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15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Thakur, Nirmalya Patel, Kesha A. Poon, Audrey Cui, Shuqi Azizi, Nazif Shah, Rishika Shah, Riyan South Dakota School of Mines and Technology Department of Electrical Engineering and Computer Science Rapid CitySD57701 United States Emory University Department of Mathematics AtlantaGA30322 United States Emory University Department of Computer Science AtlantaGA30322 United States

ISBN: (纸本)9798331507695

This study introduces the Community Sentiment and Engagement Index (CSEI)., developed to capture nuanced public sentiment and engagement variations on social media, particularly in response to major events related to COVID-19. Constructed with diverse sentiment indicators, CSEI integrates features like engagement, daily post count, compound sentiment, fine-grain sentiments (fear, surprise, joy, sadness, anger, disgust, and neutral), readability, offensiveness, and domain diversity. Each component is systematically weighted through a multi-step Principal Component Analysis (PCA)-based framework, prioritizing features according to their variance contributions across temporal sentiment shifts. This approach dynamically adjusts component importance, enabling CSEI to precisely capture high-sensitivity shifts in public sentiment. CSEI's development showed statistically significant correlations with its constituent features, underscoring internal consistency and sensitivity to specific sentiment dimensions. CSEI's responsiveness was validated using a dataset of 4,510,178 Reddit posts about COVID-19. The analysis focused on 15 major events, including the WHO's declaration of COVID-19 as a pandemic, the first reported cases of COVID-19 across different countries, nationallockdowns, vaccine developments, and crucial public health measures. Cumulative changes in CSEI revealed prominent peaks and valleys aligned with these events, indicating significant patterns in public sentiment across different phases of the pandemic. Pearson correlation analysis further confirmed a statistically significant relationship between CSEI daily fluctuations and these events (p = 0.0428), highlighting CSEI's capacity to infer and interpret the shifts in public sentiment and engagement in response to major events related to COVID-19. © 2025 IEEE.

关键词： Spatio-temporal data

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Smart Grid Security Framework for data Transmissions with Adaptive Practices Using Machine Learning Algorithm

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computers, Materials & Continua 2025年第3期82卷 4339-4369页

作者： Shitharth Selvarajan Hariprasath Manoharan Taher Al-Shehari Hussain Alsalman Taha Alfakih School of Built Environment Engineering and ComputingLeeds Beckett UniversityLeedsLS13HEUK Department of Computer Science and Engineering Chennai Institute of TechnologyChennai602109India Centre for Research Impact & Outcome Chitkara UniversityChandigarh140401India Department of Electronics and Communication Engineering Panimalar Engineering CollegeChennai600123India Department of Self-Development Skill Common First Year DeanshipKing Saud UniversityRiyadh11362Saudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

This research presents an analysis of smart grid units to enhance connected units’security during data *** major advantage of the proposed method is that the system model encompasses multiple aspects such as network flow monitoring,data expansion,control association,throughput,and *** addition,all the above-mentioned aspects are carried out with neural networks and adaptive optimizations to enhance the operation of smart grid ***,the quantitative analysis of the optimization algorithm is discussed concerning two case studies,thereby achieving early convergence at reduced *** suggested method ensures that each communication unit has its own distinct channels,maximizing the possibility of accurate *** results in the provision of only the original data values,hence enhancing *** power and line values are individually observed to establish control in smart grid-connected channels,even in the presence of adaptive settings.A comparison analysis is conducted to showcase the results,using simulation studies involving four scenarios and two case *** proposed method exhibits reduced complexity,resulting in a throughput gain of over 90%.

