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Blockchain and IoT-Based Medical Care Utilizing Secure and Protection Safeguarding Watermark

Blockchain and IoT-Based Medical Care Utilizing Secure and P...

引用

2023 International Conference on Smart Systems for Applications in Electrical Sciences, ICSSES 2023

作者： Shruthi, K. Poornima, A.S. Siddaganga Institute of Technology Dept. of Computer Science and Engineering Tumakuru India

ISBN: (纸本)9798350347296

Patient clinical information is critical information for a well-being application. Clinical information regularly assumes a significant part in illness examination and is ensnared in persuading values for instructing symptomatic techniques and altering analytic outcomes. Privacy of patient well-being reports is more significant, furthermore, subsequently, specialists have considered numerous safety efforts, counting access control, classification, and uprightness of these records. To take care of the security issue, this paper presents a high-level arrangement in light of changing over clinical information into minimized size, as a watermark before being sent on Blockchain. Along these lines, difficult tasks will be securely performed off-chain, like gathering and preprocessing patient information, while accommodation of this information is finished on an hourly premise chain. We suggest an information stream design that consolidates the IoT with blockchain using the Ethereum platform, utilizing brilliant agreements to access and store information against many assaults. The proposed model is successful in sharing and overseeing patient electronic well-being information. The tests need to be carried out on Ethereum chain arrangements on laptops, and desktops. © 2023 IEEE.

关键词： Blockchain

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Operating System Command Execution Using Voice Command 3

Operating System Command Execution Using Voice Command

引用

3rd IEEE Asian Conference on Innovation in technology, ASIANCON 2023

作者： Singh, Paras Nath Navaneetha, M. Tijare, Poonam Vijay Cmr Institute of Technology Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350302288

Recent years have seen a considerable increase in the use of voice commands as a result of the growing popularity of smart gadgets, virtual assistants, and other related technologies. In this study, we investigate the viability of voice commands for operating system commands. In this paper a system is proposed and demonstrated that enables voice commands to be recognized by the operating system and executed according to parameters. This way it is offering a fresh and more usable voice assistant to carry out typical computer chores. Python tools are used to go over how the system is put into practice, including how speech recognition and natural language processing are used, and how well it executes in terms of accuracy, speed, and usefulness. The main impact factor of this paper is to provide voice assistance to disadvantageous people whose hands and fingers are not working well. © 2023 IEEE.

关键词： Speech recognition

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Enhanced Unimodal Continuous Authentication Architecture on Smartphones for User Identification through Behavioral Biometrics 2

Enhanced Unimodal Continuous Authentication Architecture on ...

引用

2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Rayani, Praveen Kumar Changder, Suvamoy National Institute of Technology Dept. of Computer Science and Engineering Durgapur India

ISBN: (纸本)9798350347982

Nowadays intruders are smart and intelligent to initiate various attacks on the smartphone's entry-point au- thentication methods to access the user's stored information on the smartphone. Accessing users' confidential information rather than personal information by attackers can create a monetary or non-monetary gain. Thus, continuous authentication was proposed on smartphones to recognize the user from the entry point to the end of the session. Even though, we noticed that earlier works on unimodal-based continuous authentication were limited to specific applications on smartphones. This can create a vulnerable gap to access the users' information on the smartphone. Thus, we propose an enhanced unimodal-based continuous authentication architecture for user identification on the smartphone. The proposed approach instantaneously chooses the unimodal behavioral biometrics based on the foreground application of the device and performs the user identification through a machine learning model. The experimental results pro- posed approach show better results than the existing literature. © 2023 IEEE.

关键词： Smartphones

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Identification of Malignant Cells Using Convolutional Neural Network 1

Identification of Malignant Cells Using Convolutional Neural...

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1st International Conference on Circuits, Power, and Intelligent Systems, CCPIS 2023

作者： Sahu, Rekha Dash, Akshaya Kumar Silicon Institute of Technology Dept. of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350315769

Breast cancer has a high impact as a leading cause of death. Doctors face problems with the quick diagnosis of malignant tumors, which would help in the prevention of cancer at an early stage. Automatic detection of malignant cells is a life-saving boon. The deep learning approach extensively manipulates the data non-linearly to obtain suitable information. In this study, a one-dimensional convolutional neural network is implemented on a tumor dataset to detect malignant and benign tumors. The deep learning approach has achieved 99.9% classification accuracy with 45 epochs and 98.8% ROC-AUC. More experiments are needed on new tumor datasets to study the performance of the one-dimensional convolutional neural network approach in identifying malignant cells. © 2023 IEEE.

