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Functional connectivity anomalies in medication-naive children with ADHD: Diagnostic potential, symptoms interpretation, and a mediation model

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CLINICAL NEUROPHYSIOLOGY 2025年 174卷 212-219页

作者： Hu, Yingzi Zeng, Yexian Fu, Tong Hong, Danping Yang, Han Zhu, Zhihang Cheng, Daomeng Dang, Caiping Song, Yan Yang, Chanjuan Yin, Weizhen Zhou, Yanling Guangzhou Med Univ Affiliated Brain Hosp Mingxin Rd 3 Guangzhou 510370 Peoples R China Guangzhou Med Univ Sch Hlth Management Guangzhou 511436 Peoples R China Guangzhou Med Univ Key Lab Neurogenet & Channelopathies Guangdong Pro Guangzhou 510370 Peoples R China Guangzhou Med Univ Minist Educ China Guangzhou 510370 Peoples R China Guangzhou Med Univ Affiliated Brain Hosp Inst Psychiat & Psychol Guangzhou 510370 Peoples R China Shanghai Jiao Tong Univ Sch Med Shanghai Mental Hlth Ctr Shanghai 200030 Peoples R China Beijing Normal Univ IDG McGovern Inst Brain Res State Key Lab Cognit Neurosci & Learning Beijing 100875 Peoples R China Beijing Normal Univ Ctr Collaborat & Innovat Brain & Learning Sci Beijing 100875 Peoples R China

Objective: To identify reliable electroencephalography (EEG) biomarkers for attention deficit/hyperactivity disorder (ADHD) by investigating anomalous functional connectivity patterns and their clinical relevance. Methods: Resting-state EEG data were collected from 74 children aged 6-12 (33 unmedicated ADHD;41 typically developing). Functional connectivity was quantified using the imaginary part of coherency (ICOH). machine learning (ML)-based support vector machine (SVM) modeling, regression, and mediation analyses linked connectivity features to symptom severity and diagnostic classification. Results: Children with ADHD exhibited beta (beta) band hypo-connectivity in frontal regions (Fp2-F4, Fp1-Cz, F7-Cz) and theta (0) band hyper-connectivity in left parietal-central networks (C3-P7, P3-P7, etc.). An SVM classifier achieved an average area under the curve of 0.89 using three connectivity features. Left parietal 0 band hyperconnectivity (C3-P7) correlated with both inattention and hyperactivity/impulsivity and mediated their interrelationship. Conclusions: ADHD is characterized by disrupted frontoparietal connectivity, with 0 band hyper-connectivity in sensory-integration networks potentially compensating for impaired frontal regulation. Significance: These findings highlight C3-P7 0 band connectivity as both a diagnostic and mechanistic biomarker, providing novel target for neurofeedback therapies and enhancing the differential diagnosis in neurodevelopmental disorders.

关键词： ADHD biomarkers Resting-state EEG Functional Connectivity machine learning classification Mediation Analysis

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machine learning Based Functional Connectivity classification of Autistic Children and Distinct Social Interaction Subtypes

Machine Learning Based Functional Connectivity Classificatio...

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中国神经科学学会第十二届全国学术会议

作者： Xiao-Jing Shou Fan-Chao Meng Tian-Jia Song Xin-Jie Xu Ran Huo Ying Liu Xiang-Zhu Zeng Hui-Shu Yuan Yan Xing Mei-Xiang Jia Ji-Shui Zhang Song-Ping Han Rong Zhang Ji-Sheng Han Neuroscience Research Institute Peking University Department of Neurobiology School of Basic Medical Sciences Peking University Key Laboratory for Neuroscience Ministry of Education/National Health and Family Planning Commission Peking University Radiology Department Peking University Third Hospital Department of Pediatrics Peking University Third Hospital Mental Health Institute Peking University Key Laboratory of Ministry of National Health and Family Planning Commission Beijing Children’s Hospital

