India experienced a 23% rise in podcast listening after the Covid-19 pandemic. The pandemic and screen fatigue led people to seek their favourite old audio podcasts. Podcast genre classification allows listeners to compile a playlist of their favourite tracks; it also helps podcast streaming services provide recommendations to users based on the genre of the podcasts they enjoy. Since the COVID-19 pandemic, the need for educational content in all forms, including podcasts, has skyrocketed, making it even more crucial to anticipate the genre of educational podcasts. Educational podcasts are a sub-genre of the broader education genre and typically involve audio recordings of discussions, lectures, or interviews on educational topics. Education podcast genre prediction is required to efficiently classify and arrange educational content and make it simpler for listeners to access and absorb pertinent information. This study focuses on Podcast Genre Prediction, specifically for the Hindi language. In this study, our developed PodGen dataset was used, which consists of 550 five-minute podcasts with 26,867 sentences, where every podcast was manually annotated into one of the four genre categories (Horror, Motivational, Crime, and Romance). The performance comparison of state-of-the-art machine learning techniques on the PodGen dataset was used to demonstrate accuracy. The best performance on testing data was observed in the Support Vector Classifier model with balanced accuracy: 82.42%, precision (weighted): 83.09%, recall (weighted): 82.42%, and F1 score (weighted): 82.39%.

Many IoT applications run on a wireless infrastructure supported by resource-constrained nodes which is popularly known as Low-Power and Lossy Networks (LLNs). Currently, LLNs play a vital role in digital transformation of industries. The resource limitations of LLNs restrict the usage of traditional routing protocols and therefore require an energy-efficient routing solution. IETF’s Routing Protocol for Low-power Lossy Networks (RPL, pronounced “ripple”) is one of the most popular energy-efficient protocols for LLNs, specified in RFC 6550. In RPL, Destination Advertisement Object (DAO) control message is transmitted by a child node to pass on its reachability information to its immediate parent or root node. An attacker may exploit the insecure DAO sending mechanism of RPL to perform “DAO insider attack” by transmitting DAO multiple times. This paper shows that an aggressive DAO insider attacker can drastically degrade network performance. We propose a Lightweight Mitigation Solution for DAO insider attack, which is termed as “Li-MSD”. Li-MSD uses a blacklisting strategy to mitigate the attack and restore RPL performance, significantly. By using simulations, it is shown that Li-MSD outperforms the existing solution in the literature.

A first comprehensive note on systematics of identified seventy-two radiolarian taxa along with their morphological variations, distribution and comparison with other oceans from the core ABP-06, Station-I in the Central Indian Ocean. The presence of varied assemblage in the region like sub Antarctic fauna showed that vertical advection currents generate the niche which is due to the deep cold water currents. Further, the presence of important upwell taxa- C. huxleyi, L. nigriniae, T. octacantha, the geographic extent of the upwell system is extended up to 11 degrees S in the Central Indian Ocean. Predominantly the assemblage belongs to tropical region and the age of the core is Pleistocene (0.18 similar to 1.5Ma).

In recent times, the Internet of Things (IoT) has a significant rise in industries, and we live in the era of Industry 4.0, where each device is connected to the Internet from small to big. These devices are Artificial Intelligence (AI) enabled and are capable of perspective analytics. By 2023, it’s anticipated that over 14 billion smart devices will be available on the Internet. These applications operate in a wireless environment where memory, power, and other resource limitations apply to the nodes. In addition, the conventional routing method is ineffective in networks with limited resource devices, lossy links, and slow data rates. Routing Protocol for Low Power and Lossy Networks (RPL), a new routing protocol for such networks, was proposed by the IETF’s ROLL group. RPL operates in two modes: Storing and Non-Storing. In Storing mode, each node have the information to reach to other node. In Non-Storing mode, the routing information lies with the root node only. The attacker may exploit the Non-Storing feature of the RPL. When the root node transmits User Datagram Protocol (UDP) or control message packet to the child nodes, the routing information is stored in the extended header of the IPv6 packet. The attacker may modify the address from the source routing header which leads to Denial of Service (DoS) attack. This attack is RPL specific which is known as Hatchetman attack. This paper shows significant degradation in terms of network performance when an attacker exploits this feature. We also propose a lightweight mitigation of Hatchetman attack using game theoretic approach to detect the Hatchetman attack in IoT.

