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    Researchers at Daffodil International University Report Research in Machine Lear ning (Smart aquaculture analytics: Enhancing shrimp farming in Bangladesh throug h real-time IoT monitoring and predictive machine learning analysis)

    48-49页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news originating from Dhaka, Bangladesh, by NewsRx correspondents, research stated, “Water quality is a critical factor in s hrimp farming, and the success of shrimp production is closely tied to the overa ll condition of the water. Challenges such as rapid population growth, environme ntal pollution, and global warming have led to a decline in fisheries production , particularly in the freshwater shrimp sector.” Our news editors obtained a quote from the research from Daffodil International University: “This study addresses these challenges by monitoring multiple water parameters in shrimp farms, including pH, temperature, TDS, EC, and salinity. Tr aditional manual monitoring systems are known to be cumbersome, time-consuming, and lacking real-time capabilities. Consequently, a continuous and automated mon itoring system becomes imperative for efficient and real-time metrics handling. This study introduces a real-time freshwater shrimp (locally named Galda, i.e., Macrobrachium Rosenbergii) farm monitoring system. The proposed system incorpora tes technologies such as microcontroller-based physical devices, IoT, cloud stor age with service, machine learning models, and web applications. This integrated system enables users to remotely monitor shrimp farms and receive alerts when w ater parameters fall outside the optimal range. The physical implementation invo lves a set of sensors for collecting data on water metrics in shrimp farms. Regr ession analysis is employed for predicting next-day values, and a newly develope d decisionbased algorithm classifies shrimp production levels into low, medium, and maximum categories using six well-known classification algorithms.”
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    Research Data from Central South University Update Understanding of Machine Lear ning (Machine learning-based predictions and analyses of the creep rupture life of the Ni-based single crystal superalloy)

    49-49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 reporting out of Central South University by NewsRx editors, research stated, “The evaluation of creep rupture life is compl ex due to its variable formation mechanism.” Financial supporters for this research include Fundamental Research Funds For Th e Central Universities of Central South University. The news journalists obtained a quote from the research from Central South Unive rsity: “In this paper, machine learning algorithms are applied to explore the cr eep rupture life span as a function of 27 physical properties to address this is sue. By training several classical machine learning models and comparing their p rediction performance, XGBoost is finally selected as the predictive model for c reep rupture life. Moreover, we introduce an interpretable method, Shapley addit ive explanations (SHAP), to explain the creep rupture life predicted by the XGBo ost model. The SHAP values are then calculated, and the feature importance of th e creep rupture life yielded by the XGBoost model is discussed. Finally, the cre ep fracture life is optimized by using the chaotic sparrow optimization algorith m. We then show that our proposed method can accurately predict and optimize cre ep properties in a cheaper and faster way than other approaches in the experimen ts.”
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    Reports from University of Paulista Advance Knowledge in Artificial Intelligence (Artificial Intelligence To Classify the Cooling Effect of Tree-shade In Buildi ngs’ Facade: a Case Study In Brazil)

    50-50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 originating from Sao Paulo, Brazil, by NewsRx correspondents, research stated, “Urban heat islands, exacerbated by cli mate change, have become a pressing issue as summer temperatures rise. This stud y uses data mining techniques to classify the thermal impact of tree shade on bu ilding fa & ccedil;ades in the urban area of a tropical city.” Financial support for this research came from Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES).
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    Findings from University of Technology Sydney Provide New Insights into Machine Learning (A Comprehensive Survey On Poisoning Attacks and Countermeasures In Mac hine Learning)

    51-51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 Machine Learn ing have been published. According to news reporting originating in Ultimo, Aust ralia, by NewsRx journalists, research stated, “The prosperity of machine learni ng has been accompanied by increasing attacks on the training process. Among the m, poisoning attacks have become an emerging threat during model training.” Funders for this research include Australian Research Council, Australian Resear ch Council. The news reporters obtained a quote from the research from the University of Tec hnology Sydney, “Poisoning attacks have profound impacts on the target models, e .g., making them unable to converge or manipulating their prediction results. Mo reover, the rapid development of recent distributed learning frameworks, especia lly federated learning, has further stimulated the development of poisoning atta cks. Defending against poisoning attacks is challenging and urgent. However, the systematic review from a unified perspective remains blank. This survey provide s an in-depth and up-to-date overview of poisoning attacks and corresponding cou nter-measures in both centralized and federated learning. We firstly categorize attack methods based on their goals. Secondly, we offer detailed analysis of the differences and connections among the attack techniques. Furthermore, we presen t countermeasures in different learning framework and highlight their advantages and disadvantages.”
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    Studies from National Taipei University of Technology Further Understanding of A rtificial Intelligence (Artificial intelligence technique development for energy -efficient waste-to-energy: A case study of an incineration plant)

    52-52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 reporting originating from Taipei, Taiwan, b y NewsRx correspondents, research stated, “The increasing heat value and complex ity of waste types in Taiwan’s incineration plants have led to reduced facility capacity and utilization rates.” Our news editors obtained a quote from the research from National Taipei Univers ity of Technology: “Traditional control systems struggle to manage rapid and irr egular fluctuations in waste heat values, often resulti ng in poor stability and prolonged response times. This study introduces an artificial intelligencebase d heat value prediction and combustion control system that enhances system effic iency and stability without equipment upgrades. The system predicts future waste heat trends, enabling precise operational adjustments. This results in shorter response times, improved combustion stability, and higher energy recovery effici ency, effectively replacing traditional control systems for more accurate waste management. Our system evaluates waste input uniformity to ensure consistent fee d and employs a Long Short-Term Memory neural network architecture to predict wa ste combustion heat values, greatly enhancing combustion stability. The model’s R2 value of 0.96 allows for optimized control parameters that reduce system resp onse times.”
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    Study Results from University Rey Juan Carlos Provide New Insights into Robotics (Design and Validation of an Ambulatory User Support Gait Rehabilitation Robot: NIMBLE)

