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Research on the Establishment and Validation of an IDC Implementation Factor measurement Framework 25

Research on the Establishment and Validation of an IDC Imple...

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Proceedings of the 2025 4th international conference on Big Data, Information and computer Network

作者： Yulian Zhang Ping Cao Yuanhang Jiao Xi'an University of Science and Technology Xi'an Shaanxi China

ISBN: (纸本)9798400712425

With escalating owner expectations for project quality and intensifying market competition in the construction sector, the integrated project delivery model has demonstrated enhanced potential in addressing fragmented execution processes. While China encounters multifaceted challenges during industry transformation, integrated approaches present viable pathways for achieving sustainable development. This study develops a theoretical assessment framework for Integrated Design and Construction (IDC) effectiveness by synthesizing existing research on integrated systems with structural equation modeling (SEM) and integrated project delivery (IPD) Philosophy. Through empirical analysis of survey responses, the research systematically evaluates reliability, validity criteria, and model fitness using SPSS and AMOS platforms. The finalized analytical model identifies four critical drivers influencing IDC adoption, providing empirical evidence to advance genuine design-construction integration.

关键词： Integrated Design-Construction (IDC)

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From Data to Decisions: Enterprise-Level Domain-Specific Graph Retrieval-Augmented Generation systems for Advanced Question Answering

From Data to Decisions: Enterprise-Level Domain-Specific Gra...

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international conference on Big Data and Smart Computing (BIGCOMP)

作者： Sandeep Varma Shivam Shivam Sarun Natarajan Ankita Banerjee Sourodeep Roy ZS Associates Pune India Rockwell Automation Pune India

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

Retrieval-Augmented Generation (RAG) is aimed at improving the functionality of large language model (LLM) applications by incorporating specific data. This may include searching for relevant materials or files concerning a particular issue or search provided as background information to the LLM. Some prominent architectures like the Vanilla RAG architecture focuses primarily on retrieval of textual data, primarily utilizing vector databases, thus neglecting the structural intricacies of textual data, resulting in a critical gap in the generation process. To address this gap, we have introduced SAGE-QA (Scalable Advanced Graph RAG for Enterprise Question Answering), which significantly enhances both the retrieval and generation processes by emphasizing the importance of topological information for reasoning tasks on a variety of data sources. For our use case, SAGE-QA significantly outperforms current state-of-the-art RAG methods with its novel retrieval mechanism, which effectively mitigates hallucinations, and acts as a sustainable contextually relevant application.

关键词： measurement Databases Soft sensors Large language models Computational modeling Retrieval augmented generation computer architecture Question answering (information retrieval) Vectors Data models

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Musimple: A Simplified Music Generation System with Diffusion Transformer

Musimple: A Simplified Music Generation System with Diffusio...

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE international conference on

作者： Zheqi Dai Haolin He Qiuqiang Kong Electronic Engineering The Chinese University of Hong Kong

ISBN: (数字)9798331519315

ISBN: (纸本)9798331519322

Music generation has become an important area of research, with applications in automated composition, personalized soundtracks, and virtual entertainment. Recently, diffusion models such as Diffusion Transformers (DiT) have shown promising results in generating high-quality audio. However, many previous music generation systems rely heavily on large datasets and require significant computational resources. This makes them challenging to reproduce and less accessible for broader research. To address these limitations, we propose a simplified text-to-music generation system. Our model uses the small-scale GTZAN dataset and 2D mel spectrograms as the primary audio representation. By avoiding complex components like VAEs, we streamline the architecture and reduce computational overhead. We evaluate our model using metrics such as FAD, KL and CLAP, achieving scores of 5.0, 2.31, and 0.18, respectively. These results demonstrate that our system generates musically acceptable audio even with reduced data. Our code, data, and pre-trained checkpoints are fully open-sourced and available at https://***/ZheqiDAI/Musimple.

