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                <text>Aayush A.A.A., School of Sciences, Christ (Deemed to be University), Delhi, Delhi, India; Sundaram J., School of Sciences, Christ (Deemed to be University), Delhi, Delhi, India; Devaraju S., School of Computing Science and Engineering (SCSE), VIT Bhopal University, Bhopal, India; Jayaprakash S., BlueCrest University College, Accra, Ghana; Anandaram H., Centre for Excellence in Computational Engineering and Networking, Coimbatore, India; Manivasagan C., School of Computer Studies (UG), Rathnavel Subramaniam College of Arts and Science, Coimbatore, India</text>
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                <text>Dynamical analysis of a model of social behavior: Criminal vs non-criminal population</text>
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                <text>Criminal-minded population; Logistic growth; Prey predator model; Stability analysis</text>
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                <text>In this paper, we construct a model motivated by the well known predator-prey model to study the interaction between criminal population and non-criminal population. Our aim is to study various possibilities of interactions between them. First we model it using simple predator-prey model, then we modify it by considering the logistic growth of non-criminal population. We clearly deduce that the model with logistic growth is better than classical one. More precisely, the role of carrying capacity on the dynamics of criminal minded population is discussed. Further, we incorporate law enforcement term in the model and study its effect. The result obtained suggest that by incorporating enforcement law, the criminal population reduces from the very beginning, which resembles with real life situation. Our result indicates that the criminal minded population exist as long as coefficient of enforcement lc does not cross a threshold value and after this value the criminal minded population extinct. In addition, we also discuss the occurrence of saddle-node bifurcation in case of model system with law enforcement. Numerical examples and simulations are presented to illustrate the obtained results.  2017 Elsevier Ltd</text>
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                <text>Abbas S.; Tripathi J.P.; Neha A.A.</text>
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                <text>Chaos, Solitons and Fractals, Vol-98, pp. 121-129.</text>
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                <text>ISSN: 9600779; CODEN: CSFOE</text>
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                <text>Abbas S., School of Basic Sciences, Indian Institute of Technology Mandi, Mandi, 175001, H.P., India; Tripathi J.P., Department of Mathematics, Central University of Rajasthan, NH-8, Bandarsindri, Kishangarh, Distt., Ajmer, 305801, Rajasthan, India; Neha A.A., Department of Mathematics, Christ University, Bengaluru, 560029, Karnataka, India</text>
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                <text>An efficient inclusion complex based fluorescent sensor for mercury (II) and its application in live-cell imaging /</text>
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                <text>Journal of Fluorescence, Vol.32, pp.1109–1124, ISSN No: 1573-4994.&#13;
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                <text>Abbo, Hanna., Cherian, Anila Rose., Titinchi, Salam., Varghese, Anitha.</text>
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                <text>&lt;a href="https://link.springer.com/article/10.1007/s10895-022-02931-4" target="_blank" title="&amp;quot; An efficient inclusion complex based fluorescent sensor for mercury (II) and its  application in live-cell imaging&amp;quot;" rel="noreferrer noopener"&gt;https://link.springer.com/article/10.1007/s10895-022-02931-4&lt;/a&gt;</text>
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                <text>Innovative Method for Alzheimer Disease Prediction using GP-ELM-RNN</text>
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                <text>Alzheimer Disease (AD); GP (Garson-Purned); Grey Level Co-occurrence Matrix (GLCM)</text>
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                <text>Brain illnesses are notoriously challenging because of their fragility, surgical complexity, and high treatment costs. Contrarily, it is not obligatory to carry out the operation, as the outcomes of the procedure may fall short of expectations. Adult-onset Alzheimer's disease, which causes memory loss and losing information to varied degrees, is one of the most common brain diseases. This will vary from person to person based on their current health situation. This highlights the need of using CT brain scans to classify the extent of memory loss and determine the patient's risk for Alzheimer's disease. The four main goals of Alzheimer's disease detection are preprocessing the data, extracting features, selecting features, and training the model with GP-ELM-RNN. The Replicator Neural Network has been utilized earlier for AD detection, however this study offers an improved version of the network, modified with ELM learning and the Garson algorithm. From this study, it is deduced that the proposed method is not only efficient, but also quite precise. In this research, GP-ELM-RNN network is built to four groups of images representing different stages of Alzheimer's disease: very mildly demented, mildly demented, averagely demented, and non-demented. The class of very mildly demented patients was found to have the highest accuracy (99.1%) and specificity (0.984%). As compared to the ELM and RNN models, this technique achieves superior accuracy (around 99.23%).  2023 IEEE.</text>
