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                <text>DTDO: Driving Training Development Optimization enabled deep learning approach for brain tumour classification using MRI</text>
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                <text>A brain tumour; Child Drawing Development Optimization (CDDO); Driving Training Development Optimization (DTDO); Magnetic Resonance Imaging (MRI)</text>
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                <text>A brain tumour is an abnormal mass of tissue. Brain tumours vary in size, from tiny to large. Moreover, they display variations in location, shape, and size, which add complexity to their detection. The accurate delineation of tumour regions poses a challenge due to their irregular boundaries. In this research, these issues are overcome by introducing the DTDO-ZFNet for detection of brain tumour. The input Magnetic Resonance Imaging (MRI) image is fed to the pre-processing stage. Tumour areas are segmented by utilizing SegNet in which the factors of SegNet are biased using DTDO. The image augmentation is carried out using eminent techniques, such as geometric transformation and colour space transformation. Here, features such as GIST descriptor, PCA-NGIST, statistical feature and Haralick features, SLBT feature, and CNN features are extricated. Finally, the categorization of the tumour is accomplished based on ZFNet, which is trained by utilizing DTDO. The devised DTDO is a consolidation of DTBO and CDDO. The comparison of proposed DTDO-ZFNet with the existing methods, which results in highest accuracy of 0.944, a positive predictive value (PPV) of 0.936, a true positive rate (TPR) of 0.939, a negative predictive value (NPV) of 0.937, and a minimal false-negative rate (FNR) of 0.061%.  2024 Informa UK Limited, trading as Taylor &amp;amp; Francis Group.</text>
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                <text>Prasad V.; Jeba Jingle I.D.; Sriramakrishnan G.V.</text>
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                <text>Network: Computation in Neural Systems, Vol-35, No. 4, pp. 520-561.</text>
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                <text>&lt;a href="https://doi.org/10.1080/0954898X.2024.2351159" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1080/0954898X.2024.2351159&lt;/a&gt;
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                <text>ISSN: 0954898X; PubMed ID: 38801074</text>
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                <text>Prasad V., Department of Computer Science &amp;amp; Engineering, Lendi Institute of Engineering &amp;amp; Technology, Jonnada, India; Jeba Jingle I.D., Department of Computer Science &amp;amp; Engineering, Christ (Deemed to be University), Bangalore, India; Sriramakrishnan G.V., Computer Science and Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, India</text>
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                <text>Autism spectrum disorder detection using brain MRI image enabled deep learning with hybrid sewing training optimization</text>
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                <text>Autism spectrum disorder; Hybrid sewing training optimization; Magnetic resonance imaging; Pivotal region extraction; Sewing training-based optimization</text>
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                <text>Autism spectrum disorder (ASD) is brain enabled disorder representing behaviors in a repetitive manner and social deficits. In this paper, ASD is diagnosed using brain magnetic resonance imaging (MRI) enabled deep learning with a hybrid optimization algorithm. Also, the hybrid optimization algorithm utilized is hybrid sewing training optimization (HSTO) which trains ZFNet for ASD detection. Pre-processing of the MRI image is done by Wiener filter and the filtered image is fed for region of interest extraction. Moreover, pivotal region extraction is carried out by the proposed HSTO, which is finally allowed for ASD detection by ZFNet. The proposed HSTO is formed by combining sewing training-based optimization and hybrid leader-based optimization. Furthermore, the performance of HSTO_ZFNet is found by five performance metrics of accuracy with 95.7%, true negative rate with 92.6%, true positive rate with 93.7%, false negative rate with 68.7%, and false positive rate with75.9%.  2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.</text>
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                <text>Prasad V.; Sriramakrishnan G.V.; Diana Jeba Jingle I.</text>
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                <text>Signal, Image and Video Processing, Vol-17, No. 8, pp. 4001-4008.</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/s11760-023-02630-y" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/s11760-023-02630-y&lt;/a&gt;
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                <text>ISSN: 18631703</text>
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                <text>Prasad V., Department of Computer Science and Engineering, Lendi Institute of Engineering and Technology, Jonnada Village, Andhra Pradesh, Vizianagaram District, 535005, India; Sriramakrishnan G.V., Department of CSE, Mohan Babu University, Sree Sainath Nagar, Andhra Pradesh, Tirupati, 517102, India; Diana Jeba Jingle I., Department of CSE, School of Engineering and Technology, Christ University, Karnataka, Bengaluru, India</text>
