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                <text>Sliced Bidirectional Gated Recurrent Unit with Sparrow Search Optimizer for Detecting the Attacks in IoT Environment</text>
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                <text>bidirectional gated recurrent unit; internet of things; multi-head self-attention mechanism; network intrusion detection systems; network traffic; sparrow search optimization algorithm</text>
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                <text>In an era characterized by pervasive interconnectivity facilitated by the widespread adoption of Internet of Things (IoT) devices across diverse domains, novel cybersecurity challenges have emerged, underscoring the imperative for robust intrusion detection systems. Conventional security frameworks, constrained by their closed-system architecture, struggle to adapt to the dynamic threat landscape marked by the continual emergence of unprecedented attacks. This paper presents a methodology aimed at mitigating the open set recognition (OSR) challenge within IoT-specific Network Intrusion Detection Schemes (NIDS). Leveraging image-based representations of data, our approach focuses on extracting geographical traffic patterns. We observe that the Recurrent Neural Network exhibits suboptimal classification accuracy and lacks parallelizability for attack analysis tasks. Our investigation concludes that the Sparrow Search Optimization Algorithm (SSOA) serves as a foundation for constructing an effective assault classification model. This research contributes significantly to the field of network security by emphasizing the importance and ramifications of meticulous hyperparameter tuning. It represents a critical stride toward developing IDSs capable of effectively navigating the evolving cyber threat landscape. In the experimental analysis of proposed model reached the accuracy and 0.963% respectively.   2024 IEEE.</text>
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                <text>Gondkar S.R.; Siva Balan R.V.; Kavitha S.; Gondkar R.R.</text>
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                <text>3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 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/ICDCECE60827.2024.10548548" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICDCECE60827.2024.10548548&lt;/a&gt;
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                <text>ISBN: 979-835031860-9</text>
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                <text>Gondkar S.R., Bms Institute Of Technology And Management, Department Of Electronic And Communication Engineering, Bangalore, India; Siva Balan R.V., Christ (Deemed-to-be-University), Department Of Computer Science, Bangalore, India; Kavitha S., Nitte Meenakshi Institute Of Technology, Department Of Mca, Bangalore, India; Gondkar R.R., Christ (Deemed-to-be-University), Department Of Computer Science, Bangalore, India</text>
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                <text>Slow Violence in Vikram Chandras Sacred Games: An Ecocritical Reading</text>
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                <text>ecocriticism; slow violence; socio-environmentalism; waste studies</text>
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                <text>This article gives insight into the ways in which enforcement and institutional vigilante activities portrayed in Vikram Chandras Sacred Games foreshadow the urban thicket of garbage dump yards and slum dwellings. The text will be analyzed from an ecocritical perspective to establish aspects of slow violence and its explicit and implicit results. Chandras plotline, regarding several entangled human tragedies against the background of refuse, urges a study of the novel through the lens of waste studies. However, he fails to address the reasons for the characters opinion of Mumbai being uninhabitable and infamous for treating human life as expendable. The novelist also seems to normalize the issues of inequalities in waste management and justifies the anthropocentric utilitarian perception of resources. The depictions of Mumbai gang wars against a disturbingly overlooked state of dilapidated lives and misplaced ideologies mention waste as being both created and ignored. Such representation also compels a close reading of consumerism and criminal aspiration.  2023, University of Zadar. All rights reserved.</text>
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                <text>Sabu M.; Mudaliar M.</text>
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                <text>Sic, Vol-13, No. 2</text>
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                <text>University of Zadar</text>
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                <text>&lt;a href="https://doi.org/10.15291/sic/2.13.lc.5" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.15291/sic/2.13.lc.5&lt;/a&gt;
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                <text>All Open Access; Gold Open Access; Green Open Access</text>
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                <text>ISSN: 18477755</text>
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                <text>Sabu M., Christ Deemed to be University, India; Mudaliar M., Christ Deemed to be University, India</text>
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                <text>SLV voltage regulated DC/DC converter /</text>
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                <text>Patent Number: 202141046671, Applicant: Radhika S.&#13;
