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                <text>2D Photonic Crystal Nano Biosensor with IoT Intelligence</text>
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                <text>Bio-analyte; Internet of things; Nano biosensor; Photonic crystal; Photonic sensor</text>
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                <text>Optical biosensors based on photonic crystals (PCs) offer interesting possibilities for the analysis and identification of bioanalytes. PC is a periodically varying artificial dielectric material that determines the propagation of modes present in the structure. Within dielectric media, there are modes that are selected based on structural perturbations. Changes in the refractive index of biological analytes are used to identify biological samples and are therefore used as sensing media in many applications. Because these PC sensors are designed in the nano range, they have excellent selectivity and sensitivity. The PC is ultra-compact and only small amounts of analyte are required for bioanalyte detection. Quantification of bioanalytes and biochemicals is one of the greatest challenges in the medical and diagnostic fields. However, these electronic devices cannot be directly connected to biological analytes, so the most difficult task is to extract the analyte information and convert it into electronic signals. Optical biosensors offer an attractive way to interrogate the content of bioanalytes because they directly convert biological events into electrical signals. It is also called a self-contained integrated physical medium because of its many applications such as food industry, drug delivery, point-of-care diagnostic sensing devices, and environmental monitoring. Based on the analyte placed on the PC sensor, resonant wavelengths are observed and the measurements are stored in a database. Diseases are identified based on the current users cognitive value, and data is transmitted and monitored over the Internet of Things.  2024 Scrivener Publishing LLC.</text>
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                <text>Balaji V.R.; Jesuwanth Sugesh R.G.; Sreevani N.R.G.; Dhanabalan S.S.; Sridarshini T.; Hegde G.</text>
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                <text>Internet of Things in Bioelectronics: Emerging Technologies and Applications, pp. 107-124.</text>
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                <text>&lt;a href="https://doi.org/10.1002/9781394241903.ch6" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1002/9781394241903.ch6&lt;/a&gt;
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                <text>Balaji V.R., Centre for Healthcare Advancement, Innovation and Research, Vellore Institute of Technology, Tamil Nadu, Chennai, India; Jesuwanth Sugesh R.G., Department of Electronics and Communication Engineering, CHRIST University, Karnataka, Bangalore, India; Sreevani N.R.G., Department of Computer Science and Engineering, S.A. Engineering College, Tamil Nadu, India; Dhanabalan S.S., Functional Materials and Microsystems Research Group, School of Engineering, RMIT University, Melbourne, VIC, Australia; Sridarshini T., Department of Electronics and Communication Engineering, College of Engineering Guindy Campus, Anna University, Tamil Nadu, Chennai, India; Hegde G., Centre for Nanoscience and Engineering, Indian Institute of Science, Karnataka, Bengaluru, India</text>
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                <text>Jananisri, Arumugam; Geofferina, Irudhayaraj Peatrise; Vishalini, Ganesh; Sridhar, Subramaniam; Vimalraj, Pachaiyappan; Suruthi, Gunna Suresh Babu; Sujitha, Senthil; Balamuralikrishnan, Balasubramanian; Arun, Meyyazhagan; Anand, Arumugam Vijaya</text>
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                <text>2D Pnictogens for Biosensing and Imaging Applications</text>
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                <text>Two-Dimensional Nanomaterials for Biosensing and Imaging Applications;pp.375-413</text>
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                <text>&lt;a href="https://doi.org/10.1002/9781394200375.ch17" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1002/9781394200375.ch17&lt;/a&gt; &lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/pages/publications/105010804177?origin=resultslist" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/pages/publications/105010804177?origin=resultslist&lt;/a&gt;</text>
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                <text>Jananisri A., Department of Human Genetics and Molecular Biology, Bharathiar University, Tamil Nadu, Coimbatore, India; Geofferina I.P., Department of Human Genetics and Molecular Biology, Bharathiar University, Tamil Nadu, Coimbatore, India; Vishalini G., Department of Human Genetics and Molecular Biology, Bharathiar University, Tamil Nadu, Coimbatore, India; Sridhar S., Department of Biochemistry, Bharathiasan University, Tamil Nadu, Tiruchirappalli, India; Vimalraj P., Department of Human Genetics and Molecular Biology, Bharathiar University, Tamil Nadu, Coimbatore, India; Suruthi G.S.B., Department of Human Genetics and Molecular Biology, Bharathiar University, Tamil Nadu, Coimbatore, India; Sujitha S., Department of Human Genetics and Molecular Biology, Bharathiar University, Tamil Nadu, Coimbatore, India; Balamuralikrishnan B., Department of Food Science and Biotechnology, Sejong University, Seoul, South Korea; Arun M., Department of Life Sciences, CHRIST Deemed to be University, Bengaluru, India; Anand A.V., Department of Human Genetics and Molecular Biology, Bharathiar University, Tamil Nadu, Coimbatore, India</text>
