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Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

computers, Materials & Continua 2024年第10期81卷 867-883页

作者： Mahesh Thyluru Ramakrishna Kuppusamy Pothanaicker Padma Selvaraj Surbhi Bhatia Khan Vinoth Kumar Venkatesan Saeed Alzahrani Mohammad Alojail Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India School of Computer Science and Engineering VIT-AP UniversityAmaravati522241India Department of Computer Science&Technology Madanapalle Institute of Technology&ScienceMadanapalle517325India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK School of Computer Science Engineering&Information Systems(SCORE) Vellore Institute of Technology(VIT)Vellore632014India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11531Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpuraPunjab140401India

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within computer Science and engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

关键词： Deep learning MRI brain tumor cassification EfficientNetB3 computational engineering healthcare technology artificial intelligence in medical imaging tumor segmentation neuro-oncology

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Combinatorial Kinetic Dualistic Auction Model for Metaverse Service Allocation 1

Combinatorial Kinetic Dualistic Auction Model for Metaverse ...

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IEEE AITU: 1st International Student Conference: "Digital Generation", AITU 2024

作者： Singh, Raj Sarkar, Pritam Bandyopadhyay, Anjan Swain, Sujata Sardar, Tanvir Habib Pandey, Bishwajeet School of Computer Engineering KIIT 751024 India Department of Computer Science GITAM University Bengaluru India Department of Intelligent System and Cybersecurity Astana IT University Kazakhstan

ISBN: (纸本)9798350364378

The focus of this research work revolves around the utilization of a specialized"Combinatorial Kinetic Dualistic Auction Model" designed specifically for"metaverse Services." The main goal is to improve the allocation of resources in this ever-changing environment and enhance the overall experience for end-users. Through the exploration of metaverse services, we aim to further develop the comprehension and practical application of the Combinatorial Kinetic Dualistic Auction Model, ultimately contributing to a more immersive and responsive virtual experience. ©2024 IEEE |

关键词： Resource allocation

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Architecture and Control Simulation for Improved system Resiliency of a Building Microgrid 59

Architecture and Control Simulation for Improved System Resi...

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59th IEEE/IAS Industrial and Commercial Power systems Technical Conference, I and CPS 2023

作者： Correia, Alexandre F. M. Lopes, Jose M. B. Coimbra, A. Paulo Moura, Pedro de Almeida, Anibal T. University of Coimbra Institute of System and Robotics Department of Electrical and Computer Engineering Coimbra Portugal

ISBN: (纸本)9798350396515

The increasing frequency of natural disasters has led to situations where small towns and critical infrastructure, become isolated from the main utility grid. The microgrids' ability to work autonomously from the utility grid presents a viable solution to this problem. Microgrid resiliency is the characteristic related to the capacity of a microgrid to minimize the impact of disruptive events and ensure that the power supply is maintained under a variety of adverse conditions. This is specially important for critical infrastructures such as hospitals, communications and military bases. The objective of this paper is to simulate strategies and propose an algorithm for the design and management of electric microgrids with a focus on resiliency towards critical and disaster situations. The proposed solutions will be simulated, using a building microgrid implemented in the University of Coimbra as a pilot. As a result of the work, the study presents an algorithm that efficiently manages loads of a microgrid in order to increase its resilience when the microgrid is isolated from the utility grid. © 2023 IEEE.

关键词： Smart power grids

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Network Traffic Obfuscation system for IIoT-Cloud Control systems

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computers, Materials & Continua 2022年第9期72卷 4911-4929页

作者： Yangjae Lee Sung Hoon Baek Jung Taek Seo Ki-Woong Park SysCore Lab. Sejong UniversitySeoul05006Korea Department of Computer System Engineering Jungwon UniversityChungcheongbuk-do28024Korea Department of Computer Engineering Gachon UniversityGyeonggi-do13120Korea