关键词： Machine learning power systems security smart grid

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Translation Systems: A Synoptic Survey of Deep Learning Approaches and Techniques 11

Translation Systems: A Synoptic Survey of Deep Learning Appr...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Kumar, Kirti Faiz, Anas Shruti, Kumari Chhabra, Megha Setia, Sonia School of Engineering and Technology Sharda University Department of Computer Science & Engineering U.P. Greater Noida India

ISBN: (纸本)9798350350357

Within artificial intelligence, deep learning is a subset that emulates the information processing mechanism of the human brain. allowing machines to learn and make decisions on their own. This technology has revolutionized the field of language translation. Deep learning models analyze vast amounts of linguistic data, allowing visualization of context, and idiomatic expressions. This results in translations that read more naturally and capture the true essence of the source text. In order to accomplish the goal of higher accuracy, this study explores a variety of methodologies, including rule-based, neural-based, hybrid-based, and statistical-based machine translation systems, along with associated pre-processing strategies. It details the process of data collection, pre-processing, model building, training, and evaluation. The paper concludes by highlighting its key contribution in the field. © 2024 IEEE.

关键词： Deep learning

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Coopetition in Heterogeneous Cross-Silo Federated Learning 27

Coopetition in Heterogeneous Cross-Silo Federated Learning

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Huang, Chao Dachille, Justin Liu, Xin School of Computing Montclair State University United States Department of Computer Science University of California Davis United States

ISBN: (纸本)9781643685489

In cross-silo federated learning (FL), companies collaboratively train a shared global model without sharing heterogeneous data. Prior related work focused on algorithm development to tackle data heterogeneity. However, the dual problem of coopetition, i.e., FL collaboration and market competition, remains under-explored. This paper studies the FL coopetition using a dynamic two-period game model. In period 1, an incumbent company trains a local model and provides model-based services at a chosen price to users. In period 2, an entrant company enters, and both companies decide whether to engage in FL collaboration and then compete in selling model-based services at different prices to users. Analyzing the two-period game is challenging due to data heterogeneity, and that the incumbent's period one pricing has a temporal impact on coopetition in period 2, resulting in a non-concave problem. To address this issue, we decompose the problem into several concave sub-problems and develop an algorithm that achieves a global optimum. Numerical results on three public datasets show two interesting insights. First, FL training brings model performance gain as well as competition loss, and collaboration occurs only when the performance gain outweighs the loss. Second, data heterogeneity can incentivize the incumbent to limit market penetration in period 1 and promote price competition in period 2. © 2024 The Authors.

关键词： Federated learning

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Exploring the Effectiveness of Deep Learning in Audio Compression and Restoration 2

Exploring the Effectiveness of Deep Learning in Audio Compre...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Pushparani, S. Rekha, K. Sashi Sivagami, V.M. Usharani, R. Jothi, M. Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology SriperumbudurTamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence School of Computing Tamil Nadu Kattankulathur India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India

ISBN: (纸本)9798350384277

Modern audio processing requires compression and restoration for streaming services and data storage and transmission. This study examines how deep learning can address these difficulties and includes an architecture flow diagram. The suggested complex design has numerous integral stages optimized for audio compression and restoration. The input layer contains raw audio data. The pre-processing module normalizes and extracts features from raw data, frequently using Mel-frequency cepstral coefficients (MFCC) to capture audio characteristics. This module improves data quality by reducing noise and filtering. The architecture's heart is the compression module, where deep learning is used. This module compresses audio data using neural networks, maybe CNNs or RNNs. This compression is quantified by compression ratio and bitrate, with hypothetical values representing audio quality trade-offs. The compression module compacts audio data for storage or transmission. To minimize data size, quantization and encoding are common here. To accurately decompress audio, the decompression module mimics the compression module's architecture. Depending on needs, error correction or quality enhancement layers may be added. Filtering, equalization, and dynamic range compression may be used in the post-processing module to improve audio quality. If needed, a restoration module uses deep learning models to minimize noise and artifacts. This study presents four hypothetical scenarios to demonstrate different results. The scenarios emphasize high compression, balanced approaches, quality prioritization, and low resource utilization. Deep learning approaches can adapt to varied audio processing needs by balancing compression ratio, bitrate, audio quality (measured by PSNR), and restoration accuracy in each case. © 2024 IEEE.

