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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Durgarao, M.S.P. Valluru, Dinesh Saraswathi, S. Malathi, P. Thilagam, K. Loganathan, S. Madanapalle Institute of Technology & Science Department of Computer Science & Engineering Andhra Pradesh Madanapalle India Mlritm Engineering College Department of Information Technology Hyderabad India Vel Tech High Tech Dr Rangarajan Dr Sakuntala Engineering College Dept of Cse - Aiml Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Simats Department of Data Analytics Tamilnadu Chennai India Velammal Engineering College Department of Ece Tamil Nadu Chennai India Paavai Engineering College Department of Ece Tamilnadu Namakkal India

ISBN: (纸本)9798350359688

A novel blockchain-based security architecture is presented in this study as a solution to the difficulties associated with securing 5 G -enabled WSNs. To manage the uncertain and limited resources inherent in WSNs, the system employs cryptographic techniques, intelligent contracts, and decentralized consensus protocols. The findings of this exhaustive analysis indicate that the proposed approach significantly enhances the overall security stance of 5 G -enabled WSNs. This solution to reduce security risks and vulnerabilities in WSNs is practical and contributes to the growing body of knowledge regarding secure communication in the 5 G era. Subsequent investigations and practical implementations that integrate blockchain technology with 5G-enabled WSNs are made possible by these results. © 2024 IEEE.

关键词： Smart contract

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Cutting-Edge Robotic Arm Design: Harnessing Flex Sensors and Novel Controller Framework to Meet Real-World Innovations

Cutting-Edge Robotic Arm Design: Harnessing Flex Sensors and...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Syambabu, V. Kalaiselvi, K. Mahil, S. Derisha Hemalatha, S. Alekhya, Badi Kanagavalli, N. Mlr Institute of Technology Department of Electronics and Communication Engineering Hyderabad India Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Department of Data Analytics Tamil Nadu Chennai India Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India R.M.D Engineering College Department of Aiml Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

This research paper introduces a sophisticated arm movement tracking system, leveraging a multi-sensor fusion approach for enhanced precision and versatility. The comprehensive system comprises various components, including a dedicated cabinet, six Leap Motion sensors, six microcomputers, a Kinect depth camera, a KVM switcher, a router, and flex sensors. In addition to the aforementioned components, the integration of flex sensors enhances the system's capability to capture nuanced movements and gestures of the human arm. Flex sensors contribute valuable data to the multi-sensor fusion, further refining the arm movement tracking and providing a comprehensive understanding of the intricacies involved. By quantifying the Root Mean Square Error (RMSE) for each filtering technique, including the data from flex sensors, we gained valuable insights into the accuracy and precision achieved by Particle Filtering in refining data from the specified angles. This approach ensures a thorough validation of Particle Filtering's superior performance when compared to Existing filtering Methods. The systematic evaluation of RMSE serves as a robust method for assessing the effectiveness of Particle Filtering, affirming its potential as a high-performing solution for precise angle estimation and tracking within the context of the presented arm movement tracking system that incorporates flex sensors. © 2024 IEEE.

关键词： Cameras

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College Admissions Counseling Using Intelligent Question Answering System 1

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International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2021

作者： Hung, Bui Thanh Data Analytics and Artificial Intelligence Laboratory Engineering—Technology School Thu Dau Mot University 6 Tran Van On street Phu Hoa District Binh Duong Thu Dau Mot Viet Nam

ISBN: (数字)9789811900112

ISBN: (纸本)9789811900105

College admission is successful based on admission counseling for college entrance examinations. To understand the user’s intentions and provide students and parents with all necessary information about school admissions in a more rapid and efficient manner, the college admissions counseling system plays an important role. In this research, we build an intelligent question answering system to support enrollment counseling based on the question classification methodology using deep learning approach. In order to classify the question, we then use pre-train word embedding and bi-directional long short-term memory. Based on the outcomes of question classification, we will be able to get the answer for the question. As a practical experiment, we have built this application and applied for the college admissions counseling of Thu Dau Mot University. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Embeddings

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Bi-SIS Epidemics on Graphs - Quantitative Analysis of Coexistence Equilibria

Bi-SIS Epidemics on Graphs - Quantitative Analysis of Coexis...

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IEEE Conference on Decision and Control

作者： Vishwaraj Doshi Jie Hu Do Young Eun Data Science and Advanced Analytics Team at IQVIA Department of Electrical and Computer Engineering NCSU

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

We consider a system in which two viruses of the Susceptible-Infected-Susceptible (SIS) type compete over general, overlaid graphs. While such systems have been the focus of many recent works, they have mostly been studied in the sense of convergence analysis, with no existing results quantifying the non-trivial coexistence equilibria (CE) - that is, when both competing viruses maintain long term presence over the network. In this paper, we prove monotonicity of the CE with respect to effective infection rates of the two viruses, and provide the first quantitative analysis of such equilibria in the form of upper bounds involving spectral radii of the underlying graphs, as well as positive equilibria of related single-virus systems. Our results provide deeper insight into how the long term infection probabilities are affected by system parameters, which we further highlight via numerical results.

