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                <text>ThermAI: Exploring Temperature Analysis Through Diverse Machine Learning Models</text>
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                <text>Machine learning methods; Meteorological forecasting; Random forests; Temperature predictions</text>
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                <text>Meteorological forecasting is crucial in multiple industries, including agriculture, aviation, and daily routines. The objective of this inquiry is to improve temperature predictions by examining and comparing several machine learning methods, such as linear regression, decision trees, and random forests. This work aims to fill the gap in assessing machine learning models for temperature forecasting on a broader scale by utilising the comprehensive Indian meteorological dataset, which covers a wide range of geographical regions. The research utilises a thorough technique that includes gathering data, selecting relevant features, choosing appropriate models, and evaluating the results using R-squared and Mean Square Error metrics. The findings demonstrate that the Random Forest model surpasses both multiple linear regression and decision trees in terms of performance, displaying superior accuracy and reduced prediction errors. This study enhances proactive weather management and decision-making processes by offering valuable insights and tools to stakeholders in various industries. The work is organised into distinct sections that encompass a literature review, methodology, results, and conclusions, providing a comprehensive viewpoint on developments in temperature forecasting.   2024 IEEE.</text>
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                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>Thomas D.R.B., CHRIST University, Department of computer science, Bangaluru, India; Joy H.K., CHRIST University, Department of computer science, Bangaluru, India</text>
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                <text>The Development of Structured Tele Based Medicine Concept Using Programmable System</text>
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                <text>and telemedicine; Binomial law; distributed adaptation; videoconferencing</text>
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                <text>In the medical field, clinics and hospitals frequently use dispersed applications like telediagnosis. These apps must nevertheless provide information security in order to properly transit security measures like firewalls and proxies. The User Datagram Protocol (UDP) is often recommended for videoconferencing applications because of its low latency; nevertheless, security problems occur when UDP tries to pass through firewalls and proxies without a specified set of fixed ports. In order to overcome these obstacles, this study presents a revolutionary platform that uses Transmission Control Protocol (TCP) rather of UDP: VAGABOND, which stands for 'Video Adaptation framework, across security gateways, based on transcription,' Adaptation Proxies (APs) that are designed to accommodate user preferences, device variations, and dynamic changes in network capacity comprise VAGABOND. This platform's versatility at the user and network levels guarantees seamless operation in a range of scenarios. VAGABOND uses a binomial probability distribution to start making adaptation decisions. This distribution is formed from the retention of video packets inside a certain time period. VAGABOND gets beyond firewall and proxy constraints by using ordinary TCP ports (like 80 or 443) to provide videoconferencing data via TCP. But even though TCP is a dependable transport protocol, it can occasionally have latency and socket timeout problems. VAGABOND has clever adaptation techniques to deal with these problems and ensure smooth data transfer.  2024 IEEE.</text>
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                <text>Adnan M.M.; Pant R.; Nirsanametla Y.; Fatima T.; Kumar M.; Kumar N.S.</text>
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                <text>2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 1285-1289.</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/ICACITE60783.2024.10616820" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICACITE60783.2024.10616820&lt;/a&gt;
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                <text>ISBN: 979-835036016-5</text>
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                <text>Adnan M.M., The Islamic University, Najaf, Iraq; Pant R., Uttaranchal University, Uttaranchal Institute of Management, Uttarakhand, India; Nirsanametla Y., Chitkara University, Centre of Research Impact and Outreach, Punjab, Rajpura, 140401, India; Fatima T., Cmr Technical Campus, Department of Cse, Telangana, Hyderabad, India; Kumar M., Christ University, School of Business and Management, Bangalore, India; Kumar N.S., Srm Institute of Science &amp;amp; Technology, Department of Computer Science &amp;amp; Engineering, Chennai, Vadapalani, 26, India</text>
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                <text>Attention Based Meta-Module to Integrate Cervigrams with Clinical Data for Cervical Cancer Identification</text>
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                <text>attention; cervical cancer; Deep learning; EfficientNet; feature integration</text>
