The proceedings contain 298 papers. The topics discussed include: a novel framework for multi source based cloud forensic;a comparative analysis of cloudlet provisioning in mobile cloud computing environment;smart hom...
ISBN:
(纸本)9781665410281
The proceedings contain 298 papers. The topics discussed include: a novel framework for multi source based cloud forensic;a comparative analysis of cloudlet provisioning in mobile cloud computing environment;smart home monitoring system and prediction of power consumption;simulation of pick and place robotic arm using coppeliasim;effective metrics modeling of big data technology in electric power information security;microcontroller and mobile app based garments environment monitoring system for workers;study on wind energy analytics and its algorithms;tracking and monitoring of medical equipments using uwb for smart healthcare;use of nanotechnology sensors for sustainable agriculture;cloud storage and authenticated access for intelligent medical system;a survey on electronic health records (EHRS): challenges and solutions;and intelligent application of virtual reality technology in the design of elderly service station.
The primary goal of query expansion is to gather terms that are closely associated with the original query terms. To accomplish this goal, similarity measurements are used to evaluate the similarity between the query ...
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The handling of big data refers efficient management of processing and storage requirements of very large volume of structured and an unstructured data of association. The basic approach for big data classification us...
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The proposed method, HybridNet-NDM, integrates three vital algorithms-Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Graph Convolutional Networks (GCNs)-in a synergistic manner for a...
The proposed method, HybridNet-NDM, integrates three vital algorithms-Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Graph Convolutional Networks (GCNs)-in a synergistic manner for applications in predictive analytics and healthcare management of neurodegenerative diseases. CNNs play a role in extracting biomarkers from medical imaging data, identifying complex patterns crucial for pinpointing neurodegenerative diseases. Meanwhile, LSTMs take charge of modeling the temporal dimensions of patient data, utilizing longitudinal records to predict disease progression. GCNs contribute by analyzing brain connectivity patterns, shedding light on disease development through an examination of brain structure and function. This hybrid neural network marries the strengths of these algorithms, fusing their unique features to attain unparalleled predictive accuracy. An attention mechanism is incorporated to further hone the feature fusion process, ensuring precision in predictions. Demonstrating improved performance across a range of metrics, the optimized model stands out in its effectiveness for predictive analytics and healthcare management in the realm of neurodegenerative diseases.
Ho Chi Minh City, particularly Vietnamese cities in general, is so busy and crowded since tremendous numbers of motorbikes move on roads. Ho Chi Minh City leaders have encountered several challenges in fully understan...
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ISBN:
(纸本)9781665461719
Ho Chi Minh City, particularly Vietnamese cities in general, is so busy and crowded since tremendous numbers of motorbikes move on roads. Ho Chi Minh City leaders have encountered several challenges in fully understanding and effectively dealing with problems of urban traffic for the past few decades. Software-based solutions are proper and dramatically necessary, currently. This paper presents the deployment of an AI-based application at the Ho Chi Minh City Department of Transportation. The paper mainly concentrates on traffic counting problems during the outbreak of the Covid-19 pandemic from June 2021. The performance of the AI-based application was compared with medical declaration data and achieved an accuracy of 93.80%.
A very novel predicament for quantitative data science has been generated by the abundance of large, well-cured data sets in biological and social science, coupled with an extraordinary increase in computational abili...
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ISBN:
(纸本)9781728196879
A very novel predicament for quantitative data science has been generated by the abundance of large, well-cured data sets in biological and social science, coupled with an extraordinary increase in computational ability. This is the possibility of sophisticated studies combined with remedial understanding. analytics for intelligent systems should cover architecture of hardware platforms and application of software methods, technique and tools. It is anticipated that adapting dynamic memory information, processing parametric values of large data sheets with optimization, would be faster. The field of Big-dataanalytics under recent trends of data Science studies various means of pre-processing, analyzing and filtering from huge and semi-structured data sets from different sources which are complex to be handled by traditional data processing systems. In addition to extracting and aggregating data from various main performance measures, this proposal also forecasts potential values for these KPIs (Key Performance Indicators) and alerts them when unfavorable values are about to occur. As AI and ML are implemented through different platforms and sectors including chat-bots, robotics, social media, healthcare, self-driven automobile and space exploration, large companies are investing in these fields, and the demand for ML and AI experts is growing accordingly. Python is becoming the most popular language for AI (Artificial Intelligence and Machine Learning) due to its rich supported tools. This proposed applications "I-Care" (intelligent Care) provide recommendations to improve Quality of Service of Big-dataanalytics. So, the proposed paper examines the methodology and requirements, architecture, modeling and analytics with implementation and describes the architectural design and the results obtained by the pilot application using Python and its powerful tools like Pandas and Scikit-Learn.
Big dataanalytics integration with smart grid systems has become an important field of study with previously unheard-of potential to improve the sustainability, dependability, and efficiency of contemporary power net...
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ISBN:
(数字)9798331541583
ISBN:
(纸本)9798331541590
Big dataanalytics integration with smart grid systems has become an important field of study with previously unheard-of potential to improve the sustainability, dependability, and efficiency of contemporary power networks. This study explores the various uses of big dataanalytics in smart grids, with an emphasis on gathering, analysing, and interpreting enormous datasets produced by various grid components. Predictive maintenance, demand forecasting, and real-time monitoring and control using sophisticated analytics are important research fields. The study investigates how smart grids may optimise energy use, proactively reduce possible faults, and dynamically adapt to changing conditions by utilising machine learning algorithms and data-driven models. The study also looks into the cybersecurity ramifications of handling and protecting the enormous inflow of data, guaranteeing the confidentiality and integrity of vital grid data. In addition to acknowledging the revolutionary potential of big dataanalytics in smart grids, the study tackles issues including data interoperability, scalability, and the requirement for standardised protocols. The results foster a better comprehension of how big dataanalytics will influence smart grids in the future and open the door to more intelligent, resilient, and sustainable energy ecosystems.
Today, the data world that rules us made its mark not only in computer science but also in other academic sectors like medicine, finance, news, and marketing, among others. In today’s world, the healthcare business i...
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In today’s fast-changing retail industry, IoT and data-driven analytics have changed consumer buying and business operations. In IoT-enabled smart shopping environments, intelligent decision-making algorithms are cru...
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ISBN:
(数字)9798350352689
ISBN:
(纸本)9798350352696
In today’s fast-changing retail industry, IoT and data-driven analytics have changed consumer buying and business operations. In IoT-enabled smart shopping environments, intelligent decision-making algorithms are crucial. This paper presents an intelligent decision-making system for IoT-enabled smart purchasing. The program improves various shopping aspects using real-time data from IoT sensors, RFID tags, and cameras. The algorithm includes data integration, advanced analytics, decision optimization, real-time adaptation, and customization. The programme provides actionable information from customer behaviour, product inventories, and environmental elements to aid decision-making. These choices include inventory management, pricing, product recommendations, and targeted promotions to enhance the shopping experience. The real-time algorithm adapts to demand, inventory, and client preferences. Personalized recommendations and promotions boost retailer revenue and consumer engagement. The algorithm streamlines inventory management, improving efficiency, cost, and retail competitiveness.
Collecting and analyzing spherical images is one of the crucial fields supporting the digitization of indoor environments, like houses, apartments, offices, or factories, and creating virtual tours of smart buildings....
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