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检索条件"任意字段=12th International Conference on Embedded and Real-Time Computing Systems and Applications"

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Classifiers for Human Pose Recognition: Analysis for real-time applications 12th

Classifiers for Human Pose Recognition: Analysis for Real-Ti...

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12th international conference on Recent Trends in computing, ICRTC 2024

作者： Panchal, Piyush Tirkey, Nikhil Singh, Kaustubh Ranjan Bawana Road Delhi110042 India

ISBN: (纸本)9789819789450

Action detection is a rapidly growing field that involves identifying and tracking particular body key points in photos/videos to ascertain an individual’s action. Its difficulties arise from the natural fluctuations in lighting, a wide range of clothes, and the multiplicity of body actions that are seen in videos. Robust classification algorithms that can precisely anticipate pose angles and distances are necessary to tackle these problems. Action detection has many uses in several fields. the accurate analysis of body positions in fitness tracking helps create customized workout plans. In the medical field, awareness and detection of actions is essential for patient mobility monitoring and rehabilitation. Accurate action identification is beneficial to motion analysis, enabling advances in surveillance systems, sports analytics, and animation. the difficulty of detecting actions in videos is becoming increasingly popular due to its many uses in fields such as motion analysis, fitness tracking, and healthcare. this study first, compares the performance of several classifiers, including Support Vector Machine (SVM), k-nearest neighbors, logistic regression, decision trees, naive Bayes classification, and convolutional Neural Network (CNN) in the complex problems of posture detection, and secondly estimates various actions in real-time where the model is trained on video datasets after selecting the most suited machine learning model, the main motivation behind this research is to find the most accurate and effective classifier possible and using this information to classify actions while taking input from machine’s camera in real-time. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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2018 14th IEEE/ASME international conference on Mechatronic and embedded systems and applications, MESA 2018

2018 14th IEEE/ASME International Conference on Mechatronic ...

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14th IEEE/ASME international conference on Mechatronic and embedded systems and applications, MESA 2018

ISBN: (纸本)9781538646434

the proceedings contain 60 papers. the topics discussed include: sensor-embedded linear ball bearing for linear guide way pre-load and straightness monitoring;multi-robot coordination through mobile agent;research of vehicle parameter and sensor systems necessary to control autonomous vehicles;machine learning approach for predictive maintenance in Industry 4.0;filtering scheme for context-aware fog computing in cyber-physical systems;the sensing technology of applying the acoustic emission sensor to the grinding wheel loading phenomenon;accelerating Viterbi algorithm using custom instruction approach;design, development and experimental evaluation of a vortex actuation system;optimal map-based mode selection and powertrain control for a multi-mode plug-in hybrid electric vehicle;spline-based energy-optimal trajectory planning for functionally redundant robots;simulation analysis and performance evaluation of a vibratory feeder actuated by dielectric elastomers;machine learning-based approaches to analyse and improve the diagnosis of endothelial dysfunction;real-time and robust collaborative robot motion control with Microsoft Kinect® V2;control design of an electro-pneumatic gearbox actuator;modeling and simulation of hybrid electric ships with AC power busa case study;low-power wake-up system based on frequency analysis for environmental Internet of things;and scalability of GPU-processed 3D distance maps for industrial environments.

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Reliability of IoT systems for Fire Detection 16

Reliability of IoT Systems for Fire Detection

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16th international conference on ENTERprise Information systems, CENTERIS 2024 - 12th international conference on Project MANagement, ProjMAN 2024 - 14th international conference on Health and Social Care Information systems and Technologies, HCist 2024

作者： Singh, Khushwant Yadav, Mohit Barak, Dheerdhwaj Moreira, Fernando Department of Computer Science & Engineering U.I.E.T Maharshi Dayanand University Rohtak124001 India Department of Mathematics University Institute of Sciences Chandigarh University Punjab Mohali140413 India Department of Computer Science & Engineering Vaish College of Engineering Rohtak124001 India REMIT IJP Universidade Portucalense Porto & IEETA Universidade de Aveiro Aveiro Portugal