The requirement is to develop a DC/DC converter for DC microgrid and charging electric vehicle batteries. The main issue in the DC microgrid is the voltage regulation and it is necessary to implement a regulated DC/DC converter and this can be achieved by the combination of voltage lift and boost circuit giving a regulated DC output voltage of 203.1V DC at 0.4 duty ratio by using LTspice XVII software.</text>
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                <text>V, Margaret.</text>
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                <text>Intellectual Property India</text>
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                <text>2021-05-06</text>
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                <text>Sm-MOF/rGO/PANI composite as an electrode material for supercapacitor applications</text>
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                <text>Energy density; Metal organic frameworks; Polyaniline; Power density; Supercapacitors</text>
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                <text>In recent years, scientists have been paying a lot of attention to metal-organic frameworks, often known as MOFs, as a possible material for use as electrodes in supercapacitors. MOFs are capable of functioning as high-quality pseudocapacitors because of their crystalline structure, which enhances the specific surface area and provide mechanical support for composite materials. The objective of this study is to synthesize an electrode material that can be used as supercapacitors by synthesizing Sm-MOF and then combining it with reduced graphene oxide (rGO) and polyaniline (PANI) to make a hybrid material. Here, the effect of incorporation of PANI and rGO in Sm-MOF is investigated, and its application as a supercapacitor is examined. Due to the high surface area and pore size, Sm-MOF/rGO/PANI material exhibits high specific capacitance. The computed specific capacitance of the composite Sm-MOF/rGO/PANI material is 1935.6 F g?1 when subjected to a current density of 1 A g?1. The Sm-MOF/rGO/PANI device is fabricated and exhibits a specific capacitance of 218 F g?1. The specific power and energy are calculated to be 59.3 Wh kg?1 and 581 W kg?1, respectively.  2023 Elsevier Ltd</text>
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                <text>Rajasekaran S.; K R S.D.; Reghunath B.S.; Saravanakumar B.; William J.J.; Pinheiro D.</text>
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                <text>Electrochimica Acta, Vol-467</text>
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                <text>Rajasekaran S., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; K R S.D., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Reghunath B.S., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Saravanakumar B., Department of Physics, Dr. Mahalingam College of Engineering and Technology, Tamil Nadu, Pollachi, 642003, India; William J.J., Department of Physics, Dr. Mahalingam College of Engineering and Technology, Tamil Nadu, Pollachi, 642003, India; Pinheiro D., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India</text>
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                <text>In this paper, we propose a novel squeeze M-SegNet (SM-SegNet) architecture featuring a fire module to perform accurate as well as fast segmentation of the brain on magnetic resonance imaging (MRI) scans. The proposed model utilizes uniform input patches, combined-connections, long skip connections, and squeezeexpand convolutional layers from the fire module to segment brain MRI data. The proposed SM-SegNet architecture involves a multi-scale deep network on the encoder side and deep supervision on the decoder side, which uses combined-connections (skip connections and pooling indices) from the encoder to the decoder layer. The multi-scale side input layers support the deep network layers extraction of discriminative feature information, and the decoder side provides deep supervision to reduce the gradient problem. By using combined-connections, extracted features can be transferred from the encoder to the decoder resulting in recovering spatial information, which makes the model converge faster. Long skip connections were used to stabilize the gradient updates in the network. Owing to the adoption of the fire module, the proposed model was significantly faster to train and offered a more efficient memory usage with 83% fewer parameters than previously developed methods, owing to the adoption of the fire module. The proposed method was evaluated using the open-access series of imaging studies (OASIS) and the internet brain segmentation registry (IBSR) datasets. The experimental results demonstrate that the proposed SM-SegNet architecture achieves segmentation accuracies of 95% for cerebrospinal fluid, 95% for gray matter, and 96% for white matter, which outperforms the existing methods in both subjective and objective metrics in brain MRI segmentation.  2022 by the authors. Licensee MDPI, Basel, Switzerland.</text>
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                <text>Sensors, Vol-22, No. 14</text>
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                <text>Yamanakkanavar N., Department of Electronics and Communications Engineering, CHRIST University, Bangalore, 560029, India; Choi J.Y., Division of Computer Engineering, Hankuk University of Foreign Studies, Yongin, 17035, South Korea; Lee B., Department of Information and Communications Engineering, Chosun University, Gwangju, 61452, South Korea</text>
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                <text>Small finance banks and financial inclusion in India /</text>
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                <text>Management-Small Finance Banks</text>
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                <text>Research Review Journals, Vol.4, Issue 3, pp.1586-1588, ISSN No: 2455-3085.</text>
