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Alzheimer's Disease Classification Approach Using MRI Imaging 3

Alzheimer's Disease Classification Approach Using MRI Imagin...

引用

3rd international Mobile, Intelligent, and Ubiquitous computing conference, MIUCC 2023

作者： Khaled, Eman Amr, Farah Essam, Farah Nouh, Maryam Sherif, Youssef Yakan, Zyad Gayar, Ziad El Abdelraouf, Ashraf Ain Shams University Faculty of Engineering Department of Computer Engineering and Software Systems Cairo Egypt Misr International University Faculty of Computer Scinece Cairo Egypt

ISBN: (纸本)9798350306231

Alzheimer's disease (AD) is a slowly progressing, irreversible brain condition that weakens memory and negatively affects the patient's quality of life. Alzheimer's disease (AD) can be identified using Magnetic Resonance Imaging (MRI) data. For an early diagnosis of the disease, various medical and diagnostic approaches are being investigated. Even while MRI is a useful tool for locating AD-related brain symptoms, the acquisition process is time-consuming, largely because workflow bottlenecks must be manually evaluated. In order to find the best effective method for detecting the disease, this research examines the basic technique for analyzing MRI images. To carry out our study to slow progression of the disease by the use of Alzheimer's disease (AD) prognosis, a dataset from The Alzheimer's Disease Neuroimaging Initiative (ADNI) will be imported and fitted. The outcomes highlight the tremendous potential of integrating imaging data for automated categorization of Alzheimer's disease (AD) using multidisciplinary AI techniques. With a deep three-dimensional convolutional network (3D CNN) being used to handle the three-dimensional MRI input and a Transformer encoder being applied to manage the genetic sequence input, the suggested solution merges machine learning, bioinformatics, and other image processing techniques. After various experiments by checking the results accuracy, it is stated that the CNN model is never enough to provide us with the desired accuracy either by training on both skull stripped data or the GM tissue segmented data. Although, it is relatively better at the skull stripped dataset training, but the results accuracy and predicted classes show that inferring some classifiers after extracting the features from the CNN would increase the accuracy and results. After applying Support Vector Machine SVM-RBF, SVM-POLY, and XGBoost, it is concluded that the training of the Skull Stripped dataset with features extracted from the CNN model we provided an

关键词： Magnetic resonance imaging

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Research on an Intelligent Structure for Push Control of Multi-Center Cloud data Pool

Research on an Intelligent Structure for Push Control of Mul...

引用

Intelligent Control, Measurement and Signal processing and Intelligent Oil Field (ICMSP), international conference on

作者： Huang Ying Department of Information Engineering Armed Police Engineering University Xi'an China

The intelligent control of complex system must first solve the problem of data calculation, application and real-time support, which is not only the difficulty and focus of control system design, but also the key to artificial intelligence and intelligent manufacturing. It is found that the essence of control system are both processing data and controlling the direction of data flow. So, the connotation of intelligence lies in an open data processing system to increase the control of data flow, thus forming the basis of real-time data processing. Therefore, this paper proposes an intelligent push control structure of multi center cloud data pool. The structure takes multi-layer cloud data pool as the center, vertical cloud data pool processes global parameters, and uses block chain technology to keep data synchronization. Horizontal cloud data pool collects status data by different function, and parameters required by control loop are pushed by cloud data pool. So, cloud computing ensures the separation of data computing and control, and multi-layer cloud data pool can form sensor networks. The structure realizes the intelligent control target of complex large-scale system that can not only implement big data calculation, but also control the flow direction of data.

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Prediction of obesity among school going children using Machine learning algorithms

Prediction of obesity among school going children using Mach...

引用

Innovative computing, Intelligent Communication and Smart Electrical Systems (ICSES), international conference on

作者： Seeta Devi Ranjana Chavan Harshita Gupta Harikrishnan R Symbiosis College of Nursing (SCON) Symbiosis International Deemed University (SIDU) Pune India Symbiosis Institute of Technology (SIT) Pune Campus Symbiosis International Deemed University (SIDU) India

Obesity has developed as a major global concern in the modern era due to the continuing trend towards unhealthy lifestyles characterized by excessive junk food consumption, insufficient sleep patterns, and prolonged periods of sedentary behavior. This research aims to predict the possibility of obesity using machine learning algorithms. We collected data from a total of 250 samples, which included moms and school-aged children of varied ages, as well as those affected by obesity and those who were not. Six well-ML learning techniques were used to study this. We used supervised machine learning techniques including RF, DT, GB, adaboost, catboost, and MLP to evaluate the suggested model. The results showed that combining the AdaBoost and MLP algorithms produced the best results, with 96% accuracy, 92% precision, 96% recall, and 94% F1. Using machine learning to analyze public health data can improve forecasts, find complicated patterns, and improve our understanding of issues such as identifying obesity risk factors.

