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检索条件"任意字段=2nd International Conference on Computer Communications Control Technology"

共 23677 条记录，以下是161-170 订阅

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Analyzing the Hybrid Electric Vehicle's Performance for Automatic Acceleration control 2nd

Analyzing the Hybrid Electric Vehicle's Performance for Auto...

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2nd international conference on Emerging Wireless Technologies and Sciences, ICEWTS 2024

作者： Charyulu, M.L.N. Sailaxmi, M. Kushwah, Vivek Singh Gandipet Telangana Hyderabad500075 India

ISBN: (纸本)9783031878855

Hybrid electric vehicles primarily revolve around improving fuel efficiency, reducing emissions, addressing environmental concerns, and offering a more accessible transition toward electric mobility while leveraging existing automotive technology. HEVs are designed to combine the benefits of traditional internal combustion engines (ICE) with those of electric propulsion systems, in heightened total vehicle performance, decreased emissions, and increased fuel efficiency. The development of a driver, electric powertrain, and vehicle model for a hybrid electric vehicle. Examine the conduct of the Plot the real velocity power distribution curves for the car throughout a US06 drive cycle. Examine the fuel consumption and operation of the battery, ultracapacitor, and fuel cell as well. By converting to power gearbox via the motor and turning off the engine, the HEV resolves the issue. In this manner, no fuel will be used when the vehicle is idle, producing no exhaust emissions. A significant benefit of hybrid electric vehicles (HEVs) is their ability to operate entirely on electricity when the fuel tank runs empty while the engine is running. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Hybrid vehicles

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Securing Software Defined Networks from Packet Injection Attacks: A Framework for Detection and Prevention

Securing Software Defined Networks from Packet Injection Att...

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Computational Intelligence, Communication technology and Networking (CICTN), international conference on

作者： Laboni Dolai Partha Maity Tannishtha Jana Pritam Sadhu Tinku Adhikari Sandipan Ghosal Computer Science and Engineering Techno International New Town Kolkata India Information Technology Techno International New Town Kolkata India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Software Defined Networking (SDN) has changed how networks are managed by splitting the control and data layers, allowing for centralized control and flexible network setups. SDN increases programmability, flexibility, and agility but also presents serious security risks, especially with regard to packet injection attacks. These attacks specifically target the central SDN controller, which serves as a single point of failure, by taking advantage of security flaws in the SDN architecture. Attackers can send unauthorized packets into the network, causing communication issues, damaging data accuracy, and thereby leads to Denial-of-Service (DoS) attack. This paper examines the threat landscape of packet injection attack in SDN environments, with a focus on the OpenFlow protocol, which serves as a communication bridge between the control and data planes. We explore how attackers create fake hosts to get around traditional security defences. The research also provides a novel and efficient solution to cater this attack. The solution is compared with the existing state-of-the art solutions to ensure its accuracy and efficiency. We show, by direct observation, how these methods strengthen SDN security against packet injection attacks, improve network reliability, and provide a safe and dependable network environment.

关键词： Protocols Accuracy Scalability Prevention and mitigation Threat assessment Real-time systems Software reliability Software measurement Software defined networking Systematic literature review

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Hot Topic Extraction in Microblog Based on Hashtags 2nd

Hot Topic Extraction in Microblog Based on Hashtags

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2nd international conference on Applied Intelligence, ICAI 2024

作者： Feng, Xue Zhang, Can Pan, Yijie Computer School Beijing Information Science and Technology University Beijing China Eastern Institute for Advanced Study Eastern Institute of Technology Ningbo China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9789819619061

Hot topic extraction in microblog has received great interests during recent years. Traditionally, there are two different frameworks for this task, one being based on supervised learning, and the other being based on unsupervised learning, which typically exploits clustering or topic models for the task. Actually, the construction of large-scaled annotated corpus for supervised learning is extremely difficult, thus most work exploits unsupervised learning. In this paper, unsupervised learning is used. Different from previous work, the hashtags labeled by microblog authors are used as the key information to improve the performance of hot topic extraction. Finally, the experiment demonstrate that this information is highly useful for hot topic extraction in microblog. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Self-supervised learning

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Enhanced Coffee Bean Defect Detection Using Super-Resolution GANs and Deep Learning

Enhanced Coffee Bean Defect Detection Using Super-Resolution...