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                <text>Small signal stability in a Microgrid using PSO based Battery storage system</text>
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                <text>BSS (Battery Storage System); DG (Diesel Generator); Distributed Generation; Microgrid; MPPT (Maximum Power Point Tracking); PSO (Particle swarm optimization) PV System; THD (Total Harmonic Distortion)</text>
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                <text>This papers covers, modelling and analysis of a small microgrid with Battery Storage System (BSS). A sample microgrid is considered, it is analyzed for small signal stability, with and without BSS. Voltage, frequency and current THD which are considered to be the major attributes of stability in a microgrid, the behavior of these attributes is observed with and without BSS. The Battery storage system is connected to the considered microgrid through PV array, using PSO algorithm, which improves the stability of the system. Simulation is carried out using MATAB/ Simulink and the results are presented. Microgrid considered consists of PV array, Diesel Generator and Battery storage system. These sources are modelled according to the loads connected to the microgrid. BSS acts as emergency backup to the considered system and also provides small signal stability to the microgrid. Simulation is carried out with BSS and without Battery Storage in the Islanded mode. The obtained results show that microgrid with BSS is more stable during small disturbances and also acts as backup power supply. A Properly modelled microgrid can act as power backup for industries.   2022 IEEE.</text>
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                <text>Divya B.V.; Archana N.V.; Latha N.; Surendra U.</text>
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                <text>2022 IEEE Industrial Electronics and Applications Conference, IEACon 2022, pp. 55-60.</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/IEACon55029.2022.9951733" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/IEACon55029.2022.9951733&lt;/a&gt;
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                <text>ISBN: 978-166548006-2</text>
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                <text>Divya B.V., REVA University, School of EEE, Bangalore, India; Archana N.V., NIEIT, Mysore, India; Latha N., REVA University, School of EEE, Bangalore, India; Surendra U., CHRIST(Deemed to Be University), Bangalore, India</text>
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                <text>Human affect recognition (HAR) using images of facial expression and electrocardiogram (ECG) signal plays an important role in predicting human intention. This system improves the performance of the system in applications like the security system, learning technologies and health care systems. The primary goal of our work is to recognize individual affect states automatically using the multilayered binary structured support vector machine (MBSVM), which efficiently classify the input into one of the four affect classes, relax, happy, sad and angry. The classification is performed efficiently by designing an efficient support vector machine (SVM) classifier in multilayer mode operation. The classifier is trained using the 8-fold cross-validation method, which improves the learning of the classifier, thus increasing its efficiency. The classification and recognition accuracy is enhanced and also overcomes the drawback of 'facial mimicry' by using hybrid features that are extracted from both facial images (visual elements) and physiological signal ECG (signal features). The reliability of the input database is improved by acquiring the face images and ECG signals experimentally and by inducing emotions through image stimuli. The performance of the affect recognition system is evaluated using the confusion matrix, obtaining the classification accuracy of 96.88%.  2020 The British Computer Society 2020. All rights reserved.</text>
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                <text>Thamba Meshach W.; Hemajothi S.; E A M.A.</text>
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                <text>Computer Journal, Vol-65, No. 4, pp. 897-917.</text>
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                <text>&lt;a href="https://doi.org/10.1093/comjnl/bxaa125" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1093/comjnl/bxaa125&lt;/a&gt;
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                <text>ISSN: 104620; CODEN: CMPJA</text>
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                <text>Thamba Meshach W., Department of Computer Science and Engineering, Prathyusha Engineering College, Aranvoyalkuppam, Thiruvallur District, Tamilnadu, Chennai, 602025, India; Hemajothi S., Department of Electronics and Communication Engineering, Prince Shri Venkateshwara Padmavathy Engineering College, Medavakkam-Mambakkam Road, Tamilnadu, Ponmar, Chennai, 600127, India; E A M.A., Department of Computer Science and Engineering, Christ University, Mysore Rd, Kumbalgodu, Karnataka, Bangalore, 560074, India</text>
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                <text>Smart Agriculture: Machine Learning Approach for Tea Leaf Disease Detection</text>
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                <text>Agriculture; Convolutional neural network; Machine learning; Pattern identification; Tea leafs</text>
