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检索条件"任意字段=International Conference on Intelligent and Smart Computing in Data Analytics, ISCDA 2020"

共 3246 条记录，以下是41-50 订阅

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Precision Healing: Transforming Spinal Wellness with Neural Precision and K-fold Cross-validation 2

Precision Healing: Transforming Spinal Wellness with Neural ...

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2nd international conference on Sustainable computing and smart Systems (ICSCSS)

作者： Anjum, Ayesha Shivapur, Seema Sharieff, Arfath Ahmed Aziz, Faizan Tabassum, Husna HKBK Coll Engn Dept CSE Bangalore Karnataka India

ISBN: (纸本)9798350391558;9798350379990

Maintaining a good health alongside work seems hectic and also a concern if not taken care of. We always want things happen automatically and if we can integrate the technologies we work on to maintain our health, it would do wonders. In this work, we focus on developing a prototype of a smart chair integrated with intelligent technologies to help employees take care of their spinal health alongside doing their work. The intelligent identifier incorporates machine learning algorithms that continually adapt and improve based on user feedback and data analysis. The adaptive learning feature enhances the system's accuracy in recognizing subtle changes in posture and provides personalized recommendations to users for optimizing their sitting habits. Moreover, the proposed system integrates with mobile applications and wearable devices, allowing users to track their posture and health metrics seamlessly. This integration enhances user engagement and enables a holistic approach to wellness management, encompassing both posture monitoring and overall health tracking. For healthcare professionals, the proposed system offers comprehensive analytics tools that provide insights into trends and patterns in patients' sitting behaviors. This data-driven approach enables proactive interventions, early detection of potential health issues, and better management of chronic conditions related to posture and sitting habits. The proposed model is developed with the use of sensors and internet of things, giving precision in sensing the posture and correcting the postures. Furthermore, our intelligent identifier is designed with high privacy and data security, ensuring that sensitive user information is protected at all times. This commitment to privacy and security enhances user trust and confidence in the system, leading to greater adoption and long-term benefits for individuals and healthcare providers alike.

关键词： Posture detection smart chair EMG sensor Muscle strain Arduino UNO Sensors IoT smart Device

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IoT Services and Intelligence: Empowering the Internet of Things with Real-Time data analytics and decision-making 2

IoT Services and Intelligence: Empowering the Internet of Th...

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2nd IEEE international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Kilaru, Mrudula Maheswari, P. Boddepalli, Eswararao Venkataramana, K. Patel, Jaksan D Sharma, M.K. Velagapudi Ramakrishna Siddhartha Engineering College India Srm Institute of Science and Technology Faculty of Science and Humanities Department of Commerce Ramapuram Chennai India Virginia Tech. Blacksburg Bradely Department of Ece United States Vemu Institute of Technology Department of Cse A.P Chittoor India Merchant Engineering College Mechanical Engineering Basna Gujarat Mehsana India Chaudhary Charan Singh University Mathematics Uttar Pradesh Meerut250004 India

ISBN: (纸本)9798350384277

This empirical study examines the revolutionary relationship between intelligence and Internet of Things (IoT) services, with a focus on intelligent decision-making and real-time data analytics. The IoT ecosystem is made possible by a complex architecture that spans edge, communication networks, including cloud platforms, producing a significant amount of raw data. IoT services and intelligence combine to provide a real-time, uninterrupted service that defies traditional paradigms. The paper explores the importance of edge intelligence, which reduces latency as well as improves security by bringing decision-making closer to the data source. More complex decision-making processes could potentially be centrally controlled with the use of cloud-based intelligence. Advanced analytics methods, machine learning algorithms, along information visualization tools are all part of the technological approach used to extract valuable insights. As the IoT system's lifeblood, real-time data analytics develops a dynamic awareness of the changing physical environment. Real-time data analytics and edge computing have been especially revolutionary because they lower latency while enhancing responsiveness. Case studies highlight practical applications in smart cities, businesses, and healthcare, demonstrating the value of IoT intelligence and services. Cybersecurity, ethical issues, and communication interoperability are among the difficulties. Nonetheless, these difficulties offer chances for cooperation, creativity, and development. The promise of a responsive, intelligent, and interconnected world is highlighted in the paper's conclusion, along with the necessity of meticulously handling obstacles, seizing opportunities, and placing ethical concerns first to effectively harness the potential of the Internet of Things for a peaceful future. © 2024 IEEE.

