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Stirling number of the fourth kind and Lucky partitions of a finite set
The concept of Lucky k-polynomials and in particular Lucky ?polynomials was recently introduced. This paper introduces Stirling number of the fourth kind and Lucky partitions of a finite set in order to determine either the Lucky k- or Lucky ?-polynomial of a graph. The integer partitions influence Stirling partitions of the second kind. 2021 Azarbaijan Shahid Madani University. -
Ranjith cinemas - An episteme that create discourse on class, caste and religion /
Films are an important form of mass communication in India today. Apart from being mode of entertainment, films also shape the ideology in the mind of viewers because cinema is an ideological apparatus by nature of its very seamlessness. The audience do not see how the cinema creates ideology it invisibly renders and naturalizes it. -
Memes as multimodal ensemble
Memes have now become a common medium of communication. There are multiple ways memes are considered in academia. Semiotics offers information on how the media and modes that memes consist of can be interpreted and how the characteristics of semiotic resources apply to memes. Drawing from a pool of memes collected during the Kerala assembly election in 2021, this research argues that certain memes need to be categorised as multimodal ensemble. Different modalities play different roles meaning construction, and they also collaborate with each other for a uniform purpose. By comparing existing memes defined in academia and multiple methodologies to analyse memes, the paper puts forth a framework to analyse memes. 2023 De Gruyter Mouton. All rights reserved. -
Khaki on Screen: Understanding the Representation of Cops in Malayalam Cinema
[No abstract available] -
Self-Care, burnout, and compassion fatigue in oncology professionals
Context: With the rising number of cancer cases in India, the stress levels of the treating team have increased. It has affected their self-care and made them susceptible to problems like burnout and compassion fatigue that adversely affect the quality of patient care. Aims: The aim of the study was to assess and compare the levels of burnout, compassion fatigue, and self-care in three groups of oncology professionals (clinical oncologists, nurses, and psychologists). Settings and Design: The study included 134 oncology professionals working in New Delhi, Bengaluru, and Mumbai. Methods and Material: Sociodemographic data sheet, Professional Quality of Life Scale V and Self-Care Assessment Worksheet were used. Statistical Analysis Used: Kruskal-Wallis, Mann-Whitney U test, and Correlation Analysis. Results: The majority of the professionals reported moderate levels of burnout (60.4%) and compassion fatigue (56%). Oncology nurses reported an elevated risk as they scored significantly higher on these domains and had a lower degree of self-care. Interestingly, psychologists reported comparatively lower levels of burnout and compassion fatigue, despite the fact that they interact with the patients at a deeper level, looking after their psychological and emotional needs. Young age and a poor degree of self-care were identified as major risk factors. Conclusions: The moderate levels of burnout and compassion fatigue, though not severe, are a cause of concern and cannot be overlooked. The study highlights the need for self-care in this regard and suggests that individual and institutional level interventions, particularly for nurses and young professionals, would prove useful. 2020 Wolters Kluwer Medknow Publications. All rights reserved. -
Hook-up culture among young Indian adults in Indian metropolitan cities
This study aimed to explore the existence of hook-up behaviour among emerging adults in the Indian metropolitan cities of Bangalore, Mumbai, Delhi and Kolkata. The study used a survey design. The sexual behaviour subsection of the Sexual Behaviour Questionnaire was used to assess the frequency of the various types of hook-up behaviour. The results indicated that the participants frequency of hook-up behaviour was 55.13%. Overall hook-up behaviour was higher for males, whereas females reported the highest incidence of hook-up behaviour for kissing. Hook-up behaviour was highest for emerging adults living independently in apartments, followed by those living in paying guests/hostels and living with family. The findings provide evidence for a hook-up culture in Indian metropolitan cities. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Application of the Taguchi method and RSM for process parameter optimization in AWSJ machining of CFRP composite-based orthopedic implants