In this paper, a smart IoT system including the software and hardware aspects of its design, as well as its operation in the context of a city or a building, along with its advantages, disadvantages, and potential applications, all with a focus on fire safety have been analyzed. Based on the results of the aforementioned research, it is clear that an IoT-based fire system may be tailored to the specific requirements of each individual situation. the major focus of the study is on the planning and evaluation of a WSN that can detect house fires in their earliest stages. the authors address the crucial issue of giving rapid-fire alerts by including a broad range of sensors in the wireless sensor network. In order to provide precise, real-time fire detection, the wireless sensor network (WSN) is constructed from tiny, low-power sensor devices that have sensors able to sense their surroundings. Using the Global System for Mobile Communications (GSM) to reduce the number of false alarms is one strategy the authors propose for coping with sensor malfunctions. the reliability of IoT (Internet of things) systems for fire detection has been a topic of interest and continuous improvement. IoT-based fire detection systems have the potential to offer numerous benefits, such as real-time monitoring, remote management, and data-driven insights. However, their reliability depends on several factors, including technology, implementation, and maintenance. this research aids in the enhancement of residential community safety by improving the early detection and prevention of house fires. © 2025 the Author(s).

关键词： Fire detectors

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FDA-TO-FCA: Flow Direction Algorithm-Based Task Offloading in Fog-Cloud Architecture 4th

FDA-TO-FCA: Flow Direction Algorithm-Based Task Offloading i...

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4th international conference on Smart IoT systems: Innovations and computing, SSIC 2023

作者： Shukla, Prashant Pandey, Sudhakar Gupta, Shivani Agrawal, Deepika Department of IT NIT Chhattisgarh Raipur492010 India Department of CSE IIT Bihar Patna801106 India

ISBN: (纸本)9789819751990

With the development of a lot of delay-sensitive applications, it is very difficult to perform all the tasks locally because our devices have less computation power. Traditional Cloud Server (CS) has large computing power and storage but it is very far from IoT devices that’s why it consumes a lot of energy and take a huge time to execute a task. For these types of applications, local devices and CSs are not good choices. So, to overcome this delay-sensitive problem, the Fog computing (FC) concept was introduced. In FC, we can offload our task to Fog Nodes (FN) that are close to the user device and take less execution time and energy than a CS. To minimize the execution time for delay-sensitive applications, we have modeled our problem and converted our solutions from a bounded real space to a finite discrete space that uses the Flow Direction Algorithm (FDA). Our algorithm performance is 12 \% better than cloud-only, 6 \% better than Fog-only, and 6 \% better than IoT-only on average. In this, the task can execute locally or on a FN or CS. the proposed approach performed better to reduce the execution time. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Delay-sensitive applications

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Sentimental Insights: A Study of Margaret Atwood’s Oryx and Crake Using Natural Language Processing (NLP) Tool NVivo12 12th

Sentimental Insights: A Study of Margaret Atwood’s Oryx and...

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12th international conference on Frontiers of Intelligent computing: theory and applications, FICTA 2024

作者： Sharma, Mohit Balhara, Nikita Chandigarh University Mohali India

ISBN: (纸本)9789819601387

the present study is an attempt to develop a working segregation of words used in Margaret Atwood’s Oryx and Crake. It functions around the exploration of the sentiments prevailing in the text. the narration is originally divided into 53 chapters which the researcher further sectioned into five clusters containing at least ten chapters each. Furthermore, all the clusters were analyzed using NVivo12. the analysis report served that ‘time’ is one of the major themes of the novel Oryx and Crake. the analysis report also suggested that there is slight dominance of negative emotions in the novel. thus, the research makes a unique contribution by adding up to the understanding of the essential sentimental structures represented through language in the text that Atwood followed to weave the storyline and present the dystopian narrative. the study also exhibits the potential of machine learning to examine the language in a literary text. Moreover, it is an effort to make a broader point about the co-existence, and collaboration between literature and technology for richer analysis. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Natural language processing systems

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Unlocking potential: Critical success factors for AI integration in remote patient monitoring systems for post-cardiac surgery care 16

Unlocking potential: Critical success factors for AI integra...

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作者： Schmocker, Roberto Sampaio, Suzana Cormican, Kathryn Enterprise Research Centre and LERO The Irish Software Research Centre School of Engineering University of Galway H91 TK33 Ireland Federal Rural University of Pernambuco PE Recife Brazil