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                <text>Cervical cancer remains a significant burden on public health, particularly in developing countries, where its malignancy and mortality rates are alarmingly high. Early diagnosis stands as a pivotal factor in effectively treating and potentially curing the cervical cancer. This study introduces a novel approach of meta module based on recurrent gate architecture designed to enhance the classification of cervix images efficiently. This innovative framework incorporates a meta module capable of dynamically selecting image modalities most pertinent attributes. Furthermore, it integrates clinical data with extracted image features and employs a range of EfficientNet architectures (B0-B5) for image classification. Our results indicate that the EfficientNet B5 architecture outperforms its counterparts, achieving an AUC (Area Under the Curve) score of 55.1 and an F1-Score of 75.1. Overall, this work represents a crucial step towards improving the early detection of cervical cancer, which in turn can lead to more effective treatment strategies and, ultimately, better outcomes for patients worldwide.  The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.</text>
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                <text>Mukku L.; Thomas J.</text>
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                <text>Lecture Notes in Networks and Systems, Vol-1046 LNNS, pp. 286-295.</text>
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                <text>Springer Science and Business Media Deutschland GmbH</text>
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                <text>&lt;a href="https://doi.org/10.1007/978-3-031-64813-7_30" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-3-031-64813-7_30&lt;/a&gt;
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                <text>ISSN: 23673370; ISBN: 978-303164812-0</text>
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                <text>Mukku L., CHRIST (Deemed to be University), Bangalore, India; Thomas J., CHRIST (Deemed to be University), Bangalore, India</text>
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                <text>On Two-Dimensional Approximate Pattern Matching Using Fuzzy Automata</text>
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                <text>Array matching; Fuzzy automata; Pattern matching; Position specific approximate pattern matching</text>
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                <text>Pattern matching has been extensively studied in the last few decades, owing to its great contribution in various fields such as search engines, computational biology, etc. Several real-life situations require patterns that allow ambiguity in specified positions. In this paper, one-dimensional and two-dimensional approximate pattern matching models have been constructed using fuzzy automata. The similarity function used in fuzzy automata enables the occurrence of all exact and similar one-dimensional and two-dimensional patterns. This kind of searching approximate patterns is not possible with regular search models. The time complexity of the proposed algorithm has also been analyzed.  The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.</text>
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                <text>John Kaspar A., Department of mathematics, Christ University, Karnataka, Bengaluru, 560029, India; Sheena Christy D.K., Department of mathematics, SRM Institute of Science and Technology, Tamil Nadu, Kattankulathur, 603203, India</text>
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                <text>A Quality of Service Study for Downlink Scheduling Algorithms in Mobile Networks</text>
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                <text>Applications; Channel Aware; Mobile Networks; Quality of Service; Scheduling</text>
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                <text>Internet usage and the number of applications/users growth is going in an unprecedented manner. In these days, lot of users are changed themselves to use internet-based applications rather than traditional voice service. The fundamental of voice-based communication is shifted to packet data access for satisfying the human needs through internet based mobile applications. 4G network is an IP supported rising technology for the past decade and at present also because of un availability service of 5G in all the places. Still, 4G is ruling the globe and the number of subscribers kept growing only. In these days, this remains on the list of latest research topics. Under 4G technology lot of research problems are exist like QoS, Uplink and Downlink Scheduling, Security, Mobility etc., Inspite of discussing that several issues, this paper mainly focusing the QoS in Downlink scheduling algorithms. Also, it presents the issues of various existing QoS downlink scheduling algorithms, names, QoS aware/unaware, parameters used/simulated, drawbacks of those algorithms and result verifications etc. Packet scheduling plays a crucial role for providing Quality of Service (QoS) to the mobile users. Ultimately, it gives some suggestions to explore more further about QoS based research work in Mobile Networks.  The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.</text>
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                <text>Krishnamoorthy R.; Nagaraj S.; Robert N.R.; Donald A.C.; Suresh K.; Cynthia T.</text>
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                <text>Lecture Notes in Networks and Systems, Vol-1050 LNNS, pp. 436-443.</text>
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                <text>ISSN: 23673370; ISBN: 978-303164846-5</text>
