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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： P. Suthanthiradevi G Revathy P Sathish Kumar B Karthikeyan Lakshmi.V Muthu Data Science and Business Systems School of Computing SRM institute of Science and Technology India Department of Computer science and engineering Vels Institute of science Technology and Advanced studies India Computer and Communication Engineering Rajalakshmi institute of technology India School of Computing SASTRA Deemed University India Department of Computer Science and Engineering St. Joseph’s Institute of Technology India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

With the recent advent of technology, social networks are accessible 24/7 using mobile devices. During covid-19 pandemic the propagation of misinformation are mostly related to the disease, its cures and prevention. With Twitter as the medium this information was spreaded to millions of people who lack domain specific knowledge. This can lead to bloster fear and direct damage to the people. This research focuses on identifying the veracity of the Twitter posts behind pandemic situation. The authors have proposed a Transformer Model using Bat Algorithm for identifying the fake news. This model is trained and evaluated using infodemic metadata. Our experimental results shows by which proposed model accurately detected the fake news and achieved 96.5% of f1-score.

关键词： COVID-19 Social networking (online) Pandemics Blogs Predictive models Transformers Prediction algorithms

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Authentication in the Internet of Vehicles using Blockchain Technology: Review 3

Authentication in the Internet of Vehicles using Blockchain ...

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3rd International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022

作者： Sarika, Sabavath Prabakeran, S. SRM Institute of Science and Technology Dept. of Computer Science and Engineering TamilNadu India School of Computing Dept. of Networking and Comunications SRM Institute of Science and Technology TamilNadu India

ISBN: (纸本)9781665462754

The Internet of Vehicles (IoV) aims to make transportation autonomous, safe, fast, and efficient while reducing resource waste and harmful environmental impacts. To happen this, IoV interconnects pedestrians, cars, and components of urban infrastructure. It uses numerous sensors, software, in-built hardware, and diverse connections to enable reliable and continuous communication. Therefore, they are prone to attacks that may lead to life-endangering consequences, so it is vital to develop security mechanisms to detect such attacks against IoV. Blockchain technology is one of the emerging technologies that have been used recently in Cybersecurity due to its robustness and integrity-preserving properties. Blockchain technology's immutability, tamper-proof, and security by design make it a promising technology for IoV. This study is a comprehension of existing work of securing IoV using blockchain techniques. © 2022 IEEE.

关键词： Vehicles

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3D Based CNN for Improved Segmentation from the Hyperspectral Images

3D Based CNN for Improved Segmentation from the Hyperspectra...

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computer science and Emerging Technologies (CSET), International Conference on

作者： Narmadha T Arockia Raj A P Saravanan A Suresh Kumar Ramesh Sekaran Gowri Shankar J School of Computer Science and Engineering Jain Deemed to be University Bengaluru Department of Computer Science and Engineering - Data Science Madanapalle Institute of Technology and Science Madanapalle India School of Computing Science and Engineering Galgotias University Delhi NCR

In this paper, we develop a 3D based CNN for Improved Segmentation of Paddy Fields from the HSI. Within the scope of this research, we will investigate a unique deep learning model, specifically 3D-CNN. In order to evaluate the validity of the model, it is applied to three separate datasets, each of which is then subjected to one of four distinct dimensionality reduction strategies. The purpose of this is to determine which of these four models is the most effective at reducing the number of dimensions. According to the findings, the 3D-CNN model is superior to both the AlexNet and CNN models in terms of the accuracy of its segmentations. In addition to this, the findings demonstrate that the 3D-CNN models perform better than the other approaches in terms of segmentation accuracy with a smaller number of datasets.

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Beyond X-ray: Deep Learning Solutions for Gastrointestinal Bleeding

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Procedia computer science 2025年 259卷 1306-1315页

作者： S Yougesh Kumar Amruth Ganesh J M Tejasvin Vimal S Abhishek Anjali T Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri India Rolls-Royce Inc Bangalore India

