المؤلفون: Kostenko, Ernest, Šengaut, Jakov, Maknickas, Algirdas

المصدر: Applied Sciences (2076-3417); Jun2024, Vol. 14 Issue 11, p4867, 17p

مصطلحات موضوعية: BONE fractures, MACHINE learning, MACHINE theory, VETERINARY medicine, RANDOM forest algorithms, SAMPLE size (Statistics), HOUGH transforms

مستخلص: In the ever-evolving world of veterinary care, the occurrence of bone fractures in canines poses a common and complex problem, especially in extra-small breeds and dogs that are less than 1 year old. The objective of this research is to fill a gap in predicting the risk of canine bone fractures. A machine learning method using a random forest classifier was constructed. The algorithm was trained on a dataset consisting of 2261 cases that included several factors, such as canine age, gender, breed, and weight. The performance of the algorithm was assessed by examining its capacity to forecast the probability of fractures occurring. The findings of our study indicate that the tool has the capability to provide dependable predictions of fracture risk, consistent with our extensive dataset on fractures in canines. However, these results should be considered preliminary due to the limited sample size. This discovery is a crucial tool for veterinary practitioners, allowing them to take preventive measures to manage and prevent fractures. In conclusion, the implementation of this prediction tool has the potential to significantly transform the quality of care in the field of veterinary medicine by enabling the detection of patients at high risk, hence enabling the implementation of timely and customized preventive measures. [ABSTRACT FROM AUTHOR]

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المؤلفون: Yao, Penghui, Qiu, Liqi, Sun, Qun, Xu, Lipeng, Zhao, Ying, Fan, Zhongxing, Zhang, Andong

المصدر: Applied Sciences (2076-3417); Jun2024, Vol. 14 Issue 11, p4786, 24p

مصطلحات موضوعية: ORDER picking systems, PITAHAYAS, MACHINE theory, FRUIT harvesting, FRUIT growing, STRUCTURAL analysis (Engineering)

مستخلص: In order to effectively solve the problems of the complex growth state of dragon fruit and how the picking process is mostly manual, this study designed a picking and selecting integrated remote-operation-type dragon-fruit-picking device. Based on SOLIDWORKS 2020 software for the three-dimensional digital design and overall assembly of key components, the structure and working theory of the machine are introduced. By improving the high-recognition-rate dragon fruit target detection algorithm based on YOLOv5, better recognition and locating effects were achieved for targets with a small size and high density, as well as those in bright-light scenes. Serial communication, information acquisition, and the precise control of each picking action were realized by building the software and hardware platforms of the picking device control system. By analyzing the working principle of the mechanical system and the mechanics of the machine picking process, the critical factors affecting the net picking rate and damage rate of the dragon-fruit-picking device were confirmed. Based on the force and parameter analysis of the test results, it was confirmed that the machine had an optimal picking influence when the flexible claw closing speed was 0.029 m/s, the electric cylinder extending speed was 0.085 m/s, and the mechanical arm moving speed was 0.15 m/s. The net picking rate of the device reached 90.5%, and the damage rate reached 2.9%. The picking device can complete the picking of a single dragon fruit, as well as a plurality of fruits grown at a growing point, and integrates the integration of picking fruits, removing bad fruits, and sorting fruits, which can improve the efficiency of dragon fruit harvesting and replace manual work. [ABSTRACT FROM AUTHOR]

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المؤلفون: Kumar, Lalit, Afzal, Mohammad Saud

المصدر: Acta Geophysica; Jun2024, Vol. 72 Issue 3, p1895-1911, 17p

مصطلحات موضوعية: PIERS, MACHINE learning, COASTAL engineering, RELATIVE velocity, FROUDE number, FLOW velocity, MACHINE theory