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                <text>Across the globe, plant infections from pathogens such as fungi, bacteria and viruses are the major issues in the agricultural sector. Agricultural productivity is one of the most important things on which the nations economy highly depends. The detection of diseases in plants plays a major role in the agricultural field. This study proposes a multi-stage network involving Convolutional neural network, Pattern identification and Classification using Siamese network. The main objective behind this study is to enhance the disease detection technique performance. The image data of Tea leaves chosen for this study will be gathered. The algorithms based on techniques of image processing would be designed. The proposed algorithm was tested on the following diseases namely Red rust, Blister blight, Twig dieback, Stem canker, Grey Blight, Brown Blight, Brown root rot disease and Red root rot disease in Tea leaves.  The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.</text>
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              <elementText elementTextId="166612">
                <text>Rajagopal M.; Sivasakthivel R.; Pandey M.</text>
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                <text>Lecture Notes in Networks and Systems, Vol-967 LNNS, pp. 199-209.</text>
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                <text>Springer Science and Business Media Deutschland GmbH</text>
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            <elementTextContainer>
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                <text>ISSN: 23673370; ISBN: 978-981972052-1</text>
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            <description>The file format, physical medium, or dimensions of the resource</description>
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                <text>Rajagopal M., School of Business and Management, Christ University, Bangalore, India; Sivasakthivel R., Department of Computer Science, School of Sciences, Christ University, Bangalore, India; Pandey M., School of Business and Management, Christ University, Bangalore, India</text>
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          <element elementId="50">
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              <elementText elementTextId="168538">
                <text>Smart Air Pollution Monitoring System Using Arduino Based on Wireless Sensor Networks</text>
              </elementText>
            </elementTextContainer>
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          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
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              <elementText elementTextId="168539">
                <text>Air pollution; IoT; LCD; Population growth</text>
              </elementText>
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                <text>Impurity levels in air have risen throughout time as a result of several reasons, such as population expansion, increased automobile use, industry, and urbanization. All of these elements harm the health of individuals who are exposed to them, which has a detrimental effect on human well-being. We will create an air pollution monitoring system based on an IoT that uses a Internet server to track the air quality online in order to keep track of everything. An alert will sound when the level of harmful gases such CO2, smoking, alcohol, benzene, and NH3 is high enough or when the air quality drops below a specified threshold. The air quality will be displayed on the LCD in PPM.  The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.</text>
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              <elementText elementTextId="168541">
                <text>Thaiyalnayaki S.; Sambandam R.K.; K.Vidhyalakshmi M.; Shanthi S.; Jenefa J.; Vetriveeran D.</text>
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              <elementText elementTextId="168542">
                <text>Lecture Notes in Networks and Systems, Vol-840, pp. 497-504.</text>
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              <elementText elementTextId="168543">
                <text>Springer Science and Business Media Deutschland GmbH</text>
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              <elementText elementTextId="168544">
                <text>2024-01-01</text>
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                <text>&lt;a href="https://doi.org/10.1007/978-981-99-8451-0_42" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-981-99-8451-0_42&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187697753&amp;amp;doi=10.1007%2F978-981-99-8451-0_42&amp;amp;partnerID=40&amp;amp;md5=5998b84933d57db41e6f3065bda29da3" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187697753&amp;amp;doi=10.1007%2f978-981-99-8451-0_42&amp;amp;partnerID=40&amp;amp;md5=5998b84933d57db41e6f3065bda29da3&lt;/a&gt;</text>
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            <elementTextContainer>
              <elementText elementTextId="168546">
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                <text>ISSN: 23673370; ISBN: 978-981998450-3</text>
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                <text>Thaiyalnayaki S., Department of CSE, Bharath Institute of Higher Education and Research (Deemed to Be University), Tamilnadu, Chennai, India; Sambandam R.K., Department of CSE, SOET, CHRIST (Deemed to Be University), Kengeri Campus, Karnataka, Bengaluru, India; K.Vidhyalakshmi M., Department of Computing Technologies, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, Chengalpattu District, 603203, India; Shanthi S., Department of CSE, Malla Reddy College of Engineering and Technology, Telangana, Hyderabad, India; Jenefa J., Department of CSE, SOET, CHRIST (Deemed to Be University), Kengeri Campus, Karnataka, Bengaluru, India; Vetriveeran D., Department of CSE, SOET, CHRIST (Deemed to Be University), Kengeri Campus, Karnataka, Bengaluru, India</text>
