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Empowering Healthcare with AI: Brain Tumor Detection Using MRI and Multiple Algorithms

Empowering Healthcare with AI: Brain Tumor Detection Using M...

2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Arowolo, Micheal Olaolu Oluwatobiloba Ajayi, Williams Olawoye, Prisca Babajide, Oluwatomilola Aigbogun, Happiness Eric Damola Salawu, Mukhtar Adebiyi, Marion Olubunmi Adebiyi, Ayodele University of Missouri Department of Electrical Engineering and Computer Science Columbia65201 United States Landmark University Department of Computer Science Omu-Aran Nigeria Western Illinois University Department of Public Health 61455 United States Stowers Institute for Medical Research Kansas CityMO United States Kwara State University Department of Computer Science Malete Nigeria

ISBN: (纸本)9798350358155

Artificial intelligence (AI) has revolutionized neuroimaging, especially in the identification of brain cancers, by giving patients greater control and providing highly accurate diagnostic imaging. utilizing MRI data, this study investigates the feasibility of AI-driven brain tumor diagnosis utilizing methods including K-nearest neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Naive Bayes (NB). Careful data preprocessing allowed us to access Kaggle's brain MRI datasets labeled with cancer information, which allowed us to obtain accurate results. While Naive Bayes achieved 60% accuracy and 84% sensitivity, SVM achieved 82% sensitivity and 62% accuracy. Enhanced sensitivity and precision, in addition to 63% accuracy, were shared by Decision Tree and KNN. These findings suggest that AI could be helpful in the multi-method detection strategy that is essential for the correct diagnosis of brain tumors. This study provides clinicians with flexible diagnostic tools powered by artificial intelligence, opening a new chapter in the fight against neurological disorders and for better patient outcomes. The combination of artificial intelligence with magnetic resonance imaging (MRI) holds great promise for the better early detection and treatment planning of brain cancers. As AI keeps changing the healthcare landscape, these studies show how AI can revolutionize diagnostics and the fight against neurological illnesses. © 2024 IEEE.

关键词： Support vector machines

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Approach for Double Security with Watermarking and Chaos Based Encryption for e-Healthcare Systems 4

Approach for Double Security with Watermarking and Chaos Bas...

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4th IEEE International Conference on electrical, computer, and Energy Technologies, ICECET 2024

作者： Sharma, Suvita Rani Singh, Birmohan Kaur, Manpreet Singh, Harpreet Sant Longowal Institute of Engineering and Technology Department of Computer Science and Engineering Punjab Sangrur148106 India Sant Longowal Institute of Engineering and Technology Department of Electrical and Instrumentation Engineering Punjab Sangrur148106 India

ISBN: (纸本)9798350395914

Medical data of the patients includes sensitive information and needs security while transmit over the internet. The double security approach based on watermarking and encryption method has been purposed in this work to secure the patient information consisting personal information, clinical information, and diagnostic records along with the medical images. The watermarking method is introduced on the basis of the least significant bit (LSB) method that conceals the patient information in the LSB bit of the image to hide the information. As, the LSB bit of the image is consider as least informative bit. Thus, it reduces the loss of data in the image. Then, the watermarked image is secured using the hybrid encryption method based on the Self Adaptive Bald Eagle Search (SABES) optimization algorithm, multiple chaotic maps, and circular shift. Chaotic maps are employed in the proposed approach to increase randomness in the encrypted image and chaotic maps' parameters are optimized employing the SABES optimization method to increase the chaotic maps efficiency. The propounded approach is tested on different types of medical images i.e., Grayscale, RGB, and hyperspectral images having different modalities and sizes. The performance of the propounded approach has been analyzed utilizing the Normalized Correlation (NC), Peak Signal-to-Noise Ratio (PSNR), entropy, histogram, Number of Changing Pixel Rate (NPCR), Structural Similarity Measure (SSIM), and Unified Averaged Changed Intensity (UACI) test. The proposed approach performance shows that the medical information secure from different types of attacks. © 2024 IEEE.