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Computational Intelligence, Communication technology and Networking (CICTN), international conference on

作者： S. Prabu P.S. Thanigaivelu S. Poonkodi M. Anand Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai TamilNadu India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Image Super-Resolution (SR) is a key challenge in computer vision, focusing on enhancing low-resolution images while preserving details. This paper presents a novel method using Generative Adversarial Networks (GANs) for improving image quality, specifically targeting coffee bean defect detection. The proposed SR-GAN model includes a generator that upscales low-resolution inputs and a discriminator that differentiates between generated high-resolution images and real ones. Trained on a coffee bean dataset, the model optimizes loss functions to balance perceptual quality and fidelity, capturing subtle defects like cracks and discolorations. Enhanced images are processed by two Convolutional Neural Networks (CNNs): one identifies broad defect categories, such as defective black beans, brown beans, broken beans and non-defective high-quality beans. This layered approach ensures more precise defect identification and classification, overcoming limitations of previous methods that struggled with subtle defects. Performance evaluations show that SR-GAN and CNN significantly outperforms existing models in resolution enhancement and classification accuracy, offering a robust solution for improving quality control in coffee production.

关键词： Deep learning Performance evaluation Image quality computer vision Computational modeling Superresolution Quality control Production Convolutional neural networks Defect detection

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“A Novel Regularized Deep Learning Approach for Tuberculosis Risk Prediction”

“A Novel Regularized Deep Learning Approach for Tuberculosi...

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Computational Intelligence, Communication technology and Networking (CICTN), international conference on

作者： Pankaj Kumar Vishav Pratap Singh Ajay Pal Singh Department of Technical Education GPC Patiala Punjab Dept. of Computer Science & Engineering Chandigarh University Mohali Punjab India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Effective treatment of tuberculosis (TB) requires prompt and precise risk assessment, since it continues to be a major worldwide health concern. Labor-intensive and subjective approaches are frequently used in traditional methods. The neural network model for Tuberculosis risk categorization which includes the following categories: No_Risk, Low_Risk, Moderate_Risk, and High_Risk is found in this paper. Regularization, Batch normalization and the use of Reversed Linear unit (ReLU) activation functions is adopted by the model to enhance learning process. After successfully conducting the sophisticated and optimal simulation the proposed model attains 91% classification accuracy using a variety of physiological and clinical parameters. This potentially presents future deep learning techniques to automatically rate people for possible tuberculosis occurrence thus delivering a strong tool to medical practitioners to offer distinctive intervention techniques. With deep learning, the model is capable of detecting a very complex pattern and correlation between data sets inputs and its output, thus enhancing its predictive precision as well as offering recommendations that may assist in the early detection of actual clinical situations.

关键词： Correlation Tuberculosis Computational modeling Predictive models Data models Physiology communications technology Risk management Batch normalization Computational intelligence

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Linearized Gait Phase Estimation Using BiLSTM

Linearized Gait Phase Estimation Using BiLSTM

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2025 international conference on Electronics, Information, and Communication, ICEIC 2025

作者： Lee, Yonghyun Shin, Dongbin Ji, Younghoon Jang, Hyeyoun Han, Changsoo Lee, Yeonjoon Hanyang University Department Of Applied Artificial Intelligence Major In Bio Artificial Intelligence Seoul Korea Republic of RandD Center Hexar Humancare Ansan Korea Republic of Research and Development Hexar Humancare Ansan Korea Republic of CEO Hexar Humancare Ansan Korea Republic of Computer Science Hanyang University Ansan Korea Republic of