关键词： Training measurement Computational modeling Entertainment industry computer architecture Transformers Diffusion models Multiple signal classification Speech processing Spectrogram

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Self Attention-based Sparse Graph Convolutional Neural Network for Instant Parameter Prediction to Optimize Wireless Sensor Network Performance in Electrical systems

Self Attention-based Sparse Graph Convolutional Neural Netwo...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： Asma Anjum Thilak Raj Balasubramanian M. Lakshminarayana Daxa Vekariya Madhu B K Ramya Maranan Department of Computer Science and Engineering HKBK College of Engineering Bengaluru Karnataka Personalization Data Infrastructure/ ML Infrastructure Walmart Inc San Jose California USA Department of Medical Electronics Engineering M S Ramaiah Institute Of Technology Bengaluru Karnataka Department of Computer Science & Engineering Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Vidya Vikas Institute of Engineering and Technology Mysore Karnataka Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

WSNs function as key components across environmental monitoring systems as well as healthcare facilities and industrial automation networks. WSN performance optimization faces ongoing challenges because the networks must deal with restricted energy supplies in addition to shifting operational environments and restricted data transfer capabilities. The current optimization techniques implement static hierarchical approaches combined with heuristic methods yet these prove unable to adjust in real-time thus leading to deteriorating network conditions and resource inefficiency. The research puts forward Self Attention-based Sparse Graph Convolutional Neural Network (SA-SGCN) as an instant parameter prediction framework to optimize WSN performance through automatic adjustments of critical metrics including energy usage and packet success ratios as well as latency and data transfer speed. Real-time prediction becomes possible through the self-attention mechanisms used in SA- SGCN models to detect distant dependencies in the sensor network in combination with sparse graph convolution that decreases computational requirements. The Crested Porcupine Optimizer (CPO) optimizes the hyperparameters of the SA- SGCN model for better efficiency and accuracy measurement. The experimental evidence shows that using the proposed method results in superior performance compared to current approaches because it reaches a prediction accuracy level of 99.9% for WSN configurations. The research presents a dependable energy-efficient adaptive framework for WSNs which enables high scalability and reliable operation within fast-changing environments.

关键词： Wireless sensor networks Adaptation models Accuracy Convolution Computational modeling Predictive models Data transfer Real-time systems Convolutional neural networks Optimization

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Robust and Efficient Text-based Speech Editing using Noise Conditioning and Rectified Flow

Robust and Efficient Text-based Speech Editing using Noise C...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haowen Yin Kai Wang Hongli Yang Hao Huang Wushour Silamu School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Multi-Lingual Information Technology Urumqi China Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing Urumqi China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Significant advancements have been made in text-based speech editing (TSE) for clear speech, but effectively editing the noise-contaminated speech remains a challenge. Background noise degrades the quality of generated speech, and edited speech that fails to maintain noise context consistency often sounds unnatural. We propose Reflow-TSE, a robust and efficient TSE model for noise-consistent speech editing. For noise robustness, 1) we design a noise condition module to extract frame-level noise sequences as the conditioning information, and 2) we introduce an enhanced context-conditioned prediction module that predicts masked noise sequences along with conventional duration and pitch, using these conditions to guide generation. For boosting efficiency, 3) we introduce the rectified flow model, leveraging speech context and predicted conditions to achieve high-quality editing with limited sampling steps. Experimental results show that with just two steps of sampling, Reflow-TSE achieves context-consistent noisy speech editing, a capability absent in other TSE models. Additionally, for clean speech, Reflow-TSE also matches or surpasses baseline models. Audio samples are available at https://***.

关键词： Noise Predictive models Boosting Acoustics Noise robustness Noise measurement Human voice Data mining Background noise Context modeling

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Intrusion Detection in IoT Networks Using LSTM Deep Learning Models with the UNSW-NB15 Dataset

Intrusion Detection in IoT Networks Using LSTM Deep Learning...

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international conference on New Trends in Computing Sciences (ICTCS)

作者： Nazek Hassouneh Saleh Al-sharaeh King Abdallah II School of Information Technology Department of Computer Science University of Jordan Amman Jordan

