A comprehensive novel model for network speech anomaly detection system using deep learning approach
Attack classification; Deep learning; Network intrusion detection system
Network Intrusion Detection System (NIDS) is the key technology for information security, and it plays significant role for classifying various attacks in the networks accurately. An NIDS gains an understanding of normal and anomalous behavior by examining the network traffic and can identify unknown and new attacks. Analyzing and Identifying unfamiliar attacks are one of the big challenges in Network IDS research. A huge response has been given to deep learning over the past several years and novelty in deep learning techniques are also improved regularly. Deep learning based Network Intrusion Detection approach is highly essential for improved performance. Nowadays, Machine learning algorithms made a revolution in the area of human computer interaction and achieved significant advancement in imitating human brain exactly. Convolutional Neural Network (CNN) is a powerful learning algorithm in deep learning model for improving the machine learning ability in order to achieve high attack classification accuracy and low false alarm rate. In this article, an overview of deep learning methodologies for commonly used NIDS such as Auto Encoder (AE), Deep Belief Network (DBN), Deep Neural Network (DNN), Restricted Boltzmann Machine (RBN). Moreover, the article introduces the most recent work on network anomaly detection using deep learning techniques for better understanding to choose appropriate method while implementing NIDS through widespread literature analysis. The experimental results designate that the accuracy, false alarm rate, and timeliness of the proposed CNN-NIDS model are superior than the traditional algorithms. 2020, Springer Science+Business Media, LLC, part of Springer Nature.
Manimaran A.; Chandramohan D.; Shrinivas S.G.; Arulkumar N.
International Journal of Speech Technology, Vol-23, No. 2, pp. 305-313.
Manimaran A., Department of Computer Applications, Madanapalle Institute of Technology & Science, Madanapalle, India; Chandramohan D., Department of Computer Science & Engineering, Madanapalle Institute of Technology & Science, Madanapalle, India; Shrinivas S.G., Department of Computer Applications, Madanapalle Institute of Technology & Science, Madanapalle, India; Arulkumar N., Department of Computer Science, Christ (Deemed to be University), Bangalore, India
A Comprehensive Research on Deep Learning Based Routing Optimization Algorithms in Software Defined Networks
Controller attacks; Deep Learning; Deterministic Policy Gradient; Optimal routing; Software defined networking
Discovering an optimal routing in Software Defined Networks (SDNs) is challenging due to several factors like scalability issues, interoperability, reliability, poor configuration of controllers and security measures. The compromised SDN controller attacks at the control plane layer, packet losses in the topology and end-to-end delay are the most security risk factors in SDNs. To overcome this, in most of the existing researches, Deep Reinforcement Learning (DRL) algorithm with various optimization techniques was implemented for optimal routing in SDN by providing link weights to balance the end-to-end delay and packet losses. DRL used Deterministic Policy Gradient (DPG) method which acts as an actor-critic reinforcement learning agent that searches for an optimal policy to minimize the expected cumulative long-term reward. However, discovering an optimal routing with efficient security measures is still a major challenge in SDNs. This research proposes a detailed review of routing optimization algorithms in SDN using Deep Learning (DL) methods which supports the researchers in accomplishing a better solution for future research. 2023 IEEE.
2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023
Institute of Electrical and Electronics Engineers Inc.
Kumar G., Dayananda Sagar University, School of Engineering, Department of Computer Science and Engineering, Bengaluru, India; Girisha G.S., Dayananda Sagar University, School of Engineering, Department of Computer Science and Engineering, Bengaluru, India; Shamanth N., School of Engineering and Technology Christ (Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India
A comprehensive review of AI based intrusion detection system
Artificial intelligence; Deep learning; Ensemble learning; Intrusion detection system; Machine learning
In today's digital world, the tremendous amount of data poses a significant challenge to cyber security. The complexity of cyber-attacks makes it difficult to develop efficient tools to detect them. Signature-based intrusion detection has been the common method used for detecting attacks and providing security. However, with the emergence of Artificial Intelligence (AI), particularly Machine Learning, Deep Learning and ensemble learning, promising results have been shown in detecting attacks more efficiently. This review discusses how AI-based mechanisms are being used to detect attacks effectively based on relevant research. To provide a broader view, the study presents taxonomy of the existing literature on Machine Learning (ML), Deep learning (DL), and ensemble learning. The analysis includes 72 research papers and considers factors such as the algorithm and performance metrics used for detection. The study reveals that AI-based intrusion detection methods improve accuracy, but researchers have primarily focused on improving performance for detecting attacks rather than individual attack classification. The main objective of the study is to provide an overview of different AI-based mechanisms in intrusion detection and offer deeper insights for future researchers to better understand the challenges of multi-classification of attacks. 2023
https://doi.org/10.1016/j.measen.2023.100827 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163471347&doi=10.1016%2fj.measen.2023.100827&partnerID=40&md5=38c5ff8dc0c55fc5ce68b9a51dca1a02
Sowmya T., Department of CSE, Christ University Bangalore, CMR Institute of Technology, Bengaluru, India; Mary Anita E.A., Department of CSE, Christ University, Bangalore, India
A Comprehensive Review of Linear Regression, Random Forest, XGBoost, and SVR: Integrating Machine Learning and Actuarial Science for Health Insurance Pricing
Actuarial science; Artificial intelligence; Data science; Machine learning; Supervised learning
Actuarial science and data science are being studied as a fusion using Industry 4.0 technologies such as the Internet of Things, artificial intelligence, big data, and machine learning (ML) algorithms. When analyzing earlier components of actuarial science, it could have been more accurate and quick, but when later stages of AI and ML were integrated, the algorithms weren't up to the standard, and actuaries experienced some accuracy concerns. The company requires actuaries to be precise with analysis to acquire reliable results. As a result of the large amount of data these companies collect, a choice made manually may turn out to be incorrect. We will, therefore, examine alternative models in this article as part of the decision-making process. Once we have chosen the best path of action, we will use our actuarial expertise to evaluate the risk associated with specific charges features. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 355-367.