关键词： Epidemics Analytical models Upper bound Statistical analysis Stars Topology Numerical models

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Investigating Continuous Learning in Spiking Neural Networks

arXiv

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arXiv 2023年

作者： Fredieu, C. Tanner Sanghani Center for Artificial Intelligence and Data Analytics Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA United States

In this paper, the use of third-generation machine learning, also known as spiking neural network architecture, for continuous learning was investigated and compared to conventional models. The experimentation was divided into three separate phases. The first phase focused on training the conventional models via transfer learning. The second phase trains a Nengo model from their library. Lastly, each conventional model is converted into a spiking neural network and trained. Initial results from phase 1 are inline with known knowledge about continuous learning within current machine learning literature. All models were able to correctly identify the current classes, but they would immediately see a sharp performance drop in previous classes due to catastrophic forgetting. However, the SNN models were able to retain some information about previous classes. Although many of the previous classes were still identified as the current trained classes, the output probabilities showed a higher than normal value to the actual class. This indicates that the SNN models do have potential to overcome catastrophic forgetting but much work is still needed. © 2023, CC BY.

关键词： Network architecture

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Advancements in Multiple Sclerosis Disease Classification Through Machine Learning 3

Advancements in Multiple Sclerosis Disease Classification Th...

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3rd International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2023

作者： Pandya, Darshanaben D. Degadwala, Sheshang Vyas, Dhairya Solanki, Vishal Narendrakumar Sureshbhai, Sharma Vishalkumar Patel, Hiteshkumar Gunvantbhai Gujarat Visnagar India Sigma University Dept. of Comm. Engg. Gujarat India The Maharaja Sayajirao University of Baroda Gujarat Vadodara India Gujarat Himatnagar India Gujrat Visnagar India Quality Engineering - Data Tiger Analytics Tamil Nadu Chennai India

ISBN: (纸本)9798350306989

In recent years, the field of medical diagnosis has witnessed significant advancements due to the application of machine learning algorithms, especially in complex neurological diseases like Multiple Sclerosis (MS). This study delves into the progress made in MS disease classification using various machine learning techniques, including SVM, KNN, DT, RF, NB, and Extra Trees. Through a diverse dataset of clinical and neuroimaging data, this study has systematically compared the performance of these algorithms in identifying different MS subtypes. SVM and Random Forest exhibited the highest accuracy, while KNN and Decision Trees showed competitive performance. Naive Bayes and Extra Trees also demonstrated promising results in specific scenarios. The study discusses the strengths and weaknesses of each approach and explores the interpretability of the models to understand the key features influencing the classification process. These findings contribute to the growing knowledge base of machine learning in MS disease classification and open avenues for more efficient and accurate diagnostic tools, ultimately leading to personalized patient care and improved treatment planning. © 2023 IEEE.

关键词： Support vector machines

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Taxi Station Location Model Based on Spatio-Temporal Demand Cube

Taxi Station Location Model Based on Spatio-Temporal Demand ...

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2023 International Conference on Networking and Network Applications, NaNA 2023

作者： Gao, LuLu Li, XiaoNan Wei, ZhiCheng Key Lab of Network and Information Security Hebei Province Shijiazhuang China College of Computer and Cyber Security Hebei Province Shijiazhuang China Hebei Normal University Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Security Hebei Province Shijiazhuang China

ISBN: (纸本)9798350327380

With the widespread use of mobile apps, empty taxis can easily receive new orders without having to roam around. Therefore, it is necessary to plan taxi stations where empty taxis can park. In the location selection problem, the spatial and temporal distribution of passenger demand needs to be considered to ensure that the selected location can meet the actual needs of passengers. In this paper, a Spatio-Temporal Cube Demand Coverage Model (STC-DCM) is proposed for taxi station location selection. Firstly, a spatiotemporal demand cube is constructed to obtain fine-grained user demands based on time and space, and genetic algorithm is used to maximize the demand coverage rate. Then, the final location selection results are obtained using the fusion module and adjustment optimization module, where the fusion module integrates multiple location selection solutions using ensemble learning method. Experimental results show that our proposed model can better cover dynamic passenger demand compared to traditional models. © 2023 IEEE.

关键词： Genetic algorithms

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Improving Power Allocation and Installation in WSN Using Novel Nature-Inspired Optimization for Cyber Security Applications 1

Improving Power Allocation and Installation in WSN Using Nov...

