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Automated Brain Tumor Diagnosis Using Deep Residual U-Net Segmentation Model

computers, Materials & Continua 2023年第1期74卷 2179-2194页

作者： R.Poonguzhali Sultan Ahmad P.Thiruvannamalai Sivasankar S.Anantha Babu Pranav Joshi Gyanendra Prasad Joshi Sung Won Kim Department of Computer Science and Engineering Periyar Maniammai Institute of Science and TechnologyThanjavur613403India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer Science and Engineering Jain Deemed to-be UniversityBangalore560069India Department of Neurology Annapurna Neuro HospitalKathmandu44600Nepal Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

Automated segmentation and classification of biomedical images act as a vital part of the diagnosis of brain tumors(BT).A primary tumor brain analysis suggests a quicker response from treatment that utilizes for improving patient survival *** location and classification of BTs from huge medicinal images database,obtained from routine medical tasks with manual processes are a higher cost together in effort and *** automatic recognition,place,and classifier process was desired and *** study introduces anAutomatedDeepResidualU-Net Segmentation with Classification model(ADRU-SCM)for Brain Tumor *** presentedADRUSCM model majorly focuses on the segmentation and classification of *** accomplish this,the presented ADRU-SCM model involves wiener filtering(WF)based preprocessing to eradicate the noise that exists in *** addition,the ADRU-SCM model follows deep residual U-Net segmentation model to determine the affected brain ***,VGG-19 model is exploited as a feature ***,tunicate swarm optimization(TSO)with gated recurrent unit(GRU)model is applied as a classification model and the TSO algorithm effectually tunes *** performance validation of the ADRU-SCM model was tested utilizing FigShare dataset and the outcomes pointed out the better performance of the ADRU-SCM approach on recent approaches.

关键词： Brain tumor diagnosis image classification biomedical images image segmentation deep learning

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Hepatitis C Prediction Using Machine Learning and Deep Learning-Based Hybrid Approach with Biomarker and Clinical Data

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Biomedical Materials and Devices 2025年第1期3卷 558-575页

作者： Ripa, Rokiya Uddin, Khandaker Mohammad Mohi Alam, Mir Jafikul Rahman, Md. Mahbubur Department of Computer Science and Engineering Dhaka International University Dhaka 1205 Bangladesh Department of Computer Science and Engineering Southeast University Dhaka 1208 China Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka 1216 Bangladesh

Chronic liver damage is believed to be mostly caused by the Hepatitis C virus (HCV). About 90% of hepatitis C infections progress to chronic hepatitis. Acute HCV infection is a condition that frequently progresses to liver cirrhosis and eventually liver cancer;therefore, understanding this stage of the virus is essential. Molecular and serological testing approaches are often expensive and difficult to perform for diagnosing HCV infection. Machine learning technology can be effectively employed to identify patterns or associations for diagnosing HCV infection. The study utilized machine learning techniques to develop classification models for hepatitis C illness, aiming to anticipate the virus responsible for the infection. Our research integrates various machine learning algorithms, including Random Forest, Cat Boost, Bagging Classifier, SGD Classifier, Gaussian NB, Bernoulli NB, Multinomial NB, Linear Discriminant Analysis, ANN, and MLP. Prior to training the models, preprocessing methods, including normalization, filtering, and SMOTE, were used to improve the dataset’s attributes. The classification accuracy scores show encouraging results, with ANN scoring 79.63%, MLP scoring 46.29%, Bernoulli NB scoring 79.62%, Cat Boost scoring 98.14%, and Random Forest scoring 99.53%. Among classification methods, random forest demonstrates the highest accuracy in diagnosing HCV infection. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Classification Deep learning Hepatitis C virus Machine learning Predictions

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Anomaly Detection Model of Time Segment Power Usage Behavior Using Unsupervised Learning

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Journal of Internet Technology 2024年第3期25卷 455-463页

作者： Ho, Wen-Jen Hsieh, Hsin-Yuan Tsai, Chia-Wei Software Technology Institute Institute for Information Industry Taiwan Department of Computer Science and Information Engineering National Taitung University Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taiwan

