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                <text>A Hybrid Grayscale Image Scrambling Framework Using Block Minimization and Arnold Transform</text>
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                <text>Decoding; Encoding; Entropy; Image Confusion; Image processing</text>
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                <text>Image disarranging is the process of randomly rearranging picture elements to make the visibility unreadable and break the link among neighboring elements. Pixel values often don't change while they are being scrambled. There has been a slew of proposed image encryption techniques recently. The two steps that most image encryption algorithms go through are confusion and diffusion. Using a scrambling technique, the pixel positions are permuted during the confusion phase, and an inverse-able function is used to modify the pixel values during the diffusion phase. A good scrambling method practically eliminates the high relationships between adjacent pixels in a picture. In the proposed scheme, XOR based minimization operator is applied on blocks of images followed by Arnold Transform. The suggested design is assessed using a matrix comprising the Structured Similarity Index and the Peak Signal to Noise Ratio. The computed PSNR value less than 10 indicates the input image and scrambled image has high variation. The SSIM value nearer to 0 indicates no similarity in the structure of the input image and scrambled image.  2024 IEEE.</text>
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                <text>Shiju Thomas M.Y.; Krishna A.V.N.</text>
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                <text>2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2024</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/ICRITO61523.2024.10522409" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICRITO61523.2024.10522409&lt;/a&gt;
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                <text>ISBN: 979-835035035-7</text>
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                <text>Shiju Thomas M.Y., Rajagiri College of Social, Sciences(Autonomous), Department of Computer Science, Kerala, India; Krishna A.V.N., School of Engineering and Technology, Christ (Deemed to Be University), Department of Computer Science and Engineering, Karnataka, Bangalore, India</text>
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                <text>AR and Online Purchase Intention Towards Eye Glasses</text>
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                <text>Augmented reality; e-commerce; eyeglasses; online purchase intention</text>
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                <text>Augmented reality (AR) can be a potent tool for Indian online eyewear marketers by bridging the gap between online and offline purchasing experiences and meeting the needs of social validation and sensory engagement, which are preferences of Indian consumers. The present research explores how augmented reality (AR) technology affects Indian consumers' intentions to buy glasses online. A combination of descriptive and exploratory research design was used on the sample size of 236 consumers. Data was analyzed using frequency table and Structured Equation modelling (SEM) to identify the relationship amongst the variables. The findings indicate that accessibility to product information, telepresence, and perceived ease of use are important variables impacting purchase intention. AR can bridge the gap between online and offline experiences, meet consumer preferences, and create trust and confidence. Future research should explore AR's effectiveness and personalization possibilities for Indian online eyewear retailers. Future research should explore AR's effectiveness and personalization possibilities for Indian online eyewear retailers.  2024 IEEE.</text>
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                <text>Prasad A.S.; Sinha D.; Goyal S.; Mathur S.</text>
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                <text>2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2024</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/ICRITO61523.2024.10522440" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICRITO61523.2024.10522440&lt;/a&gt;
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                <text>ISBN: 979-835035035-7</text>
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                <text>Prasad A.S., School of Business and Management, Christ (Deemed to Be University), Bangalore, India; Sinha D., School of Business and Management, Christ (Deemed to Be University), Bangalore, India; Goyal S., School of Business and Management, Christ (Deemed to Be University), Bangalore, India; Mathur S., School of Business and Management, Christ (Deemed to Be University), Bangalore, India</text>
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                <text>A Cognitive Architecture Based Conversation Agent Technology for Secure Communication</text>
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              <elementText elementTextId="167757">
                <text>Agent Technology; Case Base Reasoning Systems; Decision Support System; JADE; Multi-Agent</text>
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                <text>This paper outlines a multi-agent system-based approach to provider selection. Suppliers in the supply chain are different and the demand and supply levels are high. Buy agents will find the right supply agent in our approach. First, the multi-layer classification system is used to rationally arrange and overall selection on suppliers and buyers. Secondly, the purchase information is organized by the supplier agent to improve device performance. The assessment process is then used to select the suppliers initially. In addition to selecting the correct provider and maximizing the value of the purchaser, the time negotiating mechanism is implemented.  The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.</text>