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LDVNet: Lightweight and Detail-Aware Vision Network for Image Recognition Tasks in Resource-Constrained Environments 29

LDVNet: Lightweight and Detail-Aware Vision Network for Imag...

引用

29th IEEE international conference on Parallel and Distributed Systems, ICPADS 2023

作者： Liu, Yuchen Liang, Hu Qiu, Ran Zhao, Shengrong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

In many underwater application scenarios, recognition tasks need to be executed promptly on computationally limited platforms. However, models designed for this field often exhibit spatial locality, and existing works lack the ability to capture crucial details in images. Therefore, a lightweight and detail-aware vision network (LDVNet) for resource-constrained environments is proposed to overcome the limitations of these approaches. Firstly, in order to enhance the accuracy of target image recognition, we introduce transformer modules to acquire global information, thus addressing the issue of spatial locality inherent in traditional convolutional neural networks (CNNs). Secondly, to maintain the network's lightweight nature, we integrate the transformer module with convolutional operations, thereby mitigating the substantial parameter and floating point operations (FLOPs) overhead. Thirdly, for the efficient extraction of crucial fine-grained details from feature maps, we have devised a channel and spatial attention module (C&SA). This module aids in recognizing intricate and fine-grained visual tasks and enhances image understanding. It is seamlessly integrated into LDVNet with nearly negligible parameter overhead. The experimental results demonstrate that LDVNet outperforms other lightweight networks and hybrid networks in different recognition tasks, while being suitable for resource-constrained environments. © 2023 IEEE.

关键词： Detail-aware Image recognition Lightweight

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Real-Time Clickstream data processing and Visualization Using Apache Tools

Real-Time Clickstream Data Processing and Visualization Usin...

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international conference on computing Communication Control and Automation (ICCUBEA)

作者： T. B. Patil Keshav Anand Aditya Bhateja Kashif Jamal S. T. Sawant-Patil Priyanka Paygude Dept. of Information Technology Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune India

In the modern digital generation, clickstream data analytics is a crucial component of online selling and purchasing platforms. It offers useful information for enhancing user experience, conversion rates, website design, and keeping track of the success of digital marketing efforts. A data warehouse is required to store and analyze clickstream data, which requires a lengthy and complex ETL process. Two of the leading large data processing tools and distributed streaming systems are Spark and Kafka. This paper presents a comprehensive examination of recent research pertaining to clickstream data analytics, real-time data visualization, data lifecycle management utilizing Apache technologies. Based on the review, the paper proposes an architecture that can aid organizations and local merchants in effectively visualizing the performance of their products. This paper provides a valuable resource for researchers and practitioners seeking to explore the latest developments in their future work.

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More Speaking or More Speakers?

More Speaking or More Speakers?

引用

international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Dan Berrebbi Ronan Collobert Navdeep Jaitly Tatiana Likhomanenko Carnegie Mellon University Apple

Self-training (ST) and self-supervised learning (SSL) methods have demonstrated strong improvements in automatic speech recognition (ASR). In spite of these advances, to the best of our knowledge, there is no analysis of how the composition of the labelled and unlabelled datasets used in these methods affects the results. In this work we aim to analyse the effect of number of speakers in the training data on a recent SSL algorithm (wav2vec 2.0), and a recent ST algorithm (slimIPL). We perform a systematic analysis on both labeled and unlabeled data by varying the number of speakers while keeping the number of hours fixed and vice versa. Our findings suggest that SSL requires a large amount of unlabeled data to produce high accuracy results, while ST requires a sufficient number of speakers in the labelled data, especially in the low-regime setting. In this manner these two approaches improve supervised learning in different regimes of data composition.

关键词： Systematics Supervised learning Signal processing algorithms Training data Self-supervised learning Signal processing Acoustics

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Exploration on Blended Teaching Mode of Python Programming Based on PBL

Exploration on Blended Teaching Mode of Python Programming B...

