36
20
16481
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A Novel Back-Propagation Neural Network for Intelligent Cyber-Physical Systems for Wireless Communications
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Artificial intelligence; BPNN; Cyber-physical systems; Wireless sensor network
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Wireless sensor networks, which play a significant role in monitoring complex environments that change rapidly over time, were used in the Artificial Intelligence method. External factors or the device designers themselves are both responsible for this complex behavior. Sensor networks often use machine learning techniques to adapt to such conditions, eliminating the need for excessive redesign. Cyber-physical systems (CPS) appeared as the promising option for improving physical-virtual interactions. The quality of the system containing processing information is primarily determined by the system function. There are many benefits obtained while combining Artificial Intelligence (AI) and Cyber-Physical Systems (CPSs) in buildings. In CPS-based indoor environment has various design schemes containing measurement and intelligent buildings in the control system consisting of detection, tracking, execution, and communication modules. The Multi-Agent System (MAS) is the smallest control unit that simulates among neurons and it flexibly provides the information. To mimic the interactions between human neurons, multi-agents are used. In this paper, the CPSs information world is built on the fundamental principle of granular formal concepts and the theory of granular computing is investigated. The calculation module is used by Back-Propagation Neural Network (BPNN) for pattern recognition and classification by environmental information. Various parameters namely the normalized root mean square error, peak signal-to-noise ratio, mean square error, and the mean absolute error are chosen as the objective assessment criteria to assess the benefits of the proposed method and the effectiveness of the proposed system is proven. 2024 IETE.
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Madasamy N.S.; Eldho K.J.; Senthilnathan T.; Deny J.
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IETE Journal of Research, Vol-70, No. 2, pp. 1361-1373.
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Taylor and Francis Ltd.
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2024-01-01
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<a href="https://doi.org/10.1080/03772063.2023.2173669" target="_blank" rel="noreferrer noopener">https://doi.org/10.1080/03772063.2023.2173669</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172011132&doi=10.1080%2F03772063.2023.2173669&partnerID=40&md5=ee6986534317ff366c5d43edbf3beb00" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172011132&doi=10.1080%2f03772063.2023.2173669&partnerID=40&md5=ee6986534317ff366c5d43edbf3beb00</a>
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ISSN: 3772063
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Online
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English
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Article
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Madasamy N.S., Department of CSE, Dr. Mahalingam College of Engineering & Technology, Pollachi, India; Eldho K.J., PG Department of Computer Science, Mary Matha Arts and Science College, Kerala, Wayanad, India; Senthilnathan T., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Deny J., Department of ECE, Kalasalingam Academy of Research and Education, Krishnankoil, India
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Faculty Publications
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Malik, Rohit; Dua, Sakshi; Shoran, Preety; Upreti, Kamal
Title
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A Novel Blockchain-Integrated Deep Learning Framework for Securing Smart Healthcare Communication Networks
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01-01-2025
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Computational Intelligence;Volume;41;Issue;6;Article No.;e70132;
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<a href="https://doi.org/10.1111/coin.70132" target="_blank" rel="noreferrer noopener">https://doi.org/10.1111/coin.70132</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105020379435?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105020379435?origin=resultslist</a>
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Malik R., School of Computer Applications, Lovely Professional University, Punjab, Phagwara, India; Dua S., School of Computer Applications, Lovely Professional University, Punjab, Phagwara, India; Shoran P., School of Sciences, Christ University, Uttar Pradesh, Ghaziabad, India; Upreti K., School of Sciences, Christ University, Uttar Pradesh, Ghaziabad, India
Description
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With the rapid expansion of intelligent medical equipment and their interconnectedness through the Internet of Things (IoT), addressing safety issues in the communicating system has become increasingly critical. A learning mechanism is proposed for an intelligent healthcare-based communication system that uses blockchain for secure network communication and incorporates a data evaluation layer based on cloud which actively segregates and ranks transactions into three main categories: Good, Moderate, and Malware. Fog servers are utilized to route the communicating nodes via Rician and Rayleigh channels. The learning mechanism employs a deep neural network to instruct and classify categories, thereby improving the blockchain layer's decision-making process. This paper introduces several significant contributions, such as the development of a secure blockchain framework for user authentication and a protected digital ledger for communication. Additionally, it incorporates a cloud-driven data analysis layer combined with a neural network to improve training accuracy and category classification. The developed algorithm surpassed the existing works in terms of quality of service (QoS) parameters with low latency, bit error rate (BER), higher signal to inference plus noise ratio (SINR), packet delivery ratio (PDR), true detection rate (TDR), false detection rate (FDR), and throughput. Also, a thorough comparison of consensus mechanisms like practical Byzantine fault tolerance (pBFT), proof of work (PoW), Raft, and Paxos is done to ensure which consensus helps optimize the proposed system in terms of security and fault tolerance with low latency and energy-efficient operations. It also establishes a secure and efficient communication network for smart healthcare, aimed at enhancing the overall quality of life for individuals. 2025 Wiley Periodicals LLC.