The intelligent internet of things (IoT) will become more valuable with introduction of 6G communication network, which is a sixth-sense next-generation communication network. It is certain that these two technologies will merge, opening the door for 6G wireless networks and the intelligent IoT. Ambient backscatter networks with machine learning (ML) capabilities have several applications, such as smart farming, industrial automation, and healthcare networks. Humans must provide for their own healthcare in order to thrive. A wide spectrum of disorders affecting heart as well as blood arteries are together referred to as cardiovascular disease. Novel optoelectronic materials have potential to revolutionise ongoing green shift by increasing efficiency of photovoltaic (PV) devices as well as decreasing energy consumption of devices such as LEDs as well as sensors. Both organic semiconductors, perovskites are the leading potential materials for these applications. This study suggests a unique method for detecting cardiovascular illness based on an investigation of blood artery blockages using machine learning algorithms with optoelectronic sensor analysis. Here, noise is removed from the input cardiac pictures, and the images are smoothed and normalised. The analysis of the blood artery obstruction in the right and left ventricles follows the processing of this picture. Utilising quantum dot-based Hopfield neural networks and convolutional ResNet gradient learning, the blood vessels are studied for the purpose of detecting cardiovascular illness. Different cardiac pictures are subjected to experimental investigation in terms of training accuracy, ROC, Precision, and recall. Proposed technique attained training accuracy of 98

Skin diseases are considered the emerging cause of high mortality throughout the world. To reduce the rate of death, that arise due to skin is minimized with early detection and cure. The lesions in the skin need to be evaluated significantly to increase the detection rate in the early stage. NowadaysComputer-Aided Diagnosis (CAD) is considered as the effective technique to detect lesions in the skin with the use of pattern recognition technique. Through CAD system skin lesions are classified into different classes. In other hand, medical environment uses the Internet of Things (IoT) for the monitoring and detection of diseases in patients. Hence, this paper constructed a framework of Dragon Pattern Optimization Stacked Classifier (DPOSC) automated model for the early detection of skin lesions. The DPOSC automated model is interconnected with the IoT devices implanted in the patient body for the classification of lesions as malignant, benign and normal in the early stage. The DPOSC model perform the image pre-processing with PCA (Principal component analysis) followed by the segmentation. The pattern recognition approach is developed with estimation of the features in the skin images. The technique uses quantitative analysis with the coding process to extract the pattern in the skin lesion and utilized for the automated detection system. The modifier dragon optimization model is implemented for the extraction and optimization of features in the skin lesion images. Upon the extracted features classification is performed with the stacked classification model. The DPOSC model effectively detect the lesions in the skin images and linked with the IoT environment for the detection of skin cancer in the early diagnosis. The developed automated integrated with IoT is emerging innovative technique for the detection of cancer in early stages. The accuracy of the improved DPOSCis 0.5

Use of solar energy systems and related green energy technology has spread around the world. When compared to conventional energy sources, solar energy is still not a frequently used energy source due to the comparatively high installation prices, low conversion rates, and battery capacity concerns. Despite the difficulties, there are numerous creative studies of new substances and new techniques for enhancing the efficiency of solar energy transformation to increase competitiveness of solar energy in market. This research proposes novel method in renewable energy analysis based on photovoltaic cell and machine learning technique for wind energy hybridization. The renewable analysis has been analysed using photovoltaic (PV) cell. Wind energy hybridization is carried out using convolutional kernel support regression vector machine. Experimental analysis has been carried out in terms of scalability, QoS (quality of service), power consumption, network efficiency, training accuracy. Financial advantages of using new cooling methods for photovoltaic panels are also assessed through a cost analysis.

Internet of Things (IoT) has revolutionized the networking by connecting the real world entities to the Internet. IoT connects the communication devices and has an incredible impact on perspective analytics on the massive volume of data produced every day. An attacker may exploit vulnerabilities of IoT entities and compromise users' security and privacy. Development of solutions to address security and privacy issues of IoT is in premature stage and considered as challenge. This challenge becomes more critical when the devices in the network are resource-constrained in terms of energy, processing and memory. The IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN) has emerged in recent years as an adaptation layer to carry IPv6 packets over IEEE 802.15.4. Many IoT applications use Routing Protocol for Low Power and Lossy Networks (RPL) as a network layer protocol developed for routing in 6LoWPAN. Security is challenging in resource constrained environment where encryption may not be a viable solution. Version number attack is one of the most common network layer attacks against RPL based 6LoWPAN. The RPL specification does not address the integrity of the version number and therefore leaves version number mechanism as a weak point in terms of security. This paper investigates the impact of version number attack in RPL networks while considering mobility of the sensor nodes. We propose a solution that utilizes Q-Learning strategy to detect the malicious nodes that are performing version number attack. The proposed approach detects malicious nodes with reasonable accuracy while imposing significantly less overhead on the nodes of low power and lossy networks. There are other approaches too like Message Authentication Codes (MAC) based on symmetric keys but these techniques have memory and communication overhead. So we propose different approach Q-Learning to detect the attacker nodes.

