In this paper, a novel problem in transshipment networks has been proposed. The main aims of this pa- per are to introduce the problem and to give useful tools for solving it both in exact and approximate ways. In a transshipment network it is important to decide which are the best paths between each pair of nodes. Representing the network by a graph, the union of thesepaths is a delivery subgraph of the original graph which has all the nodes and some edges. Nodes in this subgraph which are adjacent to more than two nodes are called switches because when sending the flow between any pair of nodes, switches on the path must adequately direct it. Switches are facilities which direct flows among users. The installation of a switch involves the installation of adequate equipment and thus an allocation cost. Furthermore, traversing a switch also implies a service cost or allocation cost. The Switch Location Prob- lem is defined as the problem of determining which is the delivery subgraph with the total lowest cost. Two of the three solutions approaches that we propose are decomposition algorithms based on articula- tion vertices, the exact and the math-heuristic ones. These two approaches could be embedded in expert systems for locating switches in transshipment networks. The results should help a decision maker to select the adequate approach depending on the shape and size of the network and also on the exter- nal time-limit. Our results show that the exact approach is a valuable tool if the network has less than 10 0 0 nodes. Two upsides of our heuristics are that they do not require special networks and give good solutions, gap-wise. The impact of this paper is twofold: it highlights the difficulty of adequately locating switches and it emphasizes the benefit of decomposing algorithms.
Keywords: Discrete location | Math-heuristic | Articulation vertex | Block-Cutpoint graph
Determination of the Blood, Hormone and Obesity Value Ranges that Indicate the Breast Cancer, Using Data Mining Based Expert System
تعیین محدوده ارزش خون ، هورمون و چاقی که نشان دهنده سرطان پستان است ، با استفاده از سیستم خبره مبتنی بر داده کاوی-2019
Breast cancer is a dangerous type of cancer that spreads into other organs over time. Therefore, medical studies are being done for the early diagnosis by means of the anthropometric data and blood analysis values besides the mammographic and histological findings. However, medical studies have identified only cancer-related values but the value ranges indicating the cancer have not been determined yet. Concurrently the automated diagnostic systems are being developed to assist medical specialists in biomedical engineering studies. The range of values or boundaries indicating the cancer are automatically determined in biomedical methods, but only the diagnostic result is presented. Because of this, biomedical studies don’t provide enough opportunity for medical experts to evaluate the relationship between values and result. In this study, decision trees that is one of data mining method was applied to anthropometric data and blood analysis values to complete the mentioned deficiencies in breast cancer diagnosis aiming studies. The determined value ranges were also presented visually to medical experts understand them easily. The proposed diagnostic system has accuracy rate up to 90.52% and provides value ranges indicating the breast cancer as well as mathematically presents the relations between the values and cancer.
Keywords: Breast cancer | Data mining | Obesity | Hormone | Blood analysis
استفاده از رسانه های اجتماعی برای شناسایی جذابیت گردشگری در شش شهر ایتالیا
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 18
تکامل فناوری و گسترش شبکه های اجتماعی به افراد اجازه داده است که مقادیر زیادی داده را در هر روز تولید کنند. شبکه های اجتماعی کاربرانی را فارهم می کند که به اطلاعات دسترسی دارند. هدف این مقاله تعیین جذابیت های شهرهای مختلف گردشگری ازطریق بررسی رفتار کاربران در شبکه های اجتماعی می باشد. پایگاه داده ای شامل عکس های جغرافیایی واقع شده در شش شهر می باشد که به عنوان یک مرکز فرهنگی و هنری در ایتالیا عمل می کنند. عکس ها از فلیکر که یک بستر به اشتراک گذاری داده می باشد دانلود شدند. تحلیل داده ها با استفاده از دیدگاه مدلهای یادگیری ریاضی و ماشینی انجام شد. نتایج مطالعه ما نشانگر نقشه های شناسایی رفتار کاربران، گرایش سالانه به فعالیت تصویری در شهرها و تاکید بر سودمند بودن روش پیشنهادی می باشد که قادر به تامین اطلاعات مکانی و کاربری است. این مطالعه تاکید می کند که چگونه تحلیل داده های اجتماعی می تواند یک مدل پیشگویانه برای فرموله کردن طرح های گردشگری خلق کند. در انتها، راهبردهای عمومی بازاریابی گردشگری مورد بحث قرار می گیرند.