Subject
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blockchain technology; consensus mechanisms; fog computing; IoT security; neural network classification; smart healthcare communication
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John Wiley and Sons Inc
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ISSN: 8247935; CODEN: COMIE
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English
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Article
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online
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Articles
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A novel chemical route for low-temperature curing of natural rubber using 2,4 dihydroxybenzaldehyde: improved thermal and tensile properties
Subject
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Activation energy; Dihydroxybenzaldehyde; Latex; Low-temperature vulcanization; Natural rubber
Description
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A novel method for chemically curing natural rubber (NR) using 2,4-dihydroxybenzaldehyde (DHB) at low temperatures has been discovered. Adding varying amounts of DHB to NR increases the crosslinking between the NR molecular chains. The chemical reaction between NR molecular chains and DHB was confirmed through Fourier transform infrared (FTIR) and proton nuclear magnetic resonance (NMR) spectra. From the thermogravimetric analysis (TGA), the thermal stability and activation energy of degradation were determined. The variation in glass transition temperature (Tg), as an indication of increased crosslink density, reducing the mobility of rubber chains, has been confirmed through differential scanning calorimetry (DSC). The addition of DHB to latex significantly enhanced the thermal stability of the rubber. An increase in the activation energy of 5.52% was observed upon the addition of 80mL DHB into NRL when compared to the uncured one. Furthermore, the tensile properties, in terms of tensile strength and modulus of elasticity of rubber, were drastically increased through DHB crosslinking. Tensile strength values of rubber were found to increase by reducing its elongation at break due to the formation of crosslinks between the macromolecular chains. NR cured with 80mL DHB exhibited superior tensile and thermal properties among the series of cured samples. By adding 80mL of DHB, the tensile strength increased by 390% and the elongation at break decreased by 10%. The advantage of this curing method is that, it is an effective technique for crosslinking NR directly from NR latex at comparatively low temperature. Graphical abstract: (Figure presented.) Iran Polymer and Petrochemical Institute 2024.
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Raosaheb B.V.; Adeppa A.; Aralihalli S.; Kalkornsurapranee E.; Tuljittraporn A.; Chuaybamrung A.; Vijayashankar A.V.; Johns J.
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Iranian Polymer Journal (English Edition), Vol-33, No. 7, pp. 915-925.
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Springer Science and Business Media Deutschland GmbH
Date
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2024-01-01
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<a href="https://doi.org/10.1007/s13726-024-01297-7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s13726-024-01297-7</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187928463&doi=10.1007%2Fs13726-024-01297-7&partnerID=40&md5=6b567175a0ea967a1dd6c3dd4f44d1d8" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187928463&doi=10.1007%2fs13726-024-01297-7&partnerID=40&md5=6b567175a0ea967a1dd6c3dd4f44d1d8</a>
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ISSN: 10261265; CODEN: IPJOF
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Online
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English
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Article
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Raosaheb B.V., Research Center, Department of Physics, Rajarajeswari College of Engineering, Bangalore, India; Adeppa A., Department of Aeronautical Engineering, ACS College of Engineering, Bangalore, India; Aralihalli S., Department of Chemistry, Rajarajeswari College of Engineering, Bangalore, India; Kalkornsurapranee E., Division of Physical Sciences, Faculty of Science, Prince of Songkla University, Hat-Yai, Thailand; Tuljittraporn A., Division of Physical Sciences, Faculty of Science, Prince of Songkla University, Hat-Yai, Thailand; Chuaybamrung A., Division of Physical Sciences, Faculty of Science, Prince of Songkla University, Hat-Yai, Thailand; Vijayashankar A.V., Department of Sciences and Humanities, School of Engineering and Technology, CHRIST-Deemed to be University, Bangalore, 560074, India; Johns J., Research Center, Department of Physics, Rajarajeswari College of Engineering, Bangalore, India
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Conference Papers
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A Novel CNN Approach for Condition Monitoring of Hydraulic Systems
Subject
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CNN; Deep Learning; Industry 4.0; Operations Management; Predictive Maintenance
Description
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In the dynamic landscape of Industry 4.0, the ascendancy of predictive analytics methods is a pivotal paradigm shift. The persistent challenge of machine failures poses a substantial hurdle to the seamless functioning of factories, compelling the need for strategic solutions. Traditional reactive maintenance checks, though effective, fall short in the face of contemporary demands. Forward-thinking leaders recognize the significance of integrating data-driven techniques to not only minimize disruptions but also enhance overall operational productivity while mitigating redundant costs. The innovative model proposed herein harnesses the robust capabilities of Convolutional Neural Networks (CNN) for predictive analytics. Distinctively, it selectively incorporates the most influential variables linked to each of the four target conditions, optimizing the model's predictive precision. The methodology involves a meticulous process of variable extraction based on a predetermined threshold, seamlessly integrated with the CNN framework. This nuanced and refined approach epitomizes a forward-looking strategy, empowering the model to discern intricate failure patterns with a high degree of accuracy. 2024 IEEE.
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Khan R.; Bhagat V.; Ganie A.H.
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TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/TQCEBT59414.2024.10545150" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TQCEBT59414.2024.10545150</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204501324&doi=10.1109%2FTQCEBT59414.2024.10545150&partnerID=40&md5=203d93fad9f6cc2f8de0909a5cea615b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204501324&doi=10.1109%2fTQCEBT59414.2024.10545150&partnerID=40&md5=203d93fad9f6cc2f8de0909a5cea615b</a>
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ISBN: 979-835038427-7
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Online
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English
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Conference paper
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Khan R., Dept. of Data Science, Christ (Deemed to Be) University, Bangalore, India; Bhagat V., Dept. of Data Science, Christ (Deemed to Be) University, Bangalore, India; Ganie A.H., Dept. of Data Science, Christ (Deemed to Be) University, Bangalore, India
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Articles
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Faculty Publications -Articles
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A novel congestion-aware approach for ECC based secured WSN multicasting
Subject
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Congestion Control; Multicasting; Unequal Clustering; Wireless Sensor Network
Description
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--Multicasting in Wireless Sensor Networks greatly reduces the communication complexity between The Base station and set of sensor nodes deployed in a given region. It reduces the number of packets to be sent thus minimizing the chance of congestion. Still the existence of congestion appears due to improper channel utilization resulting in low throughput. In this paper, we have addressed the issue of congestion with reference to WSN multicasting. The Simulation results have shown that our approach is better in terms of throughput and delay compared with existing approaches. 2018, Institute of Advanced Scientific Research, Inc.. All rights reserved.
