Synthesis and Characterization of Cyclopentadithiophene and Thienothiophene-Based Polymers for Organic Thin-Film Transistors and Solar Cells
donor-donor type conjugated polymers; organic solar cells; organic thin film transistors
Novel donor-donor type alternating copolymers (8CDT-TT and 16CDT-TT) derived from cyclopentadithiophene (CDT) and thienothiophene (TT) moieties that differ from solubilizing side chains were successfully synthesized and characterized. After the synthesis of CDT-TT-based conjugated polymers with dioctyl and dihexadecyl side chains, their optical, thermal, structural and semiconducting properties were investigated. Organic thin-film transistors fabricated from 8CDT-TT and 16CDT-TT exhibit carrier mobilities as high as 3.920-4 and 1.050-3 cm2V-1s-1, respectively. Bulk heterojunction solar cells fabricated using a polymer:PCBM blend ratio of 1:3 exhibit power conversion efficiencies of 2.12 and 1.84% for 8CDT-TT and 16CDT-TT, respectively. 2018, The Polymer Society of Korea and Springer Nature B.V.
Madathil P.K.; Cho S.; Choi S.; Kim T.-D.; Lee K.-S.
Macromolecular Research, Vol-26, No. 10, pp. 934-941.
Madathil P.K., Department of Advanced Materials, Hannam University, Daejeon, 34054, South Korea, Department of Chemistry, Christ University, Bangalore, 560029, India; Cho S., Department of Physics and EHSRC, University of Ulsan, Ulsan, 44610, South Korea; Choi S., Department of Advanced Materials, Hannam University, Daejeon, 34054, South Korea; Kim T.-D., Department of Advanced Materials, Hannam University, Daejeon, 34054, South Korea; Lee K.-S., Department of Advanced Materials, Hannam University, Daejeon, 34054, South Korea
CDADITagger: An Approach Towards Content Based Annotations Driven Image Tagging Integrating Hybrid Semantics
annotations driven; content driven; image annotation; image retrieval; Image tagging
Considering the rapid growth of multimedia data, especially images, image tagging is considered the most efficient way to organize or retrieve images. The significance of image tagging is growing extensively but the frameworks employed for tagging these images aren't sophisticated. These images aren't properly tagged because of a lack of resources for tagging or manual tagging is a challenging task considering such voluminous data. Already existing frameworks take both the image data and tag-related textual data but ultimately resulted in mediocre or unpalatable performance as they are dataset centered. To overcome these limitations in existing frameworks we proposed an image tagging mechanism, CDADITagger capable of automatically tagging images efficiently and much more reliable compared to existing frameworks. This framework can tackle real-world applications like tagging a new unknown image as the framework isn't powered by dataset alone but is designed to inculcate images from search engines like Google, Bing, etc. to have comprehensive knowledge of real-time data. These images are classified using CNN and tag-related textual data is classified using decision trees for enhanced performance. While tagging images from the classified tags, are sorted based on the semantic computation values, only the top 50% of the instances classified are selected. The tags which are more correlated to the image are ranked and finalized. The proposed semantically inclined framework CDADITagger outshined the well-established frameworks with an accuracy of 96.60% and a precision of 95.84% making it a more reliable approach. 2022 IEEE.
Maddineni S.; Deepak G.; Sheeba Priyadarshini J.
2022 IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2022 - Proceedings
Institute of Electrical and Electronics Engineers Inc.
Maddineni S., National Institute of Technology, Department of CSE, Andhra Pradesh, Tadepalligudem, India; Deepak G., Manipal Insitute of Technology Bengaluru, Manipal Academy of Higher Education, Department of CSE, Manipal, India; Sheeba Priyadarshini J., CHRIST (Deemed to Be University), Department of Data Science, Bangalore, India
Simulation and Experimental Analysis of L-Section in Reinforced Cement Concrete: Uncertainties in Performance and Strength
cyclic load; ductility; earthquake resilience; energy absorption; GFRP mesh; L-section; reinforced cement concrete; stiffness
The design and construction of reinforced cement concrete (RCC) flooring play a crucial role in the overall stability of a structure, particularly in regions prone to tectonic activity. RCC floors comprise various beams, including intermediate T-sections and specific L-sections at critical points such as corners and around staircases or lift openings. This paper identifies a key challenge in building frameworks to resist tectonic loads. It further explores the components of the structure that provide potential for interruption, capability, and the safe transfer of tectonic loading to the array connection, all while maintaining sufficient strength. The L-sections were experimented on using various grades of concrete and sizes to reinforce connections under diverse loading conditions. L-sections contribute to reducing floor height, solving economic and technical problems, and creating advanced composite connections that integrate the proposed structural system. The analysis was conducted both analytically and experimentally to assess methods to resist earthquake forces based on stiffness, building strength, and elasticity capacity. These approaches have been identified to safeguard buildings during substantial seismic events. The development of the L-section is detailed, highlighting the loading process and the capacity to overcome various structural challenges. 2024 by the authors. Licensee MDPI, Basel, Switzerland.