Springer Science and Business Media Deutschland GmbH
Agarwal V., Christ (Deemed to be University), Bangalore, India; Singh M., Christ (Deemed to be University), Bangalore, India; Kumar K.P., Christ (Deemed to be University), Bangalore, India
A comprehensive review of microplastic pollution in freshwater and marine environments
Aquatic fauna; Environment; Marine systems; Microplastics; Pollution
Water popularly termed the The Elixir of Life is now polluted beyond control in several regions. Microplastics, the tiny contaminants have found their way into all walks of life. They have also been found to be present in human blood, multiple organs, and even breast milk. There is an abundance of microplastics in the air we breathe, the food we eat, and the water we drink. Curbing them has to start with a ban of all forms of primary microplastics, and single use plastics with preference being given to biodegradable alternatives. India in particular banned single use plastics in 2022, which put an end to several commonly used plastic items being replaced with biodegradables. Paint is one of the largest contributors to microplastics, followed by textile industry, cosmetic, pharmaceutical industry, packaging industry are all top contributors to microplastics. The wastewater treatment plants aren't designed to filter microplastics from the source and this results in microplastics polluting all water resources. Though several novel techniques for microplastic segregation exist such as sieving, filtration, density separation, visual sorting, alkali digestion exist, they aren't fully employed as the initial process of microplastic segregation from waste is still in question. 2024 The Author(s)
Jaikumar I.M.; Tomson M.; Meyyazhagan A.; Balamuralikrishnan B.; Baskaran R.; Pappuswamy M.; Kamyab H.; Khalili E.; Farajnezhad M.
https://doi.org/10.1016/j.greeac.2024.100202 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214086364&doi=10.1016%2fj.greeac.2024.100202&partnerID=40&md5=18968a505a31452052530c1cae089ae6
Jaikumar I.M., Department of Life Sciences, Christ University, Karnataka, Bengaluru, 560029, India; Tomson M., Department of Life Sciences, Christ University, Karnataka, Bengaluru, 560029, India; Meyyazhagan A., Department of Life Sciences, Christ University, Karnataka, Bengaluru, 560029, India; Balamuralikrishnan B., Department of Food Science and Biotechnology, College of Life Sciences, Sejong University, Seoul, 05006, South Korea; Baskaran R., Department of Bioinformatics and Medical Engineering, Asia University, Taichung, 413305, Taiwan; Pappuswamy M., Department of Life Sciences, Christ University, Karnataka, Bengaluru, 560029, India; Kamyab H., UTE University, Faculty of Architecture and Urbanism, Architecture Department, TCEMC Investigation Group, Calle Rumipamba S/N and Bourgeois, Quito, Ecuador, Department of Biomaterials, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Chennai, 600077, India; Khalili E., Department of Engineering, University of Surrey, Guildford, GU2 7XH, United Kingdom; Farajnezhad M., Faculty of Business and Communication, Inti International University, N. Sembilan, Nilai, 71800, Malaysia
A Comprehensive Review of Small Building Detection in Collapsed Images: Advancements and Applications of Machine Learning Algorithms
Collapsed images; Machine learning; Small building detection
Accurately identifying small buildings in images of collapses is essential for disaster assessment and urban planning. In the context of collapsed images, this study provides an extensive overview of the methods and approaches used for small building detection. The investigation covers developments in machine learning algorithms, their uses, and the consequences for urban development and disaster management. This work attempts to give a brief grasp of the difficulties, approaches, and potential paths in the field of small building detection from collapsed imaging through a thorough investigation of the body of existing literature. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-1121 LNNS, pp. 429-438.