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

Network Intrusion Detection systems (IDS) are an essential part of the Internet of Things (IoT) Networks, addressing the increasingly severe challenges posed by Internet security threats. This research enhances (IDS) capabilities in IoT environments by investigating the performance of Deep Learning Models with Binary Classification for identify and predict if the connection is normal or attack. The Deep Learning Models; including Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU) and Bidirectional GRU. The comprehensive UNSW-NB15 Dataset is utilized that includes real-world network traffic and a variety of attacks. To effectively tackle the issue of Class Imbalance in the Dataset, we utilize the Synthetic Minority Over-sampling Technique (SMOTE). This approach enhances the representation of minority classes, leading enhances our model's performance and robustness. We train and evaluate these models, our evaluation based on Accuracy, Recall, Precision, F1-score and AUC metrics. The results indicate that the model with the best metrics is the Bidirectional LSTM BiLSTM, achieving an Accuracy of 0.9468, AUC of 0.9801 without SMOTE and an Accuracy of 0.9501, AUC of 0.9953 with SMOTE. These findings emphasize the effectiveness of the BiLSTM model for a more accurate and robust Intrusion Detection systems (IDS) using the SMOTE technique and underscore the beneficial impact of the SMOTE technique on class imbalance issues. The research contributes valuable insights towards the development of more robust and secure IoT systems using the LSTM model using the SMOTE technique.

关键词： Deep learning measurement Accuracy Computational modeling Bidirectional long short term memory Network intrusion detection Telecommunication traffic Predictive models Robustness Internet of Things

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ResNet-GRU Hybrid Model for Brain Tumor Diagnosis: A Sequential Learning Framework

ResNet-GRU Hybrid Model for Brain Tumor Diagnosis: A Sequent...

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Machine Learning and Autonomous systems (ICMLAS), international conference on

作者： Soumyarashmi Panigrahi Dibya Ranjan Das Adhikary Binod Kumar Pattanayak Bibhuti Bhusan Dash Utpal Chandra De Sudhansu Shekhar Patra Dept. of Comp. Sc. & Engineering ITER Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar India School of Computer Applications Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

This research investigates the effectiveness of integrating Gated Recurrent Units (GRUs), a variant of Recurrent Neural Network (RNN), into pre-trained Convolutional Neural Networks (CNNs) such as Residual Networks (ResNet50) for enhanced brain tumor classification using Magnetic Resonance Imaging (MRI) scans. RNNs are a class of Neural Networks (NNs) structured to analyze sequential data, while GRU is a specific type of RNN that efficiently learns long-term dependencies without suffering from vanishing gradients. ResNet50 is a robust architecture renowned for its exceptional image feature extraction capabilities. GRU specializes in modeling complex temporal relationships. By integrating these two, this approach effectively synergizes the strengths of both architectures to achieve enhanced performance. This combination enables the model to capture spatial and temporal dependencies in MRI scans, improving brain tumor classification accuracy. Using the publicly available “Br35H: Brain Tumor Detection 2020” dataset, we evaluate the effectiveness of the suggested ResNet50-GRU model, achieving state-of-the-art results. This comprehensive evaluation employs various metrics, such as Accuracy, Cohen's Kappa, Matthews Correlation Coefficient (MCC), Area Under the Curve (AUC), Precision, Recall, and F1-score. The results underscore the advantages of combining CNNs and GRUs for medical image analysis. This study provides valuable insights for developing more accurate and efficient Deep Learning (DL) based diagnostic tools, highlighting the prospects of CNN-RNN combinations in medical imaging applications.

关键词： Deep learning measurement Accuracy Image analysis Magnetic resonance imaging Brain tumors Brain modeling Convolutional neural networks Medical diagnostic imaging Residual neural networks

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Dual Active Bridge Converter Output Current Ripple Prediction in EV Battery Chargers Through Multimodal Adaptive Spatio-Temporal Graph Neural Network

Dual Active Bridge Converter Output Current Ripple Predictio...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： Abhay Chaturvedi Mudit Mittal Swapnil Uttamrao Deokar Rachit Adhvaryu G. Satish Natrayan L Department of Electronics & Communication Engineering GLA University Mathura Uttar Pradesh India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Departement of Mechanical Engineering Smt. Kashibai Navale College of Engineering Pune Maharashtra Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Post Limda Gujarat India Department of Electrical and Electronics Engineering Sree Dattha Institute of Engineering and Science Sheriguda Telangana India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

