با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.
Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study
رانندگان ، موانع و ملاحظات اجتماعی برای پذیرش هوش مصنوعی در مشاغل و مدیریت: یک مطالعه عالی-2020
The number of academic papers in the area of Artificial Intelligence (AI) and its applications across business and management domains has risen significantly in the last decade, and that rise has been followed by an increase in the number of systematic literature reviews. The aim of this study is to provide an overview of existing systematic reviews in this growing area of research and to synthesise their findings related to enablers, barriers and social implications of the AI adoption in business and management. The methodology used for this tertiary study is based on Kitchenham and Charter’s guidelines , resulting in a selection of 30 reviews published between 2005 and 2019 which are reporting results of 2,021 primary studies. These reviews cover the AI adoption across various business sectors (healthcare, information technology, energy, agriculture, apparel industry, engineering, smart cities, tourism and transport), management and business functions (HR, customer services, supply chain, health and safety, project management, decisionsupport, systems management and technology acceptance). While the drivers for the AI adoption in these areas are mainly economic, the barriers are related to the technical aspects (e.g. availability of data, reusability of models) as well as the social considerations such as, increased dependence on non-humans, job security, lack of knowledge, safety, trust and lack of multiple stakeholders’ perspectives. Very few reviews outside of the healthcare management domain consider human, organisational and wider societal factors and implications of the AI adoption. Most of the selected reviews are recommending an increased focus on social aspects of AI, in addition to more rigorous evaluation, use of hybrid approaches (AI and non-AI) and multidisciplinary approaches to AI design and evaluation. Furthermore, this study found that there is a lack of systematic reviews in some of the AI early adopter sectors such as financial industry and retail and that the existing systematic reviews are not focusing enough on human, organisational or societal implications of the AI adoption in their research objectives.
Keywords: artificial intelligence | business | machine learning | management | systematic literature review | tertiary study
A biomorphic neuroprocessor based on a composite memristor-diode crossbar
یک پردازشگر عصبی بیومورفیک بر اساس یک قطر کامپوزیت دیود ممیستور-2020
A concept of biomorphic neuroprocessor that implements hardware spiking neural network for traditional tasks of information processing and can simulate operation of brain cortical column or its fragment is proposed. The key units of hardware neural network are memory and logic matrices, previously developed on the basis of composite memristor-diode crossbar. These matrices provide high element integration and energy efficiency compared to known neuroprocessors and individual matrices. Such efficiency is achieved by application of mixed analogdigital computations, including those that use memristors integrated in composite memristor-diode crossbars. Neuron electrical model was constructed on the basis of these matrices and the Hodgkin-Huxley biomorphic model for neuron membrane. Unlike existing neural networks with synapses based on discrete memristors, the generation of new association was demonstrated in memristor-diode crossbar by SPICE modeling of associative self-learning processes. The adaptation procedure for biomorphic neural network program to neuroprocessor hardware is defined. In essence, presented neuroprocessor is a prototype of new generation computers with artificial intelligence.
Keywords: Biomorphic neuroprocessor | Biomorphic software and hardware electrical | models of neuron | Composite memristor-diode crossbar | Memory matrix | Logic matrix | Associative self-learning
Development of a chemometric-assisted spectrophotometric method for quantitative simultaneous determination of Amlodipine and Valsartan in commercial tablet
توسعه یک روش اسپکتروفتومتری با کمک شیمیایی برای تعیین کمی همزمان آملودیپین و والرسارتان در قرص تجاری-2020
In this study, two drugs named Amlodipine (AML) and Valsartan (VAL) related to the high blood pressure were simultaneously determined in synthetic mixtures and Valzomix tablet. For this purpose, the chemometric-assisted spectrophotometric method was developed without any prepreparation. Artificial intelligence techniques, including artificial neural network (ANN) and least squares support vector machine (LS-SVM) as chemometrics procedures were proposed. Feed-forward back-propagation neural network (FFBP-NN) with two different algorithms, containing Levenberg–Marquardt (LM) and gradient descent with momentum and adaptive learning rate backpropagation (GDX) was applied. To select the best model, several layers and neurons were investigated. The results revealed that layer = 5 with 6 neurons and layer = 2 with 10 neurons had lower mean square error (MSE) (1.41 × 10−24, 1.16 × 10-23) for AML and VAL, respectively. In the LS-SVM method, gamma (γ) and sigma (σ) parameters were optimized. γ and σ were obtained 50, 30 and 40, 40 with the root mean square error (RMSE) of 0.4290 and 0.5598 for AML and VAL, respectively. Analysis of the pharmaceutical formulation was evaluated through the chemometrics methods and high-performance liquid chromatography (HPLC) as a reference technique. The obtained results were statistically compared with each other using the one-way analysis of variance (ANOVA) test. There were no significant differences between them and the proposed method was satisfactory for estimating the components of the Valzomix tablet.
