Research progress in predicting the conversion from mild cognitive impairment to Alzheimer’s disease via multimodal MRI and artificial intelligence by Min Ai, Yu Liu, Dan Liu, Chengxi Yan, Xia Wang, Xun Chen

“…Artificial intelligence (AI) methods based on deep learning and machine learning, with their powerful data processing and pattern recognition capabilities, have shown great potential in mining the features of multimodal MRI data and constructing prediction models for MCI conversion. …”
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A Transformer-Based Approach for Efficient Geometric Feature Extraction from Vector Shape Data by Longfei Cui, Xinyu Niu, Haizhong Qian, Xiao Wang, Junkui Xu

“…Traditional methods rely on different machine learning algorithms tailored to specific types of line and polygon elements, limiting their general applicability. …”
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Circadian rhythm disruption by PARP inhibitors correlates with treatment toxicity in patients with ovarian cancer and is a predictor of side effectsResearch in context by Deeksha Malhan, Janina Hesse, Nina Nelson, Kay Stankov, Jessica Nguyen, Ouda Aboumanify, Josefin Garmshausen, Gunther Rogmans, Bastian Czogalla, Jens Gerber, Martin Koch, Tomáš Kupec, Oliver Tomé, Ralf Witteler, Mustafa Deryal, Michael Eichbaum, Jalid Sehouli, Elena Ioana Braicu, Angela Relógio

“…In a subset of these patients, we performed differential gene expression and rhythmicity analysis on up to 800 genes, including clock and clock-controlled genes. Machine learning algorithms and mathematical modelling were employed to simulate patient-specific toxicity profiles and to explore correlations between gene expression patterns and treatment-related side effects. …”
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Occupancy Monitoring Using BLE Beacons: Intelligent Bluetooth Virtual Door System by Nasrettin Koksal, AbdulRahman Ghannoum, William Melek, Patricia Nieva

“…In this paper, we propose an Intelligent Bluetooth Virtual Door (IBVD) OM system for the indoor/outdoor tracking of individuals using the interaction between a BLE device worn by the occupant and two BLE beacons located at the entrance/exit points of a doorway. ML algorithms are used to perform intelligent OM through pattern detection from the BLE RSSI signal(s). …”
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Infant rat ultrasonic vocalizations in the neurodevelopmental model of schizophrenia by Agnieszka Potasiewicz, Zuzanna Mincikiewicz, Piotr Popik, Agnieszka Nikiforuk

“…USV characteristics, temporal organization, clustering, and syntax were analyzed by DeepSqueak’s machine-learning algorithms. Unlike controls, which showed an increasing USV rate associated with proper vocal development, MAM-exposed pups displayed a stable emission rate across days and emitted fewer USVs on the 12th PND. …”
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Artificial Intelligence Driven Human Identification by Purushottam Sharma, Aazar Imran Khan, Samyak Jain, Abhishek Srivastava

“…Human Identification has been widely implemented to enhance the efficiency of surveillance systems, however, systems based on common CCTV (closed-circuit television) cameras are mostly incompatible with the advanced identification algorithms which aim to extract the facial features or speech of an individual for identification. …”
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Applications of artificial intelligence in neurosurgical education: a scoping review by Hector Julio Piñera-Castro, Christian Borges-García

“…Simulation training utilized neural networks to classify expertise and deliver individualized feedback, though rigid metrics risked oversimplifying skill progression. Machine learning algorithms assessed surgical performance, identifying metrics. …”
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Consensus-Based Intelligent Distributed Secondary Control for Multiagent Islanded Microgrid by Ali M. Jasim, Basil H. Jasim, Flah Aymen, Hossam Kotb, Ahmed Althobaiti

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Different oscillatory mechanisms of dementia-related diseases with cognitive impairment in closed-eye state by Talifu Zikereya, Yuchen Lin, Zhizhen Zhang, Ignacio Taguas, Kaixuan Shi, Chuanliang Han

“…We accurately and clearly identified three stable oscillation targets (theta, ∼5 Hz, alpha, ∼10 Hz, and beta, ∼18 Hz) that facilitate differentiation between AD, FTD, and HC both statistically and through classification using machine learning algorithms. Overall, the differences between AD and HC were the most pronounced, with FTD exhibiting intermediate characteristics. …”
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A Multi-Source Transfer Joint Matching Method for Inter-Subject Motor Imagery Decoding by Fulin Wei, Xueyuan Xu, Tianyuan Jia, Daoqiang Zhang, Xia Wu