Autism spectrum disorder(ASD) is a neurodevelopmental disorder, which occurs during early age and the symptoms may last throughout the lifetime of patients. Clinical diagnosis of ASD primarily relies on behavioral manifestations and currently no satisfactory biomarker is available. In the present study, the magnetic resonance imaging technique was used to obtain resting-state functional images of the brain from 4-5 years old children with ASD and matching typically developing children(TDC). Based on a machine learning method, we classified children with ASD and TDC. We further divided children with ASD into three categories, which were active, passive and aloof with Beijing Autism Subtyping Questionnaire(BASQ). The classification scores and behavioral scores were correlated and analyzed. The results showed:(1) Children with ASD and TDC were classified with 95.88% accuracy, 98.70% sensitivity and 85.11% specificity. The optimal functional connectivity features were 145.(2) The classification score of autistic children were positively correlated with ADOS(r = 0.321, p = 0.0056), CARS(r = 0.363, p = 0.0012), and ABC(r = 0.253, p = 0.0266) scores, and were negatively correlated with developmental quotient(r =-0.328, p = 0.0046) and vocabulary(r =-0.285, p = 0.0147). However, no correlations between classification score and behavioral score were found in TDC. It suggests that classification scores were consistent with symptoms in children with ASD.(3) classification of different social interaction types in autistic children was obtained with 84.42% accuracy, 84.42% sensitivity and 91.15% specificity. The optimal functional connectivity features were 340.(4) The predict probability of active type was positively correlated with active score(r = 0.496, p = 0.0001), and negatively correlated with passive score(r =-0.400, p = 0.0022) and aloof score(r =-0.470, p = 0.0003) of BASQ. The aloof score of BASQ was positively correlated with aloof predict probability(r = 0.504, p <

关键词： autism children functional connectivity machine learning classification

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Leveraging machine learning to proactively identify phishing campaigns before they strike

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JOURNAL OF BIG DATA 2025年第1期12卷 1-55页

作者： Zhang, Kun Wang, Haifeng Chen, Meiyi Chen, Xianglin Liu, Long Geng, Qiang Zhou, Yu Hainan Normal Univ Sch Informat Sci & Technol Haikou 571158 Hainan Peoples R China Hainan Trop Ocean Univ Sch Comp Sci & Technol Sanya 572022 Hainan Peoples R China Hainan Trop Ocean Univ Sch Ocean Informat Engn Sanya 572022 Hainan Peoples R China Sanya Informat Infrastruct Investment & Construct Sanya 572022 Hainan Peoples R China Haikou Univ Econ TJ YZ Sch Network Sci Haikou 571127 Peoples R China

With the increasing reliance on digital platforms for shopping, communication, and meetings, users are more exposed to cyber threats like phishing. These attacks often involve fraudulent websites designed to steal sensitive information, such as passwords and credit card details, by mimicking legitimate sites. Attackers use various deceptive techniques, including link manipulation, filter evasion, covert redirection, website forgery, and social engineering. This study introduces an advanced phishing detection framework using machine learning (ML) models. A dataset of 1,353 URLs (702 legitimate, 103 suspicious, and 548 phishing) was compiled, with nine key features extracted for classification. Four ML classifiers-Categorical Boosting, Random Forest (RF), Decision Tree (DT), and Extreme Gradient Boosting (XGB)-were employed, with cross-validation ensuring robust model evaluation. Feature selection was conducted using SHapley Additive Explanations (SHAP) and Recursive Feature Elimination (RFE) to enhance interpretability and computational efficiency. To further refine classification accuracy across legitimate, suspicious, and phishing categories, hyperparameter tuning was performed using four nature-inspired optimization algorithms: Golden Jackal Optimization, Dandelion Optimization, Coati Optimization, and Puma Optimization. These algorithms were chosen for their strong global search capabilities and adaptability to complex datasets, ensuring optimal parameter selection for improved model performance. The study's main contribution lies in integrating these optimization techniques with ML classifiers, significantly improving phishing detection accuracy while reducing computational complexity. Experimental results demonstrated that XGB-based models, particularly XGPO, achieved the highest performance across two feature-selection scenarios. In Scenario 1, Accuracy = 0.980, Precision = 0.981, Recall = 0.980, F1-score = 0.980, MCC = 0.965, AUC = 0.985. In Scenario 2, Accur