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                <text>Abd Algani Y.M.; Vidhya S.; Ghai B.; Acharjee P.B.; Kathiravan M.N.; Dwivedi V.K.</text>
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              <elementText elementTextId="174556">
                <text>Proceedings of the 2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023, pp. 723-728.</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/ICAAIC56838.2023.10140571" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICAAIC56838.2023.10140571&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163684137&amp;amp;doi=10.1109%2FICAAIC56838.2023.10140571&amp;amp;partnerID=40&amp;amp;md5=d53996823d1b1b0781a2f67268192103" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163684137&amp;amp;doi=10.1109%2fICAAIC56838.2023.10140571&amp;amp;partnerID=40&amp;amp;md5=d53996823d1b1b0781a2f67268192103&lt;/a&gt;</text>
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                <text>ISBN: 978-166545630-2</text>
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                <text>Abd Algani Y.M., The Arab Academic College for Education in Israel-Haifa, Department of Mathematics, Israel; Vidhya S., Rmk College of Engineering and Technology, S&amp;amp;H(Mathematics), Tamilnadu, Chennai, India; Ghai B., Ccsit Teerthankar Mahaveer University, Uttar Pradesh, Moradabad, India; Acharjee P.B., Christ University, Faculty Member, Maharashtra, Pune, India; Kathiravan M.N., Dr. N.G.P Arts and Science College, Department of Biotechnology, Tamil Nadu, Coimbatore, India; Dwivedi V.K., Vishwavidyalaya Engineering College, Department of Mathematics, Chhattisgarh, Surguja, India</text>
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                <text>A proposed framework for crop yield prediction using hybrid feature selection approach and optimized machine learning</text>
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                <text>CFS; Crayfish optimization; Crop yield prediction; Feature selection; Hyperparameter optimization; Information gain; Machine learning; Support vector machine; Support vector regression</text>
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                <text>Accurately predicting crop yield is essential for optimizing agricultural practices and ensuring food security. However, existing approaches often struggle to capture the complex interactions between various environmental factors and crop growth, leading to suboptimal predictions. Consequently, identifying the most important feature is vital when leveraging Support Vector Regressor (SVR) for crop yield prediction. In addition, the manual tuning of SVR hyperparameters may not always offer high accuracy. In this paper, we introduce a novel framework for predicting crop yields that address these challenges. Our framework integrates a new hybrid feature selection approach with an optimized SVR model to enhance prediction accuracy efficiently. The proposed framework comprises three phases: preprocessing, hybrid feature selection, and prediction phases. In preprocessing phase, data normalization is conducted, followed by an application of K-means clustering in conjunction with the correlation-based filter (CFS) to generate a reduced dataset. Subsequently, in the hybrid feature selection phase, a novel hybrid FMIG-RFE feature selection approach is proposed. Finally, the prediction phase introduces an improved variant of Crayfish Optimization Algorithm (COA), named ICOA, which is utilized to optimize the hyperparameters of SVR model thereby achieving superior prediction accuracy along with the novel hybrid feature selection approach. Several experiments are conducted to assess and evaluate the performance of the proposed framework. The results demonstrated the superior performance of the proposed framework over state-of-art approaches. Furthermore, experimental findings regarding the ICOA optimization algorithm affirm its efficacy in optimizing the hyperparameters of SVR model, thereby enhancing both prediction accuracy and computational efficiency, surpassing existing algorithms.  The Author(s) 2024.</text>
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                <text>Abdel-salam M.; Kumar N.; Mahajan S.</text>
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                <text>Neural Computing and Applications, Vol-36, No. 33, pp. 20723-20750.</text>
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                <text>Abdel-salam M., Faculty of Computer and Information Science, Mansoura University, Mansoura, Egypt; Kumar N., Department of CSE, Thapar Institute of Engineering and Technology, Deemed to Be University, Patiala, 147004, India; Mahajan S., Department of Computer Science, CHRIST (Deemed to be University), Delhi-NCR, India</text>
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                <text>Arrhenius activation energy; Homotopy analysis method; Newtonian heating; Sensitivity analysis; Stefan blowing</text>
