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检索条件"任意字段=7th IASTED International Conference on Advances in Computer Science and Technology"

共 1494 条记录，以下是11-20 订阅

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A Comparative Study of Brain Tumor Detection using Convolutional Neural Networks with MRI Images 7

A Comparative Study of Brain Tumor Detection using Convoluti...

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7th international conference on Information technology, Information Systems and Electrical Engineering, ICITISEE 2023

作者： Miah, Jonayet Hasan, Md Maruf Ahmed, Ashiqul Haque Ayon, Eftekhar Hossain Ca, Duc M Koli, Tahera University of South Dakota Department of Computer Science South Dakota United States University of South Dakota Economics & Decision Science South Dakota United States Gannon University Department of Computer & Info Science EriePA United States University of Tennessee Department of Economics KnoxvilleTN United States

ISBN: (纸本)9798350382266

this research emphasizes the global health challenge of brain tumors and emphasizes the importance of early detection using Convolutional Neural Networks (CNNs) on Magnetic Resonance Imaging (MRI). the dataset, including healthy and tumor MRI scans, underwent careful processing for CNN input. With a SoftMax Fully Connected layer, the CNN achieved 98% accuracy, outperforming Radial Basis Function (RBF) and Decision Tree (DT) classifiers. Feature extraction through clustering improved CNN accuracy, with the SoftMax classifier reaching 99.52% on test data. the study advances deep learning in medical image analysis, highlighting CNN-MRI synergy for precise brain tumor detection and potential advancements in early treatment and patient care. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Robotic Process Automation for Automating Business Processes: A use case 7

Robotic Process Automation for Automating Business Processes...

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7th international conference on Computing Methodologies and Communication, ICCMC 2023

作者： Bhardwaj, Vivek Virender Kumar, Mukesh thakur, Deepak Lamba, Vikas School of Computer Science and Engineering Lovely Professional University Jalandhar India Chandigarh University Punjab India School of Computer Application Lovely Professional University Jalandhar India Chitkara University Institute of Engineering and Technology Chitkara University Punjab 140401 India

ISBN: (纸本)9781665464086

Numerous advances and changes have been brought about by digitalization in both our daily lives and the business world. RPA is an all-purpose technology for developing specialized agents, or "bots," that communicate with GUI components to carry out routine, rule-based operations. Anyone can utilize the RPA to automate digital operations. RPA deployment in business processes can help to provide better service and guarantee that processes adhere to more efficiency and accuracy. the objective of this study is to enable human workers to assign their regular generic repetitive tasks to a software bot, freeing them up to concentrate on more challenging and difficult activities. this study presents a methodology for implementing RPA to extract employee information from the XMLpages. © 2023 IEEE.

关键词： Automation Anywhere Business Processes RPA Software BOT

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Overview of Current Trends in Machine Learning Approaches for EEG-Based Brain computer Interface Applications 7

Overview of Current Trends in Machine Learning Approaches fo...

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7th international conference on Informatics and Applied Mathematics, IAM 2024

作者： Ferdi, Ahmed Yassine Ghazli, Abdelkader Department of Mathematics and Computer Sciences Faculty of Exact Sciences Tahri Mohamed University Bechar Algeria Laboratory of INIE Tahri Mohamed University Bechar Algeria

the potential of the human brain to communicate and interact with the environment is promoted by advances in neuroscience and computer science, making brain-computer interface (BCI) top interdisciplinary study. Addition-ally, with recent developments in machine learning (ML), electroencephalogram (EEG)-based BCIs for AI are gaining popularity. this review article offers a look at recent research on brain-computer interfaces (BCIs) and how the technology of machine learning (ML) is used in BCIs. It highlights the role that ML has had in the execution of various BCI tasks and examines the various research methodologies used in this area. Additionally, it discusses ML techniques for detecting mental states, classifying mental tasks, classifying emotions, classifying electroencephalogram (EEG) signals, classifying event-related potential (ERP) signals, classifying motor picture data, and classifying limb movements. this paper aids readers in learning about recent advances in BCI and ML as well as upcoming discoveries required to enhance and create better BCI applications. © 2024 Copyright for this paper by its authors.