关键词： Phishing cybercrimes Uniform resource locator machine learning classification Shapely additive explanations Recursive feature elimination Hyperparameter optimization

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Optical and radar remote sensing for burn scar mapping in the grassland biome

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Remote Sensing Applications: Society and Environment 2025年 38卷

作者： Molema, Talya R. Tesfamichael, Solomon G. Fundisi, Emmanuel Department of Geography Environmental Management and Energy Studies University of Johannesburg South Africa EResearch Knowledge Centre Human Science Research Council South Africa

Wildfires remain a major ongoing threat to the integrity of the environment and therefore emphasis is placed on employing efficient assessment techniques, such as remote sensing. Grassland fires received lesser attention compared to forest fires, despite their significant contribution to global wildfire occurrences. This study, conducted in South Africa, utilized Sentinel-1 radar and Sentinel-2 optical data to map burn scars in grasslands, in a biome representative of grasslands found elsewhere. Employing the Random Forest (RF) and Support Vector machine (SVM) algorithms within the Google Earth Engine (GEE) platform to classify the data, the study achieved high producer's and user's accuracies in identifying burn scars using optical data (>90 %). Comparison of variable importance showed the infrared as well as vegetation and fuel moisture indices being the most influential variables to the classification. However, radar data produced lower accuracies (<50 %) owing to significant confusion in distinguishing grass, bare land and water bodies since these features have structural compositions similar to burnt areas. Nonetheless, radar data proved effective in differentiating burn scars from shadows. Combining optical and radar data yielded comparable accuracies to the optical-alone data but improved the discrimination between burnt areas and shadows. This discrimination capability also agrees with the importance of radar data that ranked better than the visible bands of the optical data. The benefit of merging optical and radar data underscores the importance of radar data, which remains unaffected by atmospheric interference like smoke, haze and clouds, enabling continuous monitoring even during fire events. © 2025 The Authors

关键词： Data fusion machine learning classification Sentinel-1 Sentinel-2 Wildfire

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Highly efficient secure linear algebra for private machine learning classifications over malicious clients in the post-quantum world

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2023年第9期35卷

作者： Kjamilji, Artrim Guney, Osman Berke Istanbul Commerce Univ Fac Engn Istanbul Turkiye Sabanci Univ Fac Engn & Nat Sci Istanbul Turkiye Univ Caddesi 27 Tuzla TR-34956 Istanbul Turkiye Inonu Cd 4 TR-34854 Maltepe Istanbul Turkiye

Over the last decade there has a been widespread usage of machine learning (ML) classifiers in cases such accurate disease diagnosis at clinics, credit card fraud detection in banks, cyber-attacks prevention of computer systems in different industries, etc. However, privacy and security concerns and law regulations have been an obstacle to the usage of ML classifiers. To this end, this paper addresses the scenario where a server has a private trained ML model, and one or more clients have private queries that they wish to classify using the server's model. During the process, the server learns nothing, while the clients learn only their final classifications and nothing else. Several ML classification algorithms, such as Deep Neural Networks, Support Vector machines, Logistic Regression, different flavors of Naive Bayes, etc., can be expressed in terms of linear algebra operations. To this end, initially, as building blocks, several novel secure linear algebra operations are proposed. On top of them novel secure ML classification algorithms are proposed for the aforementioned classifiers under strict security, privacy and efficiency constraints and their security is proven under the semi-honest model. Since the used underlying cryptographic primitives are shown to be resilient to quantum computer attacks, the proposed algorithms are also suitable for the post-quantum world. Furthermore, the proposed algorithms are non-interactive and, based on where the bulk of the operations are done, they have the flexibility to be server or client centric. Theoretical analysis and extensive experimental evaluations over benchmark datasets show that the proposed secure linear algebra operations, hence the secure ML algorithms build on top of them, outperform the state-of-the-art schemes in terms of computation and communication costs as well as on security and privacy characteristics. Moreover, and to the best of the authors' knowledge, for the first time in literature the securit

关键词： Novel secure linear algebra Privacy preserving algorithms machine learning classification Malicious clients Post -quantum cryptography

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Automatic Scale Severity Assessment Method in Psoriasis Skin Images Using Local Descriptors