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                <text>Preety; Singh J.; Yadav M.</text>
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                <text>Communications in Computer and Information Science, Vol-2122 CCIS, pp. 1-12.</text>
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                <text>Springer Science and Business Media Deutschland GmbH</text>
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                <text>&lt;a href="https://doi.org/10.1007/978-3-031-61298-5_1" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-3-031-61298-5_1&lt;/a&gt;
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                <text>ISSN: 18650929; ISBN: 978-303161297-8</text>
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                <text>Preety, Christ University Delhi NCR, Ghaziabad, India; Singh J., Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation, Andhra Pradesh, Vaddeswaram, India; Yadav M., Galgotias College of Engineering and Technology, Uttar Pradesh, Greater Noida, India</text>
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                <text>A Study on Factors Enhancing Immersive Virtual Reality Experiences</text>
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                <text>Experience enhancement; Immersion; Technology; Virtual Reality</text>
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                <text>The objective of this study is to identify the various influential factors of immersive virtual reality (VR) experiences and examine the relationship between the immersion factors (technology, visuals, sound, interaction, and sound) and virtual reality experiential outcomes (satisfaction and loyalty). The survey comprises 412 participants who experienced VR games at the Orion Mall in Bangalore. The study has identified the prominent factors for enhancing the immersive experience. The factors are technology, visuals, sound, interaction, and sound. It also identified that there exists a positive association between VR experiential satisfaction and technology, visuals, sound, interaction, and sound. The results imply that service providers should focus on elevating immersive experience as it is closely associated with VR experiential satisfaction and VR experiential loyalty. This will increase the revisit intention and spread positive word of mouth about the virtual experiences. This paper provided valuable insights that pay way to analyze the association between immersion factors and VR experiential outcomes.   2024 IEEE.</text>
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                <text>Rina Daline T., CHRIST (Deemed to Be University), Bangalore, India; Thomas J., CHRIST (Deemed to Be University), Bangalore, India</text>
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                <text>Abuse words; CNN; Gradient Boosting Machine and random forest; SVM; XGB classifier</text>
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                <text>Utilization of artificial intelligence contributes to the efficient examination of emotions, resulting in valuable insights into the psychological condition of users on a large scale. In this research endeavor, sentiment analysis is conducted on a dataset from Reddit, which was obtained through Kaggle. The feedback in this collection of data was divided into downbeat, neutral, and upbeat sentiments. Various machine learning techniques, like Random Forest, Extreme Gradient Boosting Classifier (XGB), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), were detected and examined to assess their effectiveness in sentiment classification. The review of these techniques comprised performance criteria such as F1 Score, accuracy, precision, and recall. Additionally, confusion matrices were utilized to assess the algorithms' proficiency in identifying abusive language. The investigation's conclusions indicate that, when it comes to sentiment analysis, the random forest method performs better than any other strategy, with a maximum accuracy of 0.99 that is on par with the CNN model's accuracy of 0.98. Moreover, random forest proves to be the most effective algorithm in recognizing negative comments and abusive language. This study underscores the significance of employing machine learning algorithms in sentiment analysis, content moderation, social media monitoring, and customer feedback analysis, emphasizing their role in enhancing automated systems that aim to comprehend user sentiments in online discussions.   2024 IEEE.</text>
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                <text>Proceedings - 2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024, pp. 312-317.</text>
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                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>ISBN: 979-835037284-7</text>
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                <text>Madhurima S., Computer Science and Engineering Christ (Deemed to Be University), Karnataka, Bangalore, India; Abhijith Ajith K., Computer Science and Engineering Christ (Deemed to Be University), Karnataka, Bangalore, India; Vs S., Computer Science and Engineering Christ (Deemed to Be University), Karnataka, Bangalore, India; Prathap B.R., Computer Science and Engineering Christ (Deemed to Be University), Karnataka, Bangalore, India</text>
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              <elementText elementTextId="167798">
