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Proceedings - 2021 IEEE international conference on Dependable, Autonomic and Secure computing, international conference on Pervasive Intelligence and computing, international conference on Cloud and Big Data computing and international conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021

Proceedings - 2021 IEEE International Conference on Dependab...

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19th IEEE international conference on Dependable, Autonomic and Secure computing, 19th IEEE international conference on Pervasive Intelligence and computing, 7th IEEE international conference on Cloud and Big Data computing and 2021 international conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021

ISBN: (纸本)9781665421744

The proceedings contain 148 papers. The topics discussed include: design and implementation of face recognition system based on raspberry pie;a privacy-preserving revocable framework in the deep-learning-as-a-service platform system based on non-software as a service;beyond homeostasis: a novel approach for assessing the stability and coherence of self-adaptive systems;a meta multi-agent reinforcement learning algorithm for multi-intersection traffic signal control;reducing model complexity and cost in the generation of efficient error detection mechanisms;quantitative security assurance case for in-vehicle embedded systems;chaos engineering for understanding consensus algorithms performance in permissioned blockchains;rethinking of reentrancy on the ethereum;self-attention based text matching model with generative pre-training;role-based approach as support for safety analysis of collaborative systems;optimizing dynamic timing analysis and reinforcement learning;impact of selective implementation on soft error detection through low-level re-execution;and applying neural networks for plant model simulation in embedded control system.

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SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical Image Segmentation 1

SliceProp: A Slice-Wise Bidirectional Propagation Model for ...

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1st IEEE international conference on Medical Artificial Intelligence, MedAI 2023

作者： Xu, Xin Lu, Wenjing Lei, Jiahao Qiu, Peng Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Department of Vascular Surgery China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9798350358780

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual labeling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise Label Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods. © 2023 IEEE.

关键词： Medical imaging

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Exploring Time Series Clustering with Head Based Aggregation

Exploring Time Series Clustering with Head Based Aggregation

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international conference on computing and Networking Technology (ICCNT)

作者： R Manjunatha Jyotirmaya Sahoo Abhinav Department of Data analytics and Mathematical Sciences School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India Department of Pharmacy ARKA JAIN University Jamshedpur Jharkhand India Department of Agriculture Vivekananda Global University Jaipur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Time series clustering is a technique of facts analysis that seeks to group hard and fast temporal statistics information into meaningful clusters based on their similarities. The goal of time collection clustering is to become aware of common styles or developments over time. Head primarily based aggregation (HBA) is an alternative approach to time collection clustering that makes use of a hierarchical clustering set of rules to without delay cluster time series records without guide intervention. This approach has been proven to produce significant results for brief temporal datasets. In this paper, we discover time collection clustering with head primarily based aggregation. We begin by discussing the fundamentals of HBA and the way it differs from conventional types of clustering. We then present outcomes from experiments with simulated temporal datasets and speak about the strengths and weaknesses of HBA. Sooner or later, we examine the consequences of making use of HBA to actual international time series datasets. Our outcomes display that HBA is a viable technique for time series clustering in some contexts but that its effectiveness varies depending on the complexity and dynamics of the information.

关键词： Time series analysis Noise Magnetic heads pattern recognition Delays Complexity theory

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Deep Learning for Real-Time Malware Detection and Classification

Deep Learning for Real-Time Malware Detection and Classifica...

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Visual Analytics and Data Visualization (ICVADV), international conference on

作者： Shantanu Sudhir Gujar Department of Cyber Security Golisano College of Computing and Information Sciences Rochester Institute of Technology Rochester NY

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

The increased deep learning styles have been adopted to improve the identification and detection of malware in real time. This improves the detection and detection of malware. The research focuses on the utilization of Convolutional Neural Networks (CNNs) to recognize malware patterns. These techniques are utilized to accomplish the aforementioned goals. The incorporation of these styles results in an improvement in both the delicacy and the speed of operation when it comes to transmitting viruses. Additionally, the research highlights the application of DeepLearning4J (DL4J), which is an important framework for deep learning, to successfully deploy these models, which enables large-scale malware analysis operations. This is demonstrated by the research. During the research, significant advancements were made in the detection of malware in real-time, and strong results were obtained in the fight against developing cyber threats. One way in which these developments are displayed is through the combination of different approaches and technologies that are utilized.

关键词： Deep learning Recurrent neural networks Visual analytics Heuristic algorithms Autoencoders Malware Real-time systems pattern recognition Convolutional neural networks Long short term memory

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Deep Neural Networks for Advanced Medication Security in IoT-Enabled Smart Robotic Dispensing Cabinets

Deep Neural Networks for Advanced Medication Security in IoT...

