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检索条件"任意字段=International Conference on Advances in Data Science and Computing Technologies, ASDC 2022"

共 6589 条记录，以下是41-50 订阅

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Transforming High-Frequency Trading Predictions with CNN-LSTM Networks 5

Transforming High-Frequency Trading Predictions with CNN-LST...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Priya, S. Baghavathi Priyanga, S. Kamisetty, Sri Surya Shobith Neelapala, Venkata Sai Vinay Nithila, G. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Chennai India Vellore Institute of Technology Department of Electronics & Communication Engineering Vellore India Vellore Institute of technology Department of Computer Science & Engineering Vellore India

ISBN: (纸本)9798350356236

High-Frequency Trading (HFT) has transformed financial markets by enabling rapid and automated trading. However, the prediction of critical market metrics, such as trading volumes, remains a challenge due to the noisy and complex nature of financial data. This paper presents a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model designed specifically for predicting trading volumes in the NIFTY50 index using high-resolution, minutelevel Yahoo Finance data. By combining CNN's spatial feature extraction capabilities with LSTM's temporal modeling strengths, the proposed approach effectively captures the intricate patterns and sequential dependencies inherent in high-frequency trading datasets. Comprehensive data preprocessing, including noise reduction and temporal segmentation, coupled with advanced cross-validation techniques, ensures robust performance across market conditions. Comprehensive evaluations using metrics such as Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2) demonstrate the model's efficacy in capturing intricate financial dynamics. This work underscores the potential of hybrid deep learning models in enhancing predictive analytics for high-frequency trading applications in emerging markets. © 2025 IEEE.

关键词： Deep learning

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Smart Contract Vulnerability Detection Method Based on Node Feature Augmentation Graph Convolutional Network 8th

Smart Contract Vulnerability Detection Method Based on Node ...

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8th international conference on Information science, Communication and computing

作者： Fan Dong Cheng Jieren Tang Xiangyan Lu Kai Hainan Univ Sch Comp Sci & Technol Haikou Hainan Peoples R China Hainan Blockchain Technol Engn Res Ctr Haikou 570228 Hainan Peoples R China Hainan Univ Sch Cyberspace Secur Sch Cryptol Haikou 570228 Hainan Peoples R China Hainan Vocat Coll Polit & Law Dept Publ Safety Technol Haikou 571100 Hainan Peoples R China

ISBN: (纸本)9789819617838;9789819617814;9789819617807

In recent years, frequent smart contract security vulnerability incidents have seriously threatened the ecological security of blockchain. This paper combines data enhancement, contrast loss and graph convolutional network, and proposes a smart contract vulnerability detection method based on node feature enhanced graph convolutional network, to improve the accuracy of the smart contract vulnerability detection method and reduce false alarm rate. First, this paper constructs a smart contract graph for the smart contract's source code and retains the source code's logic and order to the maximum extent. Then, this paper vectorizes the smart contract graph and proposes a mask-based smart contract graph node feature enhancement method, which uses the node features of the graph structure to perform attribute-level feature enhancement. Secondly, the enhanced vector and the original vector are simultaneously input into the graph convolutional network model for training. Finally, the two training results are calculated and back-propagated with contrast loss and cross-entropy loss. The purpose of adding contrast loss is to improve the generalization ability of the model and improve detection efficiency.

关键词： smart contract vulnerability detection graph convolutional networks graph neural networks data augmentation contrastive learning vulnerability detection system

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Augmented Reality-Assisted Pediatric Surgery: A Machine Learning-Based Approach for Enhancing Accuracy 1

Augmented Reality-Assisted Pediatric Surgery: A Machine Lear...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Bhardwaj, Sumedha Narla, Swapna Absur, Md Nurul Dutta, Shuvo Hyzil, J.B. Krishna Ganesh, S.M. DIT University Department of School of Computing Uttarakhand Dehradun India Tek Yantra Inc CA United States CUNY Graduate Center New YorkNY United States Western Michigan University Department of Physics United States Govt College for Women Affiliated to University of Kerala Department of Zoology Thiruvananthapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331518578