关键词： Electroencephalography

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Planning of Electric Vehicle Charging Stations for Four Wheeled Passenger Vehicles in Guwahati: An Approach through Genetic Algorithm 7

Planning of Electric Vehicle Charging Stations for Four Whee...

引用

7th international conference on computer Applications in Electrical Engineering-Recent advances, CERA 2023

作者： Siddhartha Chaturvedi, Harsh Indian Institute of Technology School of Energy Science & Engineering Guwahati India Manipal Institute of Technology Manipal Academy of Higher Education Department of Electrical & Electronics Engineering Manipal India

ISBN: (纸本)9798350305005

Strategically designing and placing electric car charging stations helps urban customers accept electric vehicles as their main form of transportation. However, policy economics, charging demand, user comfort, and road traffic circumstances affect the position and setup of these electric car charging stations. this study presents a charging station placement and capacity optimization methodology. the goal is to increase operator income and reduce user billing costs. In addition, the road time-consuming index is used to analyze the effect of road congestion on the user's extra charging expenditures, improving user satisfaction. A modified non-dominated sorting genetic algorithm (NSGA-II) is used to construct charging stations with varied constraints in this study. the paper identifies Guwahati's urban road network charging station locations and sizes for 2030 electric car charging needs. © 2023 IEEE.

关键词： Roads and streets

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Neural network-based time stepping scheme for multiscale partial differential equations 7

Neural network-based time stepping scheme for multiscale par...

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7th international conference on computer Applications in Electrical Engineering-Recent advances, CERA 2023

作者： Hamid, Asif Rafiq, Danish Nahvi, Shahkar Ahmad Bazaz, Mohammad Abid Islamic University of Science & Technology Department of Electrical Engineering J&K 192122 India National Institute of Technology Department of Electrical Engineering J&K Srinagar190006 India

ISBN: (纸本)9798350305005

the macroscopic coherent behavior in complex systems usually may occur from the interactions of microscopic agents, such as molecules, cells and individuals in a population, between themselves and their environment resulting in the multiscale behavior of such systems. Multiscale systems are computationally expensive to simulate as they exhibit behavior at multiple spatial and temporal scales. Capturing the fastest time scale requires taking extremely small steps in numerical time stepping algorithms while capturing the slow scale behavior necessitates long term solution, thus rendering the task computationally prohibitive. In this paper, we demonstrate the application of deep learning techniques to derive an accurate time stepping scheme for multiscale systems. the proposed technique can capture the multiscale PDE behavior with high accuracy while being computationally tractable. We demonstrate the scheme on two benchmark nonlinear PDEs that exhibit multiscale behavior. © 2023 IEEE.

关键词： Deep neural networks

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Protection for Turn-to-Turn Internal Fault in Shunt Reactors using Time-Domain Approach 7

Protection for Turn-to-Turn Internal Fault in Shunt Reactors...

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7th international conference on computer Applications in Electrical Engineering-Recent advances, CERA 2023

作者： Mohanty, Rabindra Pradhan, Ashok Kumar Indian Institute of Technology Delhi Department of Energy Science and Engineering Hauz Khas110016 India Indian Institute of Technology Kharagpur Department of Electrical Engineering West Bengal 721302 India

ISBN: (纸本)9798350305005

this paper presents a turn-to-turn internal fault detection technique using time-domain approach in a three-phase wye-connected shunt reactor. the proposed protection method is sensitive to a very low level turn-to-turn fault. An unbalanced component is computed using the difference of measured current and calculated balanced component of current in each phase. the balanced component is obtained by subtracting the zero sequence component from the calculated phase current in time-domain. the unbalanced current component for normal operation of shunt reactor is zero and that for internal fault case is non-zero. Since the phase current of the reactor is calculated using terminal voltage and equivalent inductance of each phase, the proposed method is compliant to change in system voltage. Various cases including turn-to-turn fault, simultaneous fault, unbalance in system voltage and switching of reactor are studied to validate the performance of the proposed time-domain protection method of reactor. © 2023 IEEE.