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                <text>2D pnictogens are considered the most promising nano agents among the family of 2D materials. The pnictogens contain phosphorus, arsenene, antimonene, and bismuthine. It has inherent tunable midrange band gaps and unparalleled mobility. It is popular due to its efficient photothermal behavior, loading capability the drug, high charge carrier mobility, in-plane anisotropic properties and less toxicity. The pnictogens nanosheet is obtained by using the Shear exfoliation method. The exfoliation is feasible when there is the existence of weak van der Waals forces between individual layers and during covalent interactions between layers, these elements will adopt the rhombohedral structure. Raman spectroscopy acts as the molecular fingerprints in identifying the number of layers and temperature dependence peak shift of pnictogen substances. The biosensor device can be made by using a layer-by-layer method for each pnictogen, cross-linking agent, and enzyme. Pnictogens can be employed in the application of photothermal therapy (PTT) to overcome the hinder which is because of the lack of efficient cell perception and cell toxicity. It can be used in biomedical applications to make the candidates absorb the ultraviolet and infrared lights as preferable. It can also be used in Biosensors, diagnosis, therapy and in anti-bacterial, anti-inflammation, anti-tumor, neurodegenerative treatment, and tissue repair. Pnictogen has many advantages in the application of biosensing and imaging techniques, but the main disadvantage of pnictogens are low thermal and chemical stability and degradation in ambient conditions. Therefore, this chapter focuses on different characteristics and various applications of pnictogen.  2025 Scrivener Publishing LLC.</text>
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                <text>2D materials; antimonene; arsenene; biosensors; bismuthene; imaging technique; phosphorene; Pnictogen</text>
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                <text>ISBN: 978-139420037-5; 978-139419992-1;</text>
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                <text>Restricted Access; Hardcopy may be available in the library</text>
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                <text>3-Sequent achromatic sum of graphs</text>
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                <text>3-Sequent vertex coloring; 3-sequent vertex coloring number; 3s achromatic number; 3s achromatic sum</text>
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                <text>Three vertices x,y,z in a graph G are said to be 3-sequent if xy and yz are adjacent edges in G. A 3-sequent coloring (3s coloring) is a function ?: V (G) ?{1, 2,...,k} such that if x,y and z are 3-sequent vertices, then either ?(x) = ?(y) or ?(y) = ?(z) (or both). The 3-sequent achromatic number of a graph G, denoted ?3s(G), equals the maximum number of colors that can be used in a coloring of the vertices' of G such that if xy and yz are any two sequent edges in G, then either x or z is colored the same as y. The 3-sequent achromatic sum of a graph G, denoted a'3s(G), is the greatest sum of colors among all proper 3s-coloring that requires ?3s(G) colors. This research initiates the study of 3-sequent achromatic sum and finds the exact values of this parameter for some known graphs. Furthermore, we calculate the a'3s(G) of corona product, Cartesian product of the graphs and some important results have been proved and a comparative study is carried out.  2021 World Scientific Publishing Company.</text>
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                <text>Dominic C.; Devassia J.V.</text>
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                <text>Discrete Mathematics, Algorithms and Applications, Vol-13, No. 4</text>
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                <text>&lt;a href="https://doi.org/10.1142/S1793830921500397" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1142/S1793830921500397&lt;/a&gt;
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                <text>ISSN: 17938309</text>
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                <text>Dominic C., Department of Mathematics, Christ (Deemed to Be University), Bangalore, Karnataka, 560029, India; Devassia J.V., Tecnologico de Monterrey, School of Engineering and Sciences, Mexico, Mexico</text>
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                <text>With the pandemic worldwide due to COVID-19, several detections and diagnostic methods have been in place. One of the standard modes of detection is computed tomography imaging. With the availability of computing resources and powerful GPUs, the analyses of extensive image data have been possible. Our proposed work initially deals with the classification of CT images as normal and infected images, and later, from the infected data, the images are classified based on their severity. The proposed work uses a 3D convolution neural network model to extract all the relevant features from the CT scan images. The results are also compared with the existing state-of-the-art algorithms. The proposed work is evaluated in accuracy, precision, recall, kappa value, and Intersection over Union. The model achieved an overall accuracy of 94.234% and a kappa value of 0.894.  2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>Leena Sri R., Department of CSE, Thiagarajar College of Engineering, Madurai, India; Vetriveeran D., Department of CSE, CHRIST (Deemed to be University), Bangalore, India; Sambandam R.K., Department of CSE, CHRIST (Deemed to be University), Bangalore, India</text>