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                <text>Even Numbered Saturdays are More Joyful for Bank Employees in India - A Critical Analysis </text>
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                <text>Journal of Exclusive Management Sciences, Vol. 5, Issue 4, ISSN No. 2277-5684</text>
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                <text>Prasad, Arcot Purna</text>
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                <text>Prasad, C. Durga; Pradeep Kumar, G.S.; Shashi Kumar, M.E.; Krupa, R.; Rajesh, M.N.; Veeresha, G.</text>
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                <text>Role of Additive Manufacturing and Thermal Spray Processed Materials in Electric Vehicle (EV) and Hybrid Electric Vehicle (HEV) Applications</text>
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                <text>01-01-2026</text>
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                <text>Lightweight Materials for Electric Vehicles: Sustainable Materials, Production Process, and Modeling Techniques;pp.97-111</text>
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                <text>&lt;a href="https://doi.org/10.1201/9781003596097-6" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1201/9781003596097-6&lt;/a&gt; &lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/pages/publications/105035825272?origin=resultslist" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/pages/publications/105035825272?origin=resultslist&lt;/a&gt;</text>
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                <text>Prasad C.D., Department of Mechanical Engineering, RV Institute of Technology and Management (RVITM), Bengaluru, India; Pradeep Kumar G.S., Department of Mechanical Engineering, Christ University, Karnataka, Bengaluru, India; Shashi Kumar M.E., Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Karnataka, Bengaluru, India; Krupa R., Department of Mechanical Engineering, Nitte Meenakshi Institute of Technology, Karnataka, Bengaluru, India; Rajesh M.N., Department of Mechanical Engineering, GITAM (deemed to be) University, Karnataka, Bengaluru, India; Veeresha G., Department of Mechanical Engineering, New Horizon College of Engineering, Karnataka, Bengaluru, India</text>
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                <text>Additive manufacturing (AM) significantly contributes to the development of electric vehicles (EVs) and hybrid electric vehicles (HEVs), providing lightweight, complex, and customized components. This study explores AMs role in advancing EV and HEV technology, with a special focus on integrating thermal spray coatings (TSCs) to enhance component performance. By employing TSCs in AM-fabricated components, manufacturers can improve surface characteristics, wear resistance, and corrosion protection critical factors for long-lasting EV/HEV systems. The synergy between AM and TSC enhances key parts such as battery enclosures, thermal management systems, and structural frameworks by optimizing their thermal insulation, durability, and energy efficiency. Additionally, AM enables efficient material use and lightweighting, which reduces vehicle weight and enhances energy conservation, addressing industry needs for sustainable solutions. This chapter reviews the current applications and future potential of TSC in AM components, highlighting its role in meeting the rigorous demands of the automotive sector. Findings suggest that combining AM and TSC opens pathways for advanced, sustainable EV and HEV designs, aligning with the global shift toward cleaner energy and resource-efficient manufacturing.  2026 selection and editorial matter, R. Suresh, C. Durga Prasad, Satish Kumar, K.N. Bharath, and Ajith G. Joshi; individual chapters, the contributors.</text>
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                <text>CRC Press</text>
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                <text>ISBN: 978-100359609-7; 978-103297505-4; 978-103297915-1;</text>
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                <text>Prasad D.D., Department of ECE, Vishnu Institute of Technology, Andhra Pradesh, Bhimavaram, India; J V.A., Department of Computer Science, CHRIST University, Karnataka, Bangalore, India; Rao K.S., Department of Computer Science and Engineering, Faculty of Science and Technology (IcfaiTech), ICFAI Foundation for Higher Education, Hyderabad, India; Kumar D.V., Freshman Engineering Department, Lakireddy Bali Reddy College of Engineering, Andhra Pradesh, Mylavaram, India; Kumar J.D., Department of ECE, Vishnu Institute of Technology, Andhra Pradesh, Bhimavaram, India; Reddy K.V.S.R., Department of ECE, Vishnu Institute of Technology, Andhra Pradesh, Bhimavaram, India</text>
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                <text>The digital revolution has changed most aspects of modern life. Nowhere has the change been more fundamental than in the field of microscopy. Researchers who use the microscope in their investigations have been among the pioneers who applied digital processing techniques to images. Vision is most powerful of the five senses of human being. Digitized visual information provides high impact on the subject. Digital image processing is concerned with the extraction of useful information from images. Visual information from microscopic images of microorganisms is analyzed regularly. This has resulted in a need to understand and implement digital processing on microscopic images. The purpose of this thesis is to bring new digital image processing techniques for the noise removal of microscopic image of microorganisms. The digitized image processing includes image representation; improving image quality by removing noise; and enhancing the quality of microscopic images.</text>
