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    Technical University of Denmark (DTU) Researcher Furthers Understanding of Machi ne Learning (Federated In-Network Machine Learning for Privacy-Preserving IoT Tr affic Analysis)

    97-97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 artificial intell igence are discussed in a new report. According to news reporting out of Kongens Lyngby, Denmark, by NewsRx editors, research stated, “The expanding use of IoT has driven machine learning (ML) based traffic analysis.” The news reporters obtained a quote from the research from Technical University of Denmark (DTU): “5G networks’ standards, requiring low-latency communications for time-critical services, pose new challenges to traffic analysis. They necess itate fast analysis and response, preventing service disruption or security impa ct on network infrastructure. Distributed intelligence on IoT edge has been stud ied to analyze traffic, but introduces delays and raises privacy concerns. Feder ated learning can address privacy concerns, but does not meet latency requiremen ts. In this paper, we propose FLIP4: an efficient federated learning-based frame work for in-network traffic analysis. Our solution introduces a lightweight fede rated tree-based model, offloaded and running within network devices.”
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    Data on Artificial Intelligence Detailed by Researchers at AmirKabir University of Technology (Slope stability analysis of the open-pit walls using artificial i ntelligence)

    100-100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 artificial intelligen ce have been presented. According to news reporting out of Tehran, Iran, by News Rx editors, research stated, “The slope stability analysis is recognized as one of the most significant issues in rock mechanics engineering.” The news editors obtained a quote from the research from AmirKabir University of Technology: “It plays a fundamental role in the design of various rock and soil structures, including mining slopes, roads, and tunnels. To date, various metho ds have been proposed to address the issue of stability, including limit equilib rium methods, numerical methods, and artificial intelligence techniques. In the present study, the stability analysis of mine wall slopes has been conducted usi ng a neuro-fuzzy integrated approach (ANFIS). For this purpose, utilizing data f rom the Choghart iron mine, two neuro-fuzzy networks were developed to analyze t he safety and stability of circular failures under static loading conditions. In the circular failure model, six parameters were identified as the most signific ant inputs, with the safety factor (SF) and stability (S) state as outputs, unde r two different scenarios for analysis. The results obtained indicate that the s tability and safety analysis networks possess low error and high correlation, su ch that the average error for the safety factor and stability was 0.05 and 0.013 , respectively, demonstrating the network’s high generalization capability.”
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    Researcher’s Work from Universidad Autonoma de Yucatan Focuses on Machine Learni ng (Geo-Sensing-Based Analysis of Urban Heat Island in the Metropolitan Area of Merida, Mexico)

    104-104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 new report. According to news originating from Merida, Mex ico, by NewsRx correspondents, research stated, “Urban Heat Islands are a major environmental and public health concern, causing temperature increase in urban a reas.” Our news journalists obtained a quote from the research from Universidad Autonom a de Yucatan: “This study used satellite imagery and machine learning to analyze the spatial and temporal patterns of land surface temperature distribution in t he Metropolitan Area of Merida (MAM), Mexico, from 2001 to 2021. The results sho w that land surface temperature has increased in the MAM over the study period, while the urban footprint has expanded. The study also found a high correlation (r> 0.8) between changes in land surface temperature and land cover classes (urbanization/deforestation).”
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    Emory University Reports Findings in Machine Learning [D-Mach ine Learning to Elevate DFT-Based Potentials and a Force Field to the CCSD(T) Le vel Illustrated for Ethanol]

    105-105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 originating from Atlanta, Georgia, by N ewsRx correspondents, research stated, “Progress in machine learning has facilit ated the development of potentials that offer both the accuracy of first-princip les techniques and vast increases in the speed of evaluation. Recently, D-machin e learning has been used to elevate the quality of a potential energy surface (P ES) based on low-level, e.g., density functional theory (DFT) energies and gradi ents to close to the gold-standard coupled cluster level of accuracy.” Our news journalists obtained a quote from the research from Emory University, “ We have demonstrated the success of this approach for molecules, ranging in size from HO to 15-atom acetyl-acetone and tropolone. These were all done using the B3LYP functional. Here, we investigate the generality of this approach for the P BE, M06, M06-2X, and PBE0 + MBD functionals, using ethanol as the example molecu le. Linear regression with permutationally invariant polynomials is used to fit both low-level and correction PESs. These PESs are employed for standard RMSE an alysis for training and test data sets, and then general fidelity tests such as energetics of stationary points, normal-mode frequencies, and torsional potentia ls are examined. We achieve similar improvements in all cases. Interestingly, we obtained significant improvement over DFT gradients where coupled cluster gradi ents were not used to correct the low-level PES.”
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    New Robotics and Automation Study Results from Guangdong University of Technolog y Described (Distillgrasp: Integrating Features Correlation With Knowledge Disti llation for Depth Completion of Transparent Objects)

