Skin cancer is caused due to unusual development of skin cells and deadly type cancer. Early diagnosis is very significant and can avoid some categories of skin cancers, such as melanoma and focal cell carcinoma. The recognition and the classification of skin malignant growth in the beginning time is expensive and challenging. The deep learning architectures such as recurrent networks and convolutional neural networks (ConvNets) are developed in the past, which are proven appropriate for non-handcrafted extraction of complex features. To additional expand the efficiency of the ConvNet models, a cascaded ensembled network that uses an integration of ConvNet and handcrafted features based multi-layer perceptron is proposed in this work. This offered model utilizes the convolutional neural network model to mine non-handcrafted image features and colour moments and texture features as handcrafted features. It is demonstrated that accuracy of ensembled deep learning model is improved to 98.3% from 85.3% of convolutional neural network model.

Music is a heavenly way of expressing feelings about the world. The language of music has vast diversity. For centuries, people have indulged in debates to stratisfy between Western and Indian Classical Music. But through this paper, an understanding can be fabricated while differentiating the types of Indian Classical Music. Classical music is one of the essential characteristics of Indian Cultural Heritage. Indian Classical Music is divided into two major parts, i.e. Hindustani and Carnatic. Models have been sculptured and trained to classify between Hindustani and Carnatic Music. In this paper, two approaches are used to implement classification models. MFCCs are used as features and implemented models like DNN (1 Layer, 2 Layers, 3 Layers), CNN (1 Layer, 2 Layers, 3 Layers), RNN-LSTM, SVM (Sigmoid, Polynomial & Gaussian Kernel) as one approach. A 3 channels input is created by merging features like MFCC, Spectrogram and Scalogram and implemented models like VGG-16, CNN (1 Layer, 2 Layers, 3 Layers), ResNet-50 as another approach. 3 Layered CNN and RNN-LSTM model performed best among all the approaches.

This paper aims at collecting and analysing temperature, rainfall, soil, seed, crop production, humidity and wind speed data (in a few regions), which will help the farmers improve the produce of their crops. Firstly, we pre-process the data in a Python environment and then apply the MapReduce framework, which further analyses and processes the large volume of data. Secondly, k-means clustering is employed on results gained from MapReduce and provides a mean result on the data in terms of accuracy. After that, we use bar graphs and scatter plots to study the relationship between the crop, rainfall, temperature, soil and seed type of two regions (Ahmednagar, Maharashtra and, Andaman and Nicobar Islands). Further, a self-designed recommender system has been used to predict the crops and display them on a Graphic User Interface designed in a Flask environment. The system design is scalable and can be used to find the recommended crops of other states in a similar manner in the future.

A Phonocardiogram (PCG) signal represents murmurs and sounds signals made by vibrations caused for the period of a cardiac cycle. Acoustic wave generated through the beat of the cardiac cycle propagates through the chest wall. It can be easily recorded by a low-cost small handheld digital device called a stethoscope. It provides information like heart rate, intensity, tone, quality, frequency, and location of various components of cardiac sound. Due to these characteristics, phonocardiogram signals can be used to detect heart status at an early stage in a non-invasive manner. In previous studies, the Convolutional Neural Network (ConvNet) is the most studied architecture, which was fed by features, namely Mel Frequency Cepstral (MFC), Chroma Energy Normalized Statistics (CENS), and Constant-Q Transform (CQT). This work has proposed a ConvNet model trained by Hybrid Constant-Q Transform (HCQT) for heart sound beat classification. CQT, Variable-Q Transform (VQT), and HCQT are extracted from each phonocardiogram signal as the acoustic features, including the dominant MFCC features, feed into five-layer regularized ConvNets. After analyzing the literature in the same domain, it can be stated that this is the first time HCQT is being utilized for PCG signals. The findings of the experiments demonstrate that HCQT is more effective than standard CQT and other variants. Also, the accuracies of the system proposed in this work on the validation datasets are 96% in multi-class classification, which outperforms the proposed work relative to other models significantly. The source code is available on the Github repository https://github.com/shamiktiwari/ PCG-signal-Classification-using-Hybrid-Constant-Q-Transform to support the research community.

In agriculture, accurate and timely detection of plant diseases is crucial for minimizing crop losses and ensuring food security. Traditional methods of plant disease detection often rely on visual inspection and single-modal data analysis, which can be limited in their diagnostic accuracy. This study introduces an innovative ensemble learning framework that integrates Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for multimodal plant disease detection to address these limitations. The proposed framework capitalizes on the strengths of both CNNs and RNNs by processing visual data from leaf images and sequential data such as time-series measurements of environmental conditions. CNNs are adept at extracting intricate spatial features from leaf images, identifying visual symptoms of diseases with high precision. Concurrently, RNNs are designed to capture temporal patterns in sequential data, providing insights into environmental factors that may influence disease development. The ensemble method employed in this study aggregates predictions from both CNN and RNN models using techniques such as majority voting and weighted averaging. Majority voting involves combining the outputs of multiple models to make a final prediction based on the most common outcome, while weighted averaging assigns different weights to the predictions of each model based on their performance, leading to a more balanced and accurate diagnostic result. Experimental evaluations were conducted on comprehensive multimodal datasets, including diverse plant species and varying environmental conditions, to assess the effectiveness of the proposed framework. The results demonstrate that the ensemble approach significantly outperforms individual CNN and RNN models, achieving higher diagnostic accuracy, precision, recall, and F1 scores. This superior performance underscores the potential of integrating diverse data streams to provide a holistic view of plant health, enabling more accurate and reliable disease diagnosis. The findings of this study highlight the importance of leveraging multimodal data and advanced machine-learning techniques in plant disease detection. By integrating spatial and temporal information, the proposed framework offers a comprehensive diagnostic tool that can be instrumental in improving agricultural practices, optimizing plant health management, and ultimately contributing to sustainable farming practices.

Plants play a crucial role in human history by providing essential resources such as food. However, they are susceptible to various diseases like leaf blight, grey spots, and rust, posing significant challenges for farmers and ranchers due to the potential damage and financial losses incurred. The current method of diagnosing plant diseases relies heavily on manual inspection by trained professionals, which becomes increasingly difficult and resource-intensive, particularly on large farms. To address this issue, a novel approach is proposed, integrating clustering methods and neural networks to enhance the efficiency of identifying and studying plant disease samples. This method aims to expedite the diagnostic process and accurately quantify disease-induced damage. It introduces a formula for assessing damage, calculating the diseased leaf area as a fraction of the total leaf area. With an impressive accuracy rate of 96–97%, the system successfully identifies diseases such as Alternaria, Anthracnose, Bacterial Blight, and Cercospora Leaf Spot. This advancement signifies a notable improvement over previous methods, highlighting the evolution of the system. By harnessing the capabilities of clustering techniques and neural networks, this innovative approach seeks to revolutionize the detection and monitoring of plant diseases in agriculture, offering a more accessible and precise solution.