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Emerging Smart computing and Informatics (ESCI), conference on

作者： Prabhu V S Gurumoorthi Gurulakshmanan C. Chandravathi D. P. Sangeetha Vinoth Kumar V B Meenakshi Department of Electronics and Communication Engineering R.M.D. Engineering College Chennai Tamil Nadu India Mphasis Corporation Texas USA Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sona College of Technology Salem Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Ensuring advanced medication security is of the utmost importance in smart robotic dispensing cabinets enabled by the Internet of Things (IoT). Strong pharmaceutical security is of the utmost importance in smart robotic dispensing cabinets that the IoT enables. To strengthen security measures in these systems, this research presents a new method that uses deep neural networks (DNNs). The proposed system improves the identification of potential medication mistakes and unauthorized access by using DNNs' anomaly detection and pattern recognition capabilities. A thorough security layer is created by incorporating DNN models into the IoT design of smart cabinets to address drug administration and dispensation weaknesses. Discovering security breaches or procedural problems entails training DNNs on large datasets to detect typical operating patterns and abnormalities. It shows that the strategy significantly improves security procedures and operational dependability in real-world conditions via simulations and validation experiments, proving its usefulness. Providing a strong answer to the problems of improving the effectiveness of drug administration in healthcare settings based on the IoT helps move healthcare technology forward. The technology improves pharmaceutical security and lays the groundwork for responsive and adaptable healthcare infrastructures. This study will make improving the dependability and safety of healthcare delivery systems possible.

关键词： Drugs Medical services Artificial neural networks Robot sensing systems pattern recognition Security Internet of Things Artificial intelligence Anomaly detection Intelligent sensors

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CNN-based Application recognition to Enhance Network Governance for Financial Networks

CNN-based Application Recognition to Enhance Network Governa...

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international conference on Computer, Information and Telecommunication Systems (CITS)

作者： Urmil Gadhiya Preet Faldu Krisha Darji Mohammad S. Obaidiat Rajesh Gupta Sudeep Tanwar Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat India King Abdullah II School of Information Tech. The University of Jordan Amman School of Computer and Communication Engineering University of Science and Technology Beijing China Department of Computational Intelligence School of Computing SRM University Kattankulathur TN India School of Engineering The Amity University Noida UP India

ISBN: (数字)9798350359091

ISBN: (纸本)9798350359107

Effective network governance is essential to guarantee optimal performance, security, and resource allocation in the increasing variety of network applications and the growing complexity of network environments. In this study, we used convolutional neural network (CNN)-based application recognition which uses deep learning techniques to identify network applications and classify them accurately. We designed and evaluated a customized CNN architecture for a recognition application, using raw data as input to capture unique patterns and features. We demonstrate the efficiency of our method in accurately identifying the variety of network applications through an experiment performed on real-world network datasets, that includes web browsing, video streaming, file transfer, and more. We then evaluate the performance of our system using various performance evaluating measures, which include accuracy, precision, recall, and F1-score, and then compare it with the traditional methods to emphasize its superiority in accuracy and efficiency. Lastly, our research on this comes up with the advancement of network governance by providing powerful and scalable solutions for application network management which makes a way to improve network performance, security, and resource development.

关键词： Deep learning Accuracy Telecommunication traffic Computer architecture Streaming media Telecommunications pattern recognition

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Physical Exergames Movements and pattern recognition using Convolutional Neural Network

Physical Exergames Movements and Pattern Recognition using C...

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international conference on Advancements in Computational Sciences (ICACS)

作者： Tanvir Fatima Naik Bukht Ahmad Jalal Faculty of Computing & Artificial Intelligence Air University Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

This paper proposes a novel framework for accurately recognizing physical exergames interaction in video sequences. The developed approach uses image processing and machine learning tools to correctly extract and classify the features. The framework comprises five main steps: Silhouette extraction, preprocessing, feature extraction, feature optimization and classification. Image contrast is improved by power law transformation, silhouette is extracted by the Multiple Object Tracking (MOT) algorithm and graph-based segmentation. We extract ORB and geometric skeleton based keypoints to capture discriminative features, and apply Fast Independent Component Analysis (FICA) to optimize feature representation. Thirdly, the Convolutional Neural Network algorithm is used to classify the optimized features, with a macro average accuracy of 0.85 and a weighted average accuracy of 0.87%. The proposed framework effectively recognizes physical exergames interaction and can be applied in many domains, such as human-human interaction and surveillance.