One of the latest technologies enabling remote control,operational efficiency upgrades,and real-time big-data monitoring in an industrial control system(ICS)is the IIoT-Cloud ICS,which integrates the Industrial Internet of Things(IIoT)and the cloud into the *** an ICS benefits from the application of IIoT and the cloud in terms of cost reduction,efficiency improvement,and real-time monitoring,the application of this technology to an ICS poses an unprecedented security risk by exposing its terminal devices to the outside *** adversary can collect information regarding senders,recipients,and prime-time slots through traffic analysis and use it as a linchpin for the next attack,posing a potential threat to the *** address this problem,we designed a network traffic obfuscation system(NTOS)for the IIoT-Cloud ICS,based on the requirements derived from the ICS characteristics and limitations of existing NTOS *** a strategy to solve this problem wherein a decrease in the traffic volume facilitates traffic analysis or reduces the packet transmission speed,we proposed an NTOS based on packet scrambling,wherein a packet is split into multiple pieces before transmission,thus obfuscating network *** minimize the ICS modification and downtime,the proposed NTOS was designed using an agentbased *** addition,for the ICS network traffic analyzer to operate normally in an environment wherein the NTOS is applied,a rule-based NTOS was adopted such that the actual traffic flow is known only to the device that is aware of the rule and is blocked for *** experimental results verified that the same time requested for response and level of difficulty of analysis were maintained by the application of an NTOS based on packet scrambling,even when the number of requests received by the server per second was *** network traffic analyzer of the ICS can capture the packet flow by using the pre-communicated NTOS *** addition,by designing an N

关键词： Cloud computing system moving-target defense container orchestration

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Embedded Machine Learning on Accelerometer Data for Exercise Classification

Embedded Machine Learning on Accelerometer Data for Exercise...

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2023 IEEE Applied Sensing Conference, APSCON 2023

作者： Nissa, Rufyid-U Karmakar, Nemai Chandra Baghini, Maryam Shojaei Indian Institute of Technology Bombay Electrical Engineering Department Mumbai India Monash University Department of Electrical and Computer System Engineering Australia

ISBN: (纸本)9781665461634

This paper describes the use of embedded machine learning (ML) on raw accelerometer data to classify three lower-limb exercises. The developed model, which uses supervised ML can accurately classify the exercises. The model performed with an accuracy of 99.41 % on the test data and F1 score of 0.99. The trained model is deployed on the microcontroller board, Arduino Nano 33 BLE Sense (ARM Cortex M4F processer). The system has been developed using open-source platforms. The developed wearable device, PhysioSens has the potential to enhance limb exercise monitoring in home-based physical therapy. © 2023 IEEE.

关键词： Machine learning

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EverydayFantasy - A Mobile Computing Application in Mental Healthcare 18

EverydayFantasy - A Mobile Computing Application in Mental H...

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18th Iberian Conference on Information systems and Technologies, CISTI 2023

作者： Triohin, Victor Leba, Monica Ionica, Andreea Cristina University of Petrosani Department of System Control and Computer Engineering Petrosani Romania University of Petrosani Department of Management and Industrial Engineering Petrosani Romania

ISBN: (纸本)9789893347928

The paper proposes a game, EverydayFantasy, based on the principles of serious games, game mechanics and gamification and intended for young people with cognitive behavior issues. The game is a mobile computing application in mental healthcare, in the architecture of which is integrated the connection to a database through a server and the use of dedicated APIs such as the one for locating on the map. The game creates a fantasy environment superimposed on the real environment in which the player is motivated to move, socialize and solve different challenges, all to improve his estimated state. © 2023 ITMA.

关键词： Serious games

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Joint Spectrum Sensing and Resource Allocation for OFDMA-Based Underwater Acoustic Communications

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IEEE Communications Letters 2025年第6期29卷 1300-1304页

作者： Kim, Minwoo Choi, Youngchol Kim, Yeongjun Seo, Eojin Yang, Hyun Jong Department of Electrical Engineering Pohang37673 Korea Republic of Daejeon34103 Korea Republic of System LSI Modem Development Team Device Solutions Samsung Electronics Hwaseong18488 Korea Republic of Seoul National University Department of Electrical and Computer Engineering Seoul08826 Korea Republic of

Underwater acoustic (UWA) communications generally rely on cognitive radio (CR)-based ad-hoc networks due to challenges such as long propagation delay, limited channel resources, and high attenuation. To address the constraints of limited frequency resources, UWA communications have recently incorporated orthogonal frequency division multiple access (OFDMA), significantly enhancing spectral efficiency (SE) through multiplexing gains. Still, the low propagation speed of UWA signals, combined with the dynamic underwater environment, creates asynchrony in multiple access scenarios. This causes inaccurate spectrum sensing as inter-carrier interference (ICI) increases, which leads to difficulties in resource allocation. As efficient resource allocation is essential for achieving high-quality communication in OFDMA-based CR networks, these challenges degrade communication reliability in UWA systems. To resolve the issue, we propose an end-to-end sensing and resource optimization method using deep reinforcement learning (DRL) in an OFDMA-based UWA-CR network. Through extensive simulations, we confirm that the proposed method is superior to baseline schemes, outperforming other methods by 42.9 % in SE and 4.4 % in communication success rate. © 1997-2012 IEEE.