Emotions are a vital and fundamental part of our existence. Whatever we do, say, or do not say somehow reflects our feelings, however not immediately. To comprehend human’s most fundamental behaviour, we must examine these feelings using emotional data. According to the extensive literature review, categorising speech text into multiple classes is now undergoing extensive investigation. The application of this research is very limited in local and regional languages such as Hindi. This study focuses on text emotion analysis, specifically for the Hindi language. In our study, BHAAV Dataset is used, which consists of 20,304 sentences, where every other sentence has been manually annotated into one of the five emotion categories (Anger, Suspense, Joy, Sad, Neutral). Comparison of multiple machine learning and deep learning techniques with word embedding is used to demonstrate accuracy. And then, the trained model is used to predict the emotions of Hindi text. The best performance were observed in case of mBERT model with loss- 0.1689 ,balanced_accuracy- 93.88%, recall- 93.44%, auc- 99.55% and precision- 94.39 % on training data, while loss- 0.3073, balanced_accuracy- 91.84%, recall- 91.74%, auc- 98.46% and precision- 92.01% on testing data.

The Internet of Things (IoT) has a vital role in communication and has many cross-platform applications which generate a massive volume of data. IoT interconnects various devices from small to big without the direct intervention of humans. The resource-constrained environment poses a significant problem in IoT applications, and it is challenging to develop secure applications. The Internet community endeavours to cope with such challenges by developing different internet protocols. IETF ROLL working group standardized a mechanism called IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN) to carry IPv6 packets over IEEE 802.15.4. 6LoWPAN which supports the constrained environment uses the Routing Protocol for Low Power and Lossy Networks (RPL) as a routing protocol. It is essential to secure such applications since the malicious attacker can breach the privacy and security of humans through a small device. Traditional security mechanisms are not prominent in a resource-constrained context. Version attack is one of the most common attacks in RPL based 6LoWPAN. The network becomes unstable due to the version attack, which results in a Denial of Service attack. The integrity of the version number is not provided by RPL specifications, leading to threats for IoT applications. The impact of a version number attack on an RPL-based network is demonstrated in this study. The implications on the constrained network when the nodes are mobile is the main objective of this paper. In many IoT applications nodes move and it is vital to address the impact of mobility in a constrained environment. This paper investigates the network’s performance in terms of packet delivery, delay, and power consumption in RPL based IoT when there is version attack. Version attacks must be prevented as quickly as possible since they have the potential to significantly disrupt mobile networks. The main contribution of this research is a performance metric-based analysis of mobile RPL-based IoT networks under attack.

The Internet of Things (IoT) has brought a revolution in technology in the last decade. IoT is susceptible to numerous internal routing attacks because of the characteristics of the sensors used in IoT networks and the insecure nature of the Internet. The majority of the IoT ecosystem's problems come during the routing phase. While routing, the attacking node causes a number of challenges with the packet transmission mechanism. Routing Protocol for Low-Power and Lossy Networks (RPL) is susceptible to numerous types of attacks. The effects could be disruptive to network performance and resource availability. In this paper, we investigate the impact of a novel attack known as the DIO suppression attack and propose a mitigation mechanism for this attack on RPL-based network. This attack disrupts the topology of a network, and as a result, certain number of nodes are disconnected. Attacker nodes exploit the trickle algorithm to execute this attack. The impact of DIO suppression attack in different topologies and scenarios is studied in this research. We have also proposed a lightweight mitigation technique to defend the networks from this attack. This technique leverages the trickling timer's DIO Redundancy Constant k for each node to identify the attacking node in the network.

