The innovation effect of administrative hierarchy on intercity connection: The machine learning of twin cities

Journal of Innovation & Knowledge, Journal Year: 2023, Volume and Issue: 8(1), P. 100293 - 100293

Published: Jan. 1, 2023

Do cities with higher administrative hierarchies always enjoy intercity connections? Based on an innovation of twin (Chengdu-Chongqing) in China, this paper uses machine learning methods (linear model, support vector machines, neural network and random forest) to predict the effect a new hierarchy connections. The key findings indicate following. (1) connections is asymmetric cities. (2) For city later-higher (LHC, Chongqing case), initially negative (approximately 10 years for Chongqing) then becomes positive. (3) (IHC, Chengdu continually (4) underlying mechanism reverse trend LHC due more financial independence, investment transportation infrastructure less transaction cost market access associated hierarchy, which have innovative after time lag Chongqing). (5) However, IHC enjoys continual net-positive connection its first-mover advantage diffusion LHC. These shed light practical implications that connections, primary concern dominant strategy cooperation trickle-down rather than siphon effect. We should tailor different strategies separate twin-city pair. This can provide contributions only if see one another politically as "siblings" "divorced couple".

Language: Английский

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Review on Alzheimer Disease Detection Methods: Automatic Pipelines and Machine Learning Techniques

Sci, Journal Year: 2023, Volume and Issue: 5(1), P. 13 - 13

Published: March 21, 2023

Alzheimer’s Disease (AD) is becoming increasingly prevalent across the globe, and various diagnostic detection methods have been developed in recent years. Several techniques are available, including Automatic Pipeline Methods Machine Learning that utilize Biomarker Methods, Fusion, Registration for multimodality, to pre-process medical scans. The use of automated pipelines machine learning systems has proven beneficial accurately identifying AD its stages, with a success rate over 95% single binary class classifications. However, there still challenges multi-class classification, such as distinguishing between MCI, well sub-stages MCI. research also emphasizes significance using multi-modality approaches effective validation detecting stages.

Language: Английский

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An Explainable AI Paradigm for Alzheimer’s Diagnosis Using Deep Transfer Learning

Diagnostics, Journal Year: 2024, Volume and Issue: 14(3), P. 345 - 345

Published: Feb. 5, 2024

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects millions of individuals worldwide, causing severe cognitive decline and memory impairment. The early accurate diagnosis AD crucial for effective intervention management. In recent years, deep learning techniques have shown promising results in medical image analysis, including from neuroimaging data. However, the lack interpretability models hinders their adoption clinical settings, where explainability essential gaining trust acceptance healthcare professionals. this study, we propose an explainable AI (XAI)-based approach disease, leveraging power transfer ensemble modeling. proposed framework aims to enhance by incorporating XAI techniques, allowing clinicians understand decision-making process providing valuable insights into diagnosis. By popular pre-trained convolutional neural networks (CNNs) such as VGG16, VGG19, DenseNet169, DenseNet201, conducted extensive experiments evaluate individual performances on comprehensive dataset. ensembles, Ensemble-1 (VGG16 VGG19) Ensemble-2 (DenseNet169 DenseNet201), demonstrated superior accuracy, precision, recall, F1 scores compared models, reaching up 95%. order transparency diagnosis, introduced novel model achieving impressive accuracy 96%. This incorporates saliency maps grad-CAM (gradient-weighted class activation mapping). integration these not only contributes model’s exceptional but also provides researchers with visual regions influencing Our findings showcase potential combining realm paving way more interpretable clinically relevant healthcare.

Language: Английский

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A Robust Distributed Deep Learning Approach to Detect Alzheimer’s Disease from MRI Images

Mathematics, Journal Year: 2023, Volume and Issue: 11(12), P. 2633 - 2633

Published: June 9, 2023

Alzheimer’s disease has become a major concern in the healthcare domain as it is growing rapidly. Much research been conducted to detect from MRI images through various deep learning approaches.However, problems of availability medical data and preserving privacy patients still exists. To mitigate this issue detection, we implement federated approach, which found be more efficient, robust, consistent compared with conventional approach. For this, need excavation on orientations transfer architectures. Then, utilize two publicly available datasets (OASIS ADNI) design cases evaluate performance The approach achieves better accuracy sensitivity approaches most cases. Moreover, robustness proposed also than In our MobileNet, low-cost architecture, highest 95.24%, 81.94%, 83.97% OASIS, ADNI, merged (ADNI + OASIS) test sets, much higher achieved Furthermore, only weights model are shared, keeps original their respective hospital or institutions, domain.