Sajitha I., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Karnataka, Bangalore, India; Sambandam R.K., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Karnataka, Bangalore, India; John S.P., Department of Computer Science and Engineering, Jyothi Engineering College, Cheruthuruthy, Kerala, Thrissur, India
A comprehensive review of the Indian retail industry growth /
International Journal of Social And Allied Research, Vol.7, Issue 2, pp.49-53, ISSN No: 2319-3611.
http://journals.foundationspeak.com/index.php/ijsar/article/view/705
International Journal of Social And Allied Research
A Comprehensive Review on Antibacterial, Anti-Inflammatory and Analgesic Properties of Noble Metal Nanoparticles
antibacterial property; antioxidant property, biomedical applications; pain management; precious metal nanoparticles; surface plasmon
Their health industry is facing challenges due to a rise in mortality rates brought on by various multi-drug-resistant bacterial strains. As a result, new and improvedantibacterial drugs are urgently needed. Similarly, when some unwanted foreign pathogensenter the cellular premises to disturb its homeostasis, inflammation develops as an immune reaction. However, these immune responses also become a double-edged sword when the inflammatory reaction lasts for a long time, and pain is also linked to inflammatory responses. Inflammation and pain are both signs of tissue injury. Pain is,by definition, an unpleasant experience that ultimately interferes with their normalwell-being. Hence, bacterial infection, inflammation, and pain need medical assistance to maintain homeostasis. Conventional medicines possess so many repercussive effects, which then demand a replacement with a less toxic and more efficient modern drug. In their review article, for the first time, they present recent advancements in biomedical applications such as the antimicrobial, anti-inflammatory, and analgesic properties of noble metal nanoparticles. Noble metals have limited availability in the earth's crust. Hence, their physicochemical characterizations and applications are greatly limited. Still, there are some interesting research findings that offer a significant ray of hope for the health sector all over the world. 2023 Wiley-VCH GmbH.
Particle and Particle Systems Characterization, Vol-41, No. 5
https://doi.org/10.1002/ppsc.202300162 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180682055&doi=10.1002%2fppsc.202300162&partnerID=40&md5=65605094fd7a54fbede7eda54ad9b629
Aswathy Waliaveettil F., Department of Physics and Electronics, Christ (Deemed to be University), Karnataka, Bangalore, 560029, India; Anila E.I., Department of Physics and Electronics, Christ (Deemed to be University), Karnataka, Bangalore, 560029, India
A comprehensive review on application of atomic force microscopy in Forensic science
Atomic force microscopy (AFM); Fingerprint and questioned document (QD); Quartz grain; Surface morphology; Time since deposition (TSD)
The primary objective of forensic investigation of a case is to recognize, identify, locate, and examine the evidence. Microscopy is a technique that provides crucial information for resolving a case or advancing the investigation process by analyzing the evidence obtained from a crime scene. It is often used in conjunction with suitable analytical techniques. Various microscopes are employed; scanning probe microscopes are available in diverse forensic analyses and studies. Among these, the atomic force microscope (AFM) is the most commonly used scanning probe technology, offering a unique morphological and physico-chemical perspective for analyzing multiple pieces of evidence in forensic investigations. Notably, it is a non-destructive technique capable of operating in liquid or air without complex sample preparation. The article delves into a detailed exploration of the applications of AFM in the realms of nanomechanical forensics and nanoscale characterization of forensically significant samples. 2024 Elsevier Ltd and Faculty of Forensic and Legal Medicine
Therassa M.; Aparna N.S.; Jose M.; Dev A.; Isukapatla A.R.
