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Automatic Detection of Hypernasality in Cleft Palate Speech Using Phase Feature 14

Automatic Detection of Hypernasality in Cleft Palate Speech ...

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Hashreta, Kanuri Naga Venkata Sai Lalitha, Duggaraju Sri Lahari, Maddula Lalitha Sri, Pittala Deepthi Dubey, Akhilesh Kumar Koneru Lakshmaiah Education Foundation Dept. of Computer Science and Engineering Guntur India

ISBN: (纸本)9798350344837

The detection of hypernasality, a speech disorder common in children with cleft palate, typically involves analyzing vowels within speech stimuli. An essential acoustic cue for hypernasality detection is the presence of a nasal peak near the first formant (F1). Traditionally, a two-step process is employed, where vowel selection from stimuli is initially performed through manual annotation, followed by automatic feature extraction and classification. This work introduces a novel method for hypernasality detection, automating the entire process, including vowel region selection, feature extraction, and classification. The method leverages vowel onset points for the automatic identification of the vowel region. Subsequently, a modified group delay cepstral coefficient feature is extracted from this automatically selected vowel region. The feature is extracted from modified group delay spectrum, offering enhanced resolution to distinguish closely spaced nasal peaks and the F1, thus improving the capture of nasality evidence. The proposed method demonstrates promising results when applied to hypernasality detection using a support vector machine classifier with the accuracy of 83.50% for /a/ vowel and 93.21% for /i/ vowel. © 2024 IEEE.

关键词： Support vector machines

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A Comprehensive Survey of Blockchain Technology-Trust as a Service 3

A Comprehensive Survey of Blockchain Technology-Trust as a S...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Muthu, S. Esakki Kartheeban, K. Kalasalingam Academy of Research and Education Dept. of Computer Science and Engineering Srivilliputtur India

ISBN: (纸本)9798350396157

In today's rapidly changing digital world, security and trust remain as an essential element supporting a range of communications, transactions, and distributed systems. With the invention of blockchain technology, a trust may now be provided as 'Trust as a Service (TaaS),' a decentralized, verifiable, and essential asset rather than a centralized digital network. In a decentralized and distributed ledger network, the blocks are connected in chronological order, and information is saved using distributed ledger techniques. Since the start of the deployment, blockchain has kept a record of all transactions. This research study analyses the explores the blockchain technology and analyze how it functions as a trusted provider across a range of industries. Also, this study investigates the fundamental ideas behind blockchain, its development, and the cryptographic security systems. This research study illustrates how blockchain maintains confidence in data quality, security, transparency, and immutability across a range of applications, including banking, supply chain, healthcare, and governance. This study also emphasizes the difficulties and possibilities present in the rapidly developing blockchain ecosystem and further discusses about the issues and challenges and various blockchain attacks, solutions, and tools. Finally, the open issues and solutions are analysed. © 2024 IEEE.

关键词： Blockchain

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A Brief Survey on Feature Extraction Models for Brain Tumor Detection 3

A Brief Survey on Feature Extraction Models for Brain Tumor ...

引用

3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Janapati, Malathi Akhtar, Shaheda Acharya Nagarjuna University Dept. of Computer Science and Engineering Guntur India Guntur and Research Supervisor Dept. of Computer Science and Engineering Guntur India

ISBN: (纸本)9781665462167

Today, tumours are the second leading cause of cancer deaths. Cancer poses a significant threat to a large population of patients. The medical community needs a quick, automated, efficient, and trustworthy method for detecting tumours like brain tumours. Detection is crucial to effective treatment. If doctors are able to catch a tumour in its earliest stages, they have a better chance of preserving the patient's health. To do this, several distinct image processing methods are used. Through this method, doctors have been able to effectively treat tumours and save the lives of many patients. Tumors are simply abnormal growths of cells that cannot be stopped. As brain tumour cells multiply, they eventually deplete the brain's supply of nutrients. Clinicians currently use MR images (MRI) of the patient's brain to manually pinpoint the location and extent of a brain tumour. Brain tumours can develop at any age in both children and adults. However, this is not the case if detection is timely and accurate. This investigation focuses on three subtypes of brain cancer: gliomas, meningiomas, and pituitary tumours. While there have been numerous publications on the topic of brain tumour classification and prediction, very few have focused on the importance of feature extraction. Manual diagnosis and conventional feature extraction methods have their limitations, and new approaches are needed to overcome them. An automated diagnostic system is necessary for extracting features and making an accurate diagnosis of brain cancer. Although advancements are being made, automatic brain tumour diagnosis continues to struggle with issues like low accuracy and a high proportion of false-positive findings. In this research work, a brief survey is provided on feature extraction for brain tumor detection using machine learning and deep learning techniques. © 2023 IEEE.