Maddodi B.S.; Naik N.; Maddikeri P.K.; Chougule S.; Abdu S.M.; Narasimha D.B.; Dubey A.K.; Devesh S.
Multidisciplinary Digital Publishing Institute (MDPI)
All Open Access; Hybrid Gold Open Access
https://doi.org/10.3390/engproc2023059228 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188245352&doi=10.3390%2fengproc2023059228&partnerID=40&md5=5f6dbd45cfe2dc02c4a0ad52fee401bc
Maddodi B.S., Department of Civil Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Karnataka, Manipal, 576104, India; Naik N., Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Karnataka, Manipal, 576104, India; Maddikeri P.K., Department of Civil Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Karnataka, Manipal, 576104, India; Chougule S., School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, 30332, GA, United States; Abdu S.M., Department of Civil Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Karnataka, Manipal, 576104, India; Narasimha D.B., Department of Environment Impact Assessment, Horizon Ventures, Karnataka, Bengaluru, 560094, India; Dubey A.K., National Highways Authority of India, PIU Baghpat, Uttar Pradesh, Baghpat, 250609, India; Devesh S., Department of Business and Management, Christ (Deemed to be University), Yeshwantpur Campus, Nagasandra, Karnataka, Bengaluru, 5750073, India
System Design for Financial and Economic Monitoring Using Big Data Clustering
Big Data; Clustering; Economic; Financial; Monitoring
Economic data executives are becoming increasingly important for the longevity and improvement of ventures due to the constant expansion in the influence of data innovation. This study lays out an undertaking economic data the executive's structure for the intricate internal undertaking economic data the board business. It also includes the application of web-based big data technology to understand the fairness, reliability, and security of system database calculations, mainly to improve office capabilities and solve daily project management problems. used in the project. The aim is to evaluate the suitability of transfer clustering computation (DCA) for managing large amounts of data in energy systems and the suitability of data economics dispatch methods for harnessing new energies. Then, combine day-ahead shipping plans with continuous shipping plans to create a multi-period, data-economic shipping model. Consider how the calculations are performed using a case study on the use of new energies. This will enable new energy in multi-period data economics shipping models while meeting his DR requirements on the customer side. 2023 IEEE.
Madem S.; Katuri P.K.; Anandasubramanian C.P.; Anandasubramanian C.P.; Kalra A.; Singh P.
Proceedings of the 2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023
https://doi.org/10.1109/ACCAI58221.2023.10200699 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170089277&doi=10.1109%2fACCAI58221.2023.10200699&partnerID=40&md5=c2f93a927d59be579b3ffc2737bfecd0
Madem S., Vignan's Institute of Information Technology (A), Department of Management Studies, Visakhapatnam, 530049, India; Katuri P.K., Vfstr Deemed to Be University, Department of Management Studies, Andhra Pradesh, India; Anandasubramanian C.P., Vels University, Tamil Nadu, Chennai, 600117, India; Anandasubramanian C.P., Christ University, School of Business and Management, Bangalore, India; Kalra A., Mba, Brandeis University, Transfer Pricing and International Economics Subject-Matter Expert, MA, Waltham, United States; Singh P., Lovely Professional University, Department of Management, India
AI-Controlled Wind Turbine Systems: Integrating IoT and Machine Learning for Smart Grids
Advances in renewable energy technologies are pivotal in addressing the challenges posed by the depletion of traditional energy sources and their associated environmental impacts. Among these, wind energy stands out as a promising avenue, with wind turbine farms proliferating globally. However, the unpredictable nature of wind and intricate interplay between turbines necessitate innovative solutions for efficient operation and maintenance. This paper reviews advancements in intelligent control systems, notably those proposed by Smart Wind technologies. These systems leverage a network of sensors and IoT devices to gather real-time data, such as wind speed, temperature, and humidity, to optimize turbine performance. A significant focus is on turbines employing doubly-fed induction generators, which offer benefits like adjustable speed and consistent frequency operation. Their integration into smart grids introduces challenges concerning power system dynamics'security and reliability. This review delves into the dynamics, characteristics, and potential instabilities of such integrations, emphasizing the uncertainties in wind and nonlinear load predictions. A noteworthy finding is the rising prominence of artificial intelligence, particularly machine and deep learning, in predictive diagnostics. These methodologies offer costeffective, accurate, and efficient solutions, holding potential for enhancing power system stability and accuracy in the smart grid context. The Authors, published by EDP Sciences, 2024.
