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                <text>Intelligent Approaches of Clinical and Nonclinical Type-1 Diabetes Data Clustering and Analysis</text>
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                <text>Analysis; Clustering; Diabetes Type 1; Intelligent approaches; K-Means; Silhouette</text>
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                <text>Every year in India, there are nearly 15,600 fresh cases being reported among these age groups. In 2011, in the United States, 18,000 children under 15 were newly reported for T1DM. Over 13years, the Karnataka state government has a list of records showing that out of 100,000, 37% of boys and 40% of girls are affected by T1DM Disease. This paper investigates two methodologies to identify significant details about Type-1 diabetes. The first methodology is applicable to clinical data. The second methodology is demonstrated for the NDA T1D dataset. The dataset is utilized further to apply machine learning techniques to group similar patient traits. Exploratory data analysis on the dataset has revealed significant information answering a few research questions. This analysis can be useful for India, China, and other countries with high populations. In this paper, a unique methodology based on Artificial Intelligence Technique is proposed for both clinical and non-clinical data. The Autoimmune Disease, Diabetes Type 1-T1D, is focused. Non Clinical data based on 2021 reports are collected to identify patterns. Substantial unique issues are addressed in this work which were never reported before. The knowledge generated can be helpful for creating new clinical datasets, methodology and new insights related to Type-1 diabetes.  The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.</text>
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                <text>Lecture Notes in Networks and Systems, Vol-818, pp. 349-360.</text>
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                <text>Goswami M., CHRIST (Deemed to be University), Bangalore, India; Elango D., CHRIST (Deemed to be University), Bangalore, India; Vishnu C., CHRIST (Deemed to be University), Bangalore, India; Tendulkar V., CHRIST (Deemed to be University), Bangalore, India</text>
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                <text>A course recommendation provides valuable guidance and support to learners navigating their educational and career journeys. Artificial Intelligence paves the way for recommending higher education courses. In this article, a framework is proposed that uses different features like learners' interest, their past performance and mainly their family talent history. This framework emphasizes the Intelligence Robotic Course Recommendation System. The system is very helpful for the learner who don't have that much of an explorer of the current trends happening in the world. When the learners similarity knowledge interest is known with respect to real-world needs, the perfect higher education is suggested for them. This paper shows that the framework gives better results when using with artificial intelligence algorithms.  2023 IEEE.</text>
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                <text>Vinay M., Christ (Deemed to Be University), Department of Computer Science, Karnataka, Bangalore, India; Jayapriya J., Christ (Deemed to Be University), Department of Computer Science, Karnataka, Bangalore, India</text>
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The present invention is related to an intelligent deep-well rescue system using ultrasonic sensors. The system for rescue a human in a narrow diameter deep-well (bore-well) comprises an ultrasonic sensor module, a grappling module, a central computing unit. The objective of the present invention is to solve the problems of the prior arts in solving issues of rescue of human being from the deep-well or bore well."</text>
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                <text>For every country which is expecting a large growth in power demand in the near future or facing a power crisis, an effective load control and power distribution strategy is a necessity. Load shedding is done whenever power demand is more than power generation in order to sustain power system stability. The current load shedding strategies fails to shed exact amount of load as per the system requirement and does not prioritize loads which are being shed. Given the dimension of the problem, it would not be feasible computationally, to use regular optimization techniques to solve the problem. The problem is typically suited for application of meta-heuristic algorithms. This paper proposes a new scheme for optimizing load shedding using ant colony algorithm in a smart grid platform considering loads at utility level. The algorithm developed considers each electrical connection from Distribution Company as one lumped load and provides an effective methodology to control the load based on various constrains such as importance of load and time of load shedding.  Springer India 2015.</text>
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                <text>Lecture Notes in Electrical Engineering, Vol-326, pp. 1149-1162.</text>
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                <text>Margaret V., Electrical and Electronics Department, Christ University, Bangalore, India; Uma Rao K., Electrical and Electronics Department, R.V. College of Engineering, Bangalore, India; Ganeshprasad G.G., Electrical and Electronics Department, Christ University, Bangalore, India</text>
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                <text>Intelligent machine learning approach for cidscloud intrusion detection system</text>
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                <text>In this new era of information technology world, security in cloud computing has gained more importance because of the flexible nature of the cloud. In order to maintain security in cloud computing, the importance of developing an eminent intrusion detection system also increased. Researchers have already proposed intrusion detection schemes, but most of the traditional IDS are ineffective in detecting attacks. This can be attained by developing a new ML based algorithm for intrusion detection system for cloud. In the proposed methodology, a CIDS is incorporated that uses only selected features for the identification of the attack. The complex dataset will always make the observations difficult. Feature reduction plays a vital role in CIDS through time consumption. The current literature proposes a novel faster intelligent agent for data selection and feature reduction. The data selection agent selects only the data that promotes the attack. The selected data is passed through a feature reduction technique which reduces the features by deploying SVM and LR algorithms. The reduced features which in turn are subjected to the CIDS system. Thus, the overall time will be reduced to train the model. The performance of the system was evaluated with respect to accuracy and detection rate. Then, some existing IDS is analyzed based on these performance metrics, which in turn helps to predict the expected output. For analysis, UNSW-NB15 dataset is used which contains normal and abnormal data. The present work mainly ensures confidentiality and prevents unauthorized access.  The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.</text>
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                <text>Sowmya T.; Muneeswari G.</text>
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                <text>Lecture Notes on Data Engineering and Communications Technologies, Vol-66, pp. 873-885.</text>
