading, fraudulent, or irrelevant content that disrupts user experience and security. The methodology involves systematic data preprocessing followed by feature extraction using TF-IDF vectorization. Several traditional models — Naive Bayes, Logistic Regression, Decision Tree, Random Forest, and Linear SVM (Calibrated) along with a Long Short-Term Memory (LSTM) model were trained and evaluated. The comparative analysis demonstrated that the LinIear SVM (Calibrated) model achieved the best overall performance among all tested algorithms, showing the highest accuracy, balanced precision-recall values, and the lowest error rates. This outcome confirms the effectiveness of combining advanced preprocessing, TF-IDF feature extraction, and hybrid machine learning techniques for spam detection. It also bridges the gap between traditional machine learning and deep learning approaches, providing a scalable foundation for real-time spam filtering. Furthermore, the study contributes to digital communication security by offering a reliable system capable of detecting and reducing unwanted or malicious text messages efficiently.
Antral Ulcer Detection Using Deep Learning
An ulcer is a break on the skin, in the lining of tissues, or on an organ. An antral ulcer is found in the antrum part of the stomach. An antral ulcer can have many complications, such as bleeding, perforation, obstruction, and even cancer, if not treated properly on time. In the remote part of Nepal, antral ulcer diagnosis is very challenging due to the scarcity of trained gastroenterologists. Most of the patients visit higher centers, which are located only in urban cities, for proper management. Those patients who cannot afford higher centers, they just take the medicine without proper diagnosis. Those who visit the higher center present very late, so there are more complications of disease. Among them, a few complications lead to serious problems, even the death of the patients. This research study has been conducted to fulfill this huge gap. Hence, AI-based applications help to eliminate delayed management and early diagnosis and treatment of the patient. It focuses on having a quick diagnosis of antral ulcer to have fast treatment that prevents the serious or complex issue. Various deep learning models based on Convolutional Neural Networks (CNNs) are used, such as ResNet50, VGG16, Mo bileNetV2, Inception-ResNet, and CNN. Here, a total of 967 images have been used to train the model, including 485 normal and 482 ulcers. The performance of ResNet50 shows better as compared to other applied models, having a validation accuracy of 96 percent, with precision, recall, and F1-scores all above 96 percent.
Development of a Drone-Based Seed Bombing Mechanism for Ecological Restoration in Challenging Terrains
This paper details the design of an advanced drone based seed bombing system engineered for ecological restoration in Nepal’s challenging and remote terrains. The existing system faced significant limitations, including a restricted seed capacity, frequent jamming, high power consumption, and an inadequate operating height. The objective of this research was to develop a new mechanism capable of increasing seed capacity to 300 seeds, ensuring continuous, jam-free operation, functioning effectively from a height of 30 meters, and minimizing electricity consump tion. The proposed solution integrates an optimized hopper with a pyramid shape and an internal mixer to prevent congestion, coupled with a robust flywheel-based shooting mechanism. This mechanism incorporates a hinged, spring-tensioned flywheel to accommodate varying seed sizes, ensuring consistent contact and precise seed acceleration to 25 m/s. Detailed calculations for motor power, spring constants, and seed trajectory informed the design. The system’s development also considered local manufacturing capabilities, aiming for a sustainable and effective reforestation tool.
Analyzing Behavior of Agricultural Commodities in Kalimati Tarkari Market using Machine Learning Technique and Prediction Strategies
Seasonal price volatility of agricultural commodities poses significant challenges to farmers and market stakeholders in Nepal due to climate variability, supply disruptions, and limited access to predictive market information. This study analyzes long-term seasonal price behavior using daily wholesale price data from Kalimati Tarkari Bazaar spanning 2013–2023. Machine learning and time-series techniques including Facebook Prophet, logistic regression, STL decomposition, and K-means clustering are employed to examine seasonal patterns, price volatility, and future price trends across six Nepali seasons. The results reveal strong and consistent seasonal dependencies, with most vegetables exhibiting peak prices during winter (He manta) and lower prices during the monsoon (Barsha). Prophet based forecasting demonstrates moderate predictive performance with an average MAE of 32.95 and an average R² of 0.42, effectively capturing trend and seasonal components for most commodities. Volatility analysis identifies high-risk commodities with substantial price dispersion, while clustering reveals distinct market segments based on price levels and variability. The findings highlight the importance of seasonal awareness and data driven forecasting in improving production planning, market participation, and policy formulation in Nepal’s agricultural sector.
