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                <text>Rani M., Kumaraguru College of Technology and Business School, Coimbatore, India; Arul Mary Rexy V., Simats Kuthambakkam, Saveetha College of Liberal Arts and Sciences, Department of Commerce, Chennai, India; Halle P.D., Skn Sinhgad Institute of Technology and Science, Department of Information Technology, Lonavala, Pune, India; Kaushal J., Geetanjali Institute of Technical Studies, Department of Computer Science and Engineering, Udaipur, India; Chauhan A., School of Sciences, Christ (Deemed to Be University), Department of Life Sciences, Bengaluru, India; Basha H.A., Saveetha Institute of Medical and Technical Sciences, Saveetha College of Liberal Arts and Sciences, Department of Computer Science, Chennai, India</text>
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                <text>IoT has transformed how devices work together. Now, billions of connected devices may share data across smart homes, energy systems, and environmental monitoring. In Internet of Things ecosystems, rapid IoT expansion has made them very vulnerable, which makes them easy targets for cyberattacks. Hackers can break into IoT devices that don't have enough protection to stop services, steal data, and invade privacy. This paper shows how to use deep learning using CNNs and LSTM networks and the HABCABO optimization algorithm to deal with these new dangers. After careful sequencing, scaling, and noise reduction, filter-based feature selection uses statistical methods to keep the most important information. To get the best detection, the CNN-LSTM model is trained with features that are carefully regulated. The suggested model is more accurate than CNN and LSTM approaches, with an accuracy rate of 98.04 %. These results show that the model can find and stop IoT cybersecurity threats. In conclusion, CNN-LSTM and HABCABO are strong and smart ways to make sure that IoT infrastructure is safe and reliable right now.  2025 IEEE.</text>
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                <text>Leslie Dass P., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Nair S.V.R., Faculty for Lean Operations and Systems, School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Kurien G.P., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Kumar Chandar S., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India</text>
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                <text>Revolutions in information technology have been helping agriculturists to increase the productivity of the cultivation. Many techniques exist for farming, but precision agriculture (PAg) is one technique that has gained popularity and has become a valuable tool for agriculture. Nowadays, farmers find it difficult to get expert advice regarding crops on time. As a solution, image processing techniques (IPTs) embedded PAg applications are developed to support farmers for the benefit of agriculture. In recent years, IPT has contributed a lot to provide a significant solution in PAg. This systematic review provides an understanding on preprocessing and feature extraction in PAg applications along with limitations. Preprocessing and feature extraction are the major steps of any application using IPTs. This study gives an overall view of the different preprocessing, feature extraction, and classification methods proposed by the researchers for PAg.  2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>Sharmila G.; Rajamohan K.</text>
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                <text>Lecture Notes on Data Engineering and Communications Technologies, Vol-114, pp. 333-354.</text>
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                <text>Sharmila G., Department of Computer Science, CHRIST (Deemed To Be University), Bangalore, 560029, India; Rajamohan K., Department of Computer Science, CHRIST (Deemed To Be University), Bangalore, 560029, India</text>
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                <text>Baranidharan, S.; Sankarkumar, Amirdha Vasani; Chandrakala, G.; Dhanalakshmi, K.; Mohan, Chippy; Victor, Melvin; Rindavanam, Rahul V.; Sembiyan, R.</text>
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                <text>A Systematic Literature Review Unveiling the Societal Impact of 5G/6G Technologies on Bridging the Digital Divide and Enabling Social Transformation</text>
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                <text>Baranidharan S., Christ University, Bangalore, India; Sankarkumar A.V., St. Francis College, India; Chandrakala G., Dayananda Sagar University, India; Dhanalakshmi K., Christ University, Bangalore, India; Mohan C., Christ University, Bangalore, India; Victor M., Christ University, Bangalore, India; Rindavanam R.V., Christ University, Bangalore, India; Sembiyan R., St. Josephs College of Arts and Science, Cuddalore, India</text>
