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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Ganeshkumar, P.V. Prasanna, S. Vels Institute of Science Technology and Advanced Studies Chennai India Vels Institute of Science Technology and Advanced Studies Department of Computer Application Chennai India

ISBN: (纸本)9798350375442

The early identification of brain tumours is crucial for improving patient prognosis and treatment planning. Recent advancements in neuroimaging techniques have paved the way for the enhanced detection and characterization of these lesions through CT or MRI image scans. During medical image analysis, the feature analysis is important factor to know the object relations, entity resolution information and disease relational properties, from the prior analysis, machine learning models are mostly covered to segment the disease region by retaining non scaling factors. Due to lower precision class, poor segmentation leads low accuracy to identifying the disease regions to produce higher false rates. In this research explores a novel approach to brain tumour identification utilizing Neuromelanin-Sensitive Magnetic Resonance Imaging (NMS-MRI). We propose a novel preprocessing methodology leveraging the Weigner filter to enhance image normalization and overall quality, facilitating more accurate subsequent analyses. Following preprocessing, we employ Cascading Slice Window Volumetry Segmentation (CSWVM) to achieve refined tumor delineation in three-dimensional MRI scans. The segmentation results are further refined through Cross Modality Particle Swarm Optimization (CMPSO)-based feature selection, which effectively identifies the most relevant features across various imaging modalities to optimize the classification process. To classify the detected tumors, further to integrate a ResNet 50-Convolutional Neural Network (ResNet50-CNN), leveraging its deep learning capabilities for robust pattern recognition in neuroimaging data. The proposed system proves higher performance in precision, recall, f1 score by attaining feature selection, and deep learning algorithms, ultimately contributing to enhanced accuracy and effectiveness in brain tumor identification. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Hierarchical Representation Learning for Improved Diagnosis of Prostate Cancer on MRI Scans

Hierarchical Representation Learning for Improved Diagnosis ...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Prabhu, A. Pandeya, Megha Kaushik, Harshita Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

It focuses on using hierarchical illustration mastering (HRL) for the progressed prognosis of most prostate cancers on MRI scans. HRL is a gadget getting-to-know technique using a hierarchy of function vectors to encode record sets, allowing extra complicated non-linear styles to be recognized and applied. In this examination, HRL is compared to a fashionable convolution neural network (CNN) classifier for the challenge of prostate cancer analysis on MRI scans. The effects show that HRL outperforms the CNN classifier, providing a higher accuracy fee and universal predictive performance. Furthermore, HRL results in fewer fake positives and extra correct type accuracy. Its improved overall performance suggests that utilizing HRL in medical decision-making can offer a more correct, practical analysis of most prostate cancers. © 2024 IEEE.

关键词： convolutional improved performance predictive representation

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Internet of Everything: A Review on Smart Healthcare Taxonomy, Research Challenges and Methodologies 14

Internet of Everything: A Review on Smart Healthcare Taxonom...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Raj, Anu Prakash, Shiva Srivastava, Jyoti Madan Mohan Malaviya University of Technolgy Information Technology & Computer Application Department Gorakhpur India

ISBN: (纸本)9798350335095

The Internet of Everything (IoE) has the potential to revolutionize many aspects of our daily lives, including healthcare, business and industrial world etc. Smart healthcare systems that utilize IoE technologies could greatly improve patient outcomes, increase efficiency, and reduce costs. However, there are several challenges that must be overcome in order to develop effective and reliable power supply systems for IoE-enabled healthcare devices. IoE healthcare devices may be deployed in a variety of settings, and the power supply system must be able to monitor and manage these devices remotely. The development of high-performance smart healthcare systems for the IoE requires the development of reliable and scalable power supply systems that can overcome the challenges of distribution, connection, charging technologies, and management. This paper mainly emphasizes on the IoE and their components that are used in the smart healthcare system and rising of the research challenges. In this paper, by addressing the taxonomy of healthcare and research challenges, researchers and businesses can develop innovative solutions that will transform healthcare delivery and improve patient outcomes. © 2023 IEEE.

关键词： Internet of Everything

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Research on Cloud Computing Security technology under computer Big Data Network 3

Research on Cloud Computing Security Technology under Comput...

