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4th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2023

作者： Sannala, Gayatri Sanjana Rohith, K.V.G. Vyas, Aashutosh G. Kavitha, C.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru560035 India

ISBN: (纸本)9789819965854

Artificial intelligence (AI) has the potential to revolutionize the field of healthcare by automating many tasks, enabling more efficient diagnosis and treatment. However, one of the challenges with AI in healthcare is the need for explainability, as the decisions made by these systems can have serious consequences for patients. AI can be particularly useful in the classification of diseases based on symptoms. This involves using machine learning algorithms to analyze a patient’s symptoms and classify them as having a particular disease or condition. While using black box machine learning algorithms can be highly accurate, there is little to no understanding on how these models work. Therefore, using techniques such as feature importance analysis and Explainable AI, it is possible to provide clear explanations for the decision-making process, which can improve trust and understanding among healthcare providers and patients. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision making

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Identity Management Dapp using Ethereum Blockchain and IPFS 15

Identity Management Dapp using Ethereum Blockchain and IPFS

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Avvaru, Ananya Aaditi Tandon, Aryan Beena, B.M. Amrita Vishwa Vidyapeetham Amrita School of Computing Bengaluru Department of Computer Science & Engineering Bangalore India

ISBN: (纸本)9798350370249

This paper explores the integration of a verification and document management DApp into the core of the Ethereum blockchain and the InterPlanetary File System (IPFS). This way, the multi-signature wallets and intelligent contracts introduced help restore many traditional problems based on the secure handling of documents and ensure increased security, data integrity, and easily accessible documents. This DApp will allow such safe, transparent interaction that mandates consensus with multi-sig functionalities during document transactions to ensure the immutability and retrievability of those documents. This implies that the solution would scale using Ethereum and IPFS while offloading pressure off the blockchain to assure high data availability and reliability. Recent performance reviews indicate relevant improvements in the processes related to document management, and this improvement may even have broader implications for the filling implementation in essential sectors that need both a secure and efficient document-handling process. © 2024 IEEE.

关键词： Blockchain DApp Ethereum IPFS

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An Integrated Study on Convolutional Neural Networks and Graph Neural Networks for Brain Tumor Classification from MRI Images 2024

An Integrated Study on Convolutional Neural Networks and Gra...

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16th International Conference on Contemporary computing, IC3 2024

作者： Ashok, Asha Hitesh, Odulapalli Naidu, Gonuguntla Praveen Abhinav, Beeneedi Krishna, Chitirala Pranav Vamsi Nair, Manjusha Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Kerala Amritapuri India

ISBN: (纸本)9798400709722

Brain tumors are one of the top diseases with less survival rate that affect humans across the globe and the development of brain tumors is influenced by many factors. The abnormal growth of cell masses in different categories of brain tumors are detected using brain Imaging techniques such as Magnetic Resonance Imaging(MRI). The purpose of this study is to examine the potential of Graph Convolutional Networks (GCNs) combined with pre-trained Convolutional Neural Network(CNN) models for brain tumor classification. Novel architectures, including VGG16, Densenet121, and Resnet18, are used to harness hierarchical features learned by CNNs and exploit graph-based representations for tumor classification. The models showed accuracy ranging from 64 to 74% and Resnet18 showed improved feature extraction compared to other models. The findings of this study highlight the models' efficacy in leveraging image features by using the pre-trained CNN models like VGG16, DenseNet121, and ResNet18 to extract rich hierarchical features from brain tumor MRI images. These extracted image features are then transformed into graph structures, enabling the application of Graph Convolutional Networks (GCNs) for classification tasks. By incorporating spatial dependencies among features through GCNs, the models can effectively learn complex patterns and relationships within the image data, leading to accurate brain tumor classification results. Visual representations using PCA offer insights into distinctive feature representations learned by each model, underscoring the significance of model architecture and backbone selection in GCN based medical image analysis. Future work aims to explore larger datasets, refine hyperparameters, and delve into interpretability aspects, further advancing GCNs' role in medical imaging and diagnostics. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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A Novel Zero-Knowledge Authentication Protocol for Secure Communication in VANET 15

A Novel Zero-Knowledge Authentication Protocol for Secure Co...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Vinod, Ardra Pranav, Mayur S.V. Vinodkumar, Malavika Remyakrishnan, P. Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham 641112 India

ISBN: (纸本)9798350370249

Vehicular Ad Hoc Network (VANET) is a particular subclass of the mobile ad-hoc network that raises several security challenges, notably how users authenticate the network. The work explores using zero-knowledge proofs for secure authentication while preserving user privacy and aims at encrypted information exchange between onboard units. Zero-knowledge proofs enhance security by protecting against impersonation attacks. We strive to reduce the dependence on roadside units for computational tasks by integrating the central authentication server and sub-authentication servers. We reduce modular exponentiation operations during authentication, enhancing efficiency without compromising security. We use the protocol verifier tool called Proverif to verify the security of our protocol. Simulation using the NS-2 simulator validates the protocol by varying the vehicle density. This paper advances VANET security by combining zero-knowledge authentication with encrypted information exchange, strengthening security, efficiency, and data confidentiality while reducing reliance on RSUs. Our protocol ensures secure communication in VANET with minimum computational and communication overhead. ©2024 IEEE.