مستخلص: The development of pier scour in coastal environments severely affects the bridge's stability. Therefore, estimating pier scour around the vertical cylinder is important for the safety of the bridge structure. The estimation of pier scour depth in combined wave-current conditions has become a challenging task for researchers in recent times. The existing empirical formulations that calculate scour in the combined action of current and wave are scarce and may not always provide accurate results. Machine-learning (ML) techniques have become increasingly popular for their prediction capabilities in the fields of hydraulics and coastal engineering in recent years. Therefore, the present study aims to develop Boosting ML techniques (i.e., AdaBoost, XGBoost, CatBoost, and LightGBM) of ML to estimate pier scour in combined wave-current conditions. The non-dimensional parameters, such as Keulegan–Carpenter (KC) number, Relative flow velocity (Ucw), and Absolute Froude number (Fra), are used as input parameters, whereas scour depth (S/D) is the output parameter in Boosting ML models. The sensitivity analysis has been performed to demonstrate the relative importance of the input parameter on S/D. The performance metrics show that the XGBoost model with the input combination of Fra, KC, and Ucw provides the highest accuracy of 92.47% and outperforms SVM, CatBoost, AdaBoost, and LightGBM models. The XGBoost model also outperforms the existing empirical formulations. Therefore, it can be concluded that the XGBoost techniques can be used as a reliable, accurate, and alternative tool to estimate pier scour depth in the combined action of current and wave. [ABSTRACT FROM AUTHOR]

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المؤلفون: Albreiki, Balqis, Habuza, Tetiana, Palakkal, Nishi, Zaki, Nazar

المصدر: Education & Information Technologies; 2024, Vol. 29 Issue 6, p6791-6820, 30p

مصطلحات موضوعية: KNOWLEDGE graphs, ONLINE education, MACHINE learning, INFORMATION technology, MACHINE theory

مستخلص: The nature of education has been transformed by technological advances and online learning platforms, providing educational institutions with more options than ever to thrive in a complex and competitive environment. However, they still face challenges such as academic underachievement, graduation delays, and student dropouts. Fortunately, by harnessing student data from institution databases and online platforms, it becomes possible to predict the academic performance of individual students at an early stage. In this study, we utilized knowledge graphs (KG), clustering, and machine learning (ML) techniques on data related to students in the College of Information Technology at UAEU. To construct knowledge graphs and visualize students' performance at various checkpoints, we employed Neo4j-a high-performance NoSQL graph database. The findings demonstrate that incorporating clustered knowledge graphs with machine learning reduces predictive errors, enhances classification accuracy, and effectively identifies students at risk of course failure. Additionally, the utilization of visualization methods facilitates communication and decision-making within educational institutions. The combination of KGs and ML empowers course instructors to rank students and provide personalized learning interventions based on individual performance and capabilities, allowing them to develop tailored remedial actions for at-risk students according to their unique profiles. [ABSTRACT FROM AUTHOR]

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المؤلفون: Faria, Isabella, Vidgren, Mathias, Canizares, Stalin, Devos, Lene, Reichel, Katrin, Oniscu, Gabriel C., Martins, Paulo N.

المصدر: Artificial Organs; Jun2024, Vol. 48 Issue 6, p686-691, 6p

مصطلحات موضوعية: TRANSPLANTATION of organs, tissues, etc., PERFUSION, MACHINE theory, DISRUPTIVE innovations, GRAFT survival, REGENERATIVE medicine, MACHINERY

مصطلحات جغرافية: ATHENS (Greece)

مستخلص: The 21st Congress of the European Society of Organ Transplantation (ESOT), held on September 17–20th, 2023, in Athens, Greece, was a pivotal event in transplantation, focusing on the theme "Disruptive Innovation, Trusted Care." The congress attracted a global audience of 2 826 participants from 82 countries, emphasizing its international significance. Machine perfusion, as a groundbreaking technology in organ transplantation, was one of the central focuses of the conference. This year's meeting had a remarkable increase in accepted abstracts on machine perfusion, evidencing its growing prominence in the field. The collective findings from these abstracts highlighted the efficacy of machine perfusion in improving organ viability and transplant outcomes. Studies demonstrated improvements in graft survival and reduction in complications, as well as novel uses and techniques. Furthermore, the integration of machine perfusion with regenerative medicine and its application across multiple organ types were significant discussion points. The congress also highlighted the challenges and solutions in implementing machine perfusion in clinical settings, emphasizing the importance of practical training and international collaboration for advancing this technology. ESOT 2023 served as a crucial platform for disseminating scientific advancements, fostering practical learning, and facilitating international collaborations in organ transplantation. The congress underscored the evolution and importance of machine perfusion technology, marking a significant step forward in enhancing patient outcomes in the field of organ transplantation. [ABSTRACT FROM AUTHOR]

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المؤلفون: Albahli, Saleh

المصدر: Multimedia Tools & Applications; May2024, Vol. 83 Issue 17, p52711-52735, 25p