    106-107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 Robotics - Ro botics and Automation have been published. According to news reporting originati ng from Guangzhou, People’s Republic of China, by NewsRx correspondents, researc h stated, “Due to the visual properties of reflection and refraction, RGB-D came ras cannot accurately capture the depth of transparent objects, leading to incom plete depth maps. To fill in the missing points, recent studies tend to explore new visual features and design complex networks to reconstruct the depth, howeve r, these approaches tremendously increase computation, and the correlation of di fferent visual features remains a problem.” Funders for this research include China Scholarship Council, National Natural Sc ience Foundation of China (NSFC), Special Research Fund (BOF) of Hasselt Univers ity, The foundation of State Key Laboratory of Public Big Data, Guangdong Innova tive Research Team Program.
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    Data from Colorado State University Advance Knowledge in Machine Learning (Retri eval of boundary layer precipitable water from GOES ABI using machine learning t echniques)

    107-107页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in artific ial intelligence. According to news originating from Fort Collins, Colorado, by NewsRx correspondents, research stated, “Low-level moisture is an important ingr edient for forecasting severe storms, especially over the Great Plains where sev ere storms often develop along the dryline.” Our news journalists obtained a quote from the research from Colorado State Univ ersity: “Although ground-based observation systems such as lidar or radiosondes on weather balloons provide accurate information on low-level moisture, data are provided at limited locations, and the low temporal resolution of radiosondes m akes it difficult to track a rapidly developing dryline. Geostationary satellite s provide high spatial and temporal observation, but the channels of current geo stationary satellites are mostly sensitive to water vapor at mid to upper levels . However, the split window difference (SWD) between the “clean” window channel (10.3 mm) and the “dirty” window channel (12.3 mm) is commonly used for estimati ng low-level water vapor. However, this estimation is complicated by surface tem perature contributions, dependence on the lapse rate, and nonlinear relationship s between SWD and moisture. This study applies machine learning techniques to in fer boundary layer precipitable water (BLPW) from Geostationary Operational Envi ronmental Satellite (GOES) Advanced Baseline Imager (ABI) data. Since there are few observations that cover wide regions for training convolutional neural netwo rks, especially for the atmosphere above the surface, High-Resolution Rapid Refr esh (HRRR) model outputs are used as the truth for training.”
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    Studies from Solent University Update Current Data on Artificial Intelligence (E nergy Efficiency Evaluation of Artificial Intelligence Algorithms)

    108-108页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on artificial intelligence is the su bject of a new report. According to news reporting from Southampton, United King dom, by NewsRx journalists, research stated, “This article advances the discours e on sustainable and energy-efficient software by examining the performance and energy efficiency of intelligent algorithms within the framework of green and su stainable computing.” Our news journalists obtained a quote from the research from Solent University: “Building on previous research, it explores the theoretical implications of Brem ermann’s limit on efforts to enhance computer performance through more extensive methods. The study presents an empirical investigation into heuristic methods f or search and optimisation, demonstrating the energy efficiency of various algor ithms in both simple and complex tasks. It also identifies key factors influenci ng the energy consumption of algorithms and their potential impact on computatio nal processes.”
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    Research in the Area of Artificial Intelligence Reported from University of Pavi a (Artificial Intelligence for the Evaluation of Postures Using Radar Technology : A Case Study)