In Taiwan, the current electricity prices for residential users remain relatively low. This results in a diminished incentive for these users to invest in energy-saving improvements. Consequently, devising strategies to encourage residential users to adopt energy-saving measures becomes a vital research area. Grounded in behavioral science, this study introduces a feasible approach where an energy management system provides alerts and corresponding energy-saving recommendations to residential users upon detecting abnormal electricity consumption behavior. To pinpoint anomalous electricity usage within specific time segments, this research employs an unsupervised machine learning method, developing an anomaly detection model for the overall electricity consumption behavior of residential users. The model focuses on analyzing 2-hour intervals of electricity consumption, enabling more effective detection of abnormal usage patterns. It is trained using power consumption data collected from five actual residential users as part of an experimental study. The results indicate that the proposed anomaly detection model achieves performance metrics such as Precision, Recall, and F1-score of 0.90 or above, showcasing its potential for practical implementation. © 2024 Taiwan Academic Network Management Committee. All rights reserved.

关键词： Energy conservation

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Blockchain-based rumor detection approach for COVID-19

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 435-449页

作者： Rani, Poonam Jain, Vibha Shokeen, Jyoti Balyan, Arnav Department of Computer Engineering Netaji Subhas University of Technology Delhi India Department of Computer Science and Engineering University Institute of Engineering and Technology Maharshi Dayanand University Haryana Rohtak India

The ubiquity of handheld devices and easy access to the Internet help users get easy and quick updates from social media. Generally, people share information with their friends and groups without inspecting the posts’ veracity, which causes false information propagation in the network. Moreover, detecting false news and rumors in such a massive load of unstructured information is a very tedious task. Results, many literature papers explored different machine learning and deep learning approaches to detect the presence of rumors on social media networks. Although detection of misleading news and rumors is not sufficient, therefore, we have proposed a model for the detection and prevention of transmitted rumors in this paper. In this paper, we use blockchain technology to verify the credibility of information and design a framework with four layers: network layer, blockchain layer, machine layer, and device layer, to prevent the propagation of rumors in the network. We also use deep learning techniques to identify the anomalies in the network. The Bi-directional Long Short Term Memory (Bi-LSTM) model is used to prevent the introduction of new rumors by continuously monitoring incoming messages in the network. The experimental results demonstrate that the proposed Bi-LSTM model outperforms state-of-the-art machine learning methods and recent baseline work. Performance is compared over different metrics such as accuracy, precision, recall, f1-score, and specificity. Experiment results show that our Bi-LSTM model outperforms all the other approaches and achieved 99.63 % accuracy. Additionally, the probability of incorrect detection is significantly low with only 0.13% false positive. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： COVID-19

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Powder spreading process monitoring of selective laser melting manufacturing by using a convolutional Takagi–Sugeno–Kang fuzzy neural network

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International Journal of Advanced Manufacturing Technology 2024年第9-10期132卷 4989-5004页

作者： Lin, Chun-Hui Lin, Cheng-Jian Wang, Shyh-Hau Department of Computer Science and Information Engineering National Cheng Kung University Tainan701 Taiwan Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung411 Taiwan

Process monitoring plays a pivotal role in elucidating the intricate interplay among process, structure, and property in additive manufacturing production. The control of powder spreading affects not only particle adhesion but also rearrangement, both of which are indispensable constituents in the selective laser melting (SLM) process. To develop a novel monitoring process, this study proposed a convolutional Takagi–Sugeno–Kang fuzzy neural network (C-TSKFNN) to assess images with uneven powder distribution. The performance of the model was compared with that of conventional image analysis and state-of-the-art deep learning models. With its fusion layer and TSKFNN design, the C-TSKFNN model retains the distinctive characteristics of input images and minimizes expenditures through trainable parameters. The experimental results demonstrated that the average F1 score of the C-TSKFNN model achieved 97.07% and that it had the lowest standard deviation compared to state-of-the-art deep learning models. The trainable parameters were only 103,506, which is lower than those of models. Defect information was also provided locally through a model interpretation technique, facilitating the monitoring of SLM manufacturing. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Process monitoring