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                <text>Sowmya T., School of Engineering and Technology CHRIST (Deemed to be University), Bangalore, India; Muneeswari G., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India</text>
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                <text>The use of intelligence in manufacturing has emerged as a fascinating subject for academics and businesses everywhere. This book focuses on various manufacturing operations and services which are provided to customers to achieve greater manufacturing flexibility, as well as widespread customization and improved quality with the help of advanced and smart technologies. It describes cyber-physical systems and the whole product life cycle along with a variety of smart sensors, adaptive decision models, high-end materials, smart devices, and data analytics. Intelligent Manufacturing and Industry 4.0: Impact, Trends, and Opportunities focuses on Intelligent Manufacturing and the design of smart devices and products that meet the demand of Industry 4.0, manufacturing and cyber-physical systems, along with real-time data analytics for Intelligent Manufacturing. The usage of advanced smart and sensing technologies in Intelligent Manufacturing for healthcare solutions is discussed as well. Popular use cases and case studies related to Intelligent Manufacturing are addressed to provide a better understanding of this topic. This publication is ideally designed for use by technology development practitioners, academicians, data scientists, industry professionals, researchers, and students interested in uncovering the latest innovations in the field of Intelligent Manufacturing. Features:  Presents cutting-edge manufacturing technologies and information to maximise product exchanges and production  Discusses the improvement in service quality, product quality, and production effectiveness  Conveys how a manufacturing companys competitiveness can increase if it can manage the turbulence and changes in the global market  Presents how intelligence production is essential in Industry 4.0 and how Industry 4.0 offers greater manufacturing flexibility, as well as widespread customisation, improved quality, and increased productivity  Covers the ways businesses handle the challenges of generating an increasing number of customised items with quick time to market and greater quality  Includes popular use cases and case studies related to intelligent manufacturing to provide a better understanding of this discipline.  2025 selection and editorial matter, Alka Chaudhary, Vandana Sharma, and Ahmed Alkhayyat individual chapters, the contributors.</text>
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                <text>Intelligent Manufacturing shows transformative paradigms in the manufacturing industry; leveraging advanced technologies such as Artificial Intelligence (AI), Internet of Things (IoT), and robotics, to develop highly automated and adaptive production systems. This chapter outlines the Intelligent Manufacturing process, including its key principles, components, challenges, and opportunities. The combination of Machine Learning (ML) techniques and AI enables decision-making, real-time optimisation, and predictive analytics of manufacturing processes, productivity, and quality of products. Robotics and IoT devices play critical roles in enabling automation, data collection, and connectivity within Intelligent Manufacturing environments. Additionally, Digital Twin technology facilitates virtual simulation, modelling, and optimisation of production systems. While Intelligent Manufacturing offers significant benefits, it also presents challenges viz. high investments, integration complexity, and workforce reskilling requirements. Overcoming the challenges requires a holistic approach involving collaboration between industry stakeholders, government agencies, academia, and technology providers. Overall, Intelligent Manufacturing represents a promising future for the manufacturing industry, offering opportunities for innovation, competitiveness, and sustainable growth in a rapidly evolving global economy.  2025 selection and editorial matter, Alka Chaudhary, Vandana Sharma, and Ahmed Alkhayyat individual chapters, the contributors.</text>
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                <text>Internet of Things (IoT) interconnects billions of devices by establishing a network that adheres to International Organization of Standardization (ISO) standards. These devices communicate with each other by sharing data regulated by the application. This is performed to accomplish a task or service that the application demands. The social or human-like behaviors are adapted in the IoT environment forming the Social IoT (SIoT). The SIoT integrates social networks in IoT-connected devices, making them unique and identifiable. Recent advancements in networking, intelligent network management, battery management, remote sensing, sensors, and other related technologies convinced users and designers to adopt IoT even for large-scale applications where the data involved is enormous. Leveraging the advancements in medical IoT, which focuses on healthcare to patients, can improve its service by removing redundant manual processes, long wait times, and providing other automated services. The advancements in real-time healthcare IoT devices and wearables make a strong case for implementing SIoT in the healthcare domain. SIoT in the healthcare domain has the potential to benefit users on a large scale. This chapter comprehends the challenges and solutions of using SIoT in medical and healthcare solutions from a networking quality of service (QoS) perspective. In addition, this chapter compares the intelligent algorithm, which can be used to improve the QoS of SIoT. Achieving higher QoS is necessary for healthcare services, especially while handling data from emergency and intensive care units. These data cannot tolerate errors and delays. Intelligent network management has become unavoidable in the health and medical services to achieve a higher degree of QoS system, which indirectly decreases data transfer time. The data from the sensor devices sent across the network leads to data loss and delay in data transmission due to congestion in the network and gateway devices. The optimized algorithms incorporated with the delay-based algorithm improves the QoS predominantly and reduces the delay in data transfer. Similarly, the particle swarm optimization algorithm allocates resources over the network and dynamically makes the network adapt to increased and reduced data flow, which reduces the delay and improves the QoS. Intelligent optimized delay algorithm (IODA) is proposed to improve the network performance by reducing the delay and using available bandwidth for data transfer in SIoT.  2023 selection and editorial matter, Gururaj H L, Pramod H B, and Gowtham M; individual chapters, the contributors.</text>
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                <text>"Patent Number: 201941023480, Applicant: Dr, Sachi Nandan Mohanty.&#13;
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The present invention present an intelligent PSO based DC motor control System and method thereof. The system for intelligent control of a DC motor, comprises a PID controller, and Particle Swarm Optimization Control unit, a PSO tuning algorithm &amp; fuzzy logic control unit.</text>
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The system for intelligent control of a DC motor, comprises a PID controller, and Particle Swarm Optimization Control unit, a PSO tuning algorithm &amp; fuzzy logic control unit. The present system and method disclosed novel &amp; efficient control techniques with using PSO algorithm and fuzzy logic techniques for DC motor control using a PID controller with solving problems of efficient control the prior arts.</text>
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