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Information Technology and Nanotechnology (ITNT), International Conference on

作者： Aleksei Golovin Nataly Zhukova Igor Kulikov Department of Software Engineering and Computer Applications Saint Petersburg Electrotechnical University ‘LETI’ Saint-Petersburg Russia St. Petersburg Federal Research Centre of the Russian Academy of Sciences (SPCRAS) Saint-Petersburg Russia

ISBN: (数字)9798350372939

ISBN: (纸本)9798350372946

In this paper, the method for knowledge graph completion based on using multi-hop reasoning is proposed. The relevance of the problem is due to the widespread use of large sparse knowledge graphs with incomplete data. Within the research, the method for restoring the structure of a knowledge graph using sequence-to-sequence multi-hop reasoning, based on the transformer architecture, is proposed. To evaluate the effectiveness of the proposed method, the study of its properties was carried out and the results were compared with the results of the existing methods used for predicting relations in the knowledge graphs. The proposed solution almost achieves the same results as methods based on embeddings. The evidential paths generated by the proposed method ensure the interpretability of the results. Our method does not rely on existing relations to create an evidential path and has the flexibility to restore missing relations in the path.

关键词： Knowledge graphs Reinforcement learning Transformers Cognition Information technology Nanotechnology Convergence

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EEG Classification Using Reinforcement Learning and Swarm-Based Deep Sparse Autoencoder

EEG Classification Using Reinforcement Learning and Swarm-Ba...

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International Conference on Inventive Research in Computing applications (ICIRCA)

作者： V. Rekha T Santhi Punitha R. Sethuraman Naveena N Md. Abul Ala Walid Aviral Srivastava Department of B.C.A. Agurchand Manmull Jain College Chennai Department of Software Applications Agurchand Manmull Jain College Department of CSE Sathyabama institute of Science and Technology Chennai Computer technology-UG Kongu Engineering College Department of Computer Science and Engineering Khulna University of Engineering & Technology (KUET) Department of CSE Amity University Rajasthan Amity University Rajasthan Jaipur

Analysis of electroencephalographic (EEG) data has shown to be a useful tool in the fields of Brain-computer interfaces (BCIs), and even business. Recently, deep learning architectures have been implemented, particularly in the interpretation of EEG readings and the knowledge it may impart about mental processes, thanks to the availability of huge EEG data sets and advancements in deep learning. In order to find useful data for brain classification and neuroimaging, Various Analytical Techniques utilized in EEG investigations have turned to machine learning. This research study combines sparse auto-encoders (SAEs) with a post-processing system, which is comprised of a linear system model dependent on the Particle Swarm Optimization (PSO) method as a unique data categorization framework. To boost classification precision, it is to prune datasets of superfluous characteristics. The Q-Learning method is used to built on reinforcement learning to reduce the overall number of features and have obtained the resulting accuracy. Evidence of this strategy leading to better classification performance is shown by experiments on both simulated and actual data. The PSO method provides a metaheuristic strategy for estimating the linear model's parameters. Using publicly available datasets, the suggested methodology is verified; When contrasted to current best practices, the results are favorable. With some adjustments to the input characteristics, hidden neurons, and output classes, the system may be utilized to address any data categorization problem.

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Weapon Detection Identification and Classification for DCNN and YOLO-V5 Techniques