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                <text>The characteristics of Stefan blowing effects in a magneto-hydrodynamic flow of a Casson fluid past a stretching sheet are investigated. The effects of radiation, heat source/sink, Newtonian heating, Arrhenius activation energy and binary chemical reaction are considered for heat and mass transfer analysis. The homotopy analysis method (HAM) was utilised to solve the transformed non-dimensionalized equations analytically. The impact of various physical parameters affecting the flow are investigated. Further, the relationship of various parameters on the skin friction and rate of heat and mass transfer was explored using correlation and probable error. A sensitivity analysis was carried out based on the Response Surface Methodology to analyse the effect of Stefan blowing parameter, magnetic parameter and stretching/shrinking parameter on the reduced Nusselt number and reduced Sherwood number. A constant positive sensitivity for the reduced Nusselt number towards the Stefan blowing parameter for all levels of magnetic parameter and stretching/shrinking parameter was found. Further, the reduced Sherwood number indicated a negative sensitivity towards the Stefan blowing parameter.  2020 Faculty of Engineering, Alexandria University</text>
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                <text>Alexandria Engineering Journal, Vol-59, No. 5, pp. 3991-4011.</text>
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                <text>Abdelmalek Z., Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam, Faculty of Medicine, Duy Tan University, Da Nang, 550000, Viet Nam; Mahanthesh B., Department of Mathematics, CHRIST (Deemed to be University), Bangalore, 560029, Karnataka, India; Md Basir M.F., Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, UTM Johor Bahru, 81310, Malaysia; Imtiaz M., Department of Social and Behavioral Sciences, National University of Medical Sciences, Rawalpindi, Pakistan; Mackolil J., Department of Mathematics, CHRIST (Deemed to be University), Bangalore, 560029, Karnataka, India; Khan N.S., Department of Mathematics, Abdul Wali Khan University, Mardan, 23200, Khyber Pakhtunkhwa, Pakistan; Nabwey H.A., Department of Mathematics, College of Science and Humanities in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia, Department of Basic Engineering Science, Faculty of Engineering, Menoufia University, Shebin El-Kom, 32511, Egypt; Tlili I., Department of Mechanical and Industrial Engineering, College of Engineering, Majmaah University, Al-Majmaah, 11952, Saudi Arabia</text>
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                <text>Wireless Sensor Data Acquisition and Control Monitoring Model for Internet of Things Applications</text>
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                <text>This article focuses on providing solutions for one important application termed as agriculture. In India, one major occupation for people living in urban and rural areas is agriculture where an economic rate depends only on the crops they yield. In such cases, if an intelligent monitoring device is not integrated then it becomes difficult for the farmers to grow their crops and to accomplish marginal income from what they have invested. Also existing methods have been analyzed in the same field where some devices have been installed and checked for increasing the productivity of horticulture crops. But existing methods fail to install an intelligent monitoring device that can provide periodic results within short span of time. Therefore, a sensor based technology with Internet of Things (IoT) has been implemented in the projected work for monitoring major parameters that support the growth and income of farmers. Also, an optimization algorithm for identifying the loss in different crops has been incorporated for maximizing the system boundary and to transmit data to farmers located in different areas. To prove the cogency of proposed method some existing methods have been compared and the results prove the projected technique produces improved results for about 58%.   2022 SulaimaLebbe Abdul Haleem et al.</text>
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                <text>Abdul Haleem S.; Kshirsagar P.R.; Manoharan H.; Prathap B.; Hemalatha S.; Pradeep Kumar K.; Tirth V.; Islam S.; Katragadda R.; Amibo T.A.</text>
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                <text>Scientific Programming, Vol-2022</text>
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&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125857423&amp;amp;doi=10.1155%2F2022%2F9099163&amp;amp;partnerID=40&amp;amp;md5=d4a9c53be3a1e2f429be7b99cc39f93f" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125857423&amp;amp;doi=10.1155%2f2022%2f9099163&amp;amp;partnerID=40&amp;amp;md5=d4a9c53be3a1e2f429be7b99cc39f93f&lt;/a&gt;</text>
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              <elementText elementTextId="113031">
                <text>All Open Access; Gold Open Access</text>
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                <text>ISSN: 10589244; CODEN: SCIPE</text>
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                <text>Abdul Haleem S., Department of Information and Communication Technology, South Eastern University of Sri Lanka (SEUSL), Oluvil, Sri Lanka; Kshirsagar P.R., Department of Artificial Intelligence, G. H. Raisoni College of Engineering, Nagpur, 440016, India; Manoharan H., Department of Electronics and Communication Engineering, Panimalar Institute of Technology, Poonamallee, Tamil Nadu, Chennai, India; Prathap B., Department of Computer Science and Engineering, CHRIST University, Bangalore, India; Hemalatha S., Department of Computer Science and Engineering, Panimalar Institute of Technology, Chennai, India; Pradeep Kumar K., Department of Computer Science and Engineering, Panimalar Institute of Technology, Chennai, India; Tirth V., Mechanical Engineering Department, College of Engineering, King Khalid University, Asir, Abha, 61411, Saudi Arabia; Islam S., Civil Engineering Department, College of Engineering, King Khalid University, Asir, Abha, 61411, Saudi Arabia; Katragadda R., Department of Business Management, V. R. Siddhartha Engineering College, Andhra Pradesh, Vijayawada, India; Amibo T.A., School of Chemical Engineering, Jimma Institute of Technology, Jimma University, Oromia, Jimma, Ethiopia</text>