关键词： Electronic health record

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Novel Design of UWB Jeans Based Textile Antenna for Body-Centric Communications

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computer Systems science & engineering 2022年第9期42卷 1079-1093页

作者： Mohammad Monirujjaman Khan Bright Yeboah-Akowuah Kaisarul Islam Eric Tutu Tchao Sumanta Bhattacharyya Rajesh Dey Mehedi Masud Fahad Alraddady Department of Electrical and Computer Engineering North South UniversityDhaka1229Bangladesh Department of Computer Engineering Kwame Nkrumah University of Science and TechnologyKumasiGhana Electronics and Communication Engineering Cambridge Institute of TechnologyRanchiIndia Department of Electronics and Communication Engineering Brainware Group of Institutions-SDETKolkataIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

This research presents an ultra-wideband (UWB) textile antenna designfor body-centric applications. The antenna is printed on a 1 mm thick denim substrate with a 1.7 relative permittivity. The jeans substrate is sandwiched between apartial ground plane and a radiating patch with a Q-shaped slot. The slotted radiating patch is placed above the substrate and measures 27.8 mm × 23.8 mm. In freespace, the antenna covers the ultra-wideband spectrum designated by the FederalCommunication Commission (FCC). Various parameters of the antenna designwere changed for further performance evaluation. Depending on the operating frequency, the antenna's realized gain varied from 2.7 to 5 dB. The antenna achievedhigh radiation efficiency with an omnidirectional radiation pattern. A parametricstudy was performed in research on varying antenna substrates and other components of the antenna. The three outermost layers of the human body are used tomodel a human phantom for on-body simulation. After that, the antenna wasplaced at five different distances from the phantom. The findings demonstrate thatat close distances to the phantom, the antenna's gain and efficiency at lower frequencies are reduced. The antenna's radiation efficiency and gain were muchhigher at higher frequencies for distances greater than 6 mm. Compared to freespace, the antenna's radiation pattern was more omnidirectional, especially athigher frequencies. This antenna is novel, compact and has an ultra wide bandwidth, a maximum of 94.60% radiation efficiency and a 5 dBi gain that will makeit a good candidate for body-centric communications.

关键词： Ultra-wideband body-centric communication on-body textile jeans slotted-patch WBAN

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An IoT-Based Sustainable Automation System for the Garments Industry in Bangladesh 3

An IoT-Based Sustainable Automation System for the Garments ...

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3rd International Conference on Emerging Frontiers in electrical and Electronic Technologies, ICEFEET 2023

作者： Ahmed, Khandakar Rabbi Salam, Towfika Rabbu, Mohammad Mahmudul Haque, F.M. Hasan, Nazmul Miraz, Shafiun Rana, Md. Masud Mansur, Maliha Tabassum, Humayra Shayer, Khandokar Shadman Miyan Research Institute International University of Business Agriculture and Technology Dhaka Bangladesh Dept. of Mechatronics Engineering Rajshahi6204 Bangladesh International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Ahsanullah University of Science and Technology Dept. of Computer Science & Engineering Dhaka Bangladesh Ahsanullah University of Science and Technology Dept. of Electrical & Electronic Engineering Dhaka Bangladesh Varendra University Dept. of Computer Science and Engineering Rajshahi Bangladesh Dept. of Computer Science and Engineering Rajshahi Bangladesh Ahsanullah University of Science & Technology-Bangladesh Dept. of Electrical & Electronic Engineering Dhaka Bangladesh American International University-Bangladesh Dept. of Electrical & Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350307399

The Readymade Garments sector accelerates Bangladesh's development. The 'Made in Bangladesh' brand has also helped to boost Bangladesh's reputation and brand recognition on a global scale. Despite its limited resources, the country has maintained a 6% annual GDP growth rate while attaining extraordinary social and human development. On the other hand, the phrase 'Internet of Things' (IoT) refers to a broad concept that can be summarized as an ecosystem of intelligent gadgets equipped with networks, sensors, and processing tools. IoT is a new technological platform that enables the many objects in our environment to connect via sensors and the Internet to improve our quality of life. Manufacturing and the industrial sector, agriculture, the health sector, smart cities, security, and emergency services, to name a few. IoT implementation in the garment sector would open a new door to growth in production and worker safety. Implementing home automation and safety systems in the garment sector will help in production and ensure the safety of the workers. The suggested IoT -based sustainable automation system for the Bangladeshi garments sector is efficient, eco-friendly, secure, reliable, and user-friendly. The system's user-friendly interface makes it straightforward to use and manage. The Bangladeshi garments industry's IoT -based sustainable automation system is a comprehensive and integrated solution that addresses the industry's primary difficulties and provides a sustainable and viable future. © 2023 IEEE.