                <text>Synergy Unleashed: Smart Governance, Sustainable Tourism, and the Bioeconomy</text>
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            <elementTextContainer>
              <elementText elementTextId="167799">
                <text>Bioeconomy; Environmental sustainability; Smart Governance; Sustainable tourism</text>
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                <text>This study investigates the transformational potential of smart Governance in the tourism sector to enhance the operational effectiveness, transparency, and efficacy of governmental actions. This research synthesises the body of knowledge regarding the use of technology and data-driven methods in Governance using a literature review methodology. A conceptual framework is suggested to highlight the complex effects of smart Governance on many stakeholders in the travel industry. The study uses a multidimensional paradigm that includes agile leadership, stakeholder alliances, network management, and adaptive Governance. It explains how these complementary components construct a revolutionary ecology that encourages creativity, adaptability, and inclusive growth. Organisations can acquire insights into visitor behaviours, preferences, and traffic patterns by utilising data analytics and digital platforms, which can improve resource allocation, infrastructure construction, and policy formation. Applications that use real-time data enable dynamic crowd control, traffic optimisation, and safety improvements. The report also highlights how local communities may be involved in smart Governance to promote inclusive decision-making. This framework helps promote deeper study into the actual application and outcomes of smart Governance, which has the potential to change the travel sector. This multidisciplinary approach fosters resilience, innovation, and responsible, inclusive development. This study promotes real-world applications that fully utilise this synergy to further the interconnected objectives of sustainable tourism, bioeconomic growth, and efficient Governance.  The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.</text>
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              <elementText elementTextId="167801">
                <text>George G.; Varghese B.; Kulkarni M.</text>
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              <elementText elementTextId="167802">
                <text>Lecture Notes in Networks and Systems, Vol-941 LNNS, pp. 411-419.</text>
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              <elementText elementTextId="167803">
                <text>Springer Science and Business Media Deutschland GmbH</text>
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              <elementText elementTextId="167804">
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                <text>&lt;a href="https://doi.org/10.1007/978-981-97-1260-1_34" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-981-97-1260-1_34&lt;/a&gt;
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              <elementText elementTextId="167806">
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                <text>ISSN: 23673370; ISBN: 978-981971259-5</text>
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              <elementText elementTextId="167809">
                <text>English</text>
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                <text>George G., Academics, Westford University College, Sharjah, United Arab Emirates; Varghese B., Tourism Management, School of Business and Management, Christ University, Bangalore, India; Kulkarni M., Symbiosis Centre for Information Technology (SCIT), Symbiosis International (Deemed) University, Pune, India</text>
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                <text>Single-Stage Bidirectional Three-Level AC/DC LLC Resonant Converter with High Power Factor</text>
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              <elementText elementTextId="167813">
                <text>AC-DC converters; LLC resonant converters; PFC; soft switching</text>
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                <text>The increasing demand for efficient and high-performance power converters in electric vehicle technology and renewable energy integration has brought attention to LLC resonant converters due to their advantages in soft switching, inherent short circuit and open circuit protection, and high efficiency. These converters are particularly well-suited for high-frequency operation, making them ideal for electric vehicle battery charging and other power conversion tasks. However, when integrated with a front-end boost power factor correction (PFC) stage in AC-DC applications, challenges arise in maintaining power balance during transients, leading to voltage fluctuations and potential operational instability. Moreover, light load conditions can result in excessive switching frequencies, causing elevated switching losses and control difficulties. Additionally, traditional LLC resonant converters face limitations related to high voltage stress on switches, which affects device reliability and overall converter performance. To address these issues, researchers have explored the use of multilevel inverters, but they introduce complexity and cost. In this context, this paper proposes a novel single-stage, three-level bidirectional AC-DC LLC-based resonant converter with features like zero voltage switching and duty ratio control for output voltage regulation. The converter achieves a unity displacement power factor naturally through discontinuous conduction mode. Simulation results demonstrate the converter's effectiveness of the proposed topology. The proposed converter offers a promising solution for Electric vehicle chargers, combining unity power factor operation and efficient bidirectional power flow control in a single topology.   2024 IEEE.</text>