Abrasive water suspension jet (AWSJ) machining on carbon fiber-reinforced polymer (CFRP) composite-based orthopedic implants yielded insightful results based on experimental data and subsequent statistical validations. Underwater AWSJ cutting consistently outperformed free air cutting, with numerical findings demonstrating its superiority. For instance, at #100 abrasive size and 5 mm standoff distance (SOD), the material removal rate (MRR) peaked at 2.44 g/min with a kerf width of 0.89 mm and a surface roughness (SR) of 9.25 ?m. Notably, the increase in abrasive size correlated with higher MRR values, such as achieving 2.15 g/min at #120 grit and 3 mm SOD. Furthermore, optimization techniques like the Taguchi method and response surface methodology (RSM) were applied to refine machining parameters. These methodologies enhanced MRR, exemplified by achieving 2.10 g/min with #120 abrasive size and 5 mm SOD in underwater cutting conditions. The research explored the impact of key process parameters, namely, the speed, feed, and SOD on the MRR, kerf width, and SR in both free air cutting and underwater cutting conditions, which is one of the novel research endeavors in the domain of abrasive jet machining of composites. 2024 the author(s) -
Earthquake and flood resilience through spatial Planning in the complex urban system
Urban Communities are exposed to different disaster risks. The paper aims at understanding the interrelation of spatial planning and the resilience of the urban communities for earthquakes and floods. Various spatial planning components were used to evaluate the community resilience to earthquake and flood in the city of Pune of Maharashtra state in India. It has been identified that spatial planning contributes to a greater extent in determining community resilience. Spatial planning results in differential resilience among communities. In the study area, economically weaker households are found to be more vulnerable to disaster risk due to their spatial locations and limited accessibility to share the resources. These factors are found to be contributing to reduced resilience in the city. 2022 The Authors -
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An Efficient Routing Strategy for Energy Management in Wireless Sensor Network Using Hybrid Routing Protocols
In these days, Wireless Sensor Networks (WSN) shows a huge impact on all appliances but one of the huge challenges in WSN is management of energy because the nodes in the network run with battery power. As the replacement of energy drained nodes is difficult, and frequent failure of links may occur and it incurs huge data loss. To avoid this issue the we proposed a Hybrid Krill Herd and Spider Monkey with Grid-Based Data Dissemination (HKHSM-GBDD) protocol with the Shortest Energy Resourceful Routing (SERR) mechanism to develop an efficient and better wireless communication channel. The presented HKHSM framework is utilized to classify malicious and energy drained nodes in earlier stage and to detect the link failure. Furthermore, the SERR mechanism is processed to recover the link and route maintenance. This novel proposed protocol has improved packet delivery ratio and energy consumption. It also enhances energy state of sensor nodes by mounting its lifetime and rerouting. Finally, the competence of the proposed mechanism is compared with existing works and it shows significant improvement over existing algorithms for the considered parameters. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Efficient Routing Strategies for Energy Management in Wireless Sensor Network
Wireless Sensor Network (WSN) refers to a group of distributed sensors that are used to examine and record the physical circumstances of the environment and coordinate the collected data at the centre of the location. This WSN plays a significant role in providing the needs of routing protocols. One of the important aspects of routing protocol in accordance with Wireless Sensor Network is that they should be efficient in the consumption of energy and have a prolonged life for the network. In modern times, routing protocol, which is efficient in energy consumption, is used for Wireless Sensor Network. The routing protocol that is efficient in energy consumption is categorized into four main steps: CM Communication Model, Reliable Routing, Topology-Based Routing and NS Network Structure. The network structure can be further classified as flat/hierarchical. The communication model can be further classified as query, coherent/non-coherent, negotiation-based routing protocol system. The topology-based protocol can be further classified as mobile or location-based. Reliable routing can be further classified as QoS (Quality of service) or multiple-path based. A survey on routing protocol that is energy-efficient on Wireless Sensor Network has also been provided in this research. The Author(s), under exclusive license to Springer Nature Switzerland AG 2022. -
Enhancing the energy efficiency for prolonging the network life time in multi-conditional multi-sensor based wireless sensor network
