IT Diagnostics of Parkinson’s Disease Based on the Analysis of Voice Markers and Machine Learning by U. A. Vishniakou, Xia YiWei

“…The results of studying the parameters of the spectra of speech signals by machine learning with the use of neural networks are presented. …”
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A novel model for higher performance object detection with deep channel attention super resolution by Ayse Berika Varol Malkocoglu, Ruya Samli

“…With the introduction of deep learning methods, target object detection studies have gained momentum and started to be used in many areas. …”
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Deep multi-view feature fusion with data augmentation for improved diabetic retinopathy classification by Boualleg Yaakoub, Daouadi Kheir Eddine, Guehairia Oussama, Djeddi Chawki, Cheddad Abbas, Siddiqi Imran, Bouderah Brahim

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Automated Digit Recognition and Measurement-Type Classification from Blood Pressure Monitor Images Using Deep Learning by Nur Ahmadi, Hansel Valentino Tanoto, Rinaldi Munir

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Collider-based movement detection and control of wearable soft robots for visually augmenting dance performance by Patrick Twomey, Vaibhavsingh Varma, Leslie L. Bush, Mitja Trkov

“…Additionally, machine learning algorithms are unsuitable for detecting the arbitrary motion of improvisational dancers due to the non-repetitive and unique nature of their movements, resulting in limited available training data. …”
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Analysis of the capabilities of an information system for improving sleep quality based on biometric data analysis by M.S. Graf, A.V. Yakoniuk, D.V. Krant, I.I. Golovach

“…The system is able to function independently thanks to the use of machine learning algorithms, including LSTM for prediction, Kalman filter for data cleaning, Isolation Forest for anomaly detection, and K-means for clustering sleep patterns.…”
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Optimizing integration techniques for UAS and satellite image data in precision agriculture — a review by Aliasghar Bazrafkan, C. Igathinathane, Nonoy Bandillo, Paulo Flores

“…Future works should delve into advanced fusion methodologies, incorporating machine learning algorithms, and conduct cross-crop application studies to broaden applicability and tailor insights for specific crops.…”
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Enhancing Vertical Trajectory Reconstruction in SASS-C: Advanced Segmentation, Outlier Detection, and Filtering Techniques by Daniel Amigo, David Sánchez Pedroche, Jesús García, José Manuel Molina, Jekaterina Trofimova, Emmanuel Voet, Benoît Van Bogaert

“…The invalid detection algorithm, while incurring a slight computational cost, significantly enhances trajectory accuracy. …”
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Prediction of Diabetes in Middle-Aged Adults: A Machine Learning Approach by Gideon Addo, Bismark Amponsah Yeboah, Michael Obuobi, Raphael Doh-Nani, Seidu Mohammed, David Kojo Amakye

“…This study focuses on this demographic to examine symptom-diabetes associations, examine the influence of symptoms in diabetes prediction, and determine an optimal machine learning (ML) model for diabetes prediction. Materials and Methods: This study utilized data from a previous cohort study conducted in Bangladesh. …”
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Predictive Analytics in Agriculture: Machine Learning Models for Coconut Tree Health by Goswami Anjali, Kirit Dhablia Dharmesh

“…We note that coconut tree health issues have been addressed using advanced ML models for early detection and prediction in this paper. Several ML algorithms are analyzed in the study for data from several sources like satellite imagery, drone based sensors, and field data, including Convolutional Neural Networks (CNNs), Random Forest and Support Vector Machines (SVMs). …”
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Machine learning for predicting Chagas disease infection in rural areas of Brazil. by Fabio De Rose Ghilardi, Gabriel Silva, Thallyta Maria Vieira, Ariela Mota, Ana Luiza Bierrenbach, Renata Fiuza Damasceno, Lea Campos de Oliveira, Alexandre Dias Porto Chiavegatto Filho, Ester Sabino

“…We analyzed data from the Retrovirus Epidemiology Donor Study (REDS) to train five popular machine learning algorithms. …”
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An Attention-Enhanced 3D-CNN Framework for Spectrogram-Based EEG Analysis in Epilepsy Detection by Ziaullah Khan, Aakanksha Dayal, Hee-Cheol Kim

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A parallel CNN architecture for sport activity recognition based on minimal movement data by Huipeng Zhao

“…Abstract Novel Human Activity Recognition (HAR) methodologies, which are built upon learning algorithms and employ ubiquitous sensors, have achieved remarkable precision in the identification of sports activities. …”
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Understanding the flowering process of litchi through machine learning predictive models by SU Zuanxian, NING Zhenchen, WANG Qing, CHEN Houbin

“…Accurately predicting the development of the inflorescence and the process of flowering duration, as well as correctly understanding the quantitative relationship between the flowering phenology and the meteorological factors, is very important for the high-yield and quality production of litchi. The machine learning algorithms can handle high-dimensional nonlinear data with complex interactions, outperform traditional statistical models in ecology, and have been effectively used for plant classification, phenology detection, crop growth detection, and yield prediction. …”
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Development and validation of a machine learning model for prediction of cephalic dystocia by Yumei Huang, Xuerong Ran, Xueyan Wang, Defang Wu, Zheng Yao, Jinguo Zhai

“…Machine learning offers unique advantages, enabling the generation of predictive models using various types of clinical data. …”
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Effective Gait Abnormality Detection in Parkinson’s Patients for Multi-Sensors Surveillance System by Abdulwahab Alazeb, Mouazma Batool, Naif Al Mudawi, Mohammed S. Alshehri, Sultan Almakdi, Nouf Abdullah Almujally, Asaad Algarni

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StopSpamX: A multi modal fusion approach for spam detection in social networking by Dasari Siva Krishna, Gorla Srinivas

“…Out of them few algorithms are implemented to detect the spam data in twitter. • We compare the outcomes in each scenario using various state-of-the-art word embedding techniques, such as Word2Vecv, GloVe, and FastText. • To account for the restrictions, two deep learning hybrid fusion classifier techniques—Text-based classifier and Combined classifier—are used in this work. …”
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Bridge Deformation Prediction Using KCC-LSTM With InSAR Time Series Data by Zechao Bai, Chang Shen, Yanping Wang, Yun Lin, Yang Li, Wenjie Shen

“…To address this challenge, we propose a novel K-shape and complete linkage hierarchical cluster long short-term memory (KCC-LSTM) approach for predicting bridge deformation based on time-series InSAR data. The approach initially combines two machine learning based clustering algorithms, K-Shape for better capturing shape features of time series and complete linkage hierarchical clustering combined with spatial geographic location captures the spatial characteristics of time series to derive clusters with unique spatiotemporal deformation behavior, improving clustering accuracy and spatiotemporal correlation. …”
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V-STAR: A Cloud-Based Tool for Satellite Detection and Mapping of Volcanic Thermal Anomalies by Simona Cariello, Arianna Beatrice Malaguti, Claudia Corradino, Ciro Del Negro

“…In contrast, advanced machine learning algorithms offer a data-driven alternative. …”
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PA2E: Real-Time Anomaly Detection With Hyperspectral Imaging for Food Safety Inspection by Jungi Lee, Myounghwan Kim, Jiseong Yoon, Kwangsun Yoo, Seok-Joo Byun

“…In the food industry, the occurrence of unknown contaminants is problematic due to the difficulty in obtaining training data, highlighting the need for anomaly detection algorithms that can identify previously unseen contaminants by learning from normal data. …”
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