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                <text>Prasad S., Department of Statistics and Data Science, CHRIST University, Karnataka, Bengaluru, India; Senthil Vadivu M., Department of Statistics and Data Science, CHRIST University, Karnataka, Bengaluru, India</text>
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                <text>Virtual reality (VR) is an innovative technology with various applications in fields such as simulation, gaming, sports, and entertainment. VR technology has extended its reach into the medical industry, leveraging computer-generated information and visuals to simulate real-world sensory experiences. Augmented reality (AR) is an expertise that covers computer-generated virtual objects onto the real world when it is seen through mobile phones, tablets, or AR glasses. VR and AR have started popular in 2016. Autism spectrum disorder (ASD) is a many-sided neurodevelopmental condition and is caused because of difficulties in social interaction, communication, and iterative behavior. ASD is increasingly predominant globally, touching roughly 2 in 100. If there is timely diagnosis and intervention, then there will be fewer problems. In recent years, AR and VR technologies have become one best option and novel means in the analysis and treatment of autism. Controlled environments that can imitate real-life social situations, contribution clinician's critical insights into the perceptions, and interactions of individuals with ASD in relation to their surroundings will be provided by AR and VR. These technologies assist customized intermediations, rendering them extremely adaptable to the distinct requirements of each individual.  2026 Scrivener Publishing LLC.</text>
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                <text>International Journal in Management and Social Science, Vol. 4, Issue 11, pp. 24-34, ISSN No. 2321-1784. </text>
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                <text>Prasad. Arcot Purna</text>
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                <text>The effect of hopeful lyrics on levels of hopelessness among college students</text>
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                <text>Hopeful; Hopelessness; Intervention; Listening; Lyrics; Music</text>
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                <text>Hopelessness is a product of negative future expectations, negative feelings toward the future, and feeling a lack of control over future improvements. College students are seen to experience hopelessness. This study aimed to reduce levels of hopelessness in college students through an intervention that involved listening to songs having hopeful lyrics. The sample consisted of college students (N = 66), who were randomly assigned to three groups, namely the lyrics-music group, music-only group, and the control group (no intervention). The Becks Hopelessness Scale was used to measure their levels of hopelessness before the intervention and at the end of four weeks. The lyrics-music group and the music group participants were exposed to songs and instrumental tracks, respectively, twice a week, for four weeks. The Wilcoxon Signed-Rank test for related samples was used to analyze the effect of the intervention on levels of hopelessness. The KruskalWallis test was used to analyze the differences across the three groups. Results indicated that the lyrics-music group had a significant decrease in levels of hopelessness after the intervention. However, the music group and the control group showed no significant decrease. There was a significant difference between the three groups with regard to the difference score obtained from pre to post intervention. Thus, the evidence suggests that hopeful lyrics do have an effect on hopelessness and can be seen as differing from the functions of music alone.  2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.</text>
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                <text>Prasadam S.E.; Thomas E.</text>
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                <text>Current Psychology, Vol-42, No. 25, pp. 21648-21658.</text>
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                <text>2023-01-01</text>
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                <text>&lt;a href="https://doi.org/10.1007/s12144-022-02934-1" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/s12144-022-02934-1&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131316386&amp;amp;doi=10.1007%2Fs12144-022-02934-1&amp;amp;partnerID=40&amp;amp;md5=1d8874ab2a4f7c51c1d0818399d75b2f" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131316386&amp;amp;doi=10.1007%2fs12144-022-02934-1&amp;amp;partnerID=40&amp;amp;md5=1d8874ab2a4f7c51c1d0818399d75b2f&lt;/a&gt;</text>
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                <text>ISSN: 10461310</text>
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                <text>Prasadam S.E., Department of Psychology, CHRIST (Deemed to be University), Hosur Road, Karnataka, Bengaluru, 560 029, India; Thomas E., Department of Psychology, CHRIST (Deemed to be University), Hosur Road, Karnataka, Bengaluru, 560 029, India</text>
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