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2023 IEEE World AI IoT Congress, AIIoT 2023

作者： Sakib, Sheikh Rahmatulla Sara, Kamarun Nahar Hossain Rasel, Md. Tahmid Asif, Md. Masudul Islam Nahid, Md. Aynul Hasan Rahman, Md. Saifur Mridha, M.F. Islam, Ashraful Bangladesh University of Business and Technology Department of Computer Science and Engineering Dhaka Bangladesh Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh American International University-Bangladesh Department of Computer Science Dhaka Bangladesh Independent University Bangladesh Center for Computational and Data Sciences Dhaka Bangladesh

ISBN: (纸本)9798350337617

In Dhaka, the capital city of Bangladesh, various sources including vehicle emissions, industrial activities, brick kilns, building sites, and open rubbish burning contribute to the air pollution problem. To assess the air quality, the Air Quality Index (AQI) is utilized, which categorizes air quality based on pollutant concentration. In this study, we have built ARIMA, Auto-ARIMA, SARIMAX, and VAR models to predict the air quality of Dhaka. Unlike previous studies, we have utilized hourly air pollutants factors such as PM2.5, PM10, SO2, CO, NO2, and O3 to forecast air quality. Our novel approach enables us to predict the monthly and weekly air quality of Dhaka city. Our analysis reveals that the SARIMAX model, which takes into account seasonal patterns, trends, and external factors, is the most accurate in predicting Dhaka city's air quality. The model's prediction performance is assessed using statistical indicators such as mean absolute percentage error and root mean square error. The study highlights that the SARIMAX model could aid policymakers in evaluating the efficacy of air pollution control measures. © 2023 IEEE.

关键词： Air quality

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6G Networks in Unmanned Ariel Vehicle Intelligent Internet of Things Secured Communication 6

6G Networks in Unmanned Ariel Vehicle Intelligent Internet o...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Beatrice Dorothy, A. Sreeja, B.P. Chamundeeswari, Vijaya Madala, Ravikiran Singh, Devesh Pratap Sucharitha, K. Department of Data Science Tiruchirappalli India Karpagam College of Engineering Department of Information Technology Tamilnadu Coimbatore India Saveetha Engineering College Department of Computer Science Engineering Tamilnadu Chennai India University of Cumberlands DurhamNC United States Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India St.Martin's Engineering College Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350304480

Unmanned airborne vehicles (UAVs) have shown enormous possible throughout recent years in conveying the expense and time-successful answers for the different social applications, including medical services, store network, and video and reconnaissance. Scientists from everywhere the world have offered various ways of protecting information against cyberattacks on the grounds that it has various information security and security concerns. Various of them have made ideas for very computationally concentrated cryptographic-based arrangements. Just few scholastics have proposed Blockchain (BC)- based arrangements, but these arrangements might have high information stockpiling costs as well as inertness, trustworthiness, and limit issues with the networks. This study introduced an InterPlanetary Record Framework and BC-based secure UAV communication convention over the 6G organization to address the previously mentioned issues. The proposed arrangement ensures information secrecy and protection, brings down the expense of information stockpiling, and further develops network proficiency. The following stage is to examine the exploration issues and future headings for improving the proposed framework. © 2023 IEEE.

关键词： Network security

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Graph-based deep learning models in the prediction of early-stage Alzheimers 46

Graph-based deep learning models in the prediction of early-...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Thapaliya, Bishal Wu, Zundong Sapkota, Ram Ray, Bhaskar Suresh, Pranav Ghimire, Santosh Calhoun, Vince Liu, Jingyu Georgia State University Department of Computer Science Atlanta United States Tri-Institutional Center for Translational Research in Neuroimaging and Data Science United States Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta United States Tribhuvan University Department of Applied Sciences and Chemical Engineering Nepal

ISBN: (纸本)9798350371499

Alzheimer's disease is the most common age-related problem and progresses in different stages, from cognitively normal to early mild cognitive impairment, and severe dementia. This study investigates the predictive potential of resting-state functional magnetic resonance imaging (rs-fMRI) and its derived functional connectivity (FC) in understanding Alzheimer's progression. Leveraging deep learning and graph-based models, we introduce two key contributions: 1) a comparative analysis of rs-fMRI time points and FC for Alzheimer's prediction. 2) an innovative graph transformer variant incorporating self-clustering for enhanced prediction accuracy. Experiments on the Alzheimer's Disease Neuroimaging Initiative dataset with 830 subjects reveal two notable conclusions. Firstly, rs-fMRI time points offer limited utility compared to functional network connectivity for transformer-based models, even when considering temporal information. Secondly, a clustering-based attention module proves effective for classifying brain networks in predicting Alzheimer's disease progression, providing valuable insights for future research and clinical applications. © 2024 IEEE.