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                <text>3D face recognition based on symbolic FDA using SVM classifier with similarity and dissimilarity distance measure</text>
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                <text>Human face images are the basis not only for person recognition, but for also identifying other attributes like gender, age, ethnicity, and emotional states of a person. Therefore, face is an important biometric identifier in the law enforcement and human-computer interaction (HCI) systems. The 3D human face recognition is emerging as a significant biometric technology. Research interest into 3D face recognition has increased during recent years due to availability of improved 3D acquisition devices and processing algorithms. A 3D face image is represented by 3D meshes or range images which contain depth information. In this paper, the objective is to propose a new 3D face recognition method based on radon transform and symbolic factorial discriminant analysis using KNN and SVM classifier with similarity and dissimilarity measures, which are applied on 3D facial range images. The experimentation is done using three publicly available databases, namely, Bhosphorus, Texas and CASIA 3D face database. The experimental results demonstrate the effectiveness of the proposed method.  2017 World Scientific Publishing Company.</text>
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                <text>&lt;a href="https://doi.org/10.1142/S0218001417560067" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1142/S0218001417560067&lt;/a&gt;
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                <text>ISSN: 2180014; CODEN: IJPIE</text>
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                <text>Hiremath M., Department of Computer Science, Christ University, Bangalore, Karnataka, 560029, India; Hiremath P.S., Department of Computer Science (MCA), KLE Technological University, BVBCET, Hubli, Karnataka, 580031, India</text>
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                <text>International Journal of Pattern Recognition and Artificial Intelligence, 31, Issue 4, ISSN: 1793-6381.</text>
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                <text>&lt;a href="https://www.worldscientific.com/doi/10.1142/S0218001417560067" target="_blank" title="3D face recognition based on symobolic FDA using SVM classifier with similarity and dissimilarity distance measure" rel="noreferrer noopener"&gt;https://www.worldscientific.com/doi/10.1142/S0218001417560067&lt;/a&gt;</text>
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                <text>In the recent literature, 3D face reconstruction received wide interest and has become one of the significant areas of research. 3D face reconstruction provides in depth details on geometrics, texture and color of the face, which are utilized in different applications. It supports a multitude of applications, ranging from face recognition and surveillance to medical imaging, gaming, animation, and virtual reality. This paper attempts to consolidate the research works that have happened in the history of 3D face reconstruction. Also, we try to classify the existing methods based on the input for the process. The databases used in the recent works are discussed and the performance evaluation of methods on different databases is analyzed. The challenges addressed in recent studies are mainly focused on the faster reconstruction of 3D Images, improved accuracy of reconstructed images, human pose identification, image reproduction with higher resolution. Researchers have also tried to address occlusion related problems. Passive methods, used by different researchers are analyzed and their effects on different parameters are discussed in this work. Finally, possible future areas for improvement in terms of reconstructions are presented for the benefit of researchers.  BEIESP.</text>
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                <text>John S.; Danti A.</text>
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                <text>International Journal of Recent Technology and Engineering, Vol-8, No. 2, pp. 4354-4364.</text>
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&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071870147&amp;amp;doi=10.35940%2Fijrte.B3152.078219&amp;amp;partnerID=40&amp;amp;md5=3d70f05bcca1d3c38767f99eb4cf2fe3" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071870147&amp;amp;doi=10.35940%2fijrte.B3152.078219&amp;amp;partnerID=40&amp;amp;md5=3d70f05bcca1d3c38767f99eb4cf2fe3&lt;/a&gt;</text>
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                <text>The representation of facial features in three-dimensional space plays a pivotal role in various applications such as facial recognition, virtual reality, and digital entertainment. However, achieving high-fidelity reconstructions from two-dimensional facial images remains a challenging task, particularly in preserving fine texture details. This research addresses this problem by proposing a novel approach that leverages a combination of advanced techniques, including Resnet, Flame model, Bi-FPN, and a differential render architecture. The primary objective of this study is to enhance texture details in reconstructed 3D facial images. The integration of Bi-FPN (Bi-directional Feature Pyramid Network) enhances feature extraction and fusion across multiple scales, facilitating the preservation of texture details across different regions of the face. The objective is to accurately represent facial features from 2D images in three-dimensional space. By combining these methods, the proposed framework achieves significant improvements in preserving fine texture details and overall facial structure. Experimental results demonstrate the effectiveness of the approach, suggesting its potential for various applications such as virtual try-on and facial animation.   2024 The Authors.</text>
