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                <text>A comprehensive novel model for network speech anomaly detection system using deep learning approach</text>
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                <text>Attack classification; Deep learning; Network intrusion detection system</text>
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                <text>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.</text>
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                <text>Manimaran A.; Chandramohan D.; Shrinivas S.G.; Arulkumar N.</text>
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                <text>International Journal of Speech Technology, Vol-23, No. 2, pp. 305-313.</text>
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                <text>Manimaran A., Department of Computer Applications, Madanapalle Institute of Technology &amp;amp; Science, Madanapalle, India; Chandramohan D., Department of Computer Science &amp;amp; Engineering, Madanapalle Institute of Technology &amp;amp; Science, Madanapalle, India; Shrinivas S.G., Department of Computer Applications, Madanapalle Institute of Technology &amp;amp; Science, Madanapalle, India; Arulkumar N., Department of Computer Science, Christ (Deemed to be University), Bangalore, India</text>
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                <text>A Comprehensive Research on Deep Learning Based Routing Optimization Algorithms in Software Defined Networks</text>
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                <text>Controller attacks; Deep Learning; Deterministic Policy Gradient; Optimal routing; Software defined networking</text>
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                <text>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.</text>
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                <text>Kumar G.; Girisha G.S.; Shamanth N.</text>
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                <text>2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023</text>
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                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>&lt;a href="https://doi.org/10.1109/EASCT59475.2023.10392911" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/EASCT59475.2023.10392911&lt;/a&gt;
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                <text>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</text>
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                <text>A comprehensive review of AI based intrusion detection system</text>
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                <text>Artificial intelligence; Deep learning; Ensemble learning; Intrusion detection system; Machine learning</text>
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                <text>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</text>
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                <text>Sowmya T.; Mary Anita E.A.</text>
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                <text>Measurement: Sensors, Vol-28</text>
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                <text>All Open Access; Gold Open Access</text>
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                <text>ISSN: 26659174</text>
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                <text>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</text>
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                <text>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</text>
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                <text>A comprehensive review on energy management strategy of microgrids</text>
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                <text>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)</text>
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                <text>Energy Reports, Vol-9, pp. 5565-5591.</text>
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                <text>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</text>
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                <text>A Comprehensive Review on Fault Data Injection in Smart Grid</text>
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                <text>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.</text>
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                <text>Lecture Notes in Networks and Systems, Vol-757 LNNS, pp. 649-660.</text>
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                <text>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</text>
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                <text>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.</text>
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                <text>2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023 - Proceedings, pp. 562-567.</text>
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                <text>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</text>
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                <text>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)</text>
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                <text>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</text>
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                <text>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 &amp;amp; Francis Group.</text>
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                <text>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</text>
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                <text>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.</text>
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                <text>Patil M.A.; Manohar M.</text>
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                <text>Lecture Notes in Networks and Systems, Vol-608, pp. 481-503.</text>
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                <text>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</text>
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                <text>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.</text>
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                <text>2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023, pp. 134-140.</text>
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                <text>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 &amp;amp; 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</text>
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