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检索条件"任意字段=3rd International Conference on Generative Programming and Component Engineering"

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3rd international conference on generative programming and component engineering, GPCE 2004

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3rd international conference on generative programming and component engineering, GPCE 2004

ISBN: (纸本)3540235809

The proceedings contain 27 papers. The special focus in this conference is on Aspect Orientation, Staged programming, Types of Meta-programming and Model-Driven Approaches. The topics include: A generative approach to aspect-oriented programming;supporting flexible object database evolution with aspects;cross-language aspect-oriented programming;meta-programming with typed object-language representations;a multi-stage, object-oriented programming language;a fresh calculus for name management;a unification of inheritance and automatic program specialization;towards generation of efficient transformations;model-driven configuration and deployment of component middleware publish/subscribe services;model-driven program transformation of a large avionics framework;automatic remodularization and optimized synthesis of product-families;a case study of a product line for versioning systems;a model-driven approach for smart card configuration;on designing a target-independent DSL for safe OS process-scheduling components;a generative approach to the implementation of language bindings for the document object model and assembling applications with patterns, models, frameworks and tools.

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Object Recognition and Audio Tactile System for Visually Impaired 3

Object Recognition and Audio Tactile System for Visually Imp...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Maheswari, S. Mangai, S. Aksharasree, J.R. Gobika, S. Janani, M. Janani, S. Department of Biomedical Engineering Velalar College of Engineering and Technology Tamil Nadu Erode India

ISBN: (纸本)9798331527549

This design focuses on designing and developing an assistive technology system tailored to the needs of visually impaired individuals, enhancing their daily lives and fostering independence. The system integrates a high-resolution camera module and an ESP32 microcontroller to capture and process images of the user’s surroundings using advanced computer vision algorithms programmed in Python. The object recognition component identifies objects and generates audio descriptions, delivered through a speaker module, while a vibration band provides tactile feedback for obstacle detection. Unique vibration patterns corresponding to specific objects or obstacles ensure intuitive navigation and increased confidence for users. The system’s technical features, including customizable vibration patterns, clear audio feedback, and a portable design, address key challenges faced by visually impaired individuals, offering a comprehensive, cost-effective, and user-friendly solution to improve mobility and awareness. The system's technical specifications include Python programming, ESP32microcontroller, high-resolution camera module, clear and loud speaker module, and customizable vibration band. The integration of object recognition, audio- tactile feedback, and vibration band creates a assistive technology system that addresses the unique challenges faced by visually impaired individualities. © 2025 IEEE.

关键词： Assistive technology

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A Hybrid Approach for Language Variety Prediction using BERT and T5 Embeddings 3

A Hybrid Approach for Language Variety Prediction using BERT...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kavuri, Karunakar Raghunadha Reddy, T. Gelli, Archana Priyanka, B.H.D.D. Department of CSE Swarnandhra College of Engineering and Technology AP Narasapuram India Department of CSE Matrusri Engineering College Telangana Hyderabad India Department of IT S R K R Engineering College AP Bhimavaram India

ISBN: (纸本)9798331527549

A crucial component of author profiling is language variety prediction, which looks for stylistic and regional differences in language usage in written texts. Applications for this activity are substantial in fields including localization, forensics, and customized marketing. Conventional methods depend on manually created features or embeddings from separate models, which might not fully represent the nuanced linguistic differences. In order to improve language variety prediction performance, we presented a hybrid model in this work that integrates statistical features and embeddings from two sophisticated transformer models, such as T5 (Text-to-Text Transfer Transformer) and BERT (Bidirectional Encoder Representations from Transformers). Different statistical features identified in this work are Lexical features, Syntactic features, Orthographic and Phonological features, Stylistic features, Semantic features, Pragmatic and discourse-level features and Temporal features. The embeddings from both models are projected into a shared dimensions space, averaged, appended, and utilized as input to a downstream classifier in order to accomplish effective integration. This method makes use of T5's generative capabilities and BERT's contextual richness to better capture syntactic and semantic patterns in text. The English language diversity dataset from the PAN 2017 competition is used to test the suggested framework. According to experimental evaluations, the hybrid embedding technique achieves better accuracy and F1-score for language variety prediction, outperforming individual transformer models by a wide margin. When compared to other models for language variety prediction, the XGBoost classifier with Statistical Features, BERT, and T5 embeddings performed better in terms of F1-Score (93.4%) and Accuracy (93.5%). These results demonstrate the importance of including complimentary transformer embeddings to improve author profiling tasks' state-of-the-art performance. ©