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                <text>This study examines the multifaceted societal impact of 5G and 6G mobile communication technologies. Employing a systematic literature review (SLR) of recent scholarly literature (2022-2024), A comprehensive search strategy was implemented across reputable academic databases (Scopus, Web of Science, IEEE Xplore) to identify relevant publications. Selected articles underwent rigorous critical appraisal and thematic analysis to synthesize key findings and recurring themes. The review reveals the potential of 5G/6G to bridge the digital divide, particularly in underserved regions, through strategic infrastructure development. Social transformation can be achieved by fostering equitable access to communication services and deploying transformative applications in critical sectors like agriculture and healthcare. However, challenges persist regarding infrastructure limitations, equitable access, and sustainable development. Effective policy frameworks and robust managerial strategies for responsible deployment are crucial to maximize the positive impact of 5G/6G technologies.  2025 by IGI Global Scientific Publishing. All rights reserved.</text>
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                <text>IGI Global</text>
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                <text>Praveenraj, D. David Winster; Yunusova, Ugiloy; Sachdeva, Lalit; Ghate, Atul Dattatraya; Khalil, Fleur Clara</text>
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                <text>Acta Innovations;Volume;55;pp.49-58</text>
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                <text>Praveenraj D.D.W., School of Business and Management, CHRIST University, Bangalore, India; Yunusova U., Department of Economics, Tashkent Institute of Irrigation and Agricultural Mechanization Engineers (TIIAME), National Research University, Tashkent, Uzbekistan; Sachdeva L., Department of Management, Kalinga University, Raipur, India; Ghate A.D., Department of Management, Kalinga University, Raipur, India; Khalil F.C., Audit Department, Business School, Holy spirit University of Kaslik, Jounieh, Lebanon</text>
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                <text>This research advances the comprehension of the processes behind individuals' environmentally friendly behavior using a comprehensive approach. A questionnaire addressed intrapersonal, motivational, relationships, and educational aspects, with environmental science as the primary catalyst for green behavior within a complete theoretical structure.The method is the CADMIACA approach, which is founded on Comprehensive Action Determining Modeling (CADM), together with various Motivational and Interpersonal (MI) theories and the Activity Competence Algorithm(ACA). This framework encompasses various control factors relevant to comprehensively characterizing the factors influencing environmentally friendly behavior, including climate change, energy conservation, recycling, sustainable buying, and contamination.The findings were gathered in the A Coru metropolitan region to experimentally evaluate the causal relationships among the parameters that formed the framework utilizing Structural Equation Modeling (SEM). Findings show that environmentalscience serves as an effective instrument for fostering eco-friendly behavior among residents. The extensive CADMIACA model aligns well with the information since all components incorporated in the framework (intrapersonal, inspiring, social, and institutional) are pivotal in shaping green conduct.Environmental instruction and intrapersonal variables emerged as the primary predictors of green conduct, but social and motivational variables were less prevalent in influencing such behavior. The findings suggest that human conduct plays a vital role in environmental protection.  2025, Rotherham Academic Press Ltd. All rights reserved.</text>
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                <text>environmental sciences; management; Meta-analysis; review</text>
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              <elementText elementTextId="274879">
                <text>Kumar, Manish; Shanbhog, Manjula; Alapatt, Bosco Paul; Upreti, Kamal; Poonia, Ramesh Chandra</text>
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                <text>A Systematic Review and Meta-Analysis of Pneumonia Diagnosis Using Machine Learning Techniques</text>
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                <text>2025 IEEE 5th International Conference on ICT in Business Industry and Government, ICTBIG 2025;</text>
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                <text>Kumar M., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India; Shanbhog M., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India; Alapatt B.P., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India; Upreti K., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India; Poonia R.C., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India</text>
