A Comprehensive Literature Review on Convolutional Neural Networks

The fields of computer vision and image processing from their initial days have been dealing with the problems of visual recognition. Convolutional Neural Networks (CNNs) in machine learning are deep architectures built as feed-forward neural networks or perceptrons, which are inspired by the research done in the fields of visual analysis by the visual cortex of mammals like cats. This work gives a detailed analysis of CNNs for the computer vision tasks, natural language processing, fundamental sciences and engineering problems along with other miscellaneous tasks. The general CNN structure along with its mathematical intuition and working, a brief critical commentary on the advantages and disadvantages, which leads researchers to search for alternatives to CNN’s are also mentioned. The paper also serves as an appreciation of the brain-child of past researchers for the existence of such a fecund architecture for handling multidimensional data and approaches to improve their performance further

Recommendation of Algorithm for Efficient Retrieval of Songs from Musical Dataset

Now-a-days, the research is more towards the entertainment like music, songs, movies, etc. There are many existing works that suggest good songs, movies to people depending on their mood, nature and time that has been savior for the society during the days of lockdown. The existing algorithms used in the literature for basic clustering  are K-means, TSNE (T- distributed Stochastic Neighborhood Embedding), PCA (Principal Component Analysis).In this paper, the music dataset considered, consists of songs with attributes as song name, genres, artists, mode, tempo, valence, year, liveness, loudness, popularity, acousticness, danceability, duration, energy, explicit, instrumentalness, key. The important feature is extracted from the other features with the support of literature survey i.e., number of music listeners, types of the songs and type of the music. Later, the dataset is divided into clusters using traditional technique that is k-means based on genre, an important attribute which is selected from the above attributes. The different classifier models like Random Forest, Extra Trees, LightGBM, XGBoost, CatBoost classifier are applied on the clustered dataset and the results have been evaluated on each individual algorithm. Thus the paper recommends not only the group of relevant songs but also suggests the best accurate classification algorithm that can be used for any mentioned musical dataset. The paper also compares all the said ensemble algorithms by calculating the precision, recall, f1-score and support. The accuracy is also calculated for all said ensemble algorithms and based on the accuracy the best suitable algorithm is suggested

A bi-directional Long Short-Term Memory-based Diabetic Retinopathy detection model using retinal fundus images

Images of the retina are widely used for diagnosing fundus disease. Low-quality retinal photos make it hard for computer-aided diagnosis systems and ophthalmologists to make a clinical diagnosis. In ophthalmology, precision medicine is based partly on the quality of retinal images. Diabetic Retinopathy (DR) is a common complication of diabetes mellitus that causes iris damage. It is difficult to detect and, if not detected early, can result in blindness. Convolutional neural networks are gaining popularity as an effective deep learning (DL) approach for medical image analysis. This study suggests using deep learning approaches at various stages of the fundus image-based diagnostic pipeline for diabetic retinopathy (DR). Many fields, including medical image classification, have adopted DL representations. Using retinal fundus images, we propose a bi-directional extended short-term memory-based diabetic retinopathy detection model. By examining images of the retinal fundus, the Bi-directional Long Short-Term Memory (LSTM) method can detect and classify various grades of DR. As a preprocessing step, the proposed model uses the Multiscale Retinex with Chromaticity Preservation (MSRCP) method to increase the difference of fundus pictures and progress the short difference of medicinal views. To prepare satisfactory results for image processing, multiscale retinex with chromaticity preservation is used. However, choosing the parameters’ values, such as the Gaussian scales, gain, offset, etc., is the main difficulty with the retinex algorithm. To achieve a practical effect, these parameters must be adjusted. The main goal of the suggested method is to obtain the ideal values for the parameters used in the MSRCP algorithm. Also, photos that have already been processed are used to make feature vectors with the help of an efficient net-based feature extractor that uses deep learning. Many experiments use the benchmark Methods to Evaluate Segmentation and Indexing Techniques in the Field of Retinal Ophthalmology (MESSIDOR) dataset. The results are analyzed in terms of various evaluation factors. The results show that the Bi-LSTM-MSRCP technique is better at diagnosing DR than more modern methods