Adaptive Denoising of Respiratory Sounds with a Hybrid Discrete Wavelet Transform and 1D CNN Framework / (Record no. 614595)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01893nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 610 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Ashraf, Naila |
| 245 ## - TITLE STATEMENT | |
| Title | Adaptive Denoising of Respiratory Sounds with a Hybrid Discrete Wavelet Transform and 1D CNN Framework / |
| Statement of responsibility, etc. | Naila Ashraf |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Islamabad : |
| Name of producer, publisher, distributor, manufacturer | SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice | 2025. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 63 |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 500 ## - GENERAL NOTE | |
| General note | Accurate analysis of respiratory sounds holds key importance in precise diagnosis of pulmonary<br/>diseases. These sounds are sometimes noisy which require an effective diagnosing method for<br/>noise reduction and correct analysis of lung sounds. Traditional denoising methods are limited to<br/>spectral overlap with background noise. This study attempts for denoising biomedical signals<br/>utilizing discrete wavelet convolutional neural network (DW-CNN), an adaptive filter consists of<br/>multiresolution ability of discrete wavelet transform (DWT) and deep feature learning ability of<br/>neural networks, which preserve the signal details. Encoder-decoder structure of DW-CNN with<br/>inverse DWT accurately reconstructs the signal, outperforming traditional wavelet denoising. LS<br/>signals under real noise conditions were denoised using DW-CNN, where DWT replaced<br/>traditional pooling layers. Results were quantitatively compared with baseline traditional methods<br/>using Signal to Noise Ratio (SNR) and Root Mean Square Error (RMSE) metrics. On average<br/>there is an improvement of 9.61 dB in SNR and a reduction of 0.35489 in RMSE. Final results<br/>highlight the better performance of implemented model over conventional methods, increasing the<br/>scope of combined model in pattern recognition, clinical diagnostics and wearable devices. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Biomedical Engineering (BME) |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Muhammad Asim Waris |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/54567">http://10.250.8.41:8080/xmlui/handle/123456789/54567</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|---|
| School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | E-Books | 08/29/2025 | 610 | SMME-TH-1153 | Thesis |