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Imidazopyridine Hydrazine Conjugates as Potent Anti-TB Agents with their Docking, SAR, and DFT Studies
Novel imidazopyridines hydrazine conjugates were designed and synthesized for their anti-tubercular (anti-TB) activity. A cytotoxicity assay was conducted with Vero cells to determine the safety profile of the most effective compounds. It was found that compound (IA3) (MIC=0.78 ?M) and (IA8) (MIC=1.12 ?M) were nearly 3.7 and 2.5 times more active than pyrazinamide. Based on Density functional theory (DFT), these molecules exhibited better charge transfer between molecular orbital's, which made them suitable for biological applications. Molecular docking on Mycobacterium tuberculosis InhA bound to NITD-916 (PDB: 4R9S) revealed that compounds possessed greater binding affinity towards proteins. In addition, the most active anti-TB compounds (IA3) and (IA8) exhibited high levels of interaction with the target protein and exceptional safety profile, suggesting they may prove to be effective leads for new drugs. 2024 Wiley-VCH GmbH. -
Comparative Study on Load Balancing Techniques in Distributed Systems
International Journal of Information Technology and Knowledge Management, Vol-6 (1), pp. 53-60. ISSN-0973-4414 -
Nexus Between Interest Rate Risk and Economic Value of Equity of Banks
This analytical study looks to provide recommendations to the banking sector on different policies and regulations by examining certain aspects of the Basel III accord, which was designed to manage specific operational, capital and market risks of banks. A review of extant literature reveals that only a few papers have been written on simulation-based approaches, using basis and re-pricing risks. We look to connect this as a source while attempting to define and measure the impact of interest rate risk (IRR) on the economic value of equity (EVE) of banks. We propose to use the driverdriven method, wherein interest rate shocks are derived through prime lending rate (PLR) for the period of 20162019 in the context of India. Monte Carlo Simulation and OLS regression was performed to predict the IRR; Granger causality was used to examine the cause and effect relationship; the impulse response function (IRF) was used for sensitivity analysis; and the vector error correction model (VECM) technique was used for co-integrating relationships. Notably, the EVE movement caused due to shocks in interest rates had to be traced as it envisages probable EVE losses. Importantly, our study is among the first few to show the relationship between IRR and EVE of banks, especially after the deregulation of Indian banking sector. 2021 International Management Institute, New Delhi. -
AI-based wavelet and stacked deep learning architecture for detecting coronavirus (COVID-19) from chest X-ray images
A novel coronavirus (COVID-19), belonging to a family of severe acute respiratory syndrome coronavirus 2 (SARs-CoV-2), was identified in Wuhan city, Hubei, China, in November 2019. The disease had already infected more than 681.529665 million people as of March 13, 2023. Hence, early detection and diagnosis of COVID-19 are essential. For this purpose, radiologists use medical images such as X-ray and computed tomography (CT) images for the diagnosis of COVID-19. It is very difficult for researchers to help radiologists to do automatic diagnoses by using traditional image processing methods. Therefore, a novel artificial intelligence (AI)-based deep learning model to detect COVID-19 from chest X-ray images is proposed. The proposed work uses a wavelet and stacked deep learning architecture (ResNet50, VGG19, Xception, and DarkNet19) named WavStaCovNet-19 to detect COVID-19 from chest X-ray images automatically. The proposed work has been tested on two publicly available datasets and achieved an accuracy of 94.24% and 96.10% on 4 classes and 3 classes, respectively. From the experimental results, we believe that the proposed work can surely be useful in the healthcare domain to detect COVID-19 with less time and cost, and with higher accuracy. 2023 Elsevier Ltd -
Recent developments in bandwidth improvement of dielectric resonator antennas /
International Journal of RF And Microwave Computer-Aided Engineering, Vol.29, Issue 6, pp.1-17 -
Cultural Expression of Anxiety Symptoms in Kannada Language: A Qualitative Study
Background: In anxiety disorders, culture is important in symptom presentation and help-seeking. Most tools for anxiety disorders are not validated in India and thus might not capture culture-specific aspects of anxiety. This study aims to identify and generate culturally specific terms to describe symptoms of anxiety as part of the development of the Kannada version of the Panic and Anxiety National Indian Questionnaire (PANIQ). The PANIQ is a tool under development to identify anxiety and panic in Indian settings. Methods: This study used qualitative methods like focus group discussions (FGDs) and in-depth interviews (IDIs) to identify and generate items related to anxiety and panic in Kannada from stakeholders like individuals with anxiety disorders, their caregivers, healthcare workers, and mental health professionals who treat individuals with anxiety and panic disorders. Five FGDs (n = 28), one triad (n = 3), and 34 IDIs (n = 34) were conducted. Results: The mean age of the participants was 38.9 (standard deviation: 12.28) years; 57.1% were from rural areas. We generated 615 Kannada items. These were classified into 21 domains and facets. Items in domains like Somatic symptoms, Fear, and Impairment in day-to-day life were higher than those noted in existing tools for anxiety that focus more on cognitive symptoms of anxiety. Conclusions: This study generated culturally specific items of anxiety through a qualitative process of tool development incorporating subjective experiences of persons with anxiety disorders and other stakeholders. This is among the first steps toward the development of PANIQ. 2022 The Author(s). -