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                <text>Stacked LSTM and Kernel-PCA-based Ensemble Learning for Cardiac Arrhythmia Classification</text>
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                <text>Arrhythmia classification; ensemble learning; extreme gradient boosting; kernel PCA; LSTM; machine learning</text>
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                <text>Cardiovascular diseases (CVD) are the most prevalent causes of death and disability worldwide. Cardiac arrhythmia is one of the chronic cardiovascular diseases that create panic in human life. Early diagnosis aids physicians in securing life. ECG is a non-stationary physiological signal representing the heart's electrical activity. Automated tools to detect arrhythmia from ECG signals are possible with Machine Learning (ML). The ensemble learning technique combines the power of two or more classifiers to solve a computational intelligence problem. It enhances the performance of the models by fusing two or more models, which extremely increases its strength. The proposed ensemble Machine learning amalgamates the potency of Long Short-Term Memory (LSTM) and ensemble learning, opening up a new direction for research. In this research work, two novel ensemble methods of Extreme Gradient Boosting-LSTM (EXGB-LSTM) are developed, which use LSTM as a base learner and are transformed into an ensemble learner by coalescing with Extreme Gradient Boosting. Kernel Principal Component Analysis (K-PCA) is a significant non-linear dimensionality reduction technique. It can manage highdimensional datasets with various features by lowering the dimensionality of the data while retaining the most crucial details. It has been applied as a preprocessing step for feature reduction in the dataset, and the performance of EXGB-LSTM is tested with and without K-PCA. Experimental results showed that the first method, fusion of EXG-LSTM, has reached an accuracy of 92.1%, Precision of 90.6%, F1-score of 94%, and Recall of 92.7%. The second proposed method, KPCA with EXGB-LSTM, attained the highest accuracy of 94.3%, with a precision of 92%, F1-score of 98%, and Recall of 94.9% for multi-class cardiac arrhythmia classification.  (2023), (Science and Information Organization). All Rights Reserved.</text>
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                <text>Abdullah A.; Nithya S.; Rani M.M.S.; Vijayalakshmi S.; Balusamy B.</text>
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                <text>International Journal of Advanced Computer Science and Applications, Vol-14, No. 9, pp. 39-48.</text>
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              <elementText elementTextId="100156">
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                <text>2023-01-01</text>
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                <text>&lt;a href="https://doi.org/10.14569/IJACSA.2023.0140905" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.14569/IJACSA.2023.0140905&lt;/a&gt;
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              <elementText elementTextId="100159">
                <text>All Open Access; Gold Open Access</text>
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                <text>ISSN: 2158107X</text>
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                <text>Abdullah A., Faculty of Applied Science and Technology, Pedana University, Kuala Lumpur, Malaysia; Nithya S., The Gandhigram Rural Institute (Deemed to be University), Gandhigram, India; Rani M.M.S., The Gandhigram Rural Institute (Deemed to be University), Gandhigram, India; Vijayalakshmi S., Christ University, Pune, India; Balusamy B., Shiv Nadar Institution of Eminence, India, Center for Global Health Research, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences, India</text>
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                <text>The increase in the rural workforce during and after the lockdown was primarily driven by women entering self-employment in agriculture, where earnings remain significantly low. The movement from casual work to self-employment in the agricultural sector also underlines a larger shift in dynamics in rural areas, arguably exacerbated by the pandemic. The non-manufacturing sector, most importantly the construction sector, absorbed men in the post-pandemic situation, but the same shift is not observed among women.  2026, Economic and Political Weekly. All rights reserved.</text>
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                <text>The effect of calcium Sulfoaluminate additives (CSA) on the compression and bending strength of mortar, as well as linear deformation of prism samples at different environmental humidity was studied. Test results indicate that bending strength of mortars with CSA and the referent at the age of 28 days are practically equal. Compressive strength of mortars with CSA reduced by 20... 23% for all dosages of CSA. Relative linear deformations depend on the humidity of the environment. At a humidity of 100%, the relative linear deformations are positive and the expansion increases with increasing dosage of the expanding additive. When hardening in dry air at a humidity of 55%, the greatest shrinkage deformations were observed for mortars with CSA. We can conclude that the expanding effect of CSA is fully manifested at high humidity, i.e. under construction conditions, this means very high-quality moisture care for concrete structures.  The Authors 2020.</text>