关键词： Tumors

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Exploring Deep Learning Techniques for Automated Cataract Detection in Digital Camera Imagery

Exploring Deep Learning Techniques for Automated Cataract De...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Kamepalli, Sujatha Veeranjaneyulu, N. Bodapati, Jyostna Devi Vignan's Foundation For Science Technology And Research Dept. Department Of Information Technology And Computer Application Guntur India Vignan's Foundation For Science Technology And Research Dept. Department Of Advnced Computer Science And Engineering Guntur India

ISBN: (纸本)9798350371567

Cataracts, a prevalent eye condition impacting vision, demand accurate and prompt identification for effective risk reduction and blindness prevention. Traditional methods of cataract diagnosis rely heavily on manual examination by ophthalmologists, which can be time-consuming and subject to human error. With the advent of digital imaging technology, there is an opportunity to enhance the efficiency of cataract detection. The deep neural network models VGG19 and ResNet-50 are recommended in this paper for automatic cataract identification on digital camera images. To optimize network performance, adjustments to loss and activation functions were made, incorporating minimal kernels, reduced training parameters, and layers. This resulted in a ResNet-50 model with significantly decreased computational cost and average operating time compared to the cutting-edge models. The proposed ResNet 50 model achieved an average accuracy of 93.41%, outperforming the VGG 19 model, which had an accuracy of 91.13%. This research contributes to the evolution of cataract detection methodologies, emphasizing the potential of deep learning models, particularly ResNet-50, in improving diagnostic accuracy and operational efficiency for enhanced eye health outcomes. © 2024 IEEE.

关键词： Digital cameras

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Visual Coherence Loss for Coherent and Visually Grounded Story Generation 8

Visual Coherence Loss for Coherent and Visually Grounded Sto...

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8th Workshop on Representation Learning for NLP, RepL4NLP 2023, co-located with ACL 2023

作者： Hong, Xudong Demberg, Vera Sayeed, Asad Zheng, Qiankun Schiele, Bernt Dept. of Computer Vision and Machine Learning MPI Informatics Germany Dept. of Language Science and Technology Dept. of Computer Science Saarland University Germany Dept. of Philosophy Linguistics and Theory of Science University of Gothenburg Sweden Saarland Informatics Campus Saarbrücken Germany

ISBN: (纸本)9781959429777

Local coherence is essential for text generation models. We identify two important aspects of local coherence within the visual storytelling task: (1) the model needs to represent re-occurrences of characters within the image sequence in order to mention them correctly in the story;(2) character representations should enable us to find instances of the same characters and distinguish different characters. In this paper, we propose a loss function inspired by a linguistic theory of coherence for learning image sequence representations. We further propose combining features from an object detector and a face detector to construct stronger character features. To evaluate visual grounding that current reference-based metrics do not measure, we propose a character matching metric to check whether the models generate referring expressions correctly for characters in input image sequences. Experiments on a visual story generation dataset show that our proposed features and loss function are effective for generating more coherent and visually grounded stories. Our code is available at https://***/vwprompt/vcl. © 2023 Association for Computational Linguistics.

关键词： Electric grounding

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An Evaluation and Comparison for Phishing Attack Detection using Machine Learning Approaches 3

An Evaluation and Comparison for Phishing Attack Detection u...

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3rd International Conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2024

作者： Sharma, Ajeet Kumar Anushree Rakesh, Nitin Verma, Pawan Kumar Sharda University School of Engineering and Technology Dept. of Computer Science & Engineering Uttar Pradesh Greater Noida India GLA University Dept. of Computer Engineering & Applications Uttar Pradesh Mathura India Nagpur Campus Pune India

ISBN: (纸本)9798350309409

The persistent and evolving threat of phishing attacks demands effective and adaptive detection techniques. This research paper presents a comprehensive evaluation and comparison of various machine learning approaches to detect phishing attacks. We investigated five prominent algorithms: Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Naive Bayes, and Extreme Gradient Boosting (XGBoost), to determine their efficacy in identifying phishing activities. Our methodology involved a systematic analysis using a large dataset of phishing and legitimate URLs, where each model was trained, tested, and validated to ensure robustness and reliability. The performance of each algorithm was assessed based on accuracy, precision, recall, and F1 score. Among the evaluated models, XGBoost demonstrated superior performance, achieving an exceptional accuracy of 99.75%. This result underscores the potential of XGBoost in phishing attack detection, offering a promising tool for cybersecurity applications. © 2024 IEEE.