A comprehensive review of AI based intrusion detection system
In today's digital world, the tremendous amount of data poses a significant challenge to cyber security. The complexity of cyber-attacks makes it difficult to develop efficient tools to detect them. Signature-based intrusion detection has been the common method used for detecting attacks and providing security. However, with the emergence of Artificial Intelligence (AI), particularly Machine Learning, Deep Learning and ensemble learning, promising results have been shown in detecting attacks more efficiently. This review discusses how AI-based mechanisms are being used to detect attacks effectively based on relevant research. To provide a broader view, the study presents taxonomy of the existing literature on Machine Learning (ML), Deep learning (DL), and ensemble learning. The analysis includes 72 research papers and considers factors such as the algorithm and performance metrics used for detection. The study reveals that AI-based intrusion detection methods improve accuracy, but researchers have primarily focused on improving performance for detecting attacks rather than individual attack classification. The main objective of the study is to provide an overview of different AI-based mechanisms in intrusion detection and offer deeper insights for future researchers to better understand the challenges of multi-classification of attacks. 2023 -
Intelligent machine learning approach for cidscloud intrusion detection system
In this new era of information technology world, security in cloud computing has gained more importance because of the flexible nature of the cloud. In order to maintain security in cloud computing, the importance of developing an eminent intrusion detection system also increased. Researchers have already proposed intrusion detection schemes, but most of the traditional IDS are ineffective in detecting attacks. This can be attained by developing a new ML based algorithm for intrusion detection system for cloud. In the proposed methodology, a CIDS is incorporated that uses only selected features for the identification of the attack. The complex dataset will always make the observations difficult. Feature reduction plays a vital role in CIDS through time consumption. The current literature proposes a novel faster intelligent agent for data selection and feature reduction. The data selection agent selects only the data that promotes the attack. The selected data is passed through a feature reduction technique which reduces the features by deploying SVM and LR algorithms. The reduced features which in turn are subjected to the CIDS system. Thus, the overall time will be reduced to train the model. The performance of the system was evaluated with respect to accuracy and detection rate. Then, some existing IDS is analyzed based on these performance metrics, which in turn helps to predict the expected output. For analysis, UNSW-NB15 dataset is used which contains normal and abnormal data. The present work mainly ensures confidentiality and prevents unauthorized access. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021. -
An Intelligent Hybrid GA-PI Feature Selection Technique for Network Intrusion Detection Systems
The development of Network Intrusion Detection Systems (NIDS) has become increasingly important due to the growing threat of cyber-attacks. However, with the vast amount of data generated in networks, handling big data in NIDS has become a major challenge. To address this challenge, this research paper proposes an intelligent hybrid GA-PI algorithm for feature selection and classification tasks in NIDS using support vector machines (SVM). The proposed approach is evaluated using two sub-datasets, Analysis and Normal, and Reconnaissance and Normal, which are generated from the publicly available UNSWNB-15 dataset. In this work, instead of considering all possible attacks, the focus is on two attacks, emphasizing the importance of the feature selection agent in determining the optimal features based on the attack type. The experimental results show that the proposed hybrid feature selection approach outperforms existing methodologies in terms of accuracy and execution time. Moreover, the selection of features can be subjective and dependent on the domain knowledge of the researcher. Additionally, the proposed approach requires computational resources for feature selection and classification tasks, which can be a limitation for resource-constrained systems. To be brief, this research paper presents a promising approach for feature selection and classification tasks in NIDS using an intelligent hybrid GA-PI algorithm. While there are some challenges and limitations, the proposed approach has the potential to contribute to the development of effective and efficient NIDS. 2023, Ismail Saritas. All rights reserved. -
Assessing the Skill Sets from the NEP Policy 2020: Scale Development and Validation
