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                <text>Enhanced Social Media Profile Authenticity Detection Using Machine Learning Models and Artificial Neural Networks</text>
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                <text>ANN; fake profile classification; Fake profile detection; GAN; Machine Learning</text>
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                <text>Fake engagement is one of the main issues with online networks or ONSs, which are used to artificially boost an account's popularity, this study examines the effectiveness of seven sophisticated Machine Learning Algorithms, Random Forest Classifier, Decision Tree Classifier, XGBoost, LightGBM, Extra Trees Classifier, and SVM, and got 93% accuracy in Decision Tree Classifier. In order to solve overfitting issues and improve model resilience, the paper proposes Generative Adversarial Networks (GANs) and uses K-Fold Cross-Validation. Furthermore, design a Gan-ANN model that combines Batch Normalization and Artificial Neural Networks (ANN) with GAN-generated synthetic data is investigated. The enhanced dataset seeks to strengthen model performance and generalization when combined with cutting-edge modeling methods. This study aims to improve model scalability, predictive accuracy, and dependability across different machine learning paradigms.   2023 IEEE.</text>
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                <text>Mahanta B., Christ (Deemed to Be University), India; Nair A.M., Christ (Deemed to Be University), India; Alapatt B.P., Christ (Deemed to Be University), India; George J.P., Christ (Deemed to Be University), India</text>
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                <text>Dynamic Behaviour Analysis of Multi-Cell Battery Packs: A Simulation Study</text>
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                <text>Battery Management System; Cell Voltage; Current; Lithium-Ion; State of Charge</text>
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                <text>In the era of IoT understanding the dynamic behavior of a Lithium-ion Battery Management System (BMS) has become gradually more important. This research investigates the dynamic behaviour of a six-cell Lithium-ion Battery Management System (BMS) through simulation. The study employs a comprehensive model encompassing key battery parameters, including cell capacity, voltage limits, temperature thresholds, and charge/discharge characteristics. Additionally, state variables such as State of Charge (SOC), State of Health, and State of Function are integrated to capture the battery's internal dynamics. The simulation incorporates a sinusoidal current profile to emulate realistic operating conditions. Notably, Coulomb counting is employed for SOC estimation, and protective measures against overvoltage, undervoltage, and overcurrent are implemented. The study also addresses balancing strategies and communication interfaces within the BMS. The results reveal nuanced interactions between voltage, temperature, SOC, and current, offering insights into the intricate behaviour of the battery system under dynamic conditions. This research not only advances our understanding of BMS functionality but also lays a crucial foundation for the evolution of battery technology and energy management systems in the IoT landscape.  The Institution of Engineering &amp;amp; Technology 2023.</text>
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                <text>Praghash K.; Jesuwanth Sugesh R.G.; Neethu P.S.; Peter G.; Yuvaraj N.</text>
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                <text>IET Conference Proceedings, Vol-2023, No. 22, pp. 203-209.</text>
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                <text>&lt;a href="https://doi.org/10.1049/icp.2023.2877" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1049/icp.2023.2877&lt;/a&gt;
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                <text>ISSN: 27324494</text>
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                <text>Praghash K., Department of Electronics and Communication Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Jesuwanth Sugesh R.G., Department of Electronics and Communication Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Neethu P.S., Department of Electronics and Communication Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Peter G., CRISD, School of Engineering and Technology, University of Technology Sarawak, Sibu, Malaysia; Yuvaraj N., Department of Computer Science and Engineering, Sri Sakthi Institute of Engineering and Technology, Coimbatore, India</text>
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                <text>A Self-Attention Bidirectional Long Short-Term Memory for Cold Start Movie Recommendation Models</text>
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                <text>Bidirectional Long Short-Term Memory; Lemmatization; Movie recommendation; Self-attention; Stemming; Term frequency-inverse document frequency</text>
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                <text>Movie recommendation systems are useful tools that help users find relevant results and prevent information overload. On the other hand, the user cold-start issue has arisen because the system lacks sufficient user data. Furthermore, they are not very scalable for use in extensive real-world applications. One of the key strategies to address the sparsity and cold-start problems is to leverage other sources of information, including item or user profiles or user reviews. Processing client feedback is typically a challenging process that involves challenging the interpretation and analysis of the textual data. Thus, this research implements an efficient deep learning-based recommendation architecture. Following the acquisition of textual data from the Amazon product reviews database, stop word removal, lemmatization, and stemming techniques are applied to the data pre-processing which eliminate inconsistent and redundant data, facilitating the process of interpreting and utilising data. Then, the Term Frequency-Inverse Document Frequency (TF-IDF) method is applied to extract the feature values from the pre-processed text data. The extracted feature values are fed to the Self-Attention Bidirectional Long Short-Term Memory (SA-BiLSTM) that utilises the matrix factorization method framework's information sources. The SA-BiLSTM model obtained 95.93% of recall, 94.76% of precision, and 97.84% of accuracy on the amazon product reviews database.  2023 IEEE.</text>
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                <text>Manohar M.; Reddy R.A.; Alzubaidi L.H.; Hameed Abdul Hussein A.; Buvaneswari P.R.</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/AIKIIE60097.2023.10390324" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/AIKIIE60097.2023.10390324&lt;/a&gt;