مصطلحات موضوعية: MACHINE learning, DECISION trees, MACHINE theory, RANDOM forest algorithms, PREDICTION theory

مستخلص: Higher education is crucial as it introduces students to various fields and then guides them to the next steps. Student's academic performance is critical and could lead to failure if it is not monitored to find the strengths and weaknesses of students and the factors that affect them. That is why the student academic prediction method should be improved so teachers can predict their students' performance. A lot of research tried to improve the prediction accuracy but had problems with imbalanced data and how to tune the algorithm. For this case, we proposed two different machine learning algorithms that handle imbalanced data by applying the Synthetic Minority Oversampling Technique and employing a hyperparameter tuning algorithm to increase the prediction during the training process in the machine learning models. The machine learning models we used are Random Forest and Decision Tree. Models were further tuned using Grid Search, Random Search and Bayesian Optimization Hyperparameter Tuning. After we compared them, the results showed that Synthetic Minority Oversampling Technique and Bayesian Optimization combined with the Decision Tree algorithm outperformed models for student academic prediction. [ABSTRACT FROM AUTHOR]

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المؤلفون: Baldini, Gianmarco, Bonavitacola, Fausto

المصدر: Electronics (2079-9292); May2024, Vol. 13 Issue 10, p1925, 17p

مصطلحات موضوعية: RADIO frequency, ELECTRONIC equipment, CONVOLUTIONAL neural networks, DEEP learning, HILBERT-Huang transform, MACHINE theory, HUMAN fingerprints

مستخلص: Radio Frequency Fingerprinting (RFF) refers to the technique for identifying and classifying wireless devices on the basis of their physical characteristics, which appear in the digital signal transmitted in space. Small differences in the radio frequency front-end of the wireless devices are generated across the same wireless device model during the implementation and manufacturing process. These differences create small variations in the transmitted signal, even if the wireless device is still compliant with the wireless standard. By using data analysis and machine-learning algorithms, it is possible to classify different electronic devices on the basis of these variations. This technique has been well proven in the literature, but research is continuing to improve the classification performance, robustness to noise, and computing efficiency. Recently, Deep Learning (DL) has been applied to RFF with considerable success. In particular, the combination of time-frequency representations and Convolutional Neural Networks (CNN) has been particularly effective, but this comes at a great computational cost because of the size of the time-frequency representation and the computing time of CNN. This problem is particularly challenging for wireless standards, where the data to be analyzed is extensive (e.g., long preambles) as in the case of the LoRa (Long Range) wireless standard. This paper proposes a novel approach where two pre-processing steps are adopted to (1) improve the classification performance and (2) to decrease the computing time. The steps are based on the application of Variational Mode Decomposition (VMD) where (in opposition to the known literature) the residual of the VMD application is used instead of the extracted modes. The concept is to remove the modes, which are common among the LoRa devices, and keep with the residuals the unique intrinsic features, which are related to the fingerprints. Then, the spectrogram is applied to the residual component. Even after this step, the computing complexity of applying CNN to the spectrogram is high. This paper proposes a novel step where only segments of the spectrogram are used as input to CNN. The segments are selected using a machine-learning approach applied to the features extracted from the spectrogram using the Local Binary Pattern (LBP). The approach is applied to a recent LoRa radio frequency fingerprinting public data set, where it is shown to significantly outperform the baseline approach based on the full use of the spectrogram of the original signal in terms of both classification performance and computing complexity. [ABSTRACT FROM AUTHOR]

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المؤلفون: Schindler, Peter, Antoniuk, Evan R., Cheon, Gowoon, Zhu, Yanbing, Reed, Evan J.

المصدر: Advanced Functional Materials; 5/10/2024, Vol. 34 Issue 19, p1-12, 12p

مصطلحات موضوعية: DENSITY functional theory, MACHINE theory, MACHINE learning, ELECTRON emission, ENERGY conversion