关键词： Feature extraction

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Using AI and Blockchain in Case of Algorithm Regulation 2

Using AI and Blockchain in Case of Algorithm Regulation

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2nd International Conference for Innovation in Technology, INOCON 2023

作者： Tripathi, Neha Mishra, Amit Kumar Dept. of Computer Science & Engineering Uttarakhand Dehradun India Graphic Era Hill University Dept. of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350320923

The implication of AI is determined by some basic applications where the important analysis is properly modulated as much as possible. All of the basic applications are determined by the growth of AI technologies for finding a better perspective appropriately. Fundamentally, there are different aspects based on the advantages of AI developed by the basic application on the organized basic application of the various variable perspective. In addition, all of the importance regarding the basic development basis is properly incremented on the functional approach. Examination of some basic applications to determine all of the appropriate resolutions has to be evaluated in this research article. © 2023 IEEE.

关键词： Machine learning

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Vyoma-ADS : An AI-Driven Tool for Adaptive Monitoring and Anomaly Detection for Space Agriculture

Vyoma-ADS : An AI-Driven Tool for Adaptive Monitoring and An...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Das, Rajdeep Sharma, Vaishnavi Kumari, Priyanka Raj, Abhishek Das, Sudipta De, Indrajit Institute of Engineering and Management Dept. of Computer Science and Business Systems Kolkata India

ISBN: (纸本)9798350390759

As humanity ventures further into space exploration, securing sustainable resources becomes paramount. The success of space farming depends not only on the ability to cultivate crops but also on the effective management of environmental conditions. Introducing Vyoma-ADS: an innovative system that integrates IoT technology with an adaptive Anomaly Detection System, specifically designed for space agriculture. Our study underscores the crucial role of environmental monitoring within space shuttles, where even minor fluctuations can have a significant impact on plant growth and productivity. To tackle these challenges, we designed a compact IoT-enabled system to monitor the moisture content in the soil, the temperature and humidity of the surrounding environment, and the ppm value of unwanted gases in the atmosphere. Through rigorous testing and analysis, we established optimal thresholds conducive to plant growth in the unique conditions of space. Recognizing the inherent variability and occasional anomalies in environmental data, we investigated three distinct machine learning models for anomaly detection: Isolation Forest, One-Class Support Vector Machines (OCSVM) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Our findings reveal that DBSCAN outperforms other techniques in identifying anomalies amidst fluctuating data. Additionally, we detail our approach to data visualization using a SaaS cloud platform, which enhances the comprehensibility of the study and facilitates better decision-making processes. This paper demonstrates how the integration of IoT monitoring and advanced anomaly detection methods can significantly improve the reliability and efficiency of space agriculture. By leveraging these technologies, we aim to create a robust system capable of maintaining optimal growing conditions for plants in space. © 2024 IEEE.

关键词： Support vector machines

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Enhancing Privacy Through Time Aggregation of Load Profiles in Energy Management 8

Enhancing Privacy Through Time Aggregation of Load Profiles ...

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8th IEEE International Energy Conference, ENERGYCON 2024

作者： Varenhorst, Ivo A.M. Hoogsteen, Gerwin Gerards, Marco E.T. Hurink, Johann L. dept. of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

ISBN: (纸本)9798350382150

Demand side management (DSM) applications rely on the exchange of load profiles to effectively manage the operation of energy systems. However, sharing detailed energy profile data raises substantial privacy concerns, such as potential misuse of personal information. To mitigate these concerns, we investigate the potential of time aggregation (TA), which involves merging multiple samples of a profile into a single value representing multiple intervals. TA reduces data exchange and computational requirements in energy management and helps preserve user privacy by reducing granularity of user data. We show that, for an effective implementation of TA in energy management, it is important to make the right choice of TA method. We compare and evaluate seven different TA methods. Furthermore, we perform TA across various time frames using an optimization based DSM approach. Our findings reveal that if we aggregate load profiles from 15 minutes to 4 hours, we obtain both enhanced privacy and a 21% decrease in the required number of iterations with the investigated DSM method, albeit at the cost of a 15% decrease in objective value performance. Based on this, we conclude that depending on the application needs, TA with a carefully selected aggregation method has the potential to bring value to energy management, even when aggregating to a considerable extent. ©2024 IEEE.

关键词： Energy management

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Deepfakes - Generating Synthetic Images, and Detecting Artificially Generated Fake Visuals Using Deep Learning 14

Deepfakes - Generating Synthetic Images, and Detecting Artif...

引用

14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Mishra, Ayushi Bharwaj, Aadi Yadav, Aditya Kumar Batra, Khushi Mishra, Nidhi Amity University Noida Dept. of Computer Science and Engineering Noida201313 India

ISBN: (纸本)9798350344837

The abstract delves into the groundbreaking realm of DeepFakes, a revolutionary synthesis of deep learning and synthetic media. Central to DeepFake generation is the utilization of Generative Adversarial Networks (GANs), particularly the innovative mechanisms of face reenactment involving DCGANs (Deep Convolutional GANs) and Autoencoders. These technologies empower a generator to transform random noise into hyper-realistic visuals, capturing intricate details such as facial expressions and lighting conditions through latent space interpolation. The adversarial interplay between generator and discriminator continuously refines the authenticity of the generated content. While DeepFakes unlocks creative possibilities for artists and filmmakers, the abstract underscores the ethical concerns surrounding misinformation, privacy breaches, and trust erosion in digital media. The discourse navigates the delicate balance between creative freedom and responsible use, highlighting how DeepFakes, with their roots in advanced deep learning techniques, redefine our perception of synthetic media, challenging notions of reality in our increasingly digital world. © 2024 IEEE.