Madeswaran A.; Bisht D.; Yuvaraj S.; Udayapal Reedy M.; Al-Attabi K.; Dhablia A.
https://doi.org/10.1051/e3sconf/202454003008 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198053163&doi=10.1051%2fe3sconf%2f202454003008&partnerID=40&md5=9660d3c6e2cf1b7cb9a4bdc5ec42e9d4
Madeswaran A., Scool of Business and Management, CHRIST University, Yeshwanthpur Campus, Bengaluru, India; Bisht D., Department of Management Uttaranchal Institute of Management, Uttaranchal University, Dehradun, 248007, India; Yuvaraj S., Department of ECE, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, 127, India; Udayapal Reedy M., Department of Computer Science and Engineering, IES College of Technology, IES University, Madhya Pradesh, Bhopal, 462044, India; Al-Attabi K., The Islamic University, Najaf, Iraq; Dhablia A., Altimetrik India Pvt Ltd, Maharashtra, Pune, India
Automatic Classification of Normal and Affected Vegetables Based on Back Propagation Neural Network and Machine Vision
Classifier; Color features; Texture features; Vegetable disease
This article presents a neural network and machine vision-based approach to classify the vegetables as normal or affected. The farmers will have great difficulty if there is a change from one disease control to another. The examination through an open eye to classify the diseases by name is more expensive. The texture and color features are used to identify and classify different vegetables into normal or affected using a neural network and machine vision. The mixture of both the features is proved to be more effective. The results of experiments show that the proposed methodology extensively supports the accuracy in automatic detection of affected and normal vegetables. The applications in packing and grading of vegetables are the outcome of this research article. 2019, Springer Nature Singapore Pte Ltd.
Communications in Computer and Information Science, Vol-1037, pp. 529-537.
Madgi M., K. L. E. Institute of Technology, Hubballi, 580030, India; Danti A., Christ (Deemed to be University), Bengaluru, 560074, India
Recognition of Green Colour Vegetables' Images Using an Artificial Neural Network
ANN classifier; feature vector; horticultural produce; vegetable recognition
Image processing is used in all the domains including agriculture. In this paper, we have introduced a computationally simple and small feature vector, as a tool for the recognition of green colour vegetable images. The RGB colour system is used and the feature set is computationally economic and performs well on locally available vegetable images. For recognition of vegetable images, an ANN-based classifier is deployed. The recognition percentage is in the scale of 74-100 for 15 vegetable types. This work finds application in the packing of vegetables, food processing, automatic vending. 2019 IEEE.
2019 International Conference on Data Science and Communication, IconDSC 2019
https://doi.org/10.1109/IconDSC.2019.8816943 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072774681&doi=10.1109%2fIconDSC.2019.8816943&partnerID=40&md5=02fb596bb288252b501a7fd22cc49ed3
Madgi M., Dept. of Computer Science and Engg., K. L. E. Institute of Technology, Hubballi, India; Danti A., Dept. of Computer Science and Engg., Christ, Deemed to be University, Bengaluru, India; Anami B., K. L. E. Institute of Technology, Hubballi, India
Climate Risks in an Unequal Society: The Question of Climate Justice in India
Climate justice; Climate risks; India's climate policy; Kerala and Odisha; Social inequities
Over the past few decades, India has witnessed the brunt of climate change impacts in multiple dimensions. Notably, recent years' experiences prove that there has been a substantial increase in the intensity, frequency, and duration of climate-related risks and extreme events, resulting in an acceleration of the nexus between climate change and inequities. Despite the growing advancements of socioeconomic research on current and future climate change risks in India, the explorations through the lens of legal perspectives are still limited and have not met the demands. This chapter argues for rethinking legal perspectives of climate justice in India by drawing insights from two recent climate extreme events. To begin with, this chapter briefly reviews the historical background of the global actions to combat climate inequities and injustices and identifies the ways in which climate injustices perpetuate. For this, it adopts three main principles of climate justice, consisting of equity, a rights-based approach, and sustainability. Following this, it discusses India's climate policy and the existing institutional framework and actions to respond to climate change at the national and state levels. Then, by focusing on the climate change impacts on India, it introduces two recent climate-related risk events in India, and it discusses the unequal structuration of climate risks and the resulting more vulnerable and precarious situation of the marginal sections of the society that already faces multiple social injustices of Indian society. At the end of the cases, it briefly offers a critique of the climate change action plans of the respective states. This chapter concludes by outlining a few strategies to create a more sustainable and equitable approach toward climate governance and justice by strengthening the legal and institutional dispensations of the climate regime in India. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.
Environment, Climate, and Social Justice: Perspectives and Practices from the Global South, pp. 161-188.