Journal of Forensic and Legal Medicine, Vol-105
https://doi.org/10.1016/j.jflm.2024.102717 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198021512&doi=10.1016%2fj.jflm.2024.102717&partnerID=40&md5=634772476741fbeaf52bd47a4a41dfd9
Therassa M., Department of Life Sciences, Christ University, Karnataka, Bengaluru, India; Aparna N.S., Department of Life Sciences, Christ University, Karnataka, Bengaluru, India; Jose M., Department of Life Sciences, Christ University, Karnataka, Bengaluru, India; Dev A., Department of Life Sciences, Christ University, Karnataka, Bengaluru, India; Isukapatla A.R., Department of Life Sciences, Christ University, Karnataka, Bengaluru, India
A comprehensive review on energy management strategy of microgrids
Demand response; Distributed energy resources; Energy management; Microgrids; Optimization
Renewable energy resources are a one-stop solution for major issues that include drastic climate change, environmental pollution, and the depletion of fossil fuels. Renewable energy resources, their allied storage devices, load supplied, non-renewable sources, along with the electrical and control devices involved, form the entity called microgrids. Energy management systems are essential in microgrids with more than one energy resource and storage system for optimal power sharing between each component in the microgrid for efficient, reliable and economic operation. A critical review on energy management for hybrid systems of different configurations, the diverse techniques used, forecasting methods, control strategies, uncertainty consideration, tariffs set for financial benefits, etc. are reviewed in this paper. The novelty of reformer based fuel cells, which generates hydrogen on demand, thereby eliminating the requirement of hydrogen storage and lowest carbon footprint is discussed for the first time in this paper. The topics requiring extended research and the existing gap in literature in the field of energy management studies are presented in the authors perspective, which will be helpful for researchers working in the same specialization. Papers are segregated based on multiple aspects such as the configuration, in particular, grid-tied, islanded, multi microgrids, the control strategies adopted besides the identification of limitations/factors not considered in each work. Moreover, at the end of each section, the literature gap related to each category of segregated group is identified and presented. 2023 The Author(s)
https://doi.org/10.1016/j.egyr.2023.04.360 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85158821181&doi=10.1016%2fj.egyr.2023.04.360&partnerID=40&md5=0551ddc0b65de1072284803aa60f9d1b
Allwyn R.G., ECE Department, Sultan Qaboos University, P.O Box 33, P.C 123, Oman, School of Engineering and Technology, Christ (Deemed to be University), Bangalore Kengeri Campus:, 560074, India; Al-Hinai A., ECE Department, Sultan Qaboos University, P.O Box 33, P.C 123, Oman, Sustainable Energy Research Center, Sultan Qaboos University, P.O Box 17, P.C 123, Oman; Margaret V., School of Engineering and Technology, Christ (Deemed to be University), Bangalore Kengeri Campus:, 560074, India
A Comprehensive Review on Fault Data Injection in Smart Grid
Cyber-attack; Fault data injection attack (FDIA); Machine learning; Smart grid
Nowadays, power generation at the utility side and transfer to the demand side have been controlled by the smart grid. Day-by-day entire power distribution process has moved in multiple directions and connects more residential and industrial sectors. Due to these phenomena, more monitoring, and security processes have been adopted in smart grid to control fault data injection, cyber-attack, and physical side attackers in smart grids. This research study analyzes the fault data injection in smart grid with respect to the malicious data, signal, and connectivity process. As a part of this research study, a survey has been done on various techniques to control the faults in smart grid. The analysis carried out in this study is very helpful to identify and determine the suitable method to control the fault in smart grid. Along with these, a countermeasure against the FDI is also summarized on the cyber-attack and physical attack. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Prakyath D.; Mallikarjunaswamy S.; Sharmila N.; Rekha V.; Pooja S.
Lecture Notes in Networks and Systems, Vol-757 LNNS, pp. 649-660.
Prakyath D., Department of Electrical and Electronics Engineering, SJB Institute of Technology, Karnataka, Bengaluru, India; Mallikarjunaswamy S., Electronics and Communication Engineering, JSS Academy of Technical Education, Karnataka, Bengaluru, India; Sharmila N., Electrical and Electronics Engineering, JSS Science and Technology University, Karnataka, Mysore, India; Rekha V., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bengaluru, India; Pooja S., Department of Electronics and Communication Engineering, KS Institute of Technology, Bengaluru, India
A Comprehensive Review on Heart Disease Risk Prediction using Machine Learning and Deep Learning Algorithms
Cardiovascular diseases claim approximately 17.9 million lives annually, with heart attacks and strokes accounting for over 80% of these deaths. Key risk factors, including hypertension, hyperglycemia, dyslipidemia, and obesity, are identifiable, offering opportunities for timely intervention and reduced mortality. Early detection of heart disease enables individuals to adopt lifestyle changes or seek medical treatment. However, conventional diagnostic methods, such as electrocardiogramscommonly used in clinics and hospitals to detect abnormal heart rhythmsare not effective in identifying actual heart attacks. Additionally, angiography, while more precise, is an invasive method, financial strain on patients, and high chances of incorrect diagnosis, highlighting the need for alternative approaches. The main goal of this study was to assess the accuracy of machine learning techniques, including both individual and combined classifiers, in early detection of heart diseases. Furthermore, the study aims to highlight areas where additional research is necessary. Our investigation covers a decade period from 2014 to 2024, including a thorough review of pertinent literature from international conferences and top journals from the databases like Springer, ScienceDirect, IEEEXplore, Web of Science, PubMed, MDPI, Hindawi and so on. The following keywords were used to search the articles: heart disease risk, heart disease prediction, data mining, data preprocessing, machine learning algorithms, ensemble classifiers, deep learning algorithms, feature selection, hyperparameter optimization techniques. We examine the methodologies used and evaluate their effectiveness in predicting cardiovascular conditions. Our findings reveal notable progress in applying machine learning and deep learning in cardiology. The study concludes by proposing a framework that incorporates current machine learning techniques to enhance heart disease prediction. The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2024.