关键词： Internet of things

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Online Learning Behavior Analysis and Prediction Based on Spiking Neural Networks

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Journal of Social Computing 2024年第2期5卷 180-193页

作者： Yanjing Li Xiaowei Wang Fukun Chen Bingxu Zhao Qiang Fu the Institute of Education Science Research Heilongjiang UniversityHarbin 150080China the School of Cyberspace Security Shandong University of Political Science and LawJinan 250014China the School of Computer Science and Technology Harbin Engineering UniversityHarbin 150001China the Faculty of Electrical and Computer Engineering University of VictoriaVictoriaV8P 5C2Canada. the Guangxi Key Lab of Brain-Inspired Computing and Intelligent Chips School of Electronic and Information EngineeringGuangxi Normal UniversityGuilin 541004China

The vast amount of data generated by large-scale open online course platforms provide a solid foundation for the analysis of learning behavior in the field of *** study utilizes the historical and final learning behavior data of over 300000 learners from 17 courses offered on the edX platform by Harvard University and the Massachusetts Institute of Technology during the 2012-2013 academic *** have developed a spike neural network to predict learning outcomes,and analyzed the correlation between learning behavior and outcomes,aiming to identify key learning behaviors that significantly impact these *** goal is to monitor learning progress,provide targeted references for evaluating and improving learning effectiveness,and implement intervention measures *** results demonstrate that the prediction model based on online learning behavior using spiking neural network achieves an impressive accuracy of 99.80%.The learning behaviors that predominantly affect learning effectiveness are found to be students’academic performance and level of participation.

关键词： online learning learning outcomes prediction learning behavior analysis spiking neural network

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Edge Computing for Efficient Storage and Low-Latency Video Streaming in Cloud Environments

Edge Computing for Efficient Storage and Low-Latency Video S...

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2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023

作者： Darwich, Mahmoud Khalil, Kasem Ismail, Yasser Bayoumi, Magdy University of Mount Union Department of Mathematics and Computer Science AllianceOH44601 United States University of Mississippi University Department of Electrical and Computer Engineering MS38677 United States Southern University and AandM College Electrical Engineering Department Baton RougeLA70807 United States University of Louisiana at Lafayette Department of Electrical and Computer Engineering LA70504 United States

ISBN: (纸本)9798350322347

Efficient storage and low-latency video streaming are critical for delivering high-quality multimedia experiences in cloud environments. This research explores the potential of edge computing as a solution to address these challenges. By strategically deploying computational resources at the network edge, edge computing minimizes latency and enhances storage capabilities, leading to an improved user experience. We propose a cutting-edge prototype system that integrates edge computing into the cloud infrastructure. Through rigorous experiments, we evaluate key performance metrics, including latency, bitrate, and storage utilization, the results show the effectiveness of the proposed approach for video streaming in the cloud. Particularly, it reduces the latency by up to 25% and storage utilization by up to 15%. © 2023 IEEE.

关键词： Edge computing

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Optimization of SVM Based on IPSO Algorithm for Tool Life Prediction 6

Optimization of SVM Based on IPSO Algorithm for Tool Life Pr...

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6th International Conference on computer Network, Electronic and Automation, ICCNEA 2023

作者： Gao, Xianghong Cao, Yan Xi'an Technological University School of Mechanical and Electrical Engineering Xi'an China Xi'an Technological University School of Computer Science and Engineering Xi'an China

ISBN: (纸本)9798350305388

As the basic parts of workshop processing, the life of the tool will affect the processing efficiency and processing quality, and the life of the tool will be affected by a variety of uncertain factors in the processing process, which makes it difficult to accurately predict. In order to improve the prediction accuracy, an improved IPSO algorithm was proposed to optimize the penalty factor c and kernel parameter g of SVM based on particle swarm optimization (PSO). Firstly, the influencing parameters of tool life are analyzed and the experimental data are extracted, and then the data is brought into the IPSO-SVM model to obtain the predicted data. IPSO algorithm can realize the adaptive change of inertia weight and introduce asynchronous learning formula to dynamically adjust the learning factor, which can effectively improve the prediction accuracy of tool life. Compared with the traditional PSO-SVM prediction model, the average relative error and average absolute error of IPSO-SVM are reduced by 5.275% and 5.006% respectively, which indicates that the SVM model optimized by IPSO algorithm has higher prediction accuracy. © 2023 IEEE.

