Biplov Paneru

Biplov Paneru, Bipul Thapa, Bishwash Paneru et al.

Brain-machine interfaces (BMIs), particularly those based on electroencephalography (EEG), offer promising solutions for assisting individuals with motor disabilities. However, challenges in reliably interpreting EEG signals for specific tasks, such as simulating keystrokes, persist due to the complexity and variability of brain activity. Current EEG-based BMIs face limitations in adaptability, usability, and robustness, especially in applications like virtual keyboards, as traditional machine-learning models struggle to handle high-dimensional EEG data effectively. To address these gaps, we developed an EEG-based BMI system capable of accurately identifying voluntary keystrokes, specifically leveraging right and left voluntary hand movements. Using a publicly available EEG dataset, the signals were pre-processed with band-pass filtering, segmented into 22-electrode arrays, and refined into event-related potential (ERP) windows, resulting in a 19x200 feature array categorized into three classes: resting state (0), 'd' key press (1), and 'l' key press (2). Our approach employs a hybrid neural network architecture with BiGRU-Attention as the proposed model for interpreting EEG signals, achieving superior test accuracy of 90% and a mean accuracy of 91% in 10-fold stratified cross-validation. This performance outperforms traditional ML methods like Support Vector Machines (SVMs) and Naive Bayes, as well as advanced architectures such as Transformers, CNN-Transformer hybrids, and EEGNet. Finally, the BiGRU-Attention model is integrated into a real-time graphical user interface (GUI) to simulate and predict keystrokes from brain activity. Our work demonstrates how deep learning can advance EEG-based BMI systems by addressing the challenges of signal interpretation and classification.

Biplov Paneru, Bishwash Paneru, Krishna Bikram Shah

This study presents a novel method for improving rice disease classification using 8 different convolutional neural network (CNN) algorithms, which will further the field of precision agriculture. Tkinter-based application that offers farmers a feature-rich interface. With the help of this cutting-edge application, farmers will be able to make timely and well-informed decisions by enabling real-time disease prediction and providing personalized recommendations. Together with the user-friendly Tkinter interface, the smooth integration of cutting-edge CNN transfer learning algorithms-based technology that include ResNet-50, InceptionV3, VGG16, and MobileNetv2 with the UCI dataset represents a major advancement toward modernizing agricultural practices and guaranteeing sustainable crop management. Remarkable outcomes include 75% accuracy for ResNet-50, 90% accuracy for DenseNet121, 84% accuracy for VGG16, 95.83% accuracy for MobileNetV2, 91.61% accuracy for DenseNet169, and 86% accuracy for InceptionV3. These results give a concise summary of the models' capabilities, assisting researchers in choosing appropriate strategies for precise and successful rice crop disease identification. A severe overfitting has been seen on VGG19 with 70% accuracy and Nasnet with 80.02% accuracy. On Renset101, only an accuracy of 54% could be achieved, along with only 33% on efficientNetB0. A MobileNetV2-trained model was successfully deployed on a TKinter GUI application to make predictions using image or real-time video capture.

Biplov Paneru

The emergence of large language models (LLMs), augmented reality (AR), and user interface/user experience (UI/UX) design in therapies for children, especially with disorders like autism spectrum disorder (ASD), is studied in detail in this review study. 150 publications were collected by a thorough literature search throughout PubMed, ACM, IEEE Xplore, Elsevier, and Google Scholar; 60 of them were chosen based on their methodological rigor and relevance to the focus area. Three of the primary areas are studied and covered in this review: how AR can improve social and learning results, how LLMs can support communication, and how UI/UX design affects how effective these technologies can be. Results show that while LLMs can provide individualized learning and communication support, AR has shown promise in enhancing social skills, motivation, and attention. For children with ASD, accessible and engaging interventions rely heavily on effective UI/UX design, but there is still a significant lack of robotics-based education and therapeutic programs specifically tailored for autistic children. To optimize the benefits of these technologies in ASD therapies and immersive education, the study emphasizes the need for additional research to address difficulties related to customization, accessibility, and integration.

Biplov Paneru, Bishwash Paneru, Bipul Thapa et al.

