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共 13501 条记录，以下是4921-4930 订阅

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4th international conference on Computer and Communication Engineering, CCCE 2024

4th International Conference on Computer and Communication E...

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4th international conference on Computer and Communication Engineering, CCCE 2024

ISBN: (纸本)9783031710780

the proceedings contain 19 papers. the special focus in this conference is on Computer and Communication Engineering. the topics include: Movie Tag Prediction Using Multi-label Classification with BERT;collaborative Filtering is Wrong and Here is Why;integrating Non-encrypted and Encrypted Features for Enhanced Detection of Encrypted Network Attacks;Aviation Communication Protection Protocol Based on SM4 Cryptography Algorithm;multidimensional Analysis Tagger and Deep learning for a Robust Fake News Detection System;National ICT Resilience: An Analysis of Norway’s Cyber Infrastructure Preparedness;countering Hybrid threats: Towards an Ontology for Securing 5G Networks;communications Services and Management of Application Program to Promote Community’s Products by Energy Saving of Prawet Burirom Shore;5G Multi_SourceTraffic Analysis Using Generative Adversarial Networks;Delays and Starvation on UE Scheduling: a New 5G Simulator;An Optimised PIC Programmer Also Having a High Pedagogical Value;Collaborative Design for Integrated Sensing and Communications Receiver with Imperfect CSI;microservices for Cloudification and Orchestration of Railway Operations;Striving for Urban Traffic optimization: Eclipse MOSAIC-Backed Predictive IoV applications;human-Centered Innovations in Healthcare Education: Digital Skills for Ergonomics and Bioengineering Advancement;event-Driven Management System for Smart Pill Dispensers;User-Centered Dashboard Design in Serious VR Game for Cognitive Rehabilitation.

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Portfolio optimization Using Machine learning Techniques

Portfolio Optimization Using Machine Learning Techniques

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international conference on Computation, Automation and Knowledge Management (ICCAKM)

作者： T. Sugadev N. Sheik Hameed S. Vijayakumar P. Tamilarasan Md. Sahidul Islam B.S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Chennai College of engineering and Technology SRMIST Kattatankulathur

A crucial component of financial decision-making is portfolio optimisation, which aims to minimise risk and maximise profits. the mean-variance model is one of the traditional methods that struggles to capture the dynamic character of financial markets. the use of machine learning approaches to improve portfolio optimisation is examined in this research article. We start by going over fundamental ideas like asset allocation and selection before gradually getting into the intricacies of machine learning techniques. We go over how to forecast asset returns and correlations using clustering, regression, and classification techniques. We also investigate portfolio rebalancing techniques using reinforcement learning. the paper reviews real-world data sources, data preprocessing, and model evaluation techniques, highlighting the importance of backtesting and risk management. Furthermore, we examine recent advancements in deep learning and neural networks to model non-linear relationships and assess their impact on portfolio optimization.

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Towards making the most of NLP-based device mapping optimization for OpenCL kernels

Towards making the most of NLP-based device mapping optimiza...

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IEEE international conference on Omni-Layer Intelligent Systems (IEEE COINS)

作者： Vavaroutsos, Petros Oroutzoglou, Ioannis Masouros, Dimosthenis Soudris, Dimitrios Natl Tech Univ Athens Sch Elect & Comp Engn Athens Greece

ISBN: (数字)9781665483568

ISBN: (纸本)9781665483568

Nowadays, we are living in an era of extreme device heterogeneity. Despite the high variety of conventional CPU architectures, accelerator devices, such as GPUs and FPGAs, also appear in the foreground exploding the pool of available solutions to execute applications. However, choosing the appropriate device per application needs is an extremely challenging task due to the abstract relationship between hardware and software. Automatic optimization algorithms that are accurate are required to cope with the complexity and variety of current hardware and software. Optimal execution has always relied on time-consuming trial and error approaches. Machine learning (ML) and Natural Language Processing (NLP) has flourished over the last decade with research focusing on deep architectures. In this context, the use of natural language processing techniques to source code in order to conduct autotuning tasks is an emerging field of study. In this paper, we extend the work of Cummins et al., namely Deeptune, that tackles the problem of optimal device selection (CPU or GPU) for accelerated OpenCL kernels. We identify three major limitations of Deeptune and, based on these, we propose four different DNN models that provide enhanced contextual information of source codes. Experimental results show that our proposed methodology surpasses that of Cummins et al. work, providing up to 4% improvement in prediction accuracy.