关键词： Semantics

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generative Adversarial Network Based Real-Time Malware Detection in Cloud Environments

Generative Adversarial Network Based Real-Time Malware Detec...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Srinivasa Rao Bittla Abothar Mahmood D. Kokila Ayesha Siddiqua Ajitha P R Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Al Diwaniyah Iraq Department of Artificial Intelligence & Data Science Excel Engineering College Namakkal India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Artificial Intelligence and Data Science Dhanalakshmi Srinivasan College of Engineering Technology Mamallapuram India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Recently, the rapid growth of cloud computing has created a vast attack surface for malware threats. The existing Support Vector Machine (SVM) accurately detected malwares although the dynamic nature of cloud environments led to high-dimensional data and class imbalance issues. Hence, this research proposes a generative Adversarial Network (GAN) to address the class imbalance by creating synthetic malware samples. Initially, the input data is collected and fed into the analysis phase with Cuckoo Sandbox to generate malware behavior report in JavaScript Objective Notation (JSON) format. After that, Trend Micro Locality Sensitive Hashing (TLSH) is employed to measure the similarity among files and then the clustering phase groups the similar files as a cluster according to hash values. Further, the features are extracted from JSON format which contains information on Application programming Interface (API) calls and their return codes. Then, the optimal features are selected using Chi-square to calculate the statistical independence on each feature and finally, GAN model is used to detect malware in cloud environments. From the results, the proposed GAN achieved better results in terms of accuracy (99.61%) when compared to existing Artificial Neural Network (ANN).

关键词： Support vector machines Cloud computing Codes Computer viruses Artificial neural networks generative adversarial networks Feature extraction Malware Real-time systems Application programming interfaces

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Smart IoT-enabled Water Quality Management using generative AI and Super-Resolution GAN for Enhanced Monitoring and Prediction

Smart IoT-enabled Water Quality Management using Generative ...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Shofia Priyadharshini D G.P. Ramesh Department of Electronics and Communication Engineering St. Peter's Institute of Higher Education and Research Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

An essential component of sustainable development is water quality management, which requires accurate monitoring and forecasting capabilities. Conventional methods for assessing water quality often face challenges such as sparse data, sensor inaccuracies, and real-time processing limitations. To address these challenges, this study proposes a Smart IoT-Enabled Water Quality Management System that integrates Super-Resolution generative Adversarial Networks (SR-GAN) with artificial intelligence (AI) for enhanced monitoring and prediction. The system utilizes IoT -based smart sensors to collect real-time data on temperature, turbidity, oxygen concentration, pH levels, and other water quality ***-GAN enhances both the temporal and spatial resolution of low-quality sensor data, enabling better feature extraction and more precise information. generative AI models are then employed for automated decision-making, anomaly detection, and forecasting, effectively identifying potential water pollution threats. Compared to conventional AI and deep learning methods, the proposed framework achieves superior performance in terms of response time efficiency (30% improvement), data quality, and prediction accuracy (98.7%). The integration of edge computing ensures low latency and real-time data processing. This innovative approach significantly enhances water quality monitoring accuracy, facilitating proactive measures and sustainable water resource management.