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Power synchronization: taming massive diversified serverless functions under power constraints

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science China(Information sciences) 2025年第3期68卷 47-65页

作者： Du LIU Lu ZHANG Yechen XU Xinkai WANG Lingyu SUN Yifei PU Xiaofeng HOU Chao LI Minyi GUO Department of Computer Science and Engineering Shanghai Jiao Tong University AI Chip Center for Emerging Smart Systems The Hong Kong University of Science and Technology

Carbon neutrality has become an important design objective worldwide. However, the on-going shift to cloud-naive era does not necessarily mean energy efficiency. From the perspective of power management, co-hosted serverless functions are difficult to tame. They are lightweight, short-lived applications sensitive to power capping activities. In addition, they exhibit great individual and temporal variability, presenting idiosyncratic performance/power scaling goals that are often at odds with one another. To date, very few proposals exist in terms of tailored power management for serverless platforms. In this work, we introduce power synchronization, a novel yet generic mechanism for managing serverless functions in a power-efficient way. Our insight with power synchronization is that uniform application power behavior enables consistent and uncompromised function operation on shared host machines. We also propose PowerSync, a synchronization-based power management framework that ensures optimal efficiency based on a clear understanding of functions. Our evaluation shows that PowerSync can improve the energy efficiency of functions by up to 16% without performance loss compared to conventional power management strategies.

关键词： serverless computing variability energy efficiency power management power synchronization

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Adaptive Marine Predator Optimization Algorithm(AOMA)-Deep Supervised Learning Classification(DSLC)based IDS framework for MANET security

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Intelligent and Converged Networks 2024年第1期5卷 1-18页

作者： M.Sahaya Sheela A.Gnana Soundari Aditya Mudigonda C.Kalpana K.Suresh K.Somasundaram Yousef Farhaoui Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600069India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai 602105India JNIAS School of Planning and Architecture Hyderabad 500034India Department of Computer Science and Engineering NPR College of Engineering and TechnologyNatham Dindigul 624401India Department of Computer Science and Engineering PSNA College of Engineering and TechnologyPoolangulathupatti 620009India Department of Computer Science and Engineering Sri Muthukumaran Institute of TechnologyChennai 600069India Department of Computer Science Moulay Ismail UniversityMeknes 5003Morocco

Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks *** MANET,the Intrusion Detection System(IDS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular *** machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of ***,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of ***,the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning ***,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of ***,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of ***,the Deep Supervise Learning Classification(DSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training *** evaluation,the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.

关键词： Intrusion Detection System(IDS) Security Mobile Ad-hoc Network(MANET) min-max normalization Adaptive Marine Predator Optimization Algorithm(AOMA) Deep Supervise Learning Classification(DSLC)

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emapDiffP: A novel learning algorithm for convolutional neural network optimization

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Neural Computing and Applications 2024年第20期36卷 11987-12010页

作者： Bhakta, Shubhankar Nandi, Utpal Changdar, Chiranjit Ghosal, Sudipta Kr Pal, Rajat Kumar Department of Computer Science Vidyasagar University West Bengal Midnapore721102 India Department of Computer Science Belda College West Bengal Belda721424 India Department of Computer Science and Technology Behala Goverment Polytechnic West Bengal Kolkata720060 India Department of Computer Science and Engineering University of Calcutta West Bengal Kolkata700106 India