Gastrointestinal (GI) bleeding is a critical medical condition that has a substantial impact and poses significant challenges for the global healthcare system due to its varying severity. GI bleeding can occur ranging from mild, self-limiting cases to severe, poten- tially fatal events, which emphasizes the need for timely and accurate diagnosis. Traditional feature extraction techniques, such as color thresholding and Scale-Invariant Feature Transform (SIFT), combined with classifiers like Support Vector Machine (SVM), often fail to detect subtle patterns within complex gastrointestinal images, especially those images captured from wireless capsule endoscopy (WCE).This work demonstrates the distinct benefits of deep learning techniques over conventional methods, especially their capacity to identify minute bleeding patterns in intricate gastrointestinal pictures. Better performance is shown by models like EfficientNetB0, which are computationally efficient, and the custom CNN, which is designed for binary classification. These discoveries aid in the creation of automated, trustworthy diagnostic instruments for use in real-time healthcare settings. Especially, In this research, we introduce the most sought-after deep learning methods namely Custom Convolutional Neural Network (CNN), EfficientNetB0, ResNet50, and InceptionV3. These models and their working have been thoroughly understood and evaluated across various parameters such as precision, accuracy, recall F1-score, etc., These algorithms have proven capability in identifying patterns that traditional methods tend to miss out. The results prove the potential of the models, particularly EfficientNetB0 and custom CNN, which offered superior performance, efficiency and reliability in tasks involving the classification of medical images. Further study of this work can be carried out, optimizing model accuracy, robustness, and computational efficiency by including varied and more diversified datasets. Parallely, exp

关键词： Custom Convolutional Neural Network Deep Learning Efficient Convolutional Neural Network (EfficientNetB0) InceptionV3 Residual Network50 (ResNet50) Wireless Capsule Endoscopy

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2D Wound Image Segmentation Using Deep Learning Algorithms

2D Wound Image Segmentation Using Deep Learning Algorithms

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Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： Soubraylu Sivakumar V. S. V. Abhiram P. Rama Vikram Kallakrinda T. Rajesh Kumar P. Saravanan Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

The purpose of wound segmentation is to accurately identify and delineate wound areas in images, enabling precise analysis of metrics like size, shape, and spread. This aids healthcare professionals in assessing wound severity and monitoring treatment progress. This research focuses on developing an automated wound image segmentation tool using the Deep learning Algorithms. The objective is to enhance wound assessment accuracy by precisely identifying and segmenting wound areas in digital images. Three models are employed to find the effectiveness in segmenting the wound which includes U- Net, DeepLabV3+ and Mask-CNN. The deep learning models are trained on a massive dataset of wound pictures and masks; they then provide segmented outputs, which are examined for wound characteristics and severity. The U-Net model has obtained better performance in segmenting the wound with an accuracy of $\mathbf{9 2. 3} \%$. This work seeks to incorporate advanced capabilities into a user-friendly interface for practical use in clinical settings. Initial results demonstrate promising segmentation accuracy, with plans for further enhancements in future.

关键词： Deep learning Image segmentation Visualization Accuracy Shape Standardization Medical services Wounds Object recognition Monitoring

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Cotton leaf disease prediction using VGG16 and RESNET50 6

Cotton leaf disease prediction using VGG16 and RESNET50

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6th Parul International Conference on engineering and Technology, PICET 2024

作者： Srivastava, Animesh Rawat, Bhupender Singh Kumar, Gautam Bhatnagar, Vivek Garg, Navin Department of Computer Science & Engineering Graphic Era Hill University Dehradun India College of Smart Computing COER University Roorkee India School of Computing Graphic Era Hill University Haldwani Campus Uttarakhand Nainital India Mullana Ambala India Uttarakhand Dehradun India

ISBN: (纸本)9798350369748

The farming business is a vital component of every economy, and its success is directly proportional to the yields of individual farmers. The micro-level illnesses that manifest themselves throughout the development of a plant often have an effect on the production of the plant under consideration. The presence of a cotton plant disease is a significant cause for worry because of the detrimental impact it has on crop output. Diagnostic procedures for these disorders need to be carried out on a consistent and accurate basis. Without this, there is a possibility that its output may suffer a major decline, which will eventually have an impact on the farmer economy. Diagnoses made manually take a significant amount of time. Consequently, this study is to make use of the advantages of deep learning to automate the diagnosis of disease in cotton plants. This study investigates the impact of deep learning on a selection of pre-trained models, including VGG-16 and ResNet50, and also proposes a hybrid method to predict cotton leaf disease. According to the examination of the results, the proposed model scored the greatest accuracy of 99.25% when compared to other CNN architectures that had been trained. Several criteria, including Precision, Recall, and F1 score, are used to measure the performance of the model. © 2024 IEEE.

关键词： Plant diseases

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Securing Smart Homes Using Hybrid Stacking Ensemble Deep Learning-Enabled Framework 2

Securing Smart Homes Using Hybrid Stacking Ensemble Deep Lea...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Moubayed, Abdallah Injadat, MohammadNoor Abdellatif Mohamed, Tamer Mohamed Almatarneh, Sattam Al-Mashagbeh, Malak Aljaidi, Mohammad School of Computing and Augmented Intelligence Arizona State University GilbertAZ United States Lebanese American University Electrical and Computer Engineering Department Byblos Lebanon Western University Electrical and Computer Engineering Department LondonON Canada Zarqa University Data Science and Artificial Intelligence Department Zarqa Jordan School of Engineering Applied Science and Technology Canadian University Dubai Dubai United Arab Emirates Zarqa University Cyber Security Department Zarqa Jordan Zarqa University Computer Science Department Zarqa Jordan