Chitoz: An Efficient and Accessible Ride-Sharing Mobile Application for Urban Environment
Urban transportation in developing regions faces challenges due to unreliable public transit, traffic congestion, and limited access to high-end mobile devices and stable internet connectivity. Although ride-sharing applications offer on-demand mobility, many existing systems are feature-heavy, leading to increased resource consumption and reduced usability in con- strained environments. This paper presents Chitoz, a lightweight ride-sharing mobile application prototype designed to provide essential services with minimal computational and data overhead. The system focuses on core functionalities, including near real-time ride requests, live trip tracking, flexible payments, driver verification, and digital receipts. Chitoz employs a modular three-tier architecture comprising a cross-platform mobile frontend, a REST-based backend, and efficient data management. Functional and inte- gration testing indicate stable performance, fast response times, and suitability for low-resource urban contexts.
Real-Time Visualization and Comparative Performance Analysis of Maze Generation and Pathfinding Algorithms
Algorithm visualization remains a critical challenge in computer science education, particularly for spatial algorithms such as maze generation and pathfinding. This paper presents an interactive educational platform that provides real-time visualiza tion of three maze generation algorithms (Recursive Backtrack ing, Prim’s, and Kruskal’s) and three pathfinding algorithms (DFS, BFS, and A*). The system features a modular architecture with comprehensive performance analysis capabilities, achieving consistent 60 FPS visualization while maintaining memory usage below 0.15 MB. Empirical evaluation demonstrates that A* pathfinding reduces node exploration by approximately 75% compared to BFS and 67% compared to DFS, while maintaining optimal path guarantees.
An Integrated System of IoT-based Agriculture, Disease Detection and News Classification
Smart agriculture systems are becoming more and more integrated based on IoT and artificial intelligence to aid farm monitoring and decision-making, but most current solutions support the assessment of single elements and are studied in laboratory settings, restricting their practical implementation. The current paper introduces a highly integrated, end-to-end smart farming system which incorporates the real-time sensing of the physical environment, automated irrigation management, AI-based detection of plant diseases, and provision of information to farmers into a single, low-cost system. The system is clearly aimed at managing the practical deployment limitations such as latency, energy efficiency, reliability of connectivity and scalability in agricultural settings with resource limitation. In order to accomplish the deployment-oriented challenges, a comparative analysis of the convolutional neural networks namely ResNet 50, DenseNet-201 and MobileNet-V2 are performed using a large scale data set. DenseNet-201 is more accurate however, MobileNet-V2 is chosen to deploy on the field due to its much low inference time and model size, which is more applicable in limited resources of agriculture facilities. In addition, an agricultural news classification module is incorporated to filter domain specific information for farmers, with limitations in dataset size and generalization explicitly discussed. Experimental data prove that the proposed system effectively balances sensing, computation and automation requirements, bridging the gap between laboratory-level performance and practical smart agriculture deployment.
Low-Cost Air Quality Forecasting in Resource-Constrained Environment
Rapid urbanization exacerbates air pollution in developing regions, yet monitoring remains limited by high costs and infrastructure gaps. This paper presents an IoTedge framework integrating Raspberry Pi Pico, ESP32-WROOM microcontrollers, and Plantower PMS7003 sensors for real-time PM2.5/PM10 monitoring and 7-day AQI forecasting. Leveraging lightweight SVR regression trained on localized meteorological data, our system achieves MAE=2.8 for 48-hour predictions while operating at <10W power. The system enables community-scale deployment validated in Kathmandu Valley, demonstrating 92% uptime with component costs under $35/unit. The architecture addresses key limitations of cloud-dependent systems through edge processing, making it viable for low-connectivity regions.
Kidney Tumor Detection in Medical Image Processing
Accurate classification of medical images, particularly CT scans, plays a vital role in diagnosing and detecting renal anomalies, including tumors, stones, and cysts. This work investigates several cutting-edge deep learning techniques for the automated identification and categorization of certain renal conditions. Large datasets comprising thousands of CT images were used to evaluate different methods according to important performance metrics such as recall, accuracy, precision, and F1-score. The proposed approaches demonstrated high classification accuracy, with several models achieving over 99% in sensitivity and specificity. Additionally, The study emphasizes the significance of incorporating explainability techniques to improve model interpretability, Facilitating clinicians’ comprehension of the decision-making process. The findings emphasize the potential of these advanced techniques in enhancing medical diagnostics, particularly in environments where computational efficiency and accuracy are critical.
