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A framework to support ubiquitous healthcare monitoring and diagnostic for sickle cell disease 11th

A framework to support ubiquitous healthcare monitoring and ...

引用

11th international conference on intelligent Computing, ICIC 2015

作者： Khalaf, Mohammed Hussain, Abir Jaafar Al-Jumeily, Dhiya Fergus, Paul Keenan, Russell Radi, Naeem Applied Computing Research Group School of Computing and Mathematical Sciences Liverpool John Moores University Byrom Street LiverpoolL3 3AF United Kingdom Liverpool Paediatric Haemophilia Centre Haematology Treatment Centre Alder Hey Children’s Hospital Eaton Road West Derby LiverpoolL12 2AP United Kingdom Al Khawarizmi International College Abu Dhabi United Arab Emirates

ISBN: (纸本)9783319221854

Recent technology advances based on smart devices have improved the medical facilities and become increasingly popular in association with real-time health monitoring and remote/personals health-care. Healthcare organisations are still required to pay more attention for some improvements in terms of cost-effectiveness and maintaining efficiency, and avoid patients to take admission at hospital. Sickle cell disease (SCD) is one of the most challenges chronic obtrusive disease that facing healthcare, affects a large numbers of people from early childhood. Currently, the vast majority of hospitals and healthcare sectors are using manual approach that depends completely on patient input, which can be slowly analysed, time consuming and stressful. this work proposes an alert system that could send instant information to the doctors once detects serious condition from the collected data of the patient. In addition, this work offers a system that can analyse datasets automatically in order to reduce error rate. A machine-learning algorithm was applied to perform the classification process. Two experiments were conducted to classify SCD patients from normal patients using machine learning algorithm in which 99 % classification accuracy was achieved using the Instance-based learning algorithm. © Springer international Publishing Switzerland 2015.

关键词： Hospitals

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intelligent multi-agent based genetic fuzzy ensemble network intrusion detection

引用

11th international conference on Neural Information Processing, ICONIP 2004

作者： Sindhu, Siva S. Sivatha Ramasubramanian, P. Kannan, A. Department of Computer Science and Engineering Anna University Chennai600025 India

ISBN: (纸本)3540239316

this paper proposes a distributed superior approach for preventing malicious access to corporate information system. It is to identify a foolproof system to obviate the manual analysis and breaches in the networking system by using a distributed approach and a technique of genetic algorithm that automates the generation of fuzzy rules. the experimental study is performed using audit data provided by MIT Lincoln labs. In order to reduce single point of failures in centralized security system, a dynamic distributed system has been designed in which the security management task is distributed across the network using intelligent Multi-Agents. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Multi agent systems

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Deep Reinforcement learning Based Dynamic Resource Allocation in Cloud Radio Access Networks 11

Deep Reinforcement Learning Based Dynamic Resource Allocatio...

引用

11th international conference on Information and Communication Technology Convergence, ICTC 2020

作者： Tasnim Rodoshi, Rehenuma Kim, Taewoon Choi, Wooyeol Chosun University Department of Computer Engineering Gwangju Korea Republic of Hallym University School of Software Chuncheon Korea Republic of

ISBN: (纸本)9781728167589

Cloud radio access network (C-RAN) is a promising architecture to fulfill the ever-increasing resource demand in telecommunication networks. In C-RAN, a base station is decoupled into baseband unit (BBU) and remote radio head (RRH). the BBUs are further centralized and virtualized as virtual machines (VMs) inside a BBU pool. this architecture can meet the massively increasing cellular data traffic demand. However, resource management in C-RAN needs to be designed carefully in order to reach the objectives of energy saving and to meet the user demand over a long operational period. Since the user demands are highly dynamic in different times and locations, it is challenging to perform the optimal resource management. In this paper, we exploit a deep reinforcement learning (DRL) model to learn the spatial and temporal user demand in C-RAN, and propose an algorithm that resizes the VMs to allocate computational resources inside the BBU pool. the computational resource allocation is done according to the amount of required resources in the associated RRHs of the VMs. through an extensive evaluation study, we show that the proposed algorithm can make the C-RAN network resource-efficiency while satisfying dynamic user demand. © 2020 IEEE.

关键词： Reinforcement learning

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the Model-Free learning Enhanced Motion Control of DC motor

The Model-Free Learning Enhanced Motion Control of DC motor

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11th international conference on Electrical Machines and Systems(第11届国际电机与系统会议)

作者： Rongmin Cao Wei Zhang Zhongsheng Hou Beiijjiing IInsttiittutte off Machiinerry IIndusttrry / Deparrttmentt off Computterr& Auttomattiion Beijing Jiaotong University / School of Electronics and Information Engineering Beijing 100044 Ch

this paper presents an approach towards learning enhanced motion control of DC motor, suitable for applications involving repeated iterations of motion trajectories. the overall structure of the control consists of a feedback and a feed-forward components. the model-free learning adaptive feedback control (MFLAC) provides for the main system stabilization and an iterative learning control (ILC) algorithm is proposed to serve as a feedforward compensation to nonlinear and unknown dynamics and disturbances, thereby enhancing the improvement achievable with PID or MFLAC alone. It serves as the basis for simulation study of the proposed control scheme. A comparison of the performance achieved with traditional PID and MFLAC is also provided to highlight the advantages of the additional intelligent feedforward mode.