The prediction of converter output current ripple in Electric Vehicle (EV) battery chargers stands as a vital condition to maintain stable charging with safe battery operation by reducing voltage fluctuations and delivering steady currents. The prediction of converter output current ripple faces difficulties because the complex dynamics of converter behavior changes according to various load conditions and environmental factors. This measurement inaccuracy threatens the capability to make optimal power transfers and reduces loss management ability thereby affecting the life span and operational performance of the EV battery. This research presents a framework to predict the amount of current oscillation in chargers for EV battery systems powered by Dual Active Bridge (DAB) Converter. The proposed technique utilizes Multimodal Adaptive Spatio-Temporal Graph Neural Network (MASTGNN) as its method. The proposed method aims at both enhancing current ripple prediction accuracy and optimizing converter performance together with improving EV battery lifespan during charging. A MAST-GNN process predicts output current ripples in DAB converters used for EV battery charging through modeling which evaluates the behavior changes of voltage and current and power when operational parameters vary. Implementation of the proposed method in MATLAB will undergo comparison testing with CNN as well as BESO-ANFIS and ANN together with other existing methods. By achieving an accuracy of 97% in current ripple prediction and maintaining a high converter efficiency of 98.5%, the proposed MASTGNN method outperforms traditional approaches without compromising the stability and performance of the EV battery charging process.

关键词： Battery chargers Accuracy Adaptive systems Switched reluctance motors Electric vehicles Real-time systems Loss measurement Graph neural networks Batteries Vehicle dynamics

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Evaluating the Impact of Discriminative and Generative E2E Speech Enhancement Models on Syllable Stress Preservation

Evaluating the Impact of Discriminative and Generative E2E S...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Rangavajjala Sankara Bharadwaj Jhansi Mallela Sai Harshitha Aluru Chiranjeevi Yarra Language Technology Research Centre International Institute of Information Technology Hyderabad India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Automatic syllable stress detection is a crucial component in computer-Assisted Language Learning (CALL) systems for language learners. Current stress detection models are typically trained on clean speech, which may not be robust in real-world scenarios where background noise is prevalent. To address this, speech enhancement (SE) models, designed to enhance speech by removing noise, might be employed, but their impact on preserving syllable stress patterns is not well studied. This study examines how different SE models, representing discriminative and generative modeling approaches, affect syllable stress detection under noisy conditions. We assess these models by applying them to speech data with varying signal-to-noise ratios (SNRs) from 0 to 20 dB, and evaluating their effectiveness in maintaining stress patterns. Additionally, we explore different feature sets to determine which ones are most effective for capturing stress patterns amidst noise. To further understand the impact of SE models, a human-based perceptual study is conducted to compare the perceived stress patterns in SE-enhanced speech with those in clean speech, providing insights into how well these models preserve syllable stress as perceived by listeners. Experiments are performed on English speech data from non-native speakers of German and Italian. And the results reveal that the stress detection performance is robust with the generative SE models when heuristic features are used. Also, the observations from the perceptual study are consistent with the stress detection outcomes under all SE models.

关键词： Computational modeling Gaussian noise Speech enhancement Feature extraction Data models Acoustics Noise measurement Background noise Stress Signal to noise ratio

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Research on Multi-Target Tracking Model of Data Fusion between Autonomous Driving Millimeter-Wave Radar and Visual Sensors 24

Research on Multi-Target Tracking Model of Data Fusion betwe...

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Proceedings of the 2024 4th international conference on Computational modeling, Simulation and Data Analysis

作者： Xinzhen Long Tao Lin Xingzhi Lin Faculty of Computer and Information Engineering GuangXi Vocational Normal University Nanning Guangxi China Guangxi Minzu University Nanning Guangxi China GuangXi Vocational Normal University Nanning Guangxi China

ISBN: (纸本)9798400711831

The research mainly addresses the multi-target tracking problem encountered in the data fusion process of millimeter-wave radar and visual sensors in unmanned systems. Multi-sensor data fusion models are investigated to optimize target detection performance and improve the accuracy of tracking models. The research analyzed the accuracy of the Kalman filter and the convolutional neural network, and used joint data association technology to achieve high-precision target detection, stable real-time tracking, and excellent adaptability to dynamic environments. The tracking accuracy reached 97.87% and the false detection rate was less than 2.13% through simulation experiments in a simulated driving environment. The experimental results verified that the fusion model performs excellently in complex driving scenarios, maintaining high-precision target tracking, proving that the technical methods used are feasible.

关键词： Autonomous driving

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