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                <text>E3S Web of Conferences, Vol-212</text>
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&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097718965&amp;amp;doi=10.1051%2Fe3sconf%2F202021202001&amp;amp;partnerID=40&amp;amp;md5=96693b4a10b68bec6c8ba6ae05a10c31" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097718965&amp;amp;doi=10.1051%2fe3sconf%2f202021202001&amp;amp;partnerID=40&amp;amp;md5=96693b4a10b68bec6c8ba6ae05a10c31&lt;/a&gt;</text>
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                <text>All Open Access; Gold Open Access; Green Open Access</text>
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                <text>ISSN: 25550403</text>
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                <text>Abdulwahid Al-Musawi K., Belarusian National Technical University, Minsk, Belarus; Bindhu Abraham K., Christ (Deemed to Be University), Bangalore, India; Potses T., Belarusian National Technical University, Minsk, Belarus; Sergey L., Belarusian National Technical University, Minsk, Belarus; Beulah M., Christ (Deemed to Be University), Bangalore, India; Obolewich J., Politechnika Bialostocka, Poland</text>
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                <text>Abhay Bhaskaran, M.T.; Joseph, Akash Varughese; Thomas, Romal; Kavitha, S.</text>
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                <text>From Producer to Consumer: AI-Blockchain Integration for Sustainable Supply Chain Tracking and Optimization</text>
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                <text>Proceedings of the 2025 International Conference on Technology Enabled Economic Changes, InTech 2025;pp.489-496</text>
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                <text>Abhay Bhaskaran M.T., Christ University, Department of Computer Science, Bangalore, India; Joseph A.V., Christ University, Department of Computer Science, Bangalore, India; Thomas R., Christ University, Department of Computer Science, Bangalore, India; Kavitha S., Christ University, Department of Computer Science, Bangalore, India</text>
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                <text>The blockchain is transforming the way supply chains operate. It is a decentralized peer-to-peer system that ensures safe and transparent data interchange. Because there is no central authority, blockchain technology can't be hacked; the data are dispersed throughout numerous nodes. Therefore, more openness, safety, and resistance to tampering are assured as opposed to conventional centralized database systems that depend on a single authority to manage all the data. This would ensure the immutability of the transaction log, which accurately traces the products from origin to destination. The supply chain refers to the transportation and distribution of goods when transactions are verified in real time and integrated into a secure, cryptographic ledger. This decentralized framework provides better tracking of various manufacturers, reduced chances of fraud, and greater trust among the participants. The early understanding based on tracking will definitely helps the stakeholders to improve the blockchain activities, reliability, it will minimize risks, and enhances efficiency. This will parallelly help those who are involved within supply chain management giving high accountability and seamlessness in the movement of details.  2025 IEEE.</text>
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                <text>AI; Anomaly detection; Autoencoders; Blockchain technology; Classification; Forest; Isolation; Optimization; Supply chain; SVM</text>
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                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>Artificial Intelligence-Monitored Procedure for Personal Ethical Standard Development Framework in the E-Learning Environment</text>
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          <element elementId="49">
            <name>Subject</name>
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              <elementText elementTextId="155073">
                <text>Ethical development; Holistic life; Personal ethics; Professional ethics</text>
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                <text>The changes in the lifestyle of human beings due to the pandemic COVID-19 have affected all walks of human life. As a pillar of human development, the arena of education has a vital role to play in this changing world. The humongous and disruptive technologies that had made inroads into the educational scene as E-learning paved the way for ethical concerns in an unimaginable manner. Artificial intelligence is prudently incorporated for developing an ethical lifestyle for students all over the world. The Personal Ethical Standard Framework would work as a vaccine for the pandemic of the cancerous growth of the unethical habits of learners.  2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>Lecture Notes in Networks and Systems, Vol-478, pp. 291-308.</text>
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                <text>Springer Science and Business Media Deutschland GmbH</text>
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                <text>Abhay R., Christ University, Bangalore, India; Abi A., Christ University, Bangalore, India; Othayoth P.K., Christ University, Bangalore, India; Kureethara J.V., Christ University, Bangalore, India; Puliyanmakkal J.K., Christ University, Bangalore, India</text>