India experienced a 23% rise in podcast listening after the Covid-19 pandemic. The pandemic and screen fatigue led people to seek their favourite old audio podcasts. Podcast genre classification allows listeners to compile a playlist of their favourite tracks; it also helps podcast streaming services provide recommendations to users based on the genre of the podcasts they enjoy. Since the COVID-19 pandemic, the need for educational content in all forms, including podcasts, has skyrocketed, making it even more crucial to anticipate the genre of educational podcasts. Educational podcasts are a sub-genre of the broader education genre and typically involve audio recordings of discussions, lectures, or interviews on educational topics. Education podcast genre prediction is required to efficiently classify and arrange educational content and make it simpler for listeners to access and absorb pertinent information. This study focuses on Podcast Genre Prediction, specifically for the Hindi language. In this study, our developed PodGen dataset was used, which consists of 550 five-minute podcasts with 26,867 sentences, where every podcast was manually annotated into one of the four genre categories (Horror, Motivational, Crime, and Romance). The performance comparison of state-of-the-art machine learning techniques on the PodGen dataset was used to demonstrate accuracy. The best performance on testing data was observed in the Support Vector Classifier model with balanced accuracy: 82.42%, precision (weighted): 83.09%, recall (weighted): 82.42%, and F1 score (weighted): 82.39%.

Wireless body area networks (WBANs) have seen an increase in popularity in recent years. Electromagnetic waves created by the body have the capacity to connect nodes all over the epidermis and throughout the body. If the gadget does not cause discomfort or harm, it can be linked to or implanted in the body. This is something that is currently being worked on. Other factors influence an individual’s genuine mobility and the ease with which they can use something. Participating in social networks may enhance the lives of members. WBANs equipped with sensors can monitor a user’s heart rate and communicate that information to the user’s physician. WBAN has been shown to be a dependable electronic health solution. WBAN technology allows you to follow your patient’s data no matter where they are, when they are, or what they are doing. However, because it runs in an open Wi-Fi environment and can conceal users’ physiological data, it is more vulnerable to assault. To deal with resource-constrained WBAN sensors and devices, a cryptographic solution that is both very efficient and extremely secure is required. Our primary priority will be the safeguarding of the WBAN network. WBAN contains several significant security weaknesses that must be addressed immediately. WBANs might benefit from certificateless signature encryption that uses a hyperelliptic curve and works over a secure channel. We are outpaced by the opposition by 4.58 milliseconds.

As the COVID-19 outbreak spread from early 2020 on, synchronous and asynchronous online learning became the predominant delivery method in the education system. This is the inaugural time that educational programs have indeed been totally given online across the state. So, this research aims to study the Indian student's perception of synchronous and asynchronous online courses amid COVID-19. This study involved 655 responses from UG students of various Indian educational institutions. In this study, we utilized basic random sampling to gather data, and SPSS was used to analyse the data. To narrow down the enormous dimensionality, the acquired data were subjected to a factor analysis utilizing a principal component analytical method. The results of the study demonstrate that synchronous can be challenging at times and puts more responsibility on the students. Asynchronous learning also gives the learners the chance to independently investigate and explore the subjects that have been given to them. Another reason why asynchronous exercises were perceived as burdensome by students was the large number of handwritten tasks that had to be turned in quickly. The COVID-19 outbreak has indeed been difficult for both students and teachers nationwide. Yet, teachers have supported students' use of digital learning tools. Therefore, asynchronous and synchronous online courses together have produced balanced learning.

Low power and lossy networks (LLN) are flourishing as an integral part of communication infrastructure, particularly for growing Internet of Things (IoT) applications. RPL-based LLNs are vulnerable and unprotected against Denial of Service (DOS) attacks. The attacks in the network intervene the communications due to the inherent routing protocol’s physical protection, security requirements, and resource limitations. This paper presents the performance analysis of the Hatchetman attack on the RPL based 6LoWPAN networks. In a Hatchetman attack, an illegitimate node alters the received packet’s header and sends invalid packets to legitimate nodes, i.e. with an incorrect route. The authorised nodes forcefully drop packets and then reply with many error messages to the root of DODAG from all other nodes. As a result, many packets are lost by authorised nodes, and an excessive volume of error messages exhausts node energy and communication bandwidth. Simulation results show the effect of Hatchetman attack on RPL based IoT networks using various performance metrics.