Madhanagopal D., School of Sustainability, XIM University, Odisha, India; Jacob V.A., School of Law, Christ University, Karnataka, Bangalore, India
Genetic Modification of Enzymes for Biomass Hydrolysis
Lignocellulose biomass is an economically viable and most abundant energy source. The synthesis of renewable energy-based fuel from lignocellulosic biomass is a replacement for fossil fuel. Cellulases are the biocatalysts that hydrolyze the ?-1,4-glycosidic bond in cellulose to release carbohydrate moieties that can be converted to ethanol, butanol, and other compounds. However, little enzymatic activity and product yield, and thermal stability are hurdles in the deconstruction of lignocellulose. Current progress in synthetic and omics technologies has resulted in several works in metabolic and genetic engineering that have paved the way for efficient conversion of lignocellulose to fuel in the last decades. Several works have attempted to apply genetic and metabolic engineering in the synthesis of stable and highly active cellulases at lower cost. This chapter reviews various genetic engineering technologies for enhancing cellulase synthesis and catalytic efficiency. 2024 selection and editorial matter, Reeta Rani Singhania, Anil Kumar Patel, Htor A. Ruiz, Ashok Pandey; individual chapters, the contributors.
Madhavan A.; Arun K.B.; Nair B.G.; Singhania R.R.; Raveendran S.; Awasthi M.K.; Pandey A.; Binod P.
Biomass Hydrolyzing Enzymes: Basics, Advancements, and Applications, pp. 101-111.
Madhavan A., School of Biotechnology, Amrita Vishwa Vidyapeetham, Kerala, Kollam, India; Arun K.B., Department of Life Sciences, Christ (Deemed to be University), Karnataka, Bengaluru, India; Nair B.G., School of Biotechnology, Amrita Vishwa Vidyapeetham, Kerala, Kollam, India; Singhania R.R., Institute of Aquatic Science and Technology, National Kaohsiung University of Science and Technology, Kaohsiung City, Taiwan, Centre for Energy and Environmental Sustainability, Uttar Pradesh, Lucknow, India; Raveendran S., Department of Food Technology, T K M Institute of Technology, Kerala, Kollam, India; Awasthi M.K., College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi Province, China; Pandey A., Centre for Energy and Environmental Sustainability, Uttar Pradesh, Lucknow, India, Centre for Innovation and Translational Research, CSIR-Indian Institute of Toxicology Research, Uttar Pradesh, Lucknow, India, Kyung Hee University, Seoul, South Korea, School of Engineering, University of Petroleum and Energy Studies, Dehradun, India; Binod P., Microbial Processes and Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology, Kerala, Thiruvananthapuram, India
Bacterial Pigments as Antimicrobial Agents
In this chapter, we discuss various bacterial derived secondary metabolites pigments which has antimicrobial properties. Though these metabolites were identified more than several decades ago, attention into their bioactivities has emerged in the last few decades. Their increasing acceptance is an outcome of their cost-effectiveness, biodegradability, noncarcinogenic property, and eco-friendly characteristics. This chapter has made an attempt to take an in-depth observation into the current bacterial derived pigments and their bioactivity against various microorganisms. 2024 selection and editorial matter, Mohammed Kuddus, Poonam Singh, Raveendran Sindhu and Rachana Singh; individual chapters, the contributors.
Madhavan A.; Arun K.B.; Reshmy R.; Abhinand K.; Menon A.M.; Nair B.G.; Sindhu R.; Kuriakose L.L.; Awasthi M.K.; Pandey A.; Binod P.
Microbial Pigments: Applications in Food and Beverage Industry, pp. 150-157.