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                <text>Hrudhya Kurian C.; Peter A.K.</text>
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                <text>Proceedings - 2024 2nd International Conference on Smart Technologies for Power and Renewable Energy, SPECon 2024</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/SPECon61254.2024.10537247" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/SPECon61254.2024.10537247&lt;/a&gt;
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                <text>Hrudhya Kurian C., Christ University, School of Engineering &amp;amp; Technology, EEE Department, Bengaluru, India; Peter A.K., Institute for Power Electronics, Hochschule Kempten University of Applied Sciences, Germany</text>
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                <text>Universal Filtered Multicarrier (UFMC) has been incorporated in 5G and is likely to be considered in future generations (B5G). The prominent limitation of UFMC manifests as a high Peak-to-Average Power Ratio (PAPR). Our suggested approach to address the Peak-to-Average Power Ratio (PAPR) issue in UFMC signals involves the application of diverse precoding matrices, including Square Root Raised Cosine Function (SRC), Discrete Cosine Transform (DCT), and Discrete Hartley Transform (DHT).This technique reduces the PAPR performance of UFMC signals over current state of the art methods. In square root raised cosine (SRC) precoding techniques, a novel precoding matrix is adapted for minimizing PAPR and improvement of BER respectively. Results show that the different subcarrier was applied and surpasses all existing techniques in reduction of PAPR and BER improvement. A novel SRC-Precoding technique reduces PAPR by 5dB for considering 512 sample points with QAM modulation as compared to 10dB for the conventional technique. Additionally, the Bit Error Rate Performance is maintaining 14dB when compared to conventional technique. Furthermore, the evaluation of Bit Error Rate (BER) performance and Peak-to-Average Power Ratio (PAPR) in the UFMC system reveals superior results compared to conventional technique.   2024 IEEE.</text>
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                <text>ISBN: 979-835035306-8</text>
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                <text>Ponmani Raja M., Christ (Deemed To Be University), Bengaluru, India; Sujatha S., Christ (Deemed To Be University), Department of ECE, Bengaluru, India; Prajoon P., Jyothi Engineering College, Department of ECE, Thrissur, India</text>
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                <text>Smart Home attains an active role in elderly care. Vision impairments caused by aging makes elders more dependent and affects the circadian rhythm or body clock. Some vision impairments can be improved by providing additional lighting. Smart lighting is the leading solution in providing adequate quality of lighting which helps elders to perform their daily activities independently. Various smart lighting solutions for elderly care are proposed in past and failed to consider about the energy loss due to over lighting. Additionally, the solutions are more independent in nature and not integrable to existing smart home solutions. To provide a solution to these ongoing challenges, an experimental design has been proposed to manage the adequate quality lighting for elderly people by controlling the illuminance and color temperature of the light with a feedback mechanism. Also, this experiment has integrated into a popular smart home platform. The proposed design keeps monitoring the ambient lighting and maintains the room's illumination as required for elderly individuals. The functional behaviors of the experimental design are evaluated using a testbed. The result shows that the proposed design reduces the energy usage more than 50% along with providing adequate lighting for elderly individuals. In addition, this experimental design promises that the proposed method can be easily integrated into any existing smart home solutions with its native scripting framework.   2024 IEEE.</text>
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                <text>Sadasivam B.; Rajamohan K.</text>
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                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195367136&amp;amp;doi=10.1109%2FICNWC60771.2024.10537598&amp;amp;partnerID=40&amp;amp;md5=d8b74fc1764679ad1b0197b874868852" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195367136&amp;amp;doi=10.1109%2fICNWC60771.2024.10537598&amp;amp;partnerID=40&amp;amp;md5=d8b74fc1764679ad1b0197b874868852&lt;/a&gt;</text>
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                <text>ISBN: 979-835036526-9</text>
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                <text>Sadasivam B., CHRIST (Deemed to be University), Department of Computer Science, Bangalore, India; Rajamohan K., CHRIST (Deemed to be University), Department of Computer Science, Bangalore, India</text>
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                <text>Exploring the Balance Between Automated Decision-Making and Human Judgment in Managerial Contexts</text>
