Article
Computer Science, Information Systems
Jonne van Dreven, Veselka Boeva, Shahrooz Abghari, Hakan Grahn, Jad Al Koussa, Emilia Motoasca
Summary: This paper provides a comprehensive survey of intelligent fault detection and diagnosis in district heating systems. It emphasizes the importance of maintaining an efficient heating system and discusses the use of artificial intelligence and machine learning techniques for automatic fault detection and diagnosis. The paper reviews 57 papers published in the last 12 years, highlights recent trends, identifies research gaps, discusses limitations, and provides recommendations for future studies.
Article
Computer Science, Artificial Intelligence
Yingying Zhu, Minjeong Kim, Xiaofeng Zhu, Daniel Kaufer, Guorong Wu
Summary: Neuroimaging investigations for early detection and diagnosis of Alzheimer's disease have been driven by the significant social and economic cost. Current computational approaches applied to longitudinal imaging data in subjects with Mild Cognitive Impairment aim to increase sensitivity for detecting changes and potentially serve as a diagnostic biomarker for AD. However, the lack of robust predictive power in current brain imaging diagnostic methods limits their utility in clinical practice.
MEDICAL IMAGE ANALYSIS
(2021)
Article
Chemistry, Multidisciplinary
Luis Chaves, Goncalo Marques
Summary: The study explores the use of data mining techniques for early diagnosis of diabetes, with results indicating that Neural Networks should be used for diabetes prediction. The proposed model shows high predictive accuracy and specificity.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Theory & Methods
Dylan Chou, Meng Jiang
Summary: This survey presents the challenges faced by data-driven network intrusion detection, including the authenticity and representativeness of datasets. Trends in the past decade are analyzed, and future directions are proposed, including the application of NID in cloud-based environments, designing scalable models for large network data, and collecting labeled datasets from real-world networks.
ACM COMPUTING SURVEYS
(2022)
Article
Health Care Sciences & Services
Md Martuza Ahamad, Sakifa Aktar, Md Jamal Uddin, Tasnia Rahman, Salem A. Alyami, Samer Al-Ashhab, Hanan Fawaz Akhdar, A. K. M. Azad, Mohammad Ali Moni
Summary: This study aims to use machine learning models and statistical methods to predict and diagnose ovarian cancer at an early stage. The analysis found significant blood biomarkers and showed that machine learning models can classify malignant and benign patients with an accuracy of 91%. This study demonstrates the importance of machine learning in cancer diagnosis.
JOURNAL OF PERSONALIZED MEDICINE
(2022)
Article
Computer Science, Information Systems
M. Sreeraj, Jestin Joy, Manu Jose, Meenu Varghese, T. J. Rejoice
Summary: Cervical Spondylosis is a chronic spinal condition that can be difficult to diagnose in early stages, but primary care detection can reduce the risk. Machine learning techniques can provide a low-cost, accurate mechanism for early stage spondylosis detection.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Review
Computer Science, Artificial Intelligence
Sejal Mistry, Naomi O. Riches, Ramkiran Gouripeddi, Julio C. Facelli
Summary: This article reviews the use of machine learning and data mining methods to understand the environmental exposures in diabetes etiology and disease prediction. The researchers found that specific external exposures were the most commonly studied and supervised models were the most commonly used methods. Well-established risk factors such as low physical activity, high cholesterol, and high triglycerides were predictive of general diabetes, type 2 diabetes, and prediabetes, while novel metabolic and gut microbiome biomarkers were implicated in type 1 diabetes.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Rokaya Rehouma, Michael Buchert, Yi-Ping Phoebe Chen
Summary: COVID-19 is a significant health challenge globally, and early detection is crucial for controlling the spread and reducing mortality rates. Machine learning has made significant progress in COVID-19 detection using medical imaging, with deep learning algorithms widely used for patient identification and achieving good predictive results.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Information Systems
AinhoaYera, Inigo Perona, Olatz Arbelaitz, Javier Muguerza, J. Eduardo Perez, Xabier Valencia
Summary: The current importance of digital competence makes it essential to enable people with disabilities to use digital devices and applications and to automatically adapt site interactions to their needs. Automatic detection of user abilities and disabilities is the foundation for building adaptive systems, contributing to diminishing the digital divide for people with disabilities.
HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Peifa Sun, Mengda Lyu, Hui Li, Bo Yang, Lizhi Peng
Summary: Cryptocurrency is gaining popularity, leading to a boom in mining, but the high energy consumption is a significant issue. This paper proposes a convolutional function-based method for identifying mining traffic by analyzing network flow. Through empirical studies and online validation, our proposal demonstrates high performance in detecting mining traffic.
COMPUTER COMMUNICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Lukasz Korycki, Bartosz Krawczyk
Summary: This paper proposes a framework for robust concept drift detection in the presence of adversarial and poisoning attacks. It introduces a taxonomy for two types of adversarial concept drifts and a robust trainable drift detector. Extensive computational experiments prove the high robustness and efficacy of the proposed framework in adversarial scenarios.
Article
Energy & Fuels
Paria Movahed, Saman Taheri, Ali Razban
Summary: Long-term operation of HVAC systems can lead to failures, higher energy consumption, and maintenance costs. Fault detection diagnostic (FDD) is commonly used to prevent malfunctions, and machine learning methods have gained interest due to their high accuracy. However, existing studies suffer from biased classification algorithms and high false positives. To address these challenges and improve diagnostic performance, this study proposes a novel data-driven framework using principal component analysis, time series anomaly detection, and random forest.
Article
Computer Science, Artificial Intelligence
Fuyuan Cao, Xiaolin Wu, Liqin Yu, Jiye Liang
Summary: This paper proposes an outlier detection algorithm for matrix-object data sets, which describes and calculates the outlier factor of matrix objects based on their coupling and cohesion. Experimental results have shown that the proposed algorithm effectively detects outliers compared to other algorithms on real and synthetic data sets.
APPLIED SOFT COMPUTING
(2021)
Article
Engineering, Biomedical
Christos Laganas, Dimitrios Iakovakis, Stelios Hadjidimitriou, Vasileios Charisis, Sofia B. Dias, Sevasti Bostantzopoulou, Zoe Katsarou, Lisa Klingelhoefer, Heinz Reichmann, Dhaval Trivedi, K. Ray Chaudhuri, Leontios J. Hadjileontiadis
Summary: This study introduces a high-frequency, privacy-aware and unobtrusive PD screening tool based on voice samples captured during routine phone calls. The proposed method outperforms other methods for language-aware PD detection.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
(2022)
Article
Engineering, Electrical & Electronic
Aleksei Shcherbak, Ekaterina Kovalenko, Andrey Somov
Summary: Parkinson's disease is a common and rapidly growing neurodegenerative disorder that significantly affects patients' physical and social activities. Early diagnosis is challenging due to similar symptoms with other neurodegenerative diseases.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)