https://doi.org/10.1201/9781003353980-9 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184528915&doi=10.1201%2f9781003353980-9&partnerID=40&md5=2dab4684e5fcabd3f9ae9ab04265c584
Madhavan A., School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, Kollam, India; Arun K.B., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Reshmy R., Department of Science and Humanities, Providence College of Engineering, Kerala, Chengannur, India; Abhinand K., School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, Kollam, India; Menon A.M., School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, Kollam, India; Nair B.G., School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, Kollam, India; Sindhu R., Department of Food Technology, T K M Institute of Technology, Kerala, Kollam, India; Kuriakose L.L., Department of Food Technology, T K M Institute of Technology, Kerala, Kollam, India; Awasthi M.K., College of Natural Resources and Environment, Northwest A & F University, Shaanxi, Yangling, China; Pandey A., Center for Innovation and Translational Research, CSIR-Indian Institute of Toxicology Research, Lucknow, India, Sustainability Cluster, School of Engineering, University of Petroleum and Energy Studies, Uttarkhand, Dehradun, India, Centre for Energy and Environmental Sustainability, Uttar Pradesh, Lucknow, India; Binod P., Microbial Processes and Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Kerala, Trivandrum, India
Design and genome engineering of microbial cell factories for efficient conversion of lignocellulose to fuel
Bacteria; Biofuel; Ethanol; Filamentous fungi; Metabolic engineering; Synthetic biology
The gradually increasing need for fossil fuels demands renewable biofuel substitutes. This has fascinated an increasing investigation to design innovative energy fuels that have comparable Physico-chemical and combustion characteristics with fossil-derived fuels. The efficient microbes for bioenergy synthesis desire the proficiency to consume a large quantity of carbon substrate, transfer various carbohydrates through efficient metabolic pathways, capability to withstand inhibitory components and other degradation compounds, and improve metabolic fluxes to synthesize target compounds. Metabolically engineered microbes could be an efficient methodology for synthesizing biofuel from cellulosic biomass by cautiously manipulating enzymes and metabolic pathways. This review offers a comprehensive perspective on the trends and advances in metabolic and genetic engineering technologies for advanced biofuel synthesis by applying various heterologous hosts. Probable technologies include enzyme engineering, heterologous expression of multiple genes, CRISPR-Cas technologies for genome editing, and cell surface display. 2022 Elsevier Ltd
Madhavan A.; Arun K.B.; Sindhu R.; Nair B.G.; Pandey A.; Awasthi M.K.; Szakacs G.; Binod P.
https://doi.org/10.1016/j.biortech.2022.128555 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145294466&doi=10.1016%2fj.biortech.2022.128555&partnerID=40&md5=db2aada628eabde002ceca6b3f8e4683
Madhavan A., School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, Kollam, 690525, India; Arun K.B., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Sindhu R., Department of Food Technology, TKM Institute of Technology, Kollam, 689 122, India; Nair B.G., School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, Kollam, 690525, India; Pandey A., Center for Innovation and Translational Research, CSIR-Indian Institute of Toxicology Research, Lucknow, 226 001, India, Sustainability Cluster, School of Engineering, University of Petroleum and Energy Studies, Uttarkhand, Dehradun, 248007, India, Centre for Energy and Environmental Sustainability, Uttar Pradesh, Lucknow 226 029, India; Awasthi M.K., College of Natural Resources and Environment, Northwest A & F University, Shaanxi, Yangling, 712 100, China; Szakacs G., Budapest University of Technology and Economics, Department of Applied Biotechnology and Food Science, Szent Gellert ter 4, Budapest, 1111, Hungary; Binod P., Microbial Processes and Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology, Trivandrum, 695 019, India
Murraya koenigii extract blended nanocellulose-polyethylene glycol thin films for the sustainable synthesis of antibacterial food packaging
Antimicrobial; Antioxidant; Food packaging; Murraya koenigii; Nanocellulose
Non-biodegradable plastics are a worldwide problem that have a negative impact on all living things, including humans. Nanocellulose, an excellent biopolymer is known for their increasing uses in food, healthcare, cosmetics, and various other fields. Nanocellulose is readily biodegradable, bioderived, and useful for creating innovative bioplastics that are employed in the production of food packaging and wound dressing. Curry leaves (Murraya koenigii) belongs to the rutaceae family and has many health benefits. Synthesis of Murraya koenigii incorporated nanocellulose thin films, and its characterisation using FT-IR, and XRD is discussed in detail. The source of nanocellulose in this study is sugar cane bagasse, an easily available agricultural residue in Kerala. Also, a biocompatible plasticizer is utilised to produce antibacterial packaging for food. The synthesised nanocomposites showed non-toxicity against THP1-derived macrophage cells and significant antibacterial activity against gram positive and gram-negative bacteria suggesting the possible application as a viable alternative for food packaging materials. 2023 Elsevier B.V.
Madhavan A.; Reshmy R.; Arun K.B.; Philip E.; Sindhu R.; Nair B.G.; Awasthi M.K.; Pandey A.; Binod P.