关键词： Edge computing Empirical Study intelligent Decision-Making Internet of Things (IoT) Real-time data analytics

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Design and Implementation of an intelligent Machine Learning System Based on Artificial Intelligence computing 2

Design and Implementation of an Intelligent Machine Learning...

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2nd international conference on data analytics, computing and Artificial Intelligence, ICDACAI 2023

作者： Cao, Yanqin Wuhan Institute of Design and Sciences Hubei Province Wuhan City430000 China

ISBN: (纸本)9798350339765

In the field of artificial intelligence, its applications have penetrated into daily life, such as smart home appliances, navigation, and medical treatment. This article introduces the design and implementation of an intelligent machine learning system based on artificial intelligence computing. This system utilizes artificial intelligence algorithms to analyze and process a large amount of data, thereby achieving automated learning and prediction of data. At the same time, the system also has good scalability and maintainability, which can adapt to the needs of different fields and application scenarios. The experimental results show that among the four algorithms, their accuracy has reached over 80%, and the time taken is within 3 seconds. The design and implementation of this system provides an effective solution for intelligent data analysis and decision-making. © 2023 IEEE.

关键词： intelligent systems

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Compressing Big OLAP data Cubes in Big data analytics Systems: New Paradigms, a Reference Architecture, and Future Research Perspectives 19th

Compressing Big OLAP Data Cubes in Big Data Analytics System...

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19th international conference on smart Business Technologies (ICSBT)

作者： Cuzzocrea, Alfredo Univ Calabria IDEA Lab Arcavacata Di Rende Italy Univ Paris City Dept Comp Sci Paris France

ISBN: (纸本)9783031451362;9783031451379

In the current big data era, big data analytics systems play a leading role due to their popularity in a wide collection of application scenarios, ranging from healthcare systems to e-science platforms, from social networks to smart cities, from intelligent transportation systems to graph analysis tools, and so forth. Among various proposals, multidimensional big data analytics methodologies, which basically are based on well-understood OLAP paradigms, are gaining the momentum due to their flexibility and expressiveness power. In this so-delineated scenario, performance issues clearly represent a significant obstacle to the effective impact of these methodologies in real-life settings. data compression techniques indeed represent successful approaches to face-off performance drawbacks in big multidimensional data analytics systems. According to this main concept, the idea of compressing big OLAP data cubes is a natural proposal arising in this field. Inspired by this main framework, in this talk we focus the attention on the issue of effectively and efficiently supporting big OLAP data cube compression in modern big data analytics systems. In particular, we first report on some recent proposals in this field, and then introduce some effective and efficient approaches we proposed in the context of the investigated research area, tailored to well-known big data requirements. Future research perspectives are presented and discussed as well.

关键词： Big data Big data management Big data analytics Big multidimensional data analytics OLAP Big OLAP data cubes ,Compression techniques for big OLAP data cubes Big data analytics systems

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BEHAVIORAL BASED DECISION-MAKING SCHEME FOR COOPERATIVE CONNECTED AUTOMATED MOBILITY 9

BEHAVIORAL BASED DECISION-MAKING SCHEME FOR COOPERATIVE CONN...

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9th international conference on Big data analytics, data Mining and Computational Intelligence, BigDaCI 2024, the 10th international conference on Connected smart Cities, CSC 2024 and the 16th international conference on e-Health, EH 2024, Part of the 18th Multi conference on Computer Science and Information Systems 2024, MCCSIS 2024

作者： Yagüe-Cuevas, David Marín-Plaza, Pablo Paz-Sesmero, María Sanchis, Araceli Intelligent Systems Lab. University Carlos III Madrid Spain Control Learning and Systems Optimization Group University Carlos III Madrid Spain

ISBN: (纸本)9789898704597

For some time now, the automotive industry has been undergoing major adjustments in recent years. Current technological solutions aimed to achieve an autonomous level 5 platform implement increasingly complex software and hardware systems. On the one hand, high-tech fields such as smart cities, the Internet of Things (IoT), and intelligent transportation systems (ITS) will benefit from connected and cooperative vehicles in the future. On the other hand, the more complex these systems are, the more knowledge, computing power and precision they require, and the more difficult they become to debug. There are still several challenges in the field of autonomous vehicles to address. From sensor perception to system localization, through arbitration and decision making. The goal of this paper is to address some of the problems regarding those latter fields by aggregating all the structured traffic knowledge extracted from the vehicle context. This knowledge will be used to deploy an effective decision-making system based on behaviors capable of choosing what to do when certain conditions are met. Actions such as changing its operation mode, selecting the most suitable trajectory or even adjusting the systems to the current situation for a better automated vehicle experience. © 2024 Big data analytics, data Mining and Computational Intelligence. All Rights Reserved.

