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Quick Response Codes for Bone Fracture Diagnosis Based on Deep Learning

Quick Response Codes for Bone Fracture Diagnosis Based on De...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Arikaran, N. Sathishkumar, R. Balaji, S. Ashwin, G. Mohamed Salman, M. Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering Puducherry India

ISBN: (纸本)9798350315127

Orthopaedic treatment and a precise diagnosis of bone fractures are essential components of contemporary healthcare. This study introduces a novel method for streamlining and improving the analyses of bone fractures through the use of QR codes and object detection methods. In this study, we present a novel technique to increase the speed and precision of bone fracture diagnosis by integrating computer vision skills with QR code technology. The workflow of the system starts with the gathering of digital medical pictures, such as X-rays or CT scans, which are afterwards tagged with QR codes that are patient specific. To locate fractures in these photos and to identify them, a deep learning-based on fracture detection model is used. The method is also well suited for telemedicine applications, enabling distant specialists to offer quick assessments. Additionally, the gathered information can be used for orthopaedic care research and initiatives to enhance quality of care. We demonstrate the system's efficacy and emphasize its potential to revolutionize orthopaedics through thorough examination and case studies. We think that the combination of object detection with QR codes can greatly improve the speed and precision of bone fracture diagnosis, leading to better patient outcomes overall. With a direct bearing on patient care and the medical community's capacity to offer prompt and accurate diagnoses in orthopaedic patients, this research represents a significant advancement in the merging of medical imaging technology with artificial intelligence. © 2023 IEEE.

关键词： Orthopedics

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Decentralized Blockchain based Online Voting System with Biometric Authentication 8

Decentralized Blockchain based Online Voting System with Bio...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Waniya, Joel Sanjeev Palmer, Matthew Kathrine, G Jaspher W. Basil Xavier, S. Aarthi, Shilpa Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350396638

Online voting system using blockchain with integration of biometric authentication have emerged as a promising solution to address the challenges of online voting such as preventing fraud and maintaining the integrity of the voting results. Blockchain technology provides a high level of security and transparency, making it difficult for hackers to manipulate or alter the voting results. Biometric authentication, on the other hand, ensures that only eligible voters can cast their votes, preventing double voting and other forms of fraud. Despite the potential benefits of using blockchain technology and biometric authentication for online voting, there are still challenges that need to be addressed. These challenges include ensuring that the system is accessible and user-friendly to all voters, protecting against cyberattacks, ensuring compliance with legal and regulatory requirements, and maintaining voter privacy. The objective of this proposed online voting system using blockchain with biometric authentication is to create a secure and reliable voting system that is accessible and user-friendly to all voters, while maintaining the privacy and integrity of the voting process. The system will use blockchain technology to create a tamper-proof and auditable record of the votes, and biometric authentication to ensure that only eligible voters can cast their votes. The system will also incorporate additional security measures such as encryption and multifactor authentication to protect against cyberattacks and ensure compliance with legal and regulatory requirements. By addressing these challenges, this proposed online voting system using blockchain with biometric authentication has the potential to increase voter confidence and trust in the election process. © 2023 IEEE.

关键词： Cryptography

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Securing Electronics Gadgets using Blockchain based Framework 2

Securing Electronics Gadgets using Blockchain based Framewor...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Prakash, P. N. Senthil Lanitha, B. Sundaram, Karthic Vellore Institute of Technology School of Computer Science and Engineering Chennai India Karpagam Academy of Higher Education Department of Artificial Intelligence and Data Science Coimbatore India KPR Institute of Engineering and Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798331534950