Sustainable Chemistry and Pharmacy, Vol-32
https://doi.org/10.1016/j.scp.2023.101021 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149967433&doi=10.1016%2fj.scp.2023.101021&partnerID=40&md5=6a6f5e39d08b5d1c5d4703a317e3a9a1
Madhavan A., School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, Kollam, 690525, India; Reshmy R., Post Graduate and Research Department of Chemistry, Bishop Moore College, Kerala, Mavelikara, 690 110, India, Department of Science and Humanities, Providence College of Engineering, Kerala, Chengannur, 689122, India; Arun K.B., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Philip E., Post Graduate and Research Department of Chemistry, Bishop Moore College, Kerala, Mavelikara, 690 110, India; Sindhu R., Department of Food Technology, TKM Institute of Technology, Kollam, 689 122, India; Nair B.G., School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, Kollam, 690525, India; Awasthi M.K., College of Natural Resources and Environment, Northwest A & F University, Yangling, Shaanxi, 712 100, China; Pandey A., Centre for Energy and Environmental Sustainability, Uttar Pradesh, Lucknow, 226 029, India, Centre for Innovation and Translational Research, CSIR- Indian Institute for Toxicology Research (CSIR-IITR), 31 MG Marg, Lucknow, 226 001, India; Binod P., Microbial Processes and Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology, Trivandrum, 695 019, India
Non-Contact Vital Prediction Using rPPG Signals
Convolutional neural networks; Deep learning; Facial expression recognition; Machine learning; Remote photoplethysmography
In this paper, we present the clinical significance of various cardiac symptoms with the use of heart rate detection, ongoing monitoring and present emotions. The development of algorithms for remote photoplethysmography has drawn a lot of interest during the past decade (rPPG). As a result, using data gathered from the video feed, we can now precisely follow the heart rate of individuals who are still seated. rPPG algorithms have also been developed, in addition to technique based on hand-crafted characteristics. Deep learning techniques often need a lot of data to train on, but biomedical data frequently lacks real-world examples. The experiment described in this work, we looked at how illumination affected the rPPG signals' SNR. The findings show that the SNR in each RGB channel varies depending on the colour of the light source. Paper describes development in video filtering for recognising the comprehending human face emotions. In our method, emotions are deduced by identifying facial landmarks and analysing their placement. 2023 IEEE.
Madhu M.; Gurudas V.R.; Manjunath C.; Naik P.; Kulkarni P.
Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications
Madhu M., CHRIST(Deemed to Be University), School of Engineering and Technology, Department of Computer Science and Engineering, Bangalore, India; Gurudas V.R., CHRIST(Deemed to Be University), School of Engineering and Technology, Department of Computer Science and Engineering, Bangalore, India; Manjunath C., CHRIST(Deemed to Be University), School of Engineering and Technology, Department of Computer Science and Engineering, Bangalore, India; Naik P.; Kulkarni P., Dayananda Sagar University, Department of Computer Science and Engineering, Bangalore, India
Performance of second law in Carreau fluid flow by an inclined microchannel with radiative heated convective condition
Carreau fluid; Convective condition; Inclined microchannel; Radiative heat transport; Second law analysis
This investigation addresses the novel characteristics of entropy production in the fully-developed heat transport of non-Newtonian Carreau fluid in an inclined microchannel. The physical effects of Roseland thermal radiation and viscous heating are included in the energy equation. The no-slip boundary condition for velocity and convective type heating boundary conditions for temperature are also accounted. Mathematical modeling included the non-Newtonian Carreau fluid model. The dimensionless two-point boundary value problem acquired from governing equations via dimensionless variables. The nonlinear system is tackled by using the Finite Element Method. A detailed discussion of the significance of effective parameters on Bejan number, entropy generation rate, temperature and velocity is presented through graphs. Our analysis established that the entropy generation is reduced at the left and right phase of the channel while the Bejan number is improved at both phases of the channel and is maximum at the center of channel by the incrementing values of Weissenberg number. 2020 Elsevier Ltd
Madhu M.; Mahanthesh B.; Shashikumar N.S.; Shehzad S.A.; Khan S.U.; Gireesha B.J.
International Communications in Heat and Mass Transfer, Vol-117
https://doi.org/10.1016/j.icheatmasstransfer.2020.104761 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088212772&doi=10.1016%2fj.icheatmasstransfer.2020.104761&partnerID=40&md5=159ddf9b5055687e2a194a75edd04db1
Madhu M., Department of Mathematics, Kuvempu University, Shimoga, 577451, India; Mahanthesh B., Department of Mathematics, CHRIST (Deemed to be University), Bangalore, 560029, India; Shashikumar N.S., Department of Mathematics, Malnad College of Engineering, Hassan, 573202, India; Shehzad S.A., Department of Mathematics, COMSATS University Islamabad, Sahiwal, 57000, Pakistan; Khan S.U., Department of Mathematics, COMSATS University Islamabad, Sahiwal, 57000, Pakistan; Gireesha B.J., Department of Mathematics, Kuvempu University, Shimoga, 577451, India
Heat transfer and entropy generation analysis of non-Newtonian fluid flow through vertical microchannel with convective boundary condition
Bejan number; Entropy generation; Mag- netic field; Microchannel; Third-grade fluid
The entropy generation and heat transfer characteristics of magnetohydro-dynamic (MHD) third-grade fluid flow through a vertical porous microchannel with a convective boundary condition are analyzed. Entropy generation due to flow of MHD non-Newtonian third-grade fluid within a microchannel and temperature-dependent viscosity is studied using the entropy generation rate and Vogels model. The equations describing flow and heat transport along with boundary conditions are first made di-mensionless using proper non-dimensional transformations and then solved numerically via the finite element method (FEM). An appropriate comparison is made with the pre-viously published results in the literature as a limiting case of the considered problem. The comparison confirms excellent agreement. The effects of the Grashof number, the Hartmann number, the Biot number, the exponential space-and thermal-dependent heat source (ESHS/THS) parameters, and the viscous dissipation parameter on the temperature and velocity are studied and presented graphically. The entropy generation and the Bejan number are also calculated. From the comprehensive parametric study, it is recognized that the production of entropy can be improved with convective heating and viscous dissipation aspects. It is also found that the ESHS aspect dominates the THS aspect. Shanghai University and Springer-Verlag GmbH Germany, part of Springer Nature 2019.