关键词： Temperature sensors Accuracy generative AI Superresolution Water quality generative adversarial networks Real-time systems Reliability Monitoring Anomaly detection

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3rd international conference on generative and component-Based Software engineering, GCSE 2001

引用

3rd international conference on generative and component-Based Software engineering, GCSE 2001

ISBN: (纸本)3540425462

The proceedings contain 15 papers. The special focus in this conference is on Software Product Lines, Aspects, Generic and generative Approaches. The topics include: A characterization of generator and component reuse technologies;a standard problem for evaluating product-line methodologies;components, interfaces and information models within a platform architecture;XVCL approach to separating concerns in product family assets;a basis for multi-paradigm design for aspectJ;aspect-oriented configuration and adaptation of component communication;a version model for aspect dependency management;an object model for general-purpose aspect-languages;generic visitor framework computing statistical estimators;reflection support by means of template metaprogramming;scenario-based generation and evaluation of software architectures;the role of design components in test plan generation;retrieving software components using directed replaceability distance and generating application development environments for java frameworks.

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Enhancing Human Resource Management Practices in Marketing Companies Using Dual Graph Attention Networks

Enhancing Human Resource Management Practices in Marketing C...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： N. Roopalatha Dhanalakshmi K G N P V Babu P. Vamsi Krishna Anvesh Perada Harun Kumar Mulagapati School of Business GITAM University Hyderabad India School of Business and Management Christ University Bangalore India School of Management and Commerce Malla Reddy University Hyderabad India Department of Electrical and Computer Engineering Drexel University Philadelphia USA Department of Engineering Mathematics and Humanities SRKR Engineering College Chinna Amiram India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Marketing organization features and strategy implementation have been studied for over 30 years. These include organizational structure, culture, leadership, and processes. HR regulations can motivate marketing professionals to support group and individual goals when correctly implemented, but this part of HR has gotten little attention. Model preparation, feature extraction, and training comprise the suggestive technique. It reviewed data quality, evaluated dataset structure, and described data types during pre-processing. Principal component analysis (PCA) ranked and evaluated decision-making units to reduce dimension. Model training used MGGAN. In comparison to GAN and CNN, the proposed model performed well. With an average accuracy rate of 94.36%, it surpassed earlier approaches and captured all dataset peculiarities. MGGAN modeling can increase predictive performance, and marketing organizations should integrate HR regulations, according to this study. This study opens up new organizational analysis and strategy execution methods.

关键词： Accuracy Shape Decision making Predictive models generative adversarial networks Regulation Integrated circuit modeling Management training Principal component analysis Recruitment

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Comparative Analyais of CNN Architectures for Deep Fake Detection

Comparative Analyais of CNN Architectures for Deep Fake Dete...

引用

international Symposium on Advanced Computing and Communication (ISACC)

作者： Parth Wani Sachin Chavan Siddhesh Paithankar Diptee Ghusse Sunita Barve School of Computer Engineering MIT Academy of Engineering Pune India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

DeepFake technology, driven by advanced generative models, threatens online authenticity and privacy. This paper presents a comparative analysis of three convolutional neural network (CNN) architectures—VGGFace16, DenseNet-121, and a custom CNN model—for DeepFake image detection. The study utilizes the 140k Faces Dataset, comprising 70,000 real and 70,000 synthetic images, and the Real and Fake Face Detection Dataset from Yonsei University, ensuring a diverse and well-balanced training set while accounting for computational constraints. Feature extraction from the final convolutional layers, dimensionality reduction via Principal component Analysis (PCA), and classification with a Support Vector Machine (SVM) using a polynomial kernel form the core methodology. DenseNet-121 achieved the highest accuracy (97%) on grayscaled images, while the augmented custom CNN balanced accuracy (86%) and interpretability, attaining a Receiver Operating Characteristic Area Under the Curve (ROC-AUC) score of 0.953. PCA visualizations confirmed the models’ ability to distinguish real from fake images. The findings underscore dataset selection’s role in model performance and the necessity of resource-efficient training. Future work will expand dataset diversity, explore cross-dataset validation, and leverage advanced computational resources to enhance generalization, contributing to more robust DeepFake detection systems.