关键词： ILC computer simulation DC motor MFLAC nonlinear systems stability

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Curbing Anomalous Transactions Using Cost-Sensitive learning 7th

Curbing Anomalous Transactions Using Cost-Sensitive Learning

引用

7th international conference on Information and Communication Technology for intelligent Systems, ICTIS 2023

作者： Aswathy, S. Viji Rajendran, V. Computer Science and Engineering NSS College of Engineering Palakkad India

ISBN: (纸本)9789819937578

Credit card fraud is one of the most common crimes in online transactions, posing a significant threat to banks, credit card companies, and consumers. there are several strategies to prevent and identify these anomalous activities, but they all struggle with the class imbalance problem. Usually, traditional algorithms tend to ignore this unequal distribution of data, thereby neglecting the misclassification of costs. this leads to poor classification, which affects the fraud prediction. Generic data level algorithms are typically used to balance such unequal data, but cost misclassification (Singh and Jain in J Inf Optim Sci 41:1–14, 2020 [1]) persists, so this paper attempts to solve both the class imbalance problem (Singh and Jain in J Inf Optim Sci 41:1–14, 2020 [1];Singh et al. in J Exper theor Artif Intell 34:571–598, 2021 [2]) and the cost misclassification problem by using a correlation-based feature selection method, optimizing these features with the help of an optimization algorithm, the Genetic algorithm (GA) (Roseline et al. in Comput Electr Eng 102:1–11, 2022 [3]), and finally, a cost-sensitive (Ling et al. in Cost sensitive learning. Encyclopedia of machine learning. Springer, Boston, pp 231–235, 2011 [4]) classifier to deal with cost misclassification. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Genetic algorithms

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Classification of Brain MRI Images Using Convolution Neural Network and Transfer learning

Classification of Brain MRI Images Using Convolution Neural ...

引用

international conference on Computing and Networking Technology (ICCNT)

作者： N Ambily K Suresh Government Engineering College Barton Hill Thiruvananthapuram

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

Magnetic Resonance Image (MRI) is a non intrusive procedure for obtaining high resolution images of living tissues. Varieties of tissue contrast images that MRI modalities produce provide important structural information, enabling diagnosis and segmentation of tumors. In this paper we come up with an integrated model of Convolutional Neural Network and Transfer learning for automated binary classification of brain MRI images. We use a sixteen layer pretrained network, to distinguish normal and abnormal images. Our experiments suggest good accuracy in classification on test data, and thus will turn out to be useful for efficient automated diagnosis.

关键词： Feature extraction Magnetic resonance imaging Tumors Training Biological neural networks Convolution Support vector machines

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A Fast Method of Function Approximation using Broad learning System 6

A Fast Method of Function Approximation using Broad Learning...

引用

6th international conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2019

作者： Ma, Yuzhuo Li, Tieshan Zuo, Yi Philip Chen, C.L. Yuan, Liang'en Shan, Qihe Dalian Maritime University Navigation College Dalian China University of Macau Faculty of Science and Technology China

ISBN: (纸本)9781728158570

In this paper, A new method is applied to approach function -the Broad learning System (BLS). Due to the complexity of the system in actual engineering, when the traditional algorithm handles the approximation problem, it faces problems such as complicated calculations and severe time consumption. To solve the above problems, the BLS uses the way of network horizontal expansion, horizontal extension and vertical immobility. Input data is mapped to feature nodes as feature vectors of the network, and then connected enhancement nodes by random weights, the enhancement node and the function node are spliced together as an input layer, and then directly connected to the output of the system. Finally, compared with traditional algorithms: back propagation neural network (BPNN), Radial Basis Function neural network (RBFNN), the results of experiments show that the function approximation method based on the BLS in this paper can make the system use less time under the premise of ensuring the approximation accuracy. this result provides the possibility of real-time and high-precision approximation for other intelligent algorithms. © 2019 IEEE.

关键词： learning systems

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Deep learning Framework-Based automated Multi-class Diagnosis for Neurological Disorders 7

Deep Learning Framework-Based Automated Multi-class Diagnosi...