关键词： Audio streaming

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Wood Type Identification via Neural Networks and Spectral Analysis: An Advanced Algorithmic Solution

Wood Type Identification via Neural Networks and Spectral An...

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2023 International Conference on data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Bhat, C.Rohith Lakshmiramana, P. Vincy, R.Fatima Rajasekar, P. Balaji, A. Rajeshkanna, R. Saveetha School of Engineering Department of Computer Science and Engineering Tamilnadu Chennai India Madanapalle Institute of Technology & Science Department of Computer Science & Technology Madanapalle India Easwari Engineering College Computer Science and Engineering Ramapuram India Sri Krishna College of Technology Department of Ece Coimbatore India Panimalar Engineering College Department of It Chennai India Bangalore India

ISBN: (纸本)9798350348910

Forestry management, woodworking, and manufacturing need wood type identification. This study introduces a neural network-spectral analysis technique for accurate and automatic wood type detection. Principal Component Analysis (PCA) is used to extract features from a heterogeneous collection of wood spectral signatures after training a neural network. The algorithm's 94.2% accuracy on a testing dataset shows its ability to distinguish wood *** model's confusion matrix shows it can recognise closely related wood species with few misclassifications. The neural network's precision, recall, and F1 score prove its wood classification accuracy. With PCA highlighting classification characteristics, spectral analysis helps the algorithm *** method is useful for forestry management and woodworking quality control. The non-destructive technology provides in-situ wood type detection, addressing environmental and conservation issues. The study explores ramifications, constraints, and future algorithm modification and application in real-world *** networks and spectral analysis provide a strong, efficient, and non-destructive wood type detection solution. The hopeful results represent a major advance in wood science and current computer methods, with applicability across sectors. © 2023 IEEE.

关键词： Timber

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Pseudo-Siamese Blind-Spot Transformers for Self-Supervised Real-World Denoising 38

Pseudo-Siamese Blind-Spot Transformers for Self-Supervised R...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Quan, Yuhui Zheng, Tianxiang Ji, Hui School of Computer Science and Engineering South China University of Technology China Department of Mathematics National University of Singapore Singapore

Real-world image denoising remains a challenge task. This paper studies self-supervised image denoising, requiring only noisy images captured in a single shot. We revamping the blind-spot technique by leveraging the transformer's capability for long-range pixel interactions, which is crucial for effectively removing noise dependence in relating pixel-a requirement for achieving great performance for the blind-spot technique. The proposed method integrates these elements with two key innovations: a directional self-attention (DSA) module using a half-plane grid for self-attention, creating a sophisticated blind-spot structure, and a Siamese architecture with mutual learning to mitigate the performance impacts from the restricted attention grid in DSA. Experiments on benchmark datasets demonstrate that our method outperforms existing self-supervised and clean-image-free methods. This combination of blind-spot and transformer techniques provides a natural synergy for tackling real-world image denoising challenges. © 2024 Neural information processing systems foundation. All rights reserved.

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Leveraging Model Distillation as a Defense Against Adversarial Attacks Based on Deep Learning 2

Leveraging Model Distillation as a Defense Against Adversari...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Suresh, K. Radha, J. Thilagaraj, T. Subramani, R. Lakineni, Prasanna Kumar Taqui, Syed Noeman Department of Computer Science Bangalore India Nandha Engineering College Department of Computer Science and Engineering Tamilnadu Perundurai India Moodlakatte Institute of Technology Department of Computer Science and Engineering Moodalakatte Karnataka Kundapura India Department of Mathematics Bangalore India Gitam School of Technology Gitam University Department of Computer Science and Engineering Visakhapatnam India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Vlsi Microelectronics Tamil Nadu Chennai India

ISBN: (纸本)9798350369960

Adversarial attacks on deep learning models threaten machine learning system security and reliability. The above attacks use modest data alterations to produce erroneous model results while being undetected by humans. This work suggests model distillation to prevent adversarial perturbations. The student model is taught to emulate the teacher model in model distillation. This is done using teacher model soft outputs. Our idea is that this strategy organically strengthens the student model against adversarial assaults by keeping the teacher model's essential knowledge and generalization capabilities while reducing weaknesses. Distilled models are more resilient to adversarial assaults than non-distilled models, according to experiments. These models also perform similarly on undamaged, uncorrupted data. The results show that model distillation may be a powerful defense against machine learning adversaries. This method protects model resilience and performance. © 2023 IEEE.