关键词： Automotive industry

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Cognitive computing for smart City Infrastructure Management 3

Cognitive Computing for Smart City Infrastructure Management

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international conference on Advances in computing, Communication and Applied Informatics (ACCAI)

作者： Murthi, P. Chavhan, Jyoti Jayesh Sarkar, Prithu Khandelwal, Kritika Chandini, Shaik Dhanraj, Joshuva Arockia SR Univ Sch Engn Dept Civil Engn Warangal Telangana India Mumbai Univ Comp Engn SIES Grad Sch Technol Mumbai Maharashtra India Amity Univ Kolkata Amity Sch Commun Kolkata India Manipal Univ Jaipur Dept Business Adm Jaipur Ajmer Express HighwayNear GVK Toll Plaza Jaipur 303007 Rajasthan India Gulf Amer Univ Dept Civil Engn Ajman U Arab Emirates Chandigarh Univ Univ Ctr Res & Dev UCRD Mohali 140413 Punjab India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

The accelerated urbanisation and the increasing complexity of municipal infrastructure require the implementation of innovative strategies to regulate and improve urban environments. By integrating By integrating Cognitive computing, which combines AI, machine learning, and data analytics, could completely change how smart cities manage their infrastructure. The main goal of this project is to see what the possible benefits are of adding cognitive computing to the systems that are already in place, architecture of smart cities, resource allocation, enable predictive maintenance, facilitate informed decision-making, and provide real-time monitoring. We demonstrate the potential of cognitive systems to enhance the durability, duration, and efficacy of urban infrastructure through the examination of case studies and contemporary technological advancements. The results suggest that cognitive computing is the most effective solution for addressing current urban challenges and, as a result, is the most suitable choice for the development of intelligent cities in the future.

关键词： Cognitive computing smart City Infrastructure Management Artificial Intelligence Machine Learning Predictive Maintenance Urban Sustainability

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Analyzing the Impact of Artificial Intelligence Integration on data Protection in Healthcare Sector

Analyzing the Impact of Artificial Intelligence Integration ...

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2024 international conference on Innovative computing, intelligent Communication and smart Electrical Systems, ICSES 2024

作者： Gautam, Purnima Sharma, Rituja Banasthali Vidyapith Department of Legal Studies India

ISBN: (纸本)9798331543617

The integration of Artificial Intelligence(AI) in the healthcare sector has revolutionized data operation and case care, presenting both significant benefits and substantial challenges. This exploration paper aims to dissect the impact of AI integration on data protection within the healthcare assiduity. By examining colorful AI operations similar to prophetic analytics, individual tools, and substantiated treatment plans, the study assesses the counteraccusations for patient sequestration and data security. crucial enterprises include the eventuality of data breaches, the acceptability of being legal fabrics, and the ethical considerations girding AI-driven decision-making processes. The paper also explores the measures espoused by healthcare associations to guard sensitive information and misbehave with data protection regulations. Through a comprehensive review of current practices and nonsupervisory norms, the study provides perceptivity into the balance between using AI advancements and icing robust data protection. This analysis is pivotal for guiding unborn programs and practices in healthcare data operation, aiming to enhance patient trust and secure the benefits of AI integration. © 2024 IEEE.

关键词： AI integration data protection data security healthcare sector patient privacy regulatory compliance

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Trust in Human-AI Interaction: Review of Empirical Research on Trust in AI-Powered smart Home Ecosystems

Trust in Human-AI Interaction: Review of Empirical Research ...