A wireless sensor network is one of the networks that is highly demanding by various real-time networking applications nowadays. A huge amount of sensor nodes is deployed in the network randomly and distributed. Most of the applications using wireless sensor network (WSN) are surveillance monitoring applications like a forest, home, healthcare, environment, and remote monitoring systems. Based on the application usage, the type of sensor, a number of sensor nodes are deployed in such a manner where the sensors can be used effectively. But the sensor nodes are restricted in the battery and sensing region. Thus, the battery of the sensor nodes is decreased based on the nodes function. The energy level of the sensor nodes highly affects the network lifetime. Improving the energy efficiency in WSN is one of the most important challenging tasks. Most of the earlier research works have proposed various methods, techniques, and routing protocols, but they are application dependent and as a common method. So, this paper is motivated to propose a Multi-Conditional Network Analysis (MCNA) framework for saving the energy level of the sensor nodes by reducing energy consumption. The MCNA framework involves two different clustering processes with cluster head selection, choosing the best nodes based on the signal strength, and the best route for data transmission. The data transmission is done by cluster based on source-destination based. The simulation results proved that the proposed MCNA framework outperforms the other existing methods. 2022 Northeastern University, China. -
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Potassium tert-Butoxide-Mediated Synthesis of 2-Aminoquinolines from Alkylnitriles and 2-Aminobenzaldehyde Derivatives
KOtBu mediates the reaction between 2-amino arylcarbaldehydes and benzyl/alkyl cyanides toward the expeditious formation of 2-aminoquinolines under transition-metal-free conditions. The described transformation proceeds through in-situ generated enimine intermediate from benzyl/alkyl cyanides under KOtBu-mediated reaction conditions. The substituted 2-aminoquinolines were realized in excellent yields at room temperature and shorter reaction time. The designed process exhibits operational simplicity and broad functional group tolerance in delivering the products of high significance. 2022 Wiley-VCH GmbH. -
Investigation on Preserving Privacy of Electronic Medical Record using Split Learning
Artificial Intelligence is deployed in multiple areas, including healthcare. Utmost research is done in AI enabled healthcare industry because of the demands like accurate result, data security, exact prediction, huge volume of data, etc. In conventional deep learning models, the training happens with the dataset that are stored in a single device. This requires a huge storage space and highly efficient machines to train the data. Usage of big data, demands for innovative models that can be deployed and used in confined storage. Split learning is one such collaborative distributed deep learning model that allows the data to be stored in a split fashion. Split learning supports desirable features like less storage, more privacy to raw data, ability to work with resource constraints, etc., making it suitable for storing electronic medical record of patients. This paper discusses the advantages of using split learning for healthcare, the possible configurations of split learning that supports data privacy in healthcare and finally discusses the open research challenges in implementing split learning for healthcare. 2024 The Authors. Published by Elsevier B.V. -
FADA: Flooding Attack Defense AODV Protocol to counter Flooding Attack in MANET
The intrinsic nature of a Mobile Ad hoc Network (MANET) makes it difficult to provide security and it is more vulnerable to network attacks. Denial of Service (DoS) attack can be executed using Flooding attack, that has the potential to bring down the entire network. This attack works by delivering an excessive number of unwanted packets that consumes too much battery life, storage space, and bandwidth, that eventually lowers the system's performance. In order to flood the network, the attacker injects fake packets into it. Both Control Packet flooding and Data flooding attacks are taken into account in this study. FADA (Flooding Attack Defense AODV) protocol is proposed to counter flooding attack that promotes greater utilization of existing resources. This research identifies the attack-causing node, isolates it and protects the network against flooding attack. Attack Detection Rate, Attack Detection Accuracy, End-to-end delay and Throughput are few metrics used for evaluation of the proposed model. NS-2.35 is used to demonstrate the efficiency of the suggested protocol and the results prove that the proposed model increases system's throughput while decreasing attack. The simulation results have shown that the proposed FADA protocol performs better than the existing models taken into consideration. 2023 IEEE. -
Anonymization Based Deep Privacy Preserving Convolutional Autoencoder Learning Technique for High Dimensional Data Clustering in Big Data Cloud