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1st Asian Conference on Communication and Networks, ASIANComNet 2024

作者： Balamurugan, K.S. Kalyani, Gedela Al-Fatlawy, Ramy Kurdi, Waleed Hadi Madhloom Alsalamy, Fatima Al-Aboudy, H.M. Manikandan, K. Karpaga Vinayaga College of Engineering and Technology Department of ECE Tamil Nadu Chengalpattu India Karpaga Vinayaga College of Engineering and Technology Big Data Analytics Tamil Nadu Chengalpattu India The Islamic University College of Technical Engineering Department of Computer Technical Engineering Najaf Iraq Altoosi University College Department of Computer Science Najaf Iraq Al-Mustaqbal University Medical Laboratories Techniques Department Hillah Iraq Mazaya University College DhiQar Department of Computer Techniques Engineering Iraq Sona College of Technology Department of Biomedical Engineering Tamil Nadu Salem India

ISBN: (纸本)9798350367003

Wireless sensor network (WSN) installation and power allocation optimization for cybersecurity purposes are difficult tasks which include a well-thought-out method that strikes a balance between goals such as the energy efficiency, connectivity and cyber threat resistance while improving overall system performance and cybersecurity. A novel multi-task walrus optimization (MTWO) technique is offered in this study. The installation and power allocation problem (IPAP) with several jobs is established in this research. The suggested MTWO is used to split the IPAP into numerous scalar components, which are consequently grouped and treated by their desired goals. We evaluate the proposed MTWO approach using simulations and show that it works well in practice. The findings demonstrate that power distribution and installation for cybersecurity activities on WSNs can be improved by the MTWO. The superiority of the problem specific MTWO over the MOGA has been demonstrated by simulation outcome in several network cases, offering a wide range of excellent network designs to aid in the decision-maker selection. © 2024 IEEE.

关键词： Energy efficiency

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A Collaborative Trajectory-Oriented Viewport Prediction for on-Demand and Live 360° VR Video Streaming

A Collaborative Trajectory-Oriented Viewport Prediction for ...

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International Symposium on High Capacity Optical Networks and Enabling Technologies, HONET

作者： Abid Yaqoob Gabriel-Miro Muntean Performance Engineering Lab and Insight SFI Centre for Data Analytics School of Electronic Engineering Dublin City University Ireland Performance Engineering Lab School of Electronic Engineering Dublin City University

Viewport prediction is critical in delivering high-quality 360° virtual reality (VR) videos to a large audience across diverse networks. Traditional computationally expensive viewport prediction mechanisms that rely on static content analysis and individual viewing preferences have proven inadequate in adapting to dynamic content and user preferences. This paper introduces TOPVR, a novel trajectory-oriented viewport prediction approach for both on-demand and live 360° VR video streaming. TOPVR overcomes the challenges introduced by diverse user preferences by leveraging the collaborative behavior of viewers and smartly capturing the relationship between different trajectories. It incorporates a user management system that identifies potential users with similar preferences and the closest trajectory changes to the current user over time. Additionally, a collaborative viewport prediction method estimates future viewing positions for each user based on recent viewing information and similar trajectories and trend changes of other users watching the same content. We evaluate the performance of TOPVR using a real-world head movement dataset recorded using on-demand and live streaming experiments. Our experimental results demonstrate that TOPVR, with its viewer management system and collaborative trajectory prediction mechanisms, outperforms existing benchmark algorithms in terms of higher prediction performance.

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AttentionFaultFormer: An attention-enhanced 3D CNN & transformer model for seismic fault detection

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Journal of Applied Geophysics 2025年 238卷

作者： Wang, Jing Ma, Siteng Liu, Yue Dong, Ruihai School of Computer Science and Engineering North China Institute of Aerospace Engineering China The Insight Centre for Data Analytics School of Computer Science University College Dublin Dublin Ireland

In seismic exploration, accurately identifying faults is fundamental for interpreting seismic data. Due to the highly sparse distribution of faults in the seismic volume and the heavy reliance on local features to reconstruct fault segmentation masks, predicting faults using 3D Vision Transformers (ViT) remains challenging. We propose a novel attention-enhanced 3D U-shaped CNN and ViT hybrid model, called AttentionFaultFormer, specifically for fault detection tasks. AttentionFaultFormer includes a transformer-based encoder, a residual-based decoder, and attention-enhanced skip connection. The skip connection integrates local feature extraction from convolutional layers with channel and spatial attention mechanisms to enhance the representation of detailed fault features. Furthermore, based on the geometric structure of faults, we designed the Multi-Axis Striped Convolution Attention (MASCA) module and incorporated it into the shallow skip connections to help the model delineate continuous long faults. We applied our model to three field seismic datasets (F3, Kerry3D, and Thebe). Comparative analysis against two CNN models and two ViT models reveals that our model predicts more continuous and interpretable faults. © 2025

关键词： Attention mechanism CNN Fault detection Seismic interpretation Vision transformer

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