Numerous advancements have been identified as potentially disruptive innovations in the surgical workplace as surgical workflow continues to improve in the digital age. The application of virtual reality (VR) and augmented reality (AR) within health care to enhance the use of data for medical reasons is unavoidable given their rapid expansion in availability, affordability, and accessibility. Using augmented reality may help paediatric orthopaedic and trauma surgeons overcome the difficult problem of comprehending the spatial linkages among the implants, anatomy, and their instruments. As needs be, the current examination proposes another AR of the surgical scene that deftly incorporates the various information sources given by a mobile C-arm and a Kinect RGB-depth sensor. This paper present a learning-based worldview to perceive the significant items or life structures in both Kinect and data of X-ray, and to produce an object unambiguous pixel-wise alpha guide that permits importance based combination of the X-ray pictures and the video inside a solitary view. We demonstrate very encouraging results in 10 simulated procedures with the goal of giving physicians a better grasp of the surgical environment and an enhanced perception. © 2025 IEEE.

关键词： Pediatrics

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Prediction of Covid and Covid-19 and Omicron data with ESPRINT, ERF, CSDLM and HMC-19HRP Models 5

Prediction of Covid and Covid-19 and Omicron data with ESPRI...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Ramya, V. Thirunavakkarasu, K.S. Vel's Institute of Science Technology and Advanced Studies Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350356236

SARS-CoV-2, also known as the Covid-19 Corona Virus, has caused widespread damage globally, and the situation is only becoming worse. Officials from all across the world are using a number of COVID-19 outbreak prediction models to help them make choices and implement pertinent control measures. Simple epidemiological and statistical models are well-liked by the media and have garnered more attention from authorities among the conventional models for COVID-19 global pandemic prediction. It's a pandemic illness that spreads daily from person to person. As a result, it's critical to monitor the total patient population impacted. The way that the computerised data is now provided by the system makes it extremely difficult to analyse and forecast the spread of disease both locally and globally. An enhanced Sprint algorithm, Enhanced Random forest algorithm, Customized sequential Deep learning Model (CSDLM), and HMC-19HRP model is proposed in this study and analysed each with two dataset that is Covid dataset and OMICRON dataset. Finally results obtained from all the method are compared. On overall comparison HMC-19HRP outperforms other methods on both the dataset with precision of 94.92 and 98.26, Recall of 99.03 and 95.86, F1-score of 96.93 and 97.04, Specificity of 78.71 and 97.06 and Accuracy of 94.98 and 96.30 on covid data and Omicron dataset respectively. © 2025 IEEE.

关键词： COVID-19

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Security-Aware Allocation of Replicated data in Distributed Storage Systems 15

Security-Aware Allocation of Replicated Data in Distributed ...

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15th international conference on Cloud computing and Services science, CLOSER 2025

作者： De Capitani di Vimercati, Sabrina Foresti, Sara Livraga, Giovanni Samarati, Pierangela Tedesco, Mauro Computer Science Department Università degli Studi di Milano via Celoria 18 Milano Italy

ISBN: (纸本)9789897587474

Distributed storage systems offer scalable and cost-effective solutions for managing large data collections. A critical factor for the adoption of these systems is the allocation of data (possibly including replicas) to the storage nodes satisfying operational and security requirements, while ensuring economic effectiveness. Appropriate data and replica management provides significant benefits, ranging from enhanced fault tolerance and improved data availability, to reduced latency and optimized workload distribution. A suboptimal placement of data and replicas can instead lead to excessive costs, security risks, and performance bottlenecks. This paper proposes a novel model for permitting data owners to specify in a friendly manner complex data and replica allocation constraints, and an approach for computing optimal (satisfying operational and security requirements and minimizing costs) data allocations in distributed storage environments. Our work aims to improve the reliability, security, and cost-effectiveness of distributed storage systems. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Information management

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Motion Planning in Automated Vehicles Based on Hierarchical MPC 1