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Wave propagation-based tests for concrete piles – an overview by Reham Samaan, Abdelsalaam Mokhtar, Mohamed Saafan, Ahmed Ebid

“…This investigation establishes the practical and theoretical basis for implementing advanced machine learning predictive models that combine previous records with pattern recognition algorithms, potentially converting traditional PIT interpretation from an uncertain process to a reliable and precise evaluation system. …”
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Assessing the Accuracy and Consistency of Cropland Datasets and Their Influencing Factors on the Tibetan Plateau by Fuyao Zhang, Xue Wang, Liangjie Xin, Xiubin Li

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Design of an Electronic Nose System with Automatic End-Tidal Breath Gas Collection for Enhanced Breath Detection Performance by Dongfu Xu, Pu Liu, Xiangming Meng, Yizhou Chen, Lei Du, Yan Zhang, Lixin Qiao, Wei Zhang, Jiale Kuang, Jingjing Liu

“…Finally, the system is used for breath detection. We employ classical machine learning algorithms to classify breath samples from different health conditions with a classification accuracy of more than 90%, which is better than the accuracy achieved in other studies of this type. …”
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Sensor‐Guided Smart Irrigation for Tomato Production: Comparing Low and Optimum Soil Moisture in Greenhouse Environments by Ibrahim Dirlik, Ferhat Uğurlar, Cengiz Kaya

“…Future research should explore the integration of advanced sensors, machine learning algorithms, and predictive models to further optimize irrigation strategies, with an emphasis on scalability and environmental impact. …”
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Mapping forest types along ecological gradient in Pakistan by Naveed Ahmad, Syed Ghias Ali

“…This study suggests that mapping of forest types through machine learning algorithms may be improved by incorporating other explanatory variables such as microclimate, soil types, nutrients, anthropogenic, demographic factors and spectral indices.…”
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Assessing the association between ADHD and brain maturation in late childhood and emotion regulation in early adolescence by Kristóf Ágrez, Pál Vakli, Béla Weiss, Zoltán Vidnyánszky, Nóra Bunford

“…Whether the difference between an individual’s brain age predicted by machine-learning algorithms trained on neuroimaging data and that individual’s chronological age, i.e. brain-predicted age difference (brain-PAD) predicts differences in emotion regulation, and whether ADHD problems add to this prediction is unknown. …”
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Geospatial brain-inspired navigation: a neurocognitive approach for autonomous systems in complex environments by Donghui Han, Tong Qin, Tianyu Yang, Hua Liao, Qiaosong Hei, Fangwen Yu, Bailu Si, Weihua Dong

“…In particular, we discuss opportunities and challenges associated with three critical aspects: (1) expanding the understanding of cognitive mechanisms from isolated regional functions to integrated functional connectivity and large-scale brain networks, (2) refining neurocognitive experiments to provide ecologically valid evidence in complex and dynamic contexts and (3) developing efficient approaches to computationally mimic and implement spatial cognition mechanisms of human brain in navigation algorithms. Addressing these difficulties would not only enable machines to navigate autonomously and effectively in complex real-world and extreme environments (e.g. space and the deep sea) but also pave the way for the development of future intelligent systems (e.g. …”
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Ensemble learning techniques reveals multidimensional EEG feature alterations in pediatric schizophrenia by Ying Mao, Ying Mao, Fang Wang, Shan Wang, Zhaowei Wang, Gang Li, Xuchen Qi, Xuchen Qi, Yu Sun, Yu Sun

“…This study aims to develop a machine learning-based framework for the auxiliary diagnosis of SCZ using multi-dimensional electroencephalogram (EEG) features and to investigate the underlying neural alterations. …”
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Leveraging Artificial Intelligence and Data Science for Integration of Social Determinants of Health in Emergency Medicine: Scoping Review by Ethan E Abbott, Donald Apakama, Lynne D Richardson, Lili Chan, Girish N Nadkarni

“…Rule-based NLP (n=5), deep learning (n=2), and pattern matching (n=4) were the most commonly used NLP techniques. …”
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Analyzing Patient Complaints in Web-Based Reviews of Private Hospitals in Selangor, Malaysia, Using Large Language Model–Assisted Content Analysis: Mixed Methods Study by Muhammad Hafiz Sulaiman, Nora Muda, Fatimah Abdul Razak

“…Fake reviews were filtered out using natural language processing and machine learning algorithms trained on yelp.com validated datasets. …”
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