4 research outputs found
A Zigbee Based Cost-Effective Home Monitoring System Using WSN
WSNs are vital in a variety of applications, including environmental
monitoring, industrial process control, and healthcare. WSNs are a network of
spatially scattered and dedicated sensors that monitor and record the physical
conditions of the environment.Significant obstacles to WSN efficiency include
the restricted power and processing capabilities of individual sensor nodes and
the issues with remote and inaccessible deployment sites. By maximising power
utilisation, enhancing network effectiveness, and ensuring adaptability and
durability through dispersed and decentralised operation, this study suggests a
comprehensive approach to dealing with these challenges. The suggested
methodology involves data compression, aggregation, and energy-efficient
protocol. Using these techniques, WSN lifetimes can be increased and overall
performance can be improved. In this study we also provide methods to collect
data generated by several nodes in the WSN and store it in a remote cloud such
that it can be processed and analyzed whenever it is required.Comment: Paper has been presented at ICCCNT 2023 and the final version will be
published in IEEE Digital Library Xplor
Unleashing the Power of Dynamic Mode Decomposition and Deep Learning for Rainfall Prediction in North-East India
Accurate rainfall forecasting is crucial for effective disaster preparedness
and mitigation in the North-East region of India, which is prone to extreme
weather events such as floods and landslides. In this study, we investigated
the use of two data-driven methods, Dynamic Mode Decomposition (DMD) and Long
Short-Term Memory (LSTM), for rainfall forecasting using daily rainfall data
collected from India Meteorological Department in northeast region over a
period of 118 years. We conducted a comparative analysis of these methods to
determine their relative effectiveness in predicting rainfall patterns. Using
historical rainfall data from multiple weather stations, we trained and
validated our models to forecast future rainfall patterns. Our results indicate
that both DMD and LSTM are effective in forecasting rainfall, with LSTM
outperforming DMD in terms of accuracy, revealing that LSTM has the ability to
capture complex nonlinear relationships in the data, making it a powerful tool
for rainfall forecasting. Our findings suggest that data-driven methods such as
DMD and deep learning approaches like LSTM can significantly improve rainfall
forecasting accuracy in the North-East region of India, helping to mitigate the
impact of extreme weather events and enhance the region's resilience to climate
change.Comment: Paper is under review at ICMC 202
A Few-Shot Approach to Dysarthric Speech Intelligibility Level Classification Using Transformers
Dysarthria is a speech disorder that hinders communication due to
difficulties in articulating words. Detection of dysarthria is important for
several reasons as it can be used to develop a treatment plan and help improve
a person's quality of life and ability to communicate effectively. Much of the
literature focused on improving ASR systems for dysarthric speech. The
objective of the current work is to develop models that can accurately classify
the presence of dysarthria and also give information about the intelligibility
level using limited data by employing a few-shot approach using a transformer
model. This work also aims to tackle the data leakage that is present in
previous studies. Our whisper-large-v2 transformer model trained on a subset of
the UASpeech dataset containing medium intelligibility level patients achieved
an accuracy of 85%, precision of 0.92, recall of 0.8 F1-score of 0.85, and
specificity of 0.91. Experimental results also demonstrate that the model
trained using the 'words' dataset performed better compared to the model
trained on the 'letters' and 'digits' dataset. Moreover, the multiclass model
achieved an accuracy of 67%.Comment: Paper has been presented at ICCCNT 2023 and the final version will be
published in IEEE Digital Library Xplor
Enhancing Knee Osteoarthritis severity level classification using diffusion augmented images
This research paper explores the classification of knee osteoarthritis (OA)
severity levels using advanced computer vision models and augmentation
techniques. The study investigates the effectiveness of data preprocessing,
including Contrast-Limited Adaptive Histogram Equalization (CLAHE), and data
augmentation using diffusion models. Three experiments were conducted: training
models on the original dataset, training models on the preprocessed dataset,
and training models on the augmented dataset. The results show that data
preprocessing and augmentation significantly improve the accuracy of the
models. The EfficientNetB3 model achieved the highest accuracy of 84\% on the
augmented dataset. Additionally, attention visualization techniques, such as
Grad-CAM, are utilized to provide detailed attention maps, enhancing the
understanding and trustworthiness of the models. These findings highlight the
potential of combining advanced models with augmented data and attention
visualization for accurate knee OA severity classification.Comment: Paper has been accepted to be presented at ICACECS 2023 and the final
version will be published by Atlantis Highlights in Computer Science (AHCS) ,
Atlantis Press(part of Springer Nature