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                <text>A Review on Deep Learning Algorithms in the Detection of Autism Spectrum Disorder</text>
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                <text>ASD; CNN; CRNN algorithms; Deep learning; LSTM; RNN</text>
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                <text>Autism spectrum disorder (ASD) is a neurodisorder that has an impact on how people interact and communicate with each other for the rest of their lives. Most autistic symptoms appear throughout the first two years of a child's life. This is why autism is called a behavioral disease. If you have a child with ASD, the problem starts in childhood and keeps going through adolescence and adulthood. Deep learning techniques are becoming more common in research on medical diagnosis. In this paper, there is an effort to see if convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory network (LSTM), and a fusion technique known as convolutional recurrent neural network (CRNN) can be used to detect ASD problems in a child, adolescents, and adults.  The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.</text>
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                <text>Lamani M.R.; Julian Benadit P.</text>
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                <text>Lecture Notes in Networks and Systems, Vol-865, pp. 283-297.</text>
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                <text>ISSN: 23673370; ISBN: 978-981999042-9</text>
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                <text>Lamani M.R., CHRIST (Deemed to Be University), Kanmanike, Kumbalgudu, Mysor Road, Karnataka, Bangalore, 560074, India; Julian Benadit P., CHRIST (Deemed to Be University), Kanmanike, Kumbalgudu, Mysor Road, Karnataka, Bangalore, 560074, India</text>
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                <text>D, Bijo M; Unnikrishnan, Sujatha</text>
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                <text>A Review on Development and Properties of Ultra-High-Performance Concrete</text>
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                <text>Lecture Notes in Civil Engineering;Volume;666 LNCE;pp.95-114</text>
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                <text>D B.M., Christ University, Bengaluru, India; Unnikrishnan S., Christ University, Bengaluru, India</text>
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                <text>This study presents a review of literature on ultra-high-performance concrete that brings in information regarding the preparation of UHPC, mix design of UHPC on the basis of various particle packing density models, microstructural analysis, durability studies, and strength characteristics. A data base is collected to study the performance, mechanical strength and durability of UHPC from various research works. UHPC can found to be a long-term solution for present day challenges that is faced in construction industry when conventional concrete is used and makes this concrete a novel concept in concrete technology. The advantages of UHPC are: less porosity, high abrasion resistance, greater mechanical properties, high packing density, and improvement in fatigue behavior though the cost of UHPC is high. The non-availability of a standard code for mix design of UHPC makes it difficult to arrive at consistent and comparable mix. The cost of UHPC can be controlled with the use of naturally available materials and utilizing agricultural and industrial waste materials in UHPC. From the data collected, it is observed that the binder content can be optimized and cement which is residual in UHPC can be replaced by industrial residue like fly ash, GGBS, glass powder, etc. and thereby brings down the cost without compromising on the strength and performance. The information shared in this paper will help the contractors, consultants, engineers, industry stakeholders and researchers to alleviate the confusions regarding the use of UHPC in construction industry and to encourage research to make this concrete construction-industry friendly.  The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.</text>
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                <text>Microstructural analysis; Particle packing density; Ultra-high-performance concrete</text>
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                <text>A Review on EMG-based Pattern Identification Methods for Effective Controlling of Hand Prostheses</text>
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                <text>Classification Algorithms; Deep Learning; Electromyography; Hand Prosthesis; Recognition Accuracy</text>
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                <text>The ability of amputees to do daily duties is significantly restricted by upper limb amputation. The myoelectric prosthesis uses impulses from the surviving muscles in the stump to gradually restore function to such severed limbs. Such myosignals are unfortunately tedious and challenging to gather and employ. The process of transforming it into a user control signal after it has been acquired often consumes a significant amount of processing resources. By modifying machine learning strategies for pattern recognition, the factors that influence the traditional electromyography (EMG)-pattern identification approaches may be significantly minimized. Although more recent developments in intelligent pattern recognition algorithms could discern between a variety of degrees of freedom with high levels of accuracy, their usefulness in practical (amputee) applications was less obvious. This review paper examined how well various pattern recognition algorithms for hand prostheses performed. Finally, we discussed the current difficulties and offered some suggestions for future research in our paper's conclusion.   2023 IEEE.</text>