关键词： Image segmentation Machine learning algorithms Accuracy Surveillance Video sequences Independent component analysis Feature extraction Classification algorithms Convolutional neural networks Optimization

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Conscious Machines for Autonomous Agents and Cybersecurity

Conscious Machines for Autonomous Agents and Cybersecurity

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IEEE international conference on Rebooting computing (ICRC)

作者： Kadin, Alan M. Princeton Junction NJ United States

ISBN: (纸本)9781665423328

Although consciousness has been difficult to define, most researchers in artificial intelligence would agree that AI systems to date have not exhibited anything resembling consciousness. But is a conscious machine possible in the near future? I suggest that a new definition of consciousness may provide a basis for developing a conscious machine. The key is pattern recognition of correlated events in time, leading to the identification of a unified self-agent. Such a conscious system can create a simplified virtual environment, revise it to reflect updated sensor inputs, and partition the environment into self, other agents, and relevant objects. It can track recent time sequences of events, predict future events based on models and patterns in memory, and attribute causality to events and agents. It can make rapid decisions based on incomplete data, and can dynamically learn new responses based on appropriate measures of success and failure. The central aspect of consciousness is the generation of a dynamic narrative, a real-time model of a self-agent pursuing goals in a virtual reality. A conscious machine of this type may be implemented using an appropriate neural network linked to episodic memories. Near-term applications may include autonomous vehicles and online agents for cybersecurity.

关键词： artificial intelligence autonomous systems cyberspace pattern recognition machine learning computer security virtual reality neural networks

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Benchmarking Annotation Procedures for Multi-channel Time Series HAR Dataset

Benchmarking Annotation Procedures for Multi-channel Time Se...

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19th IEEE international conference on Pervasive computing and Communications (IEEE PerCom)

作者： Avsar, Hulya Altermann, Erik Reining, Christopher Rueda, Fernando Moya Fink, Gernot A. ten Hompel, Michael TU Dortmund Univ Chair Mat Handling & Warehousing Joseph von Fraunhofer Str 2-4 D-44227 Dortmund Germany TU Dortmund Univ Pattern Recognit Embedded Syst Grp Joseph von Fraunhofer Str 2-4 D-44227 Dortmund Germany

ISBN: (纸本)9781665404242

This contribution evaluates semi-automated annotation for generating high-quality data for multi-channel time series Human Activity recognition. For this purpose, time series data that consists of Optical Motion Capturing and inertial measurements from on-body devices for industrial deployment is created, annotated, and revised by four individuals. The semi-automated annotation consists of predictions from a temporal convolutional neural-network, and manual revisions for generating high-quality and fine-grained annotations for Human Activity recognition. This annotation approach reduces the annotation effort keeping similar quality as compared to manual annotation.

关键词： Human Activity recognition Labelling Annotation Human Reliability Logistics Warehousing Deep Learning Semi-Automatic Annotation

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Intelligent Accident recognition and Notification System using Hybrid Deep Learning and IoT

Intelligent Accident Recognition and Notification System usi...

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soft computing for Security Applications (ICSCSA), international conference on

作者： P. Sathish Kumar Ramesh Singh Rawat Manish Shrimali Swapnil Parikh Harshal Patil Ramya Maranan Department of Robotics & Automation United Institute of Technology Coimbatore Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and IT Janardan Rai Nagar Rajasthan Vidyapeeth (Deemed to be University) Udaipur Rajasthan India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Gujarat India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Ensuring the safety of individuals should be a top priority. Everyone wants to return home safely. Many riders continue to disregard safety constraints, increasing the chance of accidents that result in death. It is critical to incorporate the latest technology into road monitoring systems to make cities smarter and to ensure rider safety. For this research, Deep Learning (DL) and the Internet of Things (IoT) were chosen for effective road surveillance and alert systems. The primary goal of the study is to detect and inform riders about accidents that occur along their path and to provide details about the location of the detected accident. Additionally, the system provides the vehicle's speed. To identify automobile accidents in the footage, a hybrid DL model that includes Convolutional Neural Network - Bidirectional Long Short-Term Memory (CNN-Bi-LSTM) is suggested. For the model's training, both accident and non-accident data were collected. The model is evaluated using three metrics: accuracy, loss, and execution time. The next step is to upload the recommended model to the ThingSpeak cloud. A Graphical User Interface (GUI) is being created in the ThingSpeak cloud. The GUI displays the vehicle's speed, accident images, and the precise location of the accident scene using the Raspberry Pi, speed sensor, Pi-camera, and Global Positioning System (GPS) module. An LED is also integrated into the GUI to alert the user. A real-world test is conducted on the designed system. The proposed solution has many advantages and will ensure driver safety.

关键词： Deep learning Training Cloud computing Accuracy Roads Surveillance Feature extraction Safety Accidents Graphical user interfaces

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