Karna V.V.R.; Karna V.R.; Janamala V.; Devana V.N.K.R.; Ch V.R.S.; Tummala A.B.
Archives of Computational Methods in Engineering
Springer Science and Business Media B.V.
https://doi.org/10.1007/s11831-024-10194-4 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207264313&doi=10.1007%2fs11831-024-10194-4&partnerID=40&md5=97deb939e860f00bbeb2adcb3f36a3ca
Karna V.V.R., Department of Electronics and Communication Engineering, Aditya University, Surampalem, 533437, India; Karna V.R., Department of Artificial Intelligence and Machine Learning, R V College of Engineering, Bengaluru, 560059, India; Janamala V., Department of Electrical and Electronics Engineering, Christ University, Karnataka, Bengaluru, 560074, India; Devana V.N.K.R., Department of Electronics and Communication Engineering, Aditya University, Surampalem, 533437, India; Ch V.R.S., Department of Electronics and Communication Engineering, Aditya University, Surampalem, 533437, India; Tummala A.B., Department of Electronics and Communication Engineering, Chaitanya Bharati Institute of Technology, Hyderabad, 500075, India
A Comprehensive Review on Image Restoration Methods due to Salt and Pepper Noise
Convolutional Neural Network; Digital image; linear and non-linear methods; noise; Peak Signal to Noise Ratio; visualized information
Digital images are well-use in various fields like satellite communication, mobile communication, medical and security. Visualized information helps the people to understand the things easily by seen. Improper capturing, age of camera lens, imperfect storage and transmission leads to introduce noise in the image. Gaussian noise, salt and pepper/impulse noise and speckle noise may affect the original image due to aforementioned reasons. Out of these, impulse noise/salt and pepper noise is one of the major types, degrades the image with black and white spots it results loss of required information. Hence, restoration of ground- truth image from such type of noisy image is a challenging task to provide quality and clarity visuals to users. Several linear and non-linear methods have been proposed by researchers since more than four decades. Nonlinear methods based on; median filtering approach; adaptive median filter approach; median filter with switching condition; and median filter with rank order type; are proposed from early 1980s onwards. All of these operated directly on pixels in spatial domain. Hence, they are very easy to implement and most of them are not that much robust at middle and higher noise density circumstances. Further, various researchers have been implemented linear methods such as wavelet transform methods like SWT and DWT. Majority of these are works well upto 50% noise density conditions and very few works well on higher and multiple noise density conditions also. To overcome these problems CNNs based methods have been developed tremendously by various researchers from last decade and these methods require huge database to train the network model. Most of these, achieved good accuracy rates at higher and multiple noise conditions. Hence, here a detailed review report is presented on impulse noise removal methods with their Peak Signal to Noise Ratio (PSNR). 2023 IEEE.
Kumar G.P.; Arputharaj V.J.; Kumar P.R.; Kumar D.V.; Satyanarayana B.V.V.; Budumuru P.R.
2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023 - Proceedings, pp. 562-567.
Kumar G.P., Department of ECE, Vishnu Institute of Technology, Bhimavaram, India; Arputharaj V.J., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Kumar P.R., Department of ECE, Sri Vishnu Engineering College for Women, Bhimavaram, India; Kumar D.V., Freshman Engineering, Department Lakireddy, Balireddy College of Engineering, Mylavaram, India; Satyanarayana B.V.V., Department of ECE, Vishnu Institute of Technology, Bhimavaram, India; Budumuru P.R., Department of ECE, Vishnu Institute of Technology, Bhimavaram, India
A comprehensive review on natural macromolecular biopolymers for biomedical applications: Recent advancements, current challenges, and future outlooks
Biocompatible; Biomedicine; Natural polymers; Regenerative medicine; Tissue engineering
Versatile material properties coupled with high degree of biocompatibility and biodegradability has made biopolymers as potential candidates for diverse applications in the biomedical field. Natural biopolymers derived from various plant, animal and microbial sources with different biochemical compositions are extensively used in biomaterial industry with or without further medication to their native form. Biopolymeric biomaterials have been employed in a wide range of biomedical applications like tissue engineering, drug delivery, bone regeneration, wound dressings and cardiovascular surgery. Carbohydrate based biopolymers and protein based biopolymers are extensively used for several applications in the biomedical field including cartilage regeneration, periodontal tissue regeneration, bone regeneration, corneal regeneration, drug delivery and wound healing. This review work presents a comprehensive outlook on the applications of various biopolymers in biomedical field. The work elaborates the biochemistry of these polymers with special focus on their crucial properties in the biomedical industry. Further a detailed description on the most recent application of various biopolymers in the biomedical filed is presented in this review. This work further summarizes the current challenges and future prospects in the use of biopolymers in biomedical field. 2024 The Author(s)
Kalpana Manivannan R.; Sharma N.; Kumar V.; Jayaraj I.; Vimal S.; Umesh M.