关键词： Authentication Protocol

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Time Series Forecasting for COVID-19 Confirmed Cases Using Transformer Based Stacked LSTM Model 8th

Time Series Forecasting for COVID-19 Confirmed Cases Using T...

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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Penumadi, Sri Naga Jathin Nambiar, Vaishakh Vignesh, G.S. Singh, Rimjhim Padam Kanchan, Sneha Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Bengaluru India Department of Internet Engineering and Computer Science Universiti Tunku Abdul Rahman Perak Malaysia

ISBN: (纸本)9783031736162

The worldwide impact of COVID-19 necessitates accurate forecasting for informed decision-making by governments and health entities. This study proposes neural network architectures for time series forecasting of confirmed COVID-19 cases based on historical data. The models, including variants of LSTM networks, CNN-GRU hybrids, RBMs, DBNs, and self-attention mechanisms, are trained on comprehensive datasets of confirmed cases, deaths, recoveries, and other variables across various countries. Comparative analysis indicates that CNN-GRU and LSTM models exhibit superior performance in forecasting accuracy, surpassing RBM, DBN, and self-attention models across metrics like Mean Absolute Error and Root Mean Squared Error. This research emphasizes the viability of employing advanced neural networks for precise COVID-19 spread predictions, pivotal in shaping public health policies during global crises. © IFIP International Federation for Information Processing 2025.

关键词： Time series

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An Ensemble based Convolutional Neural Network modelling for Classifying Medulloblastoma subtype 5

An Ensemble based Convolutional Neural Network modelling for...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Sathwik, T. Reddy, Tadi Sai Vishruth Charitha, Talluri Singh, Rimjhim Padam Kanchan, Sneha Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Internet Engineering and Computer Science Universiti Tunku Abdul Rahman Malaysia

ISBN: (纸本)9798350361155

Medulloblastoma a heterogeneous pediatric brain tumor, requires specific subtype identification in order to determine the most effective treatment strategies. To evaluate the accuracy of classification, this study examines state-of-art leading Convolutional Neural Network (CNN), models like ResNet-50, MobileNet, Xception model, ensembles, etc in differentiating between medulloblastoma subtypes using histopathological images. The models undergo rigorous training and fine-tuning using a comprehensive benchmark dataset encompassing a variety of medulloblastoma subtypes. We also employ advanced image pre-processing techniques to optimize feature extraction. Through comparative analyses based on the accuracy, sensitivity, and specificity and precision of each model to uncover their unique strengths and weaknesses, the work identifies the top performing model for medulloblastoma subtypes classification. Then considering unique strengths of these models, we propose an ensemble methodology that combines top performing models for the underlying problem. Through experimentation, our ensemble approach proves to have the highest accuracy in medulloblastoma subtype classification. The combination of ResNet-50's depth, MobileNet's efficiency, and Xception's feature extraction abilities contribute to the ensemble's outstanding performance with 97% of acuuracy and F-score. © 2024 IEEE.

关键词： Convolutional neural networks

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Reinforcing Artificial Neural Networks through Traditional Machine Learning Algorithms for Robust Classification of Cancer

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computers, Materials & Continua 2023年第5期75卷 4293-4315页

作者： Muhammad Hammad Waseem Malik Sajjad Ahmed Nadeem Ishtiaq Rasool Khan Seong-O-Shim Wajid Aziz Usman Habib Department of Computer Sciences and Information Technology University of Azad Jammu and KashmirMuzaffarabad13100Pakistan Department of Computer Science and AI College of Computer Science and EngineeringUniversity of JeddahJeddah23890Saudi Arabia Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah23890Saudi Arabia Department of AI&DS School of ComputingNational University of Computer and Emerging SciencesIslamabad44000Pakistan

Machine Learning(ML)-based prediction and classification systems employ data and learning algorithms to forecast target ***,improving predictive accuracy is a crucial step for informed *** the healthcare domain,data are available in the form of genetic profiles and clinical characteristics to build prediction models for complex tasks like cancer detection or *** ML algorithms,Artificial Neural Networks(ANNs)are considered the most suitable framework for many classification *** network weights and the activation functions are the two crucial elements in the learning process of an *** weights affect the prediction ability and the convergence efficiency of the *** traditional settings,ANNs assign random weights to the *** research aims to develop a learning system for reliable cancer prediction by initializing more realistic weights computed using a supervised setting instead of random *** proposed learning system uses hybrid and traditional machine learning techniques such as Support Vector Machine(SVM),Linear Discriminant Analysis(LDA),Random Forest(RF),k-Nearest Neighbour(kNN),and ANN to achieve better accuracy in colon and breast cancer *** system computes the confusion matrix-based metrics for traditional and proposed *** proposed framework attains the highest accuracy of 89.24 percent using the colon cancer dataset and 72.20 percent using the breast cancer dataset,which outperforms the other *** results show that the proposed learning system has higher predictive accuracies than conventional classifiers for each dataset,overcoming previous research ***,the proposed framework is of use to predict and classify cancer patients ***,this will facilitate the effective management of cancer patients.

