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Deep Learning Model for computer Vision: Sustainable Image Classification Using Machine Learning 1st

Deep Learning Model for Computer Vision: Sustainable Image C...

1st International Conference on Sustainable Development through Machine Learning, Artificial Intelligence, and IoT, ICSD 2023

作者： Alankar, Bhavya Chauhan, Ritu Kaur, Harleen Ebadati, E. Omid Mahdi Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University UP Noida India Department of Mathematics and Computer Science Kharazmi University Tehran Iran

ISBN: (纸本)9783031470547

The core challenge within computer vision is image classification, which involves assigning an image a label and categorizing it using the features that have been extracted from it. Even though this work appears rudimentary, it requires the application involving artificial intelligence, specifically Deep Learning, a subfield of Machine Learning. In this paper, we have discussed how Convolutional Neural Networks (CNN), the most modern technique for image classification, are used in Deep Learning to classify images. We have discussed the applications of image classification and drawn the conclusion that using deep learning is preferable to using machine learning. Additionally, we have also presented a comparative study of various CNN models to observe how they could potentially be employed as pre-trained models for the image classification job. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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Advancing Non-intrusive Suppression on Enhancement Distortion for Noise Robust ASR

Advancing Non-intrusive Suppression on Enhancement Distortio...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Wei Zhao, Siyi Qian, Yanmin Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350368741

Recent advancements in speech enhancement (SE) techniques have greatly improved speech clarity and intelligibility in challenging acoustic environments. However, integrating SE into automatic speech recognition (ASR) systems often results in performance degradation due to artifacts introduced during the enhancement process. While various methods have enhanced recognition accuracy in SE-ASR systems, they often require fine-tuning or re-training of SE or ASR models, which is impractical in many real-world applications. In this paper, we propose a lightweight distortion suppression (DS) network that addresses these artifacts without modifying the SE or ASR models, treating them as fixed black boxes. The DS module operates on the time-frequency (T-F) bands of the original and enhanced complex spectrograms, efficiently compensating for SE distortions using the original T-F information. We validate our approach through experiments on both Mandarin and English ASR tasks using monaural and multi-channel SE frontends, across various ASR backends. Results show that the DS module significantly improves the performance of SE-ASR systems, even when used with robust commercial ASR backends. © 2025 IEEE.

关键词： non-intrusive robust speech recognition speech distortion speech enhancement

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Advanced Zero-Shot Text-to-Speech for Background Removal and Preservation with Controllable Masked Speech Prediction

Advanced Zero-Shot Text-to-Speech for Background Removal and...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Leying Zhang, Wangyou Chen, Zhengyang Qian, Yanmin Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350368741

The acoustic background plays a crucial role in natural conversation. It provides context and helps listeners understand the environment, but a strong background makes it difficult for listeners to understand spoken words. The appropriate handling of these backgrounds is situation-dependent: Although it may be necessary to remove background to ensure speech clarity, preserving the background is sometimes crucial to maintaining the contextual integrity of the speech. Despite recent advancements in zero-shot Text-to-Speech technologies, current systems often struggle with speech prompts containing backgrounds. To address these challenges, we propose a Controllable Masked Speech Prediction strategy coupled with a dual-speaker encoder, utilizing a task-related control signal to guide the prediction of dual background removal and preservation targets. Experimental results demonstrate that our approach enables precise control over the removal or preservation of background across various acoustic conditions and exhibits strong generalization capabilities in unseen scenarios. © 2025 IEEE.

