Unsupervised 3D Prostate Segmentation Based on Diffusion-Weighted Imaging MRI Using Active Contour Models with a Shape Prior by Xin Liu, Masoom A. Haider, Imam Samil Yetik

“…Recently, magnetic resonance imaging (MRI) is proposed as a promising modality to detect and monitor prostate-related diseases. In this paper, we propose an unsupervised algorithm to segment prostate with 3D apparent diffusion coefficient (ADC) images derived from diffusion-weighted imaging (DWI) MRI without the need of a training dataset, whereas previous methods for this purpose require training datasets. …”
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Distribution Network Ground Fault Location Method Based on VMD-TEO and Full Signal Acquisition by GUO Liang, WANG Jian ping, GUO Xin

“…Through full signal measurement, the traveling wave signals of fault current, phase voltage and zero sequence voltage are detected, and the traveling wave fault line selection algorithm is used to judge the range of fault line. …”
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An Improved Vital Signal Extraction Method Based on Laser Doppler Effect by Yu Li, Haiyang Zhang, Bowen Zhang, Yujiao Qi, Si Chen

“…The mixed signal of respiratory waveform and heartbeat waveform detected by the Laser-Doppler system is processed with an intermediate-frequency (IF) interference filtering method, an enhanced extraction method and a waveform-fixing method. …”
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Fault Diagnosis of Industrial Process Based on FDKICA-PCA by ZHANG Jing, ZHU Fei-fei, LIU Jia-xing, WANG Jiang-tao

“…Because the dynamic characteristics of autocorrelation and lag correlation in production process are neglected in fault diagnosis,Kernel Independent Component AnalysisPrincipal Component Analysis (KICAPCA) is very poor in detecting small and gradual faults because of lacking available variable contribution analysis.In this paper, a dynamic kernel independent component analysis (KICAPCA) fault diagnosis method based on wavelet packet filtering is proposed.This method integrates wavelet packet filtering theory and AR model prediction data characteristics into KICAPCA to extract the feature information of process variable autocorrelation and lagrelated .In this paper, KICAPCA algorithm is used to extract the independent components and principal components of process variables to determine the control limits of three monitoring indicators T2, SPE,I2.Nonlinear contribution graph is used for fault diagnosis, and the advantage of FDKICAPCA method is verified by simulation results of Tennessee process.…”
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Artificial intelligence in personalized cardiology treatment by Abbas Mohammadi, Sheida Shokohyar

“…This study examines the advancements and challenges in personalized cardiovascular medicine, highlighting the need to address issues such as data privacy, algorithmic bias, and accessibility to promote the equitable application of personalized medicine.…”
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Prediction Models for Diabetes in Children and Adolescents: A Review by Livija Cveticanin, Marko Arsenovic

“…It highlights models for predicting both type 1 and type 2 diabetes in this population, emphasizing the inclusion of risk factors that facilitate the identification of potential occurrence and early detection of diabetes in young individuals. Newly identified factors for differentiating between types of diabetes are discussed, alongside an overview of various machine learning and deep learning algorithms specifically adapted for diabetes prediction in children and adolescents. …”
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Application of unified microprocessor relay protection units in electrical machine diagnostics by V. I. Polishchuk, M. V. Kritsky, D. M. Bannov, S. V. Malyshev

“…They also presented the results of experiments for the breakage in the short-circuit rotor of an asynchronous motor and proved that the microprocessor-based BMRZ device is capable of digitizing at a sampling rate that meets the requirements, and in conjunction with the data processing algorithms that carry out the selective determination of hard-to-detect defects, is applicable in the diagnosis of electrical machines.…”
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Intelligent Infrastructure for Traffic Monitoring Based on Deep Learning and Edge Computing by Jaime Villa, Franz García, Rubén Jover, Ventura Martínez, José M. Armingol