关键词： Time domain analysis

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Adaptive Silo Networks with Cloud Computing and Reinforcement Learning for Responsive Grain Storage 7

Adaptive Silo Networks with Cloud Computing and Reinforcemen...

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7th international conference on Inventive Computation Technologies, ICICT 2024

作者： Jehan, C. Kumaresh, P.S. Suguna, M. Raja Rosaline, R. Anto Arockia Suguna, M. Murugan, S. Vel Tech Multitech Dr. Rangarajan Dr. Sagunthala Engineering College Department of Computer Science And Engineering Chennai Tamil Nadu Avadi India K.L.N.College of Engineering Department of Electronics And Communication Engineering Tamil Nadu Sivagangai India Rmk College of Engineering And Technology Department of Computer Science And Engineering Tamil Nadu Chennai India Srm Institute of Science And Technology Department of Computing Technologies Kattankulathur Campus Chennai Tamil Nadu Chengalpattu India Department of Computer Science And Engineering Tamil Nadu Salem India Saveetha Institute of Medical And Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350359299

this research study presents a novel method for managing grain storage facilities by combining adaptive silo networks, cloud computing, and reinforcement learning (RL). the suggested system's goal is to improve grain storage efficiency by automatically modifying storage parameters in response to changes in the surrounding environment and real-time data. the adaptive silo network uses cloud computing capabilities to keep track of things like temperature, humidity, and grain quality in real-time. the proposed system is able to learn and adapt to new circumstances due to the use of RL algorithms. Interacting with environmental elements such as weather patterns, grain kinds, and demand changes, the model learns optimum storage solutions. the proposed approach improves storage efficiency with the use of advanced algorithms, adaptive control mechanisms, and real-time monitoring. Contributing to the long-term viability and financial success of agricultural supply chains, it optimizes grain storage systems to reduce losses and provide responsive management. With cloud computing, scalability and dispersed data processing, analysis, and decision-making are improved. the device enhances storage efficiency and gives advanced warning of impending problems, allowing for preventative maintenance and protection. To increase the responsiveness and efficiency of grain storage facilities, suggest using cloud computing and RL to create adaptive silo networks. By integrating advanced technology to overcome challenges in grain storage and management it advances the development of smart agriculture practices. © 2024 IEEE.

关键词： Cloud computing

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Robotic Restroom Hygiene Solutions with IoT and Recurrent Neural Networks for Clean Facilities 7

Robotic Restroom Hygiene Solutions with IoT and Recurrent Ne...

引用

7th international conference on Inventive Computation Technologies, ICICT 2024

作者： Nasreen, A. Kathija Shenbagapriya, M. Seeni, Senthil Kumar Veda, Parthiban Meenakshi, B. Murugan, S. Rmk College of Engineering And Technology Department of Computer Science And Engineering Tamil Nadu Chennai India Department of Biomedical Engineering Tamil Nadu Namakkal India Cognizant Technology Solutions Department of Mobile Application Development Buffalo GroveIL United States Srm Institute of Science And Technology Centre For Yoga College of Engineering And Technology Chenagalpattu Chennai Tamil Nadu Kattankulathur India Sri Sairam Engineering College Department of Electrical And Electronics Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350359299