关键词： background preservation background removal flow-matching text-to-speech

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Generalising Violence Detection with a New Near-Real-World Violence Dataset 3

Generalising Violence Detection with a New Near-Real-World V...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Haque, Mahmudul Nyeem, Hussain Ovi, Tareque Bashar Nahid, Al Molla, Md. Sabbir Hossain Tuhin, Md. Tanjim Mahmud Shahab, Fardin Alam, Ayat Subah Alam, Saadia Binte Mirpur Cantonment Dhaka1216 Bangladesh Independent University Department of Computer Science and Engineering Dhaka1229 Bangladesh Independent University Bangladesh Center for Computational & Data Sciences Dhaka1229 Bangladesh

ISBN: (纸本)9798350388282

Detecting and classifying violence is crucial for public safety and addressing societal violence. DL models have greatly improved the automation of violence detection systems by effectively capturing intricate visual patterns. However, the quality and diversity of the training data greatly impact the effectiveness of these models. Existing datasets may be biased towards specific situations, limiting their practical use. To address this limitation, we introduce the Movie Clip (MC) dataset to enhance the generalisability of Automated Violence Detection and Classification (AVDC) systems. The MC dataset encompasses a broad spectrum of near-real-world violent actions, incorporating diverse demographics, environmental circumstances, and cultural elements extracted from movies. Consequently, it accurately reflects the complexity and diversity of real-world violent scenarios. The potential of the new dataset is investigated against its existing counterparts, like Hockey Fight (HF) and AIRTLab datasets. These datasets are used to train the ConvLSTM models based on the VGG16 and VGG19 architectures. The proposed dataset significantly improves AVDC model generalisation, outperforming the generalisability of current datasets, thereby advancing violence detection and facilitating the development of more robust and efficient AVDC systems. © 2024 IEEE.

关键词： Long short-term memory

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Regulation of regeneration in Arabidopsis thaliana

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aBIOTECH 2023年第4期4卷 332-351页

作者： Md Khairul Islam Sai Teja Mummadi Sanzhen Liu Hairong Wei Computational Science and Engineering Program Michigan Technological UniversityHoughtonMI 49931USA Computer Science Michigan Technological UniversityHoughtonMI 49931USA Department of Plant Pathology Kansas State UniversityManhattanKS 66506USA College of Forest Resources and Environmental Science Michigan Technological UniversityHoughtonMI 49931USA

We employed several algorithms with high efficacy to analyze the public transcriptomic data,aiming to identify key transcription factors(TFs)that regulate regeneration in Arabidopsis ***,we utilized CollaborativeNet,also known as TF-Cluster,to construct a collaborative network of all TFs,which was subsequently decomposed into many subnetworks using the Triple-Link and Compound Spring Embedder(CoSE)*** analysis of these subnetworks led to the identification of nine subnetworks closely associated with *** further applied principal component analysis and gene ontology(GO)enrichment analysis to reduce the subnetworks from nine to three,namely subnetworks 1,12,and *** for TF-binding sites in the promoters of the co-expressed and co-regulated(CCGs)genes of all TFs in these three subnetworks and Triple-Gene Mutual Interaction analysis of TFs in these three subnetworks with the CCGs involved in regeneration enabled us to rank the TFs in each ***,six potential candidate TFs-WOx9A,LEC2,PGA37,WIP5,PEI1,and AIL1 from subnetwork 1-were identified,and their roles in somatic embryogenesis(GO:0010262)and regeneration(GO:0031099)were discussed,so were the TFs in Subnetwork 12 and 17 associated with *** TFs identified were also assessed using the CIS-BP database and Expression *** analyses suggest some novel TFs that may have regulatory roles in regeneration and embryogenesis and provide valuable data and insights into the regulatory mechanisms related to *** tools and the procedures used here are instrumental for analyzing high-throughput transcriptomic data and advancing our understanding of the regulation of various biological processes of interest.