ISBN: (纸本)9798331510756

This paper presents a novel deep learning-based approach to address time-delay challenges arising from nonlinear transformations in Gait Phase Estimation, a critical factor in analyzing human gait for lower limb exoskeleton robots. The proposed model leverages hip joint angles and angular velocities as feature data, organized using a sliding window technique, to predict the Linearized Gait Phase (LGP)-a continuous repre-sentation of gait progression between Heel Strikes. Motivated by the need for real-time and precise gait phase estimation to enhance exoskeleton control, a Bidirectional LSTM network was employed to reduce delays and improve accuracy. The model's effectiveness was validated through comprehensive performance evaluations using metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Square Error (MSE), and R2 score, alongside a temporal analysis of delay reduction. The results demonstrated superior performance, with an MAE of 2.8±0.8%, RMSE of 3.9±1.2%, MSE of 0.1±0.1%, and an R2 score of 98.1±1.8%. Particularly, temporal analysis revealed a marked improvement, achieving an MAE of 0%, compared to 2.74±0.92% reported in prior studies. These findings underscore the proposed model's potential for real-time gait phase estimation, offering significant implications for enhancing the responsiveness and adaptability of lower limb exoskeleton robots. © 2025 IEEE.

关键词： Exoskeleton (Robotics)

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Research on PM2.5 Concentration Prediction Based on SARIMA-RBF Concatenated Modeling 2nd

Research on PM2.5 Concentration Prediction Based on SARIMA-R...

引用

2nd international conference on Applied Intelligence, ICAI 2024

作者： Jiang, FengYue Zhang, YinTong Zhao, ChenXi Shen, Zhen Wang, BaoSheng Liu, Wei School of Computer and Software Nanyang Institute of Technology Changjiang Road 80 Henan Nanyang473004 China

ISBN: (纸本)9789819619030

Aiming at the problems of poor prediction accuracy and overfitting of traditional time series prediction models in the inter-seasonal PM2.5 concentration prediction. In this paper, a parallel fusion model, the seasonal differential autoregressive sliding average model (SARIMA)-radial basis neural network (RBF) model, is proposed. Firstly, the seasonal factors of PM2.5 are extracted using the SARIMA model to analyze and predict the seasonal trend of PM2.5;then the effects of SO2, NO2 and CO on PM2.5 are extracted through the characteristic factor analysis, and the RBF neural network model, which has these three factors as inputs, is established to predict PM2.5;finally, the weighted fusion is used to achieve the accurate prediction of PM2.5 concentration by weighted fusion. Through the comparative test analysis of SARIMA model, RBF model and SARIMA-RBF parallel model, it is found that the prediction accuracy of SARIMA-RBF parallel model has been significantly improved, and the MAE of the parallel model reaches 3.23. This study proves that the SARIMA-RBF parallel model has a significant advantage in the prediction of PM2.5 concentration, which provides an important tool for the effective development of air pollution control strategies. This study demonstrates that the SARIMA-RBF concurrent model has significant advantages in the prediction of PM2.5 concentration, which provides important support for the effective formulation of air pollution control strategies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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2nd international conference on Innovations of Intelligent Informatics, Networking and Cybersecurity, 3INC 2024

2nd International Conference on Innovations of Intelligent I...

引用

2nd international conference on Innovations of Intelligent Informatics, Networking and Cybersecurity, 3INC 2024

ISBN: (纸本)9783031810640

The proceedings contain 15 papers. The special focus in this conference is on Innovations of Intelligent Informatics, Networking and Cybersecurity. The topics include: Real-Time Arabic Speech Recognition from Lips Movement Based on Deep Learning;parkinson's Disease Prediction and Progression Based on Voice Analysis: A Literature Survey;BGWO-Based Classification of Parkinson's Disease via MEG Signals;enhancing Twitter Comment Classification Using Convolutional Neural Networks and Support Vector Machines;high-Stakes Deception Detection Leveraging 3D Facial Landmarks;enhancement of Low Light Images Using Residual Deep Learning;hybrid Model for Hospital Services Quality Prediction Based on Patient Viewpoint;advanced Text Vectorization and Deep Learning Models for Enhanced Fake News Detection on Social Media;utilizing Graph Neural Networks for the Detection of Fake News Through Analysis of Relationships Among Various Social Media Entities;classification of Internet of Things Cybersecurity Attacks Using a Hybrid Deep Learning Approach;hybrid Multimodal Biometric Identification System: Integrating Face and Palmprint Traits Through Feature-Level Fusion;Improving Machine Learning-Based Intrusion Detection Systems: A Comparative Study on NSL-KDD Dataset;enhancing Penetration Testing: Leveraging Machine Learning for Ethical Hacking.