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                <text>The study delves into the dynamic and evolving discussion surrounding the balance between automated and human judgment within the realm of managerial decision-making. The primary objective of this research is to gain insight into how AI is evolving to mitigate ethical biases that are inherent in managerial decision-making. To accomplish this goal, the study adopts a theoretical approach, supported by qualitative analysis through an extensive review of existing literature. By systematically investigating AI techniques for managerial decision-making, the research contributes to a broader understanding of how AI is progressing to promote ethically sound managerial decisions in future. The findings from this study are pertinent to business leaders, policymakers, and researchers, offering guidance as they navigate the intricate relationship between automation and human judgment in todays managerial landscape.  The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.</text>
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                <text>Alex V.P.; Anirudh H.V.; Siby A.</text>
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                <text>Lecture Notes in Networks and Systems, Vol-946, pp. 159-167.</text>
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                <text>Springer Science and Business Media Deutschland GmbH</text>
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                <text>&lt;a href="https://doi.org/10.1007/978-981-97-1323-3_13" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-981-97-1323-3_13&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195469441&amp;amp;doi=10.1007%2F978-981-97-1323-3_13&amp;amp;partnerID=40&amp;amp;md5=111bdc9848a5a1786fb22f74700a15d8" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195469441&amp;amp;doi=10.1007%2f978-981-97-1323-3_13&amp;amp;partnerID=40&amp;amp;md5=111bdc9848a5a1786fb22f74700a15d8&lt;/a&gt;</text>
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                <text>ISSN: 23673370; ISBN: 978-981971322-6</text>
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              <elementText elementTextId="167865">
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                <text>Alex V.P., CHRIST (Deemed to be University), Bengaluru, India; Anirudh H.V., CHRIST (Deemed to be University), Bengaluru, India; Siby A., CHRIST (Deemed to be University), Bengaluru, India</text>
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                <text>Kapoor A., Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, Delhi, New Delhi, India; Kapoor S., Department of Computer Science and Engineering, Akhilesh Das Gupta Institute of Technology and Management, Delhi, New Delhi, India; Mishra K., Department of Computer Science and Engineering, Akhilesh Das Gupta Institute of Technology and Management, Delhi, New Delhi, India; Jain H., Department of Computer Science and Engineering, Akhilesh Das Gupta Institute of Technology and Management, Delhi, New Delhi, India; Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Ghaziabad, India; Verma A., Department of Computer Science and Engineering, Akhilesh Das Gupta Institute of Technology and Management, Delhi, New Delhi, India</text>
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                <text>One out of three cancers now is skin cancer, a figure that has exploded in the previous several decades. Melanoma is the worst kind of skin cancer and occurs in 4% of cases. It is also the most aggressive type. The health and economic impact of skin cancer is substantial, especially given its rising incidence and fatality rates. However, with early detection and treatment, the 5-year survival rate for skin cancer patients is much improved. As a result, a lot of money has gone into studying the disease and developing methods for early diagnosis in the hopes of stopping the rising tide of cancer cases and deaths, particularly melanoma. In order to enhance non-invasive skin cancer diagnosis, this research examines a range of optical modalities that have been utilized in recent years. The suggested system uses image processing to identify, remove, and categorize lesions from dermoscopy images; this system will greatly aid in the detection of melanoma, a type of skin cancer. A median filter is employed for preprocessing. Using watershed and clever edge detector, it can segment objects. The BOF plus SURF method is employed for feature extraction. It employs SCA-RELM, which performs better than the other two conventional approaches, to train the model.  2024 IEEE.</text>
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                <text>Subramanian M., Sri Sairam Engineering College, Department of Electronics and Instrumentation Engineering, Chennai, India; Taley R.A., College of Engineering and Technology, Maharashtra, Akola, India; Sampath V., Sri Sankara Arts and Science College (Autonomous), Department of Biochemistry, Kanchipuram, India; Kartheeban K., Kalasalingam Academy of Research and Education, Department of CSE, Krishnankoil, Anandnagar, India; Palanimuthu K., Institute of Health Sciences, Pediatric and Child Health Nursing, Dambi Dollo, Ethiopia; Acharjee P.B., Christ University, Pune Lavasa Campus, Karnataka, Bengaluru, India</text>
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                <text>AI Driven Finite Element Analysis on Spur Gear Assembly to Enhance the Fatigue Life and Minimized the Contact Pressure*</text>
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                <text>AI-based; Contact pressure; Fatigue life; FEM analysis; Hysteresis stressstrain; Spur gear; Structural analysis</text>