Deep neural networks (DNNs) having multiple hidden layers are very efficient to learn large volume datasets and applied in a wide range of applications. The DNNs are trained on these datasets using learning algorithms to learn the relationships among different variables. The base method that makes DNNs successful is stochastic gradient descent (SGD). The gradient reveals the way that a function’s steepest rate of alteration is occurring. No matter how the gradient behaves, the key issue with basic SGD is that all parameters must adjust in equal-sized increments. Consequently, creating adaptable step sizes for every parameter is an effective method of deep model optimization. Gradient-based adaptive techniques utilize local changes in gradients or the square roots of exponential moving averages of squared previous gradients. However, current optimizers continue to struggle with effectively utilizing optimization curved knowledge. The novel emapDiffP optimizer suggested in this study utilizes the prior two parameters to generate a non-periodic and non-negative function, and the upgrade parameter makes use of a partly adaptive value to account for learning rate adjustability. Thus, the optimization steps become smoother with a more accurate step size for the immediate past parameter, a partial adapting value, and the largest two momentum values as the denominator of parameter updating. The rigorous tests on benchmark datasets show that the presented emapDiffP performs significantly better than its counterparts. In terms of classification accuracy, the emapDiffP algorithm gives the best classification accuracy on CIFAR10, MNIST, and Mini-ImageNet datasets for all examined networks and on the CIFAR100 dataset for most of the networks examined. It offers the best classification accuracy on the ImageNet dataset with the ResNet18 model. For image classification tasks on various datasets, the suggested emapDiffP technique offers outstanding training speed. With MNIST, CIFAR1

关键词： Deep neural networks

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A review of explainable AI in medical imaging: implications and applications

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International Journal of computers and Applications 2024年第11期46卷 983-997页

作者： Kinger, Shakti Kulkarni, Vrushali Department of Computer Engineering and Technology WPU School of Computer Science & Engineering Dr. Vishwanath Karad MIT World Peace University Maharashtra India

Deep learning approaches have attained remarkable success across various artificial intelligence applications, spanning healthcare, finance, and autonomous vehicles, profoundly impacting human existence. However, their black-box nature, lack of transparency, and inability to elucidate conclusions have hindered their adoption in high-risk applications. Explainable Artificial Intelligence (XAI) encompasses a suite of tools, approaches, and algorithms aimed at furnishing highly accurate explanations while preserving robust accuracy. This research paper investigates the intricate and evolving domain of Explainable Artificial Intelligence (XAI), particularly its implications in healthcare, with the objective of fostering trust in AI utilization within this domain. It encompasses a spectrum of topics, including a compendium of tasks XAI should fulfil in medical imaging, an examination of current methodologies for yielding transparent and understandable outcomes in medical imaging, criteria for assessing AI system explainability, and recommendations for integrating XAI into medical imaging practices. This review facilitates the selection of suitable and efficient XAI techniques for medical imaging while aiding developers in grasping the fundamentals of these methodologies. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Medical imaging

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An Advanced Brain Tumour Detection Framework Using Improved Fast Mask R-CNN Techniques for Enhanced Accuracy

An Advanced Brain Tumour Detection Framework Using Improved ...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Shankar, G. Kumar, V.R. Abhishek Srikanth, Akkaru Reddy, Thummala Jathin Sahil, Shaik Rmd Engineering College Department of Computer Science and Engineering India Rmd Engineering Department of Computer Science and Engineering India

ISBN: (纸本)9798331510022

To enhance the precision of diagnosis, this research provides a new structure for identifying brain tumors that integrates an Improved Fast Mask Region based Convolutional Neural Network (IFMRCNN) with complex image processing algorithms. Identification of brain tumors is still a significant difficulty in medical imaging since successful therapy depends on early and accurate identification. By precisely identifying tumor boundaries in brain MRI scans, the proposed model makes use of an enhanced version of FMRCNN is intended to enhance separating and categorizing performance. Sophisticated image processing methods are used to improve the extraction of features, allowing the model to more accurately differentiate between tumor and non-tumor areas. By lowering false positives and true negatives, the structure seeks to maintain computing power while greatly increasing detection rates. The proposed strategy outperforms conventional techniques in terms of reliability, specificity, and sensitivity, according to the results of experiments, which makes it a viable tool for support with clinical choices as well as initial diagnosis in neuroimaging. IFRCNN was applied using the Open CV package in conjunction with an SVM algorithm. Using 218 photos as an initial training set, this study's approach demonstrated a 100% success rate in classifying meningiomas and 87.5% accuracy in classifying gliomas, with an average conviction score of 94.6% in the segments of meningioma tumors. A neurologist does a manual examination and ground truth assessment to verify the divided tumor areas. © 2024 IEEE.

关键词： Convolutional neural networks

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