关键词： Regeneration Pluripotency Transcription factor Collaborative network

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Novel Method for Recognizing Sign Language using Regularized Extreme Learning Machine 2

Novel Method for Recognizing Sign Language using Regularized...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Moorthi, K. Asokan, Anju Sri Sathya, K.B. Chellammal, P. Kalpana, V. Rastogi, Ravi Srm Institute of Science and Technology Department of Computational Intelligence Tamilnadu Chengalpattu India Sri Eshwar College of Engineering Department of Computer and Communication Engineering Tamilnadu Coimbatore India Kpr Institute of Engineering and Technology Computer Science and Engineering Tamilnadu Coimbatore India J J College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Trichy India R. M. K. College of Engineering and Technology Tamilnadu India Nielit Gorakhpur Uttar Pradesh India

ISBN: (纸本)9781665456302

The ability of humans to effectively communicate through the use of hand signs has a wide range of practical applications. People with speech problems around the world have embraced them due to their intuitive design. Around 1% of Indians are in this group, which is a quite high percentage. It is for this reason that the incorporation of a framework familiar with Indian Sign Language would have such a profoundly positive effect on the lives of the people of India. A median filter is used to an input image to remove unnecessary details and improve clarity. Feature extraction is performed using principal component analysis (PCA), and the YCbCr color space is used for hand segmentation. The model is then trained through Regularized Extreme Learning. Using regularization to achieve peak structural performance for precise prediction, RELMs are a subclass of ELMs. This method exceeds popular alternatives like the support vector machine (SVM), Extreme Learning Machine (ELM), and CNN in terms of accuracy (around 97.8%). © 2023 IEEE.

关键词： Principal component analysis

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M-VMT-PC: Metaverse-Enabled Virtual Medical Training and Patient Care Framework Underlying 5G

M-VMT-PC: Metaverse-Enabled Virtual Medical Training and Pat...

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2024 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2024

作者： Kumari, Aparna Obaidat, Mohammad Panchal, Shikhar Raval, Nikhil Kumar, Prasun Kumar Yadav, Dileep Nirma University Department of CSE Ahmedabad India University of Science and Technology Beijing China School of Computing SRM University Department of Computational Intelligence SRM Nagar TN Kattankulathur603203 India School of Engineering The Amity University UP Noida201301 India Nirma University Department of Computer Science and Engineering Ahmedabad India SSET Sharda University Department of Biotechnology Greater Noida India Bennett University Department of Computer Science and Engineering SCSET Greater Noida India

ISBN: (纸本)9798350349832

As technology advances, merging virtual environments, blockchain, and 5G networks opens new possibilities, especially in medical training. This paper introduces a framework that combines these technologies to create an innovative medical training system. In this paper, we proposes Metaverse-enabled Virtual Medical Training and Patient Care framework, i.e., M-VMT-PC. In the proposed framework, metaverse comprises a dig-ital space where users can interact in 3D, offers exciting opportunities for medical training. It allows for realistic and interactive simulations, where trainees can practice medical scenarios and procedures in a virtual setting. More, the M-VMT-PC uses blockchain technology within this framework to ensure that the data is securely stored, enhancing its reliability. Next, 5G technology is used to make this framework effective. With ultra-reliable, low-latency communication (uRLLC) and high-speed data transfer (eMBB), 5G supports real-Time interactions and smoothly handles large volumes of data. This technology ensures the training environment is responsive and can handle extensive data needs, essential for ongoing updates and improvements. Our proposed framework M-VMT-PC addresses the current challenges in medical training by providing a scalable, secure, and engaging learning environment. It improves the medical professionals training and sets the stage for future advancements in medical education and patient care. © 2024 IEEE.

关键词： Virtual environments

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Enhancing identity and access management using Hyperledger Fabric and OAuth 2.0: A block-chain-based approach for security and scalability for healthcare industry

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 49-67页

作者： Sutradhar, Shrabani Karforma, Sunil Bose, Rajesh Roy, Sandip Djebali, Sonia Bhattacharyya, Debnath Department of Computational Sciences Brainware University West Bengal India Department of Computer Sciences The University of Burdwan West Bengal India Department of Computer Science & Engineering JIS University West Bengal India Pôle universitaire Léonard De Vinci Research Center Paris La Défense 92 916 France Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh522302 India

