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    New Findings Reported from Autonomous University of Zacatecas Describe Advances in Machine Learning (Annual Daily Irradiance Analysis of Clusters in Mexico by M achine Learning Algorithms)

    38-38页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Research findings on artificial intelligence are discussed in a new report. According to news reporting from Zacatecas, Mexico, b y NewsRx journalists, research stated, "The assessment of solar resources involv es the utilization of physical or satellite models for the determination of sola r radiation on the Earth's surface." Funders for this research include Consejo Zacatecano De Ciencia, Tecnologia E In novacion,. Our news editors obtained a quote from the research from Autonomous University o f Zacatecas: "However, a critical aspect of model validation necessitates compar isons against ground-truth measurements obtained from surface radiometers. Given the inherent challenges associated with establishing and maintaining solar radi ation measurement networks-characterized by their expense, logistical complexiti es, limited station availability and the imperative consideration of climatic cr iteria for siting-countries endowed with substantial climatic diversity face dif ficulties in station placement. In this investigation, the measurements of annua l solar irradiation, from meteorological stations of the National Weather Servic e in Mexico, were compared in different regions clustered by similarities in alt itude, TL Linke, albedo and cloudiness index derived from satellite images; the main objective is to find the best ratio of annual solar irradiation in a set of clusters. Employing machine learning algorithms, this research endeavors to ide ntify the most suitable model for predicting the ratio of annual solar irradiati on and to determine the optimal number of clusters."
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    Research from Universiti Teknologi PETRONAS Yields New Data on Artificial Intell igence (Modeling the effect of implementation of artificial intelligence powered image analysis and pattern recognition algorithms in concrete industry)

    39-39页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Data detailed on artificial intelligence have bee n presented. According to news reporting originating from Perak, Malaysia, by Ne wsRx correspondents, research stated, "AI-powered image analysis and pattern rec ognition algorithms (IAPRA) are renowned for their capacity to identify concrete flaws, assess strength characteristics, and anticipate the service life of conc rete. However, its execution in a concrete building is challenging due to severa l unknown aspects." The news editors obtained a quote from the research from Universiti Teknologi PE TRONAS: "This research aims to evaluate the challenges encountered by AI-powered IAPRA and their influence on the concrete industry's digital transformation suc cess. We conducted a quantitative research methodology to identify impediments a nd success variables associated with AI-powered picture analysis and pattern rec ognition algorithms. Structural Equation Modeling (SEM) tests were conducted to determine the critical hurdles associated with AI-powered picture analysis and p attern recognition algorithms. Three reliable and valid formative constructs wer e identified: complexity and privacy, economic and legal, and technology and int egration. The developed model revealed the significance of three reflecting cons tructs: quality control, predictive maintenance, and enhanced productivity. The practical implications include, addressing the identified challenges related to AI-powered IAPRA is crucial for the concrete industry's digital transformation."
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    New Data from Jozef Stefan Institute Illuminate Findings in Robotics (Simulation -aided Handover Prediction From Video Using Recurrent Image-to-motion Networks)

    40-40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 have been pr esented. According to news reporting originating from Ljubljana, Slovenia, by Ne wsRx correspondents, research stated, "Recent advances in deep neural networks h ave opened up new possibilities for visuomotor robot learning. In the context of human-robot or robot-robot collaboration, such networks can be trained to predi ct future poses and this information can be used to improve the dynamics of coop erative tasks." Financial supporters for this research include Slovenian Research Agency - Slove nia, European Union (EU), New Energy and Industrial Technology Development Organ ization (NEDO), Grants-in-Aid for Scientific Research (KAKENHI), Japan Science a nd Technology Agency (JST) Mirai Program, Tateishi Science and Technology Founda tion. Our news editors obtained a quote from the research from Jozef Stefan Institute, "This is important, both in terms of realizing various cooperative behaviors, a nd for ensuring safety. In this article, we propose a recurrent neural architect ure, capable of transforming variable-length input motion videos into a set of p arameters describing a robot trajectory, where predictions can be made after rec eiving only a few frames. A simulation environment is utilized to expand the tra ining database and to improve generalization capability of the network. The resu lting architecture demonstrates good accuracy when predicting handover trajector ies, with models trained on synthetic and real data showing better performance t han when trained on real or simulated data only."
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    Tongji University School of Medicine Reports Findings in Machine Learning (Machi ne learning based on functional and structural connectivity in mild cognitive im pairment)