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ASCE international conference on computing in Civil Engineering (I3CE)

作者： He, Tianzhi Jazizadeh, Farrokh Virginia Tech Dept Civil & Environm Engn Blacksburg VA 24061 USA

ISBN: (数字)9780784485224

ISBN: (纸本)9780784485224

The growing socio-technical influence of artificial intelligence (AI) is increasing in various dimensions of our daily lives. The monitoring and decision-making role of smart home AI agents could go beyond simple operations, and a lack of users' trust could lead to the underuse or misuse of their capabilities. As such, lying at the intersection of trust in human-AI interactions and smart home ecosystems, this paper attempts to explore the driving factors of building trustworthy AI-powered smart home ecosystems. To this end, through a systematic literature review, we have identified the characteristics of human-AI trust in intelligent environments, factors that can influence users' trust in AI, features that can be implemented to improve users' trust, and challenges and opportunities that need to be considered. The findings can help develop a pathway for building long-term users' trust in AI-powered smart homes for integrated human-AI interactions.

关键词： Ecosystems

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Cloud based intelligent Nutrient Management System for Precision Agriculture using CNN 2

Cloud based Intelligent Nutrient Management System for Preci...

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2nd international conference on Sustainable computing and smart Systems (ICSCSS)

作者： Banu, M. Shafiya Riazulhameed, Arshadh Ariff Mohamed Abuthahir Mohandas, R. Rajan, D. Antony Joseph Meenakshi, B. Mohankumar, N. Rajalakshmi Engn Coll Artificial Intelligence & Machine Learning Chennai Tamil Nadu India Blackstraw Technol Pvt Ltd Chennai Tamil Nadu India Chenai Inst Technol Dept Elect & Commun Engn Chennai Tamil Nadu India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Comp Sci & Engn Chennai Tamil Nadu India Sri Sairam Engn Coll Dept Elect & Elect Engn Chennai Tamil Nadu India Symbiosis Int Deemed Univ Symbiosis Inst Technol Nagpur Maharashtra India

ISBN: (纸本)9798350391558;9798350379990

Precision agriculture struggles with scalability, data integration, and decision-making, prompting innovative solutions. Recent advancements like CNNs and cloud computing provide interesting answers but confront challenges. data quality, processing needs, interoperability, and real-time decision-making are issues. These difficulties demand innovative data synchronization, model dependability, and system scalability methods. This project uses sophisticated analytics and cloud technology to improve nutrient management systems. Improving scalability, accuracy, and efficiency while presenting unique data fusion and userfriendly interfaces are goals. Clear problems and goals guide the study's scope and purpose. Wireless soil nutrient sensors can capture real-time data on important soil factors such as pH levels, the amount of nitrogen, phosphate, and potassium present, and more. This information is then sent to a platform hosted in the cloud, where it is processed using sophisticated data analytics and machine learning algorithms, in addition to previous crop performance data and agronomic models. The program will create tailored suggestions for nutrient management, including topics such as the kinds of fertilizer, application rates, and the best time. These proposals give farmers more agency, allowing them to make educated decisions via an easily navigable interface. Because of the system's ability to react to changing circumstances and its continual monitoring, nutrient management techniques are kept in sync with those changes throughout the crop's life cycle.

关键词： Internet of Things (IoT) Precision Agriculture Soil Health Monitoring Nutrient Management data analytics

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Predictive analytics for Parkinson's Disease Detection Leveraging Deep Learning Algorithms

Predictive Analytics for Parkinson's Disease Detection Lever...

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2024 international conference on Innovative computing, intelligent Communication and smart Electrical Systems, ICSES 2024

作者： Kalpana, T. Thamilselvan, R. Chitra, K. Priyanka, S. Jahaan, R. Reshmi Thenmalar, T. Kongu Engineering College Department of Computer Application Erode India Kongu Engineering College Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798331543617

Parkinson disorder is a neurological disease that progresses gradually which is typified by the brain's dopamine-producing neurons becoming exhausted. This neuronal loss results by symptoms including tremors, muscle stiffness, bradykinesia, and balance difficulties. Early detection and accurate prediction of PD progression are especially crucial for improving management and intervention strategies. This study builds a prediction model with the use of MRI datasets. To detect Parkinson's disease from MRI data, this study uses deep learning algorithms, such as Inception, Xception, and VGG19, and evaluates how well they perform in terms of accuracy. © 2024 IEEE.

关键词： Neurons

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