Weapon Detection Identification and Classification for DCNN ...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： J. Ashok Kumar G. Dhivya P. Selvaraj V. Ganesh Karthikeyan S. P. Ramesh S. Jeya Lakshmi R. Saravanakumar Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Science and Engineering School of Computer Science and Engineering Galgotias University Greater Noida Uttar Pradesh India School of Computing SASTRA Deemed to be University Thirumalaisamudram Thanjavur Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Over 850,000 people die every year as a direct result of gun violence, yet civilians hold more than 85% of the world's weapons. Detecting weapons via manual surveillance has not been successful. It is critical to automate a system that detects weapons. A large number of convolution neural network CNN-based automated weapon identification systems have shown promising results. These methods are crucial for weapon identification but expensive. These devices produce a large amount of false negatives since monitoring videos are not high enough quality or visible enough to identify weapons. Finding the sweet spot between fast detection and a few false positives and negatives in weapon detection is the main goal of this research. In addition to defining an Area of Interest, the suggested structure uses YOLO. The models use previously processed images with backgrounds that have been blurred using a median filter. Metrics such as F1 scores, accuracy, recall, False Positive, and False Negative will be used to assess the suggested architecture. This investigation shows that YOLO-v5 can quickly identify and has a high recall rate. The deep convolution neural network DCNN required 0.17 ms for each frame, but this one only required 0.010 ms, which would indicate that security and weapon detection have good promise. Starting with a labeled collection of weapon and non-weapon photos is the initial stage in DCNN and YOLOv5 weapon detection. Use YOLOv5's ability to recognize multiple things within frames to train a real-time object localization and identification model. DCNNs categorize weapons and extract complex data effectively. DCNN classifies objects, whereas YOLOv5 detects, bounds, and tracks them. Optimize both models' hyperparameters and apply data augmentation for better performance. Install the integrated system on cutting-edge equipment for real-time weapon identification and classification.

关键词： YOLO Accuracy Convolution Weapons Neural networks Feature extraction Real-time systems Object recognition Web sites Videos

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Delay analysis of Admission Control mechanism for supporting QoS in 802.11e

Delay analysis of Admission Control mechanism for supporting...

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IEEE International Conference on Management of Innovation and Technology, ICMIT

作者： Ching-Hsun Chen Chin-Yu Huang Jun-Ru Chang Jenn-Wei Lin Security software Department Zyxel Communications Corporation Hsinchu Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science & Information Engineering Fu Jen Catholic University Taipei Taiwan

IEEE 802.11e standard is developed for QoS provisioning in WLANs. Admission Control is a mechanism that controls stations to be served in WLANs. In practice, evaluation of admission control plays an important role for QoS support. The goal of this paper is to calculate the WLAN delays with ACs. The delay calculation can be used to guarantee the QoS of the VoIP and video streams, which is divided into three parts. One is the propagation delay. Another is that packets queue up in stations or APs. Lastly, the delay is taken due to collisions. For VoIP and video streams, the second delay is more important than other two delays. Consequently, we will focus on the calculation of the second delay. Some simulations and numerical results are provided and discussed in detail. Experimental results show that the proposed method is more accurate in calculating the delay of packets in WLANs.

关键词： Delay Quality of service Admission control Downlink Mathematical model Throughput Propagation delay

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Sports Event Detection Using a 3D Convolutional Neural Network with Equiangular Basis Vector in Video Processing

Sports Event Detection Using a 3D Convolutional Neural Netwo...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： M. Ashok Kumar Laith H. Jasim Sudhakar K R. Archana Reddy N. Naga Saranya Department of Computer Science Software Engineering and Cyber Security Skyline University Nigeria Kano State Nigeria Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Dept. of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India School of Computer Science and Engineering SR University Warangal India Department of Computer Applications Saveetha College of Liberal Arts & Science SIMATS Chennai India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

As sports video collections continue to expand at a rapid pace, there is a need for advanced classification methods that can accurately and efficiently categorize and manage the massive amount of content available. These methods must be able to dynamically adapt to changes in the collection, ensuring that the videos are properly organized and easily searchable for users. Traditional classification methods are not able to keep up with the rapid growth and complexity of sports video collections, leading to incomplete classification and impacting search and retrieval processes. This paper addresses this challenge using the UCF sports dataset and proposes a Visual Geometry Group-19 (VGG-19) based CNN classifier and Equiangular Basis Vector (EBV) representation introduced for efficient classification and category representation. Furthermore, a 3D Convolutional Neural Network (3D-CNN) architecture extracts both spatial and temporal features from videos for accurate action classification. The Adam optimizer in the optimization algorithm is employed to optimize the learning process. This paper shows that the proposed 3D-CNN model outperforms existing methods a Hybrid Convolutional Neural Network with a Bidirectional Recurrent Neural Network (HCNN-BiRNN) model, Deformable Convolution Networks (DCN), 2D-Convolutional Neural Network (2D-CNN), Convolutional Neural Network - Long Short-Term Frequency (CNN-LSTM) in terms of accuracy (99.45%), precision (98.00%), sensitivity (96.98%), and F1-score (97.05%).