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                <text>The major goal of the current research is to carry out mathematical and finite element analysis on spur gear assemblage to improve fatigue life as well as minimize contact pressure among contact teeth by modifying the face width of spur gear. AI automates FEA simulations and analyses, speeding up the design process. The investigation presented above was conducted using three separate 3d models of driving gear. The equivalent stress for the spur gear assembly of design-3 has decreased up to 13.45% in comparison to design-1, and the fatigue life has increased up to 81.59% at 600 N m, according to the results. Further AI models shall predict stress distribution, contact pressure, and other relevant factors in spur gear assemblies.  The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.</text>
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                <text>Lecture Notes in Networks and Systems, Vol-958 LNNS, pp. 535-544.</text>
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                <text>Dwivedi R., Department of Mechanical Engineering, Sagar Institute of Science and Technology, MP, Taipei City, Bhopal, India; Sharma S., Department Mechanical Department, AEG Consultancy Services Pvt. Ltd, Bhopal, India; Patil S.S., Dr. D.Y. Patil Vidyapeeths Centre for Online Learning, Dr. D.Y. Patil Vidyapeeth, Pune (Deemed to be University), Pune, India; Datere G., Dr. D.Y. Patil Vidyapeeths Centre for Online Learning, Dr. D.Y. Patil Vidyapeeth, Pune (Deemed to be University), Pune, India; Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India; Haque M., Dr. D.Y. Patil Vidyapeeths Centre for Online Learning, Dr. D.Y. Patil Vidyapeeth, Pune (Deemed to be University), Pune, India</text>
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                <text>This paper examines the potential of cloud computing to revolutionize the Indian agricultural sector, government operations, and rural connectivity. It highlights the benefits and challenges associated with cloud computing in agriculture and proposes a structured model to implement it effectively. Cloud computing allows farmers to access real-time information, make informed decisions, and improve access to markets. The paper examines the difficulties and advantages of cloud computing for the government in transitioning to a cloud-based version of itself for its operations. Additionally, it draws attention to specific areas related to the agricultural sector in India and certain applications offered by the government to enhance the consumer experience for stakeholders. The Government of India has demonstrated its commitment to developing technology-driven agriculture through e-NAM, Kisan Suvidha, and Agri-market initiatives. However, some challenges must be addressed to ensure the successful adoption of cloud computing in the agricultural sector. The proposed implementation model outlines the essential stages of the process, including the needs assessment, the selection of cloud providers, the automation of workflow, the modernization of applications, the implementation of security measures, and the implementation of continuous improvement. The model emphasizes the importance of training, feedback mechanisms, and collaboration. Furthermore, the paper underscores the need for a specific feedback system and grievance redress for agricultural cloud applications to enhance user experiences. To reap the full benefits of cloud computing in the Indian agricultural sector, a comprehensive strategy is necessary. This strategy necessitates technology adoption, awareness-raising, education, and stakeholder engagement. Utilizing cloud technologies, the Indian agricultural sector can realize sustainable growth, increased efficiency, and equitable development. This paper emphasizes the importance of cloud computing in transforming the Indian agrarian landscape.  The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.</text>
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                <text>Mitra S.; Agrawal A.; Joseph J.J.</text>
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                <text>Kumar S., Department of LPM, Bihar Animal Sciences University, Patna, India; Kumari S., Department of LPM, Bihar Animal Sciences University, Patna, India; Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Ghaziabad, India; Vats P., Department of CSE, Manipal University Jaipur, Rajasthan, Jaipur, India</text>
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                <text>The need to preserve the environment, lower pollution levels, expand the amount of green space, and encourage environmentally responsible behavior has grown in recent years, all of which will contribute to a more sustainable society. This study seeks to determine the probability that demographic variables of students in higher education in Delhi NCR will influence their desire to participate in environmental education. Binary Logistics Regression has been used on the data gathered from 302 respondents and the model has been found to have been a good one as shown by Omnibus Test. It is found that 'Gender' and 'Field of Study' are the two most significant variables, which have a higher probability impact on students' willingness to join environmental education. Specifically, female students vis-vis male students and students with engineering &amp;amp; and science background vis-vis other students have more chance of joining environmental education courses.  2024 IEEE.</text>
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                <text>Ganguly D.G., School of Business &amp;amp; Management, Christ (Deemed to Be University), Delhi NCR, India; Sinha D., School of Business &amp;amp; Management, Christ (Deemed to Be University), Delhi NCR, India; Das T., School of Business &amp;amp; Management, Christ (Deemed to Be University), Delhi NCR, India</text>