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                <text>Manohar M., Christ (Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India; Reddy R.A., School of Computer Science and Artificial Intelligence, Sr University, Warangal, India; Alzubaidi L.H., The Islamic University, Najaf, Iraq; Hameed Abdul Hussein A., College of Pharmacy Ahl Al Bayt University, Karbala, Iraq; Buvaneswari P.R., School of Electrical and Electronic Engineering Vit Bhopal University, Sehhore, India</text>
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                <text>Nowadays, large amounts of unstructured data are currently trending on social media and the Web. Text summarising is the process of extracting pertinent information in a concise manner without altering the content's core meaning. Summarising text by hand requires a lot of time, money, and effort. Although deep learning algorithms are commonly applied in abstractive text summarization, further research is clearly needed to fully understand their conjunction with semantic-based or structure-based approaches. The resume dataset is taken for this research work, which is gathered from Kaggle and the dataset includes 1,735 Resumes. This paper presents a unique framework based on the combination of semantic data transformations and deep learning approaches for improving abstractive text summarization. In an attempt to tackle the problem of unregistered words, a solution called Decoder Attention with Pointer Network (DA-PN) has been introduced. This method incorporates the use of a coverage mechanism to prevent word repetition in the generated text summaries. DA-PN is utilized for protecting the spread of increasing errors in generated text summaries. The performance of the proposed method is estimated using the evaluation indicator Recall Oriented Understudy for Gisting Evaluation (ROUGE) and attains an average of 26.28 which is comparatively higher than existing methods.  2023 IEEE.</text>
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                <text>Manohar M.; Sunil G.; Al-Attabi K.; Mohammed I.H.; Buvaneswari P.R.</text>
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                <text>IEEE 1st International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023</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/AIKIIE60097.2023.10389942" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/AIKIIE60097.2023.10389942&lt;/a&gt;
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                <text>ISBN: 979-835031646-9</text>
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                <text>Manohar M., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India; Sunil G., School of Computer Science &amp;amp; Artificial Intelligence, Sr University, Warangal, India; Al-Attabi K., The Islamic University, Najaf, Iraq; Mohammed I.H., Al Rafidain University College, Department of Computer Communications Engineering, Bagdad, Iraq; Buvaneswari P.R., School of Electrical and Electronic Engineering, Vit Bhopal University, Sehhore, India</text>
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                <text>Wood Type Identification via Neural Networks and Spectral Analysis: An Advanced Algorithmic Solution</text>
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            <name>Subject</name>
            <description>The topic of the resource</description>
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              <elementText elementTextId="170606">
                <text>Forestry Management; Machine Learning; Neural Networks; Spectral Analysis; Wood Identification</text>
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            <description>An account of the resource</description>
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                <text>Forestry management, woodworking, and manufacturing need wood type identification. This study introduces a neural network-spectral analysis technique for accurate and automatic wood type detection. Principal Component Analysis (PCA) is used to extract features from a heterogeneous collection of wood spectral signatures after training a neural network. The algorithm's 94.2% accuracy on a testing dataset shows its ability to distinguish wood kinds.The model's confusion matrix shows it can recognise closely related wood species with few misclassifications. The neural network's precision, recall, and F1 score prove its wood classification accuracy. With PCA highlighting classification characteristics, spectral analysis helps the algorithm succeed.The method is useful for forestry management and woodworking quality control. The non-destructive technology provides in-situ wood type detection, addressing environmental and conservation issues. The study explores ramifications, constraints, and future algorithm modification and application in real-world contexts.Neural networks and spectral analysis provide a strong, efficient, and non-destructive wood type detection solution. The hopeful results represent a major advance in wood science and current computer methods, with applicability across sectors.  2023 IEEE.</text>
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                <text>Bhat C.R.; Lakshmiramana P.; Vincy R.F.; Rajasekar P.; Balaji A.; Rajeshkanna R.</text>
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                <text>2023 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023</text>
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            <description>An entity responsible for making the resource available</description>
            <elementTextContainer>
              <elementText elementTextId="170610">
                <text>Institute of Electrical and Electronics Engineers Inc.</text>
              </elementText>
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            <description>A point or period of time associated with an event in the lifecycle of the resource</description>
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              <elementText elementTextId="170611">
                <text>2023-01-01</text>
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                <text>&lt;a href="https://doi.org/10.1109/ICDSAAI59313.2023.10452657" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICDSAAI59313.2023.10452657&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187805581&amp;amp;doi=10.1109%2FICDSAAI59313.2023.10452657&amp;amp;partnerID=40&amp;amp;md5=af3469e4aad02e6e4e780cf892563b6e" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187805581&amp;amp;doi=10.1109%2fICDSAAI59313.2023.10452657&amp;amp;partnerID=40&amp;amp;md5=af3469e4aad02e6e4e780cf892563b6e&lt;/a&gt;</text>
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          <element elementId="47">