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Raja Vikram G.; Krishna A.V.N.; Shahu Chatrapati K.
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Journal of Advanced Research in Dynamical and Control Systems, Vol-10, No. 10, pp. 83-91.
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Institute of Advanced Scientific Research, Inc.
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2018-01-01
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<a href="" target="_blank" rel="noreferrer noopener"></a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074469247&partnerID=40&md5=adb373b00ff3669b8505b9b7e196f51b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074469247&partnerID=40&md5=adb373b00ff3669b8505b9b7e196f51b</a>
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ISSN: 1943023X
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Online
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English
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Article
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Raja Vikram G., Dept. of CSE, Vignan Institute of Technology & Science, Hyderabad, Telangana, India; Krishna A.V.N., Dept. of CSE, Faculty of Engineering, Christ University, Bangalore, India; Shahu Chatrapati K., Dept. of CSE, J.N.T.U.H College of Engineering, Karimnagar, India
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Faculty Publications
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Mukku, Lalasa; Thomas, Jyothi
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A Novel Cross-Validation Fusion Model Combining Vision Transformer and DenseNet161 for Enhanced Cervical Lesion Classification
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01-01-2025
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Lecture Notes in Networks and Systems;Volume;1276;pp.437-448
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<a href="https://doi.org/10.1007/978-981-96-2697-7_32" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-2697-7_32</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105008675698?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105008675698?origin=resultslist</a>
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Mukku L., CHRIST (Deemed to be University), Bangalore, India; Thomas J., CHRIST (Deemed to be University), Bangalore, India
Description
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Cervical cancer is fourth most common cancer in women across the world with highest impact in low- and middle-income countries. World Health Organization sent out a call for all UN nations to work toward the elimination of cervical cancer. Deep learning and artificial intelligence have been the go-to solutions for medical image analysis for diagnosis and prognosis. This paper aims to classify lesions in a colposcope captured cervix image with help of artificial intelligence models. To further advance automated cervical lesion classification, the study proposes a novel hybrid model that combines the complementary strengths of a vision transformer and DenseNet architecture. The paper also addresses ongoing challenges, such as interference from specular reflection areas and the difficulty in distinguishing between different lesion grades due to subtle visual differences. The proposed cross-validation decision fusion strategy aims to improve the reliability and robustness of the classification process. The results of the study affirm that deep learning and fusion technologies will steer the future direction of research in medical image analysis. DenseNet model has performed with an accuracy score of 0.695, sensitivity of 0.912, specificity of 0.979 and F1 score of 0.9100. These metrics are significantly improved versions of state of the art used in this study for comparative analysis. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Subject
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Cervical cancer; Deep learning; Fusion; Lesion; Transformer
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Springer Science and Business Media Deutschland GmbH
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ISSN: 23673370; ISBN: 978-981962696-0;
Language
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English
Type
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Conference paper
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https://archives.christuniversity.in/files/original/ba7d80d910b653c7424ca0923cc3e8c7.jpg
0b6dfeeadbd8dbf97f5aef738454f6b9
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Patents
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Patent
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Patent
Patent
Patents
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A novel data mining and machine learning-based framework for cyber security intrusion detection /
Subject
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Bio-Medical Engineering
Description
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Patent Number: 202231027007, Applicant: Lingaraj Sethi.
This survey paper describes a focused literature survey of machine learning (ML) and data mining (DM) methods for cyber analytics supporting intrusion detection. Short tutorial descriptions of each ML/DM method are provided. Based on the number of citations or the relevance of emerging practice, papers representing each method were identified, read, and summarised. Because data are so crucial in ML/DM approaches, some well- known cyber data sets used in ML/DM are described.
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Gupta, Chabi.
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<a href="https://drive.google.com/file/d/1MTaj7V560TwPNDRUjaduypCJWchusuuG/view" target="_blank" title="A novel data mining and machine learning-based framework for cyber security intrusion detection /" rel="noreferrer noopener">https://drive.google.com/file/d/1MTaj7V560TwPNDRUjaduypCJWchusuuG/view</a>
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Intellectual Property India
Date
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2022-05-20
Language
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English
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Patent
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Faculty Publications
Article
Faculty Publications -Articles
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Kola, Sangeetha Supriya; Lovesum, Jeno S.P.