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                <text>Pneumonia is an infection that results in inflammation of the lungs and, if not identified in time, can be life-threatening. The most frequent method of diagnosing pneumonia is chest X-rays; the pictures are scrutinized closely. Pneumonia is still a global health burden. The accurate and timely diagnosis is difficult, especially in low-resource settings. X-rays have served as a primary key for the identification of pneumonia for many years. However, with the recent advancement in artificial intelligence technologies, especially deep learning and machine learning, there's now a potential to automatically detect and classify pneumonia using chest x-ray images. This review examines the research from 2019 to 2024 to understand the current trends and future direction in various deep learning and machine learning models. These encompass the convolutional neural networks, transfer learning methods, combined network designs, explainable AI models, and the use of radiomics with conventional machine learning techniques. However, the three significant challenges remain differences in the data, an imbalance between the classes, and a limited ability to apply these methods in real clinical settings. Based on the review, this paper suggests more future research on machine learning techniques for detecting pneumonia. In this work, a new system is also introduced to improve both case identification and the clinical diagnosis process. The proposed model was evaluated using the Key Parameter Indicator (KPI) as a feature and was compared with an earlier model. Finally, recommendations are provided for future research on trustworthiness, clinical usefulness, and multi-modal AI systems.  2025 IEEE.</text>
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                <text>convolutional neural networks; Deep learning; machine learning; transfer learning</text>
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              <elementText elementTextId="274887">
                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>Potluri S., Department of CSE, Faculty of Science and Technology, IcfaiTech, ICFAI Foundation for Higher Education, Donthanpally, Shankarpalli Road, Telangana, Hyderabad, 501203, India; Satpathy S., Faculty of Emerging Technologies, Sri Sri University, Odisha, Cuttack, 751002, India; Mahapatra S., Department of Computer Application, Institute of Technical Education and Research, Siksha O Anusandhan Deemed to be University, Bhubaneswar, 751030, India; Nanjundan P., Department of Data Science, CHRIST University, Pune, Lavasa, India; Mohanty S.N., Vardhaman College of Engineering (Autonomous), Telangana, Hyderabad, 501218, India</text>
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                <text>Machine learning (ML) techniques are the backbone of Prediction and Recommendation systems, widely used across banking, medicine, and finance domains. ML techniques effectiveness depends mainly on the amount, distribution, and variety of training data that requires varied participants to contribute data. However, its challenging to combine data from multiple sources due to privacy and security concerns, competitive advantages, and data sovereignty. Therefore, ML techniques must preserve privacy when they aggregate, train, and eventually serve inferences. This survey establishes the meaning of privacy in ML, classifies current privacy threats, and describes state-of-the-art mitigation techniques named Privacy-Preserving Machine Learning (PPML) techniques. The paper compares existing PPML techniques based on relevant parameters, thereby presenting gaps in the existing literature and proposing probable future research drifts.  2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>Each sector of the digital world generates enormous data as human life continues to transform. Areas like data analytics, data science, knowledge discovery in databases (KDD), machine learning, and artificial intelligence depend on highly distributed data which requires appropriate storage in a data lake. Collecting the data from different heterogeneous sources and creating a single lake of data is called data ingestion. Ironically, data ingestion has been treated as a less important stage in data analysis because it is considered a minor first step. There are several misconceptions in the data and analytics domain about data ingestion. The survey employed in this research presents a list of significant challenges faced by information technology (IT) industries during data ingestion. The available frameworks are compared in terms of standard parameters that are set against the existing challenges and myths. The findings from the comparison are compiled in a tabular format for easy reference. The paper places emphasis on the significance of data ingestion and attempts to present it as a major activity on the big data platform.  2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135020977&amp;amp;doi=10.1007%2F978-981-19-2211-4_43&amp;amp;partnerID=40&amp;amp;md5=4dd335dd06cbffb4fc534d41c7d7213e" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135020977&amp;amp;doi=10.1007%2f978-981-19-2211-4_43&amp;amp;partnerID=40&amp;amp;md5=4dd335dd06cbffb4fc534d41c7d7213e&lt;/a&gt;</text>
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                <text>ISSN: 23673370; ISBN: 978-981192210-7</text>
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              <elementText elementTextId="193828">
                <text>anti-inflammatory; fish biomaterial; in vitro studies; in vivo studies; wound healing</text>