This study offers a revolutionary strategy to developing wheelchairs based on the Brain-Computer Interface (BCI) that incorporates Artificial Intelligence (AI) using a The device uses electroencephalogram (EEG) data to mimic wheelchair navigation. Five different models were trained on a pre-filtered dataset that was divided into fixed-length windows using a sliding window technique. Each window contained statistical measurements, FFT coefficients for different frequency bands, and a label identifying the activity carried out during that window that was taken from an open-source Kaggle repository. The XGBoost model outperformed the other models, CatBoost, GRU, SVC, and XGBoost, with an accuracy of 60%. The CatBoost model with a major difference between training and testing accuracy shows overfitting, and similarly, the best-performing model, with SVC, was implemented in a tkinter GUI. The wheelchair movement could be simulated in various directions, and a Raspberry Pi-powered wheelchair system for brain-computer interface is proposed here.

Luniva Chitrakar, Ishan Panta, Biplov Paneru et al.

Financial management has been revolutionized by mobile banking, but increasing usefulness and satisfaction requires a better user experience. This study aims to provide an improved customer experience by offering user-friendly interfaces, and real-time notifications by user-centric design of mobile banking application UI. A survey was carried out on the target audience in which 81% of respondents to a study of 103 people said they regularly used mobile banking apps, while 77% said they had problems with the ones they were using at the time. Furthermore, 44.7% of respondents expressed unhappiness with the current solutions by depending on third-party apps like e-Sewa and Khalti for everyday transactions. Language obstacles, lengthy loading times, unclear terminology, and navigational challenges were among the problems found. With 84% asking for a budgeting function and 46% complaining about biometric authentication, users indicated a need for more individualized interfaces, improved customer service, and increased security. The study included Think Aloud testing, heat maps, and remote usability testing to determine user preferences and pain spots to solve these. Feedback from a wider audience was obtained informally through guerrilla usability testing. The results highlight how important it is for mobile banking apps to guarantee security, increase functionality, simplify navigation, and improve visual design. App grouping and layout can be further enhanced by utilizing Gestalt psychology concepts like closeness and symmetry. The goal of these user-centered insights is to promote greater happiness and adoption of mobile banking.

Biplov Paneru, Bipul Thapa, Durga Prasad Mainali et al.

Accurately estimating the Remaining Useful Life (RUL) of a battery is essential for determining its lifespan and recharge requirements. In this work, we develop machine learning-based models to predict and classify battery RUL. We introduce a two-level ensemble learning (TLE) framework and a CNN+MLP hybrid model for RUL prediction, comparing their performance against traditional, deep, and hybrid machine learning models. Our analysis evaluates various models for both prediction and classification while incorporating interpretability through SHAP. The proposed TLE model consistently outperforms baseline models in RMSE, MAE, and R squared error, demonstrating its superior predictive capabilities. Additionally, the XGBoost classifier achieves an impressive 99% classification accuracy, validated through cross-validation techniques. The models effectively predict relay-based charging triggers, enabling automated and energy-efficient charging processes. This automation reduces energy consumption and enhances battery performance by optimizing charging cycles. SHAP interpretability analysis highlights the cycle index and charging parameters as the most critical factors influencing RUL. To improve accessibility, we developed a Tkinter-based GUI that allows users to input new data and predict RUL in real time. This practical solution supports sustainable battery management by enabling data-driven decisions about battery usage and maintenance, contributing to energy-efficient and innovative battery life prediction.

Biplov Paneru, Bishwash Paneru

Ensuring safe water supplies requires effective water quality monitoring, especially in developing countries like Nepal, where contamination risks are high. This paper introduces various hybrid deep learning models to predict on the CCME dataset with multiple water quality parameters from Canada, China, the UK, the USA, and Ireland, with 2.82 million data records feature-engineered and evaluated using them. Models such as CatBoost, XGBoost, and Extra Trees, along with neural networks combining CNN and LSTM layers, are used to capture temporal and spatial patterns in the data. The model demonstrated notable accuracy improvements, aiding proactive water quality control. CatBoost, XGBoost, and Extra Trees Regressor predicted Water Quality Index (WQI) values with an average RMSE of 1.2 and an R squared score of 0.99. Additionally, classifiers achieved 99% accuracy, cross-validated across models. SHAP analysis showed the importance of indicators like F.R.C. and orthophosphate levels in hybrid architectures' classification decisions. The practical application is demonstrated along with a chatbot application for water quality insights.

Bipul Thapa, Biplov Paneru, Bishwash Paneru et al.