Motion Planning in Automated Vehicles Based on Hierarchical ...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Kapila, Pooja Kumar, Pankaj Negi, Rahul Bharti, Bhupender Kumar Punhani, Akash Bisht, Suraj Singh IIMT College of Engineering Department of Artifical Intelligence & Data Science India Amity University Department of Statistics Kolkata India Tula's Institute Department of ECE Dehradun248197 India Department of Mechanical Engineering VMSB UTU Campus Pithoragarh India KIET Group of Institutions Department of Computer Science Noida India Bipin Tripathi Kumaon Institute of Technology Department of ECE Dwarahat Almora India

ISBN: (纸本)9798331518578

In this study, a thorough hierarchical control structure that supports autonomous decision-making that arises in autonomous systems and robots is proposed. Distinct state and decision/control sets are frequently used to describe high-level decision making in a conventional hierarchical control architecture. To keep up with proficiency, wellbeing, and solace, robotized vehicles generally depend serious areas of strength for on powerful movement arranging calculations. The capacity of Model Prescient Control (MPC) to oversee imperatives and upgrade directions progressively has made it a strong system for movement arranging. To approve the proposed strategy, this exploration utilizes MATLAB recreations to look at various levelled MPC for movement arranging in robotized vehicles. With the low-level regulator guaranteeing exact direction observing and the undeniable level organizer computing a worldwide direction, a two-layer progressive MPC structure is fabricated. The discoveries show how well the progressive MPC handles street ebb and flow, dynamic hindrances, and continuous computational impediments. It is demonstrated that the suggested autonomous decision-making scheme's recursive practicality and stability are ensured by carefully crafting its essential components. An intelligent vehicle's autonomous lane-changing system is developed using the suggested framework. Simulation demonstrates that it has a potential capacity to manage a variety of behaviours in complex and dynamic contexts. It is anticipated that the suggested framework will be useful for autonomous decision-making in a variety of robotics along with autonomous technologies where safety as well as optimality are important factors. © 2025 IEEE.

关键词： Motion planning

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CorLMI-FSA: An Efficient Feature Selection Approach for Stress Classification Using Physiological Signals 5

CorLMI-FSA: An Efficient Feature Selection Approach for Stre...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Shikha, Shikha Sethia, Divyashikha Indu, S. Delhi Technological University Department of Computer Science and Engineering New Delhi India Delhi Technological University Department of Software Engineering New Delhi India Delhi Technological University Department of Electronics and Communication New Delhi India

ISBN: (纸本)9798350356236

Mental stress in academic settings significantly impacts students' health and performance. Physiological signals such as Electrodermal Activity (EDA) and Heart Rate Variability (HRV) offer valuable insights for stress classification. However, the multidomain nature of these signals often introduces redundancy and irrelevant data, reducing both classification accuracy and computational efficiency [1]. Hence, this paper proposes an efficient feature selection algorithm, CorLMI-FSA (Correlation-Logistic Mutual Information Feature Selection Algorithm), which dynamically adjusts redundancy weights through a logistic function to capture nonlinear dependencies among features. The proposed algorithm achieves accuracies of 96.82% and 95.84% for binary and three-level stress classification tasks using Gradient Boosting (GB), enhancing computational efficiency by selecting only the most relevant features. These results underscore CorLMI-FSA's potential as a low-complexity solution for realtime stress monitoring in wearable devices, facilitating timely intervention. © 2025 IEEE.

关键词： Stress analysis

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Enhanced Facial Emotion Detection Using Convolutional Neural Networks and Attention Mechanisms 5

Enhanced Facial Emotion Detection Using Convolutional Neural...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Indhumathy, D. Yeswanth, S. Sri Ramakrishna Engineering College Department of Mathematics Coimbatore India Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam Erode India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356236

Convolutional neural networks are now facing a number of technological obstacles in the field of facial expression identification, including the low automatic recognition rate, the difficulty of effectively recognizing minor changes in facial emotions, and the complexity of feature extraction. In order to enhance automatic facial expression identification, this research suggests a Height Performance Module Implement (HPMI) attention mechanism based on spatial attention. The model concentrates on important spatial characteristics by incorporating this attention mechanism, which improves recognition during training. The VGG-16 network incorporates a spatial attention-based HPMI module that efficiently reduces over fitting, amplifies key information, and suppresses irrelevant data. This facilitates effective information exchange between the network model and the image's key properties. It also deals with the problem of dimensional mismatch between output and input. On the CK+ expression datasets, the approach yields accuracy rates of 97.05%, respectively. According to experimental results, the incorporation of the HPMI module improved accuracy by an average of 3.94%. © 2025 IEEE.