Rising healthcare costs have led to a shift towards cost-effective medical technologies that enhance clinical outcomes. Remote Patient Monitoring (RPM) systems enable real-time data collection, reducing hospital stays, reducing healthcare providers' workload, and improving patient satisfaction. Artificial Intelligence (AI) holds promise in transforming healthcare tasks by uncovering hidden patterns and trends. Despite its potential, AI adoption faces several multifaceted challenges such as data quality, privacy and security, interpretability of AI algorithms, and ethical and regulatory considerations. To successfully implement digital advancements these challenges must be addressed and critical success factors must be defined and prioritized. this study adopts a narrow perspective;it focuses on a context-specific domain and explores critical success factors (CSFs) for AI implementation in RPM systems for post-cardiac surgery patient care. A qualitative approach was used to collect data. Two sets of structured interviews were employed to elicit deep insights and experiences with end users and medical workers. Findings emphasize the importance of stakeholder input, revealing valuable insights into CSFs relevant to post-cardiac patient care. We found that technical aspects such as system reliability, ease of use, and data quality are particularly important and warrant attention. this research is crucial for the scientific and academic community as it addresses the multifaceted barriers to AI integration in healthcare. Illuminating and addressing these issues is critical for enhancing AI applications in healthcare. © 2025 the Author(s).

关键词： Cardiovascular surgery

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Decision systems Optimization for Cybersecurity Resilience 12th

Decision Systems Optimization for Cybersecurity Resilience

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12th international conference on Frontiers of Intelligent computing: theory and applications, FICTA 2024

作者： Ikhar, Sharayu Mondal, Dipannita Khubalkar, Deepti Yadav, Ranjeev Deshpande, Pallavi Patil, Sonali Vrushabhasagar Yashika Journal Publications Pvt. Ltd Maharashtra Wardha India Artificial Intelligence and Data Science Department Dr. Y. Patil College of Engineering and Innovation Telegaon410507 India Nagpur Campus Pune India Computer Science and Engineering Arya College of Engineering Jaipur India EnTC VIIT Pune India Nutan Maharashtra Institute of Engineering and Technology Maharashtra Pune India

ISBN: (纸本)9789819601424

the increasing danger in the online environment highlights the necessity for strong and durable cybersecurity solutions. this study aims to improve Intrusion Detection systems (IDS) by introducing a new hybrid method that merges Variational Autoencoders (VAEs) and Ant Colony Optimization (ACO) to enhance anomaly detection. the main goal is to enhance decision-making systems in IDS to attain a high level of accuracy, with a proven success rate of 99.3%. the Variational Autoencoder (VAE) functions as a tool for extracting features and reducing dimensionality by capturing latent representations of typical network behavior. the model can detect anomalous activities more accurately by using VAE to identify subtle patterns and deviations in network traffic. the Ant Colony Optimization (ACO) algorithm is incorporated into the decision-making process of the IDS to complement the VAE. Ant Colony Optimization (ACO) is used to optimize the parameters of Intrusion Detection systems (IDS) and adjust the decision boundaries to improve the detection performance. the combined interaction of VAE and ACO forms a robust framework that can accurately detect anomalies and adjust in real-time to changing cyber threats. the hybrid model proposed was assessed with real-world cybersecurity datasets, showing an impressive accuracy of 99.3%. the experimental results highlight the VAE-ACO hybrid’s effectiveness in achieving high detection accuracy while remaining efficient in terms of computational resources. Combining Variational Autoencoders with Ant Colony Optimization shows great potential for enhancing decision systems in Intrusion Detection systems. the 99.3% accuracy demonstrates the capability of this hybrid approach to greatly enhance cybersecurity resilience against advanced and evolving cyber threats. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ant colony optimization

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AI-Driven Incident Response systems for Cybersecurity: A Hybrid Approach 12th

AI-Driven Incident Response Systems for Cybersecurity: A Hyb...

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12th international conference on Frontiers of Intelligent computing: theory and applications, FICTA 2024

作者： Soni, Monika Pawar, Avinash M. Godbole, Aditee Sharma, Ashutosh Tiwaskar, S.A. Kokane, Chandrakant Deelip Auricle Global Society of Education and Research Bikaner India Bharati Vidyapeeth’s College of Engineering for Women Maharashtra Pune India Nagpur Campus Pune India Electronics and Communication Engineering Arya College of Engineering Jaipur India Computer Engineering VIIT Pune India Nutan Maharashtra Institute of Engineering and Technology Maharashtra Pune India