This paper presents an Artificial Intelligence (AI) integrated novel approach to Brain-Computer Interface (BCI)-based wheelchair development, utilizing a motor imagery right-left-hand movement mechanism for control. The system is designed to simulate wheelchair navigation based on motor imagery right and left-hand movements using electroencephalogram (EEG) data. A pre-filtered dataset, obtained from an open-source EEG repository, was segmented into arrays of 19x200 to capture the onset of hand movements. The data was acquired at a sampling frequency of 200Hz. The system integrates a Tkinter-based interface for simulating wheelchair movements, offering users a functional and intuitive control system. We propose a BiLSTM-BiGRU model that shows a superior test accuracy of 92.26% as compared with various machine learning baseline models, including XGBoost, EEGNet, and a transformer-based model. The Bi-LSTM-BiGRU attention-based model achieved a mean accuracy of 90.13% through cross-validation, showcasing the potential of attention mechanisms in BCI applications.

Biplov Paneru

This research discusses the critical need for early detection and treatment for early prediction of Freezing of Gaits (FOG) utilizing a wearable sensor technology powered with LoRa communication. The system consisted of an Esp-32 microcontroller, in which the trained model is utilized utilizing the Micromlgen Python library. The research investigates accurate FOG classification based on pertinent clinical data by utilizing machine learning (ML) algorithms like Catboost, XGBoost, and Extra Tree classifiers. The XGBoost could classify with approximately 97% accuracy, along with 96% for the catboost and 90% for the Extra Trees Classifier model. The SHAP analysis interpretability shows that GYR SI degree is the most affecting factor in the prediction of the diseases. These results show the possibility of monitoring and identifying the affected person with tracking location on GPS and providing aid as an assistive technology for aiding the affected. The developed sensor-based technology has great potential for real-world problem solving in the field of healthcare and biomedical technology enhancements.

Bishwash Panerua, Biplov Paneru

This study focuses on membrane-based systems for CO$_2$ separation, addressing the urgent need for efficient carbon capture solutions to mitigate climate change. Linear regression models, based on membrane equations, were utilized to estimate key parameters, including porosity ($ε$) of 0.4805, Kozeny constant (K) of 2.9084, specific surface area ($σ$) of 105.3272 m$^2$/m$^3$, mean pressure (Pm) of 6.2166 MPa, viscosity ($μ$) of 0.1997 Ns/m$^2$, and gas flux (Jg) of 3.2559 kg m$^{-2}$ s$^{-1}$. These parameters were derived from the analysis of synthetic datasets using linear regression. The study also provides insights into the performance of the membrane, with a flow rate (Q) of 9.8778 $\times$ 10$^{-4}$ m$^3$/s, an injection pressure (P$_1$) of 2.8219 MPa, and an exit pressure (P$_2$) of 2.5762 MPa. The permeability value of 0.045 for CO$_2$ indicates the potential for efficient separation. Optimizing membrane properties to selectively block CO$_2$ while allowing other gases to pass is crucial for improving carbon capture efficiency. By integrating these technologies into industrial processes, significant reductions in greenhouse gas emissions can be achieved, fostering a circular carbon economy and contributing to global climate goals. This study also explores how artificial intelligence (AI) can aid in designing membranes for carbon capture, addressing the global climate change challenge and supporting the Sustainable Development Goals (SDGs) set by the United Nations.

Bishwash Paneru, Biplov Paneru, Tanka Mukhiya et al.

In Nepal, air pollution is a serious public health concern, especially in cities like Kathmandu where particulate matter (PM2.5 and PM10) has a major influence on respiratory health and air quality. The Air Quality Index (AQI) is predicted in this work using a Random Forest Regressor, and the model's predictions are interpreted using SHAP (SHapley Additive exPlanations) analysis. With the lowest Testing RMSE (0.23) and flawless R2 scores (1.00), CatBoost performs better than other models, demonstrating its greater accuracy and generalization which is cross validated using a nested cross validation approach. NowCast Concentration and Raw Concentration are the most important elements influencing AQI values, according to SHAP research, which shows that the machine learning results are highly accurate. Their significance as major contributors to air pollution is highlighted by the fact that high values of these characteristics significantly raise the AQI. This study investigates the Hydrogen-Alpha (HA) biodegradable filter as a novel way to reduce the related health hazards. With removal efficiency of more than 98% for PM2.5 and 99.24% for PM10, the HA filter offers exceptional defense against dangerous airborne particles. These devices, which are biodegradable face masks and cigarette filters, address the environmental issues associated with traditional filters' non-biodegradable trash while also lowering exposure to air contaminants.