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2020年第2期24卷 577-585页

作者： George, Yasmeen Aldeen, Mohammad Garnavi, Rahil Univ Melbourne Dept Elect & Elect Engn Melbourne Vic 3010 Australia IBM Res Australia Southbank Vic 3006 Australia

Psoriasis is a chronic skin condition. Its clinical assessment involves four measures: erythema, scales, induration, and area. In this paper, we introduce a scale severity scoring framework for two-dimensional psoriasis skin images. Specifically, we leverage the bag-of-visual words (BoVWs) model for lesion feature extraction using superpixels as key points. BoVWs model is based on building a vocabulary with specific number of words (i.e., codebook size) by using a clustering algorithm with some local features extracted from a constructed set of key points. This is followed by three-class machine learning classifiers for scale scoring using support vector machine (SVM) and random forest. Besides, we examine eight different local color and texture descriptors, namely color histogram, local binary patterns, edge histogram descriptor, color layout descriptor, scalable color descriptor, color and edge directivity descriptor (CEDD), fuzzy color and texture histogram, and brightness and texture directionality histogram. Further, the selection of codebook and superpixel sizes are studied intensively. A psoriasis image set, consisting of 96 images, is used in this study. The conducted experiments show that color descriptors have the highest performance measures for scale severity scoring. This is followed by the combined color and texture descriptors, whereas texture-based descriptors come last. Moreover-means algorithm shows better results in vocabulary building than Gaussian mixed model, in terms of accuracy and computations time. Finally, the proposed method yields a scale severity scoring accuracy of using the following setup: a superpixel of size , a combined color and texture descriptor (i.e., CEDD), a constructed codebook of size 128 using means, and SVM for scale scoring.

关键词： Psoriasis image analysis PASI assessment scale severity scoring local feature extraction bag of visual words machine learning classification

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Causality reasoning about network events for detecting stealthy malware activities

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COMPUTERS & SECURITY 2016年 58卷 180-198页

作者： Zhang, Hao Yao, Danfeng (Daphne) Rarnakrishnan, Naren Zhang, Zhibin Virginia Tech Dept Comp Sci Blacksburg VA USA Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

Malicious software activities have become more and more clandestine, making them challenging to detect. Existing security solutions rely heavily on the recognition of known code or behavior signatures, which are incapable of detecting new maiware patterns. We propose to discover the triggering relations on network requests and leverage the structural information to identify stealthy malware activities that cannot be attributed to a legitimate cause. The triggering relation is defined as the temporal and causal, relationship between two events. We design and compare rule- and learning-based methods to infer the triggering relations on network data. We further introduce a user-intention based security policy for pinpointing stealthy malware activities based on a triggering relation graph. We extensively evaluate our solution on a DARPA dataset and 7 GB real-world network traffic. Results indicate that our dependence analysis successfully detects various maiware activities including spyware, data exfiltrating malware, and DNS bots on hosts. With good scalability for large datasets, the learning-based method achieves better classification accuracy than the rule-based one. The significance of our traffic reasoning approach is its ability to detect new and stealthy malware activities. (C) 2016 The Authors. Published by Elsevier Ltd.

关键词： Network security Anomaly detection Stealthy maiware Traffic analysis Dependence analysis machine learning classification

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Diagnosis of malaria disease by integrating chi-square feature selection algorithm with convolutional neural networks and autoencoder network

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2023年第5期45卷 975-985页

作者： caliskan, Abidin Batman Univ Fac Engn & Architecture Dept Comp Engn Batman Turkiye Batman Univ Fac Engn & Architecture Dept Comp Engn TR-72060 Batman Turkiye

Malaria is a febrile illness caused by a parasite called plasmodium. This life-threatening disease is preventable and treatable if diagnosed early. The World Health Organization aims to reduce the global malaria incidence and death rates by at least 90% until 2030. This disease is diagnosed by visually analyzing red blood cells with a microscope by experienced radiologists. Therefore, this situation may be erroneous due to subjective interpretations. In this study, red blood cells were trained with deep learning-based convolutional neural networks to diagnose malaria, and thus, their deep features were obtained. These obtained features are also trained with autoencoder networks. Thus, the chi-square feature selection algorithm was used to obtain distinctive features. Finally, the unique feature set obtained is given as an introduction to machine learning algorithms, and then a unique diagnostic model is proposed. As a result, 100% accuracy rate was obtained. The results are promising for the diagnosis of malaria disease.

