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                <text>A Study on Near Proper Coloring of Graphs  </text>
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                <text>An equitable coloring of a graph G is a proper vertex coloring in which the number of vertices in any two color classes are equal or almost equal. In this graph coloring, there is a partition of tasks into subsets which perform at the same time. Equitable coloring play an important role when there is a requirement of dividing a system with binary conand#64258;ict free subsystems with equal or newlinenearly equal elements. The non-availability of sufcient number of colors leads to diand#64256;erent defective coloring problems. An equitable near proper coloring of a graph G is an improper coloring in which the vertex set can be partitioned into k color classes V1, V2, . . . , Vk ; (1 lt k lt and#967;e(G)) such that the number of vertices in any two color classes diand#64256;er by at most one and the resulting monochromatic edges are minimised by restricting the number of color classes that can have adjacency among their own elements. The minimum number of monochromatic newlineedges obtained from an equitable near proper coloring of G is called equitable defective number. This study introduces the notion of equitable near proper coloring of a graph G and investigates the equitable defective number for a few graph classes. In this study, the equitable near proper coloring of various graph classes and derived graphs are discussed, and the corresponding equitable newlinedefective number for any k where 2 and#8804; k and#8804; and#967;e(G) and#8722; 1 is obtained.</text>
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                <text>N K, Sudev</text>
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                <text>Surface tempering of poly-(3 thiophene acetic acid) coated carbon fiber paper electrode with spine-like cobalt inorganic phosphate: An efficacious electrochemical metol sensor /</text>
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                <text>Surfaces and Interfaces, Vol.35, ISSN No: 2468-0230.&#13;
N-methyl-p-aminophenol sulfate (metol) is a photographic developing agent that has a toxic effect on humans and aquatic life. A cost-effective and sensitive electrochemical sensor was developed by electrodepositing Co-Pi over poly-(3 thiophene acetic acid) coated carbon fiber paper electrode (Co-Pi/PTAA/CFP) for the determination of metol (ML). Surface modification of Co-Pi facilitates superior electrocatalytic performance by offering more active sites and faster electron transport kinetics. The Physico-chemical characterization of the fabricated electrode was carried out by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS) Field emission scanning electron microscopy (FESEM) with energy-dispersive X-ray spectroscopy (EDS), Optical profilometer, Fourier transform infrared spectroscopy (FTIR), and electroanalytical techniques.</text>
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                <text>Jose, Sandra., Rajeev, Rijo., Thadathil, Ditto Abraham., Varghese, Anitha., Hegde, Gurumurthy.</text>
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                <text>Jose, Sebastian; Hiremath, Manjunatha; Pohrmen, Fabiola</text>
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                <text>An Intelligent Framework for Evaluating Handwritten Responses: Integrating Bloom's Taxonomy with Adaptive Assessment</text>
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                <text>Jose S., Christ Deemed to be University, Bengaluru, India; Hiremath M., Christ Deemed to be University, Bengaluru, India; Pohrmen F., Christ Deemed to be University, Bengaluru, India</text>
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                <text>Traditional manual grading of descriptive-type answer scripts is inefficient and laborious, while existing technologies rely on strict keyword matching and cosine similarity, which fail to capture the semantic meaning and argumentative quality. This paper proposes a multi-layered intelligent framework for evaluating handwritten descriptive answer scripts by integrating Revised Bloom's Taxonomy with adaptive assessment methods. The system consists of a multi-dimensional evaluation strategy comprising four valuation metrics, namely, content relevance, coherence, depth, and argumentation quality. When compared with conventional methods of keyword matching or computing the cosine similarity, this proposed framework evaluates the semantic meaning and argumentative structure while adapting to varying response styles and contextual differences for personalised assessment.  2025 IEEE.</text>
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                <text>Jose S., Department of Physics and Electronics, Christ University, Karnataka, Bengaluru, 560029, India; Avani A.V., Department of Physics and Electronics, Christ University, Karnataka, Bengaluru, 560029, India; Anila E.I., Department of Physics and Electronics, Christ University, Karnataka, Bengaluru, 560029, India</text>
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                <text>ZnS quantum dots exhibit remarkable versatility with novel properties and diverse applications. Highly crystalline ZnS quantum dots with cubic structure were prepared using a simple wet-chemical route by varying the sulphur concentration. This work offers an in-depth study of the influence of sulphur concentration on the optical, surface and structural characteristics of ZnS quantum dots. Structural analysis using XRD affirmed the cubic structure of ZnS. FESEM disclosed non-uniform nanosphere-like morphology, while TEM was utilized for particle size determination. Optical characteristics were assessed utilizing UVVis spectroscopy and photoluminescence spectroscopy. The ZnS quantum dots synthesized with sulphur concentration double that of the zinc concentration in the precursor solution exhibited the appropriate stoichiometry with minimum point defects. Owing to their high crystallinity, small crystallite size, excellent stability, and suitable optical properties, ZnS quantum dots are favourable candidates for optoelectronic applications.  Indian Association for the Cultivation of Science 2025.</text>