    41-42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 out of Shanghai, People's Rep ublic of China, by NewsRx editors, research stated, "Alzheimer's disease (AD) is a chronic, degenerative neurological disorder characterized by progressive cogn itive decline and mental behavioral abnormalities. Mild cognitive impairment (MC I) is regarded as a transitional stage in the progression from normal elderly in dividuals to patients with AD." Our news journalists obtained a quote from the research from the Tongji Universi ty School of Medicine, "While studies have identified abnormalities in brain con nectivity in patients with MCI, including functional and structural connectivity, accurately identifying patients with MCI in clinical screening remains challen ging. We hypothesized that utilizing machine learning (ML) based on both functio nal and structural connectivity could yield meaningful results in distinguishing between patients with MCI and normal elderly individuals, so as to provide valu able information for early diagnosis and precise evaluation of patients with MCI . Following clinical criteria, we recruited 32 patients with MCI for the patient group, and 32 normal elderly individuals for the control group. All subjects un derwent examinations for resting-state functional magnetic resonance imaging (rs -fMRI) and diffusion tensor imaging (DTI). Subsequently, significant functional and structural connectivity features were selected and combined with a support v ector machine for classification of the patient and control groups. We observed significantly different functional connectivity in the frontal lobe and putamen between the MCI group and normal controls. The results based on functional conne ctivity features demonstrated a classification accuracy of 71.88% and an area under the curve (AUC) value of 0.78. In terms of structural connecti vity, we found that decreased fractional anisotropy in patients with MCI was sig nificantly associated with Montreal Cognitive Assessment scores, specifically in regions such as the precuneus and cingulate gyrus. The classification results u sing the structural connectivity feature yielded an accuracy of 92.19% and an AUC value of 0.99. Lastly, combining functional and structural connectivi ty features resulted in a classification accuracy and AUC value of 93.75 % and 0.99, respectively. In this study, we demonstrated a high classification per formance, underscoring the potential of both brain functional and structural con nectivity in distinguishing patients with MCI from normal elderly individuals."
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    Department of Radiology Reports Findings in Artificial Intelligence (Research Pr ogress of Artificial Intelligence in the Grading and Classification of Meningiom as)

    41-41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 Artificial Intelligenc e is the subject of a report. According to news reporting originating in Zhuhai, People's Republic of China, by NewsRx journalists, research stated, "A meningio ma is a common primary central nervous system tumor. The histological features o f meningiomas vary significantly depending on the grade and subtype, leading to differences in treatment and prognosis." The news reporters obtained a quote from the research from the Department of Rad iology, "Therefore, early diagnosis, grading, and typing of meningiomas are cruc ial for developing comprehensive and individualized diagnosis and treatment plan s. The advancement of artificial intelligence (AI) in medical imaging, particula rly radiomics and deep learning (DL), has contributed to the increasing research on meningioma grading and classification. These techniques are fast and accurat e, involve fully automated learning, are non-invasive and objective, enable the efficient and non-invasive prediction of meningioma grades and classifications, and provide valuable assistance in clinical treatment and prognosis. This articl e provides a summary and analysis of the research progress in radiomics and DL f or meningioma grading and classification." According to the news reporters, the research concluded: "It also highlights the existing research findings, limitations, and suggestions for future improvement, aiming to facilitate the future application of AI in the diagnosis and treatme nt of meningioma."
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    Studies from Manipal Academy of Higher Education Add New Findings in the Area of Intelligent Systems (Noise estimation based on optimal smoothing and minimum co ntrolled through recursive averaging for speech enhancement)

    43-43页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Fresh data on intelligent systems are presented i n a new report. According to news originating from Karnataka, India, by NewsRx e ditors, the research stated, "One of the most significant challenges in the real time speech processing applications is the elimination of noise in the corrupte d speech data. This noise can significantly impact the efficacy/performance of t he applications of speech processing." The news journalists obtained a quote from the research from Manipal Academy of Higher Education: "However, developing a robust noise reduction algorithm is cru cial for improving the accuracy of automatic speech recognition (ASR) and other speech processing systems under uncontrolled conditions. In this paper, we propo se an algorithm to reduce the background noise in the degraded speech data under highly nonstationary conditions. The proposed optimal smoothing and minima con trolled (OSMC) technique uses recursive averaging to enhance degraded speech dat a. Initially, a smoothed periodogram and local minima of the degraded speech dat a are computed and determined the time-frequency dependent threshold factor. The ratio of smoothed periodogram to local minima is used to find the active region s of speech in the degraded speech data by adapting the Bayesian minimum cost de cision rule. To calculate the estimated noise spectrum for each frequency bin, a time-frequency smoothing factors are used."
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    Study Data from Jerusalem College of Technology Provide New Insights into Machin e Translation (Machine Translation for Historical Research: A Case Study of Aram aic-Ancient Hebrew Translations)