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                <text>Krishnamoorthy R., School of CS and IT, JAIN (Deemed to Be University), Bangalore, India; Nagaraj S., Departmentof CSE, Alliance University, Bengaluru, India; Robert N.R., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, India; Donald A.C., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, India; Suresh K., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, India; Cynthia T., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, India</text>
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                <text>Analyzing the Performance of Conformable and Non-Conformable Patch Antennas</text>
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              <elementText elementTextId="165563">
                <text>Graphene; MCNT; Patch Antenna; Return Loss; SCNT; VSWR</text>
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                <text>This paper presents a performance analysis between a conventional triangular shaped patch antenna and a future reconfigurable patch antenna. There are different materials with different electronic properties for the simulation of triangular shaped patch antenna. All the materials for the triangular patch antenna are simulated using FEKO tool. Materials selected for triangular patch antenna are Copper, Single-wall Carbon Nano-tube (SCNT), Multiple-wall Carbon Nano-tube (MCNT) and Graphene. For the futuristic antennas, cotton fabric based reconfigurable patch antenna is also analyzed and compared with triangular shaped patch antenna. Graphene based triangular patch antenna has been analyzed best out of other materials. Reconfigurable cotton fabric-based patch antenna provides better bandwidth and results are validated through simulation and experimental setup.  2024 IEEE.</text>
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                <text>Dhariwal S.; Lamba V.K.; Pathak J.; Choudhary A.; Kumar G.</text>
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                <text>2024 5th International Conference for Emerging Technology, INCET 2024</text>
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                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>&lt;a href="https://doi.org/10.1109/INCET61516.2024.10592925" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/INCET61516.2024.10592925&lt;/a&gt;
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                <text>ISBN: 979-835036115-5</text>
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                <text>Dhariwal S., Alliance University, Bengaluru, India; Lamba V.K., Symbiosis Institute of Technology, Pune, India; Pathak J., School of Engg. and Techn., Christ University, Bangalore, India; Choudhary A., Alliance University, Bengaluru, India; Kumar G., Alliance University, Bengaluru, India</text>
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                <text>The Optimization of Output of Wind Turbine with the Ongoing Grid System through BP Method Using ANN</text>
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                <text>ANN; automation; BP; design optimization; wind turbines</text>
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                <text>Wind turbines are intricate devices that need careful planning, evaluation, and installation to guarantee peak performance under a range of environmental circumstances. Comprehensive load calculations, performance evaluations, and iterative optimisation processes are all part of the design process. However, complex simulation techniques are required to adequately depict the non-linear behaviour of wind turbine systems because of their complicated structure. Automation of optimisation processes and simulation executions is crucial to optimise the design process and manage the large number of simulations that are needed. This work provides a thorough framework using back propagation (BP) and artificial neural networks (ANN) for simulation and optimization that will make it easier to manage and automate the execution of iterative simulations during the design and development of wind turbines. The framework's main goals are to make design load case simulations easier and optimise activities more automatically. The framework makes it possible to optimise wind turbine systems and explore design options more effectively by automating these procedures. Three example optimisation jobs illustrate the framework's versatility and functionality.  2024 IEEE.</text>
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                <text>Prabha B.; Donald C.; Dhandapani V.K.; Sura S.R.; Sayed R.M.; Jweeg M.</text>
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                <text>2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 400-403.</text>
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              <elementText elementTextId="165581">
                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>&lt;a href="https://doi.org/10.1109/ICACITE60783.2024.10616799" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICACITE60783.2024.10616799&lt;/a&gt;
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                <text>Rani T., SRM-IST, Fsh, Institute of Science and Technology, Department of Commerce, Chennai, Ramapuram, India; Vijayakumar L., Saveetha Institute of Medical and Technical Sciences, Saveetha College of Liberal Arts &amp;amp; Sciences, Department of Commerce, Tamil Nadu, Kanchipuram, India; Chandirasekar B., Saveetha Institute of Medical and Technical Sciences, Saveetha College of Liberal Arts &amp;amp; Sciences, Department of Commerce, Tamil Nadu, Kanchipuram, India; Geethanjali N., Psna College of Engineering and Technology, Department of Management Studies, Tamil Nadu, Dindigul, India; Roja M.P., Psna College of Engineering and Technology, Department of Management Studies, Tamil Nadu, Dindigul, India; Salunkhe H.A., Christ University (Deemed to Be University), Department School of Business and Management, Bangalore, India</text>
