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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Mohan Kumar, S. Reddy, Elangovan Guruva Chandramohan, S. Prabagar, S. Latha, N. CMR University Karnataka Bangalore India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Tamil Nadu Kanchipuram India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bangalore India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

One of the most severe issues in the context of rapidly growing intelligent cities is the safety of pedestrians at urban crosswalks. To address this issue, the system developed an embedded vision system for real-time pedestrian detection and to enhance safety at crosswalks. A camera module captures real-time video, an image processor analyzes the captured data using a pedestrian detection algorithm, and a microcontroller manages the system's other functions. Our system can accurately identify pedestrians inside the crossing area using deep learning-based object detection models. If pedestrians are detected, an LED warning system alerts drivers to either slow down or stop, drastically decreasing the possibility of an accident. A real-time pedestrian count and any applicable safety warnings are also shown on an LCD screen. By connecting with existing systems, this smart technology may significantly improve the security of smart cities for pedestrians. Preliminary testing has demonstrated the technology has great potential for deployment in real-world urban environments. © 2023 IEEE.

关键词： computer Vision Intelligent Transportation Systems Pedestrian Detection Pedestrian Safety Smart Cities

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Driving Towards Efficiency: Adaptive Resource-Aware Clustered Federated Learning in Vehicular networks

Driving Towards Efficiency: Adaptive Resource-Aware Clustere...

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Communication and computer Networking Conference (MedComNet), Mediterranean

作者： Ahmad Khalil Majid Lotfian Delouee Victoria Degeler Tobias Mcuser Antonio Fernandez Anta Boris Koldehofe Multimedia Communications Lab Technical University of Darmstadt Germany Bernoulli Institute University of Groningen Netherlands Informatics Institute University of Amsterdam Netherlands IMDEA Networks Institute Spain Institute of Applied Computer Science Technische Universitat Ilmenau Germany

ISBN: (数字)9798350390476

ISBN: (纸本)9798350390483

Guaranteeing precise perception for au-tonomous driving systems in diverse driving conditions requires continuous improvement and training of the perception models. In vehicular networks, federated learning (FL) facilitates this by enabling model training without sharing raw sensory data. Based on federated learning, clustered federated learning reduces communication overhead and aligns well with the dynamic nature of these networks. However, current literature on this topic does not consider critical aspects, including (1) the correlation between perception performance and the networking overhead, (2) the limited data storage on vehicles, (3) the need for training with freshly captured data, and (4) the impact of data heterogeneity (non-IID) and varying traffic densities. To fill these research gaps, we introduce AR-CFL, an Adaptive Resource-aware Clustered Federated Learning framework. AR-CFL dynam-ically enhances system efficiency by adaptively adjusting the number of clusters and specific in-cluster participant selection strategies. Using AR-CFL, we systematically study the online detection model training scenario on non-IID data across varied conditions. The evaluation results highlight the robust detection performance exhibited by the trained model employing the clustered federated learning approach, despite the constraints posed by limited vehicle storage capacity. Furthermore, our study reveals that utilizing clustered feder-ated learning enhances the training efficiency of participating nodes by up to 25% and decreases cellular communication by 33 % in contrast to conventional federated learning methods.

关键词： Training Adaptation models Correlation Adaptive systems Federated learning Memory data models

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AI for Smart Agriculture – A Deep Learning Based Turmeric Leaf Disease Detection

AI for Smart Agriculture – A Deep Learning Based Turmeric L...

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International Conference on Communication and Electronics Systems (ICCES)

作者： R. Selvaraj M. S. Geetha Devasena T. Satheesh C. Sathishkumar Department of Computer Science and Engineering Dr.N.G.P. Institute of Technology Coimbatore Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Department of Artificial Intelligence and Data Science Dr.N.G.P. Institute of Technology Coimbatore Department of Computer Science and Engineering RVS Technical Campus-Coimbatore Coimbatore

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Plants is one of the major food sources for world population. In addition to that, some plants and their outcomes are used in food preparation. One of the plants which is widely used in foods and having huge medicinal value is turmeric. Curcuma long the botanically named spice turmeric has large anti-inflammatory properties, antioxidants, etc., the tropical perennial plant is cultivated worldwide. Diseases in plant leaves is one of the serious factors that affects the turmeric cultivation. Leaf disease reduces the production and it requires continuous monitoring to avoid total loss. Manual monitoring is challenging when the crop is planted in large fields. Due to industrial revolution and smart agriculture concepts, artificial intelligence- based disease detection procedure is presented in recent times. Thus, in this research work a deep learning-based turmeric leaf disease detection model is presented using AlexNet. The leaf disease classification model is experimented with our own dataset which includes diseases like Leaf Spot Disease, Leaf Blight, Leaf Rot, and Leaf Curl. The experimentation results provide the maximum accuracy of proposed leaf disease classification model as 95.5% which is better than traditional machine learning models.