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                <text>Journal of Machine and Computing, Vol-4, No. 2, pp. 392-399.</text>
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                <text>John S., Department of Computer Science and Engineering, Christ University, Bangalore, India; Danti A., Department of Computer Science and Engineering, Christ University, Bangalore, India</text>
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                <text>Purpose: The purpose of this paper is to discuss the 3D micropolar hybrid (Ag-CuO/H2O) nanofluid past rapid moving surface, where porous medium has been considered. Design/methodology/approach: The model of problem was represented by highly partial differential equations which were deduced by using suitable approximations (boundary layer). Then, the governing model was converted into five combined ordinary differential equations applying proper similarity transformations. Therefore, the eminent iterative RungeKuttaFehlberg method (RKF45) has been applied to solve the resulting equations. Findings: Higher values of vortex viscosity, spin gradient viscosity and micro-inertia density parameters are reduced in horizontal direction, whereas opposite behaviour is noticed for vertical direction. Originality/value: The work has not been done in the area of hybrid micropolar nanofluid. Hence, this article culminates to probe how to improve the thermal conduction and fluid flow in 3D boundary layer flow of micropolar mixture of nanoparticles driven by rapidly moving plate with convective boundary condition.  2020, Emerald Publishing Limited.</text>
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                <text>Multidiscipline Modeling in Materials and Structures, Vol-16, No. 6, pp. 1691-1707.</text>
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                <text>ISSN: 15736105</text>
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                <text>Manjunatha S., Department of Mathematics, Christ University, Bangalore, India; Ammani Kuttan B., Department of Mathematics, Christ University, Bangalore, India; Ramesh G.K., Department of Mathematics, KLE'S J T College, Gadag, India; Gireesha B.J., Department of P.G. Studies and Research in Mathematics, Kuvempu University, Shimoga, India; Aly E.H., Department of Mathematics, Faculty of Science, University of Jeddah, Jeddah, Saudi Arabia, Department of Mathematics, Faculty of Education, Ain Shams University, Cairo, Egypt</text>
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                <text>3D Modelling and Rendering Using Autodesk 3ds Max</text>
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                <text>Blended Education System; Effective Education System; Outcome Based Learning; Sector Skill Development.</text>
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                <text>This is outlined how to create a 3D custom kitchen design, including how to set up the sources, details, work with managing various modifiers like edit poly, slice, mesh select, turbo smooth, lattice, bend, shell modifier, so to provide the kitchen an authentic appearance. The method materials are fitted to the model output, together with illuminating the environment leveraging Arnold lights that are intended to be utilized with this renderer only. It has features that are optimised for rendering with Arnold. Procedures and methods regarding rendering are indeed specified. The final rendering was made out of several drawings. Our report's intention is to develop a kitchen design that enriches models with materials and ample shapes from standard extended primitive along with the mostly utilization of pro-boolean. Finally, a material editor was used to improve the model. target illumination, too.  2023 IEEE.</text>
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                <text>Dangi P.; Jain A.; Samanta D.; Dutta S.; Bhattacharya A.</text>
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                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>Dangi P., Christ University, Dept. of Computer Science, Karnataka, Bangalore, India; Jain A., Christ University, Dept. of Computer Science, Karnataka, Bangalore, India; Samanta D., Rit Kosovo (A.U.K), Dept. of Computing and Information Technologies, Kosovo, Europe, Prishtina, 10000, Serbia, Rochester Institute of Technology, Rit Global Campus, Kosovo, Europe, Prishtina, 10000, Serbia; Dutta S., Institute of Engineering &amp;amp; Management, Dept. of Computer Science &amp;amp; Applications, Kolkata, India; Bhattacharya A., Institute of Engineering &amp;amp; Management, Dept. of Computer Science &amp;amp; Applications, Kolkata, India</text>
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                <text>Patent Number: 202241048127, Applicant: Ramesh Chandra Poonia.&#13;
The effect of technological advancements has made an impact on the way medical applications are used in the treatment of fracture. The possibility of medical application and technology has immensely grown, and 3D printing and its applications in medical sciences are much explored and found to be acceptable and applicable financially and technically during recent years. While 3D technology is used in diagnosis, 3D printing technology is useful for making treatment and rehabilitation tools.</text>