Public restrooms needs to be frequently cleaned to maintain public health and hygiene. Traditional bathroom cleaning techniques may not solve current cleanliness issues. this research proposes an advanced robotics, Internet of things (IoT), and Recurrent Neural Networks (RNNs) solution to these problems. the proposed system uses autonomous robots with IoT sensors and cameras. Robots identify cleaning and maintenance needs in restrooms. A central system receives data from IoT sensors on cleanliness indicators, including toilet paper, soap, and foot movement. Recurrent Neural Networks (RNN) processes this data to predict and prioritize cleaning requirements. the RNN monitors toilet conditions in real time and reacts to changing use and cleaning needs. this dynamic technique optimizes cleaning resource allocation and maintains facility cleanliness. the device also warns cleaning personnel when certain areas need quick attention. this novel technique makes restroom care more cost-effective, responsive, and environmentally friendly by combining robots, IoT, and RNNs. this study advances smart facility management, which uses technology to improve public space cleanliness, user experience, and resource usage. © 2024 IEEE.

关键词： Internet of things

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Designing a Resilient SDN Framework with IoT Integration for Enhanced Network Reliability 7

Designing a Resilient SDN Framework with IoT Integration for...

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7th international conference on Inventive Computation Technologies, ICICT 2024

作者： Jegadeesan, S. Aishwarya, S.S. Harshini, R.G. Diraviam, A. Justin Sujatha, S. Krishnan, R. Santhana Velammal College of Engineering And Technology Department of Information Technology Madurai India Scad College of Engineering And Technology Department of Computer Science And Engineering Cheranmahadevi India Adhiyamaan College of Engineering Department of Information Technology Hosur India Scad College of Engineering And Technology Department of Electronics And Communication Engineering Cheranmahadevi India

ISBN: (纸本)9798350359299

the ever-expanding Internet of things (IoT) landscape poses significant challenges for network reliability and resilience. this research investigates the design and implementation of a resilient Software-Defined Networking (SDN) framework with Internet of things (IoT) integration, aiming to enhance network reliability. the proposed model, referred to as HybridSDN-IoT Resilience (HyRes), introduces a hybrid SDN approach by amalgamating traditional and distributed SDN strategies. this hybridization seeks to balance the advantages of centralized control and distributed resilience within the network architecture. To estimate the efficacy of the HyRes model implementation, comprehensive simulation analyses are conducted, comparing its performance against established algorithms in the SDN domain. Metrics such as network latency, packet loss, and throughput are employed to assess the reliability of the network under varying conditions. the simulation results offer significant performance results for the resilience and adaptability of HyRes in comparison to existing algorithms, showcasing its potential to mitigate network failures and enhance overall reliability. the proposed HyRes model not only establishes a foundation for resilient SDN architectures but also discourses the precise challenges in the integration of diverse IoT devices. By emphasizing adaptability and responsiveness, the research advances the discourse on the practical realization of reliable SDN frameworks in the context of emerging IoT technologies. © 2024 IEEE.

关键词： Software defined networking

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Analyzing the Computational Efficiency of LLM Models for NLP Tweet Classification During Emergency-Crisis 7th

Analyzing the Computational Efficiency of LLM Models for NLP...

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7th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Raja, Sabarish Raja Ramesh Antony Vigil, M.S. Pattaiah, Muthukumar Sudarson, B. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9783031699818

Social media platforms which include Twitter, Reddit, Instagram, and YouTube have emerged as key information sources during emergencies or natural disasters. However, in the case of Twitter, it might be challenging to distinguish the tweets that are genuinely informative due to the overwhelming number of tweets that are sent during these occurrences. this study proposes a method for classifying the tweets as "informative" and "non-informative" using natural language processing. the technique considers the tweets’ subjects, hashtags segmentation, and locations for pre-processing. BERT, RoBERTa, LSTM, and DistilBERT are four different NLP models that have been trained and tested. the findings will indicate the highest classification accuracy for determining if a tweet is informative or not. Our findings reveal that DistilBERT achieved the highest accuracy of 92.0% in testing and 89% in precision of the Twitter dataset. © IFIP international Federation for Information Processing 2024.

关键词： Tweets

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