Title
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A NOVEL DATA SECURE MODEL FOR INTERNET OF HEALTH THINGS WITH A NEW LIGHTWEIGHT CRYPTOGRAPHY ALGORITHM AND STEGANOGRAPHY TECHNIQUE
Date
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01-01-2026
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Journal of Theoretical and Applied Information Technology;Volume;104;Issue;1;pp.378-390
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<a href="https://doi.org/10.5281/zenodo.18260371" target="_blank" rel="noreferrer noopener">https://doi.org/10.5281/zenodo.18260371</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033630615?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033630615?origin=resultslist</a>
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Kola S.S., Department of CSE, Christ University, India; Lovesum J.S.P., Department of CSE, Christ University, India
Description
An account of the resource
Ensuring the security of data in Internet of Things (IoT) based healthcare systems (HS) presents considerable challenges due to the limitations of traditional embedding methods and cryptography techniques, leading to more memory consumption, more execution time, less security, inadequate payload capacity, and performance inefficiencies. To address these issues, the Bernoulli Fish-based Stego Algorithm (BFBSA) is introduced as an innovative solution. Specifically designed for IoT healthcare data, this algorithm is validated through the encryption and embedding of healthcare data. The process involves initializing IoT healthcare data, encrypting it using the BFBSA algorithm, and embedding the encrypted data within steganographic images. Performance analysis is conducted using key metrics such as payload capacity, encryption time, memory usage, PSNR, and MSE. Comparative analysis with existing approaches highlights the BFBSA models efficiency and its effectiveness in ensuring secure and optimized data management in IoT healthcare environments. Little Lion Scientific
Subject
The topic of the resource
Crypto Algorithm; Data Security; Embedding Image; Encryption; IoT Healthcare
Publisher
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Little Lion Scientific
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ISSN: 19928645;
Language
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English
Type
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Article
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Restricted Access; Hardcopy may be available in the library
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online
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Faculty Publications
Conference Paper
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Sreelatha, Bolisetty; Rajamohanan, Rajasree; Kalaiarasi, G.; Praveen, Rvs; Bugge, Bhagya Prasad; John, Tegil J
Title
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A Novel Decision Tree and LSTM Powered Intelligent Agent System for Early Detection of Vegetable Plant Diseases
Date
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01-01-2025
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Proceedings of the 6th International Conference on Electronics and Sustainable Communication Systems, ICESC 2025;pp.1617-1622
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<a href="https://doi.org/10.1109/ICESC65114.2025.11212443" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICESC65114.2025.11212443</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105022632840?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105022632840?origin=resultslist</a>
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Sreelatha B., Geethanjali College of Engineering and Technology, Department of Electronics and Communication Engineering, Telangana, Hyderabad, India; Rajamohanan R., SP Jain School of Global Management, Department of Artificial Intelligence and Data Science, Dubai, United Arab Emirates; Kalaiarasi G., Dhanalakshmi Srinivasan College of Engineering, Department of Food Technology, Coimbatore, India; Praveen R., Director - Utilities Americas LTIMindtree, Houston, TX, United States; Bugge B.P., S.R.K.R Engineering College, Department of Electronics and Communication Engineering, Andhra Pradesh, India; John T.J., Christ University Dharmaram College Hosur, Department of Computer Science, Karnataka, Bengaluru, India
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The early and precise diagnosis of vegetable plant diseases is crucial for sustainable agriculture since these diseases have a major impact on crop output and quality. Disease identification performance is examined in this work through a robust detection pipeline that examines the effects of several preprocessing techniques, class imbalance handling strategies, and deep learning classifiers. To better represent data and increase model knowledge of illness characteristics, the GLCM was used to extract texture information. By combining XGBoost with LSTM networks, a new hybrid model was created. While XGBoost is great at classifying structured data, the LSTM component is great at evaluating sequential data, which allows it to capture patterns and trends in the evolution of plant diseases over time. Better and more meaningful forecasts are made possible by this supplementary integration. By surpassing more conventional methods of illness classification, the suggested LSTM-XG model attained a remarkable prediction accuracy of 99.34%. An important factor in achieving this outcome was the use of hybrid modeling in conjunction with thorough preprocessing and correction of class imbalance. Finally, the LSTM-XG model shows great promise for practical use in precision farming. Its precision and efficiency in identifying illnesses in vegetable plants might facilitate prompt action, lessen crop loss, and encourage better farming methods. 2025 IEEE.
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Extreme Gradient Boosting (XGBoost); Gray-Level Co-occurrence Matrix (GLCM); the Long Short-Term Memory (LSTM)
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Institute of Electrical and Electronics Engineers Inc.
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ISBN: 979-833155503-0;
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English
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Conference paper
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A Novel Deep Learning Approach for Identifying Interstitial Lung Diseases from HRCT Images
Subject
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Binary classification; Deep learning; HRCT; ILD; Multi-label classification
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Interstitial lung diseases (ILDs) are defined as a group of lung diseases that affect the interstitium and cause death among humans worldwide. It is more serious in underdeveloped countries as it is hard to diagnose due to the absence of specialists. Detecting and classifying ILD is a challenging task and many research activities are still ongoing. High-resolution computed tomography (HRCT) images have essentially been utilized in the diagnosis of this disease. Examining HRCT images is a difficult task, even for an experienced doctor. Information Technology, especially Artificial Intelligence, has started contributing to the accurate diagnosis of ILD from HRCT images. Similar patterns of different categories of ILD confuse doctors in making quick decisions. Recent studies have shown that corona patients with ILD also go on to sudden death. Therefore, the diagnosis of ILD is more critical today. Different deep learning approaches have positively impacted various image classification problems recently. The main objective of this proposed research work was to develop a deep learning model to classify the ILD categories from HRCT images. This proposed work aims to perform binary and multi-label classification of ILD using HRCT images on a customized VGG architecture. The proposed model achieved a high test accuracy of 95.18% on untrained data. 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
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Raju N.; Augustine D.P.; Anita H.B.
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SN Computer Science, Vol-4, No. 2
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Springer
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2023-01-01
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<a href="https://doi.org/10.1007/s42979-022-01579-y" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s42979-022-01579-y</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145388976&doi=10.1007%2Fs42979-022-01579-y&partnerID=40&md5=f86e32cc9e314ad800bc4ebf2c7f778b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145388976&doi=10.1007%2fs42979-022-01579-y&partnerID=40&md5=f86e32cc9e314ad800bc4ebf2c7f778b</a>
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ISSN: 2662995X
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Online
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English
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Article
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Raju N., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Augustine D.P., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Anita H.B., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India
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A Novel Deep Learning Approach for Retinopathy Prediction Using Multimodal Data Fusion
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Clustering; Deep Learning; Multimodal; Retinopathy
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In contemporary research on mild cognitive disorders (MCI) and Alzheimer's disease (AD), the predominant approach involves the utilization of double data modalities for making predictions related to AD stages. However, there is a growing recognition of the potential benefits that could be derived from the fusion of multiple data modalities to obtain a more comprehensive perspective in the analysis of AD staging. To address this, we have employed deep learning techniques to holistically assess data from various sources, including, genetic (single nucleotide polymorphisms (SNPs)), imaging (magnetic resonance imaging (MRI)), and clinical tests, with the objective of categorizing patients into distinct groups: AD, MCI, and controls (CN). For the analysis of imaging data, convolutional neural networks have been employed. Moreover, we have introduced a novel approach for data interpretation, enabling the identification of the most influential features learned by these deep models. This interpretation process incorporates clustering and perturbation analysis, shedding light on the crucial aspects of the data contributing to our classification results. Our experimentation, conducted on the dataset (i.e., ADNI), has yielded compelling results. Furthermore, our findings have underscored the significant advantage of integrating multi-modality data over solely relying on double modality models, as it has led to improvements in terms of accuracy, precision, recall, and mean F1 scores. 2024, Ismail Saritas. All rights reserved.