关键词： Adversarial Attacks Deep Learning Defensive Approach Machine Learning Model Distillation

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An Ensemble based Learning for Parkinson’s Disease Prediction 22

An Ensemble based Learning for Parkinson’s Disease Predicti...

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22nd OITS International Conference on Information Technology, OCIT 2024

作者： Dash, Dipti Kumar, Mukesh Senapati, Rajashree Rapate, Gauri Sameer Chatterjee, Pinaki Sankar School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India School of Computer Science BJB Autonomous College Bhubaneswar India Department of Computer Science and Engineering PES University Karnataka Bangalore India

ISBN: (纸本)9798331510404

Parkinson’s disease (PD) is a severe neurodegenerative condition impacting a significant number of individuals globally, underscoring the critical necessity for prompt diagnosis and efficacious intervention strategies. This paper introduces a collaborative learning approach for predictive modeling of PD onset and progression utilizing various machine learning algorithms. Through the amalgamation of ensemble algorithms like random forests, gradient boosting, and Support vector machine our framework harnesses the collective intelligence of multiple models to enhance prediction accuracy and efficiency. The model selection process is meticulously employed to discern vital predictors from an array of clinical and biological data, encompassing demographics, medical history, genetic markers, and neuroimaging data. Leveraging a substantial database comprising extended longitudinal records of PD patients and controls for model training and assessment, a rigorous power analysis, incorporating parameters such as accuracy, F1 score, precision and the area under the curve (AUC), attests to the model’s predictive capability in forecasting PD. Furthermore, interpretive analysis delineates the impact of individual data points within the dataset and underscores pivotal features contributing to prediction performance. The collaborative learning approach demonstrates exceptional predictive prowess and offers valuable insights for early PD detection and self-management. This study enriches the realm of predictive medicine research, fostering enhanced clinical decision-making and superior patient outcomes in Parkinson’s disease treatment. © 2024 IEEE.

关键词： Neurodegenerative diseases

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PITCOR: Privacy-assured Time-controlled Revocation in Blockchain Transactions 6

PITCOR: Privacy-assured Time-controlled Revocation in Blockc...

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6th International Conference on Blockchain Computing and Applications, BCCA 2024

作者： Chhabra, Archana Saha, Rahul Conti, Mauro School of Computer Science and Engineering Lovely Professional University Punjab India Department of Mathematics University of Padua Padua Italy

ISBN: (纸本)9798350351538

The proliferation of blockchain technology is based on decentralization, transparency, and security. The inherent openness and transparency of blockchain are beneficial for trust and accountability but pose a significant challenge to privacy. Besides, outdated access to the blockchain data also leads to privacy leakage. This needs time-controlled access revocation for the blockchain transactions. In this paper, we introduce a privacy-preserving framework, PrIvacy-ensured Time-COntrolled Revocation (PITCOR), leveraging advanced techniques including Public key Encryption-based Keyword search (PEKS), InterPlanetary File System (IPFS), and Private Information Retrieval (PIR). PITCOR also uses time-controlled revocation to update the access rights of the blockchain data. We implement PITCOR on Ethereum and HyperLedger Fabric (HLF). PICTOR shows a throughput of 56 Transaction Per Second (TPS) and 50 TPS with latency of 8.5 ms and 7.7 ms for Ethereum and HLF, respectively. Demonstrating efficacy against privacy attacks, PITCOR provides robust protection, making it a promising solution for addressing privacy concerns in blockchain applications. © 2024 IEEE.

关键词： Differential privacy

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