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                <text>Ramkumar S.; Rema D.; Devi T.A.; Elavarasi K.; Selvaganapathi T.; Gokila S.</text>
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                <text>3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023 - Proceedings, pp. 517-523.</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/ICIMIA60377.2023.10426249" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICIMIA60377.2023.10426249&lt;/a&gt;
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                <text>ISBN: 979-835034363-2</text>
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                <text>Ramkumar S., Christ (Deemed to Be University), Department of Computer Science, Bangalore, India; Rema D., Karpagam Academy of Higher Education, Department of Food Technology, Coimbatore, India; Devi T.A., Karpagam Academy of Higher Education, Department of Food Technology, Coimbatore, India; Elavarasi K., Karpagam Academy of Higher Education, Department of Electrical and Electronics Engineering, Coimbatore, India; Selvaganapathi T., Karpagam Academy of Higher Education, Department of Electrical and Electronics Engineering, Coimbatore, India; Gokila S., Karpagam Institute of Technology, Department of CSE, Coimbatore, India</text>
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                <text>A review on ensembles-based approach to overcome class imbalance problem</text>
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                <text>Bagging; Boosting; Class imbalance; Classification; Ensemble; Ensemble approach for class imbalance; Sampling</text>
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                <text>Predictive analytics incorporate various statistical techniques from predictive modelling, machine learning and data mining to analyse large database for future prediction. Data mining is a powerful technology to help organization to concentrate on most important data by extracting useful information from large database. With the improvement in technology day by day large amount of data are collected in raw form and as a result necessity of using data mining techniques in various domains are increasing. Class imbalance is an open challenge problem in data mining and machine learning. It occurs due to imbalanced data set. A data set is considered as imbalanced when a data set contains number of instance in one class vastly outnumber the number of instances in other class. When traditional data mining algorithms trained with imbalanced data sets, it gives suboptimal classification model. Recently class imbalance problem have gain significance attention from data mining and machine learning researcher community due to its presence in many real world problem such as remote-sensing, pollution detection, risk management, fraud detection and medical diagnosis. Several methods have been proposed to overcome the problem of class imbalance problem. In this paper, our goal is to review various methods which are proposed to overcome the effect of imbalance data on classification learning algorithms.  Springer Nature Singapore Pte Ltd 2019.</text>
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                <text>Saccharomyces cerevisiae is one of the prominent strains in the brewing and bioethanol industries and has been used for many industrial purposes for ages. Though the organism is an outstanding ethanol producer, the major limiting factor is the stress the organism undergoes during fermentation. One of the significant stresses is the ethanol stress, created by ethanol accumulation in the medium. The ethanol starts to interact with the yeast cell membrane; further, as ethanol concentration increases, it affects a lot of cell organelles. Thereby, cellular activities get disrupted, causing cell death and hence reducing ethanol production. The organism has developed many strategies to overcome this stress by activating the stress response pathway, which regulates many genes involved in modifying the cell membrane cell wall, renaturation of proteins, and altering the metabolism. However, with higher ethanol concentrations, the yeast cells will be unable to tolerate, leading to cell death. Hence, to minimize cell death at higher ethanol concentrations, there is a need to understand the effect of ethanol and its response by the organism; this helps improve the ethanol tolerance of the organism and, thereby, ethanol production. Although many research works are carried out to understand the vital aspect of the tolerance and are reported, very few review papers cover all these points. Hence, this review is designed to include information on all the elements of ethanol tolerance, i.e., ethanol tolerance of different strains of S. cerevisiae, the effect of ethanol on the yeast cells, the mechanism used to tolerate the ethanol, and various techniques developed to improve the ethanol tolerance of the yeast cells.  2024 Elsevier Ltd</text>
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                <text>Sahana G.R., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, 560 029, India; Balasubramanian B., Department of Food Science and Biotechnology, College of Life Science, Sejong University, Seoul, South Korea; Joseph K.S., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, 560 029, India; Pappuswamy M., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, 560 029, India; Liu W.-C., Department of Animal Science, College of Coastal Agricultural Sciences, Guangdong Ocean University, Zhanjiang, 524088, China; Meyyazhagan A., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, 560 029, India; Kamyab H., Faculty of Architecture and Urbanism, UTE University, Calle Rumipamba S/N and Bourgeois, Quito, Ecuador, Department of Biomaterials, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Chennai, 600 077, India, Process Systems Engineering Centre, Department of Chemical and Energy Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor, Skudai, Malaysia; Chelliapan S., Engineering Department, Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Kuala Lumpur, Jln Sultan Yahya Petra, 54100, Malaysia; Joseph B.V., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, 560 029, India</text>