关键词： ANN decision support systems gene-expression data hybrid classification machine learning predictive analytics

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Segmenting the Brain Tumor using Deep Learning Techniques 3

Segmenting the Brain Tumor using Deep Learning Techniques

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Pranitha, Ch. Lakshmi Sumathi, R. Harshitha, V. Kavya, N. Soni, A. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu India Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798331509675

This study presents a U-Net architecture with EfficientNet-B7 as the feature extraction backbone is used to propose a robust brain tumor segmentation method. Using cutting-edge deep learning techniques, the suggested model precisely segments MRI images with an emphasis on identifying and defining tumor anomalies. A thorough preprocessing pipeline that handles missing values, augments data with albumentations, and normalizes data guarantees the consistency and resilience of the incoming data. The segmentation procedure uses a hybrid loss function that combines Soft Dice Loss and Binary Cross-Entropy (BCE), improving the model's ability to strike a compromise between segmentation consistency and pixel-level precision. To ensure efficiency and generalizability, the model training is optimized using the Adam optimizer in conjunction with a learning rate scheduler. Additionally, an early halting mechanism is incorporated to minimize overfitting. Using a standardized dataset, performance evaluation is carried out with 70%, 15%, and 15% splits for training, validation, and testing, respectively. To evaluate the quality of segmentation, important metrics are employed, such as the Dice Coefficient and the Intersection over Union (IoU). The accuracy of the model in identifying tumor boundaries is illustrated by visualizations of the anticipated segmentation results. The potential of combining U-Net with cutting-edge feature extractors such as EfficientNet-B7 to produce dependable, effective, and clinically significant segmentation results in medical imaging is demonstrated by this approach. © 2025 IEEE.

关键词： Deep learning

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Fibrillar adhesives with unprecedented adhesion strength,switchability and scalability

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National science Review 2024年第10期11卷 20-33页

作者： Changhong Linghu Yangchengyi Liu Xudong Yang Dong Li Yee Yuan Tan Mohamed Haziq Bin Mohamed Hafiz Mohammad Fadhli Bin Rohani Zihao Du Jiangtao Su Yan Li Yucheng Huo Hanyan Xu Xiufeng Wang Yifan Wang Jing Yu Huajian Gao K.Jimmy Hsia School of Mechanical and Aerospace Engineering Nanyang Technological University School of Materials Science and Engineering Xiangtan University Department of Engineering Mechanics Zhejiang University School of Materials Science and Engineering Nanyang Technological University Institute of High-Performance Computing A*STAR Mechano-X Institute Applied Mechanics Laboratory Department of Engineering Mechanics Tsinghua University School of Chemistry Chemical Engineering and BiotechnologyNanyang Technological University

Bio-inspired fibrillar adhesives have received worldwide attention but their potentials have been limited by a trade-off between adhesion strength and adhesion switchability, and a size scale effect that restricts the fibrils to micro/nanoscales. Here, we report a class of adhesive fibrils that achieve unprecedented adhesion strength（～2 MPa）, switchability（～2000）, and scalability（up to millimeter-scale at the single fibril level）,by leveraging the rubber-to-glass（R2G） transition in shape memory polymers（SMPs）. Moreover, R2G SMP fibrillar adhesive arrays exhibit a switchability of >1000（with the aid of controlled buckling） and an adhesion efficiency of 57.8%, with apparent contact area scalable to 1000 mm2, outperforming existing fibrillar adhesives. We further demonstrate that the SMP fibrillar adhesives can be used as soft grippers and reusable superglue devices that are capable of holding and releasing heavy objects >2000 times of their own weight. These findings represent significant advances in smart fibrillar adhesives for numerous applications,especially those involving high-payload scenarios.

关键词： JKR-DMT transition rubber-to-glass (R2G) transition R2G fibrillar adhesives shape memory polymers adhesion switchability and scalability

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Deep learning on medical image analysis

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CAAI Transactions on Intelligence Technology 2025年第1期10卷 1-35页

作者： Jiaji Wang Shuihua Wang Yudong Zhang School of Computing and Mathematical Sciences University of LeicesterLeicesterUK Department of Biological Sciences Xi'an Jiaotong-Liverpool UniversitySuzhouJiangsuChina Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia School of Computer Science and Engineering Southeast UniversityNanjingJiangsuChina

Medical image analysis plays an irreplaceable role in diagnosing,treating,and monitoring various *** neural networks(CNNs)have become popular as they can extract intricate features and patterns from extensive *** paper covers the structure of CNN and its advances and explores the different types of transfer learning strategies as well as classic pre-trained *** paper also discusses how transfer learning has been applied to different areas within medical image *** comprehensive overview aims to assist researchers,clinicians,and policymakers by providing detailed insights,helping them make informed decisions about future research and policy initiatives to improve medical image analysis and patient outcomes.

关键词： CNN deep learning medical image transfer learning

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