关键词： Forecasting

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Near-sensor computing-assisted simultaneous viral antigen and antibody detection via integrated label-free biosensors with microfluidics

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InfoMat 2023年第10期5卷 80-95页

作者： Byungjoon Bae Yongmin Baek Jeongyong Yang Heesung Lee Charana S.S.Sonnadara Sangeun Jung Minseong Park Doeon Lee Sihwan Kim Gaurav Giri Sahil Shah Geonwook Yoo William APetri Kyusang Lee Department of Electrical and Computer Engineering University of VirginiaCharlottesvilleVirginiaUSA School of Electronic Engineering Soongsil UniversitySeoulRepublic of Korea Department of Electrical and Computer Engineering University of MarylandCollege ParkMarylandUSA Department of Chemical Engineering University of VirginiaCharlottesvilleVirginiaUSA Division of Infectious Diseases and International Health Department of MedicineSchool of MedicineUniversity of VirginiaCharlottesvilleVirginiaUSA Department of Material Science and Engineering University of VirginiaCharlottesvilleVirginiaUSA Department of Intelligent Semiconductors Soongsil UniversitySeoulRepublic of Korea

Precise diagnosis and immunity to viruses,such as severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)and Middle East respiratory syndrome coronavirus(MERS-CoV)is achieved by the detection of the viral antigens and/or corresponding antibodies,***,a widely used antigen detection methods,such as polymerase chain reaction(PCR),are complex,expensive,and time-consuming Furthermore,the antibody test that detects an asymptomatic infection and immunity is usually performed separately and exhibits relatively low *** achieve a simplified,rapid,and accurate diagnosis,we have demonstrated an indium gallium zinc oxide(IGZO)-based biosensor field-effect transistor(bio-FET)that can simultaneously detect spike proteins and antibodies with a limit of detection(LOD)of 1 pg mL–1 and 200 ng mL–1,respectively using a single assay in less than 20 min by integrat-ing microfluidic channels and artificial neural networks(ANNs).The near-sensor ANN-aided classification provides high diagnosis accuracy(>93%)with significantly reduced processing time(0.62%)and energy consumption(5.64%)compared to the software-based *** believe that the development of rapid and accurate diagnosis system for the viral antigens and antibodies detec-tion will play a crucial role in preventing global viral outbreaks.

关键词： artificial intelligence biosensor edge-computing integration microfluidic

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Mean-field chaos diffusion models 24

Mean-field chaos diffusion models

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Proceedings of the 41st International Conference on Machine Learning

作者： Sungwoo Park Dongjun Kim Ahmed M. Alaa Department of Electrical Engineering and Computer Sciences UC Berkeley Department of Computer Science Stanford Department of Electrical Engineering and Computer Sciences UC Berkeley and UCSF

In this paper, we introduce a new class of score-based generative models (SGMs) designed to handle high-cardinality data distributions by leveraging concepts from mean-field theory. We present mean-field chaos diffusion models (MF-CDMs), which address the curse of dimensionality inherent in high-cardinality data by utilizing the propagation of chaos property of interacting particles. By treating high-cardinality data as a large stochastic system of interacting particles, we develop a novel score-matching method for infinitedimensional chaotic particle systems and propose an approximation scheme that employs a subdivision strategy for efficient training. Our theoretical and empirical results demonstrate the scalability and effectiveness of MF-CDMs for managing large high-cardinality data structures, such as 3D point clouds.

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Dataset Ratio Influence on kNN Classification Results 33rd

Dataset Ratio Influence on kNN Classification Results

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33rd International Conference on Organization and Technology of Maintenance, OTO 2024

作者： Gaće, Marin Galba, Tomislav Baumgartner, Alfonzo Livada, Časlav Faculty of Electrical Engineering Josip Juraj Strossmayer University of Osijek Computer Science and Information Technology Osijek Kneza Trpimira 2B Osijek31000 Croatia

ISBN: (纸本)9783031805967

Classification is used in many fields today, and for most of them machine learning algorithms can be used to make a decision. This article investigates the effects of different sizes of training and test datasets on the accuracy of classification using both classical k-nearest neighbors (kNN) and weighted k-nearest neighbors (wkNN) methods. This study systematically evaluates how different ratios of training to test data affect classification performance and provides a comprehensive analysis of the interplay between the ratios of the datasets and the parameter k. The results show that the ratio of training to test datasets exerts a more significant influence on classification accuracy than the choice of the parameter k. In particular, extreme ratios, where the training dataset is either much larger or much smaller than the test dataset, lead to suboptimal classification results. This emphasizes the importance of carefully selecting an appropriate balance between training and test data to ensure robust and reliable classification results. By highlighting the critical role of the ratio between datasets, this paper provides valuable insights for optimizing kNN and weighted kNN methods in practical applications. The results have far-reaching implications for the design and implementation of machine learning models and emphasize the need for a balanced data distribution to achieve high classification accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： k-nearest neighbors

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