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                <text>Over the past few decades, there was significant increase in research concerning resources that have certain desirable characteristics like renewability, ease of availability, economic value, excellent mechanicalthermal properties, biocompatibility and biodegradability. Cellulose is one such resource that possesses these characteristics and yet various sources that constitute ample quantities of lignocellulose are discarded, as their peculiarities and applications were not widely known to the population. Agro wastes, which are generated every year at a tremendous rate, are viewed as a promising substrate for the commercial extraction of cellulose. Hence in this review, an appropriate utilization of these agricultural by-products, with respect to extraction of cellulose is discussed, so as to ameliorate their applications in an aim to diminish the disposal rate of essential commodities.  2021 World Research Association. All rights reserved.</text>
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                <text>Kariyadan S.; Umesh M.</text>
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                <text>Kariyadan S., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Umesh M., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, India</text>
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                <text>A review on feature selection algorithms</text>
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                <text>Classification; Feature selection; Genetic algorithm; Wrapper method</text>
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                <text>A large number of data are increasing in multiple fields such as social media, bioinformatics and health care. These data contain redundant, irrelevant or noisy data which causes high dimensionality. Feature selection is generally used in data mining to define the tools and techniques available for reducing inputs to a controllable size for processing and analysis. Feature selection is also used for dimension reduction, machine learning and other data mining applications. A survey of different feature selection methods are presented in this paper for obtaining relevant features. It also introduces feature selection algorithm called genetic algorithm for detection and diagnosis of biological problems. Genetic algorithm is mainly focused in the field of medicines which can be beneficial for physicians to solve complex problems. Finally, this paper concludes with various challenges and applications in feature selection.  Springer Nature Singapore Pte Ltd 2019.</text>
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                <text>Advances in Intelligent Systems and Computing, Vol-906, pp. 133-153.</text>
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                <text>Colaco S., Department of CSE, Christ(Deemed to Be University), Bangalore, 560029, India; Kumar S., Department of CSE, Christ(Deemed to Be University), Bangalore, 560029, India; Tamang A., Department of CSE, Christ(Deemed to Be University), Bangalore, 560029, India; Biju V.G., Department of CSE, Christ(Deemed to Be University), Bangalore, 560029, India</text>
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                <text>A Review on Fish Skin-Derived Gelatin: Elucidating the Gelatin PeptidesPreparation, Bioactivity, Mechanistic Insights, and Strategies for Stability Improvement</text>
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                <text>Fish skin-derived gelatin has garnered significant attention recently due to its abundant availability and promising bioactive properties. This comprehensive review elucidates various intricacies concerning fish skin-derived gelatin peptides, including their preparation techniques, bioactive profiles, underlying mechanisms, and methods for stability enhancement. The review investigates diverse extraction methods and processing approaches for acquiring gelatin peptides from fish skin, emphasizing their impact on the peptide composition and functional characteristics. Furthermore, the review examines the manifold bioactivities demonstrated by fish skin-derived gelatin peptides, encompassing antioxidant, antimicrobial, anti-inflammatory, and anticancer properties, elucidating their potential roles in functional food products, pharmaceuticals, and nutraceuticals. Further, mechanistic insights into the functioning of gelatin peptides are explored, shedding light on their interactions with biological targets and pathways. Additionally, strategies aimed at improving the stability of gelatin peptides, such as encapsulation, modification, and integration into delivery systems, are discussed to extend the shelf life and preserve the bioactivity. Overall, this comprehensive review offers valuable insights into using fish skin-derived gelatin peptides as functional ingredients, providing perspectives for future research endeavors and industrial applications within food science, health, and biotechnology.  2024 by the authors.</text>
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              <elementText elementTextId="193333">
                <text>All Open Access; Gold Open Access</text>
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                <text>ISSN: 23048158</text>
