Signal analysis of Hindustani classical music

This book presents a comprehensive overview of the basics of Hindustani music and the associated signal analysis and technological developments. It begins with an in-depth introduction to musical signal analysis and its current applications, and then moves on to a detailed discussion of the features involved in understanding the musical meaning of the signal in the context of Hindustani music. The components consist of tones, shruti, scales, pitch duration and stability, raga, gharana and musical instruments. The book covers the various technological developments in this field, supplemented with a number of case studies and their analysis. The book offers new music researchers essential insights into the use of the automatic concept for finding and testing the musical features for their applications. Intended primarily for postgraduate and PhD students working in the area of scientific research on Hindustani music, as well as other genres where the concepts are applicable, it is also a valuable resource for professionals and researchers in musical signal processing

[53] Modified Genetic Algorithm with Deep Learning for Fraud Transactions of Ethereum Smart Contract.

 Recently, the Ethereum smart contracts have seen a surge in interest  from the scientific community and new commercial uses. However, as  online trade expands, other fraudulent practices—including phishing,  bribery, and money laundering—emerge as significant challenges to trade  security. This study is useful for reliably detecting fraudulent  transactions; this work developed a deep learning model using a unique  metaheuristic optimization strategy. The new optimization method to  overcome the challenges, Optimized Genetic Algorithm-Cuckoo Search  (GA-CS), is combined with deep learning. In this research, a Genetic  Algorithm (GA) is used in the phase of exploration in the Cuckoo Search  (CS) technique to address a deficiency in CS. A comprehensive experiment  was conducted to appraise the efficiency and performance of the  suggested strategies compared with those of various popular techniques,  such as k-nearest neighbors (KNN), logistic regression (LR), multi-layer  perceptron (MLP), XGBoost, light gradient boosting machine (LGBM),  random forest (RF), and support vector classification (SVC), in terms of  restricted features and we compared their performance and efficiency  metrics to the suggested approach in detecting fraudulent behavior on  Ethereum. The suggested technique and SVC models outperform the rest of  the models, with the highest accuracy, while deep learning with the  proposed optimization strategy outperforms the RF model, with slightly  higher performance of 99.71% versus 98.33%. </p