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                <text>Objective: To conduct a systematic literature review to study the effects of fish-based biomaterials on wound healing in both in vivo and in vitro animal models. Approach: This review covers the study reported in different articles between 2016 and August 2022 concentrating mainly on the cytotoxicity evaluation of different fish-based biomaterials on inflammation, reepithelialization and wound healing. Significance: This review shows considerable amount of research work carried out with fish-based biomaterials and collagen for treating burn wounds. Surprisingly there are only a few commercial products developed so far in this particular regard for surgical purpose and therefore, there is a way out and need for developing medical support product from fish-based biomaterials to treat and cure wounds. Recent Advances: Three-dimensional skin bioprinting technique is a large-scale solution for severe burn wounds that requires collagen as a raw material for printing, wherein fish collagen can be used in place of bovine and porcine, as it is biocompatible, promotes cell proliferation, adhesion, and migration, and degrades enzymatically. In the recent times, there are a few fish-based surgical products that have been formulated by Kerecis in United States. Critical Issues: The different fish-based biomaterial products are all mere supplements taken in orally as food or supplements till date and there is no proper proven medications that has been formulated so far in the field of wound healing and inflammation based on fish biomaterials except the surgical products that can be finger counted. Future Directions: Fish-based biomaterials are known for the medicinal properties that are used throughout the world and further investigations should be carried out to understand the actual physiochemical properties of its derivatives for the discovery of novel products and drugs. Copyright 2024 by Mary Ann Liebert, Inc.</text>
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                <text>Gomathy M.; Paul A.J.; Krishnakumar V.</text>
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                <text>Advances in Wound Care, Vol-13, No. 2, pp. 83-96.</text>
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                <text>Mary Ann Liebert Inc.</text>
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                <text>&lt;a href="https://doi.org/10.1089/wound.2022.0142" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1089/wound.2022.0142&lt;/a&gt; &lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164495478&amp;amp;doi=10.1089%2Fwound.2022.0142&amp;amp;partnerID=40&amp;amp;md5=fb37451cd34127c879075bf727d5428b" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164495478&amp;amp;doi=10.1089%2fwound.2022.0142&amp;amp;partnerID=40&amp;amp;md5=fb37451cd34127c879075bf727d5428b&lt;/a&gt;</text>
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              <elementText elementTextId="193835">
                <text>Restricted Access</text>
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                <text>ISSN: 21621918; PubMed ID: 37166397; LS; 2023-2024; Vol-1; 0737-0750</text>
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                <text>Gomathy M., Department of Life Science, CHRIST (Deemed to be University), Bangalore Central Campus, Karnataka, India; Paul A.J., Department of Zoology, St. Josephs University, Bengaluru, India, Department of Zoology, St. Josephs University, Karnataka, Bangalore, 560027, India; Krishnakumar V., Department of Life Science, CHRIST (Deemed to be University), Bangalore Central Campus, Karnataka, India</text>
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          <element elementId="50">
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            <elementTextContainer>
              <elementText elementTextId="91063">
                <text>A Systematic Review of Green Apparel Manufacturing</text>
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              <elementText elementTextId="91064">
                <text>cutting department and sewing department; e-supply chain management; green practices; sustainable</text>
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                <text>The purpose of this paper is to conduct a systematic review of the literature on green manufacturing practices in the apparel industry to map green practices across various apparel manufacturing departments. The review includes academic journal articles that were retrieved between March 2013 and March 2023 from several different databases. As part of a comprehensive literature assessment, content analysis was applied to 138 publications that were published in peer-reviewed journals over ten years. Green practices in garment manufacturing process are covered, including product design, raw material procurement, fabric spreading, cutting, sewing and assembly, washing, printing and embroidery, finishing, and packing. The review of eco-friendly production practices at each phase of the production process shows the variety and complexity of green practices in apparel production companies. However, there is a lack of research on the conditions of developing countries, where the majority of apparel production takes place, as well as on the methods used in the manufacture of garments. The study is distinct in that it focuses solely on the garment manufacturing industry, and will not include textiles because the production processes for textiles and clothing are fundamentally different. This study assists managers in building a companys sustainability competency by outlining best practices at various phases of production. It also provides scholars with a uniform representation of environmentally sustainable practices to spur additional scholarly investigation.  2023, Kauno Technologijos Universitetas. All rights reserved.</text>
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                <text>Jain S.; Kalapurackal J.J.</text>