    44-44页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on machine translati on have been published. According to news reporting out of Jerusalem, Israel, by NewsRx editors, research stated, "In this article, by the ability to translate Aramaic to another spoken languages, we investigated machine translation in a cu ltural heritage domain for two primary purposes: evaluating the quality of ancie nt translations and preserving Aramaic (an endangered language)." The news editors obtained a quote from the research from Jerusalem College of Te chnology: "First, we detailed the construction of a publicly available Biblical parallel Aramaic-Hebrew corpus based on two ancient (early 2 nd to late 4 th century) Hebrew-Aramaic translations: Targum Onkelus and Targum Jonathan. Then using the statistical machine translation approach, which in our use case signif icantly outperforms neural machine translation, we validated the excepted high q uality of the translations. The trained model failed to translate Aramaic texts of other dialects. However, when we trained the same statistical machine transla tion model on another Aramaic-Hebrew corpus of a different dialect (Zohar, 13 th century), a very high translation score was achieved. We examined an additional important cultural heritage source of Aramaic texts, the Babylonian Talmud (ear ly 3 rd to late 5 th century)."
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    Hefei University of Technology Researchers Illuminate Research in Virtual Realit y and Intelligent Hardware (Audio2AB: Audio-driven collaborative generation of v irtual character animation)

    44-45页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on virtual reali ty and intelligent hardware have been published. According to news originating f rom Hefei University of Technology by NewsRx editors, the research stated, "Cons iderable research has been conducted in the areas of audio-driven virtual charac ter gestures and facial animation with some degree of success. However, few meth ods exist for generating full-body animations, and the portability of virtual ch aracter gestures and facial animations has not received sufficient attention." The news journalists obtained a quote from the research from Hefei University of Technology: "Therefore, we propose a deep-learning-based audio-to-animation-and -blendshape (Audio2AB) network that generates gesture animations andARK it's 52 facial expression parameter blendshape weights based on audio, audio-correspondi ng text, emotion labels, and semantic relevance labels to generate parametric da ta for full- body animations. This parameterization method can be used to drive full-body animations of virtual characters and improve their portability. In the experiment, we first downsampled the gesture and facial data to achieve the sam e temporal resolution for the input, output, and facial data. The Audio2AB netwo rk then encoded the audio, audio- corresponding text, emotion labels, and semant ic relevance labels, and then fused the text, emotion labels, and semantic relev ance labels into the audio to obtain better audio features. Finally, we establis hed links between the body, gestures, and facial decoders and generated the corr esponding animation sequences through our proposed GAN-GF loss function. By usin g audio, audio-corresponding text, and emotional and semantic relevance labels a s input, the trained Audio2AB network could generate gesture animation data cont aining blendshape weights. Therefore, different 3D virtual character animations could be created through parameterization."
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    Findings from Universidad del Norte in Support Vector Machines Reported (A dista nce-based kernel for classification via Support Vector Machines)

    45-46页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on have been publish ed. According to news originating from Barranquilla, Colombia, by NewsRx corresp ondents, research stated, "Support Vector Machines (SVMs) are a type of supervis ed machine learning algorithm widely used for classification tasks." The news editors obtained a quote from the research from Universidad del Norte: "In contrast to traditional methods that split the data into separate training a nd testing sets, here we propose an innovative approach where subsets of the ori ginal data are randomly selected to train the model multiple times. This iterati ve training process aims to identify a representative data subset, leading to im proved inferences about the population. Additionally, we introduce a novel dista nce-based kernel specifically designed for binary- type features based on a simil arity matrix that efficiently handles both binary and multi-class classification problems. Computational experiments on publicly available datasets of varying s izes demonstrate that our proposed method significantly outperforms existing app roaches in terms of classification accuracy. Furthermore, the distance-based ker nel achieves superior performance compared to other well-known kernels from the literature and those used in previous studies on the same datasets. These findin gs validate the effectiveness of our proposed classification method and distance -based kernel for SVMs."
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    University of Zaragoza Researcher Highlights Research in Robotics (Real-Time Pro duction Scheduling and Industrial Sonar and Their Application in Autonomous Mobi le Robots)

    46-47页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Researchers detail new data in robotics. Accordin g to news reporting originating from Zaragoza, Spain, by NewsRx correspondents, research stated, "In real-time production planning, there are exceptional events that can cause problems and deviations in the production schedule." The news reporters obtained a quote from the research from University of Zaragoz a: "These circumstances can be solved with real-time production planning, which is able to quickly reschedule the operations at each work centre. Mobile autonom ous robots are a key element in this real-time planning and are a fundamental li nk between production centres. Work centres in Industry 4.0 environments can use current technology, i.e., a biomimetic strategy that emulates echolocation, wit h the aim of establishing bidirectional communication with other work centres th rough the application of agile algorithms. Taking advantage of these communicati on capabilities, the basic idea is to distribute the execution of the algorithm among different work centres that interact like a parasympathetic system that ma kes automatic movements to reorder the production schedule. The aim is to use al gorithms with an optimal solution based on the simplicity of the task distributi on, trying to avoid heuristic algorithms or heavy computations."
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