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                <text>2025 World Skills Conference on Universal Data Analytics and Sciences, WorldSUAS 2025;</text>
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                <text>Josemon F., Christ University, Department of Computer Science and Engineering, Kengeri Campus, Bangalore, India; Anil A., Christ University, Department of AI, ML and Data Science, Kengeri Campus, Bangalore, India; Jayapandian N., Christ University, Department of Computer Science and Engineering, Kengeri Campus, Bangalore, India</text>
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                <text>Natural Language Processing (NLP) is one of the important technologies in recent days, because language detection this NLP is play a vital role. This research focuses on detecting languages using various machine learning algorithms. FastText, Recurrent Neural Networks (RNN), Support Vector Machines (SVM) algorithms are used for this experiment. The following datasets are used to take this result that is Europarl and Tatoeba. The proposed method is to preprocess, train, and test these models. Evaluation is done by measuring precision, recall, and F1 score of the three algorithms. Results show that RNN provides precision close perfect or near-perfect results in both bilingual and multilingual datasets. SVM performs with high precision and recall, but less than RNN. Its performance slightly decreases as the dataset increases. On the other hand, FastText, although fast and efficient, drops significantly in performance as the dataset grows, especially with the inclusion of a third language. It provides an all-inclusive methodology that has pinned the strengths and weaknesses of each algorithm, providing valuable insight into which one best fit real-world language detection task: RNN with their ability to handle complex sequences, SVM for large-scale high-dimensional sparse features, and FastText for simpler, smaller dataset.   2025 IEEE.</text>
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                <text>FastText; Long Short-Term Memory; Natural Language Processing; Recurrent Neural Networks; Support Vector Machines</text>
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                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>Lora-WAN Powered by Renewable Energy, and Its Operation with Siri / Google Assistant</text>
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              <elementText elementTextId="123471">
                <text>Artificial Intelligence; Energy Harvesting; Google Assistant; LoRa; LoRa WAN; Siri; Siri Shortcuts</text>
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                <text>LoRa WAN is a newly emerged game changing communication technology for sending small data packets of size 50 bytes or less, wirelessly over an area of up to 10 Km without the need of an internet connection. LoRa WAN has its own frequency band and the band is different for every country. This technology is now starring to boost WSN technology better than ever before. This paper aims to, power up a LoRa Enabled Device or a LoRa Gateway by using a reliable dual mode non-conventional energy resource for storage and utilization, find peak performances altering the data rate that can be achieved in a LoRa WAN Communication (using Indoor RAK Gateway), make use data compression techniques, data packet encoding/decoding, Coding Apple Shortcuts, setting up Siri and Google Assistant for voice control and future scope.  2020, Asian Research Association. All rights reserved.</text>
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              <elementText elementTextId="123473">
                <text>Joseph A.; Balamurugan M.</text>
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              <elementText elementTextId="123474">
                <text>International Research Journal of Multidisciplinary Technovation, Vol-2, No. 5, pp. 26-34.</text>
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                <text>Asian Research Association</text>
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                <text>&lt;a href="https://doi.org/10.34256/irjmt2055" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.34256/irjmt2055&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166928003&amp;amp;doi=10.34256%2Firjmt2055&amp;amp;partnerID=40&amp;amp;md5=577b5aec4eaefc5c74beb2f47cc02a3d" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166928003&amp;amp;doi=10.34256%2firjmt2055&amp;amp;partnerID=40&amp;amp;md5=577b5aec4eaefc5c74beb2f47cc02a3d&lt;/a&gt;</text>
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              <elementText elementTextId="123478">
                <text>All Open Access; Gold Open Access</text>
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                <text>ISSN: 25821040</text>
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                <text>Joseph A., Department of Computer Science and Engineering, Christ University, Bangalore, India; Balamurugan M., Department of Computer Science and Engineering, Christ University, Bangalore, India</text>
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                <text>Efficient hydrogen evolution reaction performance of Ni substituted WS2 nanoflakes</text>
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              <elementText elementTextId="74020">
                <text>Hydrogen evolution reaction; Transition metal dichalcogenides; Two dimensional materials; WS&lt;sub&gt;2&lt;/sub&gt;</text>