ISBN: (纸本)9798350373363

The added deployment of Internet-of-Things (IoT) devices in various environments has led to people increasingly adding this technology to their homes to make them more comfortable and have a more convenient living environment. Despite the promise and the growth potential of the smart home market, one main concern is the security and privacy of smart home devices. This is because smart home devices equipped with IoT technology are prone to various network-based attacks. With smart home devices having limited computation and memory capabilities, many IoT manufacturers often overlook the security aspect by focusing more on the functionality. To address this issue, the concepts of machine learning (ML) and deep learning (DL) have emerged as viable and promising solutions. To that end, this paper proposes a hybrid stacking ensemble DL-enabled framework that combines both classical ML models with sophisticated DL models. The goal is to make use of the simplicity and interpretability of the classical ML models with the superior time-dependent patterns that DL models can detect. The proposed framework's performance is evaluated using multiple datasets representing different smart home devices. Experimental results show that the proposed framework achieved better detection accuracy, precision, recall, and F-score. © 2023 IEEE.

关键词： Intrusion detection

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Surveying the Landscape of Image Captioning Evaluation: A Comprehensive Taxonomy, Trends and Metrics Analysis

arXiv

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arXiv 2024年

作者： Berger, Uri Stanovsky, Gabriel Abend, Omri Frermann, Lea School of Computer Science and Engineering The Hebrew University of Jerusalem School of Computing and Information Systems University of Melbourne Australia

The task of image captioning has recently been gaining popularity, and with it the complex task of evaluating the quality of image captioning models. In this work, we present the first survey and taxonomy of over 70 different image captioning metrics and their usage in hundreds of papers, specifically designed to help users select the most suitable metric for their needs. We find that despite the diversity of proposed metrics, the vast majority of studies rely on only five popular metrics, which we show to be weakly correlated with human ratings. We hypothesize that combining a diverse set of metrics can enhance correlation with human ratings. As an initial step, we demonstrate that a linear regression-based ensemble method, which we call ENSEMBEVAL, trained on one human ratings dataset, achieves improved correlation across five additional datasets, showing there is a lot of room for improvement by leveraging a diverse set of metrics.1 Copyright © 2024, The Authors. All rights reserved.

关键词： Taxonomies

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Introduction to the Special Issue on computer-Assisted Imaging Processing and Machine Learning Applications on Diagnosis of Chest Radiograph

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computer Modeling in engineering & sciences 2022年第9期132卷 707-709页

作者： Shuihua Wang Zheng Zhang Yuankai Huo School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK School of Computer Science and Technology Harbin Institute of TechnologyHarbin150006China Electrical and Computer Engineering University of VanderbiltNashville37240USA

The chest radiograph has been one of the most frequently performed radiological investigation *** clinical medicine,the chest radiograph can provide technical basis and scientific instruction to recognize a series of thoracic diseases(such as Atelectasis,Nodule,and Pneumonia,etc.).Importantly,it is of paramount importance for clinical screening,diagnosis,treatment planning,and efficacy ***,it remains challenging for automated chest radiograph diagnosis and interpretation at the level of an experienced *** recent years,many studies on biomedical image processing have advanced rapidly with the development of artificial intelligence especially deep learning techniques and *** to build an efficient and accurate deep learning model for automatic chest radiograph processing is an important scientific problem that needs to be solved.

关键词： diagnosis clinical artificial

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Deep Learning-Powered Camouflaged Object Recognition Model

Deep Learning-Powered Camouflaged Object Recognition Model

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Riya Tomar Naman Chauhan Chhavi Chauhan Yash Tomar Soumyendra Singh Nishant Chauhan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Computer Science and Engineering CMR University Bengaluru India Department of Mathematics Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Noida India

This research paper delves into the intersection of advanced technology and military strategies, specifically focusing on the imperative task of camouflaged object detection within the Indian Paramilitary forces. Leveraging cutting-edge DL Frameworks, like CNNs, Faster RCNN, and YOLOv8, our methodology aims to enhance the precision and efficiency of identifying concealed weapons and camouflaged objects in real-world scenarios. The collaboration with the Indian Paramilitary has enabled the creation of a dataset tailored to the challenges faced on the ground, reflecting a commitment to addressing contemporary threats. The study explores the global positioning of India in military technology, emphasizing the nation’s role as a key player in adopting sophisticated solutions for defense. Beyond technological innovation, this research underscores a noble cause the protection of lives and the fortification of national security. The fusion of advanced technology and a profound sense of duty forms the core of this work, exemplifying a commitment to the greater good.

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