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                <text>Joy J.M., Biochemistry and Nutrition Division, ICAR-Central Institute of Fisheries Technology, Kerala, Cochin, 682029, India, Faculty of Marine Sciences, Cochin University of Science and Technology, Kerala, Cochin, 682022, India, Department of Zoology, St. Teresas College (Autonomous), Kerala, Ernakulam, 682011, India; Padmaprakashan A., Biochemistry and Nutrition Division, ICAR-Central Institute of Fisheries Technology, Kerala, Cochin, 682029, India, Faculty of Marine Sciences, Cochin University of Science and Technology, Kerala, Cochin, 682022, India, Department of Life Sciences, Christ University, Hosur Main Road, Bhavani Nagar, Karnataka, Bangalore, 560029, India; Pradeep A., Biochemistry and Nutrition Division, ICAR-Central Institute of Fisheries Technology, Kerala, Cochin, 682029, India, Faculty of Marine Sciences, Cochin University of Science and Technology, Kerala, Cochin, 682022, India; Paul P.T., Biochemistry and Nutrition Division, ICAR-Central Institute of Fisheries Technology, Kerala, Cochin, 682029, India, Faculty of Marine Sciences, Cochin University of Science and Technology, Kerala, Cochin, 682022, India; Mannuthy R.J., Biochemistry and Nutrition Division, ICAR-Central Institute of Fisheries Technology, Kerala, Cochin, 682029, India, Faculty of Marine Sciences, Cochin University of Science and Technology, Kerala, Cochin, 682022, India; Mathew S., Biochemistry and Nutrition Division, ICAR-Central Institute of Fisheries Technology, Kerala, Cochin, 682029, India</text>
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                <text>A Review on Flood Prediction Algorithms and A Deep Neural Network Model for Estimation of Flood Occurrence</text>
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                <text>Gated Recurrent Units (GRU); Long Short-Term Memory (LSTM)network; Multilayer Perception Layer (MLP) architecture; Precipitation; Recurrent Neural Network (RNN)</text>
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            </elementTextContainer>
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                <text>Flood occurs as often as possible happens due to many environmental changes in our planet in the present years. The occurrence and damages caused by flood is very high. Major cause of flood is due to heavy rainfall which in turn increases the water level of the rivers and other water bodies. The various factors that play a major role in the occurrence of rainfall are rise in temperature, humidity level, dew point, pressure in and around the area of concern, wind speed, etc. In order to reduce the number of victims due to flood it is necessary to have a system to predict flood occurrence. In this paper, we classify and analyzed the various prediction algorithms which show usage of Deep Neural Network produces better results. In addition, a design model has been proposed to predict the flood by training the Deep Neural Network with the above-mentioned factors.  2020, Asian Research Association. All rights reserved.</text>
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              <elementText elementTextId="123612">
                <text>Ullah T.F.; Gnana Prakasi O.S.; Kanmani P.</text>
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                <text>International Research Journal of Multidisciplinary Technovation, Vol-2, No. 5, pp. 8-14.</text>
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              <elementText elementTextId="123614">
                <text>Asian Research Association</text>
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                <text>&lt;a href="https://doi.org/10.34256/irjmt2052" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.34256/irjmt2052&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130881065&amp;amp;doi=10.34256%2Firjmt2052&amp;amp;partnerID=40&amp;amp;md5=a7e437201302286858f3e007e63e7c80" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130881065&amp;amp;doi=10.34256%2firjmt2052&amp;amp;partnerID=40&amp;amp;md5=a7e437201302286858f3e007e63e7c80&lt;/a&gt;</text>
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            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="123617">
                <text>All Open Access; Gold Open Access</text>
              </elementText>
            </elementTextContainer>
          </element>
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                <text>ISSN: 25821040</text>
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          <element elementId="42">
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            <description>The file format, physical medium, or dimensions of the resource</description>
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                <text>Ullah T.F., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Gnana Prakasi O.S., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Kanmani P., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India</text>
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          <elementContainer>
            <element elementId="50">
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                <elementText elementTextId="51377">
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      <name>Conference Paper</name>
      <description>Faculty Publications- Conference Papers</description>
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                <text>A Review On Geospatial Intelligence For Investigative Journalism</text>
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          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
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              <elementText elementTextId="177217">
                <text>Geospatial Intelligence; Investigative Journalism; Satellite Imagery; Suez Canal; Uttarakhand Landslide</text>