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Kumar A.; Sharma N.Kr.; Reshmi B.; Saini R.; Thanganadar H.; Singhal A.; Rao P.V.
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International Journal of Intelligent Systems and Applications in Engineering, Vol-12, No. 11s, pp. 70-77.
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Ismail Saritas
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2024-01-01
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<a href="" target="_blank" rel="noreferrer noopener"></a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184869157&partnerID=40&md5=175474b83934b66be3cc09d10d252be3" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184869157&partnerID=40&md5=175474b83934b66be3cc09d10d252be3</a>
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ISSN: 21476799
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Online
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English
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Article
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Kumar A., School of Computing Science & Engineering, Galgotias University, India; Sharma N.Kr., Department of MCA, IIMT College of Engineering, Gr.Noida, Dr. A.P.J. Abdul Kalam Technical University, UP, Lucknow, India; Reshmi B., Department of Computer Science and Engineering, Ahalia School of Engineering and Technology, Kerala, Palakkad, India; Saini R., Department of Computer Science and Engineering, G. B. Pant Institute of Engineering and Technology, Pauri Garhwal, Uttarakhand, India; Thanganadar H., Department of Health Informatics, College of Public Health and Tropical Medicine, Jazan University, Jazan, 45142, Saudi Arabia; Singhal A., School of Sciences, Christ (Deemed to be University) Delhi-NCR, Uttar Pradesh, Ghaziabad, 201003, India; Rao P.V., Gokaraju Rangaraju Institute of Engineering and Technology(GRIET), Bachupally, Hyderabad, 500090, India
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Kaushik, Ruchi; Pradeep, S.; Shanbhog, Manjula; Tamizhselvi, A.; Jagdale, Jayashree; Mahajan, Rashima
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A novel deep learning based multimedia video retrieval framework using may fly optimization
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01-01-2025
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International Journal of Information Technology (Singapore);
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<a href="https://doi.org/10.1007/s41870-025-02587-w" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s41870-025-02587-w</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105007443959?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105007443959?origin=resultslist</a>
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Kaushik R., Xavier Institute of Management and Entrepreneurship, Bengaluru, India; Pradeep S., Department of CSE, Malla Reddy Engineering College for Women, Maisammaguda, Kompally, Telangana, Secunderabad, India; Shanbhog M., School of Science, Christ University, Bengaluru, India; Tamizhselvi A., IT, St. Josephs College of Engineering, Chennai, India; Jagdale J., Information Technology, Pune Institute of Computer Technology, Pune, India; Mahajan R., Manav Rachna International Institute of Research and Studies, Faridabad, India
Description
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Developing a video retrieval framework in multimedia management is a main challenge due to the massive growth of video content on the internet. A major drawback of video retrieval is its long search response time and low accuracy. To tackle these issues, this paper introduces a novel deep learning-based Multimedia video retrieval system (DL-MVR) to minimize the search response time with high accuracy. The collected video is initially converted into key frames and pre-processed with contrast adaptive histogram equalization to remove noise artifacts thereby improving image quality. After pre-processing, the images are fed to Efficient Net to extract patch features. Finally, to retrieve the similar video, matching is done using may fly optimization (MFO), that compares the query frame features to the video database. Several performance metrics are analysed to measure the effectiveness of the proposed strategy in terms of accuracy and response time. Experimental results indicate that the proposed system has a search response time of 0.71s, which is lower than existing methods. The proposed DL-MVR method achieves 99.26% of accuracy. The proposed method improves the overall accuracy by 9.32%, 22.04%, and 19.40% which is better than CNN-AlexNet (convolutional neural network), Pyramid regional graph network and CBVR respectively. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.
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Efficient net; Histogram equalization; May fly optimization (MFO); Multimedia; Video retrieval
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Springer Science and Business Media B.V.
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ISSN: 25112104;
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English
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Article
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online
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Title
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A novel discrete slash family of distributions with application to epidemiology informatics data
Subject
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Discrete distributions; Exponential distribution; Geometric distribution; Heavy tails; Maximum likelihood estimation; Slash distributions
Description
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This study puts forward a new class of discrete distribution that can be used by the epidemiologists and medical scientists to model data relating to epidemiology informatics. The proposed distribution is superior to traditional discrete modeling alternatives, viz., discrete Weibull and geometric distributions in terms of its model fit and flexibility to handle heavy-tailed dataset. It is a flexible three-parameter discrete distribution, grounded in the slash family and can be considered as a refined extension to the geometric distribution. We explored the diverse properties of this novel distribution thoroughly by evaluating the mathematical properties. The models parameters are estimated using the maximum likelihood estimation method, where the methodology validity is confirmed through an extensive simulation study. Furthermore, the practical utility of the distribution to model epidemiology informatics was examined with the help of eight different datasets representing three different dimensions of the epidemiology informatics, viz., mortality, infection and medication statistics. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.