关键词： Digital storage

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Performance Comparison of Deep Learning Methods for Lung Nodule Benign and Malignancy Classification 18

Performance Comparison of Deep Learning Methods for Lung Nod...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Bhagwat, Keshav S. Thirupurasundari, D.R. Bharath Institute of Higher Education & Research Dept of Computer Science and Engineering Chennai India

ISBN: (纸本)9789380544519

Today, lung tumors is the major cause of malignancy death. Doctors use the CT scan, x-ray, and manual method for lung nodule detection and diagnosis. However, such methods fail to correctly classify the malignancy of nodules. For early, automatic, and accurate detection of lung nodules, good lung nodule classification methods are required for benign and malignancy classification. To improve accuracy and lung nodule classification performance, many scholars have proposed some deep learning algorithms to solve this problem. In this paper, various deep-learning methods are compared while classifying lung nodule malignancy. Researchers have used deep learning algorithms such as CNN, 2DCNN, 3DCNN, VGG16, ResNet50, and RNN algorithms for lung nodule malignancy classification. We compared all methods performance and accuracy using a confusion matrix. After comparing experimental results, we found that the Deep Residual Network (ResNet50) gives a good result with 98.91% accuracy. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Bag of features Chest X-ray Images Convolutional neural network Deep learning Deep Residual Network Lung tumor Multi-Task CNN

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MSCE-LT: Multi-Label Supervised Contrastive Enhancement for Long-Tailed Retinal Diseases Recognition

MSCE-LT: Multi-Label Supervised Contrastive Enhancement for ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Tingyao Sheng, Bin Shanghai Jiao Tong University Dept. Computer Science and Engineering Shanghai China

ISBN: (纸本)9798350386226

Retinal diseases are leading causes of blindness globally. In real-world clinical practice, a patient may suffer from multiple retinal diseases, and these diseases are often under a long-tailed distribution, which poses significant challenges for accurate diagnosis. In this work, we propose a novel contrastive learning(CL)-based framework for multi-label retinal disease recognition. It consists of two parallel branches, a multi-label supervised contrastive learning branch and a classifier branch. The positive sets are created by the extent of proportional label overlap between samples and the anchor in calculating contrastive loss. For minority information enhancement, we design a hybrid-proxy model to generate class-dependent proxies, which are updated alongside the network. We capture rich relations samples, proxies, and labels by introducing the hybrid sample-proxy contrastive loss. Taking both label co-occurrence and data imbalance into consideration, we further utilize Distribution Balanced (DB) binary cross-entropy loss to guide the classifier branch learning. Experimental results on four public retinal disease datasets have demonstrated the superiority and effectiveness of our method. © 2024 IEEE.

关键词： Contrastive Learning

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Design and Implementation of Low Power Circuits Based on 5nm Double Gate MOSFET Technology Using Different Materials 3

Design and Implementation of Low Power Circuits Based on 5nm...

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3rd IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Kundu, Shrabanti Mandal, Jyotsna Kumar. University of Kalyani Dept. of Computer Science and Engineering WB Kalyani Nadia India

ISBN: (纸本)9798350374643

This paper presents a comprehensive investigation of 5nm double gate MOSFETs (DG MOSFETs) circuits using different substrate semiconductor materials for low-power devices. A comparative analysis of characteristics of SiGe, InGaAs and GaN as substrate materials is performed. The study reveals that InGaAs offers superior performance due to its higher electron mobility, lower threshold voltage and thermal stability than other materials studied here for low-power applications. The electrical properties of DG MOSFETs on these substrates have been investigated by calculating key parameters such as threshold voltages (Vth), Ion and Ioff currents. The results show that InGaAs-based DG MOSFETs exhibit superior performance with threshold voltage, improved leakage current and Ion/Ioff ratio compared to other substrate materials. Additionally, a lower threshold voltage has been observed for InGaAs-based devices, indicating reduced power consumption. These findings demonstrate the potential of InGaAs as a preferred substrate material for low-power, high-performance DG MOSFET devices. Double gate MOSFET circuits are designed using InGaAs and power dissipation is calculated. The results show a significant reduction in power consumption and improved circuit performance compared to traditional Si-based devices. This research demonstrates the potential of InGaAs-based DG MOSFETs for low-power electronic devices, enabling the development of energy-efficient circuits for various applications. © 2024 IEEE.

关键词： III-V semiconductors

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