            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
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              <elementText elementTextId="170613">
                <text>Restricted Access</text>
              </elementText>
            </elementTextContainer>
          </element>
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            <description>A related resource</description>
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              <elementText elementTextId="170614">
                <text>ISBN: 979-835034891-0</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="42">
            <name>Format</name>
            <description>The file format, physical medium, or dimensions of the resource</description>
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              <elementText elementTextId="170615">
                <text>Online</text>
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            <description>A language of the resource</description>
            <elementTextContainer>
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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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              <elementText elementTextId="170618">
                <text>Bhat C.R., Saveetha Institute of Medical and Technical Sciences, (SIMATS), Saveetha School of Engineering, Department of Computer Science and Engineering, Tamilnadu, Chennai, India; Lakshmiramana P., Madanapalle Institute of Technology &amp;amp; Science, Department of Computer Science &amp;amp; Technology, Madanapalle, India; Vincy R.F., Easwari Engineering College, Computer Science and Engineering, Ramapuram, India; Rajasekar P., Sri Krishna College of Technology, Department of Ece, Coimbatore, India; Balaji A., Panimalar Engineering College, Department of It, Chennai, India; Rajeshkanna R., Christ (Deemed to Be University), Bangalore, India</text>
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          <elementContainer>
            <element elementId="50">
              <name>Title</name>
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              <elementTextContainer>
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      <name>Conference Paper</name>
      <description>Faculty Publications- Conference Papers</description>
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        <elementContainer>
          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
            <elementTextContainer>
              <elementText elementTextId="170591">
                <text>Design and Implementation of an Optimized Mask RCNN Model for Liver Tumour Prediction and Segmentation</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="170592">
                <text>convolution neural network; CT- Image; Liver Tumour segmentation; Machine learning; Mask RCNN</text>
              </elementText>
            </elementTextContainer>
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            <description>An account of the resource</description>
            <elementTextContainer>
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                <text>Segmentation of liver tumour is a tedious job due to their large variation in location and closeness to nearby organs. In this research, a novel Mask RCNN prototype is developed which uses ResNet-50 model. The architecture utilizes the masked location of convolution neural network to precisely detect liver tumours by recognizing liver sites to deal with changes in liver and CT snaps with distinct metrics. The preprocessed CT scans are subjected to ResNet-50 model. The data samples used here comprises 130 instances recorded from several clinical sites that are publicly available on the LiTS weblink. The designed model upon deployment generates a promising outcome thereby obtaining a DSC of 0.97%. Thus, we can conclude that the developed model is capable enough to accurately assess liver tumours and thus help patients in early diagnosis.   2023 IEEE.</text>
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            <name>Creator</name>
            <description>An entity primarily responsible for making the resource</description>
            <elementTextContainer>
              <elementText elementTextId="170594">
                <text>Thakur R.; Volety D.R.; Sharma V.; Mishra S.; Iwendi C.; Osamor J.</text>
              </elementText>
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            <elementTextContainer>
              <elementText elementTextId="170595">
                <text>2023 4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023</text>
              </elementText>
            </elementTextContainer>
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            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
            <elementTextContainer>
              <elementText elementTextId="170596">
                <text>Institute of Electrical and Electronics Engineers Inc.</text>
              </elementText>
            </elementTextContainer>
          </element>
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            <name>Date</name>
            <description>A point or period of time associated with an event in the lifecycle of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="170597">
                <text>2023-01-01</text>
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              <elementText elementTextId="170598">
                <text>&lt;a href="https://doi.org/10.1109/ICCAKM58659.2023.10449653" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICCAKM58659.2023.10449653&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187551622&amp;amp;doi=10.1109%2FICCAKM58659.2023.10449653&amp;amp;partnerID=40&amp;amp;md5=1e2b2e3462bdbb323f77143cdf836f45" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187551622&amp;amp;doi=10.1109%2fICCAKM58659.2023.10449653&amp;amp;partnerID=40&amp;amp;md5=1e2b2e3462bdbb323f77143cdf836f45&lt;/a&gt;</text>
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            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="170599">
                <text>All Open Access; Green Open Access</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="46">
            <name>Relation</name>
            <description>A related resource</description>
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              <elementText elementTextId="170600">
                <text>ISBN: 979-835039324-8</text>
              </elementText>
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          </element>
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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="170601">
                <text>Online</text>
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            <description>A language of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="170602">