关键词： Smart agriculture Support vector machines Deep learning Accuracy Production Manuals Monitoring Tuning Random forests Diseases

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Cross-Layer Optimization Strategies for Energy-Efficient and Reliable Machine-Type Communication in LPWANs

Cross-Layer Optimization Strategies for Energy-Efficient and...

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Emerging Research in Computational science (ICERCS), International Conference on

作者： M. Amanullah J. Manoranjini V. Helen Sathiya P. Prabbu Sankar Department of Computer Science & Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Engineering Chennai India Department of Computer Science & Engineering Panimalar Engineering College Chennai India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

In the massive Machine-Type Communication (mMTC), the exponential growth of Internet of Things (IoT) devices over Low Power Wide Area networks (LPWANs) presents substantial issues regarding energy efficiency and stability. The existing frameworks frequently fails in the the intricate relationships among the MAC-layer and physical parameters. To enhanced cross-layer evaluation methodology that integrates adaptive data rate (ADR) optimization, real-time energy consumption monitoring, and dynamic downlink message handling to address the problems. To maintain stability and performance with the network situations, the framework makes use of a Control-Lyapunov function. To enable effective resource allocation uses the Successive Convex Approximation that improve the effects of various transmission systems under quasi-static and dynamic channel conditions. To verify the cross-layer interactions and it impacts the energy efficiency, the proposed method begins with the data collection and it moves to payload size and packet length, are regularly changed to examines the affect throughput and energy consumption. The findings show the high-density node situations, energy consumption that reduced while retaining reliable communication. Furthermore, the results show the interdependencies among network parameters, highlighting the necessity of an integrated strategy. This framework helps to ensure the mMTC scenarios that optimized by improving the long-term viability and dependability of IoT networks.

关键词： Energy consumption Cross layer design Scientific computing Scalability Throughput Energy efficiency Reliability Internet of Things Optimization Low-power wide area networks

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Intelligent Deep Learning Based Multi-Retinal Disease Diagnosis and Classification Framework

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computers, Materials & Continua 2022年第12期73卷 5543-5557页

作者： Thavavel Vaiyapuri S.Srinivasan Mohamed Yacin Sikkandar T.S.Balaji Seifedine Kadry Maytham N.Meqdad Yunyoung Nam College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesSaveetha NagarThandalamChennai602105India Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesChennai602105India Department of Electronics and Communication Engineering College of Engineering&TechnologySRM Institute of Science and TechnologyVadapalani CampusChennai600026India Department of Applied Data Science Noroff University CollegeKristiansand4608Norway Department of Computer Techniques Engineering Al-Mustaqbal University CollegeHillahBabil51001Iraq Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

In past decades,retinal diseases have become more common and affect people of all age grounds over the *** examining retinal eye disease,an artificial intelligence(AI)based multilabel classification model is needed for automated *** analyze the retinal malady,the system proposes a multiclass and multi-label arrangement ***,the classification frameworks based on features are explicitly described by ophthalmologists under the application of domain knowledge,which tends to be time-consuming,vulnerable generalization ability,and unfeasible in massive ***,the automated diagnosis of multi-retinal diseases becomes essential,which can be solved by the deep learning(DL)*** this motivation,this paper presents an intelligent deep learningbased multi-retinal disease diagnosis(IDL-MRDD)framework using fundus *** proposed model aims to classify the color fundus images into different classes namely AMD,DR,Glaucoma,Hypertensive Retinopathy,Normal,Others,and Pathological ***,the artificial flora algorithm with Shannon’s function(AFA-SF)basedmulti-level thresholding technique is employed for image segmentation and thereby the infected regions can be properly *** addition,SqueezeNet based feature extractor is employed to generate a collection of feature ***,the stacked sparse Autoencoder(SSAE)model is applied as a classifier to distinguish the input images into distinct retinal *** efficacy of the IDL-MRDD technique is carried out on a benchmark multi-retinal disease dataset,comprising data instances from different *** experimental values pointed out the superior outcome over the existing techniques with the maximum accuracy of 0.963.