关键词： Prediction models

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Almost Minimax Optimal Best Arm Identification in Piecewise Stationary Linear Bandits 38

Almost Minimax Optimal Best Arm Identification in Piecewise ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hou, Yunlong Tan, Vincent Y.F. Zhong, Zixin Department of Mathematics National University of Singapore Singapore Department of Mathematics Department of Electrical and Computer Engineering National University of Singapore Singapore Data Science and Analytics Thrust Hong Kong University of Science and Technology Guangzhou China

We propose a novel piecewise stationary linear bandit (PSLB) model, where the environment randomly samples a context from an unknown probability distribution at each changepoint, and the quality of an arm is measured by its return averaged over all contexts. The contexts and their distribution, as well as the changepoints are unknown to the agent. We design Piecewise-Stationary Ε-Best Arm Identification+ (PSΕBAI+), an algorithm that is guaranteed to identify an Ε-optimal arm with probability ≥ 1 − δ and with a minimal number of samples. PSΕBAI+ consists of two subroutines, PSΕBAI and NAÏVE Ε-BAI (NΕBAI), which are executed in parallel. PSΕBAI actively detects changepoints and aligns contexts to facilitate the arm identification process. When PSΕBAI and NΕBAI are utilized judiciously in parallel, PSΕBAI+ is shown to have a finite expected sample complexity. By proving a lower bound, we show the expected sample complexity of PSΕBAI+ is optimal up to a logarithmic factor. We compare PSΕBAI+ to baseline algorithms using numerical experiments which demonstrate its efficiency. Both our analytical and numerical results corroborate that the efficacy of PSΕBAI+ is due to the delicate change detection and context alignment procedures embedded in PSΕBAI. © 2024 Neural information processing systems foundation. All rights reserved.

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Self-Transriber: Few-Shot Lyrics Transcription With Self-Training 48

Self-Transriber: Few-Shot Lyrics Transcription With Self-Tra...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Gao, Xiaoxue Yue, Xianghu Li, Haizhou National University of Singapore Department of Electrical and Computer Engineering Singapore Shenzhen Research Institute of Big Data Shenzhen China The Chinese University of Hong Kong School of Data Science Shenzhen China

ISBN: (纸本)9781728163277

The current lyrics transcription approaches heavily rely on supervised learning with labeled data, but such data are scarce and manual labeling of singing is expensive. How to benefit from unlabeled data and alleviate limited data problem have not been explored for lyrics transcription. We propose the first semi-supervised lyrics transcription paradigm, Self-Transcriber, by leveraging on unlabeled data using selftraining with noisy student augmentation. We attempt to demonstrate the possibility of lyrics transcription with a few amount of labeled data. Self-Transcriber generates pseudo labels of the unlabeled singing using teacher model, and augments pseudo-labels to the labeled data for student model update with both self-training and supervised training losses. This work closes the gap between supervised and semi- supervised learning as well as opens doors for few-shot learning of lyrics transcription. Our experiments show that our approach using only 12.7 hours of labeled data achieves competitive performance compared with the supervised approaches trained on 149.1 hours of labeled data for lyrics transcription. © 2023 IEEE.

关键词： Lyrics transcription music information retrieval singing processing and self-training

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Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

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computers, Materials & Continua 2024年第1期78卷 105-126页

作者： Tayyaba Farhat Sheeraz Akram Hatoon SAlSagri Zulfiqar Ali Awais Ahmad Arfan Jaffar Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Faculty of Computer Science and Information TechnologyThe Superior UniversityLahore54600Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive *** and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like *** study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local *** research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate *** addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the *** findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation ***,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test *** validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD *** research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

关键词： Autism Autism Spectrum Disorder(ASD) disease segmentation features optimization deep learning models facial images classification

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Apache Kafka on Big data Event Streaming for Enhanced data Flows 8

Apache Kafka on Big Data Event Streaming for Enhanced Data F...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Padmanaban, K. Babu, T.R. Ganesh Karthika, K. Pattanaik, Balachandra Dhanabhavithra, K. Srinivasan, C. Department of Electronics and Communication Engineering Tamil Nadu Namakkal India Kcg College of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Wollega University Department of Electrical and Computer Engineering Nekemte Ethiopia Agni College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376425

