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AttCDCNet: Attention-Enhanced Chest Disease Classification Using X-Ray Images

AttCDCNet: Attention-Enhanced Chest Disease Classification U...

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IEEE SSD international Multi-conference on systems, Signals and Devices

作者： Omar H. Khater Abdullahi S. Shuaibu Sami Ul Haq Abdul Jabbar Siddiqui Computer Engineering Department Computer Engineering Department SDAIA-KFUPM Joint Research Center for Artificial Intelligence KFUPM

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Chest X-rays (X-ray images) have been proven to be effective for the diagnosis of chest diseases, including Pneumonia, Lung Opacity, and COVID-19. However, relying on traditional medical methods for diagnosis from X-ray images is prone to delays and inaccuracies because the medical personnel who evaluate the X-ray images may have preconceived biases. For this reason, researchers have proposed the use of deep learning-based techniques to facilitate the diagnosis process. The preeminent method is the use of sophisticated Convolutional Neural Networks (CNNs). In this work, we introduce an innovative detection model named AttCDCNet for the task of X-ray image diagnosis, enhancing the popular DenseNet121 model by adding an attention block to help the model focus on the most relevant regions, utilizing the focal loss as a loss function to overcome the imbalance of the dataset problem, and utilizing depth-wise convolution to make the model lighter by reducing the parameters. The proposed model demonstrates novelty by effectively integrating these enhancements to address critical challenges in medical image classification, including dataset imbalance and computational efficiency. Through extensive experimental evaluations, the proposed model demonstrates exceptional performance, surpassing the original DenseNet121 and other state-of-the-art models. The proposed model achieved an accuracy, precision and recall of 94.94%, 95.14% and 94.53%, respectively, on the COVID-19 Radiography Dataset. Underscoring its potential to improve diagnostic accuracy and efficiency in real-world medical applications.

关键词： COVID-19 Performance evaluation Accuracy Pneumonia Computational modeling Medical services Personnel X-ray imaging Medical diagnostic imaging Diseases

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Ultra-High-Frequency Harmony: mmWave Radar and Event Camera Orchestrate Accurate Drone Landing 25

Ultra-High-Frequency Harmony: mmWave Radar and Event Camera ...

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Proceedings of the 23rd ACM conference on Embedded Networked Sensor systems

作者： Haoyang Wang Jingao Xu Xinyu Luo Xuecheng Chen Ting Zhang Ruiyang Duan Yunhao Liu Xinlei Chen Shenzhen International Graduate School Tsinghua University Shenzhen Guangdong China Carnegie Mellon University Pittsburgh USA Tsinghua University Shenzhen Guangdong China Tsinghua-Berkeley Shenzhen Institute Shenzhen International Graduate School Tsinghua University Shenzhen Guangdong China Meituan Technology Shenzhen Guangdong China Tsinghua University beijing China Pengcheng Laboratory Shenzhen Guangdong China RISC-V International Open Source Laboratory Shenzhen Guangdong China

ISBN: (纸本)9798400714795

For precise, efficient, and safe drone landings, ground platforms should real-time, accurately locate descending drones and guide them to designated spots. While mmWave sensing combined with cameras improves localization accuracy, lower sampling frequency of traditional frame cameras compared to mmWave radar creates bottlenecks in system throughput. In this work, we replace traditional frame camera with event camera, a novel sensor that harmonizes in sampling frequency with mmWave radar within ground platform setup, and introduce mmE-Loc, a high-precision, low-latency ground localization system designed for drone landings. To fully leverage the temporal consistency and spatial complementarity between these modalities, we propose two innovative modules, consistency-instructed collaborative tracking and graph-informed adaptive joint optimization, for accurate drone measurement extraction and efficient sensor fusion. Real-world experiments in landing scenarios from a drone delivery company demonstrate that mmE-Loc outperforms SOTA methods in both accuracy and latency.

关键词： drone ground localization

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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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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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Boosting Software Quality: A Machine Learning Technique for Predicting Defects

Boosting Software Quality: A Machine Learning Technique for ...

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Mobile Computing and Sustainable Informatics (ICMCSI), international conference on

作者： Akilan E. Daniel Alwin Infant P Jainish G R Computer science & Engineering Panimalar Engineering College Chennai City Campus Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Computer Science and Engineering Loyola ICAM College of Engineering & Technology Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

In order to increase automation, strengthen decision- making, and improve efficiency while reducing costs and maintaining precision, the development of intelligent machinery is becoming more and more dependent on the integration of data, embedded software, and modern technologies. Reliability of embedded software becomes essential for guaranteeing high system performance and an improved user experience as technology and hardware merge to build smart devices. This work explores methods to quantify software reliability, which measures the likelihood of residual failures in a system. The objective is to build a statistical relationship between quality parameters and product or process metrics through the analysis of software metrics produced from development data. These indicators aid in evaluating the software's dependability and performance, enabling developers to spot and fix possible problems early on. Using pattern recognition algorithms to pinpoint software's fault-prone areas is a crucial strategy for enhancing software reliability prediction. These algorithms enable developers to focus attention on software areas that have a higher likelihood of failure by identifying patterns in the product's code or structure that suggest increased risk. In summary, these methods focus on enhancing the reliability of intelligent machines by equipping developers with advanced tools to reduce defects, improve dependability, and ensure that smart systems perform optimally. This approach aligns with modern trends in using machine learning and data-driven models to tackle complex technological challenges.

关键词： Computational modeling Software algorithms Software quality Predictive models Prediction algorithms Data models Software reliability Decision trees Software measurement Embedded software

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