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3rd IEEE International Conference on Electronic technology, Communication and Information, ICETCI 2023

作者： Zhou, Qiang Ou, Wenjisi Zheng, Wei Jiangxi Vocational and Technical College of Information Application Department of Computer Technology Nanchang330043 China

ISBN: (纸本)9798350398410

In this paper, a parameter estimation-based security access integration method for computer network smart data is designed. The scale of data clustering is determined according to the distribution and evolution characteristics in the clustering process. Finally, this paper proposes a new security access control scheme. The system uses local abnormal factors to judge non-safe data and eliminates abnormal data according to the judgment results. The system sorts and outputs the factors that may have abnormal data points in the current time window. The Java object-oriented programming language is used. The network attack will be divided into fuzzy measures of DoS attack and non-DoS attack, and finally, the results of various attacks will be displayed. Simulation results show that this paper's entire access control scheme is highly feasible. The proposed algorithm has a high recognition rate. © 2023 IEEE.

关键词： Access control

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An autoencoder based unsupervised clustering approach to analyze the effect of E-learning on the mental health of Indian students during the Covid-19 pandemic

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Multimedia Tools and applications 2024年 1-21页

作者： Banerjee, Pritha Jana, Chandan Saha, Jayita Chowdhury, Chandreyee Computer Application and Science Institute of Engineering and Management West Bengal Kolkata700091 India Computer Science and Engineering Jadavpur University West Bengal Kolkata700032 India Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal Karnataka Bengaluru560064 India

Due to the Covid-19 pandemic, the education system in India has changed to remote that is, online study mode. Though there are works on the effect of teaching learning on Indian students, the effect of online mode and associated mental state, particularly when the entire country is going through a crisis could not be found in the literature. Our goal is to analyze data and find some pattern through which we can understand the effectiveness of the online study and also try to figure out the stress level. The dataset we collected from 500 undergraduate college students during April-May, 2021 is in questionnaire format. Our contribution in this paper are - (i) publishing a dataset of student feedbacks, and (ii) designing a data processing pipeline involving autoencoders followed by clustering approach. The dataset is in text format so for our analysis we have converted the dataset into a numerical format using the concept of a binary bag of words. Dimensionality reduction is applied through autoencoder for an effective latent space representation. Finally, for finding patterns out of this dimensionally reduced feature space, we have applied unsupervised learning algorithms - kMeans and DBSCAN. A thorough analysis of the clustering process reveals that the absence of social communication in purely online education provokes isolation irrespective of the urban or rural background of the students. However, it could supplement offline classes as a substantial number of students welcomed the concept of online learning as reported in the data. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Students

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Single object tracking based on OSTrack and NAS head 4

Single object tracking based on OSTrack and NAS head

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4th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2022

作者： Li, Conghao Wang, Haofeng Liu, Pengjie Beijing Institute of Computer Technology and Application Beijing China

ISBN: (纸本)9781450398343

Neural architecture search (NAS) has attracted much attention in recent years. Especially, Differentiable Architecture Search (DARTS) has become a mainstream NAS algorithm because of its differentiable architecture hyper-parameter and low time complexity, but few people apply it in the field of object tracking due to its high memory complexity. This paper redesigns the cell structure according to the characteristics of the target tracking head network and defines a simplified search space. We propose a NASHT model to replace the three head networks of OSTrack with the modified DARTS cell, which automatically searches for a target structure suitable for its task. We use two benchmarks, PTB-TIR and LSOTB-TIR, to train and evaluate the model. The accuracy of the proposed model reached 86.5% and the success rate reached 69.0% on the PTB-TIR benchmark. Compared with OSTrack, the accuracy has increased by 1.5%, and the success rate has a 0.8% improvement. The accuracy of the proposed model on the LSOTB-TIR reached 84.9% and the success rate was 71.4%, which achieved comparable effects to OSTrack while performing better in situations such as motion blur, low resolution, and aspect ratio changes. © 2022 ACM.

关键词： Aspect ratio

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Deep Hash Model for Similarity Text Retrieval 5

Deep Hash Model for Similarity Text Retrieval

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5th IEEE International Conference on Artificial Intelligence and Big Data, ICAIBD 2022

作者： Li, Zhiwen Zhang, Dan Yuan, XiaoGuang Zheng, Jun Beijing Institute of Computer Technology and Application Beijing China

ISBN: (纸本)9781665499132

Text hashing transforms a text into a binary hash code, making similar texts have similar hash codes. Text hashing can reduces storage and improves retrieval efficiency of similar texts retrieval, but integrating semantic characteristics into hash code of text is difficult and is a hot topic. A deep hashing network model based on SE-Inception + Bi-LSTM + Attention mechanism is proposed in this paper. The Bert pre-training language model is used to preprocess text content to obtain the input vector, and the hash layer is added to generate the binary hash code of the text. The proposed model deeply combines semantic information to generate hash code and adds SE module with attention mechanism to improve the performance of neural network. The experimental results showed that the proposed model is superior to other model in extraction accuracy and classification accuracy. © 2022 IEEE.

