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    Tongji University Reports Findings in Alzheimer Disease (Automatic speech analys is for detecting cognitive decline of older adults)

    29-29页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Neurodegenerative Dise ases and Conditions - Alzheimer Disease is the subject of a report. According to news reporting originating from Shanghai, People’s Republic of China, by NewsRx correspondents, research stated, “Speech analysis has been expected to help as a screening tool for early detection of Alzheimer’s disease (AD) and mild-cognit ively impairment (MCI). Acoustic features and linguistic features are usually us ed in speech analysis.” Our news editors obtained a quote from the research from Tongji University, “How ever, no studies have yet determined which type of features provides better scre ening effectiveness, especially in the large aging population of China. Firstly, to compare the screening effectiveness of acoustic features, linguistic feature s, and their combination using the same dataset. Secondly, to develop Chinese au tomated diagnosis model using self-collected natural discourse data obtained fro m native Chinese speakers. A total of 92 participants from communities in Shangh ai, completed MoCA-B and a picture description task based on the Cookie Theft un der the guidance of trained operators, and were divided into three groups includ ing AD, MCI, and heathy control (HC) based on their MoCA-B score. Acoustic featu res (Pitches, Jitter, Shimmer, MFCCs, Formants) and linguistic features (part-of -speech, type-token ratio, information words, information units) are extracted. The machine algorithms used in this study included logistic regression, random f orest (RF), support vector machines (SVM), Gaussian Naive Bayesian (GNB), and k- Nearest neighbor (kNN). The validation accuracies of the same ML model using aco ustic features, linguistic features, and their combination were compared. The ac curacy with linguistic features is generally higher than acoustic features in tr aining. The highest accuracy to differentiate HC and AD is 80.77% achieved by SVM, based on all the features extracted from the speech data, while the highest accuracy to differentiate HC and AD or MCI is 80.43% achieved by RF, based only on linguistic features.”
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    Study Results from University of Bonn Provide New Insights into Machine Learning (Perspective Uncovering and Tackling Fundamental Limitations of Compound Potenc y Predictions Using Machine Learning Models)

    30-30页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on Machine Learning are discussed in a new report. According to news originating from Bonn, Germany, by NewsRx correspondents, research stated, “Molecular property predictions play a central role in computer-aided drug discovery. Although a variety of physicoc hemical (e.g., solubility or chemical reactivity) or physiological properties (e .g., metabolic stability or toxicity) can be predicted, biological activity is b y far the most frequently investigated compound feature.” Our news journalists obtained a quote from the research from the University of B onn, “Activity predictions are carried out in a qualitative (target-based activi ty, through compound classification) or quantitative (compound potency or studie s have evaluated and compared different machine learning methods for activity an d potency predictions, recently with a focus on deep learning. Regardless of the methods used, these studies generally rely on conventional benchmark settings. Recent work has shown that potency prediction benchmarks have severe general lim itations that have long been unnoticed but prevent a reliable assessment of diff erent methods and their relative performance.”
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    New Data from OFFIS - Institute for Information Technology Illuminate Findings i n Androids (Is the Robot Spying On Me? a Study On Perceived Privacy In Teleprese nce Scenarios In a Care Setting With Mobile and Humanoid Robots)

    31-31页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Robotics - Androids h ave been presented. According to news reporting from Oldenburg, Germany, by News Rx journalists, research stated, “The number of robots that are in use worldwide is increasing, and they are starting to be used in new areas, where a use of ro botics was impossible in the past, such as nursing care. This brings about new c hallenges that need to be addressed, one of them is the challenge of privacy pre servation.” Financial support for this research came from Federal Ministry of Education & Research (BMBF).
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    Data on Machine Learning Reported by Researchers at University of Patras (Adapti ve Augmentation Framework for Domain Independent Few Shot Learning A)

    32-32页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting from Patras, Greece, by NewsR x journalists, research stated, “Few-Shot learning is a research area of machine learning, which aims to develop a prediction model based on a limited set of tr aining instances. In contrast to human learners, who are able to quickly learn a nd adapt to new tasks, machine learning models require large amounts of training instances in order to generalize efficiently.” The news correspondents obtained a quote from the research from the University o f Patras, “Image augmentation provides a potential solution to this challenge in few-shot learning by enlarging the training dataset. However, an excessive and uncontrollable enlargement of the initial dataset may potentially add noise, whi ch could significantly impair the learning efficacy, especially in the few-shot context. Our motivation lies in the fact that since the least confident instance s are the hardest to classify, performing targeted augmentations on these instan ces could efficiently enhance the limited representational sample space in a few shot context. In this work, we propose a new augmentation-based prediction frame work, which adaptively enlarges the few-shot training samples by performing targ eted image augmentations for the hardest to identify instances. Given the inhere ntly limited size of data, their proper identification is challenging. Therefore , we adopt a Least Confident Augmentation strategy based on the output confidenc es of an embedding-based estimator. In addition, we introduce an adaptive cleani ng step in order to remove the potential noise added during the targeted augment ations.”
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    Studies from Southeast University Provide New Data on Machine Learning (An Effic ient Position Optimization Method Based On Improved Genetic Algorithm and Machin e Learning for Sparse Array)