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                <text>We have investigated the structural, optical and electrocatalytic hydrogen evolution reaction (HER) performance of pristine, Co and Ni substituted WS2 nanoflakes synthesised by facile hydrothermal method. The XRD pattern confirms the formation of hexagonal WS2 for both pristine and substituted WS2 nanoflakes. The FESEM images validate the flake-like structure for both pristine and substituted WS2. In addition, we have also analysed the Raman and UV-Vis absorbance spectra of the samples. The electrocatalytic studies reveal that the nickel-substituted WS2 (Ni-WS2) nanoflakes show superior hydrogen evolution (HER) performance compared to cobalt-substituted WS2 (Co-WS2) nanoflakes. Hence, we have varied the Ni concentration and investigated the dependence of Ni content on the electrocatalytic performance. It is found that the electrocatalytic performance of the Ni-WS2 nanoflakes increases with an increase in Ni content owing to the modified edge structures. Thus, our studies suggest Ni substitution in WS2 nanostructures can boost electrocatalytic HER performance.  The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.</text>
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              <elementText elementTextId="74022">
                <text>Joseph A.; Chacko L.; Sanal K.C.; Pineda-Aguilar N.; Jasna M.; Antony A.; Aneesh P.M.</text>
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                <text>Applied Physics A: Materials Science and Processing, Vol-130, No. 12</text>
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                <text>EDGE INCIDENT 2-EDGE COLORING SUM OF GRAPHS</text>
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                <text>Joseph A., Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, India; Dominic C., Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, India, Department of Mathematical Sciences, University of Essex, Essex, Colchester, CO4 3SQ, United Kingdom</text>
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                <text>The edge incident 2-edge coloring of a graph G is an edge coloring of the graph G such that not more than two colors are assigned to the edges incident to an edge e = uv in G. In other words, for every edge e in G, the edge e and all the edges that are incident to the edge e is in at most two different color classes. The edge incident 2-edge coloring number ?n2(G) is the maximum number of colors in any edge incident 2-edge coloring of G. The main objective of this paper is to study the edge incident 2-edge coloring concept and apply the same to some graph classes. Besides finding the exact values of these parameters, we also obtain some bounds.  World Scientific Publishing Company.</text>
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                <text>Joseph A., Department of Mathematics, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Dominic C., Department of Mathematics, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India</text>
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                <text>Vertex neighborhood restricted edge achromatic sums of graphs</text>
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                <text>vertex incident 2-edge coloring number; vertex incident 2-edge coloring sum; Vertex induced 2-edge coloring number; vertex induced 2-edge coloring sum</text>
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                <text>The vertex induced 2-edge coloring number ?vi2?(G) of a graph G is the highest number of colors that can occur in an edge coloring of a graph G such that not more than two colors can be used to color the edges in the induced subgraph (N[v]) generated by the closed neighborhood N[v] of a vertex v in V (G). The vertex induced 2-edge coloring sum of a graph G denoted as vi2?(G), is the greatest sum among all the vertex induced 2-edge coloring of a graph G which concedes ?vi2?(G) colors. The vertex incident 2-edge coloring number of a graph G is the highest number of colors required to color the edges of a graph G such that not more than two colors can be ceded to the edges incident at the vertex v of G. The vertex incident 2-edge coloring sum of a graph G denoted as vi2?(G), is the maximum sum among all the vertex incident 2-edge coloring of graph G which receives maximum ?vin2?(G) colors. In this paper, we initiate a study on the vertex induced 2-edge coloring sum and vertex incident 2-edge coloring sum concepts and apply the same to some graph classes. Besides finding the exact values of these parameters, we also obtain some bounds and a few comparative results.  2023 World Scientific Publishing Company.</text>
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                <text>Joseph A., Department of Mathematics, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560029, India; Dominic C., Department of Mathematics, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560029, India, Department of Mathematical Sciences, University of Essex, Colchester, Essex, CO4 3SQ, United Kingdom</text>
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                <text>VERTEX INDUCED k-EDGE COLORING AND VERTEX INCIDENT k-EDGE COLORING OF GRAPHS</text>
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                <text>vertex incident 2-edge coloring number; vertex incident k-edge coloring number; vertex induced 2-edge coloring number; vertex induced k-edge coloring number</text>
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                <text>Let k ? 2 be a natural number. Then the vertex induced k-edge coloring number ? vik(G) of a simple connected graph G = (V,E) is the highest number of colors needed to color the edges of a graph G such that the edges of the subgraph induced by the closed neighborhood N[v] of the vertex v ? V (G) receives not more than k colors. The vertex incident k-edge coloring number ? vink(G) of a simple connected graph G = (V,E) is the highest number of colors required to color the edges of a graph G such that the edges incident to a vertex v in graph G receives not more than k colors. In this paper, we initiate the study on ? vik(G) and ? vink(G). We also determine the exact values of ? vik(G) and ? vink(G) for k = 2 for some special graphs.  2023 Yarmouk University. All rights reserved.</text>