مستخلص: The work function is the key surface property that determines the energy required to extract an electron from the surface of a material. This property is crucial for thermionic energy conversion, band alignment in heterostructures, and electron emission devices. This work presents a high‐throughput workflow using density functional theory (DFT) to calculate the work function and cleavage energy of 33,631 slabs (58,332 work functions) that are created from 3,716 bulk materials. The number of calculated surface properties surpasses the previously largest database by a factor of ≈27. Several surfaces with an ultra‐low (<2 eV) and ultra‐high (>7 eV) work function are identified. Specifically, the (100)‐Ba‐O surface of BaMoO3 and the (001)‐F surface of Ag2F have record‐low (1.25 eV) and record‐high (9.06 eV) steady‐state work functions. Based on this database a physics‐based approach to featurize surfaces is utilized to predict the work function. The random forest model achieves a test mean absolute error (MAE) of 0.09 eV, comparable to the accuracy of DFT. This surrogate model enables rapid predictions of the work function (≈ 105 faster than DFT) across a vast chemical space and facilitates the discovery of material surfaces with extreme work functions for energy conversion and electronic device applications. [ABSTRACT FROM AUTHOR]

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المؤلفون: Nassrullah, Karrar Sahib, Stepanyan, Ivan Viktorovich, Nasrallah, Haider Sahib, Florez, Neder Jair Mendez, Zidoun, Abdelrrahmane Mohamed, Mohammed, Shahad Raouf

المصدر: International Journal of Intelligent Engineering & Systems; 2024, Vol. 17 Issue 3, p401-416, 16p

مصطلحات موضوعية: MACHINE learning, MACHINE theory, SUPERVISED learning, GENETIC programming, MOBILE robots, SEARCH algorithms, MATRIX functions

مستخلص: The focus of this study is a relatively new development in the field of control: machine learning control. This study offers a mathematical framework for machine learning control and explores three symbolic regression-based techniques for supervised and unsupervised learning. One of the challenges associated with machine learning control pertains to the control general synthesis. This entails figuring out a control function contingent upon the object's state, guaranteeing the attainment of the control objective while optimizing the quality criterion value across all possible initial states within a permissible zone where finding a satisfactory solution occurs within the space of codes. The implementation of the small variations principle within the basic solution is suggested as a viable technique for developing the algorithms of search. This paper extensively discusses three symbolic regression techniques, including Cartesian genetic programming (CGP), synthesized genetic programming (SGP) and parse-matrix evolution (PME). Notably, synthesized genetic programming, being a novel technique, and PME get utilized for the first time to address the general synthesis of control problems. The mathematical expression's SGP code is a six-row integer matrix; the first row of the matrix represents the functions that take two arguments, while the second and fourth rows represent the functions that take one argument. The third and fifth rows represent the arguments of the mathematical expression, and the sixth row represents the priority. The computational example demonstrates the potential of symbolic regression approaches as unsupervised machine learning control techniques for addressing the machine learning control challenge of general synthesis of control in order to achieve the stability of a mobile robot system. Likewise, practical experience shows that synthesized genetic programming has faster efficiency than Cartesian genetic programming and parse-matrix evolution in discovering solutions, about 2.33 and 2.11 times on average, respectively. [ABSTRACT FROM AUTHOR]

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المؤلفون: Laref, Slimane, Harrou, Fouzi, Sun, Ying, Gao, Xin, Gojobori, Takashi

المصدر: International Journal of Molecular Sciences; May2024, Vol. 25 Issue 9, p4897, 25p

مصطلحات موضوعية: THERMODYNAMICS, MACHINE learning, MACHINE theory, PHOSPHORENE, ANTIVIRAL agents, DENSITY functional theory

مستخلص: Favipiravir (FP) and ebselen (EB) belong to a diverse class of antiviral drugs known for their significant efficacy in treating various viral infections. Utilizing molecular dynamics (MD) simulations, machine learning, and van der Waals density functional theory, we accurately elucidate the binding properties of these antiviral drugs on a phosphorene single-layer. To further investigate these characteristics, this study employs four distinct machine learning models—Random Forest, Gradient Boosting, XGBoost, and CatBoost. The Hamiltonian of antiviral molecules within a monolayer of phosphorene is appropriately trained. The key aspect of utilizing machine learning (ML) in drug design revolves around training models that are efficient and precise in approximating density functional theory (DFT). Furthermore, the study employs SHAP (SHapley Additive exPlanations) to elucidate model predictions, providing insights into the contribution of each feature. To explore the interaction characteristics and thermodynamic properties of the hybrid drug, we employ molecular dynamics and DFT calculations in a vacuum interface. Our findings suggest that this functionalized 2D complex exhibits robust thermostability, indicating its potential as an effective and enabled entity. The observed variations in free energy at different surface charges and temperatures suggest the adsorption potential of FP and EB molecules from the surrounding environment. [ABSTRACT FROM AUTHOR]

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