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Joseph J.; Gillariose J.
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International Journal of Data Science and Analytics
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Springer Science and Business Media Deutschland GmbH
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2024-01-01
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<a href="https://doi.org/10.1007/s41060-024-00590-1" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s41060-024-00590-1</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197372284&doi=10.1007%2Fs41060-024-00590-1&partnerID=40&md5=38a2f5cb0e22a3cc97a82cca557907b9" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197372284&doi=10.1007%2fs41060-024-00590-1&partnerID=40&md5=38a2f5cb0e22a3cc97a82cca557907b9</a>
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ISSN: 2364415X
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Online
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English
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Article
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Joseph J., SCAPS, Marian College Kuttikkanam, Kuttikkanam P.O, Peermade, Kerala, Idukki, 685531, India; Gillariose J., Department of Statistics and Data Science, CHRIST (Deemed to be University), Dharmaram College Post, Hosur Road, Karnataka, Bangalore, 560029, India
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Faculty Publications
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Joseph, Joshin; Gillariose, Jiju
Title
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A novel discrete slash family of distributions with application to epidemiology informatics data
Date
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01-01-2025
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International Journal of Data Science and Analytics;Volume;20;Issue;3;pp.2227-2243
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<a href="https://doi.org/10.1007/s41060-024-00590-1" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s41060-024-00590-1</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85197372284?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85197372284?origin=resultslist</a>
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Joseph J., SCAPS, Marian College Kuttikkanam, Kuttikkanam P.O, Peermade, Kerala, Idukki, 685531, India; Gillariose J., Department of Statistics and Data Science, CHRIST (Deemed to be University), Dharmaram College Post, Hosur Road, Karnataka, Bangalore, 560029, India
Description
An account of the resource
This study puts forward a new class of discrete distribution that can be used by the epidemiologists and medical scientists to model data relating to epidemiology informatics. The proposed distribution is superior to traditional discrete modeling alternatives, viz., discrete Weibull and geometric distributions in terms of its model fit and flexibility to handle heavy-tailed dataset. It is a flexible three-parameter discrete distribution, grounded in the slash family and can be considered as a refined extension to the geometric distribution. We explored the diverse properties of this novel distribution thoroughly by evaluating the mathematical properties. The models parameters are estimated using the maximum likelihood estimation method, where the methodology validity is confirmed through an extensive simulation study. Furthermore, the practical utility of the distribution to model epidemiology informatics was examined with the help of eight different datasets representing three different dimensions of the epidemiology informatics, viz., mortality, infection and medication statistics. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.
Subject
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Discrete distributions; Exponential distribution; Geometric distribution; Heavy tails; Maximum likelihood estimation; Slash distributions
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Springer Science and Business Media Deutschland GmbH
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ISSN: 2364415X;
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English
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Article
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Restricted Access; Hardcopy may be available in the library
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online
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Articles
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A Novel Dynamic Physical Layer Impairment-Aware Routing and Wavelength Assignment (PLI-RWA) Algorithm for Mixed Line Rate (MLR) Wavelength Division Multiplexed (WDM) Optical Networks
Subject
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mixed-line-rate optical networks; physical layer impairment-aware routing and wavelength assignment; physical layer impairments; XPM
Description
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The ever-increasing global Internet traffic will inevitably lead to a serious upgrade of the current optical networks' capacity. The legacy infrastructure can be enhanced not only by increasing the capacity but also by adopting advance modulation formats, having increased spectral efficiency at higher data rate. In a transparent mixed-line-rate (MLR) optical network, different line rates, on different wavelengths, can coexist on the same fiber. Migration to data rates higher than 10 Gbps requires the implementation of phase modulation schemes. However, the co-existing on-off keying (OOK) channels cause critical physical layer impairments (PLIs) to the phase modulated channels, mainly due to cross-phase modulation (XPM), which in turn limits the network's performance. In order to mitigate this effect, a more sophisticated PLI-Routing and Wavelength Assignment (PLI-RWA) scheme needs to be adopted. In this paper, we investigate the critical impairment for each data rate and the way it affects the quality of transmission (QoT). In view of the aforementioned, we present a novel dynamic PLI-RWA algorithm for MLR optical networks. The proposed algorithm is compared through simulations with the shortest path and minimum hop routing schemes. The simulation results show that performance of the proposed algorithm is better than the existing schemes. 2016 by De Gruyter.
Creator
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Iyer S.
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Journal of Optical Communications, Vol-37, No. 4, pp. 349-356.
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Walter de Gruyter GmbH
Date
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2016-01-01
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<a href="https://doi.org/10.1515/joc-2015-0018" target="_blank" rel="noreferrer noopener">https://doi.org/10.1515/joc-2015-0018</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-84999040321&doi=10.1515%2Fjoc-2015-0018&partnerID=40&md5=b080aa924c104a78a6086387523aafbe" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-84999040321&doi=10.1515%2fjoc-2015-0018&partnerID=40&md5=b080aa924c104a78a6086387523aafbe</a>
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ISSN: 1734911; CODEN: JOCOD
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Online
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English
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Article
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Iyer S., Department of Electronics and Communication Engineering, Faculty of Engineering, Christ University, Mysore Road, Bangalore, India
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https://archives.christuniversity.in/files/original/9294c2c2bb4149958417ea1a61c46d2e.jpg
e19d503f48ac0cbfb088a02a6e9ac6ca
Dublin Core
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A novel dynamic Physical Layer Impairment-Aware Routing and Wavelength Assignment (PLI-RWA) algorithm for Mixed Line Rate (MLR) Wavelength Division Multiplexed (WDM) optical networks /
Subject
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Electronics and Communication Engineering
Description
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Journal of Optical Communications, Vol.37, Issue 4, pp.349-356, ISSN: 2191-6322 (Online) 0173-4911 (Print).
