Blood biomarker discovery for autism spectrum disorder: A proteomic analysis. by Laura Hewitson, Jeremy A Mathews, Morgan Devlin, Claire Schutte, Jeon Lee, Dwight C German

“…All 12 proteins were significantly different in ASD compared with TD boys, and 4 were significantly correlated with ASD severity as measured by ADOS total scores. Using machine learning methods, a panel of serum proteins was identified that may be useful as a blood biomarker for ASD in boys. …”
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On the use of clustering workflows for automated microstructure segmentation of analytical STEM datasets by Zhiquan Kho, Andy Bridger, Keith Butler, Ercin C. Duran, Mohsen Danaie, Alexander S. Eggeman

“…This study considers the issue of automated segmentation of scanning transmission electron microscopy (STEM) datasets using unsupervised machine learning approaches. To this end, a systematic comparison of two clustering workflows that had been established in previous literature was performed on two distinct material systems—an experimentally acquired Co2FeSi Heusler alloy and a simulated Au-matrix and Al2Cu precipitate. …”
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From Baseline to Best Practice: An Advanced Feature Selection, Feature Resampling and Grid Search Techniques to Improve Injury Severity Prediction by Soukaina EL Ferouali, Zouhair Elamrani Abou Elassad, Sara Qassimi, Abdelmounaîm Abdali

“…As far as we know and as of right now, there hasn’t been much interest in supporting a fusion-based system that critically reviews machine learning techniques using grid search optimization, feature selection, and smote technique and examines how injury severity prediction is affected by road crashes.…”
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A Fused Multiscale Pictorial Sequence Learning Mechanism Applied to Pavement Detection by Zhang Jiazhen, Wang Xiulai, Zhao Shuxu

“…The experimental results show that the multi-scale fusion features capture more complete information about the temporal trajectory than the single latent variable feature information, suggesting that the proposed algorithm exhibits better comprehensive performance and generalization ability than unimproved machine learning algorithms and typical deep learning classifiers.…”
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A Bee Farm Records Management System: A Case Study of Kanyabugunga Kashambya Sub-County. by Maanige, Barnet, Tayebwa, Onesmas

“…Future avenues for enhancement include IoT integration for real-time hive monitoring, machine learning for pest detection, a dedicated mobile application, and expansion to other regions. …”
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Assessment model of ozone pollution based on SHAP-IPSO-CNN and its application by Xiaolei Zhou, Xingyue Wang, Ruifeng Guo

“…Then three mainstream machine learning models are compared for SHAP analysis to obtain the significance results of relevant features. …”
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Usage of Optimized Least Square SVR to Volume Expansion Estimation of Cement Paste Including Fly Ash and Mgo Expansive Additive by Mazharul Islam, Sadia Afrin

“…To construct and assess machine learning-based algorithms to assess the volume expansion (𝑉𝑒) of cement paste, which consists of 𝐹𝐴 and 𝑀𝐸𝐴, 170 experimental findings from published studies are employed. …”
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Understanding the coupled relationship between regional longevity and physical geographical environment in Hechi, Guangxi, China by Qucheng Deng, Yaqing Liu, Yongping Wei, Wei Liang, Kaixian Zhu, Yonghua Li, Lihai Zhang

“…First, the basic characteristics of the geographical environmental factors in Hechi City were analysed, and then geographical information system techniques and eXtreme Gradient Boosting machine learning methods were applied to systematically analyse the coupling relationship between the spatial distribution of the longevity population and the geographical environmental factors. …”
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Identifying optimal substrate classes of membrane transporters. by Andreas Denger, Volkhard Helms

“…Unlike previous approaches for reducing redundancy in GO datasets, we employ machine learning to identify a subset of functional annotations in a training dataset. …”
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Open Soil Spectral Library (OSSL): Building reproducible soil calibration models through open development and community engagement. by José L Safanelli, Tomislav Hengl, Leandro L Parente, Robert Minarik, Dellena E Bloom, Katherine Todd-Brown, Asa Gholizadeh, Wanderson de Sousa Mendes, Jonathan Sanderman