Sentine1X: Hybrid Human–AI Collaboration for Real-Time Web Threat Mitigation
Modern web applications face increasingly sophisticated threats that exploit application logic, API misconfigurations, and behavioral patterns often missed by signature-based defenses. While machine learning offers scalable detection, fully autonomous systems lack transparency and may produce excessive false positives or overblocking. In this paper, we introduce SentinelX, a hybrid human–AI framework for real-time web threat mitigation that integrates zero-trust policies, predictive uncertainty, and explainable decision-making. SentinelX fuses supervised classification and unsupervised anomaly detection with dynamic trust scoring and SOAR-based response actions. It incorporates SHAP/LIME explanations, human-in-the-loop escalation, and an active learning loop to adapt to novel threats. Evaluation on web traffic datasets and simulated attack scenarios shows that SentinelX significantly improves detection precision, reduces mean time to detect and respond, and minimizes analyst workload compared to state-of-the-art baselines. The system provides a practical, trustworthy blueprint for deploying safe automation in modern security operations.
GlassBox: Causal Runtime Policy Enforcement for Web Applications
Web applications routinely combine first-party code, third-party scripts, and multi-service backends. Point defenses such as Content Security Policy, Subresource Integrity, Trusted Types, and Fetch Metadata help, but they judge effects in isolation and often miss cross-tier causality. We present GlassBox, a causal runtime policy enforcement framework that links client side sinks, network hops, and server actions into a Causal Event Graph (CEG). Policies are written over causes (provenance, integrity, request context) and compiled to two enforcement points: a lightweight inlined reference monitor in the browser and a reverse-proxy/server middleware on the backend. Our prototype for Chromium and Node.js/NGINX composes with existing headers rather than replacing them. On vulnerable apps and a microservice testbed, GlassBox blocks a broad set of attacks (97% DOM XSS, 95% cross-site request abuse, 98% supply chain swaps) with low false positives (1.1%) and modest overhead (median +5.1ms page-load, +1.3ms API p95). The results suggest that causal enforcement is a practical next step for hardening modern web stacks while preserving compatibility.
SCALE-GNN: Advanced Graph Reduction for Scalable Test Case Prioritization Using GNN
Test Case Prioritization (TCP) is crucial for efficient regression testing, especially in CI pipelines where rapid feed back is critical. While Graph Neural Network (GNN) methods outperform traditional prioritization heuristics, their scalability is severely limited on large software systems due to exploding graph size and GPU memory requirements. This paper introduces Scale-GNN, a scalable and fault-aware GNN framework that aggressively reduces graph size through a three-stage pipeline combining neural feature compression, fault-centric pruning, and spectral graph coarsening. Experiments across two datasets with 150 and 5000 test cases demonstrate that Scale-GNN reduces graph size by up to 78%, cuts training time by 3.1x, reduces GPU memory by 61%, and preserves or improves fault detection effectiveness (APFD 0.85 0.93). Notably, Scale-GNN detects up to 88% of faults within the first 10% of test executions, outperforming both classical heuristics and unreduced GCN baselines. These results establish Scale-GNN as a practical approach for integrating GNN-driven prioritization into industrial-scale regression testing.
Evaluation of Machine Learning Techniques in Winner Predicting of The Hundred Games
Cricket, being one of the most popular sports worldwide, attracted significant interest in developing accurate result prediction models. The Hundred is one of the several leagues that are contested in the world. It was important to research on accurate result prediction model in this league as the fan following and attention towards this league were increasing rapidly. The dataset was divided into training and test sets, and the models were evaluated on both datasets to measure their generalization performance. The findings demonstrated the potential of machine learning techniques in accurately forecasting Hundred match outcomes, enabling stakeholders to make informed decisions in the dynamic and unpredictable domain of cricket. Logistic Regression, Decision Tree and Random Forest models were implemented for match-winner prediction, while Gradient Boosting Regressor was used for score forecasting. This study gathered and analyzed Hundred data spanning multiple years, including player, match, team, and ball-to-ball information, to generate several conclusions that helped improve a team’s performance. The study highlights the applicability of supervised learning methods for enhancing decision-making in the dynamic and unpredictable environment of The Hundred.
Real-Time Student Attentiveness Monitoring System using YOLOv5
This paper presents real-time classroom monitoring system designed to detect and quantify student attentiveness using deep learning and computer vision. By leveraging YOLOv5 for behavior recognition (reading, writing, hand-raising, and focused gaze) and integrating head pose estimation, the system provided objective, dynamic engagement metrics. The model was trained using the SCB-Dataset merged with a custom focus dataset, achieving a precision of 1.00 and mAP@0.5 of 0.681 on validation data. With real-time video stream processing, mobile webcam integration, and a responsive dashboard, ClassCam supported educators in fostering engagement through data-driven decisions.