|مقاله ترجمه شده|
Artificial intelligence and machine learning | applications in musculoskeletal physiotherapy
هوش مصنوعی و یادگیری ماشین | برنامه های کاربردی در فیزیوتراپی عضلانی اسکلتی-2019
Introduction: Artificial intelligence (AI) is a field of mathematical engineering which has potential to enhance healthcare through new care delivery strategies, informed decision making and facilitation of patient engagement. Machine learning (ML) is a form of narrow artificial intelligence which can be used to automate decision making and make predictions based upon patient data. Purpose: This review outlines key applications of supervised and unsupervised machine learning in musculoskeletal medicine; such as diagnostic imaging, patient measurement data, and clinical decision support. The current literature base is examined to identify areas where ML performs equal to or more accurately than human levels. Implications: Potential is apparent for intelligent machines to enhance various areas of physiotherapy practice through automization of tasks which involve data analysis, classification and prediction. Changes to service provision through applications of ML, should encourage physiotherapists to increase their awareness of and experiences with emerging technologies. Data literacy should be a component of professional development plans to assist physiotherapists in the application of ML and the preparation of information technology systems to use these techniques.
Keywords: Artificial intelligence | Machine learning | Low back pain | Physiotherapy
Towards an integrated machine-learning framework for model evaluation and uncertainty quantification
به سمت یک چارچوب یکپارچه یادگیری ماشین برای ارزیابی مدل و کمیت عدم اطمینان-2019
We introduce a new paradigm for treating and exploiting simulation data, serving in parallel as an alternative workflow for model evaluation and uncertainty quantification. Instead of reporting simulations of base-case and specific variations scenarios, databases covering a wide spectrum of operational conditions are built by means of machine-learning using sophisticated mathematical algorithms. While the approach works for all sorts of computer-aided engineering applications, the present contribution addresses the CFD/CMFD sub-branch, with application to a widely used benchmark of convective flow boiling. In addition to comparing simulation and experimental results on a case-by-case basis, machine-learning is used to create their respective (CFD and experiment) data-driven models (DDM), which will in a later stage serve for assessing the predictive performance of the CFD models over a wider range of experimental conditions, hence providing a high-level classification of their range of applicability.
Keywords: Fluid flow simulation | Wall boiling | Data analytics | Digital Twin | Machine-learning | Data-driven models (DDM)
Computation of PUG concentration in human blood using the combination of photonics and machine learning
محاسبه غلظت PUG در خون انسان با استفاده از ترکیبی از فوتونیک و یادگیری ماشین-2019
The concentration of potassium chloride, urea and glucose (PUG) in human blood is computed by utilizing the photonic crystal structure and machine learning technique to realize an accurate investigation. The present computation manipulates with the triangular photonic crystal structure, where absorption, propagation, scattering, reflection and diffraction losses are thoroughly dissected to obtain the output power emerging from the photonic structure. Further, the output data(power) is fitted to linear ship through regression analysis. Finally, a mathematical model is disclosed to obtain the variation of output power and concentration of potassium chloride, urea, and glucose.
Keywords: PUG | Photonic crystal | Machine learning | Regression analysis
Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study
طراحی یادگیری عمیق خودکار برای طبقه بندی تصویر پزشکی توسط متخصصان مراقبت های بهداشتی و بدون تجربه برنامه نویسی: یک مطالعه امکان سنجی-2019
Background Deep learning has the potential to transform health care; however, substantial expertise is required to train such models. We sought to evaluate the utility of automated deep learning software to develop medical image diagnostic classifiers by health-care professionals with no coding—and no deep learning—expertise. Methods We used five publicly available open-source datasets: retinal fundus images (MESSIDOR); optical coherence tomography (OCT) images (Guangzhou Medical University and Shiley Eye Institute, version 3); images of skin lesions (Human Against Machine [HAM] 10000), and both paediatric and adult chest x-ray (CXR) images (Guangzhou Medical University and Shiley Eye Institute, version 3 and the National Institute of Health [NIH] dataset, respectively) to separately feed into a neural architecture search framework, hosted through Google Cloud AutoML, that automatically developed a deep learning architecture to classify common diseases. Sensitivity (recall), specificity, and positive predictive value (precision) were used to evaluate the diagnostic properties of the models. The discriminative performance was assessed using the area under the precision recall curve (AUPRC). In the case of the deep learning model developed on a subset of the HAM10000 dataset, we did external validation using the Edinburgh Dermofit Library dataset. Findings Diagnostic properties and discriminative performance from internal validations were high in the binary classification tasks (sensitivity 73·3–97·0%; specificity 67–100%; AUPRC 0·87–1·00). In the multiple classification tasks, the diagnostic properties ranged from 38% to 100% for sensitivity and from 67% to 100% for specificity. The discriminative performance in terms of AUPRC ranged from 0·57 to 1·00 in the five automated deep learning models. In an external validation using the Edinburgh Dermofit Library dataset, the automated deep learning model showed an AUPRC of 0·47, with a sensitivity of 49% and a positive predictive value of 52%. Interpretation All models, except the automated deep learning model trained on the multilabel classification task of the NIH CXR14 dataset, showed comparable discriminative performance and diagnostic properties to state-of-the-art performing deep learning algorithms. The performance in the external validation study was low. The quality of the open-access datasets (including insufficient information about patient flow and demographics) and the absence of measurement for precision, such as confidence intervals, constituted the major limitations of this study. The availability of automated deep learning platforms provide an opportunity for the medical community to enhance their understanding in model development and evaluation. Although the derivation of classification models without requiring a deep understanding of the mathematical, statistical, and programming principles is attractive, comparable performance to expertly designed models is limited to more elementary classification tasks. Furthermore, care should be placed in adhering to ethical principles when using these automated models to avoid discrimination and causing harm. Future studies should compare several application programming interfaces on thoroughly curated datasets.