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Iyer, Sridhar.
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<a href="https://www.degruyter.com/document/doi/10.1515/joc-2015-0018/html" target="_blank" title="A novel dynamic Physical Layer Impairment-Aware Routing and Wavelength Assignment (PLI-RWA) algorithm for Mixed Line Rate (MLR) Wavelength Division Multiplexed (WDM) optical networks /" rel="noreferrer noopener">https://www.degruyter.com/document/doi/10.1515/joc-2015-0018/html</a>
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DE GRUYTER
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2016-02-08
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English
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Journal Article
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Articles
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A Novel Edge-Based Trust Management System for the Smart City Environment Using Eigenvector Analysis
Description
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The proposed Edge-based Trust Management System (E-TMS) uses an Eigenvector-based approach for eliminating the security threats present in the Internet of Things (IoT) enabled smart city environment. In most existing trust management systems, the trust aggregation process completely depends on the direct trust ratings obtained from both legitimate and malicious neighboring IoT devices. E-TMS possesses an edge-assisted two-level trust computation approach for ensuring the malicious free trust evaluation of IoT devices. The E-TMS aims at removing the false contribution on aggregated trust data. It utilizes the properties of the Eigenvector for identifying compromised IoT devices. The Eigenvector Analysis also helps to avoid false detection. The analysis involves a comparison of all the contributed trust data about every single connected device. A spectral matrix will be generated corresponding to the contributions and the received trust will be scaled based on the obtained spectral values. The absolute sum of obtained values will contain only true contributions. The accurate identification of false data will remove the effect of malicious contributions from the final trust value of a connected IoT device. Since the final trust value calculated by the edge node contains only the trustworthy data, the prediction about the malicious nodes will be accurate. Eventually, the performance of E-TMS has been validated. Throughput and network resilience are higher than the existing system. 2022 G. Nagarajan et al.
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Nagarajan G.; Simpson S.V.; Venkatachalam K.; Alrasheedi A.F.; Askar S.S.; Abouhawwash M.; Parthasarathi P.
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Journal of Healthcare Engineering, Vol-2022
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Hindawi Limited
Date
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2022-01-01
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<a href="https://doi.org/10.1155/2022/5625897" target="_blank" rel="noreferrer noopener">https://doi.org/10.1155/2022/5625897</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131455042&doi=10.1155%2F2022%2F5625897&partnerID=40&md5=ea30f1395b138a7e4c82ffab0098865b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131455042&doi=10.1155%2f2022%2f5625897&partnerID=40&md5=ea30f1395b138a7e4c82ffab0098865b</a>
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All Open Access; Gold Open Access; Green Open Access
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A related resource
ISSN: 20402295; PubMed ID: 35663279
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Online
Language
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English
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Article
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Nagarajan G., Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India; Simpson S.V., Department of Computer Science and Engineering, SCMS School of Engineering and Technology, Kerala, India; Venkatachalam K., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bangalore, 560074, India; Alrasheedi A.F., Department of Statistics and Operations Research, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia; Askar S.S., Department of Statistics and Operations Research, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia; Abouhawwash M., Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt, Department of Computational Mathematics Science and Engineering (CMSE), Michigan State University, East Lansing, 48824, MI, United States; Parthasarathi P., Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Erode, India
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Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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A Novel Energy-Efficient Hybrid Optimization Algorithm for Load Balancing in Cloud Computing
Subject
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Bird Swarm Optimization; Cloud Computing; Firefly Algorithm; Load Balancing; Makespan
Description
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In the field of Cloud Computing (CC), load balancing is a method applied to distribute workloads and computing resources appropriately. It enables organizations to effectively manage the needs of their applications or workloads by spreading resources across numerous PCs, networks, or servers. This research paper offers a unique load balancing method named FFBSO, which combines Firefly algorithm (FF) which reduces the search space and Bird Swarm Optimization (BSO). BSO takes inspiration from the collective behavior of birds, exhibiting tasks as birds and VMs as destination food patches. In the cloud environment, tasks are regarded as autonomous and non-preemptive. On the other hand, the BSO algorithm maps tasks onto suitable VMs by identifying the possible best positions. Simulation findings reveal that the FFBSO algorithm beat other approaches, obtaining the lowest average reaction time of 13ms, maximum resource usage of 99%, all while attaining a makespan of 35s. 2023 IEEE.
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Prasanna Kumar K.R.; Gm S.; Yamsani N.; Kiran Kumar T.M.; Pani A.K.
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IEEE 1st International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023
Publisher
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Institute of Electrical and Electronics Engineers Inc.
Date
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2023-01-01
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<a href="https://doi.org/10.1109/AIKIIE60097.2023.10390196" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/AIKIIE60097.2023.10390196</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187244305&doi=10.1109%2FAIKIIE60097.2023.10390196&partnerID=40&md5=40617eebd2c0a51b68f15f0b6c109385" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187244305&doi=10.1109%2fAIKIIE60097.2023.10390196&partnerID=40&md5=40617eebd2c0a51b68f15f0b6c109385</a>
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Restricted Access
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ISBN: 979-835031646-9
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Online
Language
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English
Type
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Conference paper
Coverage
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Prasanna Kumar K.R., Msrit, Department of Cse, Bangalore, India; Gm S., Msrit, Department of Ai&ds, Bangalore, India; Yamsani N., School of Computer Science and Artificial Intelligence, Sr University, Warangal, India; Kiran Kumar T.M., Siddaganga Institute of Technology, Department of Cse, Tumkur, India; Pani A.K., Christ (Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India
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Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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A Novel Ensemble based Model for Intrusion Detection System
Subject
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accuracy; Intrusion Detection System; Network security; Random Forest algorithm
Description
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In the present interconnected world, the increasing reliance on computer networks has made them susceptible to multiple security threats and intrusions. Intrusion Detection Systems (IDS) is essential for shielding these networks by detecting and mitigating potential threats in real-time. This research paper presents an in-depth study of employing the Random Forest algorithm for building an effective intrusion detection System. The proposed IDS uses the power of the Random Forest algorithm, a popular ensemble learning technique, to detect various types of intrusions in network traffic effectively. The algorithm integrates more than one decision trees to produce a robust and accurate classifier, capable of handling large-scale and complex datasets typical of network traffic. The proposed system can be used in various industries and sectors to protect critical assets, ensuring the uninterrupted operation of computer networks. Evolving cyber threats have encouraged further research into ensemble analytics methods to increase the resilience of Intrusion Detection Systems in an ever-changing threat landscape. 2024 IEEE.