“…However, a bottleneck to its more widespread adoption is the need for establishing large reference datasets for training machine learning (ML) models, which are called soil spectral libraries (SSLs). …”
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Digital fundus image quality assessment by V. V. Starovoitov, Y. I. Golub, M. M. Lukashevich

“…At present, the level of accuracy of classification of diseases caused by DR by systems based on machine learning is comparable to the level of qualified medical doctors.The article shows variants for representation of the retina in digital images by different cameras. …”
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A SYSTEMATIC ANALYSIS OF AI-EMPOWERED EDUCATIONAL TOOLS DEVELOPED IN INDIA FOR DISABLED PEOPLE by Shivani, Monika Gupta, Satinder Bal Gupta

“…The Notebook platform utilizes Machine Learning algorithms that provide students with a personalized learning experience. …”
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Effects of Operation Prediction Sharing for Collaborative Avatar Robot by Hyuga Suzuki, Hikari Yukawa, Kouta Minamizawa, Yoshihiro Tanaka

“…The robotic arm was controlled by two operators, while the system predicts operations 0.3 seconds ahead using machine learning based on one operator’s actions. …”
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Resonant Frequency Modeling of Microwave Antennas Using Gaussian Process Based on Semisupervised Learning by Jing Gao, Yubo Tian, Xie Zheng, Xuezhi Chen

“…For the optimal design of electromagnetic devices, it is the most time consuming to obtain the training samples from full wave electromagnetic simulation software, including HFSS, CST, and IE3D. Traditional machine learning methods usually use only labeled samples or unlabeled samples, but in practical problems, labeled samples and unlabeled samples coexist, and the acquisition cost of labeled samples is relatively high. …”
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An Ensemble Learning Based Intrusion Detection Model for Industrial IoT Security by Mouaad Mohy-Eddine, Azidine Guezzaz, Said Benkirane, Mourade Azrour, Yousef Farhaoui

“…This study designs an intrusion detection model exploiting feature engineering and machine learning for IIoT security. We combine Isolation Forest (IF) with Pearson’s Correlation Coefficient (PCC) to reduce computational cost and prediction time. …”
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Classification of Dyslexia Among School Students Using Deep Learning by Alia Hussein, Ahmed Talib Abdulameer, Ali Abdulkarim, Husniza Husni, Dalia Al-Ubaidi

“…This research contributes to the field of dyslexia diagnosis by harnessing the power of machine learning and audio analysis techniques. Facilitating faster and more accurate identification of Dyslexia in children, ultimately improving their educational outcomes and quality of life. …”
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Implementation Of Convolutional Neural Network (Cnn) Based On Mobile Application For Rice Quality Determination by Muhammad Zainal Altim, Abdullah Basalamah, kasman kasman

“…The method applied is an experimental method that utilizes machine learning technology by using many rice images that are used as datasets. …”
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Lameness Recognition of Dairy Cows Based on Compensation Behaviour Analysis by Swing and Posture Features from Top View Depth Image by Ruihong Zhang, Kaixuan Zhao, Jiangtao Ji, Jinjin Wang

“…Lameness classification models were constructed using machine learning and threshold discrimination methods, achieving classification accuracies of 81.6% and 83.05%, respectively. …”
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Brain activity patterns reflecting security perceptions of female cyclists in virtual reality experiments by Mohammad Arbabpour Bidgoli, Arian Behmanesh, Navid Khademi, Phromphat Thansirichaisree, Zuduo Zheng, Sara Saberi Moghadam Tehrani, Sajjad Mazloum, Sirisilp Kongsilp

“…Subsequently, four supervised machine learning methods, random forest, support vector machine, logistic regression, and multilayer perceptron, are utilized to classify influential factors on security perception using clustered EEG data. …”
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CS-FL: Cross-Zone Secure Federated Learning with Blockchain and a Credibility Mechanism by Chongzhen Zhang, Hongye Sun, Zhaoyu Shen, Dongyu Wang

“…Federated learning enables multiple intelligent devices to collaboratively perform machine learning tasks while preserving local data privacy. …”
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