Effect of Plan Inclination on Seismic Response on Residential RC Buildings
In earthquake prone areas like Nepal, the plan irregularity significantly influences the seismic response of the buildings. This study analyses how progressive inclination of plan influences the seismic performance of the RC residential buildings. Seven analytical models were created: six irregular models with one wall inclined at 5°, 10°, 15°, 20°, 25°, and 30°, and one regular model with parallel lateral load resisting systems (0° inclination). ETABS 2022 was used to analyze all models in compliance with IS 875 for gravity loads and NBC 105:2020. Base shear, basic period, center of mass–center of rigidity eccentricities, story drift, displacement irregularity ratios, and internal forces (shear forces and bending moments) in beams are important response factors taken into account. The findings indicate that while all storey drift ratios remain well within the Ultimate Limit State (ULS) and Serviceability Limit State (SLS) limits specified by NBC 105:2020, increasing inclination results in a slight increase in fundamental time period and base shear (less than 1% variation between 0° and 30°).The study comes to the conclusion that, when low-rise RC buildings are designed in accordance with NBC 105:2020, minor to moderate plan irregularity does not significantly affect global seismic stability; however, it does change local demand and detailing requirements close to irregular zones. Recommendations for mid- and high-rise buildings and other complex irregularities and nonlinear analysis are given.
Impact of Photovoltaic Penetration on Voltage Stability of Radial Distribution Networks using Monte Carlo Simulation
In countries like Nepal, solar PV and wind are seen as the most efficient forms of distributed generation. Adding PV as a renewable energy source to the power distribution system has helped improve voltage levels and cut down energy losses. However, there is a limit to how much PV can be added, as it can cause various problems in the power system including increased line losses and voltage stability issues. In Nepal, most consumers are connected through radial distribution networks that can be very long, sometimes hundreds of kilometers, leading to bigger voltage drops and more line losses. Adding small amounts of PV doesn’t cause any issues, but when the amount goes beyond 30-40%, some negative effects start showing up. So, there’s a balance between improving voltage and reducing losses, and the main aim of this paper is to calculate the best level of PV penetration. In this research paper, the Voltage Stability Index (VSI) is utilized to locate the buses that have the lowest VSI and are at risk of collapsing under heavy loads. The PVDG is then placed at the bus which has lowest VSI, and the PV penetration level is increased from 0 to 80% in the steps of 20 to see the results. These results are then compared and analyzed to find the optimum solution. The study was carried out on the IEEE-33 radial distribution system and the East feeder of Rajbiraj DC, Saptari. The results show that with the optimal amount of PV integration, the voltage profile and VSI of the system improve significantly.
Change Detection in Land Surface Temperature After Construction of Pokhara International Airport using Remote Sensing
This study quantifies the thermal environmental impact of Pokhara International Airport in Nepal by analyzing land surface temperature (LST) trends before and after its construction. Using the Google Earth Engine (GEE) platform, TerraClimate (1987-2025) and MODIS datasets are processed to perform a spatiotemporal analysis across two distinct periods from pre-construction (i.e., 1987-2017) and post-construction (i.e., 2018-2025) of Pokhara International Airport. The derived LST trends is validated through zonal analysis of urban heat island (UHI) intensity using urban (500 m, 1 km) and rural (2-4 km) buffers and correlated with a time-series analysis of the Normalized Difference Vegetation Index (NDVI). The results indicate a pronounced acceleration in warming following the airport’s construction, with the LST trend rising from 0.44◦C/decade in the pre-construction period to 2.53◦C/decade afterwards. Statistical validation via the Mann–Kendall test and Sen’s slope estimator confirmed a significant increasing trend (p < 0.001). This warming is coupled with a strengthening of the UHI effect, where the mean urban–rural LST difference increased from 0.5◦C to 1.34◦C, and a sustained decline in NDVI, signifying vegetation loss. The findings demonstrate that major infrastructure projects can rapidly alter local microclimates, exacerbating surface warming and UHI effects.
Unveiling the Conceptual Barriers in Geometric Visualization through Newman's Error Analysis
Students are facing difficulties in the use of reasoning and problem solving skills in geometry. It causes significantly low performance in mathematics. The data was collected from sixteen students of grade IX from a community schools in the Kathmandu district. This research aims to investigate the learners’ difficulties encountered in the experimental verification of the geometric statements. For this purpose, the answer sheets of sixteen students were divided in four groups according to their errors in proving the geometry statement. After that, researchers randomly selected fours students S1, S2, S3, and S4 based on the homogenous groups. These answer sheets were analyzed for the exploration of errors in the geometrical reasoning. The students reasoning skills in the proof of the statement 'Midpoint theorem' were analyzing by using Newman's Error Analysis methods. The findings revealed that students struggled in experimental proof of the geometric problem, exhibiting a lack of mastery over the subject matter and an understanding of experimental proof in geometric reasoning. Eventually, the issues of errors of the students themselves, particularly their deficits skills in applying prior concepts.