SeDeM expert system for directly compressed tablet formulation: A review and new perspectives
سیستم خبره SeDeM برای فرمول بندی قرص مستقیما فشرده شده: مرور و دیدگاه های جدید-2019
The pharmaceutical tablet formulation design is a risky and challenging process since it largely depends on experience. The fundamental reasons lie in the lack of understanding of powder properties and the interactions between the active pharmaceutical ingredients and excipients. To compensate these shortness, the use of expert system (ES) in the formulation development has gradually drawn attentions during the last two decades. The SeDeM expert system is one such intelligent tool aiming at designing direct compression (DC) tablet. It gathers almost all the frequently used physical parameters to fully characterize the compressibility of powdered substances. Themathematical equations for selection of excipients reflect the state of art knowledge of DC tablet formulation. In this paper, the detailed history, principles, applications and derived forms of the SeDeM expert system were reviewed. Contributions of the SeDeM expert system to the manufacturing classification system (MCS) were illustrated. A SeDeM database named iTCM was innovatively proposed. All in all, the functions and application scopes of the originally developed SeDeM expert system have been continuously extended and more improvement could be achieved in the future.
Keywords: SeDeM expert system | Tablet formulation | Direct compression | Quality by design | Database
Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming
بهینه سازی تحت عدم اطمینان در عصر داده های بزرگ و یادگیری عمیق: وقتی یادگیری ماشین با برنامه نویسی ریاضی ملاقات می کند-2019
This paper reviews recent advances in the field of optimization under uncertainty via a modern data lens, highlights key research challenges and promise of data-driven optimization that organically integrates machine learning and mathematical programming for decision-making under uncertainty, and identifies potential research opportunities. A brief review of classical mathematical programming techniques for hedging against uncertainty is first presented, along with their wide spectrum of applications in Process Systems Engineering. A comprehensive review and classification of the relevant publications on data- driven distributionally robust optimization, data-driven chance constrained program, data-driven robust optimization, and data-driven scenario-based optimization is then presented. This paper also identifies fertile avenues for future research that focuses on a closed-loop data-driven optimization framework, which allows the feedback from mathematical programming to machine learning, as well as scenario- based optimization leveraging the power of deep learning techniques. Perspectives on online learning- based data-driven multistage optimization with a learning-while-optimizing scheme are presented.
Keywords: Data-driven optimization | Decision making under uncertainty | Big data | Machine learning | Deep learning
Machine learning as a contributor to physics: Understanding Mg alloys
یادگیری ماشین به عنوان مشارکت در فیزیک: شناخت آلیاژهای منیزیم-2019
Machine learning (ML) methods have played an increasingly important role in materials design. Take Mg alloys as an example,we showtheMLmethods not only supplymathematical solutions butmore importantly also contribute to understand the physics in the problem. Hitherto, the role of ML methods is widely applied in highthroughput predictions, while their contribution to understand the physical mechanisms has been rarely explored. In this study, we firstly demonstrate that the Gaussian Process Classification algorithm reliably and efficiently predicts promising solutes for ductile Mg alloys, and then use these results to evaluate the correlation between two recently proposed mechanisms. Our results help clarify the controversy regarding the ductility mechanisms that can be used as the guide for materials design.
Keywords: Mg alloys | Machine learning | Gaussian process classification