The usage of electronic gadgets has grown dramatically in the current digital era. Devices like mobile phones and smart watches are unavoidable and has ingrained themselves into every person's daily routine. Almost everyone owns at least one portable electronic device and these devices are usually expensive. Due to this widespread use, there is also an increase in the number of lost or stolen cases reported. In a recent study, it has been revealed that the number of devices lost is mostly because of misplacing in public places like home, office, public transport. Such incidents will not only cause financial loss but it may also lead to data breaches stored in the portable devices. Hence there is a need for a secure eco-system which helps to identify the genuine owners of electronic devices. A blockchain-based framework is deployed on an Ethereum-based network and adopts the Proof of Stake (PoS) mechanism to protecting gadgets. Further, it is designed using the smart contract defined using solidity programming language. The system enables the owner's details to be recorded upon purchase of a device. The complete specification details of the devices along with owner details is contained in a block and then appended to network. Later on, if the owner wants to resale the device, then the transfer of ownership information is stored in a block and added to the network. The proposed system provides an appropriate interface to check the genuine owner. The proposed framework has been implemented using solidity language, Remix IDE and Sepolia testnet. Obtained results highlights the suitability of the method and its performance in terms of gas consumption and execution latency. © 2024 IEEE.

关键词： Ethereum

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A Novel Knowledge Semantic Video game Prototype to treat patients with Absence Epilepsy 5

A Novel Knowledge Semantic Video game Prototype to treat pat...

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Kumar, Srikoti Rishi Reshmitha, Uppala Sarath, Tenneti Sai Challa, Tanmayi Akhil, Karnam Vnr Vignana Jyothi Institute Of Engineering And Technology Department Of Computer Science And Business Systems Hyderabad India Vnr Vignana Jyothi Institute Of Engineering And Technology Department Of Computer Sience And Engineering Hyderabad India

ISBN: (纸本)9798350330861

In recent times, children are increasingly experiencing anxiety and depression, nervousness, emotional distress, and a susceptibility to conditions such as Absence Epilepsy, Juvenile Myoclonic Epilepsy (JME), and Migraine. Absence Epilepsy is one of the major challenging issues of current neurological research. Absence Epilepsy is a common form of childhood epilepsy that can have significant impact on children, leading problems to memory loss, lack of cognitive ability, attention deficit hyperactivity disorder (ADHD). Recent studies of neurological research highlight that specific video games have positive effects on cognitive and motor functions in children. The proposed research presents a novel approach to create a video game prototype exclusively for children with Absence Epilepsy. Concepts of semantic web are used to build the various relations among depression, anxiety, Absence Epilepsy to understand its cause, impact on human brain and memory. Additionally, the prototype aims to assess the game's usability to ensure it is accessible and effective for its intended audience. The research methodology involves a multidisciplinary approach, drawing from neurology, pediatrics, Game designers, and Semantic Web technologies. The initial phase involves an in-depth analysis of the cognitive and motor challenges faced by children with Absence Epilepsy, enabling the integration of suitable therapeutic elements into the game mechanics. Subsequently, Semantic Web technologies are utilized to construct an ontology capturing crucial information about the player's condition, medical history, and game play preferences. In conclusion, the research bridges the gap between pediatric healthcare and therapeutic gaming. The innovative video game prototype promises in enhancing the lives of children with Absence Epilepsy, enhancing their cognitive abilities, memory, and cognitive attention, and paving the way for further exploration of video games as a valuable complement to traditio

关键词： Ontology

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Underwater Object Detection Using Synthetic Data 11

Underwater Object Detection Using Synthetic Data

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11th International Symposium on Electronic Systems Devices and Computing, ESDC 2023

作者： Reddy Nandyala, Nitish Kumar Sanodiya, Rakesh Institute of Information Technology Department of Computer Science and Engineering Sri City India

ISBN: (纸本)9781665455725

The ability to detect life in challenging underwater environments holds the potential to preserve many aquatic species and coral reefs. Recent object detection research has witnessed a remarkable upsurge in natural images but not in Underwater, due to the imbalanced lighting, inadequate contrast, frequent occlusions, and the mimicry displayed by aquatic life forms. The assessment of object recognition models utilized in various contexts has augmented the need for annotated datasets. Due to the labor-intensive nature of generating these datasets, we have opted to undertake training using synthetic images as an alternative. In this study, we train the cutting-edge YOLO object detection system on a synthetic underwater dataset, with the aim of achieving category-agnostic object detection and then evaluated through practical assessments conducted on real underwater images. In addition, we provide benchmarking results for different YOLO versions in this work, assessing their performance on both real-world and synthetic datasets. Our investigation reveal that YOLOv5 shines in its ability to perform on synthetic data, whereas the latest YOLOv8, excels in real data domains, outpacing other two models tested. These findings have far reaching implications for the design and development of object detection in underwater environments. © 2023 IEEE.