关键词： Multi-retinal disease computer aided diagnosis fundus images deep learning segmentation intelligent models

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Investigation of Time Series data Augmentation Methods for Improving Deep Learning Models in Conveyor Belt Load Classification

Investigation of Time Series Data Augmentation Methods for I...

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IEEE Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE)

作者： Tadas Žvirblis Armantas Pikšrys Damian Bzinkowski Artûras Kilikevičius Institute of Data Science and Digital Technologies Vilnius University Vilnius Lithuania Institute of Computer Science Vilnius University Vilnius Lithuania Kazimierz Pulaski University of Technology and Humanities in Radom Radom Poland Institute of Mechanical science Vilnius Gediminas Technical University Vilnius Lithuania

ISBN: (数字)9798331527761

ISBN: (纸本)9798331527778

This study explores time series data augmentation techniques to enhance the classification accuracy of deep learning models for conveyor belt tension signals. The study examines conveyor belt load data for weights of 0.5 kg, 1 kg, 2 kg, 3 kg, and 5 kg. Various time series data augmentations, including magnitude warping, Laplace noise, Gaussian noise, and uniform noise, are applied and evaluated. Additionally, the TimeVAE model is employed to synthesize new tension signals for the conveyor belt load datasets. The research investigates the impact of time series augmentation methods on the accuracy of deep learning models. A CNN-LSTM deep learning model is developed for classifying signal data from conveyor belt loads. Results indicate that the addition of Laplace noise yields the most significant improvement in classification accuracy, boosting the baseline accuracy (without augmentation) of 0.6 second signal data by 1.85% and of 1.2 second signal data by 0.59% to achieve an accuracy of 94.38%.

关键词： Deep learning Electrical engineering Accuracy Gaussian noise Conferences Time series analysis Belts

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Time Series data Augmentation Methods for Deep Learning Models in Conveyor Belt Load Classification

Time Series Data Augmentation Methods for Deep Learning Mode...

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Electrical, Electronic and Information sciences (eStream), Open Conference of

作者： Tadas Žvirblis Armantas Pikšrys Damian Bzinkowski Mirosław Rucki Artūras Kilikevičius Institute of Data Science and Digital Technologies Vilnius University Vilnius Lithuania Institute of Computer Science Vilnius University Vilnius Lithuania Kazimierz Pulaski University of Technology and Humanities in Radom Radom Poland Institute of Mechanical science Vilnius Gediminas Technical University Vilnius Lithuania

ISBN: (数字)9798350352412

ISBN: (纸本)9798350352429

This work explores time series data augmentation methods for deep learning and their application for conveyor belt tension signals. In this research, conveyor belt load data was collected and analyzed for five different weights: 0.5 kg, 1 kg, 2 kg, 3 kg, and 5 kg. The research includes applying time series data augmentations like Laplace noise, Gaussian noise, uniform noise, magnitude warping, and channel permutation. Furthermore, new conveyor belt tension signals were generated using a Time VAE model. This study investigates the influence of time series augmentation methods on the accuracy of deep learning models. A CNN-LSTM deep learning model capable of classifying conveyor belt signal data has been developed. The biggest positive impact on the classification accuracy was the addition of Laplace noise, which improved the baseline (no augmentation) accuracy by 4.07 % to 98.64 % for 0.8 second signal data.

关键词： Deep learning Training Gaussian noise Time series analysis Belts data augmentation Generative adversarial networks

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Multi-Class Abnormality Classification in Video Capsule Endoscopy Using Deep Learning

arXiv

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arXiv 2024年

作者： Samal, Arnav Batsyas, Ranya Department of Computer Science and Engineering National Institute of Technology Rourkela India Department of Artificial Intelligence and Data Sciences Indira Gandhi Delhi Technical University for Women Delhi India