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                <text>Singh J., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Dharani M., Department of Electronics and Communication Engineering, K. S. R. Institute for Engineering and Technology, Tiruchengode, India; Shelke N.A., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Sajid M., Department of Computer Science, Aligarh Muslim University, Aligarh, India; Alsahlanee A.T.R., Development and Continuous Education Center, University of Thi-Qar, Thi-Qar, Iraq; Upreti K., Department of Computer Science, CHRIST (Deemed to Be University), Ghaziabad, India</text>
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                <text>Gupta S., Chitkara University, Chitkara Centre for Research and Development, Himachal Pradesh, India; Al-Fatlawy R.R., The Islamic University, Najaf, Iraq; Aggarwal S., Uttaranchal University, Uttaranchal Institute of Technology, Department of Mechanical Engineering, Dehradun, India; Divya Shree V., Christ (Deemed to Be University), School of Business and Management, Bangalore Yeshwanthpur campus, India; Devadas G., Cmr College of Engineering &amp;amp; Technology, Department of Eee, India; Lazer N., Saveetha Institute of Medical and Technical Sciences, Saveetha University, Electronics and Communication Engineering, Saveetha School of Engineering, Chennai, India</text>
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                <text>Over the past ten years, the importance of financial literacy has been growing across the world. Prior research has found that a lack of financial knowledge can have several negative consequences, the inability to make correct financial decisions, high levels of debt, high-cost borrowing and misuse of credit. Limited knowledge of financial concepts also has an impact on the economy as a whole. This study attempts to measure the level of financial literacy of college students in Goa, India. A total of 378 respondents were surveyed and their level of financial literacy was measured through a percentage analysis. The respondents level of financial literacy was also studied concerning various demographic characteristics. The results show an association between financial literacy and sex, level of education, field of education, percentage of respondents and income level. The findings of the study suggest a need for the strengthening of initiatives by policymakers to introduce the concept of financial literacy for students all over the state as well as the country, in every field.  The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.</text>
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                <text>Lecture Notes in Networks and Systems, Vol-1080 LNNS, pp. 412-425.</text>
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                <text>Dave K.A., St. Josephs College of Commerce (Autonomous), Karnataka, Bengaluru, India; Ashfaque M.Z., St. Josephs College of Commerce (Autonomous), Karnataka, Bengaluru, India; Guruprasad D., Department of Commerce, Manipal Academy of Higher Education, Bengaluru, India; Narasimha M.H., CHRIST (Deemed to be University), Bengaluru, India; Kazim S., Jain (Deemed to be University) CMS, Bengaluru, India; Mukthar K.P.J., Kristu Jayanti College (Autonomous), Karnataka, Bengaluru, India</text>
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                <text>Facial Recognition (FR); Field Programmable Gate Arrays (FPGA); Payment System</text>
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                <text>An essential component of agricultural output is pest management, especially in fertigation-based farming. Although fertigation systems in Malaysia are beneficial for irrigation and fertilization, they frequently don't have effective pest control techniques. Because pests usually live beneath crop leaves, hand spraying is difficult and labor-intensive. Insect pests have the power to seriously harm, weaken, or even kill agricultural plants, which can lead to lower yields, worse-quality goods, and unsalable outcomes. Furthermore, insects may still cause harm to processed or stored items after harvest. Therefore, creating an autonomous pesticide sprayer specifically designed for chilli fertigation systems is the main goal of this research. The main goal is to create a sprayer arm that is flexible enough to reach under crop leaves. The goal of this project is to build an autonomous, unmanned pesticide sprayer. The goal of autonomous operation is to reduce the amount of dangerous pesticides that people are exposed to, especially in enclosed spaces like greenhouses. In addition, the sprayer arm's adaptability to different agricultural circumstances makes it a valuable tool in both greenhouse and outdoor settings. It is expected that the successful adoption of the autonomous pesticide sprayer would completely transform fertigation-based farming's approach to pest management.  2024 IEEE.</text>
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                <text>Babu P.B., Aditya Engineering College, Department of Electronics and Communication Engineering, Surampalem, India; Bisht D., Uttaranchal Institute of Management, Uttaranchal University, Department of Management, Dehradun, 248007, India; Shilpa K., Cmr Technical Campus, Department of Cse, Telangana, Hyderabad, India; Alzubaidi L.H., The Islamic University, Najaf, Iraq; Sharma A., Chitkara University, Chitkara Centre for Research and Development, Himachal Pradesh, 174103, India; Rajesh Kanna R., Christ(Deemed to Be University), Department of Cse, Bangalore, India</text>