关键词： Object detection

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Lightweight and Homomorphic Security Protocols for IoT 18th

Lightweight and Homomorphic Security Protocols for IoT

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18th International Conference on Data science and Artificial Intelligence, ICDSAI 2022

作者： Singh, Ishaan Jain, Aakarshee Dhody, Ikjot Singh Chandavarkar, B.R. Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal India

ISBN: (纸本)9783031161773

The rise in usage of IoT devices for data collection in various fields has been astronomical in recent times. There has been an increased requirement to process the collected data on various cloud providers as IoT devices are compute-constrained. However, online data processing presents a substantial security challenge, especially in sensitive data, such as finance and medicine. The motivation behind this chapter comes from the observation that the encryption algorithms used for IoT devices need to be lightweight because IoT devices are not capable of heavy computation and the algorithm must be homomorphic. This is important because when the encrypted data moves from the device’s private environment to the public network, the data integrity is a major factor for such sensors and measurement devices. In this way, the data never needs to be in its decrypted form outside the organization’s ecosystem. This chapter aims to first present the limitations of IoT devices in the context of IoT networks. Then, the chapter analyses some of the most popular security protocols for IoT networks and subsequently understands the need for lightweight and homomorphic encryption. Then, the chapter presents and compares the most widely used lightweight and homomorphic algorithms/schemes, finally presenting the observations and conclusions based on the study. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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ResNet-18 comparative analysis of various activation functions for image classification 6

ResNet-18 comparative analysis of various activation functio...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Pandey, Gaurav Kumar Srivastava, Sumit Computer Science and Engineering Department Birla Institute of Technology Mesra Ranchi India

ISBN: (纸本)9798350398496

Deep neural network and Machine learning are a latestemerging concept in the field of data science. Due to multilayer hierarchical feature extraction in conjunction with control variables like number of hidden layers, activation functions, and variable parameters like learning rates, initial weights, and decay functions, deep network models perform better than machine learning techniques. While most of these parameter control the learning dynamics or complexity of representation a neural network can deal with, it is only activation function which introduces non-linearity in a network and current state of activation function poses multiple challenges to both practitioners and researchers some of which are: • Vanishing & Exploding gradients during back-propagation • Zero-mean and range of outputs • Compute complexity of function • Predictive performance Due to this reason our objective in current work in focused to explain with reasoning and experiments the landscape of activation functions available. According to a recent study, we have enough cutting-edge activation functions to modify the architecture of the well-known deep network model. Building on top of widely adopted ResNet-18 network architecture in this study. Subsequently, we evaluate the effectiveness of ResNet-18 for image classification using various activation functions. © 2023 IEEE.

关键词： Image classification

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EEG evoked automated emotion recognition using deep convolutional neural network 5

EEG evoked automated emotion recognition using deep convolut...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Abgeena, Abgeena Garg, Shruti Birla Institute of Technology Mesra Department of Computer Science and Engineering Ranchi India