                <text>English</text>
              </elementText>
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          </element>
          <element elementId="51">
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            <description>The nature or genre of the resource</description>
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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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              <elementText elementTextId="170604">
                <text>Thakur R., Kalinga Institute of Industrial Technology, Deemed to Be University, Bhubaneswar, India; Volety D.R., Kalinga Institute of Industrial Technology, Deemed to Be University, Bhubaneswar, India; Sharma V., Christ (Deemed to Be University), Computer Science Department, Delhi NCR, India; Mishra S., Kalinga Institute of Industrial Technology, Deemed to Be University, Bhubaneswar, India; Iwendi C., School of Creative Technologies, University of Bolton, United Kingdom; Osamor J., Glasgow Caledonian University, Department of Cybersecurity and Networks, Glasgow, G4 OBA, United Kingdom</text>
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          <elementContainer>
            <element elementId="50">
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              <elementTextContainer>
                <elementText elementTextId="51377">
                  <text>Conference Papers</text>
                </elementText>
              </elementTextContainer>
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          </elementContainer>
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      <description>Faculty Publications- Conference Papers</description>
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        <elementContainer>
          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
            <elementTextContainer>
              <elementText elementTextId="170577">
                <text>QSAR Approach for Drug Discovery Targeting the Glucagon Receptor Using Machine Learning</text>
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            </elementTextContainer>
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          <element elementId="49">
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            <description>The topic of the resource</description>
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              <elementText elementTextId="170578">
                <text>Computational modeling; Drug Discovery; Glucagon receptor; Ligand efficiency; Machine Learning; QSAR</text>
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            <description>An account of the resource</description>
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                <text>Metabolic disorders like type 2 diabetes are increasing day by day so the study focusing drug discovery of glucagon receptor has become important.One of the method to study the binding strength between chemical compounds is Quantitative Structure-Activity Relationship (QSAR) which is discussed in this paper.We gathered a curated dataset of glucagon receptor ligands from the ChEMBL bio activity dataset and studied the physical and chemical properties of the molecules using factors like molecular weight and logarithm of the partition coefficient.Then Random forest regression model was applied for prediction of the binding strength of ligands. The efficiency information of ligand was extracted which contributed to study of the molecular features concerning the activity of glucagon receptor in a much easier manner. These findings highlight the potential of QSAR in elucidating the key determinants of ligated-receptor interactions and guiding the rational design of novel glucagon receptor modulators. The integration of computational approaches with experimental validation holds promise for accelerating the development of effective therapies for metabolic disorders, addressing unmet clinical needs in this field.   2023 IEEE.</text>
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              <elementText elementTextId="170580">
                <text>Sharma V.; Sarkar O.; Mishra S.; Sinha S.</text>
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            <elementTextContainer>
              <elementText elementTextId="170581">
                <text>2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023, pp. 702-706.</text>
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            <elementTextContainer>
              <elementText elementTextId="170582">
                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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            <name>Date</name>
            <description>A point or period of time associated with an event in the lifecycle of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="170583">
                <text>2023-01-01</text>
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                <text>Sharma V., Christ (Deemed to Be University), Delhi NCR, India; Sarkar O., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Mishra S., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Sinha S., Kalinga Institute of Industrial Technology, Deemed to Be University, India</text>
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                <text>In recent years, the digital evolution of education has significantly shaped the landscape of learning, steering it away from traditional classroom settings towards more agile e-learning platforms. This shift has underscored the urgency for comprehensive online examination systems, tailored to meet the unique challenges and demands of virtual education. Online learning platforms have seen a rapid rise in popularity, given their flexibility, cost-effectiveness, and capability to cater to learners worldwide. Such a widespread audience brings along the challenge of conducting exams without the constraints of geography and scale. Traditional examinations, with their manual paper based formats, fail to fit within this digital mold due to their logistical challenges and inefficiencies. Consequently, an online examination system not only introduces convenience but also operational efficiency, eliminating many of the logistical nightmares associated with manual exams. While existing tools might provide online testing capabilities, the integration of Artificial Intelligent driven proctoring in this portal elevates the standards of academic integrity to unprecedented levels. The main aim of this article is to create online test platform with the support of Artificial Intelligence technology. The result detect the malpractice activity and electronic device usage detection while online examination.  2023 IEEE.</text>