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                <text>Throughout the ongoing Russian invasion of Ukraine, satellite images like the vast convoy of Russian military vehicles approaching the beleaguered Ukrainian city of Kyiv, Russian aircraft deployed at Zyabrovka, Belarus and many more such visuals have been in circulation and are being used as a tool to facilitate investigative journalistic studies. Such satellite-based images are one of the latest means of accessing vital data that can help in expanding the scope and impact of investigative journalism. Geospatial intelligence uses varied graphical content to convey information about the activities that occur on the surface of the earth. It includes colour and panchromatic (black and white) aerial photographs, multispectral or hyperspectral digital imagery, and products such as shaded relief maps or three-dimensional images produced from digital elevation models. The improving technology in geospatial spectra has broadened the scope of its usage to investigative journalism which lies at the core of this review paper. Some of the path-breaking journalistic stories that have come up in the past decade - imaging of the Uttarakhand landslide in 2021 using satellite images, coverage of the Fukushima nuclear plant since 2011, and 2021 tracking of Asia's border disputes emerging due to climate change and the satellite journalism built around the blockage of Suez canal in 2021 - showcase the potential that geospatial intelligence has in the domain of journalism. All four identified stories point out how we can practice satellite-based investigative studies, especially, for scrutinizing and comparing historical records regarding cross-border issues as well as the disappearance of pastures and forests in vast open lands. However, the arena of using geospatial intelligence, enabled by satellite images, remains underutilized and limited to specific journalistic organizations, based in a few countries. This exploratory review of the four mentioned journalistic accounts identifies the contexts where such efforts are feasible, methods that are required, sources that could be tapped, associated skill sets needed for its usage, the dynamics of such investigative approaches, and their scope and limitations. This review and the conclusions drawn from the above-mentioned cases provides a direction for journalists from small organizations and low income countries to explore the potential of satellite-based images in furthering their investigative reporting with a technological edge that persists to be sovereign in the geopolitical powerplay. Copyright  2022 by the International Astronautical Federation (IAF). All rights reserved.</text>
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              <elementText elementTextId="177219">
                <text>Harshitha V.; Nainani M.; Padmakumar M.M.; Kumar R.</text>
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              <elementText elementTextId="177220">
                <text>Proceedings of the International Astronautical Congress, IAC, Vol-2022-September</text>
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              <elementText elementTextId="177221">
                <text>International Astronautical Federation, IAF</text>
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            <description>A point or period of time associated with an event in the lifecycle of the resource</description>
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                <text>2022-01-01</text>
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                <text>&lt;a href="" target="_blank" rel="noreferrer noopener"&gt;&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167595784&amp;amp;partnerID=40&amp;amp;md5=3e834fe2e5a4b64512ff88cf42a96939" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167595784&amp;amp;partnerID=40&amp;amp;md5=3e834fe2e5a4b64512ff88cf42a96939&lt;/a&gt;</text>
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            <name>Rights</name>
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              <elementText elementTextId="177224">
                <text>Restricted Access</text>
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            </elementTextContainer>
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              <elementText elementTextId="177225">
                <text>ISSN: 741795</text>
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            <description>The file format, physical medium, or dimensions of the resource</description>
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              <elementText elementTextId="177229">
                <text>Harshitha V., Department of Media and Communication Studies, Christ Deemed to be University, Hosur Road, Bhavani Nagar, S G Palya, Karnataka, Bengaluru, 560054, India; Nainani M., Department of Media and Communication Studies, Christ Deemed to be University, Hosur Road, Bhavani Nagar, S G Palya, Karnataka, Bengaluru, 560054, India; Padmakumar M.M., Department of Media and Communication Studies, Christ Deemed to be University, Hosur Road, Bhavani Nagar, S G Palya, Karnataka, Bengaluru, 560054, India; Kumar R., Grahaa Space, Lomonosov Moscow State University (MSU), Karnataka, Bengaluru, India</text>
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                <text>A Review on Influence of Cutting Fluid on Improving the Machinability of Inconel 718</text>
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                <text>Ceramic inserts; CNC milling machine; Flank surface; Inconel 718; Rake surface</text>