Purpose: The central aspect of this paper is the National Education Policy (NEP) 2020, an education-based policy in the Indian context. The article captured the opportunities from the perspective of the skill set it offers to its important stakeholders, students; the current research has aimed to propose and validate an instrument to measure the skill set identified from the NEP document and academic expertise, which could be used to measure students skill-based performance. Design/Methodology/Approach: The first section used EFA to establish four skill sets: Employability Skills, Communal-Based Skills, Social and Emotional Skills, and Individualistic Skills. The CFA model was further deployed to confirm the factors; the study was administered to a sample of 820 students from various commerce and management colleges from South and North Bangalore. Findings: The research results could be applied to evaluate the effectiveness of skill sets on students performance to create holistic education. Originality: The first step is to develop and validate scales for measuring skill sets under NEP 2020. Second, in accordance with the pupils order of priority, to close the gap between the talents stated and those observed. Research Limitations/Implications: The study was conducted only with students of commerce and management disciplines in Bangalore. Practical Implications: NEP has placed a greater emphasis on competency development and skill enhancement, which aids in the development of higher-order cognitive, social-emotional, and 21st-century abilities necessary for employment in the future. NEP gave greater autonomy to students to choose their learning pathway and develop skill sets, which are likely to make them job creators, thereby giving rise to the entrepreneurial culture. Social Implications: To cultivate an aspirational student body, it is critical to instill the necessary skill sets in pupils. Policymakers and decision-makers could then organize their courses using these skill sets. 2024, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
A novel African buffalo based greedy routing technique for infrastructure and cluster based communication in vehicular ad-hoc network
In this modern era, the wire free replica is utilized Vehicular Ad hoc Networks (VANETs) to converse each other. Also, the VANET paradigm not required any specific fixed infrastructure. Furthermore, the vehicle in VANET framework is movable like as mobile nodes. Also, the wireless connectivity between the vehicular nodes is not stable in all cases, it often changes their structure. Research have recommended various responses to control these issues and furthermore to lessen blockage in VANET environment. Therefore, the infrastructure of a network changes frequently which results in communication overheads, energy consumption and lifetime of the nodes. Consequently, in this paper a novel African Buffalo based Greedy Routing (ABGR) technique is to improve the performance of infrastructure and cluster based communication of the node. Moreover, the routing overhead and infrastructure communication can be enhanced by this proposed protocol. Consequently, the energy consumption solution is enhanced based on the CH. Sequentially, the proposed routing protocol is compared with existing protocols in terms of end-to-end delay, throughput, Data transmission Ratio (DTR), and energy consumption and so on. Therefore, it shows that the energy utilization and lifetime of the nodes in the proposed network has been enhanced. 2021 Little Lion Scientific. -
A structural approach towards reinvigorating student satisfaction in industrial training institutes a contemplating outlook
The research paper focused to conceptualize and empirically test the conceptual model of student satisfaction proposed for Indian vocational education and training (VET), precisely industrial training institutes (ITIs). Even though the upgradation of ITIs through public-private partnership (PPP) is emphasized from the previous decade, little empirical evidence exists about the quality of the institutes. Improved quality in ITIs helps in increased employability of the students and would help in meeting Indias projected skill demand of 191 million youths by 2022. Empirical data were collected from upgraded ITIs of Andhra Pradesh and Telangana states to assess student satisfaction. Student satisfaction gives the measure of student feedback on the quality of the courses. PLS-SEM was applied to develop measurement and structural models. Subsequently, statistical values were used to estimate the validity and reliability of the models. Besides, the predictive accuracy of the model was also tested. The data analysis assisted to ascertain whether to accept or reject the hypothesized relations proposed based on the conceptual model. The results proved that institute quality factors were positively correlated with student satisfaction. Eventually, it was observed that industry exposure was a significant determinant of student satisfaction followed by training facilities & equipment, trainer credibility, learning environment, and placement and counseling services. Above all being said, it can be posited that focusing on the above all quality factors would help in enhancing the quality of ITIs. 2021, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
A Study of Successful Strategies of Unorganized Pharmacy Retailers - A Case of South Bangalore
PES Business Review, Vol-8 (2), pp. 15-24. ISSN-0973-919X -
Toxic heavy metal ion detection by fluorescent nanocarbon sensor derived from a medicinal plant
In the twenty-first century, the importance of environmental pollution sensing cannot be overstated. Cadmium is a well-known poisonous heavy metal that seriously endangers human health. In terms of screening for poisons and diagnosing illnesses, the sensitive and focused detection of cadmium in cells is crucial. In this work, we developed Green fluorescent Carbon nanomaterial (Carbon nanomaterial) synthesized from a novel precursor, Justicia Wynaadensis, by the most eco-friendly, cost-effective hydrothermal method, which acts as a fluorescent probe for Cadmium fluorescence sensing technique with the concentration range of 1 nM1 M. The sensor displays remarkable linear detection with a 5.235 nM detection limit. 2022 The Author(s) -