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                <text>Vivek M., Department of CSE, CHRIST (Deemed to be University), India; Clintford P.S., Department of CSE, CHRIST (Deemed to be University), India; Prithvi R., Department of CSE, CHRIST (Deemed to be University), India; Maurya A.K., School of Engineering and Technology, CHRIST (Deemed to be University), India; Jayapandian N., School of Engineering and Technology, CHRIST (Deemed to be University), India</text>
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                <text>Machine Learning Approaches for Suicidal Ideation Detection on Social Media</text>
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                <text>Deep learning; Detection; Early intervention; Machine learning; Mental health; Natural language processing; Online posts; Public health; Sentiment analysis; Social media; Suicidal ideation</text>
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                <text>Social media suicidal ideation has become a serious public health issue that requires creative solutions for early diagnosis and management. An extensive investigation of machine-learning techniques for the automated detection of suicidal thoughts in internet postings is presented in this research. We start off by talking about the concerning increase in information on social media about mental health issues and the pressing need to create efficient monitoring mechanisms. The research explores the several methods used to identify the subtleties of suicidal thought conveyed in text, photographs, and audio-visual information. These methods include sentiment analysis, natural language processing, and deep learning models. We look at the problems with unbalanced data, privacy issues, and the moral ramifications of keeping an eye on user-generated material. We also go over the research's practical ramifications, such as the creation of instruments for real-time monitoring and crisis response techniques. Through comprehensive experiments and benchmarking, we demonstrate the potential of machine learning in providing timely support for those in need, thereby reducing the impact of suicidal ideation on society.   2023 IEEE.</text>
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                <text>Nandhini B.; Jayanthi; Shrinivas K.; Vinod P.</text>
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                <text>ISBN: 979-835039324-8</text>
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                <text>Nandhini B., School of Management Studies, Bannari Amman Institute of Technology, Sathyamangalam, India; Jayanthi, Hindusthan College of Engineering and Technology, Department of Management Sciences, Coimbatore, India; Shrinivas K., School of Business and Management, Christ (Deemed to Be University), Bangalore, India; Vinod P., School of Business and Management, Christ (Deemed to Be University), Bangalore, India</text>
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                <text>Intricate Plane of Adversarial Attacks in Sustainable Territory and the Perils faced Machine Intelligent Models</text>
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                <text>Adversarial AI; Cyber security; Deep Learning; Generative Adversarial Networks(GANs); Machine learning; Sustainability</text>
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                <text>The issue of model security and reliability in Artificial Intelligence (AI) is a concern due to adversarial attacks. In order to tackle this issue, researchers have developed sustainable defense strategies, but certain challenges remain. These challenges involve transferability, higher computing costs, and adaptability. Striking a balance between accuracy and robustness is difficult, as defense mechanisms often come with trade-offs between the two. Real-world situations demonstrate the practical implications of sustainable adversarial AI. For example, it improves the security of self-driving vehicles, enhances the accuracy of medical imaging diagnoses, and incorporates AI-driven defenses into network intrusion detection and phishing detection systems. It is crucial to consider ethical aspects throughout this process. Future trends in adversarial AI research for cybersecurity will involve ensemble defense mechanisms, adversarial learning from limited data, and hybrid attacks. By embracing the evolving landscape, researchers and practitioners can develop sustainable AI systems that are more secure and resilient, effectively countering adversarial threats.   2023 IEEE.</text>
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                <text>Sai Susmitha K.S.; Srimlika N.; Sharma V.; Almusawi M.; Mishra S.</text>
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                <text>International Conference for Technological Engineering and its Applications in Sustainable Development, ICTEASD 2023, pp. 233-239.</text>
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                <text>ISBN: 979-835033647-4</text>
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                <text>Sai Susmitha K.S., Deemed to Be University, Kalinga Institute of Industrial Technology, India; Srimlika N., Deemed to Be University, Kalinga Institute of Industrial Technology, India; Sharma V., Deemed to Be University, CHRIST, Delhi NCR, India; Almusawi M., The Islamic University, College of Technical Engineering, Najaf, Iraq; Mishra S., Deemed to Be University, Kalinga Institute of Industrial Technology, India</text>
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                <text>In this research work, a low voltage analog multiplier has been realized through the utilization of a flipped voltage follower (FVF). The multiplier is characterized by its capacity to function at low power while exhibiting high gain. The exclusive use of transistors in its implementation renders it highly appropriate for fully integrated circuit applications. The multiplier has been developed using a supply voltage of 500 mV and an operating frequency of 25 KHz. The design consumes power of 8.23 uW. Moreover, a comparative study between the proposed multiplier and the conventional gilbert multiplier is presented in the paper. All simulations and layout designs have been conducted through the virtuoso analog design environment (ADE) of Cadence at 45 nm CMOS technology.   2023 IEEE.</text>
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                <text>Nayak A.G., BMS College of Engineering, Department of ECE, Bangalore, India; Bharath G.S., BMS College of Engineering, Department of ECE, Bangalore, India; Bhan I., BMS College of Engineering, Department of ECE, Bangalore, India; Mishra G., BMS College of Engineering, Department of ECE, Bangalore, India; Kumar V., Christ University, Department of ECE, Bangalore, India</text>
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                <text>Circuit Breaker: A Resilience Mechanism for Cloud Native Architecture</text>