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                <text>Crime profoundly impacts individuals, communities, and families. Technological advancements have provided perpetrators with new opportunities for criminal activities. The primary objective of the police department is to resolve crimes, ensuring justice for the victims. Additionally, preventing such incidents is crucial for creating a safer world. The landscape of criminal justice has undergone a significant shift with the integration of machine learning techniques, unlocking unparalleled potential for accuracy and efficiency. This study thoroughly examines the concept of various applications of machine learning in crime detection, prediction, and prevention. We examine the evolution of these technologies, from early developments to state- of-the-art methodologies, conducting a thorough analysis of their strengths, limitations, and ethical considerations. Moreover, the paper sheds light on crimes discussed in academic circles, serving as a repository for scholars and researchers. This facilitates informed discussions and guides future research endeavours.  2024 IEEE.</text>
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                <text>Rachana N., Christ (Deemed To Be University), Department Of Computer Science, Bangalore, India; Norton Stanley S.A., Christ (Deemed To Be University), Department Of Computer Science, Bangalore, India; Sandeep J., Christ (Deemed To Be University), Department Of Computer Science, Bangalore, India; Sreeja C.S., Christ (Deemed To Be University), Department Of Computer Science, Bangalore, India</text>
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                <text>The Role of IOT in Creating SC'S through Ultra Fast Updation of the Status for Accurate Action Plan</text>
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                <text>Hariharasudhan S., Prince Shri Venkateshwara Padmavathy Engineering College, Department of Information Technology, India; Subasree S., Nehru Institute of Engineering and Technology, Department of Computer Science and Engineering, Tamil Nadu, T.M. Palayam, India; Bhuvaneswari M., Karpagam Academy of Higher Education, Department of Biomedical Engineering, TN, Coimbatore, India; Victor M., Christ University, School of Business and Management, Bangalore, India; Kumar R., Ies University, Department of Mech Engg, Ies Institute of Technology and Management, M.P., Bhopal, 462044, India; Kumar K.S., Vel Tech Rangarajan Dr. Sagunthala R D Inst. of Science and Technology (Deemed to Be University), Chennai, Tamil Nadu, Avadi, India</text>
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                <text>Amarnath Reddy B., Vignana Jyothi Institute of Management, Department of Finance, Telangana, Hyderabad, India; Vinesh Kumar G., Vardhaman College of Engineering, Department of Management Studies, Telangana, Hyderabad, India; Dhiliphan Kumar M., Deemed University, Business Administration, Kalasalingam Academy of Research and Education, Virudhunagar, India; Veena S., Deemed University, Business Administration, Kalasalingam Academy of Research and Education, Virudhunagar, India; Chauhan A., School of sciences, CHRIST (Deemed to be university), Department of life sciences, Karnataka, Bengaluru, India; Anwer Basha H., Saveetha College of Liberal Arts and Sciences, Saveetha Institute of Medical and Technical Sciences, Department of Computer Science, Chennai, India</text>
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                <text>Priyadharsini P., Nehru Institute of Engineering and Technology, T.M.Palayam, Coimbatore, India; Khare N., Ies University, Ies Insititute of Technology and Management, M.P., Bhopal, 462044, India; Praveenraj D.D.W., Christ University, Bangalore, India; Senthurya S., Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, India; Arunkumar B., Karpagam Academy of Higher Education, Coimbatore, India; Reddy G.V., Cmr Technical Campus, Hyderabad, Medchal, India</text>
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                <text>Cervical cancer ranks as the fourth most common malignancy worldwide and poses a significant threat, particularly in resource-constrained regions. Automated diagnostic approaches, leveraging colposcope image analysis, hold great promise in curbing the impact of this disease. In this study, we introduce an ensemble of machine learning and deep learning models, including DenseNet 121, ResNet 50, and XGBoost to classify the cervical intraepithelial neoplasia. A novel feature integration is proposed which ensembles the results of the individual models in five fold validation process. Our methodology is deployed on a dataset sourced from the International Agency for Cancer Research. The results from the proposed framework have shown to be accurate, robust and dependable. This method can be utilized for achieving automatic identification of cervical cancer in early stages so it can be treated appropriately.  The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.</text>
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                <text>Mukku L.; Thomas J.</text>
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                <text>Lecture Notes in Networks and Systems, Vol-1046 LNNS, pp. 112-122.</text>
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                <text>ISSN: 23673370; ISBN: 978-303164812-0</text>
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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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                <text>Mukku L., CHRIST (Deemed to be University), Bangalore, India; Thomas J., CHRIST (Deemed to be University), Bangalore, India</text>
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