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                <text>Nickel-based superalloys are widely used in the production and manufacturing sectors that require processes or applications that endure or operate at very high superheating temperatures. With the properties of high tensile strength, high melting point, and lightweight structural arrangement of molecules within the alloy material composition makes it more suitable for industrial utilization in aerospace industries and marine applications. This review paper discusses the use of various coolant lubricants that improves the machinability of Inconel 718 based on parameters such as surface roughness and tool wear under the influence of cutting speed, feed rate, and depth of cut. The machine used for analysis is CNC milling machine which will be used for experimentation using ceramic inserts as end milling tool. Various cooling techniques such as hybrid cooling, flood emulsion cooling, minimum quantity lubrication, and cryogenic cooling are being summarized in this paper from various experimentations and conclusions of other authors. On the basis of review, the hybrid cooling technique is found to be better than other cooling techniques because of its ability to obtain long tool life and smoother surface finish on the workpiece. With the use of these reviewed data, further research for finding a more compatible and effective cooling lubricant has to be done by experimentation in order to obtain an improved machining process for Inconel 718 material.  2020, Springer Nature Singapore Pte Ltd.</text>
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                <text>Springer Proceedings in Materials, Vol-8, pp. 497-504.</text>
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                <text>A review on metal nanoparticles from medicinal plants: Synthesis, characterization and applications</text>
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                <text>Anti-cancerous; Anti-diabetic; Anti-larval; Anti-microbial; Dye degradation; Food preservation; Green nanotechnology; Medicinal plants; Plant growth enhancement</text>
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                <text>Plant extracts contain secondary metabolites which have the potential to act as reducing and stabilizing agents contributing to a greener and more efficient method to synthesize nanoparticles. Rapid growth of Nanotechnology has led to an increased demand in various fields. This review summarizes the use of potent medicinal plant extracts to synthesize metal nanoparticles, methods employed to characterize the properties of the nanoparticles and its application. Characterization of the nanoparticle based on its shape, size, chemical bonds, surface properties, hydrodynamic diameter and crystalline structure using techniques such as UV-Visible Spectroscopy, XRD (X-ray Diffraction), TEM (Transmission Electron Microscopy), SEM (Scanning Electron Microscopy), EDS (X-ray energy dispersive spectroscopy), DLS (Dynamic Light Scattering), Zeta Potential and FTIR (Fourier Transform-Infrared Spectroscopy) are elaborated. The synthesized metal nanoparticles have wide ranges of applications such as antimicrobial activity, antioxidative capability, anticancer effect, antidiabetic properties, plant growth enhancement, dye degradation effects and anti-larval properties. Recent advances in nanotechnology with special emphasis on plant metabolites provide an insight into their usage as plant-derived edible nanoparticles (PDNPs). Applications, limitations and future prospects of this technology have also been briefly discussed.  2021 Bentham Science Publishers.</text>
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                <text>George J.A.; Sundar S.; Paari K.A.</text>
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                <text>Nanoscience and Nanotechnology - Asia, Vol-11, No. 5</text>
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                <text>&lt;a href="https://doi.org/10.2174/2210681210999201020142942" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.2174/2210681210999201020142942&lt;/a&gt;
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                <text>ISSN: 22106812</text>
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                <text>George J.A., Department of Life Sciences, CHRIST (Deemed to be University), Bangalore, India; Sundar S., Department of Life Sciences, CHRIST (Deemed to be University), Bangalore, India; Paari K.A., Department of Life Sciences, CHRIST (Deemed to be University), Bangalore, India</text>
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                <text>A review on power quality issues in electric vehicle interfaced distribution system and mitigation techniques</text>
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            <name>Subject</name>
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              <elementText elementTextId="197028">
                <text>Distribution systems; Electric vehicles; Harmonic imbalance; Harmonics and voltage imbalance; Power quality issue mitigation</text>
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                <text>Electric vehicles (EV) penetration in the distribution systems is evident and intended to grow day by day. Power quality issues pop up in the distribution system with an increase in EV penetration. Distribution networks need to consider the power quality issues developed due to the penetration of EVs for planning and designing the system. The power quality issues, including voltage imbalance, total harmonic distortion, distribution transformer failure, and related issues, are anticipated due to EV penetration in distribution systems. Detailed review of power quality issues and mitigation techniques are detailed in this paper. Discussion on the effect of these power quality issues on the distribution systems and corresponding mitigation measures are detailed. Power quality impact mitigation techniques have been discussed recently, which exploits the bidirectional power flow of vehicle to grid vehicle to grid (V2G) and grid to vehicle grid-to-vehicle (G2V). Methods and methodologies that mitigate power quality problems in the EV penetrated distribution system is discussed. Bidirectional power flow during EV charging and discharging and power quality issues in this topology is detailed in this review paper. A discussion on future trends and different possible future research paradigms is discussed as the review's conclusion.  2022 Institute of Advanced Engineering and Science. All rights reserved.</text>