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                <text>Unconscious thoughts more than often are seen to precede conscious contemplations of the surroundings. The Present chapter attempts to explore how subliminal priming of known and unknown human faces could impact product selection and decision-making time of consumers. 2 (Known face X Unknown face) X 2 (Product selection X Decision-making time) within-subject design was used for the study. A stimulus-priming experiment designed in E-prime software was used to subliminally expose the participants to both known and unknown human faces They were then asked to select a product that they were willing to buy from an option of four products, of which one of the products was primed along with Human face (Known Vs Unknown). The product selection rates as well as the time taken to select the product were recorded. A total of 100 Participants falling in the age category of young adults (18-39) took part in the study. The chapter discusses the results and dives deeper into the implications that they hold in the world of marketing.  2024, IGI Global. All rights reserved.</text>
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                <text>Teachers serve as torchbearers to the vast expanse of knowledge, leaving a lasting impact on the minds of their students through their teaching styles and pedagogical practices. With the advancement of technology, the integration of digital technologies has increasingly been observed in pedagogical approaches. The current study seeks to explore various subjective factors related to teachers, such as their teaching styles, age, classes taught, educational qualifications, and years of experience, in relation to their individual digital competencies and the extent of digital inclusivity in their pedagogies. Three psychometric scales were employed for data collection (TSI-Q, DiPeCoS, and TDiCoS). Data were gathered from 215 school teachers in the United Arab Emirates (UAE) and subsequently analysed. The results revealed a significant influence of teaching styles (p = 0.045), classes taught (p = 0.001), age (p = 0.006), and years of teaching experience (p = 0.016) on the digital inclusivity of the teachers' pedagogical practices.  2026 by IGI Global Scientific Publishing. All rights reserved.</text>
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                <text>Feminism bears the promise of liberation of and equality for women. Reading and teaching feminist texts, within the academia and in activist spaces, has provided the opportunity to explore what it means to become and be a woman. This article explores the experience of teaching a course on women's writing at the undergraduate level during the COVID-19 pandemic. Normally, a course on feminist writings is an occasion for self-reflection, thereby providing an opportunity to establish a dialogue between the domestic and the public. Such dialogues took place in secure institutional spaces such as classrooms or conference halls, without the intrusion of the domestic. However, as the teacher-student interaction shifted to an online mode during the pandemic, all the participants in this dialogue, including the instructor and the students, found themselves in domestic spaces, with family members listening. The article chronicles the anxieties of a woman instructor, as she teaches feminist texts from home to learners who are sitting behind computer screen in their homes and the possible impact of feminist ideas on the domestic spaces of all participants.   2022 The Author(s). Published by Oxford University Press on behalf of the English Association. All rights reserved.</text>
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                <text>English, Vol-70, No. 271, pp. 350-358.</text>
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                <text>God has signed: Nature, divinity and mysticism in the poetry of Kuvempu</text>
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                <text>Kuvempu wrote a large number of poems on the mysteries of nature. Kuvempu hails from the heart of Western Ghats and he spent his childhood and youth exploring the forests around his house. Untrammelled nature was both mysterious and beautiful; hence nature turned out to be a primary inspiration to write poetry. Kuvempu looks outward, seeking to comprehend the oneness of all in nature through his senses. But he is also struck by the inability to comprehend and explain nature through senses. Often he expresses his awe at natural sights such as dawn (which appears to him as a God's signature) or the greenery of Western Ghats (which seems to have painted everything in nature in green, including poet's soul and the blood in the stomach). This leads Kuvempu to resort to mysticism in order to relate, comprehend and sing about nature. He sees in nature the divine presence. The paper will analyze poems such as Devaru Rujumadidanu, Ba Phalguna Ravidarshanake, and Prakriti Upasane, and explore the poetic perception of nature as divine through mysticism.  2014 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (Dharmaram Vidya Kshetram, Bangalore).</text>
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                <text>Journal of Dharma, Vol-39, No. 1, pp. 39-46.</text>
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                <text>Abhaya N.B., Department of English, Christ University, Bangalore, India</text>
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