Data analysis involves the deployment of sophisticated approaches from data mining methods, information theory, and artificial intelligence in various fields like tourism, hospitality, and so on for the extraction of knowledge from the gathered and preprocessed data. In tourism, pattern analysis or data analysis using classification is significant for finding the patterns that represent new and potentially useful information or knowledge about the destination and other data. Several data mining techniques are introduced for the classification of data or patterns. However, overfitting, less accuracy, local minima, sensitive to noise are the drawbacks in some existing data mining classification methods. To overcome these challenges, Support vector machine with Red deer optimization (SVM-RDO) based data mining strategy is proposed in this article. Extended Kalman filter (EKF) is utilized in the first phase, i.e., data cleaning to remove the noise and missing values from the input data. Mantaray foraging algorithm (MaFA) is used in the data selection phase, in which the significant data are selected for the further process to reduce the computational complexity. The final phase is the classification, in which SVM-RDO is proposed to access the useful pattern from the selected data. PYTHON is the implementation tool used for the experiment of the proposed model. The experimental analysis is done to show the efficacy of the proposed work. From the experimental results, the proposed SVM-RDO achieved better accuracy, precision, recall, and F1 score than the existing methods for the tourism dataset. Thus, it is showed the effectiveness of the proposed SVM-RDO for pattern analysis.

The growth of the Indian economy is mainly based on agricultural production. A method for crop disease detection based on pre-processing and segmentation processes using filtering and neural network techniques is proposed. The dataset here has been collected based on the pre-historic cultivation data and disease-affected data of the crop. Live images from the field have been collected and the dataset has been created. This data has been initially processed using a pre-processing technique based on convoluted Gaussian filtering. Then the processed image has been segmented using a deep active contour convolutional neural network (DACCNN) to formulate new loss functions which incorporate the region and information about size in the disease detection while training. From the results of the experiment, the proposed method is a vigorous method for crop disease detection and also segments main diseases of plant leaves like Cercospora Leaf Spot, Bacterial Blight, Powdery Mildew, and Rust.

In metro cities, the effective and efficient management of traffic is one of the most demanding and time taking tasks. Vehicular ad hoc networks (VANET) provide unfailing, low-cost solutions for transportation systems with intelligence. VANET, a subclass of mobile ad hoc networks (MANET), allows exchange of information among vehicles and/or roadside devices. VANET can be implemented in numerous application areas such as effective traffic management, safety, and user comfort for drivers as well as passengers. It provides phenomenal growth to both in industries and research communities. Secure mobility and handoff management are the most promising and challenging research issues in VANET. In this paper, we have introduced the security in intra domain mobility handoff in PMIPv6 for VANET. Existing intra handover schemes does not include the authentication cost while evaluating the total packet delivery cost for intra domain handoff. Our proposed scheme includes the authentication cost of an intra-domain handover for evaluating the total packet delivery cost of handover for next-generation mobility management protocols, which is PMIPv6 for the vehicular network. We have considered the vital parameters such as the number of MAGs, setup cost, binding update cost, unit transmission cost for analyzing the total packet delivery cost. Furthermore, a comparative study with authentication and without authentication cost for the considered parameters shows that our proposed scheme secures the handover process with slight variation in cost.

Chemical graph theory is an emerging field in research and attracts people mainly for its applications in Chemistry. Topological indices have been extensively used in this regard. A lot of topological indices have been introduced in the recent days. A recent study has shown that it is possible to even calculate the boiling point of a molecule using topological indices. This paper aimed at calculating the most trending degree based topological index for a family of graphs.

This essay clarifies the knowledge and abilities needed to instruct online courses in higher education. The introduction of the study included a general review of the problems with online teaching and learning. This study looked at one training strategy for new online educators and evaluated how it affected the quality of their instruction and integration of their subject matter. The conceptual supporting was the Technological Pedagogical Content Knowledge (TPACK) model. Three information sources were utilized in a quantitatively-determined blended techniques plan: (1) the pre-and post-instructional class schedules of the educators; (2) pre-and post-preparing understudy evaluations of showing scores; and (3) the results of a subsequent web-based study. As per the review's discoveries, educators showed (a) measurably huge changes by the way they integrated components into the overhaul of their course schedules and (b) enhancements in their showing abilities as expressed without help from anyone else in the subsequent overview Three information sources were utilized in a quantitatively-determined blended techniques plan: (1) the pre-and post-instructional class prospectuses of the teachers; (2) pre-and post-preparing understudy evaluations of showing scores; and (3) the results of a subsequent web-based study. As per the review's discoveries, educators showed (a) measurably huge changes by the way they integrated components into the overhaul of their course schedules and (b) enhancements in their showing abilities as expressed without anyone else in the subsequent study. Be that as it may, there were no apparent contrasts between the pre-and post-preparing understudy appraisals of their guidance. In general, educators showed exceptionally moderate additions in their capacity to train.