Madhu M.; Shashikumar N.S.; Mahanthesh B.; Gireesha B.J.; Kishan N.
Applied Mathematics and Mechanics (English Edition), Vol-40, No. 9, pp. 1285-1300.
Springer Science and Business Media B.V.
https://doi.org/10.1007/s10483-019-2516-9 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077781675&doi=10.1007%2fs10483-019-2516-9&partnerID=40&md5=c71b3e8370bfd23a13a798cfe2bab32b
Madhu M., Department of Studies and Research in Mathematics, Kuvempu University, Shankaraghatta, 577451, India; Shashikumar N.S., Department of Studies and Research in Mathematics, Kuvempu University, Shankaraghatta, 577451, India; Mahanthesh B., Department of Mathematics, Christ Deemed to be University, Bangalore, 560029, India; Gireesha B.J., Department of Studies and Research in Mathematics, Kuvempu University, Shankaraghatta, 577451, India; Kishan N., Department of Mathematics, Osmania University, 500007, Telangana, India
Irreversibility analysis of the MHD Williamson fluid flow through a microchannel with thermal radiation
entropy generation; Finite element method; microchannel; thermal radiation; Williamson fluid
The heat transport and non-Newtonian fluid (Williamson fluid) flow through a micro-channel are considered to analyze the entropy generation minimization using the thermodynamic second law. The energy equations have been modeled with the addition of joule heating, heat source, and thermal radiation. The use of suitable dimensionless transformation helps to convert the modeled flow equations into non-dimensional coupled ODEs. The numerical simulations are done via the Finite Element Method. The current outcomes are constructed to examine the behavior of various flow parameters and presented via graphs. It is found that the rise of heat source and Reynolds number Re decays/boosts the entropy rate Ns and the Bejan number Be profile near the left/right plate, and reverse behavior is noticed for the thermal radiation parameter. 2022 Informa UK Limited, trading as Taylor & Francis Group.
Madhu M.; Shashikumar N.S.; Thriveni K.; Gireesha B.J.; Mahanthesh B.
Madhu M., Department of Mathematics, Chaitanya Bharathi Institute of Technology, Hyderabad, India; Shashikumar N.S., Department of Mathematics, Malnad College of Engineering, Hassan, India; Thriveni K., Department of Mathematics, CHRIST (Deemed to be University), Bangalore, India; Gireesha B.J., Department of Mathematics, Kuvempu University, Shimoga, India; Mahanthesh B., Department of Mathematics, CHRIST (Deemed to be University), Bangalore, India
Covid-19 Classification and Detection Model using Deep Learning
Classification; Convolutional Neural Network; COVID 19; Darknet model; Deep Learning; pneumonia
One of the deadly diseases in recent years is covid19 which is affecting the lives of peoples. Also leading to severe adverse problems and death. Prevention is done using early diagnosis and medication which in turn helps in early detection of the disease. The basic aim of the paper is to identify and further classify the patients using the chest x-rays. From scratch the Convolutional Neural Network is diagnosed producing a very high accurate and optimum results. In recent years, researchers found out that in the radiological images such as like x-rays, the traces of covid-19 can be found. In few areas, a good accuracy of the covid-19 detection cannot be achieved due to lack of the people who test so the artificial intelligence is combined with the radiological image. In machine learning the models used are deep learning by automatizing the actions and making it certain by swift, skillful and proficient outcome produced by the chest images provided by the patients. There are several layers like convolutional layer, max pooling layer etc. which are initiated and are used with aid of ReLU activation function. These images given as inputs are also classified accordingly. There is a sequence of neurons being given as input to the active dense layer and there is a result to the input by a sigmoidal function. There is a rise in efficiency because the models are trained and there is a decline of loss at the same time. If there is a model where fitting is done earlier to the overfitting and is restricted from implementing in the data augmentation. There is a better and efficient involvement of suggestions to models of deep learning. Further there is a classification of chest images for identifying and analyzing covid19. So, to check the Covid detection, the images are used as raw. In this paper a model is proposed to have good accuracy in the classification between Covid and normal and further it can be classified into three categories like Covid, pneumonia, normal. There is a 98.08% for the first one and 87.02% for the second one. By introducing 17 convolutional layers and using the Darknet model used for classifying you only look once (YOLO) for the live identification of the objects and multiple layers of filters are used. In the model there is an initial screening. 2022 IEEE.