引用

7th international conference on Automation, Control and Robots, ICACR 2023

作者： Ali, Syed Saad Azhar Memon, Khuhed Yahya, Norashikin Sattar, Karim Asif El Ferik, Sami King Fahd University of Petroleum & Minerals Interdisciplinary Research Center for Smart Mobility Logistics Aerospace Engineering Department Dhahran31261 Saudi Arabia Universiti Teknologi Petronas Centre for Intelligent Signal & Imaging Research Department of Electrical & Electronic Engineering Perak Seri Iskandar32610 Malaysia King Fahd University of Petroleum & Minerals Interdisciplinary Research Center for Smart Mobility & Logistics Dhahran31261 Saudi Arabia King Fahd University of Petroleum & Minerals Interdisciplinary Research Center for Smart Mobility & Logistics Control Instrumentation Engineering Department Dhahran31261 Saudi Arabia

ISBN: (纸本)9798350302882

Magnetic Resonance Imaging, being a harmless, non-invasive and highly informative modality, has proved to be one of the most widely accepted and used neuroimaging modality for visualizing human brain. Brain MR images possess similar features specially in case of neurodegenerative disorders containing very subtle and intricate changes rendering the diagnosis process very challenging. Manual inspection by experts of such images results in diagnoses based on their expertise, with a probability of misdiagnosis due to subtle changes in such images depending on disease stage, overlapping features, among other factors. In addition, in case of unavailability of experts in remote areas, accurate and timely diagnosis can be a problem. the advent of humongous multi-modal data and Deep learning techniques has enabled researchers to develop intelligent classification methods with adequate performance accuracies. A review of the literature suggests that a lot of research has been carried out in the direction of automatic diagnosis of neurological disorders, but to date, no consolidated framework has been developed with the capabilities to classify multiple diseases and their sub-types with adequate accuracy from structural and functional MR images of varying types and planes of orientation. the contributions of this research include the design of a unified framework for multiple neurological disease diagnosis resulting in the development of a generic assistive tool for hospitals and neurologists to precisely and briskly diagnose disorders that might result in saving lives in addition to increasing the quality of life of patients suffering from neurodegenerative disorders. To materialize this idea, Deep learning has been deployed to train a three class model to classify Brain Tumors, Parkinson's disease and normal subjects. A test accuracy of 83.69% has been achieved even with limited dataset used for training, thereby encouraging the idea of a unified framework to diagnose neurod

关键词： Computer aided diagnosis

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On Geometric Properties of Adversarial Examples

On Geometric Properties of Adversarial Examples

引用

IEEE international Workshop on intelligent data Acquisition and Advanced Computing Systems: Technology and Applications

作者： Andrii Ivaniuk Galyna Kriukova National University of Kyiv-Mohyla Academy Kyiv Ukraine

ISBN: (纸本)9781665426060

Majority of widely used machine learning models are vulnerable to adversarial attacks, which are slight modifications of inputs that change the model's prediction with high confidence. the detection of such attacks and adversarial samples is still an open problem. In this work, we continue exploration of a novel method for adversarial image detection by means of linear algebra approach. the method is built on a comparison of distances to the centroids for a given point and its neighbours. the method of adversarial examples detection is considered theoretically, and the numerical experiments are done to illustrate the approach. A number of methods of adversarial examples generation are considered. It is shown, that the method is prone to error in case of certain adversarial attacks and training sets.

关键词： Training Dimensionality reduction conferences Neural networks data acquisition Linear algebra Predictive models

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Design and Development of a Transformer Condition Diagnosis System Application to Predict Purification Time and Life of a Transformer Based on Machine learning 11

Design and Development of a Transformer Condition Diagnosis ...

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11th international conference on Electrical engineering, Computer Science and Informatics, EECSI 2024

作者： Bukit, Ferry R.A. Zulkarnain, Hendra Putra, Andre Yuanda Universitas Sumatera Utara Department of Electrical Engineering Medan Indonesia

ISBN: (纸本)9798350355314

Transformers are equipment in high-voltage engineering that often experience abnormalities originating from internal or external sources. this abnormality causes disturbances, so it is necessary to diagnose faults in the transformer as early as possible so that fault predictions and maintenance actions can be carried out to maintain the quality of work on the transformer. An application was designed to diagnose the transformer's condition and predict purification time and transformer life to maintain the quality of the transformer's work. this application is designed to help make it easier to predict transformer conditions using dissolved gas analysis test results, oil scores, furan, and tap changer. the application design is done using the Python programming language and then converted into a mobile app using the React Native library framework with a simulator using Expo and connected to a database to store user data. the predicted data is for the GT 2.1 Excitation transformer, GT 2.1 Main Transformer, and GT 2.1 UAT transformer. Based on the application design, the test results were 100% successful;the system validated all the tests from the ten tests. the GT 2.1 Excitation transformer oil purification time is predicted to be 5697 days since the last test, and the transformer lifetime is predicted to be more than 15 years. the GT 2.1 Main Trafo transformer oil purification time is predicted to be 2697 days since the last test, and the transformer lifetime is predicted to be more than 15 years. the GT 2.1 Excitation transformer oil purification time is predicted to be 1866 days since the last test, and the transformer lifetime is predicted to be more than 15 years. © 2024 IEEE.

关键词： Electric transformer testing

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