关键词： Geometry Deformable models Visualization Three-dimensional displays Sensitivity Event detection Feature extraction

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2D Simulator for Eddy Current Field Filters

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Procedia engineering 2011年 25卷 427-430页

作者： F. Liakou P. Dimitropoulos G. Stamoulis Center For Research and Technology Thessaly Technology Park Volos 38500 Greece SENSAP Microsystems Electronic Systems and Software Applications SA Ydras 2 Athens 18346 Greece University of Thessaly Computer & Communications Engineering DepartmentGlavani 37 Volos 38221 Greece

Hall devices are magnetic field current sensors based on the Hall effect. A bias current is applied via contacts through the Hall plates which are placed along magnetic field. High frequency components affect the Hall plates in a non- linear manner, disturbing the uniform biasing current flow. This problem can be alleviated by the development of a flux shaping device. A CAD tool is presented here, which can simulate the behavior of shields with different shape in order to choose the one that may be optimally used.

关键词： Eddy Current Hall Plate Magnetic Field Simulator

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CEPS: A Cross-Blockchain based Electronic Health Records Privacy-Preserving Scheme

CEPS: A Cross-Blockchain based Electronic Health Records Pri...

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IEEE International Conference on Communications (ICC)

作者： Sheng Cao Jing Wang Xiaojiang Du Xiaosong Zhang Xiaolin Qin School of Computer Science and Engineering Center for Cyber Security University of Electronic Science and Technology of China China School of Information and Software Engineering Center for Cyber Security University of Electronic Science and Technology of China China Department of Computer and Information Sciences Temple University Philadelphia USA CAS Chengdu Institute of Computer Applications Chengdu China

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

The Electronic Health Record (EHR) has been widely used in cloud-based medical data platforms. Since the owner of the EHR is a patient and the manager is a doctor (or hospital), this separation of ownership of privacy data leads to privacy leakages of the EHR stored in the cloud environment. The tamper-proof and traceable features of the blockchain make it a promising way to solve EHR privacy protection issues. Nevertheless, the latest research findings on blockchain-based schemes for EHR privacy preservation are focused on single blockchain that corresponds to a designated medical institution, which are not compatible with the privacy anti-leakage demands since private data is transferred through multiple blockchains. In this regard, we put forward relay-chain as a service (RaaS), and propose a cross-blockchain based EHR Privacy-preserving scheme (CEPS), which uses relay-chain to achieve secure access to EHR data when patients visit different hospitals. Furthermore, our scheme ensures that patients can delete the link of EHR freely and effectively. Security analysis and performance evaluations are performed, which prove that CEPS is highly secure and efficient impressively.

关键词： Hospitals Data privacy Privacy DH-HEMTs

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AI-Driven Strategic Foresight: Anticipating Future Trends and Modelling Business Strategies

AI-Driven Strategic Foresight: Anticipating Future Trends an...

