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...
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.
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 hi...
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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 i...
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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 environm...
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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 su...
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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 environm...
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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).
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 su...
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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.
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...
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.
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 profes...
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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.
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, De...
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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.
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