“…The proposed system employs deep learning algorithms and vision techniques that perform vehicle detection, classification, tracking, speed estimation, and vehicle geolocation.…”
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Leveraging transformers and explainable AI for Alzheimer's disease interpretability. by Humaira Anzum, Nabil Sadd Sammo, Shamim Akhter

“…To detect Alzheimer's disease, brain scans such as computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET) can be performed. …”
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Quantifying Upper-Arm Rehabilitation Metrics for Children through Interaction with a Humanoid Robot by Douglas A. Brooks, Ayanna M. Howard

“…The analysis shows that our algorithmic results compare closely to the results obtain from the ground truth data, with an average algorithmic error is less than 9% for the range of motion and less than 8% for the peak angular velocity of each subject.…”
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Cross paradigm fusion of federated and continual learning on multilayer perceptron mixer architecture for incremental thoracic infection diagnosis by Tianshuo Zhou, Boyuan Wang

“…This model was applied to the COVID-XRAY and COVID-CT datasets using 3 and 6 clients, respectively, implementing 4 continual learning algorithms across ten different models. Notably, we integrated MLP-Mixer with Learning without Forgetting (LwF) techniques, achieving an accuracy of 54.34%. …”
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Network analysis of comorbidity patterns in heart failure patients using administrative data by Francesca Ieva, Daniele Bitonti

“…Network analysis techniques and community detection algorithms are applied to comorbidities registered in hospital discharge papers of HF patients, in 7 cohorts between 2006 and 2012. …”
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Classification of diabetic retinopathy stages based on neural networks by M. M. Lukashevich, Y. I. Golub

“…The development of methods and algorithms for the detection and classification of this disease, the automation of this process are the actual and costeffective goals.The article focuses on the classification of the stages of diabetic retinopathy using neural networks based on human retinal images. …”
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Investigating the dynamics and uncertainties in portfolio optimization using the Fourier-Millen transform. by Muhammad Hilal Alkhudaydi, Aiedh Mrisi Alharthi

“…These are then compared with other algorithms, such as vector autoregression, in portfolio optimisation tests. …”
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Artificial Intelligence Powered Automated and Early Diagnosis of Acute Lymphoblastic Leukemia Cancer in Histopathological Images: A Robust SqueezeNet-Enhanced Machine Learning Fram... by Vineet Mehan

“…Combining DL and ML algorithms addresses the complexity of understanding histopathological images and the classification process. …”
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Object Ontologies as a Priori Models for Logical-Probabilistic Machine Learning by D. N. Gavrilin, A.V. Mantsivoda

“…In the paper we introduce the approach to integration of these two formalisms and provide some algorithmic basis for the implementation of the resulting hybrid formalism on the bSystem platform.…”
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Transfer Learning for Induction Motor Health Monitoring: A Brief Review by Prashant Kumar

“…These sophisticated deep learning algorithms have been widely used for induction motor health monitoring due to their autonomous feature extraction abilities and end-to-end learning capabilities. …”
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Advancing Breast Cancer Diagnosis: A Comprehensive Machine Learning Approach for Predicting Malignant and Benign Cases with Precision and Insight in a Neutrosophic Environment usin... by Nihar Ranjan Panda, R. Rajalakshmi, Surapati Pramanik, Mana Donganont, Prasanta Kumar Raut

“…Breast cancer is still among the deadliest diseases globally, and its detection in an early stage still represents a big challenge in medical diagnostics. …”
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A survey: Breast Cancer Classification by Using Machine Learning Techniques by Ruaa Hassan Mohammed Ameen, Nasseer Moyasser Basheer, Ahmed Khazal Younis

“…Using automated feature extraction and classification algorithms, physicians' experience in diagnosing and detecting breast cancer can be aided. …”
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A highly granular temporary migration dataset derived from mobile phone data in Senegal by Paul Blanchard, Stefania Rubrichi

“…The article presents a suite of methodological tools that not only includes algorithmic methods for the detection of temporary migration events in digital traces, but also addresses key challenges in aggregating individual trajectories into coherent migration statistics. …”
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