关键词： Semantics

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Illumination-Free Clustering Using Improved Slime Mould Algorithm for Acute Lymphoblastic Leukemia Image Segmentation

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Journal of Bionic Engineering 2023年第6期20卷 2916-2934页

作者： Krishna Gopal Dhal Swarnajit Ray Sudip Barik Arunita Das Department of Computer Science and Application Midnapore College(Autonomous)Paschim MedinipurMidnaporeWest BengalIndia Department of Computer Science and Engineering Maulana Abul Kalam Azad University of TechnologyKolkataWest BengalIndia Department of Computer Science and Engineering Kalyani Government Engineering CollegeKalyaniWest BengalIndia

Partitional clustering techniques such as K-Means(KM),Fuzzy C-Means(FCM),and Rough K-Means(RKM)are very simple and effective techniques for image ***,because their initial cluster centers are randomly determined,it is often seen that certain clusters converge to local *** addition to that,pathology image segmentation is also problematic due to uneven lighting,stain,and camera settings during the microscopic image capturing ***,this study proposes an Improved Slime Mould Algorithm(ISMA)based on opposition based learning and differential evolution’s mutation strategy to perform illumination-free White Blood Cell(WBC)*** ISMA helps to overcome the local optima trapping problem of the partitional clustering techniques to some *** paper also performs a depth analysis by considering only color components of many well-known color spaces for clustering to find the effect of illumination over color pathology image *** and visual results encourage the utilization of illumination-free or color component-based clustering approaches for image ***-KM and“ab”color channels of CIELab color space provide best results with above-99%accuracy for only nucleus ***,for entire WBC segmentation,ISMA-KM and the“CbCr”color component of YCbCr color space provide the best results with an accuracy of above 99%.Furthermore,ISMA-KM and ISMA-RKM have the lowest and highest execution times,*** the other hand,ISMA provides competitive outcomes over CEC2019 benchmark test functions compared to recent well-established and efficient Nature-Inspired Optimization Algorithms(NIOAs).

关键词： Pathology image Image segmentation Clustering Color space White blood cell Optimization Swarm intelligence Fuzzy clustering Rough clustering

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A Time Sensitive Network-Based Data Distribution Service Implementation Method 3

A Time Sensitive Network-Based Data Distribution Service Imp...

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3rd International Symposium on Advances in Informatics, Electronics and Education, ISAIEE 2022

作者： Ren, Jianxin Zhao, Manfei Wang, Haofeng Gao, Tong Yao, Peng Shao, Zonglin Beijing Institute of Computer Technology and Application Beijing China

ISBN: (纸本)9781665463577

This paper proposes a TSN-based data distribution service (DDS) implementation method for solving the application flexibility problem of Time Sensitive Network (TSN). First, we propose stream monitoring method for a system-level network controlling;Second, a network management methods is proposed, which adjusts the gate control strategy in accordance with real-time flow;Finally, we have merged TSN driver to DDS for improving the publishing and subscription performance. The experiment results show that the proposed method has established a certainty DDS system with great communication efficient and flexibility. © 2022 IEEE.

关键词： Network management

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Prediction of Epileptic Seizures based on EEG Signal using CNN Model 8

Prediction of Epileptic Seizures based on EEG Signal using C...

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

作者： Kumar, Suraj Prakash, Shiva Madan Mohan Malaviya University of Technology Department of Information Technology and Computer Application Uttar Pradesh Gorakhpur273010 India

ISBN: (纸本)9798350396638

Millions of patients suffer from epilepsy each year, a chronic nervous illness with a growing global prevalence. In a lot of situations, it might cause critical injuries or patient deaths. So, the automatic prediction of the epileptic seizure saves the patients from injury as well as from death. In this age of technological advancements, the internet of things (IoT) has started to offer a variety of solutions in the healthcare sector through the use of machine learning, deep learning, and cloud-based services. Internet of thing, Edge computing, and Cloud computing helps in the detection of such nervous disorders using deep learning and machine learning. Most of the models were developed preciously but there are many issues related to the prediction and security of the data of patients. This research study proposed an autonomous cloud-edge integrated seizure prediction model based on a convolutional neural network. The proposed model detects and classifying the epileptic seizure using an EEG dataset. The proposed model shows average training accuracy and average validation accuracy are 99.27% and 96.37%, respectively and the average losses for training and validation are 2.10 % and 14.23%, respectively. The result shows that overall accuracies and losses are better than the wellknown existing works. All implementation for seizure prediction is done in Python language. © 2023 IEEE.

关键词： Internet of things

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