ISBN: (纸本)9789819601424

the increasing complexity and sophistication of cybersecurity threats necessitate the development of strong and flexible incident response systems. this study presents a hybrid methodology that utilizes various machine learning models such as Random Forest (RF), Decision Trees (DT), Isolation Forest, Convolutional Neural Network (CNN), and a unique Hybrid Gated Recurrent Unit—CNN (GRU-CNN) design. the proposed model seeks to improve the precision and effectiveness of incident detection and response in real-time. the study shows that combining various models is effective in creating a robust defense mechanism against cyber threats. Random Forest and Decision Trees improve model interpretability, while Isolation Forest boosts anomaly detection abilities. the Convolutional Neural Network integration aids in extracting spatial features from input data, while the Hybrid GRU-CNN enhances the model’s capacity to capture temporal dependencies in cybersecurity incidents. the hybrid approach demonstrated superior performance, achieving an impressive accuracy of 98.86% in incident detection and response, as shown by experimental results. the proposed model shows significant progress in managing spatial and temporal aspects of cybersecurity data, surpassing individual models and conventional methods. Combining machine learning methods creates a strong base for developing flexible and durable AI-powered incident response systems in the changing field of Cybersecurity. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cybersecurity

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Pronunciation Correction for Hearing Impairment by Web Connection with Volunteers 12th

Pronunciation Correction for Hearing Impairment by Web Conne...

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12th international conference on Frontiers of Intelligent computing: theory and applications, FICTA 2024

作者： Park, JungHyun Chadwick International School Incheon Korea Republic of

ISBN: (纸本)9789819601462

Pronunciation plays a crucial role in shaping first impressions during initial conversations, and unclear pronunciation is often misconstrued to be closely linked to intelligence. Consequently, individuals with hearing impairments who struggle with pronunciation are susceptible to such misunderstandings. this paper aims to address this issue by describing a prototype web service that provides remote assistance for pronunciation correction among individuals with hearing impairments. the service allows individuals with hearing impairments to upload recorded audio files, which are then reviewed by non-hearing-impaired volunteers. Subsequently, feedback is provided by comparing the submitted pronunciation with standard pronunciation. Prior research has explored various methods to address similar issues, including mimicking mouth movements and automatic recognition using AI. this study analyzed these programs, considering the characteristics of the Korean language, and applied the most suitable platform and technology to create a service tailored for individuals with hearing impairments. the web service emphasizes accessibility, enabling individuals to receive pronunciation correction assistance without constraints related to time, resources, or location. the development process involved utilizing Figma for web design and coding, forming the primary technologies for the web user interface. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Web Design

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Decision systems for Adaptive Cybersecurity Incident Response 12th

Decision Systems for Adaptive Cybersecurity Incident Respons...

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12th international conference on Frontiers of Intelligent computing: theory and applications, FICTA 2024

作者： Goyal, Dinesh Gandhi, Yatin Dongre, Deepika Shailesh Bhagat, Gorakh P. Pawar, Rohit Department of Computer Engineering Poornima Institute of Engineering and Technology Rajasthan Jaipur India Competent Softwares Maharashtra Pune India Artificial Intelligence and Data Science VIIT Pune India Electronics and Communication Engineering Arya College of Engineering Jaipur India Department of Mechanical Engineering Dr. D. Y. Patil Institute of Technology Pimpri Maharashtra Pune India Shri Ramdeobaba College of Engineering and Management Maharashtra Nagpur India

ISBN: (纸本)9789819601462

Traditional methods of responding to cybersecurity incidents often use set playbooks or routines that don’t change, which might not work well with the constantly changing nature of modern cyber dangers. the idea behind this paper is to create and use decision systems that are specifically designed for adapting how to handle hacking incidents. Advanced artificial intelligence (AI) methods, such as machine learning and natural language processing, are used by these decision systems to analyze security events in real time and help people make quick, well-informed decisions. these systems improve the speed and efficiency of hacking reaction by constantly learning from past events and responding to new threats. Automatic danger recognition, risk assessment, and reaction planning are important parts of the suggested decision systems. Automated systems that look for threats use AI algorithms to look for signs of bad behavior in network traffic, system logs, and other relevant data sources. the risk assessment tools look at the dangers and how bad they could be, considering things like how sensitive the data is, how important the system is, and the rules that need to be followed. Decision systems actively plan responses based on danger recognition and risk assessment results. these responses may include separating affected systems, blocking suspicious IP addresses, or sending events to human experts for further study. Over time, these systems get better at making decisions by using adaptive learning methods and constant feedback loops. this makes it easier for them to spot and stop new cyber threats. the creating and using decision systems for adaptive cybersecurity incident reaction is a big step toward making organizations’ defenses more resilient and effective in the face of changing cyber dangers. By using AI and machine learning, these systems stop threats before they happen, giving cybersecurity teams the tools they need to stay ahead of attackers in the ongoing battl

关键词： Cyber attacks

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