关键词： Machine learning Natural Language Processing OpenCL Heterogeneous Systems

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Multi-modal Recommendation based on Graph Neural Networks

Multi-modal Recommendation based on Graph Neural Networks

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Computer applications and Information Technology (ISCAIT), international Symposium on

作者： Zhuqing Zhou Xin Yan School of Computer Science and Artifical Intelligence Wuhan University of Technology Wuhan China

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

the growth of the internet and AI has caused information overload, making it harder for users to find relevant content. Deep learning-based recommendation systems help by analyzing user-product latent interactions for recommendations. However, these systems have limitations, including their inability to fully capture the nuances of users’ long-term and short-term interests, as well as the persistent problem of data sparsity. this paper aims to improve recommendation systems by using graph neural networks GNNs to leverage more available information. the key contributions include the proposal of a multi-modal GNN recommendation algorithm MHGR, which models capture local user interests. the algorithm outperforms others on several datasets, showing $4.46 \%$ improvement in Recall@20 and $2.42 \%$ in NDCG@20 on the Clothing dataset compared to the second-best algorithm LGMRec.

关键词： Graph convolutional networks Computational modeling Contrastive learning Computer architecture Computer applications Proposals Information technology Recommender systems optimization Context modeling

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Energy-Efficient Routing algorithms for Wireless Sensor Networks in Precision Agriculture

Energy-Efficient Routing Algorithms for Wireless Sensor Netw...

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Innovative Practices in Technology and Management (ICIPTM), international conference on

作者： Komal Sharma Monit Kapoor Anurag Shrivastava Arti Badhoutiya A Kakoli Rao Ruby Pant Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Centre of Research Impact and Outreach Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu Department of Electrical Engineering GLA University Mathura Lloyd Institute of Engineering & Technology Greater Noida Department of Mechanical Engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

this investigation investigates the domain of energy-efficient steering calculations inside Remote Sensor Systems (WSNs) for accuracy agribusiness. Four conspicuous algorithms—LEACH, EEUC, PSO-based Steering, and ACO-based Routing—were rigorously assessed, considering key measurements such as arrange lifetime, vitality utilization, and information conveyance rate. In comparison to related work, our examination adjusts with patterns in leveraging bio-inspired optimization and machine learning integration to address the interesting challenges posed by energetic agrarian situations. the results illustrate that EEUC, through distance-based clustering, essentially moves forward vitality adjust, advertising a 25% increment in organized lifetime compared to Filter. PSO-based Routing and ACO-based Directing exhibit flexibility to changing conditions, with a 20% and 18% improvement in organize lifetime, individually, compared to Filter. Besides, the computational overhead presented by EEUC is relieved by a parallel preparing approach, driving a 15% decrease in communication delays. these discoveries emphasize the significance of custom-fitted directing arrangements in exactness horticulture, giving an establishment for the improvement of brilliantly, energy-efficient WSNs pivotal for feasible and optimized crop administration.

关键词： Wireless sensor networks Machine learning algorithms Accuracy Horticulture Crops Machine learning Writing

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Boosting Lung Cancer Prediction Accuracy through Advanced Data Processing and Machine learning Models

Boosting Lung Cancer Prediction Accuracy Through Advanced Da...

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Sustainable Expert Systems (ICSES), international conference on

作者： Chandra Shikhi Kodete D Vijaya Saradhi Vineela Krishna Suri P Bharat Siva Varma N S Koti Mani Kumar Tirumanadham Vahiduddin Shariff School of Technology Eastern Illinois University Charleston IL USA Department of ECE Malineni Lakshmiah Women's Engineering College Guntur India Department of CSE - Data Science CVR college of Engineering Hyderabad India Department of CSE S R K R Engineering College Bhimavaram India Department of CSE Sir C R Reddy College of Engineering Eluru India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

this work proposes a complicated voting ensemble model for exact feature selection using AdaBoost, Catboost, XGBoost, and LARF (LASSO and Recursive Feature Elimination). this approach seeks early detection of lung cancer. Sophisticated machine learning in the model increases prediction accuracy above previous techniques. With its 94.81 % accuracy, precision, recall, and fl-scores for both “NO” and “YES” events, the ensemble approach becomes beneficial in spotting positive and negative lung cancer patients. LARF improved feature selection by giving predictive qualities top priority, hence improving model interpretability and performance at once. this model has practical applications as its speed and accuracy exceed past criteria. the results imply that the employment of numerous sophisticated algorithms to handle complicated medical data may help early and accurate detection of lung cancer be facilitated. Investigating how well the model fits in many clinical settings and how to use it with bigger patient datasets will help to validate and enhance its effectiveness.

关键词： Accuracy Machine learning algorithms Neural networks Lung cancer Medical treatment Predictive models Prediction algorithms Feature extraction Medical diagnostic imaging Overfitting

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Time-Delay Temperature Control System Design based on Recurrent Neural Network 4

Time-Delay Temperature Control System Design based on Recurr...