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                <text>Joseph A.; Dominic C.</text>
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                <text>Jordan Journal of Mathematics and Statistics, Vol-16, No. 2, pp. 187-202.</text>
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                <text>&lt;a href="https://doi.org/10.47013/16.2.1" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.47013/16.2.1&lt;/a&gt;
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                <text>ISSN: 20757905</text>
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                <text>Joseph A., Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, India; Dominic C., Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, India</text>
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              <elementText elementTextId="111983">
                <text>VERTEX INDUCED 2-EDGE COLORING AND VERTEX INCIDENT 2-EDGE COLORING OF SOME GRAPH PRODUCTS</text>
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              <elementText elementTextId="111984">
                <text>Cartesian product; rooted product; strong product; vertex incident 2-edge coloring number; Vertex induced 2-edge coloring number</text>
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                <text>Let G = (V, E) be a simple connected graph with vertex set V and edge set E. The vertex induced 2-edge coloring number ?vi2? (G) is the maximum number of colors used in coloring the edges of a graph G such that for each vertex v ? V, at most two edges in the induced subgraph ?N[v]?, generated by the closed neighborhood N[v], receive different colors. The vertex incident 2-edge coloring number ?vin2? (G) of graph G is the maximum number of colors required to color the edges of G such that at most two edges incident to a vertex v in G receive different colors. In this paper, the vertex induced 2-edge coloring number and vertex incident 2-edge coloring number of some graph products such as Cartesian product and strong product are discussed. The ?vi2?(G) and??vin2(G) number in the rooted product of a general connected graphs with some graph classes are also discussed in this paper.  Palestine Polytechnic University-PPU 2022.</text>
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                <text>Palestine Journal of Mathematics, Vol-11, No. 3, pp. 86-95.</text>
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                <text>Joseph A., Department of Mathematics, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Dominic C., Department of Mathematics, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India</text>
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                <text>Conversational Agents and Chatbots: Current Trends</text>
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                <text>Languages facilitate the communication and interaction process among people. Computers learn to communicate with humans intelligently with the help of conversational agents and chatbots based on Natural Language Processing (NLP). Conversational agents and chatbots are gaining popularity in various applications. The development of chatbots or conversational agents is tightly coupled with an organizations customer service requirement. However, the background procedures that power the bots brain are more or less dependent on Artificial Intelligence-based processes. NLP mechanisms powered by various Deep Learning techniques are often used in the training and development of such intelligent agents. These bots inevitably become more competent as they interact with more people. The interactions between a customer and the bot are usually used as data in further training iterations. Chatbots are likely to respond with faster and more precise suggestions leading to solutions for frequently asked questions. Therefore, the current trends indicate the need for a supplementary system rather than substituting human agents existing customer service. The customer experience and intelligence of the chatbots are improved with the help of data analysis and training with the use of Deep Learning techniques. The chapter covers the current trends of conversational agents and chatbots, how the various Artificial Intelligence techniques have transformed the development of multiple architectures of these intelligent systems, and it compares the different state-of-the-art NLP-based chatbot architectures.  2024 selection and editorial matter, Anitha S. Pillai and Roberto Tedesco.</text>
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                <text>Joseph A.; Eapen N.G.</text>
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                <text>Machine Learning and Deep Learning in Natural Language Processing, pp. 17-52.</text>
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                <text>CRC Press</text>
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                <text>2023-01-01</text>
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                <text>&lt;a href="https://doi.org/10.1201/9781003296126-4" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1201/9781003296126-4&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173831295&amp;amp;doi=10.1201%2F9781003296126-4&amp;amp;partnerID=40&amp;amp;md5=bf39bfcb76274ce0212916d5232a7711" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173831295&amp;amp;doi=10.1201%2f9781003296126-4&amp;amp;partnerID=40&amp;amp;md5=bf39bfcb76274ce0212916d5232a7711&lt;/a&gt;</text>
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                <text>Restricted Access</text>
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                <text>ISBN: 978-100096085-3; 978-103226463-9</text>
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                <text>Joseph A., Department of Data Science, CHRIST Deemed to be University, Pune Lavasa Campus, India; Eapen N.G., Department of Data Science, CHRIST Deemed to be University, Pune Lavasa Campus, India</text>
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