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.

In recent years, unmanned aerial vehicles (UAVs) have become the trend for different types of research and applications. UAVs can accomplish some technical and risky tasks while still being safe, mobile, and inexpensive to operate. However, UAVs need flying ad-hoc networks (FANET) to operate in inaccessible or infrastructure-less areas. Subsequently, in many military and civil applications, the UAVs are connected ad hoc. FANET-based UAV systems have been developed for search and rescue, wildlife surveys, real-time monitoring, and delivery services. Maintaining the reliability and connectivity among UAV nodes in FANET becomes challenging because of the UAV movement, environmental conditions, energy efficiency, etc. Energy-aware routing protocols have become essential for developing advanced and effective FANETs. This paper presents a proposed Cross-Layer and Energy-Aware Ad-hoc On-demand Distance Vector (CLEA-AODV) routing protocol for improving FANET performance. The CLEA-AODV protocol is mainly divided into three sections: routing with AODV protocol, Glow Swarm Optimization (GSO)-based Cluster Head Selection, and Cooperative Medium Access Control (MAC). The cross-layer approach is implemented on the network layer and the data layer. The major parameters considered to evaluate the performance of the FANET are Packet Success Rate (PSR), Throughput (TP), End-to-End (E2E) delay, and packet drop ratio (PDR). The Network Simulator version 2 (NS2) is used to implement the CLEA-AODV protocol and evaluate the network performance. The results are compared with the standard AODV, Self-Organization Clustering-GSO (SOC-GSO), and Energy Efficient Neuro-Fuzzy Cluster-based Topology Construction with Meta-Heuristic Route Planning (EENFC-MRP) protocols. The results show that the CLEA-AODV surpasses these protocols in terms of PSR, TP, E2E delay, and PDR.

Automatic information extraction from online published scientific documents is useful in various applications such as tagging, web indexing and search engine optimization. As a result, automatic information extraction has become among the hottest areas of research in text mining. Although various information extraction techniques have been proposed in the literature, their efficiency demands domain specific documents with static and well-defined format. Furthermore, their accuracy is challenged with a slight modification in the format. To overcome these issues, a novel ontological framework for information extraction (OFIE) using fuzzy rule-base (FRB) and word sense disambiguation (WSD) is proposed. The proposed approach is validated with a significantly wider document domains sourced from well-known publishing services such as IEEE, ACM, Elsevier, and Springer. We have also compared the proposed information extraction approach against state-of-the-art techniques. The results of the experiment show that the proposed approach is less sensitive to changes in the document format and has a significantly better average accuracy of 89.14% and F-score as 89%.

Mobile ad hoc network (MANET) can be described as a group of wireless mobile nodes that form a temporary dynamic and independent infrastructure network or a central administration facility. High energy consumption is one of the main problems associated with the MANET technology. The wireless mobile nodes used in this process rely on batteries because the network does not have a steady power supply. Thus, the rapid battery drain reduces the lifespan of the network. In this paper, a new Bat Optimized Link State Routing (BOLSR) protocol is proposed to improve the energy usage of the Optimized Link State Routing (OLSR) protocol in the MANET. The symmetry between OLSR of MANET and Bat Algorithm (BA) is that both of them use the same mechanism for finding the path via sending and receiving specific signals. This symmetry resulted in the BOLSR protocol that determines the optimized path from a source node to a destination node according to the energy dynamics of the nodes. The BOLSR protocol is implemented in a MANET simulation by using MATLAB toolbox. Different scenarios are tested to compare the BOLSR protocol with the Cellular Automata African Buffalo Optimization (CAABO), Energy-Based OLSR (EBOLSR), and the standard OLSR. The performance metric consists of routing overhead ratios, energy consumption, and end-to-end delay which is applied to evaluate the performance of the routing protocols. The results of the tests reveal that the BOLSR protocol reduces the energy consumption and increases the lifespan of the network, compared with the CAABO, EBOLSR, and OLSR.

The key schedule algorithm (KSA) is a crucial element of symmetric block ciphers with a direct security impact. Despite its undeniable significance, the KSA is still a less focused area in the design of an encryption algorithm. PRESENT is a symmetric lightweight block cipher that provides the optimal balance between security, performance, and minimal cost in IoT. However, the linear functions in KSA lead to a slow and predictable bit transition, indicating the relationship between round keys. A robust KSA should produce random and independent round keys irrespective of the secret key. Therefore, this research aims to improve the KSA PRESENT-128 block cipher with enhanced randomness, round key bit difference, and the avalanche effect. The experiments on round keys and ciphertext with random, low density and high-density secret key datasets endorse the expected improvements. Moreover, the results show that the improved KSA produces random round keys that successfully pass the NIST randomness test. The bit transition from one round key to another is increased from 20% to 40%, where a greater inclination of the avalanche effect has an increased effect with 50% bit change. On the other hand, the improved KSA PRESENT requires an additional $0.001871$ s to generate round keys, as a security cost trade-off.

Radio frequency identification (RFID) systems are emerging as the primary object identification mechanism, especially in supply chain management. However, RFID naturally generates a large amount of duplicate readings. Removing these duplicates from the RFID data stream is paramount as it does not contribute new information to the system and wastes system resources. Existing approaches to deal with this problem cannot fulfill the real time demands to process the massive RFID data stream. We propose a data filtering approach that efficiently detects and removes duplicate readings from RFID data streams. Experimental results show that the proposed approach offers a significant improvement as compared to the existing approaches.