Language: Английский

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Deep Learning for Alzheimer’s Disease Prediction: A Comprehensive Review

Diagnostics, Journal Year: 2024, Volume and Issue: 14(12), P. 1281 - 1281

Published: June 17, 2024

Alzheimer’s disease (AD) is a neurological disorder that significantly impairs cognitive function, leading to memory loss and eventually death. AD progresses through three stages: early stage, mild impairment (MCI) (middle stage), dementia. Early diagnosis of crucial can improve survival rates among patients. Traditional methods for diagnosing regular checkups manual examinations are challenging. Advances in computer-aided systems (CADs) have led the development various artificial intelligence deep learning-based rapid detection. This survey aims explore different modalities, feature extraction methods, datasets, machine learning techniques, validation used We reviewed 116 relevant papers from repositories including Elsevier (45), IEEE (25), Springer (19), Wiley (6), PLOS One (5), MDPI (3), World Scientific Frontiers PeerJ (2), Hindawi IO Press (1), other multiple sources (2). The review presented tables ease reference, allowing readers quickly grasp key findings each study. Additionally, this addresses challenges current literature emphasizes importance interpretability explainability understanding model predictions. primary goal assess existing techniques identification highlight obstacles guide future research.

Language: Английский

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Prediction and detection of terminal diseases using Internet of Medical Things: A review

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 188, P. 109835 - 109835

Published: Feb. 24, 2025

Language: Английский

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Prediction Models for Early Detection of Alzheimer: Recent Trends and Future Prospects

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

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Transfer Learning-Based Multi-Scale Denoising Convolutional Neural Network for Prostate Cancer Detection

Cancers, Journal Year: 2022, Volume and Issue: 14(15), P. 3687 - 3687

Published: July 28, 2022

Prostate cancer is the 4th most common type of cancer. To reduce workload medical personnel in diagnosis prostate and increase diagnostic accuracy noisy images, a deep learning model desired for detection.

Language: Английский

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MedAi: A Smartwatch-Based Application Framework for the Prediction of Common Diseases Using Machine Learning

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 12342 - 12359

Published: Jan. 1, 2023

Health information technology is one of today's fastest-growing and most powerful technologies. This used predominantly for predicting illness obtaining medications quickly because visiting a doctor performing pathological tests can be time-consuming expensive. has prompted many researchers to contribute by developing new disease prediction systems or improving existing ones. paper presents smartwatch-based system named 'MedAi' multiple diseases such as ischemic heart disease, hypertension, respiratory hyperthyroidism, hypothyroidism, stroke, myocardial infarction, kidney failure, gallstones, diabetes, dyslipidemia using machine learning algorithms. It comprises three core modules: prototype smartwatch 'Sense O'Clock' equipped with eleven sensors collect bodily statistics, model analyze the data make prediction, mobile application display result. A dataset consisting patient statistics was obtained from local hospital according ethical guidelines, prior consent both patients doctors. We employ several algorithms, including Support Vector Machine (SVM), Regression (SVR), K-Nearest Neighbor (KNN), Extreme Gradient Boosting (XGBoost), Long Short Term Memory (LSTM), Random Forest (RF) investigate best algorithm. Experimentation our shows that RF algorithm outperforms other algorithms SVM, KNN, XGBoost, etc., in aforementioned an accuracy 99.4%. The provides full-time assistance user reporting his her body condition suggesting requisite remedies. notable addition early predict vulnerabilities before they reach irrecoverable stage. Finally, we compare method related methods.

Language: Английский

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Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

Bioengineering, Journal Year: 2023, Volume and Issue: 10(7), P. 850 - 850

Published: July 18, 2023

Artificial neural networks (ANNs) ability to learn, correct errors, and transform a large amount of raw data into beneficial medical decisions for treatment care has increased in popularity enhanced patient safety quality care. Therefore, this paper reviews the critical role ANNs providing valuable insights patients’ healthcare efficient disease diagnosis. We study different types existing literature that advance ANNs’ adaptation complex applications. Specifically, we investigate advances predicting viral, cancer, skin, COVID-19 diseases. Furthermore, propose deep convolutional network (CNN) model called ConXNet, based on chest radiography images, improve detection accuracy disease. ConXNet is trained tested using image dataset obtained from Kaggle, achieving more than 97% 98% precision, which better other state-of-the-art models, such as DeTraC, U-Net, COVID MTNet, COVID-Net, having 93.1%, 94.10%, 84.76%, 90% 94%, 95%, 85%, 92% respectively. The results show performed significantly well relatively compared with aforementioned models. Moreover, reduces time complexity by dropout layers batch normalization techniques. Finally, highlight future research directions challenges, algorithms, insufficient available data, privacy security, integration biosensing ANNs. These require considerable attention improving scope diagnostic

Language: Английский

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