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                <text>Environmental Research, Engineering and Management, Vol-79, No. 4, pp. 124-133.</text>
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              <elementText elementTextId="91068">
                <text>Kauno Technologijos Universitetas</text>
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                <text>&lt;a href="https://doi.org/10.5755/j01.erem.79.4.34368" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.5755/j01.erem.79.4.34368&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182198805&amp;amp;doi=10.5755%2Fj01.erem.79.4.34368&amp;amp;partnerID=40&amp;amp;md5=5dbbe6b64846043bcda67e82d3b19545" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182198805&amp;amp;doi=10.5755%2fj01.erem.79.4.34368&amp;amp;partnerID=40&amp;amp;md5=5dbbe6b64846043bcda67e82d3b19545&lt;/a&gt;</text>
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              <elementText elementTextId="91071">
                <text>All Open Access; Gold Open Access</text>
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                <text>ISSN: 13921649</text>
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                <text>Jain S., School of Business and Management, Christ (Deemed to be University), India; Kalapurackal J.J., School of Business and Management, Christ (Deemed to be University), India</text>
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      <name>Book Chapter</name>
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        <description>The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.</description>
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              <elementText elementTextId="285305">
                <text>Baranidharan, S.; Sridevi, M.; Krishnaveni, N.; Mohan, Chippy; Pradeep Kumar, S.V.; Sandhya, D.; Vinod, Priya; Kurup, Rahul R.</text>
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                <text>A Systematic Review of Mental Health and Productivity Challenges and Navigating Turbulence in Academia</text>
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              <elementText elementTextId="285308">
                <text>Mental Health Challenges in Academia: Stressors Faced by Students and Faculty;pp.61-84</text>
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                <text>&lt;a href="https://doi.org/10.4018/979-8-3373-0928-6.ch004" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.4018/979-8-3373-0928-6.ch004&lt;/a&gt; &lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/pages/publications/105026206780?origin=resultslist" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/pages/publications/105026206780?origin=resultslist&lt;/a&gt;</text>
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                <text>Baranidharan S., Christ University, Bangalore, India; Sridevi M., Christ Academy Institute for Advanced Studies, India; Krishnaveni N., Nirmala College for Women, India; Mohan C., Christ University, Bangalore, India; Pradeep Kumar S.V., Christ University, Bangalore, India; Sandhya D., Christ University, Bangalore, India; Vinod P., Christ University, Bangalore, India; Kurup R.R., Christ University, Bangalore, India</text>
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                <text>An essential component of agricultural output is pest management, especially in fertigation-based farming. Although fertigation systems in Malaysia are beneficial for irrigation and fertilization, they frequently don't have effective pest control techniques. Because pests usually live beneath crop leaves, hand spraying is difficult and labor-intensive. Insect pests have the power to seriously harm, weaken, or even kill agricultural plants, which can lead to lower yields, worse-quality goods, and unsalable outcomes. Furthermore, insects may still cause harm to processed or stored items after harvest. Therefore, creating an autonomous pesticide sprayer specifically designed for chilli fertigation systems is the main goal of this research. The main goal is to create a sprayer arm that is flexible enough to reach under crop leaves. The goal of this project is to build an autonomous, unmanned pesticide sprayer. The goal of autonomous operation is to reduce the amount of dangerous pesticides that people are exposed to, especially in enclosed spaces like greenhouses. In addition, the sprayer arm's adaptability to different agricultural circumstances makes it a valuable tool in both greenhouse and outdoor settings. It is expected that the successful adoption of the autonomous pesticide sprayer would completely transform fertigation-based farming's approach to pest management.  2024 IEEE.</text>
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                <text>Babu P.B., Aditya Engineering College, Department of Electronics and Communication Engineering, Surampalem, India; Bisht D., Uttaranchal Institute of Management, Uttaranchal University, Department of Management, Dehradun, 248007, India; Shilpa K., Cmr Technical Campus, Department of Cse, Telangana, Hyderabad, India; Alzubaidi L.H., The Islamic University, Najaf, Iraq; Sharma A., Chitkara University, Chitkara Centre for Research and Development, Himachal Pradesh, 174103, India; Rajesh Kanna R., Christ(Deemed to Be University), Department of Cse, Bangalore, India</text>
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