Carbohydrate Polymer Technologies and Applications, Vol-8
https://doi.org/10.1016/j.carpta.2024.100536 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199339390&doi=10.1016%2fj.carpta.2024.100536&partnerID=40&md5=e98a42030665e5eb5597c6cc6ff5bf43
Kalpana Manivannan R., Institute of Biotechnology, Department of Bioengineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai, 602105, India; Sharma N., Department of Biochemistry, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai, 602105, India; Kumar V., Biomaterials and Tissue Engineering (BITE) Laboratory, Department of Community Medicine, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai, 602105, India; Jayaraj I., Department of Bioengineering, Institute of Biotechnology, Saveetha School of Engineering, SIMATS, Chennai, 602105, India; Vimal S., Department of Biochemistry, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai, 602105, India; Umesh M., Department of Life Sciences, Christ University, Karnataka, Bengaluru, 560029, India
A Comprehensive Review on the Antimicrobial and Photocatalytic Properties of Green Synthesized Silver Nanoparticles
antibacterial; antimicrobial; green synthesis; photocatalysis; silver nanoparticles
With the advancement of technology, there is a growing demand for new nanoparticles that are viable, eco-friendly, non-toxic, and non-hazardous, as well as having unique chemical and physical properties. Silver nanoparticles are currently promising for antibacterial, antimicrobial, and photocatalytic applications. Because of their toxicity, nanosilver particles are now widely used in various applications, including cosmetics, clothing, sunscreen, medicinal, sensing, antibacterial, antimicrobial, and photocatalytic. The importance of plant extracts in the synthesis of AgNPs is emphasized. The various mechanisms and characterization techniques used in the study of silver nanoparticles will also be covered. This review also discusses the role of green synthesized AgNPs in antimicrobial and photocatalytic applications, which adds to our understanding of improving health, and the environment and preventing contagious diseases. 2022 by the authors.
Narayanaswamy S.; Bhaskar R.; Jayadevappa R.K.K.; Ramachandran S.K.M.; Prasad M.A.
Letters in Applied NanoBioScience, Vol-12, No. 4
https://doi.org/10.33263/LIANBS124.140 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151958470&doi=10.33263%2fLIANBS124.140&partnerID=40&md5=54318f0ec590b38ac7acd50f3d1be812
Narayanaswamy S., Department of Physics, RV Institute of Technology and Management, Bengaluru, 560 076, India; Bhaskar R., Department of Life Sciences, Christ (Deemed to be University), Hosur Road, Bengaluru, 560 029, India; Jayadevappa R.K.K., Department of Chemistry, RV Institute of Technology and Management, Bengaluru, 560 076, India; Ramachandran S.K.M., Department of Physics, RV Institute of Technology and Management, Bengaluru, 560 076, India; Prasad M.A., Department of Physics, Dayananda Sagar College of Engineering, Kumaraswamy Layout, Karnataka, Bangalore, 560 078, India
A Comprehensive Review on the Electrochemical Sensing of Flavonoids
Electrocatalytic; electrochemical sensing; electrode modifiers; flavonoids; polyphenolic compounds
Flavonoids are bioactive polyphenolic compounds, widespread in the plant kingdom. Flavonoids possess broad-spectrum pharmacological effects due to their antioxidant, anti-tumor, anti-neoplastic, anti-mutagenic, anti-microbial, anti-inflammatory, anti-allergic, immunomodulatory, and vasodilatory properties. Care must be taken, since excessive consumption of flavonoids may have adverse effects. Therefore, proper identification, quantification and quality evaluations of flavonoids in edible samples are necessary. Electroanalytical approaches have gained much interest for the analysis of redox behavior and quantification of different flavonoids. Compared to various conventional methods, electrochemical techniques for the analysis of flavonoids offer advantages of high sensitivity, selectivity, low cost, simplicity, biocompatibility, easy on-site evaluation, high accuracy, reproducibility, wide linearity of detection, and low detection limits. This review article focuses on the developments in electrochemical sensing of different flavonoids with emphasis on electrode modification strategies to boost the electrocatalytic activity and analytical efficiency. 2022 Taylor & Francis Group, LLC.