    33-33页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators discuss new findings in Machine Lea rning. According to news originating from Nanjing, People’s Republic of China, b y NewsRx correspondents, research stated, “Sparse array antennas are significant in communication and radar systems due to the advantages of high resolution, lo w complexity, and immunity to interference. In this letter, a novel array synthe sis method based on an improved genetic algorithm (IGA) is proposed to optimize the antenna position in the sparse array.” Financial supporters for this research include Natural Science Foundation for Ex cellent Young Scholars of Jiangsu Province, State Key Laboratory of Integrated C hips and Systems, National Key Laboratory of Wireless Communications Foundation, National Key Laboratory on Electromagnetic Environmental Effects and Electro-op tical Engineering, ISN State Key Lab, National Natural Science Foundation of Chi na (NSFC).
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    Investigators from Jiangxi Normal University Release New Data on Machine Learnin g (Developing Machine Learning Models for Predicting Multiple Physical Propertie s of Ionic Liquids Through a Combined Constitution-structure-interaction Descrip tor)

    34-34页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on Machine Learning are discussed in a new report. According to news reporting originating from Nanc hang, People’s Republic of China, by NewsRx correspondents, research stated, “Io nic liquids (ILs) have garnered significant research interest due to their wide- range applications in separation, catalysis, and synthesis. However, the combina tion of an extensive quantity of diverse anions and cations makes it a grand cha llenge for traditional methods (theoretical calculations and experiments) to ana lyze the properties of unexplored ILs.” Financial support for this research came from National Natural Science Foundatio n of China (NSFC).
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    Study Results from University of Milano Bicocca Update Understanding of Machine Learning (Assessment of Few-hits Machine Learning Classification Algorithms for Low-energy Physics In Liquid Argon Detectors)

    35-35页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Fresh data on Machine Learning are presented in a new report. According to news reporting from Milan, Italy, by NewsRx journalist s, research stated, “The physics potential of massive liquid argon TPCs in the l ow-energy regime is still to be fully reaped because few-hits events encode info rmation that can hardly be exploited by conventional classification algorithms. Machine learning (ML) techniques give their best in these types of classificatio n problems.” Funders for this research include Universita degli Studi di Milano - Bicocca wit hin the CRUI-CARE Agreement, CERN through the CERN QTI, Horizon Marie Sklodowska -Curie actions, Ministry of Education, Universities and Research (MIUR).
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    Data on Photocatalytics Described by Researchers at Southeast University (Machin e Learning-assisted Design of Nitrogen-rich Covalent Triazine Frameworks Photoca talysts)

    36-37页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Nanotechnology - Photocatalytics is the subject of a report. According to news reporting originating from Nanjing , People’s Republic of China, by NewsRx correspondents, research stated, “Covale nt triazine frameworks (CTFs), noted for their rich nitrogen content, have attra cted significant attention as promising photocatalysts. However, the structural complexity introduced by the diversity of nitrogen atoms in nitrogen-rich CTFs p oses a substantial challenge in discovering high-performance CTFs.” Funders for this research include National Key Research & Developm ent Program of China, National Natural Science Foundation of China (NSFC), Basic Research Program of Jiangsu Province, Jiangsu Provincial Scientific Research Ce nter of Applied Mathematics, Fundamental Research Funds for the Central Universi ties.
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    University of Science and Technology Beijing Reports Findings in Machine Learnin g (Integrating automated machine learning and metabolic reprogramming for the id entification of microplastic in soil: A case study on soybean)

    37-37页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating in Beijing, Peopl e’s Republic of China, by NewsRx journalists, research stated, “The accumulation of polyethylene microplastic (PE-MPs) in soil can significantly impact plant qu ality and yield, as well as affect human health and food chain cycles. Therefore , developing rapid and effective detection methods is crucial.” The news reporters obtained a quote from the research from the University of Sci ence and Technology Beijing, “In this study, traditional machine learning (ML) a nd H2O automated machine learning (H2O AutoML) were utilized to offer a powerful framework for detecting PE-MPs (0.1 %, 1 %, and 2 % by dry soil weight) and the co-contamination of PE-MPs and fomesafen (a common h erbicide) in soil. The development of the framework was based on the results of the metabolic reprogramming of soybean plants. Our study stated that traditional ML exhibits lower accuracy due to the challenges associated with optimizing com plex parameters. H2O AutoML can accurately distinguish between clean soil and co ntaminated soil. Notably, H2O AutoML can detect PE-MPs as low as 0.1 % (with 100 % accuracy) and co-contamination of PE-MPs and fomesafen (with 90 % accuracy) in soil. The VIP and SHAP analyses of the H2 O AutoML showed that PE-MPs and the co-contamination of PE-MPs and fomesafen sig nificantly interfered with the antioxidant system and energy regulation of soybe an.”
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    Researcher at Institute of Information Technology Describes Research in Artifici al Intelligence (UAV networks DoS attacks detection using artificial intelligenc e based on weighted machine learning)

    38-38页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on artificial intelligence is now available. According to news reporting from Baku, Azerbaijan, by NewsRx journalists, research stated, “While Unmanned Aerial Vehicles (UAVs) have found applications across numerous industries, they still remain vulnerable to various cybersecurity challenges. Different types of cyberattacks target UAVs.” Our news journalists obtained a quote from the research from Institute of Inform ation Technology: “Early detection of these cyberattacks is considered the most important step in ensuring the cybersecurity of UAVs. In this article, an artifi cial intelligence method based on machine learning was developed for detecting d ifferent types of Denial of Service (DoS) attacks targeting the UAV network. Ini tially in this work, feature selection methods are implemented to select the mos t important features. Then, machine learning methods are used to classify attack s. According to the conducted experiments, the proposed method outperformed othe rs with an accuracy of 99.51 % and a prediction time of 0.1 s. Add itionally, a novel dataset is used in this work, which offers several advantages .”
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