Deep Learning Assisted Neonatal Cry Classification via Support Vector Machine Models.

Neonatal infants communicate with us through cries. The infant cry signals have distinct patterns depending on the purpose of the cries. Preprocessing, feature extraction, and feature selection need expert attention and take much effort in audio signals in recent days. In deep learning techniques, it automatically extracts and selects the most important features. For this, it requires an enormous amount of data for effective classification. This work mainly discriminates the neonatal cries into pain, hunger, and sleepiness. The neonatal cry auditory signals are transformed into a spectrogram image by utilizing the short-time Fourier transform (STFT) technique. The deep convolutional neural network (DCNN) technique takes the spectrogram images for input. The features are obtained from the convolutional neural network and are passed to the support vector machine (SVM) classifier. Machine learning technique classifies neonatal cries. This work combines the advantages of machine learning and deep learning techniques to get the best results even with a moderate number of data samples. The experimental result shows that CNN-based feature extraction and SVM classifier provides promising results. While comparing the SVM-based kernel techniques, namely radial basis function (RBF), linear and polynomial, it is found that SVM-RBF provides the highest accuracy of kernel-based infant cry classification system provides 88.89% accuracy.

Synthesis, characterization, DNA-binding, and DNA-photocleavage properties of [Co(bpy)2(7-NO2-dppz)]3+, [Co(dmb)2(7-NO2-dppz)]3+, and [Co(phen)2(7-NO2-dppz)]3+ complexes: (7-nitro-dppz = 7-nitro dipyrido[3,2-a:2'-3'-c]phenazine; bpy = 2,2'-bipyridine; dmb = 4,4'-dimethyl-2,2'-bipyridine; phen = 1,10-phenanthroline) and their toxicity on different microorganisms.

The polypyridyl ligand 7-Nitro dipyrido[3,2-a:2'-3'-c]phenazine (7-Nitro-dppz) and its complexes [Co(bpy)(2)(7-NO(2)-dppz)](3+)(1) (bpy = 2,2'-bipyridine), [Co(dmb)(2)(7-NO(2)-dppz)](3+)(2), (dmb = 4,4'-dimethyl-2,2'-bipyridine), and [Co(phen)(2)(7-NO(2)-dppz)](3+)(3) (phen = 1,10-phenanthroline) were synthesized and characterized by UV/VIS, IR, elemental analysis, (1)H and (13)C-NMR, and mass spectra. The binding properties of the three complexes to CT-DNA were investigated by different spectroscopic methods and viscosity measurements and DNA cleavage assay. The experimental results suggest that these complexes bind to CT-DNA through an intercalative mode. Also, the three complexes promote the photocleavage of plasmid pBR-322 DNA under irradiation. Toxicological effects of the selected complexes were estimated with different microorganisms.

DNA Binding and Photocleavage Studies of Cobalt(III) Ethylenediamine Pyridine Complexes: [Co(en)2(py)2]3+ and [Co(en)2(mepy)2]3+.

Two novel cobalt(III) pyridine complexes (1)[Co(en)2(py)2]3+ and (2)[Co(en)2(mepy)2]3+ (en=ethylenediamine, py=pyridine, and mepy=methylpyridine) have been synthesized and characterized. The interaction of these complexes with calf thymus DNA was investigated by absorption, emission spectroscopy, viscosity measurements, DNA melting, and DNA photocleavage. Results suggest that the two complexes bind to DNA via groove mode and complex 2 binds more strongly to CT DNA than complex 1. Moreover, these Co(III) complexes have been found to promote the photocleavage of plasmid DNA pBR322 under irradiation at 365 nm, cytotoxicity results of complexes are also showing anticancer activity.