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Bhattacharya N.; Subudhi A.; Mishra S.; Sharma V.; Aderemi A.P.; Iwendi C.
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Proceedings - International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024, pp. 620-624.
Publisher
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Institute of Electrical and Electronics Engineers Inc.
Date
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2024-01-01
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<a href="https://doi.org/10.1109/IC2PCT60090.2024.10486584" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IC2PCT60090.2024.10486584</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191163463&doi=10.1109%2FIC2PCT60090.2024.10486584&partnerID=40&md5=7d3812cec85742684deecb43f57c261b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191163463&doi=10.1109%2fIC2PCT60090.2024.10486584&partnerID=40&md5=7d3812cec85742684deecb43f57c261b</a>
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Restricted Access
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A related resource
ISBN: 979-835038352-2
Format
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Online
Language
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English
Type
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Conference paper
Coverage
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Bhattacharya N., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Subudhi A., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Mishra S., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Sharma V., Christ (Deemed to Be University), Delhi NCR, India; Aderemi A.P., University of Bolton, School of Creative Technologies, United Kingdom; Iwendi C., Christ (Deemed to Be University), Delhi NCR, India
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Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Articles
Article
Faculty Publications -Articles
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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A novel fibrinolytic enzyme producer from mangrove soil sediments: Screening, isolation, strain improvement, and fermentation
Subject
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Bacillus subtilis; Cardiovascular diseases; Fermentation; Fibrinolytic enzyme; Mangroves; Strain improvement
Description
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Fibrinolytic enzymes are used for addressing many cardiovascular disease conditions. However, current fibrinolytic enzymes are highly expensive with many side effects which necessitate the development of alternative cost-effective processes for low-cost production of these lifesaving enzymes. Since the availability of a suitable strain is considered the basic requirement for any process development effort, we started our work in this direction with screening and isolation of fibrinolytic enzyme producers. The goal of this study was to screen and isolate fibrinolytic enzyme producers from a relatively unexplored environment, i.e., the mangroves. Mangroves are wetlands and are one of the unique and less studied habitats for the production of therapeutic molecules. Microbiota that produces fibrinolytic enzymes from Coringa mangroves located in Kakinada District, Andhra Pradesh, India, is lacking. Therefore, in this study, fibrinolytic enzyme-producing bacterium was screened from samples obtained from Coringa mangroves. Out of 200, protease enzyme-producing isolates obtained from screening 30 mangrove soil samples, 7 isolates exhibiting fibrinolytic activity were selected. Out of these 7, the highest fibrinolytic enzyme-producing bacterial strain (AIBL_AMSB2) was characterized by biochemical and genomic methods, which was finally identified as Bacillus subtilis subsp. Inaquosorum by 16S rRNA analysis. The strain was also found to be an amylase producer. AIBL_AMSB2 was subjected to strain improvement using random mutagenesis techniques (i) Ultraviolent radiations (UV) and (ii) Ethyl methyl Sulphonate (EMS), which resulted in an improved mutant strain AIBL_AMSB2_M7E32 exhibiting 54.70% improvement in fibrinolytic activity. Batch fermentation in controlled experimental conditions using the obtained mutant strain resulted in a 133.54% increase in growth OD and a 391.11% increase in enzyme activity. Thus, the study reports an increased fibrinolytic enzyme activity producing mangrove isolate and its production by submerged fermentation. Further studies to prove the potential of the enzyme on blood clots are necessary to utilize for industrial application. 2024 Bhavana Sompalli and Alok Malaviya.
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Sompalli B.; Malaviya A.
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Journal of Applied Biology and Biotechnology, Vol-12, No. 2, pp. 182-190.
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Open Science Publishers LLP Inc.
Date
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2024-01-01
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<a href="https://doi.org/10.7324/JABB.2024.161429" target="_blank" rel="noreferrer noopener">https://doi.org/10.7324/JABB.2024.161429</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186882107&doi=10.7324%2FJABB.2024.161429&partnerID=40&md5=c69c3439af32a1573c79c67c4bfcc7bc" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186882107&doi=10.7324%2fJABB.2024.161429&partnerID=40&md5=c69c3439af32a1573c79c67c4bfcc7bc</a>
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All Open Access; Gold Open Access
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A related resource
ISSN: 2347212X | LS; 2023-2024; Vol-1; 0009-0017
Format
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Online
Language
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English
Type
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Article
Coverage
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Sompalli B., Applied and Industrial Biotechnology Laboratory, CHRIST (Deemed-to-be University), Karnataka, Bengaluru, India; Malaviya A., Applied and Industrial Biotechnology Laboratory, CHRIST (Deemed-to-be University), Karnataka, Bengaluru, India, Department of Life Sciences, CHRIST (Deemed-to-be University), Karnataka, Bengaluru, India, QuaLife Biotech Private Limited, Karnataka, Bengaluru, India