This report outlines Team Seq2Cure’s deep learning approach for the Capsule Vision 2024 Challenge, leveraging an ensemble of convolutional neural networks (CNNs) and transformer-based architectures for multi-class abnormality classification in video capsule endoscopy frames. The dataset comprised over 50,000 frames from three public sources and one private dataset, labeled across 10 abnormality classes. To overcome the limitations of traditional CNNs in capturing global context, we integrated CNN and transformer models within a multi-model ensemble. Our approach achieved a balanced accuracy of 86.34% and a mean AUC-ROC score of 0.9908 on the validation set, earning our submission 5th place in the challenge. Code is available at this https URL. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Cloud-Based AI Solutions for Early Wound Infection Detection and Treatment Recommendations 4

Cloud-Based AI Solutions for Early Wound Infection Detection...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Rajasekaran, Mothiram Ranganathan, Chitra Sabapathy Manikandan, G. Bhuvaneswari, G. Ganeshbabu, T.R. Rajmohan, M. Pine Candle Way Saint AugustineFL United States Mphasis Corporation ChandlerAZ United States Rmk Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Electronics and Communication Tamil Nadu Namakkal India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

Wound infections are a major problem for healthcare systems worldwide. Infections cause patients to remain in the hospital longer., which increases healthcare expenses and negatively impacts their health outcomes. For patient care to be successful, it is crucial to identify these illnesses quickly and treat them appropriately. Recent developments in cloud computing and artificial intelligence (AI) have encouraged optimism for the potential of these technologies to improve wound infection diagnosis and treatment. Within healthcare systems, this research outlines a new method for early wound infection diagnosis and treatment recommendations generation using cloud-based AI algorithms. The proposed method employs machine learning to appropriately detect early indications of infection by analyzing a variety of patient data inputs, such as wound features, vital signs, laboratory findings, and medical history. Also, healthcare practitioners may benefit from real-time monitoring and decision assistance due to cloud-based architecture's easy interaction with existing electronic health record (HER) systems. The system is designed with a dynamic feedback loop that can be updated with fresh data inputs to make it more accurate and flexible. The proposed cloud-based AI system has been thoroughly tested and proven reliable in improving the early diagnosis and treatment of wound infections. This has led to better patient outcomes and more efficient use of healthcare resources. Key challenges include data privacy issues, the integration of diverse technologies, the reliability of precise forecasts, and maintaining system resiliency in changing environments. © 2024 IEEE.

关键词： Electronic health record

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Cuttlefish Algorithm-Based Deep Learning Model to Predict the Missing data in Healthcare Application

Cuttlefish Algorithm-Based Deep Learning Model to Predict th...

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International Conference on data Analytics and Management, ICDAM 2023

作者： Sasi Kumar, A. Rajesh Kumar, T. Balamanigandan, R. Meganathan, R. Karwa, Roshan Mahaveerakannan, R. Inurture Education Solutions Pvt. Ltd. Bangalore India Department of Cloud Technology and Data Science Institute of Engineering and Technology Srinivas University Srinivas Nagar Mukka Surathkal Mangalore India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai602105 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of CSE Prof Ram Meghe Institute of Technology and Research Badnera-Amravati India

ISBN: (纸本)9789819965526

Countless sensors embedded in IoT devices produce an ocean of data. The quality of IoT services depends on this information;hence, its accuracy is critical. Unfortunately, noise, collision, unreliable network connectivity, failed equipment, and manual system shutdown are all common causes of missing and partial values in IoT data. The use of predictive analytics to EMRs, which include healthcare data, inspired this effort. Predictive models based on such data are inherently flawed because they include noise, missing data, and inconsistencies between classes of interest. We argue for the need to create specialised methods of data preprocessing and categorisation since typical data mining approaches frequently provide unsatisfactory performance measurements. To build the long-term predictor in the attendance of significant nonlinearity and noise in the missing data, in this study, each of us provides a bidirectional self-attentive encoding and decoding architecture (BEDA). Prior to any further processing, the raw data is subjected to a wavelet threshold filter for denoising purposes. The weights are optimised via a mapping-based cuttlefish optimisation technique, and the bidirectional long short-term-memory is chosen as the basic device for isolating temporal and serial features (MCFA). The prediction performance is then enhanced by adding the multi-head self-attention device into the encoder–decoder architecture. It demonstrates that the projected technique yields quick, accurate, and resilient classification consequences by comparing them to results from other models on public data sets with unbalanced classes and missing values in health-related applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： data mining

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