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Information and Education Technology (ICIET), international conference on

作者： Yingxue Zhu Mingyou Liu Yue Wang Tao Wu School of Biology and Engineering Guizhou Medical University Guiyang Guizhou China

Python programming is widely used in scientific computing, data processing, visualization, image processing, natural language processing, machine learning, artificial intelligence and other fields, and more and more majors offer this course to provide strong support for cultivating high-quality composite "new engineering" talents. This paper explores the teaching of Python programming course for biomedical engineering majors in view of the shortcomings of the current teaching of Python programming, from several aspects such as the objectives and ideas of hybrid teaching, the content and structure of hybrid teaching in PBL mode, the design of online and offline teaching steps and teaching activities, and the design of course assessment system. The aim is to enhance students' self-learning ability and motivate them to learn, so as to cultivate their comprehensive and innovative practical application ability.

关键词： Training Scientific computing Image processing Education data visualization Machine learning Natural language processing

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Terpsichora: A Tool to Generate Synthetic MP-Declare Process Models

Terpsichora: A Tool to Generate Synthetic MP-Declare Proce...

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international Workshops which were held in conjunction with the 6th international conference on Process Mining, ICPM 2024

作者： da Silva Santos, Wesley Coutinho, Juliana Rezende Baião, Fernanda Spyrides, Georges Miranda Lopes, Hélio Côrtes Vieira Department of Informatics PUC-Rio Rio de Janeiro Brazil Department of Industrial Engineering PUC-Rio Rio de Janeiro Brazil

ISBN: (纸本)9783031822247

Process models play a fundamental role in the Business Process Management lifecycle and are crucial for assessing the robustness of proposed algorithms and conducting benchmarks among different tools. However, public models are limited as they expose strategic knowledge. While some researchers developed public repositories of imperative models, there remains a lack of diverse, publicly available multi-perspective declarative models. Our work aims to bridge this gap by providing a tool for generating synthetic MP-Declare process models. We leverage Large Language Models to generate these models, ensuring coverage of diverse aspects and enhancing the resource pool for the BPM community. © The Author(s) 2025.

关键词： declarative process management large language models mp-declare process models synthetically generated data

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Secure Encryption Scheme for Medical data based on Homomorphic Encryption

Secure Encryption Scheme for Medical Data based on Homomorph...

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data Science and Network Security (ICDSNS), international conference on

作者： Liaoran Xu Chenyang Zhao Weili Jiang Jun Ye Yan Zhao Zhengqi Zhang School of Cyberspace Security Hainan University Key Laboratory of Internet Information Retrieval of Hainan Province Haikou China Hainan Provincial Public Security Department Haikou China Shanwei Institute of Techmology Shanwei China Key Laboratory of Internet Information Retrieval of Hainan Province Haikou China

With the development of cloud computing, cloud healthcare has brought convenience to users and improved the utilization of healthcare resources, but it also brings some security risks. To this end, this paper proposes a new solution that combines homomorphic encryption technology and domestic cryptographic algorithms for protecting the privacy of cloud medical electronic medical records. It is found that by localizing user keys, data leakage due to malicious damage can be effectively prevented. In addition, this study proposes a hybrid encryption scheme and a ciphertext digest-based search and statistics approach to reduce the computational overhead required to decrypt all electronic medical records. The scheme is the first to use a combination of domestic cryptographic algorithms and homomorphic encryption techniques in a cloud medical data storage and computing application scenario to achieve secure and reliable cloud medical data storage and computing. In addition, the study provides several recommendations for healthcare organizations and cloud platform service providers to help them better protect the security and privacy of healthcare data.

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Algorithm optimization of multi-source heterogeneous big data in the context of cloud computing

Algorithm optimization of multi-source heterogeneous big dat...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Yajie Liu Tianjin University of Technology Tianjin China

Nowadays, Internet technology has rapidly spread to all over the world, which brings a large amount of information data, and how to achieve efficient processing in these rapidly growing heterogeneous data has become a research hotspot in the field of big data nowadays. For this hotspot, this paper firstly introduces multi-source heterogeneous and cloud computing, and then proposes a multisource heterogeneous big data optimization method, which obtains big data information sources for data scheduling priority calculation by threshold comparison in the context of cloud computing, determines data by priority value, and selects suitable processors for execution, and proves that this algorithm has the advantages of higher acceleration ratio and throughput by comparing this algorithm with traditional big data scheduling methods.

关键词： Cloud computing Program processors Processor scheduling Optimization methods Computer applications Big data Throughput

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