    53-54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 robotics have bee n published. According to news reporting out of Madrid, Spain, by NewsRx editors , research stated, “Relearning to walk requires progressive training in real sce narios-overground-along with assistance in basic tasks, such as balancing.”Our news correspondents obtained a quote from the research from University Rey J uan Carlos: “In addition, user ability must be maximized through compliant robot ic assistance as needed. Despite decades of research, gait rehabilitation roboti c devices yield controversial results. This article presents the conceptual desi gn of a novel walking assistance and rehabilitation robot, the NIMBLE robot, aim ed at providing ambulatory, bodyweight-supported gait training, assisting the us er’s center of mass trajectory to aid weight transfer and dynamic balance during walking. NIMBLE consists of a robotic mobile frame, a partial bodyweight suppor t (PBWS) system, an ambulatory lower-limb exoskeleton (Exo-H3) and a cable-drive n pelvis-assisting robot. Designed as a modular structure, it differentiates hie rarchical communication levels through a Robot Operating System (ROS) 2 network. We present the mechatronic design and experimental results assessing the impact of the mechatronic coupling between the robotic modules on the walking kinemati cs and the frame movement control performance.”
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    Hainan University Researchers Add New Findings in the Area of Robotics (Multi-ro bot collision avoidance method in sweet potato fields)

    53-53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 originating from Haikou, People’s Republic of China, by NewsRx correspondents, research stated, “Currently, precise spraying of sweet potatoes is mainly accomplished through semi-mechanized or single spraying robo ts, which results in low operating efficiency. Moreover, it is time-consuming an d labor-intensive, and the pests and diseases cannot be eliminated in time.” The news editors obtained a quote from the research from Hainan University: “Bas ed on multi robot navigation technology, multiple robots can work simultaneously , improving work efficiency. One of the main challenges faced by multi robot nav igation technology is to develop a safe and robust collision avoidance strategy, so that each robot can safely and efficiently navigate from its starting positi on to the expected target. In this article, we propose a low-cost multi-robot co llision avoidance method to solve the problem that multiple robots are prone to collision when working in field at the same time. This method has achieved good results in simulation. In particular, our collision avoidance method predicts th e possibility of collision based on the robot’s position and environmental infor mation, and changes the robot’s path in advance, instead of waiting for the robo t to make a collision avoidance decision when it is closer. Finally, we demonstr ate that a multi-robot collision avoidance approach provides an excellent soluti on for safe and effective autonomous navigation of a single robot working in com plex sweet potato fields.”
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    New Pattern Recognition and Artificial Intelligence Findings from National Taiwa n University of Science and Technology Published (Application of Generative Adve rsarial Networks in Semi-Annotated Defect Synthesis and Detection Under Limited ...)

    54-55页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on pattern recognition and artificial intelligence are presented in a new report. According to news reporti ng out of National Taiwan University of Science and Technology by NewsRx editors , research stated, “Defect detection is a crucial technology that is extensively employed in the manufacturing industry to monitor and ensure the quality of out put. Deep learning models have shown remarkable potential for defect detection.”Our news reporters obtained a quote from the research from National Taiwan Unive rsity of Science and Technology: “However, the success of these models heavily r elies on voluminous training data. Collecting substantial amounts of defect data is challenging in practical settings, and the tedious process of pixel-level de fect annotation further complicates the task. Among the common defects encounter ed in manufacturing, scratches are particularly significant. To address these ch allenges, this study proposes a two-phase generative adversarial network (GAN) a pproach for synthesizing defect images and generating semi-automatic pixel-wise labels for anomaly detection. The first phase primarily focuses on synthesizing images, while the second phase involves the pixel-wise labeling of the images. T he synthesized paired images generated by the GANs serve as input to the semanti c network.”
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    Research Results from Universitas AMIKOM Yogyakarta Update Understanding of Mach ine Learning (Comparison of Feature Extraction Methods for Conducting Sentiment Classification in Ternate Malay Language using Machine Learning Approaches)

    55-56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 artificial intell igence have been published. According to news originating from the Universitas A MIKOM Yogyakarta by NewsRx correspondents, research stated, “Local people in Ter nate, North Maluku, often use local languages to communicate on social media.” Our news editors obtained a quote from the research from Universitas AMIKOM Yogy akarta: “This poses a challenge for newcomers to understand the implied meaning and emotions of the messages conveyed through social media. This research aims t o develop a natural language processing (NLP)-based emotion classification metho d that can be applied to Ternate Malay text datasets. The application of NLP is expected to improve the accuracy of emotion detection and classification in the text. The research was conducted by applying and comparing the performance of se veral classification models trained using Ternate Malay text datasets. The model s used include SVM (Support Vector Machine), K-Nearest Neighbors (KNN) Random Fo rest, Decision Tree and Logistic Regression. Each model is applied using BoW (Ba g-of-Words) and Word2Vec vectorization representations.”
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    Findings from Forschungszentrum Julich GmbH in Machine Learning Reported (Practi cal feature filter strategy to machine learning for small datasets in chemistry)

    56-57页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting from the Forschungszentrum Ju lich GmbH by NewsRx journalists, research stated, “Many potential use cases for machine learning in chemistry and materials science suffer from small dataset si zes, which demands special care for the model design in order to deliver reliabl e predictions.” Financial supporters for this research include Bundesministerium Fur Bildung Und Forschung; Deutsche Forschungsgemeinschaft; Forschungszentrum Julich Gmbh.
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