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                <text>Harish B.N.; Surendra U.</text>
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                <text>Indonesian Journal of Electrical Engineering and Computer Science, Vol-25, No. 2, pp. 656-665.</text>
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                <text>Institute of Advanced Engineering and Science</text>
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                <text>&lt;a href="https://doi.org/10.11591/ijeecs.v25.i2.pp656-665" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.11591/ijeecs.v25.i2.pp656-665&lt;/a&gt;
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              <elementText elementTextId="197035">
                <text>All Open Access; Gold Open Access; Green Open Access</text>
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                <text>ISSN: 25024752</text>
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                <text>Harish B.N., Department of Electrical and Electronics Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Surendra U., Department of Electrical and Electronics Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India</text>
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                <text>A review on prediction of cardiac arrest analysis in early stage</text>
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            <description>The topic of the resource</description>
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                <text>Cardiac Arrest; Early prediction; Electrocardiogram</text>
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                <text>Cardiac arrest occurs as the heart muscle fails to contract properly, resulting in a sudden loss of blood supply. The ECG signal is one of the techniques for detecting cardiac electrical activity and is used to investigate heart block. In this paper different standardized work for early detection of cardiac arrest is described. Stages of ECG signal pre-processing involves denoised using digital filtering algorithms and extracting different features from clean ECG predicting cardiac arrest in early stage. Several other body parameters were also considered for this purpose. In this work denoising validation parameters were compared for showing effectiveness of the filtering algorithm and several body parameters and its implication on cardiac arrest was described.  2022 Author(s).</text>
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                <text>Mukherjee C.; Selvi J.T.; Ramesh M.; Thomas L.; Anusuya M.; Lakwal V.R.</text>
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                <text>Mathew A.A., Department of Physics and Electronics, Christ University, Karnataka, Bangalore, 560029, India; Balachandran M., Department of Physics and Electronics, Christ University, Karnataka, Bangalore, 560029, India</text>
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                <text>Rural womens entrepreneurship has contributed significantly to the countrys economy. Entrepreneurship rates have fluctuated in recent years, according to a variety of reasons including economic, social, and cultural influences. Therefore, machine learning models are used to assess the features to make better business decisions. In this research paper, papers from 2009 to 2022 were studied and found that machine learning models are being used to improve womens entrepreneurship. In this paper, nine machine learning models have been described in detail which include multiple regression, lasso regression, logistic regression, decision tree, Naive Bayes, clustering, classification, deep learning, artificial neural network, etc. In the study of all these models, it was found how accurately this model has been used in womens entrepreneurship work. It has been observed that by using different machine learning models with the data acquired from rural entrepreneurship, women entrepreneurs may use a new way of understanding the dynamics of rural entrepreneurship. Various machine learning models have been studied to improve rural development for women working in rural areas. Thus, we have proposed a comparative study of various machine learning models to predict entrepreneurship-based data. The findings of this study may be used to assess how rural women entrepreneurs may change the decisions made in several domains, such as making use of different economic policies and promoting the long-term viability of women entrepreneurs for the countrys economic growth.  The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.</text>
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                <text>Lecture Notes in Networks and Systems, Vol-680 LNNS, pp. 375-395.</text>
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                <text>Pareek S., Department of Computer Applications, Manipal University Jaipur, Rajasthan, Jaipur, India; Bhatnagar V., Department of Computer Applications, Manipal University Jaipur, Rajasthan, Jaipur, India; Poonia R.C., Professor, Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Uttar Pradesh, Ghaziabad, India; Sharma S., Department of Computer Applications, Manipal University Jaipur, Rajasthan, Jaipur, India; Samanta D., Department of Computing Information Technologies, RIT Kosovo (A.U.K), Rochester Institute of Technology  RIT Global Campus n.n., Germia Campus, Pristina, Kosovo, 10000</text>
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