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              <elementText elementTextId="170508">
                <text>Circuit Breaker; Cloud Native Applications; Fault Tolerance; Microservice Patterns; Resilience</text>
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                <text>Over the past decade, the utilization of cloud native applications has gained significant prominence, leading many organizations to swiftly transition towards developing software applications that leverage the powerful, accessible, and efficient cloud infrastructure. As these applications are deployed in distributed environments, there arises a need for reliable mechanisms to ensure their availability and dependability. Among these mechanisms, the circuit breaker pattern has emerged as a crucial element in constructing resilient and trustworthy cloud native applications in recent times. This research article presents a comprehensive review and analysis of circuit breaker patterns and their role within cloud-native applications. The study delves into various aspects of circuit breakers, encompassing their design, implementation, and recommended practices for their utilization in cloud native applications. Additionally, the article examines and compares different circuit breaker libraries available for employment in modern software development. The paper also presents a concept for improving the circuit breaker pattern, which will be pursued in our upcoming research.  2023 IEEE.</text>
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                <text>Norton Stanley S.A.; Sebastian S.</text>
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                <text>2023 Global Conference on Information Technologies and Communications, GCITC 2023</text>
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              <elementText elementTextId="170512">
                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>ISBN: 979-835030816-7</text>
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                <text>Norton Stanley S.A., Christ (Deemed to Be University), Department of Computer Science, Bangalore, India; Sebastian S., Christ (Deemed to Be University), Department of Computer Science, Bangalore, India</text>
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          <element elementId="50">
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              <elementText elementTextId="170493">
                <text>Smart Sensory Approach for Soil Health Tracking based Precision Farming</text>
              </elementText>
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          </element>
          <element elementId="49">
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              <elementText elementTextId="170494">
                <text>humidity; Internet of Things; Precision farming; predictive analytics; remote sensing; Sensory computing</text>
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                <text>Internet of Things (IoT) technology will have an impact on every area in the future as it will make everything intelligent, which will affect everyone's daily lives. It is a network composed of many devices that can configure themselves. The use of IoT in smart farming is transforming traditional agricultural practices by reducing crop loss, improving them, and making them more cost-effective for farmers. The study's goal is to propose a technological model for soil health monitoring that uses smart sensors and intelligent methods to communicate with farmers through a variety of channels. Farmers will benefit from the real-time farm data (temperature, humidity, soil moisture, UV index, and IR) that allows them to practice smart farming while increasing crop yields and conserving resources.   2023 IEEE.</text>
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              <elementText elementTextId="170496">
                <text>Bhattacharyya A.; Saini T.; Sharma V.; Mishra S.</text>
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              <elementText elementTextId="170497">
                <text>3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023 - Proceedings, pp. 563-570.</text>
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              <elementText elementTextId="170498">
                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>2023-01-01</text>
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                <text>&lt;a href="https://doi.org/10.1109/ICIMIA60377.2023.10425922" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICIMIA60377.2023.10425922&lt;/a&gt;
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              <elementText elementTextId="170502">
                <text>ISBN: 979-835034363-2</text>
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                <text>Online</text>
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              <elementText elementTextId="170506">
                <text>Bhattacharyya A., Deemed to Be University, Kalinga Institute of Industrial Technology, India; Saini T., Deemed to Be University, Kalinga Institute of Industrial Technology, India; Sharma V., CHRIST (Deemed to Be University), Department of Computational Sciences, Delhi NCR, India; Mishra S., Deemed to Be University, Kalinga Institute of Industrial Technology, India</text>
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                <text>Enhancing Disease Prediction in Healthcare: A Comparative Analysis of PSO and Extreme Learning Approach</text>
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          <element elementId="49">
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              <elementText elementTextId="170480">
                <text>Disease Prediction System; Extreme Learning Machine (ELM); Particle Swarm Optimization (PSO)</text>
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                <text>The healthcare business generates a tremendous quantity of data, and the goal is to collect it and use it effectively for analysis, prediction, and treatment. The best approach to disease management is disease prevention through early intervention. There are a number of methods that can advise you on how to treat a specific sickness, but much fewer that can tell you with any degree of certainty if you will actually get sick in the first place. Preprocessing, feature selection, feature extraction, and model training are all parts of the proposed method. The suggested layout includes a preprocessing stage that takes care of things like moving average, missing values, and normalization. Feature selection describes the process of selecting the most relevant features from a dataset. After gathering features, the models are trained using PSO-ELM. The proposed strategy is superior to the widely used PSO and ELM.   2023 IEEE.</text>