Proceedings of the International Conference on Electronics and Renewable Systems, ICEARS 2022, pp. 1457-1462.
Madhu M., School of Engineering and Technology Christ (Deemed to Be University), Bangalore, India; Xavier A., School of Engineering and Technology Christ (Deemed to Be University), Bangalore, India; Jayapandian N., School of Engineering and Technology Christ (Deemed to Be University), Bangalore, India
Audience perception on movies related to social issues: A case study on Ranjith movies /
Film alludes to an arrangement of pictures moving constantly, in a steady progression which makes an illusionary impact of development. Ranjith is a Malayalam screenwriter, filmmaker, producer and actor. The researcher here aims to analyse four films by Director Ranjith and study the audience perception or understanding on films related social issues.
Master of Arts in Media and Communication Studies
IoT and AI for Real-Time Customer Behavior Analysis in Digital Banking
AI (Artificial Intelligence); Context-aware computing; Customer behavior analysis; Customer experience; Data analytics; Digital banking; IoT (Internet of Things); Machine learning; Personalization; Predictive modeling; Real-time; Smart devices
Digital transformation has revolutionized the banking industry, ushering in an era of enhanced customer experiences and operational efficiency. The convergence of Internet of Things (IoT) and Artificial Intelligence (AI) technologies has further propelled this evolution by providing real-time insights into customer behavior. This research explores the integration of IoT and AI for real-time customer behavior analysis in the context of digital banking. The proliferation of connected devices, ranging from smart phones to wearables, has generated an unprecedented volume of data. IoT facilitates the collection of diverse data points, such as transaction history, location information, and device interactions, creating a comprehensive digital footprint for each customer. Simultaneously, AI algorithms leverage this wealth of data to analyze, predict, and respond to customer behavior dynamically. In the realm of digital banking, understanding and adapting to customer behavior in real-time is crucial for providing personalized services, preventing fraud, and optimizing operational processes. This research delves into the mechanisms by which IoT sensors and devices, coupled with AI algorithms, enable banks to gain deeper insights into customer behavior patterns. Key components of the proposed system include data acquisition through IoT devices, secure data transmission protocols, and AI-driven analytics engines. In conclusion, this research advocates for the symbiotic relationship between IoT and AI in digital banking to enable real-time customer behavior analysis. 2024 IEEE.
Madhumita G.; Das T.; Das S.; Khatri E.; Ravisankar P.; Hemachandu P.
5th International Conference on Recent Trends in Computer Science and Technology, ICRTCST 2024 - Proceedings, pp. 198-203.
Madhumita G., Vels Institute of Science, Technology and Advanced Studies, Department of MBA, Tamil Nadu, Chennai, India; Das T., Christ University, Department of School of Business Management, UP, Ghaziabad, India; Das S., Christ University, Department of School of Commerce, Finance & Accountancy, UP, Ghaziabad, India; Khatri E., Nitte Meenakshi Institute of Technology, Department of Management Studies, Karnataka, Bengaluru, India; Ravisankar P., Saveetha College of Liberal Arts and Sciences, Department of Commerce, Thandalam, Chennai, India; Hemachandu P., Sasi Institute of Technology and Engineering, EEE Department, Andhrapradesh, Tadepallidusem, 534101, India
Coloring of n-inordinate invariant intersection graphs
Fix of a permutation; Intersection graphs; Invariant intersection graph; n-inordinate invariant intersection graphs; Permutation group
In the literature of algebraic graph theory, an algebraic intersection graph called the invariant intersection graph of a graph has been constructed from the automorphism group of a graph. A specific class of these invariant intersection graphs was identified as the n-inordinate invariant intersection graphs, and its structural properties has been studied. In this article, we study the different types of proper vertex coloring schemes of these n-inordinate invariant intersection graphs and their complements, by obtaining the coloring pattern and the chromatic number associated. 2024 The Author(s)
Journal of Combinatorial Mathematics and Combinatorial Computing, Vol-123, pp. 369-381.
https://doi.org/10.61091/jcmcc123-26 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215416029&doi=10.61091%2fjcmcc123-26&partnerID=40&md5=41484988e5338520eda2ada92acc505e
Madhumitha S., Department of Mathematics, Christ University, Bangalore, India; Naduvath S., Department of Mathematics, Christ University, Bangalore, India