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An Advanced Cognitive Approach for Personalized Drug Formulation in Type 2 Diabetes Using Deep Neural Networks and Feature-based Fusion

An Advanced Cognitive Approach for Personalized Drug Formula...

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2025 international conference on Intelligent control, Computing and communications, IC3 2025

作者： Singh, Brijendra Pratap Singh, Jagendra Suryavanshi, Madhuri Jailani, S. Amer, A. Beer A. Kumar, Rakesh School of Computer Science Engineering & Technology Bennett University Greater Noida India Pimpri Chinchwad College of Engineering Pune India Alpha Pharma Kaec R&d Formulation Dev. Rabigh Saudi Arabia Sadat academy for management and Sciences Canob street Alexandria Egypt Magadh University Department of Computer Science and I.T. Bodh Gaya India

ISBN: (纸本)9798331527495

Personalized medicine is an innovative approach toward achieving improved therapeutic outcomes, notably in chronic diseases like Type 2 Diabetes (T2D), where individualised drug formulations represent the crux of treatment. Conventional methods of drug prescription tend to focus on generic approaches without considering the specific genetic, physiological, and lifestyle characteristics of individuals. This research fills this gap by developing an advanced deep learning-based method for personalised drug formulation in T2D. It integrates diverse patient data, including demographics, medical history, clinical measurements, genomic information, pharmacokinetic information, real-time monitoring data, and responses to previous treatments. For optimal drug formulation, it makes use of a combination of deep learning models, such as Recurrent Neural Network (RNN) for time-series prediction, Convolutional Neural Network (CNN) for genomic analysis, GAN for producing synthetic data, and auto-encoder for feature extraction. The dataset consists of 2700 patient records collected over one year, wherein the pre-processing phase includes handling missing values, normalising continuous variables, and encoding categorical features. Trained models were assessed based on precision, recall, F1-score, and AUC ROC levels;the best performance came from the RNN model with a maximum accuracy of 97.35%. CNN, GAN, and Autoencoder also obtained similar competitive results. However, the RNN-based model received the highest score in recommending the appropriate drugs. The application of this research can contribute to the broader discipline of precision medicine. © 2025 IEEE.

关键词： Theranostics

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Augmented Reality in Education: Enhancing Learning Experiences

Augmented Reality in Education: Enhancing Learning Experienc...

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Computational Intelligence, Communication technology and Networking (CICTN), international conference on

作者： Rakshit Sharma Naman Gupta Rohan Choudhary Aastha Budhiraja School of Computer Applications Manav Rachna International Institute of Research & Studies Faridabad India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

The integration of Augmented Reality (AR) into learning environments is still an area of significant challenge in light of increasingly dynamic technological landscapes. Whereas the traditional methods of learning do not appeal much to digitally native learners, the existing gap in terms of the systematic improvement of outcomes lies in the understanding of immersive technologies. This paper combines both quantitative assessments of learning and qualitative phenomenological analyses from three different educational settings: schools at the secondary level, institutions of higher learning, and professional training centers. Through an effective intervention involving 742 participants using cutting-edge AR learning platforms, our study has revealed significant cognitive and engagement-related improvements. The groups experiencing AR-enhanced curricula showed a 37% increase in knowledge retention, as well as 42% higher engagement rates, along with significantly improved spatial reasoning abilities. Qualitative data further support the idea that motivation improves, as participants indicated better conceptual understanding and greater independence in learning. The findings indicate that AR is transformative in educational practice, making it simple to bring theoretical knowledge and experiential learning together. Here, the merging of digital information with the students' physical learning environment is not merely a technological intervention, but a fundamental redesign of pedagogical approaches that promises a more interactive, personalized, and immersive education paradigm.

关键词： Knowledge engineering Systematics Merging Vocational training Cognitive load communications technology Cultural differences Augmented reality Computational intelligence Digital information

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