Apache Kafka's distributed architecture and message queuing capabilities offer significant improvements in real-time and batch data processing efficiency and reliability. This research aims to optimize Kafka setups, data partitioning, and Kafka Connect integration to create a robust and scalable data streaming infrastructure. The study focuses on enhancing data input, processing, and dissemination across systems and applications. By optimizing Kafka configurations and leveraging its capabilities, the research seeks to achieve significant improvements in data processing speed, real-time analytics, and scalable data pipelines. The evaluation of Kafka Event Stream Throughput Over Time and Latency Distribution Across Brokers demonstrates the system's performance and efficiency. The results indicate a throughput of 750-1340 events per hour and a latency distribution of 6-15 milliseconds. Additionally, Consumer Lag Over Time analysis reveals consistent performance with values ranging from 70-140. This research contributes to the advancement of big data processing by demonstrating the effectiveness of Apache Kafka in creating a robust and efficient data streaming infrastructure. The findings provide valuable insights for organizations seeking to optimize their data pipelines and leverage the power of real-time analytics. © 2024 IEEE.

关键词： data streams

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Medical Diagnostic System using Embedding JADE in jCOLIBRI

Medical Diagnostic System using Embedding JADE in jCOLIBRI

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Singh Dhatterwal, Jagjit Malik, Kiran Das, Purushottam Singh Kaswan, Kuldeep Koneru Lakshmaiah Education Foundation Department of Artificial Intelligenceand Data Science AP Vaddeswaram India Greater Noida India Graphic Era Hill University Research Scholar Graphic Era Deemed to be University Department of Computer Scienceand Engineering Dehradun India Galgotias University Department of Computer Scienceand Engineering Greater Noida203201 India

ISBN: (纸本)9798350378092

Case-based reasoning (CBR) methodology presents a foundation for a new technology of building intelligent computer-aided diagnoses systems. This Technology directly addresses the problems found in the traditional Artificial Intelligence (AI) techniques, e.g., the problems of knowledge acquisition, remembering, robustness and maintenance. This paper discusses the CBR methodology, the research issues and the technical aspects of implementing intelligent medical diagnoses systems. It develops a celebrative framework of MAS-CBR to criticize the medical problem to get the best treatment against dengue disease. This paper also embedding the framework of JADE and jCOLIBRI to improve the quality of decision- making in the medical diagnosis field. © 2024 IEEE.

关键词： Knowledge acquisition

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AI-Based Image Classification for Indian Temple Iconography: A CNN-Based Approach

AI-Based Image Classification for Indian Temple Iconography:...

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Artificial Intelligence and data engineering (AIDE), International Conference on

作者： Vanishree Kavi Mahesh Lubna Ansari Aprna Tripathi Department of Computer Science and Engineering Mangalayatan University Department of Data Science and Engineering Manipal University Jaipur

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

India is a country that encompasses a vast and diverse cultural and historical legacy, including numerous architecturally significant sculptures in temples. Analyzing and identifying entities in temple sculptures is a highly challenging and complex task. Digital archives with images, videos, books, interviews, and other resources can significantly accelerate iconographic research by building on past studies. This study explores image classification techniques applied to archives to enhance iconographic study. The findings contribute to the broader discourse on Indian art and culture, providing valuable insights into the intricate iconographic representations found in temple architecture.

关键词： Image recognition Art Convolution data engineering data models Cultural differences Artificial intelligence Interviews Image classification Videos

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FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-i.i.d. data 41

FedSC: Provable Federated Self-supervised Learning with Spec...

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41st International Conference on Machine Learning, ICML 2024

作者： Jing, Shusen Yu, Anlan Zhang, Shuai Zhang, Songyang Department of Radiation Oncology University of California San FranciscoCA United States Department of Electrical and Computer Engineering Lehigh University BethlehemPA United States Department of Data Science New Jersey Institute of Technology NewarkNJ United States Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA United States

Recent efforts have been made to integrate self-supervised learning (SSL) with the framework of federated learning (FL). One unique challenge of federated self-supervised learning (FedSSL) is that the global objective of FedSSL usually does not equal the weighted sum of local SSL objectives. Consequently, conventional approaches, such as federated averaging (FedAvg), fail to precisely minimize the FedSSL global objective, often resulting in suboptimal performance, especially when data is non-i.i.d.. To fill this gap, we propose a provable FedSSL algorithm, named FedSC, based on the spectral contrastive objective. In FedSC, clients share correlation matrices of data representations in addition to model weights periodically, which enables inter-client contrast of data samples in addition to intra-client contrast and contraction, resulting in improved quality of data representations. Differential privacy (DP) protection is deployed to control the additional privacy leakage on local datasets when correlation matrices are shared. We also provide theoretical analysis on the convergence and extra privacy leakage. The experimental results validate the effectiveness of our proposed algorithm. Copyright 2024 by the author(s)

关键词： Federated learning

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