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                <text>Sharma D., Department of General Surgery, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai, 602105, India; Sharma N., Department of Biochemistry, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai, 602105, India; Umesh M., Department of Life Sciences, Christ University, Karnataka, Bangalore, 560029, India; Chakraborty P., School of Allied Healthcare and Sciences, JAIN (Deemed to be University), Whitefield, Karnataka, Bangalore, 560066, India; Kumar V., Biomaterials and Tissue Engineering Laboratory (BITE LAB), Department of Community Medicine, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai, 602105, India</text>
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                <text>3D-Printed MOFCuO/TiO2 Energy Device for Continuous Self-Charging Power Generation</text>
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                <text>Mathias R., Department of Physics and Electronics, Christ University, Bangalore, India; Jaksani B., Department of Energy and Environmental Engineering, CSIR-Indian Institute of Chemical Technology, Hyderabad, India, Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India; Mishra S., Materials Research Centre, Indian Institute of Science, Bangalore, India; Singh A.K., Materials Research Centre, Indian Institute of Science, Bangalore, India; Kumbhakar P., Department of Physics and Electronics, Christ University, Bangalore, India; Pal U., Department of Energy and Environmental Engineering, CSIR-Indian Institute of Chemical Technology, Hyderabad, India, Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India</text>
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                <text>The increasing industrialisation and fast growth do not only pose problems related to the allocation of resources and powers, but also severely challenge the natural environment. Environmental degradation such as contaminated water, sinking groundwater levels, unhealthy soils, and polluted air has become a harsh reality in many parts of the world. One result of a rapid urbanisation, a slowly reducing gap between urban and rural, changing consumption patterns, and a growing population is the problem of waste. However, although it is the duty of the urban local bodies (ULBs) to address the issue of waste, tight budgets, inefficient organisation, has rendered a situation that has little hope for alleviation in the near future. This chapter aims to understand the concept of waste management through 3Rs. The focus is to identify the contemporary 3Rs practices and also develop advanced strategies for the same.  2024 by IGI Global.</text>
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                <text>Jaiwant S.V.; Dheepa T.; Haridasan A.</text>
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                <text>The advancement of crop yield prediction through artificial intelligence (AI) has gained significant attention. However, the existing AI-based approaches for maximizing agricultural productivity, specifically in crop yield prediction, have not consistently delivered satisfactory results. In response to this challenge, we propose a novel framework named as Three Tier Feature Learning with XLnet based Crop Prediction (3TFL-XLnet-CP) that enhances agricultural productivity by accurately predicting crop yield. The 3TFL-XLnet-CP framework employs a three-tier feature learning approach in combination with the powerful XLnet transformer-based crop prediction model. The three-tier feature learning involves the integration of Spiking Neural Network (SNN), Graphical Neural Network (GNN), and Convolutional Neural Network (CNN) to extract distinct feature vectors from the preprocessed data. These feature vectors are then concatenated using Jaccard Similarity to measure their similarity score. Additionally, a weighted Loss function is introduced to optimize feature learning, further enhanced by a novel self-adaptive Spider Monkey Optimization algorithm (SASMO). The concatenated features are subsequently fed into the classification layer for making precise crop yield predictions. The proposed model is implemented using the Python platform and evaluated against existing models such as ANN, RNN, DNN, and BiLSTM. The comparison demonstrates the superiority of our proposed 3TFL-XLnet-CP framework in accurately predicting crop yield.  The Author(s) 2025.</text>
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                <text>Sikkim is one of the smallest states located in the Eastern Himalayas of India. This paper is framed within the context of Sikkim commemorating 50 years of democracy on 16 May 2025, following its integration into the Indian Union on 16 May 1975. Over the past five decades, Sikkim has claimed several accolades; the state was declared Indias first fully organic state in 2016 and acclaimed as the best performing small state in cleanliness and most improved small state in governance in 2020. However, there are some paradoxes in Sikkims developmental paradigm. This paper focuses solely on the healthcare system in Sikkim, exploring various aspects such as the allocation of budget to the health sector, the condition of health institutions, the execution of different health programmes and schemes, the accessibility of health facilities, and the availability of human resources. An additional significant factor is the reliance of the states population on other states for healthcare services. Furthermore, it examines the factors that contribute to the increasing disparity between planning and implementation.  2026 The Round Table Ltd.</text>
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                <text>Gracia S., Christ University, Department of Computer Science, Bangalore, India; Soundarabai P.B., Christ University, Department of Computer Science, Bangalore, India; Raj P., Reliance Jio Platforms Ltd., Site Reliability Engineering Coe, Bangalore, India</text>
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