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                <text>When it comes to agricultural output, nation, India, ranks first in the world, and agriculture is unparalleled. The need to categorize and trade agricultural goods is paramount. Manual organization, which is tedious and laborious, is not a choice. When agricultural products are graded automatically, a lot of time is saved. The application of image processing techniques facilitates the examination and evaluation of the products. A technique for identifying diseased vegetables is the focus of this effort. Feature extraction, preprocessing, segmentation, and training the model are all heavily dependent on sequence. Among the preprocessing technologies at disposal are image segmentation and filtering. Using Kapur's thresholding based segmentation method, the image's sick areas can be located during the segmentation process. Use k-means clustering for feature extraction to identify vegetable plant diseases. The training of an MDTW-LSTM model relies heavily on feature selection. In terms of performance, the proposed method surpasses two cutting-edge algorithms: LSTM and DTW. The results showed an accuracy of 97.35 percent, indicating a remarkable improvement.  2024 IEEE.</text>
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                <text>Chaturvedi A., Gla University, Department of Electronics and Communication Engineering, Uttar Pradesh, Mathura, India; Nadgaundi S.K., Bharati Vidyapeeth College of Engineering, Instrumentation Engineering Department, Navi Mumbai, India; Raja M.K., Sri Eshwar College of Engineering, Department of Computer Science and Engineering, Coimbatore, India; Dubey V.R., Jai Narain College of Technology, Department of Computer Science and Engineering, Lambakheda, Bhopal, India; Singhal A., Christ (Deemed to Be University), School of Sciences, Delhi-NCR, Uttar Pradesh, Ghaziabad, India; Gupta V., Christ (Deemed to Be University), School of Sciences, Delhi-NCR, Uttar Pradesh, Ghaziabad, India</text>
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                <text>character recognition; Convolutional Neural Network (CNN); Handwritten Telugu character recognition</text>
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                <text>The Telugu language is the most prominent representative within the Dravidian language family, predominantly spoken in the southeastern regions of India. Handwritten character recognition in Telugu has significant applications across diverse fields such as healthcare, administration, education, and paleography. Despite its importance, the Telugu script differs significantly from English, presenting distinct challenges in recognizing characters due to its complexity and diverse character shapes. This study explores the application of machine learning, particularly delving into deep learning techniques, to improve the accuracy of Telugu character recognition. This paper proposes a model to recognize handwritten Telugu characters using Convolutional Neural Network (CNN). The proposed study demonstrates the accuracy in identifying diverse handwritten Telugu characters. We assess the system's performance against conventional and machine learning methodologies and preprocess an extensive dataset to guarantee strong model training. The proposed model excels in accurately predicting visually similar but distinct characters, achieving an impressive accuracy rate of 96.96%.   2024 IEEE.</text>
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                <text>ISBN: 979-835038295-2</text>
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                <text>Karapu B.M., Christ Deemed to Be University, Department of Computer Science &amp;amp; Engineering, Bengaluru, India; Anoop G.L., Christ Deemed to Be University, Department of Computer Science &amp;amp; Engineering, Bengaluru, India; Elappila M., Christ Deemed to Be University, Department of Computer Science &amp;amp; Engineering, Bengaluru, India; Mithun B.N., Christ Deemed to Be University, Department of Computer Science &amp;amp; Engineering, Bengaluru, India</text>
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                <text>Effective Techniques Non-linear Dynamic Model Calibration using CNN</text>
              </elementText>
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            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="167981">
                <text>Calibration; Convolutional Neural Network; Mapping; Nonlinear Dynamic</text>
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                <text>This paper proposes an efficient method to estimate nonlinear dynamic models using convolutional neural networks (CNNs). The proposed method combines the power of statistical optimization and machine learning to obtain more accurate and efficient estimates of complex models by training CNNs to recognize maps featuring input models and between results, thereby reducing the computational cost of measurements and then using the trained CNN to generate surrogate models -The method can determine accuracy for a range of exposed cases in various nonlinear dynamic models, including differential equation model of chemical reactor and stochastic model of biological systems The results show that the proposed methods are effective for measuring these models, if at most with such accuracy and reducing the computational cost in terms of both frequency and magnitude, the proposed method represents a promising method for estimating nonlinear dynamic models, offering significant advantages in terms of accuracy, efficiency and in scalability   2024 IEEE.</text>
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              <elementText elementTextId="167983">
                <text>Devanathan M.; Prasannan D.; Singh P.; Joshua O.; Adity Pai H.; Jakhar P.</text>
