Raga Classification Based on Novel Method of Pitch Co-Occurrence

-74813b3e17f460286df-5b37 20220402031233977 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Recent Technology and Engineering (IJRTE) IJRTE 22773878 10.35940/ijrte.2277-3878 https://www.ijrte.org/ 05 30 2022 11 1 Raga Classification Based on Novel Method of Pitch Co-Occurrence Department of E&TC, Modern College of Engineering, Pune (Maharashtra), India. Vibhavari Rajadnya Dr. Kalyani Joshi Department of E&TC, Modern College of Engineering, Pune (Maharashtra), India. Automatic identification of raga is a growing research area and has captured significant attention from movie making industry. It is the need of time to develop efficient tools for data mining the vast audio visual data on internet. In particular, to search for a specific raga. Applications of raga search are in musicological studies, similarity based search. Ascending and descending pattern of swaras is an important feature in the raga classification. Pitch tracks of swaras are obtained from raw audio recordings. This research has utilised the pattern developed due to co-occurrence of pitches of swaras for classification. This pattern gives a concise representation of the signal which contains time and frequency information of the raga. K Nearest Neighbour (KNN) has been used as the classifier. 05 30 2022 23 27 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.A6886.0511122 https://www.ijrte.org/portfolio-item/a68860511122/ Development of WARKS for Accessing Supply-Chain Management Assistant Professor, Department of Master of Computer Applications, Jadavpur University, Kolkat (West Bengal), India. Dipankar Barai Rajib Chakrabarty Department of Mathematics, Jadavpur University, Kolkata (West Bengal), India Suvankar Barai Ph.D, Department of Mathematics, Jadavpur University, Kolkata (West Bengal), India. In our modern society, the demand of wireless communications increases exponentially. All the indoor and outdoor everything converting from wire to wireless. Even the newly invented devices, cars, TV, refrigerator, washing machine all the advanced things uses wireless technology. Because of the reason, there are more fields to do research in this area. WiFi is one of the important technology in wireless communications. In this work, we have developed a device which will useful to build an wireless network. The device used to monitor and to control supply-chain management of any organization. We have used WiFi which works as two different modes; one is AP (Access Point) and another is STA (STAtion). In this paper, AP acts as a Server where STA act as a Client. We have developed a wireless network system using self organized sensor nodes (each node has one AP, one STA and one Controller) to communicated each other serially to exchange data and request task accordingly. Because of its serial distributed formation the WiFi range is also be increased with different topology. All the command and request can be done using computer or smartphone. This system (we named it WARKS) can be implemented in home, industrial, hospital, farms, forest, agriculture and many more. To verify the system capabilities and work performance, we do the experiment in indoor and outdoor using required hardware and software. 05 30 2022 28 34 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.A6908.0511122 https://www.ijrte.org/portfolio-item/a69080511122/ Artificial Intelligence and Machine Learning Based Models for Prediction and Treatment of Cardiovascular Diseases: A Review Associate Professor, Department of Electronics & Communication Engineering, Siddartha Educational Academy Group of Institutions, C. Gollapalli, Tirupati (A.P), India. Dr. Sreedevi Gandham Dr. Balaji Meriga Associate Professor, Department of Biochemistry, Sri Venkateswara University, Tirupati (A.P), India. Advances in Machine Learning (ML) algorithms, computing and Artificial Intelligence (AI)-based systems have been gradually finding applications in several domains including medical and health care systems. By using big data analytics and machine learning methodologies, AI has become a promising tool in the diagnosis and treatment of cardiovascular diseases. AI-ML based applications enhance our understanding of different parameters and phenotypes of heart diseases and lead to newer therapeutic strategies to tackle different types of cardiovascular ailments, a newer approach to cardiovascular drug therapy and a post-marketing survey of prescription drugs. Although AI has wide range of applications, it is in infant stage and has certain limitations in the clinical use of results and their interpretations such as data privacy, selection bias etc, which may result in wrong conclusions. Thus, AI-ML is a transformative technology and has immense potential in health care systems. This review covers various aspects of cardiovascular diseases (CVDs) and illustrate AI and ML based methods including supervised, unsupervised and deep learning and their applications in cardiovascular imaging, cardiovascular risk prediction and newer drug targets. 05 30 2022 35 40 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.D6632.0511122 https://www.ijrte.org/portfolio-item/d66321110421/ Channel Estimation for DS-CDMA Rake Receiver using Sparse Recovery Approach Department of E&TC, Sanjivani Engineering College, Kopargaon (Maharashtra), India. Rajendrakumar Govinda Zope Balasaheb Shrirangrao Agarkar Department of E&TC, Sanjivani Engineering College, Kopargaon (Maharashtra), India. In the literature of direct-sequence code-division multiple-access (DS-CDMA), for issues related to channel estimation, some exclusive architectures have been proposed, which are characterized by known channel noise statistics and noise observation. But in reality, the channel parameters are frequently assessed utilizing training sequences that lead to difficulty in obtaining the channel noise statistics. Channel estimation quality has been proved to play an important role in the performance of rake receiver. This paper addresses the issues of optimizing DS–CDMA rake receiver channel estimation equipped with an Iterative least square sparse recovery (IL2SR) channel estimator. Moreover, the ambient noises corrupt the signal received and multiple-access interference further aggravates it. Because of this observation noises become hard to acquire. Hence this paper proposes as an iterative least square structure for channel estimation algorithm in rake receiver employed in DS-CDMA communication systems. Further, examination of blind channel estimation problem for rake-based DS-CDMA communication framework having multi-path fading channels with time variation is also attempted. The validity of the proposed techniques has been verified through results obtained from simulation for different channel parameters and spreading codes. Further exploration has been carried out with execution of the IL2SR with Rake receiver in DS-CDMA framework for multi-path fading channels. It is found that better performance is obtained with this framework under various channels with different spreading codes. The proposed system is compared with Kalman based techniques and it was found that DS-CDMA framework under additive white gaussian noise (AWGN)channel with IL2SR receiver reveals better outcomes in terms of bit error rate (BER). Also, there has been improvement in video quality while using the proposed IL2SR receiver with increase in the values of ratio of signal to noise ratio. 05 30 2022 41 46 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F6841.0511122 https://www.ijrte.org/portfolio-item/f68410310622/