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      <description>Faculty Publications- Book Chapter</description>
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          <element elementId="50">
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                <text>Smart Antenna for Home Automation Systems</text>
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                <text>5G (Sub-6GHz) band; home automation systems; impedance bandwidth; slot antenna; Smart antenna; X-band</text>
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              <elementText elementTextId="153507">
                <text>A smart antenna for home automation systems is suggested in this design. An antenna is a device that minimizes human movements while dealing with electronics systems and software. This smart antenna helps reduce physically challenged peoples movement and supports home automation systems. The presented antennas could be used for any small device, like a mobile, tablet, laptop, Wi-Fi, or WiMAX, which are essential in the current scenario. The presented smart antenna is capable of radiating a large frequency band from 3 to 13.8 GHz, which covers the .5-7 GHz (5G(I) Sub-6 GHz band), Wimax 3.5 and 5.5 GHz bands, WLAN 5.2 and 5.8 GHz bands, C 4-8 GHz Band, X 8-12 GHz Band, and other home automation applications with high efficiency. The impedance bandwidth of the smart antenna is 128%, with a size of 15x15x1.5 mm3. The suggested design includes a modified patch in the shape of a square patch attached with one circular element fed by a microstrip line. Circular pieces have been designed for better resonances at lower modes. The antenna is simulated with an FR4 substrate using a CST Simulator. The design is investigated by simulations and corresponding S-parameter results are presented. The robotics process automation is well described in Table 7.3. The proposed structure also demonstrates stable radiation patterns across the operating bandwidth. The proposed radiator has a high gain of 5.21 dBi and an efficiency of 80%.  2023 Scrivener Publishing LLC.</text>
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              <elementText elementTextId="153508">
                <text>Yadav M.V.; Yadav S.V.</text>
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              <elementText elementTextId="153509">
                <text>Robotic Process Automation, pp. 113-124.</text>
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                <text>wiley</text>
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                <text>Patil M.A., Vasavi College of Engineering, Department of It, Hyderabad, Telangana, India; Parane K., Nit Surathkal, Dept. of Cse, Karnataka, India; Sivaprasad D.D., Kampala International University, School of Computing and It, Kampala, Uganda; Poojara S., Institute of Cs, University of Tartu, Tartu, Estonia; Lamani M.R., Christ (Deemed to Be University), Kengeri Campus, Dept. of Cse, Bangalore, Karnataka, India</text>
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                <text>Smart car - accident detection and notification using amazon alexa</text>
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                <text>Amazon alexa; Arduino mega; Arduino UNO; NodeMCU; Raspberry pi</text>
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                <text>The high demand for automobiles has increased traffic hazards and road accidents. Life of the people is under high risk. This is because of the lack of the best emergency facilities available in the country. The proposed system can detect accidents in significantly less time and sends the basic information first to aid center and relatives of the victim on mobile and Amazon Alexa within a few seconds covering geographical coordinates. Various devices like Arduino UNO for car movement demonstration, Arduino Mega for accident detection and Raspberry Pi 3B for internet services gateway, accelerometer and impact sensor working together to detect an accident. All connected over the internet to generate a huge amount of data which holds a lot of information about the occurrence of the accident based on the speed and location and can be used to detect accident hotspots. The system also focuses on the safety of pedestrians where a safety band is programmed to perform the notification services using an emergency push button. The ESP8266 NodeMCU invokes the same services using a button on the module. The data generated may be used for the prediction, analysis to prevent future accidents and contribute to future road safety.  Springer Nature Switzerland AG 2020.</text>
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                <text>Grover L.; Kirubanand V.B.; Paulose J.</text>
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                <text>Internet of Things (IoT): Concepts and Applications, pp. 391-407.</text>
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                <text>&lt;a href="https://doi.org/10.1007/978-3-030-37468-6_21" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-3-030-37468-6_21&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089322858&amp;amp;doi=10.1007%2F978-3-030-37468-6_21&amp;amp;partnerID=40&amp;amp;md5=d5e07bcc838bbe1a21bfa679a6925fa6" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089322858&amp;amp;doi=10.1007%2f978-3-030-37468-6_21&amp;amp;partnerID=40&amp;amp;md5=d5e07bcc838bbe1a21bfa679a6925fa6&lt;/a&gt;</text>
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                <text>Grover L., Department of Computer Science, Christ University, Bangalore, India; Kirubanand V.B., Department of Computer Science, Christ University, Bangalore, India; Paulose J., Department of Computer Science, Christ University, Bangalore, India</text>
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