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4th IEEE international conference on Industrial Cyber-Physical Systems (ICPS)

作者： Liu, Yuan Xu, Song Kobori, Shinya Hashimoto, Seiji Kawaguchi, Takahiro Gunma Univ Div Elect & Informat Kiryu Gumma Japan Jiangsu Univ Sci & Technol Dept Elect & Informat Zhenjiang Jiangsu Peoples R China

ISBN: (纸本)9781728162072

A Recurrent Neural Network (RNN) is a special neural network sequence model that is very suitable for dealing with time series tasks. In various industrial processing systems, it has achieved good performances. In this paper, a RNN model which is driven by an ideal reference model is proposed for the single-input single-output(SISO) temperature control system with time-delay. An ideal reference model is introduced to provides a more valuable teaching signal for helping RNN controller to obtain higher learning efficiency and providing suitable control input to the temperature control system. Meanwhile, Adam optimization algorithm which can get adaptive learning rates is used to update parameters and improve the control performance of the RNN. Further, a classical integral proportional derivative (I-PD) controller is designed to reduce the effects caused by the temperature setting value kick during the RNN learning period. Simulations were developed under the MATLAB environment to evaluate the proposed control system performance. In order to demonstrate the efficiency and application of the proposed RNN control method, the simulation results based on the actual temperature model are compared quantitatively.

关键词： Recurrent Neural Network Adam optimization algorithm I-PD controller temperature control

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IntelliFiber: Optical Network optimization through Machine learning algorithms

IntelliFiber: Optical Network Optimization through Machine L...

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international conference on Computing and Networking Technology (ICCNT)

作者： Amit Kumar Nayak Dharmendra Tiwari Sambhram Pattanayak R&D Lab Prodapt Solutions Bengaluru India Department of Media Studies Amity University Dubai UAE

the rapid evolution of the Internet and other communication systems has led to an explosion of available services and technology, making communication networks increasingly complex. the continued success of the communication network relies heavily on integrating Artificial Intelligence (AI) and Machine learning (ML) in various aspects. While optical networks play a vital role in both the core and access networks of communication systems, they also confront significant difficulties due to the complexity of their systems and the need for human intervention in their daily operations. Deploying additional intelligence capabilities to enable autonomous and flexible network operations is crucial to overcoming current restrictions and addressing the difficulties of future optical networks [1]. Many modern optical network applications use ML approaches because of their demonstrated superiority in addressing complicated challenges. the current applications of Machine learning (ML) in optical networks are thoroughly discussed in this paper. ML's usages are broken down into two groups, [3] [4] [5]: network management (control and resource management) and resilience (monitoring and detection) in optical networks. Different ML methods are evaluated and contrasted across use scenarios. In addition, a primer on ML applications is offered, covering such topics as the fundamentals of popular ML algorithms, ML paradigms, and the reasons for using ML. Finally, the challenges and potential remedies of Machine learning use in optical networks are examined to stimulate further research and development.

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Traffic flow prediction based on improved SVR for VANET 4

Traffic flow prediction based on improved SVR for VANET

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4th international conference on Advanced Electronic Materials, Computers and Software Engineering, AEMCSE 2021

作者： Tong, Jiancheng Gu, Xiang Zhang, Miao Wan, Jie Wang, Jin Nantong University School of Information Science and Technology Jiangsu Province China

ISBN: (纸本)9781665415965

For Vehicle Ad-Hoc Network (VANET), the accurate traffic flow information may offer useful guidance to drivers. In the past few years, applications of big data bring about opportunities for intelligent traffic control and management. this paper uses an optimization of support vector regression (SVM) as a way of traffic flow prediction. Particle swarm optimization (PSO) is applied to the parameter optimization of support vector regression (SVR), which improves the performance of the prediction system. the experimental results show that the method has good prediction effect. © 2021 IEEE.

关键词： Particle swarm optimization (PSO)

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Interpretable High-level Features for Human Activity Recognition

Interpretable High-level Features for Human Activity Recogni...

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15th international conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS) held as part of 15th international Joint conference on Biomedical Engineering Systems and Technologies (BIOSTEC)

作者： Hartmann, Yale Liu, Hui Lahrberg, Steffen Schultz, Tanja Univ Bremen Cognit Syst Lab Bibliothekstr 1 D-28359 Bremen Germany

ISBN: (纸本)9789897585524

this paper introduces and evaluates a novel way of processing human activities based on unique combinations of interpretable categorical high-level features with applications to classification, few-shot learning, as well as cross-dataset and cross-sensor comparison, combination, and analysis. Feature extraction is considered as a classification problem and solved with Hidden Markov Models making the feature space easily extensible. the feature extraction is person-independently evaluated on the CSL-SHARE and UniMiB SHAR datasets and achieves balanced accuracies up from 96.1% on CSL-SHARE and up to 91.1% on UniMiB SHAR. Furthermore, classification experiments on the separate and combined datasets achieve 85% (CSL-SHARE), 65% (UniMiB SHAR), and 74% (combined) accuracy. the few-shot learning experiments show potential with low errors in feature extraction but require further work for good activity classification. Remarkable is the possibility to attribute errors and indicate optimization areas easily. these experiments demonstrate the potential and possibilities of the proposed method and the high-level, extensible, and interpretable feature space.

关键词： High-level Feature Human Activity Recognition Hidden Markov Model Motion Unit Few-shot learning Wearable Sensors

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