关键词： Deep reinforcement learning

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Preventive Power Outage Estimation Based on A Novel Scenario Clustering Strategy

Preventive Power Outage Estimation Based on A Novel Scenario...

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2024 IEEE Power and Energy Society General Meeting, PESGM 2024

作者： Wang, Xiaofei Liu, Weijia Ding, Fei Yao, Yiyun Zhao, Junbo Power System Engineering Center National Renewable Energy Laboratory Golden United States Department of Electrical and Computer Engineering University of Connecticut Storrs United States

The increasing occurrence of extreme weather events is challenging power grid operation. For extreme weather events, the system operator is responsible for estimating the power outages and scheduling the restoration resources. This paper proposes an outage evaluation framework to identify the possible unserved load profiles, vulnerable areas, and mobile energy adequacy. The outputs of an outage prediction model tool are used to generate numerous faulted line scenarios. Next, each scenario's nodal unserved load profile is obtained by solving a three-phase restoration model that considers repair crews and mobile energy resources (MERs). Then, a novel scenario clustering strategy is developed to cluster the unserved load profiles into multiple representative profiles which the system operator can focus on. Finally, case studies on a distribution system evaluate the damage caused by an extreme weather event and verify the effectiveness of the proposed scenario clustering strategy. © 2024 IEEE.

关键词： Mobile energy resources (MERs) power system resilience preventive outage evaluation scenario clustering

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Enhanced Video Streaming Based Computing Encoder for Digital Visual Processing 2

Enhanced Video Streaming Based Computing Encoder for Digital...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Kumar, Tarun Shukla, Amita Galgotias University Department of Computer Science and Engineering Greater Noida India Noida Institute of Engineering and Technology Department of Computer Science and Business System Greater Noida India

ISBN: (纸本)9798350347456

The rise of video streaming for digital visual processing has been a boon for the industry of visual processing. Video streaming technology has made it easier for companies to capture, analyze, and interpret visual data faster than ever before. It has allowed for the storage and transmission of large amounts of visual data at high speeds, providing businesses with the ability to process and interpret this data in real-time. Video streaming technology can be used in a wide variety of applications, including facial recognition, 3D mapping, and object recognition. By streaming video data, companies can quickly and accurately identify individuals, recognize objects, and track movement. This technology can also be used in security applications, such as surveillance and monitoring, as well as in medical imaging, such as MRI and CT scans. Video streaming technology has also allowed companies to create more efficient visual processing systems. The streaming video data can be used to automate the process of image recognition and object classification. This has allowed companies to reduce the amount of time and effort needed to interpret visual data. Additionally, streaming video data can be used to create virtual reality experiences, providing users with an immersive experience when viewing digital images. © 2023 IEEE.

关键词： Video streaming

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The Innovation Development of Data Prediction and Clustered Compressive Sensing (CCS) in Environmental Application 2

The Innovation Development of Data Prediction and Clustered ...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Kukreja, Sonia Jain, Garima Galgotias University Department of Computer Science and Engineering Greater Noida India Noida Institute of Engineering and Technology Department of Computer Science and Business System Greater Noida India

ISBN: (纸本)9798350347456

Data prediction in environmental applications is a quickly evolving field that provides tremendous potential for improving environmental management and decision-making. By leveraging the power of data, predictive models can be developed to identify potential environmental risks, mitigate the impacts of climate change, and improve the efficiency of environmental management. Data prediction in environmental applications can be used to better understand the relationships between environmental phenomena and the environment. For instance, predictive models can be used to identify areas that are more likely to experience extreme weather events and inform the development of strategies for responding to such events. Predictive models can also be used to analyze the effects of changing climate on ecosystems and help inform decisions regarding the management of natural resources. Data prediction in environmental applications can also be used to develop more effective management strategies. Predictive models can be used to determine which areas are more likely to experience water scarcity or air pollution, and which areas are more likely to benefit from certain conservation practices. By leveraging data, predictive models can also help predict how certain management practices will affect the environment and inform decision-makers on how to best allocate resources. © 2023 IEEE.

关键词： Decision making

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