关键词： Convolutional neural networks

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Cricket Shot Classification from Video Using Body Pose Landmarks 5

Cricket Shot Classification from Video Using Body Pose Landm...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Rao, Chava Govardhana Varma, N. Sai Harshith Kashish, K.C. Mazumder, Priyangshu Ramasamy, Prema Pes University Dept of Computer Science Bengaluru India

ISBN: (纸本)9798350356236

The primary focus of this study is to use deep learning techniques to classify cricket shots. data acquisition begins by gathering video clips of different cricket shots and manually annotating them. To address imbalance in class, generated synthetic examples are used to ensure each shot type has equal representation. Advanced techniques for handling missing data points using combined object detection and 3D landmark extraction to track the batsman's move-ments are employed for efficiency. To enhance accuracy of the analysis, complex methods are used to deal with missing data. By integrating object detection methods with 3D land-mark extraction, effective tracking of batsman movements is captured. Tests with multiple machine learning models such as simple neural networks and more complicated models which could capture movement sequence over time are tested on the data. The most successful results were achieved with systems that could process video frames in both forward and reverse directions, leading to a more precise identification of cricket shots. This research illustrates the potential of machine learning techniques and how advanced models can be used to analyze and classify sports activities in video data. We contribute to the growing community and knowledge base of automated sports analytics, paving the way for future innovations in real-time action recognition and commentary generation. © 2025 IEEE.

关键词： Deep learning

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Estimation of Response Time with Neural Networks in Cloud computing System 1

Estimation of Response Time with Neural Networks in Cloud Co...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： More, Rajesh Suresh, Sanjna Veerappampalayam Easwaramoorthy, Sathish Kumar Rizvi, Haider Ali Kunnathully, Kannan Gupta, Sahil K. Cloudwerx Wordwide Departmen of AIML Hyderabad India Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India School of Engineering and Technology Sunway University Selangor Darul Ehsan Malaysia Yogananda School of Artificial Intelligence Shoolini University Department of Artificial Intelligence and Machine Learning India Veltech University Department of Research Chennai India Tula's Institute Department of Computer Science and Engineering Dehradun248197 India

ISBN: (纸本)9798331518578

Resource allotment, responsiveness, connection, research of unexplored assets, related resource modeling, and asset scheduling are just a few of the numerous factors and concerns that need to be considered to enhance cloud computing efficiency. The functionality of the cloud has a significant impact on the propagation of cloud architecture. Consequently, it is important to determine the components that influence cloud function to analyze it. Response duration is the primary aspect that significantly affects cloud computation functioning, according to the research. The response duration assistance is among the numerous different variables that may be used to organize resource applications. Artificial neural networks (ANNs) were employed as a technique for evaluating the parameter of pursuit, and a fork-join framework or an architecture with demand partitioning was selected as the queuing paradigm. As a result, this study suggests a load-managing method depending on cloud demand response duration using the ARIMA Estimation of Response Duration Method. The fundamental concept is to employ ARIMA methods to estimate the future response duration, providing a more efficient method of allocating resources with limited values. The results of the investigation demonstrated that the estimations were more precise compared to those that had been recently established. The results of the study indicate that estimation is an outstanding path for load balance and has possibility and value for load balance with displayed response duration. Additionally, the suggested method makes it feasible to acquire estimations for a wider range of cloud structure functioning signs compared to earlier feasible and also to extend the evaluation of the cloud platform to the instance of a structure with a non-Poisson data flow and non-exponential assistance duration. © 2025 IEEE.

关键词： Cloud platforms

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