Secure key exchange scheme: A DNA computing-based approach to resist MITM in DHKE
Diffie-Hellman key exchange (DHKE) protocol was a pioneering work and considered as a new direction in the field of cryptography though it is not an encryption protocol. DHKE is a method to exchange the keys securely, based on the discrete logarithm problem. It has applications in internet security protocols including SSL, IP Sec and SSH. The major issue with DHKE is its vulnerability to man in the middle attack (MITM). Various techniques have been proposed to resist the MITM including digital signatures. This paper proposes DNA computing-based encryption techniques to resist MITM in DHKE. DNA cryptography builds on the concepts of biomolecular computations which are considered as one of the emerging directions in the cryptography. The proposed methodology also includes an encryption technique based on DNA-based codebook, secret sharing and DNA cryptography to exchange parameters securely. The security analysis of the proposed scheme is evaluated by theoretical analysis. Formal analysis of the proposed protocol is done using Scyther and all the modelled claims are validated and positive results are obtained. Copyright 2021 Inderscience Enterprises Ltd. -
Implementing Innovative Weed Detection Techniques for Environmental Sustainability
Agriculture, supporting over half of India's population, grapples with the challenge of weed control. Current methods applied in plantation crops lack efficiency and pose environmental and health risks. This paper advocates a paradigm shift, emphasizing the critical need for effective weed detection using cluttered unmanned aerial vehicle (UAV) images. The research methodology integrates image processing, Mask R-Convolutional Neural Networks (R-CNN), and Internet of Things (IoT). A dataset of 200 UAV images was subjected to a thorough preprocessing. In the initial phase, weeds and crops were identified with precision employing an UAV-tailored Mask R-CNN with instance segmentation. This was found to surpass traditional methods in terms of communication between the model and the agricultural environment. For timely decision-making, real-time data were collected using IoT. Average Precision (AP) values reveal high accuracy, notably 89.1% for weeds, 88.9% for crops, and an overall precision of 89.4%. The Mask R-CNN network segments and classifies images, marking weed zones communicated to farmers via Raspberry Pi with a GSM module, enabling real-time alerts and informed decision-making for efficient weed control. This holistic approach, providing object classifications, detailed bounding boxes, and masks, addresses weed control challenges, highlighting the transformative potential of advanced technologies in agriculture. 2024, Institute for Environmental Nanotechnology. All rights reserved. -
N'-[(E)-1-(2-Fluorophenyl)ethylidene]pyridine-4-carbohydrazide
Acta Crystallographica Section E, Vol-E70, ISSN-1600-5368 -
N?-[(1E)-1-(2-Fluorophenyl)ethylidene]pyridine-3-carbohydrazide
The title compound, C14H12FN3O, adopts an E conformation with respect to the azomethine double bond whereas the N and methyl C atoms are in a Z conformation with respect to the same bond. The ketonic O and azomethine N atoms are cis to each other. The non-planar molecule [the dihedral angle between the benzene rings is 7.44(11)] exists in an amido form with a C=O bond length of 1.221(2) In the crystal, a bifurcated N - H(O,N) hydrogen bond is formed between the amide H atom and the keto O and imine N atoms of an adjacent molecule, leading to the formation of chains propagating along the b-axis direction. Through a 180 rotation of the fluorophenyl ring, the F atom is disordered over two sites with an occupancy ratio of 0.632(4):0.368(4). Sreeja et al. 2014. -
N?-[(E)-2-Fluorobenzylidene]benzohydrazide
The asymmetric unit of the title compound, C14H 11FN2O, contains two independent molecules, both of which adopt the E conformation with respect to the azomethine C=N bond. The molecules are non-planar, with dihedral angles of 26.92 (12) and 11.36 (11) between the benzene and phenyl rings. In the crystal, molecules are linked through N-H?O=C and N-H?N hydrogen bonds into chains along [101]. C-H?O contacts link these chains into layers parallel to (001). The three-dimensional crystal packing is stabilized by ?-? interactions, the shortest separation between the centroids of benzene rings being 3.884 (1) -
N?-[(E)-1-(2-fluorophenyl)ethylidene]pyridine-4-carbohydrazide
The title compound, C14H12FN3O, adopts an E conformation with respect to the azomethine bond. The pyridyl and fluorobenzene rings make dihedral angles of 38.58(6) and 41.61(5) respectively with the central C(=O)N2CC unit, resulting in a non-planar molecule. The intermolecular interactions comprise two classical N - H?O and N - H?N hydrogen bonds and four non-classical C - H?O and C - H?F hydrogen bonds. These interactions are augmented by a weak ?-? interaction between the benzene and pyridyl rings of neighbouring molecules, with a centroid-centroid distance of 3.9226(10) This leads to a three-dimensional supramolecular assembly in the crystal system. The F atom is disordered over two sites in a 0.559(3): 0.441(3) ratio, through a 180 rotation of the fluorobenzene ring. 2014 CrossMark.