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              <elementText elementTextId="170482">
                <text>Prabakaran M.; Bhole M.K.; Kalpana V.; Dixit S.; Divya K.; Chauhan D.A.</text>
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                <text>Prabakaran M., Madanapalle Institute of Technology &amp;amp; Science, Department of Computer Science and Engineering (Data Science), Andhra Pradesh, India; Bhole M.K., Bharati Vidyapeeth College of Engineering, Department of Instrumentation Engineering, Navi Mumbai, India; Kalpana V., RMK College of Engineering and Technology, Puduvoyal, Department of Science and Humanities, Tamilnadu, Thiruvallur, India; Dixit S., Vellore Institute of Technology, School of Computer Science Engineering, Tamilnadu, Vellore, India; Divya K., Prince Shri Venkateshwara Padmavathy Engineering College, Department of ECE, Chennai, India; Chauhan D.A., CHRIST (Deemed to Be University), School of Sciences, Department of Life Sciences, Karnataka, Bengaluru, India</text>
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                <text>Deuteron photodisintegration; Model independent theoretical formalism; Proton polarization</text>
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                <text>In addition to other photonuclear reactions, the study of photonuclear reactions on deuterium targets is important for laser physics, nuclear physics, astrophysics, and a number of applications, including nondestructive testing of nuclear materials. In this paper, we have carried out a model independent analysis of proton polarization in photodisintegration of deuterons with initially unpolarized beam and unpolarized target. The angular dependence of the polarization is studied by expressing it in terms of multipole amplitudes.  2023 Elsevier Ltd. All rights reserved.</text>
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                <text>Materials Today: Proceedings, Vol-100, pp. 129-131.</text>
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                <text>ISSN: 22147853</text>
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                <text>Kusuma M., K S School of Engineering and Management, Karnataka, Bengaluru, 560109, India, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India; Shastri V., School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India; Shilpashree S.P., School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India</text>
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                <text>Digital Transaction Cyber-Attack Detection Using Particle Swarm Optimization</text>
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              <elementText elementTextId="170452">
                <text>Cyber-attacks; Deep Learning; Digital Transaction; Genetic Algorithm; Machine Learning; Particle Swarm Optimization</text>
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                <text>The cyber digital world is an essential variant in day-to-day life in advanced technology. There is a better change in the lifestyle as intelligent technology. In larger excite to increase the advanced technology which can be developed to humans in major dependent on network and internet users. Now, in modern times, the internet has changed the primary need in human lifestyle by giving access to everything in the world while sitting in one place knowing and updating the information and usage of online subscribers or Revolution. The world is moving in Rapid and Faster communications within a fraction of a second, at a lesser cost, and it has minimal paper-based processes and relies on the digitization document instead of a paperless environment. The data is handled by finch security practices, which are used in security worldwide to establish protected data management systems like digital lending, credits, mobile Banking, and mobile payment. Cryptocurrency and blockchain, B-trading, and banking as a service are included. At the same time, leveraging the new technologies is to resist hacking cyber-attacks. This article is also involved in artificial intelligence and machine learning (AI&amp;amp;ML) in different cyber-attacks. This article focuses on genetic algorithms to detect the cyber-attack. The main aim of the detection is future to prevent these cyber-attacks. The comparison will take two sample genetic algorithms. The first one is taken for Ant Colony Optimization (ACO), and the proposed model is taken for Particle Swarm Optimization. The average attack detection of ACO algorithm is 45 packets at the same time PSO algorithm will detect 50 packets.   2023 IEEE.</text>
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                <text>Monisha N.; Gunvanth G.; Jayapandian N.</text>
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                <text>Proceedings of the 4th IEEE International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023</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/ICSTCEE60504.2023.10585244" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICSTCEE60504.2023.10585244&lt;/a&gt;
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                <text>ISBN: 979-835038462-8</text>
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                <text>Monisha N., Christ (Deemed To Be University), Department Of Cse, India; Gunvanth G., Christ (Deemed To Be University), Department Of Civil Engineering, India; Jayapandian N., Christ (Deemed To Be University), Department Of Cse, India</text>
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                <text>A Review on EMG-based Pattern Identification Methods for Effective Controlling of Hand Prostheses</text>
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                <text>Classification Algorithms; Deep Learning; Electromyography; Hand Prosthesis; Recognition Accuracy</text>
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                <text>The ability of amputees to do daily duties is significantly restricted by upper limb amputation. The myoelectric prosthesis uses impulses from the surviving muscles in the stump to gradually restore function to such severed limbs. Such myosignals are unfortunately tedious and challenging to gather and employ. The process of transforming it into a user control signal after it has been acquired often consumes a significant amount of processing resources. By modifying machine learning strategies for pattern recognition, the factors that influence the traditional electromyography (EMG)-pattern identification approaches may be significantly minimized. Although more recent developments in intelligent pattern recognition algorithms could discern between a variety of degrees of freedom with high levels of accuracy, their usefulness in practical (amputee) applications was less obvious. This review paper examined how well various pattern recognition algorithms for hand prostheses performed. Finally, we discussed the current difficulties and offered some suggestions for future research in our paper's conclusion.   2023 IEEE.</text>