George S.A.; Rajeev R.; Thadathil D.A.; Varghese A.
Critical Reviews in Analytical Chemistry, Vol-53, No. 6, pp. 1133-1173.
ISSN: 10408347; PubMed ID: 35001755; CODEN: CCACB
George S.A., Department of Chemistry, CHRIST (Deemed to be University), Bangalore, India; Rajeev R., Department of Chemistry, CHRIST (Deemed to be University), Bangalore, India; Thadathil D.A., Department of Chemistry, CHRIST (Deemed to be University), Bangalore, India; Varghese A., Department of Chemistry, CHRIST (Deemed to be University), Bangalore, India
A comprehensive review on the need for integrated strategies and process modifications for per- and polyfluoroalkyl substances (PFAS) removal: Current insights and future prospects
PFAS degradation; PFAS removal; PFAS removal strategies; PFAS toxicity; PFAS transport
Alarming concern over the persistence and toxicity of per- and polyfluoroalkyl substances (PFAS) in the environment has created an imperative need for designing and redesigning strategies for their detection and remediation. Conventional PFAS removal technologies that uses physical, chemical, or biological methods. Increase in the diversity and quantity of PFAS entering the environment has necessitated the need for developing more advanced and integrated strategies for their removal. Despite of the advances reported in this domain, there exist a huge research gap that need to be mentored to tackle the problems associated with mitigation of combined toxicity of wide variety of PFAS in the environment. The possibility of PFAS to combine with other emerging contaminants poses an additional threat to the existing treatment methods thereby stressing the need for a continuous monitoring and updating the treatment processes. This review work aims at understanding the structure, entry, and fate of different types of PFAS in to the environment. Further an in-depth discussion regarding the different levels of toxicity associated with PFAS is elaborated in the review. The process description of recent PFAS remediation techniques along with their significance, limitations and possibility of integration is discussed in detail. Further a detailed outlook on the advantages and limitations of PFAS removal methods and an insight into the recently developed PFAS removal methods is outlined in this review. 2024 The Authors
Sharma N.; Kumar V.; Sugumar V.; Umesh M.; Sondhi S.; Chakraborty P.; Kaur K.; Thomas J.; Kamaraj C.; Maitra S.S.
Case Studies in Chemical and Environmental Engineering, Vol-9
https://doi.org/10.1016/j.cscee.2024.100623 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182878208&doi=10.1016%2fj.cscee.2024.100623&partnerID=40&md5=2dfb842c05f9d530babe6395d37a0537
Sharma N., Department of Biochemistry, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai, 602105, India; Kumar V., Bioconversion and Tissue Engineering (BITE) Laboratory, Department of Community Medicine, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai, 602105, India; Sugumar V., Department of Biochemistry, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai, 602105, India; Umesh M., Department of Life Sciences, CHRIST (Deemed to Be University), Karnataka, Bengaluru, 560029, India; Sondhi S., Haryana State Pollution Control Board, Haryana, Panchkula, India; Chakraborty P., School of Allied Healthcare and Sciences, Jain (Deemed to Be) University, Whitefield, Karnataka, Bangalore, 560066, India; Kaur K., Punjab Agricultural University, Institute of Agriculture, Gurdaspur, Punjab, 143521, India; Thomas J., Department of Biotechnology, Mar Athanasius College, Kerala, India; Kamaraj C., Interdisciplinary Institute of Indian System of Medicine (IIISM), SRM Institute of Science and Technology (SRMIST), Kattankulathur, Tamilnadu, Chennai, 603203, India; Maitra S.S., Metagenomics and Bioprocess Design Laboratory, School of Biotechnology, Jawaharlal Nehru University, New Delhi, India
A comprehensive review on tissue culture studies and secondary metabolite production in Bacopa monnieri L. Pennell: a nootropic plant
Agrobacterium rhizogenes; Bacopa monnieri; bacoside A; multiple shoot culture; plant cell culture; secondary metabolites
Bacopa monnieri L. Pennell, commonly known as Brahmi, is an important medicinal plant that belongs to the family Plantaginaceae. Brahmi is rich in innumerable bioactive secondary metabolites, especially bacosides that can be employed to reduce many health issues. This plant is used as a neuro-tonic and treatment for mental health, depression, and cognitive performance. Brahmi is also known for its antioxidant, anti-inflammatory, and anti-hepatotoxic activities. There is a huge demand for its raw materials, particularly for the extraction of bioactive molecules. The conventional mode of propagation could not meet the required commercial demand. To overcome this, biotechnological approaches, such as plant tissue culture techniques have been established for the production of important secondary metabolites through various culture techniques, such as callus and cell suspension cultures and organ cultures, to allow for rapid propagation and conservation of medicinally important plants with increased production of bioactive compounds. It has been found that a bioreactor-based technology can also enhance the multiplication rate of cell and organ cultures for commercial propagation of medicinally important bioactive molecules. The present review focuses on the propagation and production of bacoside A by cell and organ cultures of Bacopa monnieri, a nootropic plant. The review also focuses on the biosynthesis of bacoside A, different elicitation strategies, and the over-expression of genes for the production of bacoside-A. It also identifies research gaps that need to be addressed in future studies for the sustainable production of bioactive molecules from B. monnieri. 2022 Informa UK Limited, trading as Taylor & Francis Group.