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Decision Aid Sciences and Application (DASA), International Conference on

作者： Ahmed Abdel Qader Ismail Alnajem Amer Ibrahim Mayasa M. Abdulrahman Abdulrazak A. Abdulrazak Al-Kubaisi Jamal Fadhil Tawfeq Mohammad Khishe Ali Saeed Business Administration Department Baghdad College of Economic Sciences Department of Computer Science and Software Engineering College of Information Technology Computer Engineering Department College of Engineering University of Baghdad Baghdad Iraq Department of Medical Instrumentation Technical Engineering Medical Technical College Al-Farahidi University Baghdad Iraq Innovation Center for Artificial Intelligence Applications Yuan Ze University Taiwan Jadara research center Jadara University Jordan Computer Center Northern Technical University Ninevah Iraq

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

In a generation characterized with the aid of speedy technological advancements, socio-monetary shifts, and environmental challenges, groups need to undertake proactive strategies to stay aggressive and resilient. Strategic foresight, which entails expecting destiny tendencies and modeling enterprise techniques, accordingly, emerges as a vital device in navigating this uncertainty. The trouble this have a look at addresses is the growing trouble groups face in predicting and making ready for destiny disruptions the use of conventional forecasting techniques. The number one goals of this have a look at are to discover the important thing methodologies and gear of strategic foresight, display how those techniques may be implemented to assume destiny tendencies, and offer insights on integrating foresight into enterprise approach formulation. This has a look at contributes to the present frame of information with the aid of using providing a complete evaluation of strategic foresight techniques, which includes environmental scanning, situation planning, the Delphi method, fashion evaluation, SWOT and PESTLE evaluation, and era street mapping. Research findings show that forward-looking strategies provide an organization with the ability to identify potential risks and opportunities, develop adaptive strategies, and achieve long-term growth Specifically, strategic organizations using anticipation reports a 30% increase in the ability to anticipate market fluctuations, a 25% improvement in strategic haste and a 20% reduction in unexpected disruptions, of consumer goods etc. Case studies of various industries demonstrate the practical application and usefulness of forecasting in real-world situations The study concludes that incorporating forward-looking strategies into business planning does not that it not only prepares organizations for future challenges but empowers them to shape their future and ensure sustainable competitive advantage.

关键词： Industries Uncertainty Fluctuations Shape Navigation Organizations Market research Planning Forecasting Sustainable development

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Integrating GPS and GSM Technologies for Enhanced Women’s Safety: A Fingerprint-Activated Device Approach

Integrating GPS and GSM Technologies for Enhanced Women’s S...

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Automation and Computation (AUTOCOM), International Conference on

作者： Kamred Udham Singh Varun Chaudhary Pradeep Kumar Sharma Pushpendra Kumar Neeraj Varshney Teekam Singh School of Computing Graphic Era Hill University Dehradun India IMS ENGINEERING COLLEGE Ghaziabad India Technical Architect HCL Technologies Noida UP Sr Software Engineer Scanno System Greater Noida Department of Computer Engineering & Applications GLA University Mathura UP India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun

The creation of a safety system that is based on GPS and is developed exclusively for the health and safety of women via the incorporation of novel elements in order to meet the rising concerns about crimes committed against women. The gadget is capable of contacting authorities, conveying the user’s position via SMS, and generating a loud noise to attract attention. It may be triggered in anticipation of potential danger and can be activated in the event that danger is imminent. In order to guarantee that only authorized users are able to activate the device, the system makes use of fingerprint recognition in a very unique way. Using technologies such as global positioning system (GPS), global system for mobile communications (GSM), and microcontrollers, this study makes a contribution to the continuing efforts to improve the safety of women via technological innovation. It provides a proactive approach to prevent violence against women and save lives. The research emphasizes the significance of combining hardware and software in order to provide a complete safety tool. It also illustrates the vital role that quick support plays and the potential of such gadgets to promote confidence among women in public places.

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Forewords

Proceedings of the Confluence 2020 - 10th International Conf...

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Proceedings of the Confluence 2020 - 10th International Conference on Cloud Computing, Data Science and engineering 2020年 xviii页

作者： Debnath, Narayan C. School of Computing and Information Technology Eastern International University Viet Nam Department of Software Engineering Eastern International University Viet Nam International Society for Computers and Their Applications United States Computer Science Department Winona State University United States

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