    108-109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 new report. According to news originating from Pavia, Ital y, by NewsRx correspondents, research stated, “In the last few decades, major pr ogress has been made in the medical field; in particular, new treatments and adv anced health technologies allow for considerable improvements in life expectancy and, more broadly, in quality of life.”The news correspondents obtained a quote from the research from University of Pa via: “As a consequence, the number of elderly people is expected to increase in the following years. This trend, along with the need to improve the independence of frail people, has led to the development of unobtrusive solutions to monitor daily activities and provide feedback in case of risky situations and falls. Mo nitoring devices based on radar sensors represent a possible approach to tackle postural analysis while preserving the person’s privacy and are especially usefu l in domestic environments.”
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    Researchers from University of the Free State Report Recent Findings in Machine Learning (Effectiveness of Lof, Iforest and Ocsvm In Detecting Anomalies In Stre am Sediment Geochemical Data)

    110-111页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Machine Learning. According to news originating from Bloemfontein, South Africa, by NewsRx correspondents, research stated, “This paper compares three unsupervi sed machine-learning algorithms - local outlier factor (LOF), Isolation Forest ( iForest) and oneclass support vector machine (OCSVM) - for anomaly detection in a multivariate geochemical dataset in northeastern Iran. This area contains sev eral Au, Cu and Pb-Zn mineral occurrences.” Our news journalists obtained a quote from the research from the University of t he Free State, “The methodology incorporates single-element geochemistry, multiv ariate data analysis and application of the three unsupervised machine-learning algorithms. Principal component analysis unveiled diverse elemental associations for the first seven principal components (PCs): PC1 shows a Co-Cr-Ni-V-Sn assoc iation indicating a lithological influence; PC2 shows a Au-Bi-Cu-W association s uggesting epithermal Au mineralization; PC3 shows variability in Zn-V-Co-Sb-Cu-C r; PC4 shows a Au-Cu-Ba-Sr-Ag association indicating Au and polymetallic mineral ization; PC5 reflects Zn-Ag-Ni-Pb related to hydrothermal mineralization; and PC 6 and PC7 show element associations suggesting epithermal and intrusive-related polymetallic mineralization. It was found that OCSVM performed slightly better t han LOF and iForest in detecting anomalies associated with known Cu occurrences, and it successfully delineated dispersion from all known Au occurrences. LOF ou tperformed iForest and OCSVM in identifying all four Pb-Zn occurrences, and the three methods substantially limited the areas of the anomaly class. The analysis showed that LOF produced a less cluttered anomaly map compared to the isolated patterns in the iForest map. LOF was accurate in identifying anomalies associate d with Au-Pb mineralization, while iForest detected anomalies associated with Pb -Zn-Cu occurrences and neighbouring Pb-Zn occurrence. OCSVM performed similarly in the northern and western areas but displayed unique discrepancies in the SE a nd west by detecting anomalies associated with two Cu occurences and a Pb-Cu occ urrence. This study examined the influence of contamination fraction on detectio n of geochemical anomalies, revealing a noteworthy rise in the count of mineral occurrences delineated by anomalies when the contamination fraction increases fr om 5 to 10 %.”
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    Data on Artificial Intelligence Reported by Luca Massimino and Colleagues (Artif icial intelligence: A new tool in the pathologist’s armamentarium for the diagno sis of IBD)

    111-112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 originating from Milan, Italy, b y NewsRx correspondents, research stated, “Inflammatory bowel diseases (IBD) are classified into two entities, namely Crohn’s disease (CD) and ulcerative coliti s (UC), which differ in disease trajectories, genetics, epidemiological, clinica l, endoscopic, and histopathological aspects. As no single golden standard modal ity for diagnosing IBD exists, the differential diagnosis among UC, CD, and non- IBD involves a multidisciplinary approach, considering professional groups that include gastroenterologists, endoscopists, radiologists, and pathologists.” Our news journalists obtained a quote from the research, “In this context, histo logical examination of endoscopic or surgical specimens plays a fundamental role . Nevertheless, in differentiating IBD from non-IBD colitis, the histopathologic al evaluation of the morphological lesions is limited by sampling and subjective human judgment, leading to potential diagnostic discrepancies. To overcome thes e limitations,artificial intelligence (AI) techniques are emerging to enable au tomated analysis of medical images with advantages in accuracy, precision, and s peed of investigation, increasing interest in the histological analysis of gastr ointestinal inflammation.”
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