ISBN: (纸本)9781665493604

As life continues to change in the digital era, it is crucial to perceive a person's emotional state. Affective computing is receiving more attention with the increase in the human-computer interface (HCI). Human emotion recognition employing electroen-cephalogram (EEG) signals has been studied to obtain a person's emotional status for different stimuli. However, it is difficult to identify clear patterns in EEG signals because they have low electrical impulses and are highly sensitive to noise. A deep convolutional neural network (DCNN) was employed in the present study to recognize emotions in EEG signals. For this purpose, a publicly available dataset, DREAMER, was utilized in this study to assess the applicability of the model for emotion classification. The dataset consisted of three-dimensional emotions, that is, valence, arousal, and dominance (VAD). 2D emotions arousal and valence were the most-recognized emotions in existing research. The present study identified the 3D emotions present in the above-mentioned dataset. In this study, raw EEG signals from the DREAMER dataset were pre-processed. Subsequently, three EEG rhythms, theta, alpha, and beta, were extracted using a bandpass filter. The power spectral density (PSD) was computed using fast Fourier transform (FFT) in the feature extraction. Finally, a 1D CNN model is applied to the classification of emotions. In addition, the performance of the proposed model was compared with two machine learning (ML) classifiers: random forest (RF) and extreme Gradient Boosting (XGBoost) classifiers. The highest accuracy (ACC) of 97.6% was obtained using the proposed model in the dominance dimension. The working principles were compared and discussed to determine the suitability of the model for emotion recognition applications. © 2023 IEEE.

关键词： Emotion Recognition

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Enhancing Medical Information Retrieval with a Language Model 1

Enhancing Medical Information Retrieval with a Language Mode...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Natarajan, Yuvaraj Sri Preethaa, K.R. Pandian, Sasikumar Geetha, Shruti Umapathi Senthilkumar, Sujadharshini Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kyungpook National University Department of Robot and Smart System Engineering Daegu41566 Korea Republic of

ISBN: (纸本)9798350372748

In this ever-evolving healthcare landscape, with the integration of cutting-edge technology, this study has exemplified a medical chatbot by harnessing the advanced Llama2 model. This chatbot places significant emphasis on instance training by changing and adding the meta, which enables it to continuously adapt and stay updated with the latest medical developments. Its adaptability also encompasses capabilities such as question-answering and mining knowledge from an extensive meta dataset. By leveraging its extensive ability to learn medical terminology, the chatbot provides precise and real-time responses to medical inquiries by establishing itself as a reliable information source. Access to a specially collected meta dataset further enhances its ability to deliver comprehensive medical insights while self-supervised learning techniques enhance the model efficacy. Consequently, this chatbot reshapes the way individuals access precise medical information by enabling a new era of advanced digital healthcare assistants in the digital age. © 2024 IEEE.

关键词： Supervised learning

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Hyperparameter Tuned Hybrid Convolutional Neural Network (H-CNN) for Accurate Plant Disease Classification

Hyperparameter Tuned Hybrid Convolutional Neural Network (H-...

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2023 International Conference on Communication, Circuits, and Systems, IC3S 2023

作者： Sharma, Shubham Vardhan, Manu National Institute of Technology Department of Computer Science and Engineering Raipur492010 India

ISBN: (纸本)9798350325904

Accurate identification and classification of plant diseases are essential for maintaining crop health and maximizing yields. Deep learning techniques have shown great promise in this field, and this study presents a Hybrid Convolutional Neural Network (H-CNN) model for plant disease classification. The proposed model architecture incorporates multiple Convolutional (Conv), and Fully Connected (FC) layers, which are trained on a publicly available dataset of plant leaf images. The model utilizes both visual and spectral features of plant leaves, contributing to its high accuracy in classifying various plant diseases. The visual features are obtained using a pre-trained VGG-16 network, while the spectral features are extracted using Principal Component Analysis (PCA). The hyperparameters for the CNN layers are optimized using a grid search algorithm. Our proposed model achieves impressive performance, outperforming state-of-the-art methods with an accuracy of 99.2% on the test dataset. The resulting H-CNN model provides a robust and accurate tool for plant disease detection and classification, with potential applications in precision agriculture and plant disease management. This study significantly contributes to the field of plant disease classification and has practical implications for agricultural researchers and practitioners. © 2023 IEEE.

关键词： Spectrum analysis

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