What Will It Take to Stay Relevant in a World Where Skills Expire Overnight?
Rapid technological disruption is accelerating skills obsolescence, exposing the limits of traditional education systems and threatening employability and workforce resilience. This keynote positions micro - credentials not as add-ons, but as a fundamental re-thinking of lifelong learning, responding to shifting industry demands driven by digital transformation, sustainability pressures, and education – industry misalignment. It highlights both the potential and the challenges of embedding micro - credentials at scale and calls for coordinated action to establish them as trusted and sustainable bridges between learning and work in a rapidly changing world.
2D/3D Image Morphing Technology from Traditional to Modern
Image morphing technology is a crucial area in computer graphics and computer vision, enabling various effects such as transformation, rotation, and deformation of 2D and 3D images. It has become essential in the metaverse, virtual reality (VR), industrial design, and so on. In recent years, the emergence of artificial intelligence (AI) has brought significant changes to image-morphing technology, impacting deformation effects and technological development profoundly. A comprehensive introduction of image morphing strategy is provided from three perspectives: (1) Linear deformation methods based on control points of topological network structures, focusing on techniques such as free-form deformation, Laplacian deformation, mesh deformation, etc., and introducing and exploring the fundamental principles of these methods. (2) Nonlinear deformation methods based on conditional constraints, addressing the limitations of linear deformation by using constraints on position, volume, and area, as well as linear blend deformation method of dual quaternions, and discussing the technical limitations of nonlinear deformation. (3) Image generative morphing methods based on large neural network models, analyzing cutting-edge techniques of the Diffusion models, Generative Adversarial Networks (GANs), and Sora world model architecture. They can fuse information from different datasets or modalities, and learn hidden distributions to achieve image generation, style transfer, and so on.
A Hyper Connected Wireless Future with Immersive, AI Native, and Sustainable Communications
The future of mobile connectivity will be defined by seamless hyper connection, where wireless networks intelligently link people, machines, and environments. In this context, 6G networks are envisioned to operate across terrestrial, underwater, and space domains, enabling ubiquitous connectivity and reliable service coverage even in the most remote parts of the world, such as dense forests, deep oceans, and polar areas. In this talk, we highlight how immersive communication experiences, powered by extended reality and multisensory interaction, will transform the way we live, work, and collaborate across both physical and digital spaces in the 6G era. Use cases in entertainment (e.g., extended reality and high-resolution 360-degree video streaming), personalized education, and digital healthcare are expected to benefit significantly from immersive communication. However, these applications impose unprecedented requirements on wireless networks, including gigabit-per-second data rates, ultra-low latency on the order of sub-millisecond levels, and near-perfect reliability. Meeting these stringent performance demands while supporting massive device density, seamless mobility, and energy efficiency poses major challenges for the design and operation of future 6G wireless networks. A major enabler of this transformation, which sets a new precedent for integrating multisensory human perception into digital communication systems, is the emergence of AI-native intelligent networks capable of real-time learning, adaptation, and self-optimization. AI-intelligent wireless networks will leverage on machine learning, deep learning, and reinforcement learning to embed AI models across the physical, MAC, and network layers of the communication stack. At the physical layer, beyond conventional digital signal processing and optimization, integration of intelligence will support advanced functionalities such as modulation recognition, channel estimation, intelligent waveform detection, and beamforming design. Our talk will also emphasize the growing importance of sustainability, with energy efficient architectures and green technologies guiding these future deployments. Modern wireless networks, including 4G LTE and 5G systems, are significant contributors to global energy consumption, as the continuous operation of base stations, cooling infrastructure, and computationally intensive technologies substantially increases both the CAPEX and OPEX costs associated with network deployment and maintenance. As a solution, green and sustainable communication techniques and practices seek to minimize energy consumption in emerging wireless network paradigms by leveraging innovative solutions such as energy-harvesting communication systems, massive MIMO, AI/ML-optimized protocols, and low power hardware. Collectively, these advancements in immersive communications, AI-integrated paradigms, and energy-efficient transmission technologies will establish a highly connected, resilient, and sustainable communication ecosystem that forms the foundation for next-generation communication innovation.