Data Clustering is a primary research focus in large data-driven application domains in the big data cloud as performance of the clustering dynamic data with high dimensionality is highly challenging due to major concern in the effectiveness and efficiency on data representation. Machine learning is a conventional approach to distribute the data into soft partition still it leads to increasing sparsity of data and increasing difficulties in distinguishing distance between data points. In addition, securing the personnel and confidential information of the user is also becoming vital. In order to tackle those issues, a new anonymization based deep privacy preserving learning paradigm has been presented in this paper. The proposed model is represented as deep privacy preserving convolutional auto encoder learning architecture for secure high dimensional data clustering on inferring the distribution of the data over time. Initially dimensionality reduction and feature extraction is carried out and those extracted feature has been taken for clustering on generation of objective function to produce maximum margin cluster. Those clusters are further fine tuned to feature refinement on the hyper parameter of various layers of deep learning model network to establish the minimum reconstruction error by feature refinement. Softmax layer minimizes the intra cluster similarity and inter cluster variation in the feature space for cluster assignment. Hyper parameter tuning using stochastic gradient descent has been enabled in the output layer to make the data instance in the cluster to be close to each other by determining the affinity of the data on new representation. It results significant increase in the clustering performance on the discriminative informations. Detailed experimental analysis has been performed on benchmarks datasets to compute the proposed model performance with conventional approaches. The performance outcome represents that anonymization based deep privacy preserving clustering learning architecture can produce good scalability and effectiveness on high dimensional data. 2023 American Institute of Physics Inc.. All rights reserved. -
An enhanced biometric attendance monitoring system using queuing petri nets in private cloud computing with playfair cipher
Every educational institutions needs to analyse and monitor participation. Educationists believe that there should be a fair number of students available in the majority of their classes. In colleges participation is used a measure of consistency. To deal with this kind of a challenging situation, biometric based participation monitoring framework is being proposed. This proposed method with the assistance of face recognition will help in maintaining every detail about the present students in a classroom save the same in the class database. The camera captures the image of students and compares them with the existing visual data available in the database. In case, the software is not able to find a match for the captured data in the student database, the particular student is marked as absent. Queuing Petri nets help in fulfilling customised demands of various institutions along with providing better performance in terms of hold up time. With the application of this technology, classroom participation is recorded and saved every hour. The database is accessed and maintained using cloud services and necessary security measured are incorporated as provided by major private cloud service providers with playfair cipher technique. 2020, Institute of Advanced Scientific Research, Inc. All rights reserved. -
Data encryption and decryption using graph plotting
Cryptography plays a vital role in today's network. Security of data is one of the biggest concern while exchanging data over the network. The data need to be highly confidential. Cryptography is the art of hiding data or manipulating data in such a way that where no third party can understand the original data while transmission from source to destination. In this paper, a modified affine cipher algorithm has been used to encrypt the data. The encrypted data will be plot onto a graph. Later, graph will be converted into image. This system allows sender to select his/her own keys to encrypt the original data before plotting graph. Then, Receiver will use the same key to decrypt the data. This system will provide the better security while storing the data in cloud in the form of secret message embedding in graphical image file in network environment. IAEME Publication. -
Calculator using brain computer interface
This paper is undertaken with an aspiration to provide a new way to calculate that can be availed by exploiting BCI. Often it's said things moved at the blink of eye, now the time has come to make it true. This project is developed to ease the efforts in two different ways. First a mind controlled image viewer is build which can be used to change images at the blink of an eye. Second is a simple single digit calculator which lets the user choose the number and the operators just by focusing.Brain-Computer Interface aims to improve the detection and decoding of brain signals acquired by electroencephalogram (EEG). IAEME Publication.