Block-chain-based Identity and access management framework is a promising solution to privacy and security issues raised during the exchange of patient data in the healthcare industry. This technology ensures the confidentiality and integrity of sensitive information by providing a decentralized and immutable ledger. In our research, we propose an identity and access management system that employs Hyper-ledger Fabric and OAuth 2.0 for improved security and scalability. This combination allows for transparency and immutability of user transactions and minimizes the risk of fraud and unauthorized access. Additionally, Hyper-ledger Fabric's privacy, security, and scalability features enable granular access control to sensitive information, while OAuth 2.0 authorizes only trusted third-party applications to access specific data on the Fabric network. The proposed approach can handle large volumes of data and support multiple applications, thus providing a secure and scalable solution for managing access to the Fabric network. Moreover, our solution employs Role-based access control based on the patient's role, ensuring privacy and confidentiality. Our statistical analysis demonstrates that the proposed approach can efficiently and securely manage patient identity and access, potentially transforming the healthcare industry by enhancing data interoperability, reducing fraud and errors, and improving patient privacy and security. Furthermore, our solution can facilitate compliance with regulatory requirements such as HIPAA and GDPR. © 2023 The Authors

关键词： Scalability

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Practical Phase Retrieval Using Double Deep Image Priors 21

Practical Phase Retrieval Using Double Deep Image Priors

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IS and T International Symposium on Electronic Imaging: 21st computational Imaging, COIMG 2023

作者： Zhuang, Zhong Yang, David Hofmann, Felix Barmherzig, David A. Sun, Ju Electrical & Computer Engineering University of Minnesota Minneapolis United States Department of Engineering Science University of Oxford Oxford United Kingdom Center for Computational Mathematics Flatiron Institute New York City United States Computer Science & Engineering University of Minnesota Minneapolis United States

Phase retrieval (PR) concerns the recovery of complex phases from complex magnitudes. We identify the connection between the difficulty level and the number and variety of symmetries in PR problems. We focus on the most difficult far-field PR (FFPR), and propose a novel method using double deep image priors. In realistic evaluation, our method outperforms all competing methods by large margins. As a single-instance method, our method requires no training data and minimal hyperparameter tuning, and hence enjoys good practicality. Our paper is also available at: https: // arxiv. org/ abs/ 2211. 00799 . © 2023, Society for Imaging science and Technology.

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Game Strategies for Data Transfer Infrastructures Against ML-Profile Exploits

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 925-938页

作者： Rao, Nageswara S. V. Ma, Chris Y. T. He, Fei Oak Ridge National Laboratory Computational Sciences and Engineering Division Oak RidgeTN37831 United States The Hang Seng University of Hong Kong Department of Computer Science Siu Lek Yuen Hong Kong Texas A&M University-Kingsville Department of Mechanical and Industrial Engineering KingsvilleTX78363 United States

Data transfer infrastructures composed of Data Transfer Nodes (DTN) are critical to meeting distributed computing and storage demands of clouds, data repositories, and complexes of supercomputers and instruments. The infrastructure's throughput profile, estimated as a function of the connection round trip time using Machine Learning (ML) methods, is an indicator of its operational state, and has been utilized for monitoring, diagnosis and optimization purposes. We show that the inherent statistical variations and precision of throughput profiles estimated by ML methods can be exploited for unauthorized use of DTNs' computing and network capacity. We present a game theoretic formulation that captures the cost-benefit trade-offs between an attacker that attempts to hide under the profile's statistical variations and a provider that attempts to balance compromise detection with the cost of throughput measurements. The Nash equilibrium conditions adapted to this game provide qualitative insights and bounds for the success probabilities of the attacker and provider, by utilizing the generalization equation of ML-estimate. We present experimental results that illustrate this game wherein a significant portion of DTN computing capacity is compromised without being detected by an attacker that exploits the ML estimate properties. © 2023 CCBY.

关键词： Data transfer

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