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                <text>Proceedings - International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024, pp. 832-836.</text>
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              <elementText elementTextId="167985">
                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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            <elementTextContainer>
              <elementText elementTextId="167988">
                <text>Restricted Access</text>
              </elementText>
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                <text>ISBN: 979-835038352-2</text>
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            <name>Format</name>
            <description>The file format, physical medium, or dimensions of the resource</description>
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              <elementText elementTextId="167990">
                <text>Online</text>
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            <description>A language of the resource</description>
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              <elementText elementTextId="167991">
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            <description>The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant</description>
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                <text>Devanathan M., Christ University, Karnataka, Bengaluru, India; Prasannan D., Karpagam College of Engineering, Department of Civil Engineering, Coimbatore, India; Singh P., Sgt University, Faculty of Science, Department of Physics, Gurugram, India; Joshua O., Afe Babalola University Ado-Ekiti, Civil and Environmental Engineering, Ekiti, Nigeria; Adity Pai H., Graphic Era Deemed to Be University, Department of Computer Science &amp;amp; Engineering, Uttarakhand, Dehradun, India; Jakhar P., Indian Institute of Technology Jodhpur, Rajasthan, Jodhpur, India</text>
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            <description>A name given to the resource</description>
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                <text>ATRSI: Automatic Tag Recommendation for Videos Encompassing Semantic Intelligence</text>
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          <element elementId="49">
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              <elementText elementTextId="167995">
                <text>Bag of Words model; CoSim Rank; Lin similarity; RNN</text>
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                <text>There is a requirement for an automatic semantic-oriented framework for Web video tagging in the epoch of Web 3.0, as Web 3.0 is much denser, intelligent, but more cohesive compared to Web 2.0. This paper proposes the ATRSI framework which is the Automatic Tag Recommender framework which encompasses the semantic-oriented Artificial Intelligence that outgrows the dataset by making the use of informative terms using TF-IDF and bag of words model to build the intermediate semantic network which is further organized using an Lin similarity measure and is optimized using red deer optimization by encompassing the entities from the World Wide Web to focused crawling. RNN is a classifier that is used for the classification of the dataset, it is a strong deep-learning classifier. Semantic-oriented Intelligence is achieved using the CoSim rank and Morisita's overlap index. The bag of lightweight graphs is obtained from the semantic network which is an intermediate knowledge representation mechanism that is further embedded in the intrinsic model. A semantically consistent system for video recommendation, ATRSI outperforms the other baseline models in terms of average accuracy, average precision and F-measure for a variety of recommendations.   2024 IEEE.</text>
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                <text>Deepak G.; Iyer K.; Shukla S.</text>
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                <text>Proceedings of ICWITE 2024: IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, pp. 594-599.</text>
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              <elementText elementTextId="167999">
                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>&lt;a href="https://doi.org/10.1109/ICWITE59797.2024.10503160" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICWITE59797.2024.10503160&lt;/a&gt;
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            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="168002">
                <text>Restricted Access</text>
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              <elementText elementTextId="168003">
                <text>ISBN: 979-835038328-7</text>
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            <name>Format</name>
            <description>The file format, physical medium, or dimensions of the resource</description>
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              <elementText elementTextId="168004">
                <text>Online</text>
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            <description>A language of the resource</description>
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                <text>English</text>
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                <text>Deepak G., Manipal Institute of Technology Bengaluru, Manipal Academy of Higer Education, Department of Cse, Manipal, India; Iyer K., Manipal Institute of Technology Bengaluru, Manipal Academy of Higer Education, Department of Cse, Manipal, India; Shukla S., School of Engineering, Christ (Deemed to Be University), Department of Cse, Kengeri Campus, Bengaluru, India</text>
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