关键词： Support vector machines Training Deepfakes Accuracy Computational modeling Computer architecture Feature extraction Polynomials Convolutional neural networks Principal component analysis

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Object Recognition and Audio Tactile System for Visually Impaired

Object Recognition and Audio Tactile System for Visually Imp...

引用

Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： S. Maheswari S. Mangai J. R Aksharasree S Gobika M Janani S Janani Department of Biomedical Engineering Velalar College of Engineering and Technology Erode Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

This design focuses on designing and developing an assistive technology system tailored to the needs of visually impaired individuals, enhancing their daily lives and fostering independence. The system integrates a high-resolution camera module and an ESP32 microcontroller to capture and process images of the user's surroundings using advanced computer vision algorithms programmed in Python. The object recognition component identifies objects and generates audio descriptions, delivered through a speaker module, while a vibration band provides tactile feedback for obstacle detection. Unique vibration patterns corresponding to specific objects or obstacles ensure intuitive navigation and increased confidence for users. The system's technical features, including customizable vibration patterns, clear audio feedback, and a portable design, address key challenges faced by visually impaired individuals, offering a comprehensive, cost-effective, and user-friendly solution to improve mobility and awareness. The system's technical specifications include Python programming, ESP32microcontroller, high-resolution camera module, clear and loud speaker module, and customizable vibration band. The integration of object recognition, audio-tactile feedback, and vibration band creates a assistive technology system that addresses the unique challenges faced by visually impaired individualities.

关键词： Vibrations Navigation Microcontrollers Tactile sensors Assistive technologies programming Cameras Real-time systems Object recognition Python

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A Hybrid Approach for Language Variety Prediction Using BERT and T5 Embeddings

A Hybrid Approach for Language Variety Prediction Using BERT...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Karunakar Kavuri T. Raghunadha Reddy Archana Gelli B H D D Priyanka Department of CSE Swarnandhra College of Engineering and Technology Narasapuram AP India Department of CSE Matrusri Engineering College Hyderabad Telangana India Department of IT S R K R Engineering College Bhimavaram AP India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

A crucial component of author profiling is language variety prediction, which looks for stylistic and regional differences in language usage in written texts. Applications for this activity are substantial in fields including localization, forensics, and customized marketing. Conventional methods depend on manually created features or embeddings from separate models, which might not fully represent the nuanced linguistic differences. In order to improve language variety prediction performance, we presented a hybrid model in this work that integrates statistical features and embeddings from two sophisticated transformer models, such as T5 (Text-to-Text Transfer Transformer) and BERT (Bidirectional Encoder Representations from Transformers). Different statistical features identified in this work are Lexical features, Syntactic features, Orthographic and Phonological features, Stylistic features, Semantic features, Pragmatic and discourse-level features and Temporal features. The embeddings from both models are projected into a shared dimensions space, averaged, appended, and utilized as input to a downstream classifier in order to accomplish effective integration. This method makes use of T5's generative capabilities and BERT's contextual richness to better capture syntactic and semantic patterns in text. The English language diversity dataset from the PAN 2017 competition is used to test the suggested framework. According to experimental evaluations, the hybrid embedding technique achieves better accuracy and Fl-score for language variety prediction, outperforming individual transformer models by a wide margin. When compared to other models for language variety prediction, the XGBoost classifier with Statistical Features, BERT, and T5 embeddings performed better in terms of Fl-Score (93.4 % ) and Accuracy (93.5 % ). These results demonstrate the importance of including complimentary transformer embeddings to improve author profiling tasks' state-of-the-art performanc

关键词： Location awareness Accuracy Semantics Bidirectional control Predictive models Syntactics Transformers Encoding Internet of Things Pragmatics

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