关键词： Cybersecurity

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Efficiency Improvement Mechanism Analysis of Sidewall Passivation GaN based Micro-LEDs by Atomic Layer Deposition

Efficiency Improvement Mechanism Analysis of Sidewall Passiv...

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International Symposium, Seminar, and Exhibition, Display Week 2024

作者： Zhanghu, Mengyuan Liu, Yibo Liu, Zhaojun Dept. of Electric and Electronic Engineering Southern University of Science and Technology Shenzhen China Dept. of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Sidewall passivation using atomic layer deposition (ALD) can efficiently improve external quantum efficiency (EQE). Such treatment increases EQE, a combination of internal quantum efficiency (IQE) and light extraction efficiency (LEE), primarily by curtailing surface recombination according to the ABC model. Determined by the ratio of radiative recombination rate to total recombination rate, the IQE of an LED holds a significant correlation with the diode's ideality factor in the Shockley model. In this paper, we analysis the mechanism of ALD sidewall passivation enhancing device performance in detail from the aspects of electrical performance improvement and optical performance improvement. © 2024, John Wiley and Sons Inc. All rights reserved.

关键词： Gallium nitride

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A Deep Learning Based Image Processing Technique for Early Lung Cancer Prediction

A Deep Learning Based Image Processing Technique for Early L...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Tasnim, Nowshin Noor, Kazi Rifah Islam, Mursalina Huda, Mohammad Nurul Sarker, Iqbal H. Chittagong University of Engineering & Technology Dept. of Computer Science & Engineering Chittagong4349 Bangladesh United International University Dept. of Computer Science & Engineering Dhaka1212 Bangladesh Edith Cowan University PerthWA6027 Australia

ISBN: (纸本)9798350372229

Lung cancer is the primary cause of cancer mor-tality all over the world due to the increase of tobacco consumption, and industrialization in developing nations. As the early-stage diagnosis can reduce the mortality rate significantly, early detection with the availability of high-tech Medical facilities is highly necessary. In this research, we used deep learning (DL) methods initially on patient's 1190 CT scan images from the Kaggle IQ-OTH lung cancer dataset, and after significant image preprocessing steps we found augmented images including normal, malignant, and benign cases to identify high-risk in-dividuals to detect lung cancer and also predict the malignancy and thus, taking early actions to prevent long-term consequences. A thorough performance comparison between several classifiers, including the conventional CNN, Resnet50, and InceptionV3, has been presented. Here, affine transformation, gaussian noise, and other rigorous image preprocessing techniques are used. The contribution obtained a 98% validation accuracy while reducing the model's complexity with the previous preprocessing stage. The comparison method shows that the suggested preprocessing method yields a higher F1 score value of 97%, validating our suggested methodology. © 2024 IEEE.

关键词： CT scans deep learning healthcare analytics image processing lung cancer prediction

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'Hastabhashya'-Interpretation of Indian Sign Language using SqueezeNet and YoloV3

'Hastabhashya'-Interpretation of Indian Sign Language using ...

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2023 International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2023

作者： Anbazhagan, Adithya Deeksha, D.V. Kumar Reddy, Ambavaram Tharun Singh, Paras Nath Cmr Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350314878

The primary means of communication for persons who are hearing and voice-impaired is sign language, which is sadly not understood by a significant section of the public. To solve this problem, a naïve method is offered that converts sign language symbols into comparable plain text alphabets almost instantly using image processing and deep learning principles. Beyond providing healthcare for the deaf and dumb community, this technology has the potential to evolve into a general-purpose symbol-to-text converter with a variety of uses. The difficulties with character classification for Indian Sign Language (ISL) is tried to solve in this paper and work. Even though American Sign Language (ASL) has been the subject of much research, this cannot be said of Indian Sign Language (ISL) due to the absence of standardized datasets, obscured aspects, and regional variances in the language. ISL research has been hampered by these issues, but our initiative aims to fix them. © 2023 IEEE.

关键词： Object detection

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