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                <text>Ramkumar S.; Rema D.; Devi T.A.; Elavarasi K.; Selvaganapathi T.; Gokila S.</text>
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                <text>3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023 - Proceedings, pp. 517-523.</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/ICIMIA60377.2023.10426249" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICIMIA60377.2023.10426249&lt;/a&gt;
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                <text>ISBN: 979-835034363-2</text>
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                <text>Ramkumar S., Christ (Deemed to Be University), Department of Computer Science, Bangalore, India; Rema D., Karpagam Academy of Higher Education, Department of Food Technology, Coimbatore, India; Devi T.A., Karpagam Academy of Higher Education, Department of Food Technology, Coimbatore, India; Elavarasi K., Karpagam Academy of Higher Education, Department of Electrical and Electronics Engineering, Coimbatore, India; Selvaganapathi T., Karpagam Academy of Higher Education, Department of Electrical and Electronics Engineering, Coimbatore, India; Gokila S., Karpagam Institute of Technology, Department of CSE, Coimbatore, India</text>
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                <text>The intrinsic nature of a Mobile Ad hoc Network (MANET) makes it difficult to provide security and it is more vulnerable to network attacks. Denial of Service (DoS) attack can be executed using Flooding attack, that has the potential to bring down the entire network. This attack works by delivering an excessive number of unwanted packets that consumes too much battery life, storage space, and bandwidth, that eventually lowers the system's performance. In order to flood the network, the attacker injects fake packets into it. Both Control Packet flooding and Data flooding attacks are taken into account in this study. FADA (Flooding Attack Defense AODV) protocol is proposed to counter flooding attack that promotes greater utilization of existing resources. This research identifies the attack-causing node, isolates it and protects the network against flooding attack. Attack Detection Rate, Attack Detection Accuracy, End-to-end delay and Throughput are few metrics used for evaluation of the proposed model. NS-2.35 is used to demonstrate the efficiency of the suggested protocol and the results prove that the proposed model increases system's throughput while decreasing attack. The simulation results have shown that the proposed FADA protocol performs better than the existing models taken into consideration.   2023 IEEE.</text>
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                <text>Kiruthika M., School of Computing, SRM Institute of Science and Technology, Department of Computational Intelligence, Tamilnadu, Kattankulathur, India; Krishnasamy L., School of Engineering and Technology, Christ University, Department of Computer Science and Engineering, Karnataka, Bengaluru, India; Samuel P., School of Computing, SRM Institute of Science and Technology, Department of Computational Intelligence, Tamilnadu, Kattankulathur, India; Kanagaraj K., School of Computing, SRM Institute of Science and Technology, Department of Computational Intelligence, Tamilnadu, Kattankulathur, India</text>
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                <text>Synthesis of 1, 8-Naphthyridine-3-Carbonitriles under solvent-free conditions using ceric ammonium nitrate</text>
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                <text>Materials Today: Proceedings, Vol-100, pp. 218-222.</text>
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                <text>ISSN: 22147853</text>
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                <text>Tabassum S., Department of Chemistry, Surana College, Bengaluru, 560004, India; Servesh A., Department of Chemistry, CHRIST- Deemed to be University, Bengaluru, 570029, India; Daniel N.K., Department of Sciences and Humanities, CHRIST- Deemed to be University, Bengaluru, 570029, India; Govindaraju S., Department of Sciences and Humanities, CHRIST- Deemed to be University, Bengaluru, 570029, India</text>
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                <text>NLP-based Health Care- Hospital Recommendation Systems with Online Text Reviews by Patients Satisfaction</text>
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                <text>Recent times, these recommendations based on reviews play a vital role in the service industry. The hospital is assessing its quality of service using these surveys or studies posted in online forums. The ongoing pandemic also played a vital role in making the online review more popular. These statistical data and visualization are informative in representing the views of patient satisfaction towards health service. As the size of data is large and it is of varied size and format it is difficult to get consolidated results. The users share their emotions and feelings through this review. So, it is a challenge to assess the emotions of the patients. Sentiment analysis using machine learning makes our work easy in evaluating the scores visually. The reviews are analyzed using natural language processing (NLP), and the sentiment of the studies is analysed as positive, negative, and neutral using polarity ranking, which in turn is converted as the recommendation system based on patient reviews. This paper aims to propose a new method of recommending the hospital based on the sentiment of the previous user review. The thought of the user is collected from the various hospitals. The proposed (Healthcare Recommendation System) HRS system has nearly 0.5 mean absolute error, which states that the proposed HRS system is significantly effective.   2023 IEEE.</text>
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                <text>Sabarmathi G.; Chinnaiyan R.; Muthulakshmi R.</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/ICCAMS60113.2023.10525742" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICCAMS60113.2023.10525742&lt;/a&gt;
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                <text>ISBN: 979-835031706-0</text>
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                <text>Sabarmathi G., CHRIST(Deemed to Be University), School of Business Management, Bengaluru, India; Chinnaiyan R., Alliance University, Department of Cse, Bengaluru, India; Muthulakshmi R., Environmental Engineering, Kec, Andhra Pradesh, India</text>
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