关键词： Malaria convolutional neural network autoencoder chi-square feature selection machine learning classification

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Multivariate Time-Frequency Analysis of Electrohysterogram for classification of Term and Preterm Labor

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2019年第2期14卷 897-916页

作者： You, Jiwoo Kim, Youngjoo Seok, Woojoon Lee, Seungmin Sim, Donggyu Park, Kwang Suk Park, Cheolsoo Kwangwoon Univ Dept Comp Engn Seoul South Korea Univ Groningen Fac Sci & Engn Groningen Netherlands Kookmin Univ Coll Creat Engn Sch Elect Engn Seoul South Korea Seoul Natl Univ Coll Med Dept Biomed Engn Seoul South Korea

Non-invasive electrohysterogram (EHG) could be a promising technique for the preterm birth prediction, which could enable us to diagnose the preterm birth before the labor and reduces the infant mortality and morbidity. Previous studies on the preterm birth prediction with EHG have conducted comprehensive researches on various signal features and classification algorithms, but most of them adopted prefilters based on the linear transforms using fixed basis function, although they are suboptimal for the nonlinearity and nonstationarity of the EHG signal. In this paper, multivariate empirical mode decomposition (MEMD) is applied to decompose the electrical activity signal measured on the uterus. After the decomposition, features are calculated for the corresponding oscillations to the uterine contraction. To investigate the performance of the features, three-channel EHG signals of 254 patients (224 term, 30 preterm) are chosen among 300 patients from Physionet term-preterm electrohysterogram (TPEHG) database to extract features from the EHG signals and classify the features using machine learning algorithms. classification results shows that the proposed method with MEMD achieved 94.66% correctly classified rate (CCR) and 0.987 area under the curve (AUC), which outperformed those with IIR filter implying MEMD provides a new prospect to improve the current preterm birth prediction approach.

关键词： Preterm birth detection EHG Uterine EMG machine learning classification NA-MEMD

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A class imbalance-aware review rating prediction using hybrid sampling and ensemble learning

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第5期80卷 6911-6938页

作者： Mahadevan, Anbazhagan Arock, Michael Madanapalle Inst Technol & Sci Dept Comp Sci & Engn Chittoor Andhra Pradesh India Natl Inst Technol Dept Comp Applicat Trichy Tamil Nadu India

Imbalanced distribution of instances across the classes is a challenging issue when the underlying problem is of type classification. The reason is that classifiers will tend to favor the classes with a large number of instances i.e. instances of minority classes may be identified as instances of majority classes by the classifiers. In recent years, plenty of researches have been done to resolve the class imbalance issue in binary classification problems which resulted in many class imbalance learning techniques for binary classification problems. But, the class imbalance in multi-class classification problems did not draw much attention from the research community. Unlike binary class imbalance learning, multi-class imbalance learning techniques experience more than one majority class and more than one minority class. This paper tries to come up with a multi-class imbalanced learning technique that can overcome the effects of multi-class imbalance problem in review rating prediction tasks. The proposed model handles the multi-class imbalance issue by using the combination of hybrid sampling and ensemble learning techniques. Sampling techniques such as Random Under Sampling (RUS) and Synthetic Minority Over-sampling TEchnique(SMOTE) are jointly used in the proposed model to create balanced training sets for base learners. Also, the proposed model creates a powerful ensemble structure by amalgamating a manually created bagging ensemble and AdaBoost boosting ensembles. Experiments are done using the Amazon product dataset in order to investigate the performance of the proposed model. The experimental results show that the proposed Class Imbalance-Aware Review rating prediction(CIAR) model outperforms almost all the baseline models in-terms of G-mean, F-Score, and ROC_AUC_Score.

关键词： Class imbalanced learning Ensemble learning machine learning classification Over sampling Under sampling Review rating prediction

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