Sudheer W.N.; Thiruvengadam M.; Nagella P.
Critical Reviews in Biotechnology, Vol-43, No. 6, pp. 956-970.
ISSN: 7388551; PubMed ID: 35819370; CODEN: CRBTE
Sudheer W.N., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India; Thiruvengadam M., Department of Crop Science, College of Sanghuh Life Science, Konkuk University, Seoul, South Korea; Nagella P., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India
A Comprehensive Review onCrop Disease Prediction Based onMachine Learning andDeep Learning Techniques
Classification; Crop disease; DL; Leaf image; ML; Segmentation
Leaf diseases cause direct crop losses in agriculture, and farmers cannot detect the disease early. If the diseases are not detected early and correctly, the farmer must undergo huge losses. It may lead to the wrong pesticide or over pesticide, directly affect crop productivity and economy, and indirectly affect human health. Sensitive crops have various leaf diseases, and early prediction of these diseases remains challenging. This paper reviews several machine learning (ML) and deep learning (DL) methods used for different crop disease segmentation and classification. In the last few years, computer vision and DL techniques have made tremendous progress in object detection and image classification. The study summaries the available research on different diseases on various crops based on machine learning (ML) and deep learning (DL) techniques. It also discusses the data sets used for research and the accuracy and performance of existing methods. It does mean that the methods and available data sets presented in this paper are not projected to replace published solutions for crop disease identification, perhaps to enhance them by finding the possible gaps. Seventy-five articles are analysed and reviewed to find essential issues that involve additional study for future research in this domain to promote continuous progress for data sets, methods, and techniques. It mainly focuses on image segmentation and classification techniques used to solve agricultural problems. Finally, this paper provides future research scope and challenges, limitations, and research gaps. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Lecture Notes in Networks and Systems, Vol-608, pp. 481-503.
Patil M.A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ (Deemed to be University), Karnataka, Bangalore, 560074, India, Department of Information Technology, Vasavi College of Engineering, Telangana, Hyderabad, 500032, India; Manohar M., Department of Computer Science and Engineering, School of Engineering and Technology, Christ (Deemed to be University), Karnataka, Bangalore, 560074, India
A Comprehensive Study of Blockchain Technology Based Decentralised Ledger Implementations
Blockchain; blockchain applications; P2P network; Smart contracts; Smart healthcare
Information management and decentralized, secure transactions are made possible by the groundbreaking idea of blockchain technology. Blockchain has attracted considerable attention from a wide range of businesses as a result of the rising popularity of cryptocurrencies like Bitcoin and Ethereum. The primary goal of this paper is to present a thorough examination of decentralized ledger systems based on blockchain technology. It examines at the basic ideas, underpinnings, and real-world uses of blockchain in many industries. The ability to scale, effectiveness, privacy, security, seamless integration, governing scenarios, and consumption of energy are just a few of the technical factors that are looked at in relation to the deployment of blockchain technology. In this research paper we also pinpoint forthcoming developments and future prospects for the blockchain industry like Solutions for scalability at the subsequent layer, protocols for seamless integration, integration with the Internet of Things (IoT), apps for decentralized finance (DeFi), and digital currencies that are issued by central banks (CBDCs) are a few examples. 2023 IEEE.
Haque M.; Upreti K.; Ohol A.; Pisolkar G.; Vats P.; Kumar N.
2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023, pp. 134-140.
Haque M., D.Y Patil Vidyapeeth's Centre for Online Learning Pune (Deemed to Be University), Pune, India; Upreti K., Dept. of Computer Science CHRIST(Deemed to Be University) Delhi Ncr, Ghaziabad, India; Ohol A., Faculty of Management Savitribai Phule Pune University, Pune, India; Pisolkar G., Faculty of Management St. Joseph's Degree & Pg College, Hyderabad, India; Vats P., Dept. of Cse, Scse, Manipal University Jaipur, Rajasthan, Jaipur, India; Kumar N., School of Business and Management CHRIST(Deemed to Be University), Bangalore, India