Keywords: Spectrophotometry | Amlodipine | Valsartan | Artificial neural network | Least squares support vector machine
An extensive study on the evolution of context-aware personalized travel recommender systems
یک مطالعه گسترده در مورد تکامل سیستمهای توصیه گر سفر شخصی آگاه از متن-2020
Ever since the beginning of civilization, travel for various causes exists as an essential part of human life so as travel recommendations, though the early form of recommendations were the accrued experiences shared by the community. Modern recommender systems evolved along with the growth of Information Technology and are contributing to all industry and service segments inclusive of travel and tourism. The journey started with generic recommender engines which gave way to personalized recommender systems and further advanced to contextualized personalization with advent of artificial intelligence. Current era is also witnessing a boom in social media usage and the social media big data is acting as a critical input for various analytics with no exception for recommender systems. This paper details about the study conducted on the evolution of travel recommender systems, their features and current set of limitations. We also discuss on the key algorithms being used for classification and recommendation processes and metrics that can be used to evaluate the performance of the algorithms and thereby the recommenders.
Keywords: Recommender system | Personalization | Context aware | Big data | Travel and tourism
A novel intelligent option price forecasting and trading system by multiple kernel adaptive filters
رویکرد پیش بینی قیمت و گزینه سیستم تجاری با فیلترهای انطباقی چند هسته ای-2020
Derivatives such as options are complex financial instruments. The risk in option trading leads to the demand of trading support systems for investors to control and hedge their risk. The nonlinearity and non-stationarity of option dynamics are the main challenge of option price forecasting. To address the problem, this study develops a multi-kernel adaptive filters (MKAF) for online option trading. MKAF is an improved version of the adaptive filter, which employs multiple kernels to enhance the richness of nonlinear feature representation. The MKAF is a fully adaptive online algorithm. The strength of MKAF is that the weights to the kernels are simultaneous optimally determined in filter coefficient updates. We do not need to design the weights separately. Therefore, MKAF is good at tracking nonstationary nonlinear option dynamics. Moreover, to reduce the computation time in updating the filter, and prevent overadaptation, the number of kernels is restricted by using coherence-based sparsification, which constructs a set of dictionary and uses a coherence threshold to restrict the dictionary size. This study compared the new method with traditional ones, we found the performance improvement is significant and robust. Especially, the cumulated trading profits are substantially increased
Keywords: Artificial intelligence | Adaptive filter | Multiple Kernel Machine | Big data analysis | Data mining | Financial forecasting
A new approach for identifying the Kemeny median ranking
یک روش جدید برای شناسایی رتبه بندی متوسط Kemeny-2020
Condorcet consistent rules were originally developed for preference aggregation in the theory of social choice. Nowadays these rules are applied in a variety of fields such as discrete multi-criteria analysis, defence and security decision support, composite indicators, machine learning, artificial intelligence, queries in databases or internet multiple search engines and theoretical computer science. The cycle issue, known also as Condorcets paradox, is the most serious problem inherent in this type of rules. Solutions for dealing with the cycle issue properly already exist in the literature; the most important one being the identification of the median ranking, often called the Kemeny ranking. Unfortunately its identification is a NP-hard problem. This article has three main objectives: (1) to clarify that the Kemeny median order has to be framed in the context of Condorcet consistent rules; this is important since in the current practice sometimes even the Borda count is used as a proxy for the Kemeny ranking. (2) To present a new exact algorithm, this identifies the Kemeny median ranking by providing a searching time guarantee. (3) To present a new heuristic algorithm identifying the Kemeny median ranking with an optimal trade-off between convergence and approximation .
Keywords : Decision analysis | Combinatorial optimisation | Social choice| Multiple criteria | Artificial intelligence| Defence and security| Big data
Forecasting crude oil price with multilingual search engine data
پیش بینی قیمت نفت خام با داده های موتور جستجو چند زبانه-2020
In the big data era, search engine data (SED) has presented new opportunities for improving crude oil price prediction; however, the existing research were confined to single-language (mostly English) search keywords in SED collection. To address such a language bias and grasp worldwide investor attention, this study proposes a novel multilingual SED-driven forecasting methodology from a global perspective. The proposed methodology includes three main steps: (1) multilingual index construction, based on multilingual SED; (2) relationship investigation, between the multilingual index and crude oil price; and (3) oil price prediction, with the multilingual index as an informative predictor. With WTI spot price as studying samples, the empirical results indicate that SED have a powerful predictive power for crude oil price; nevertheless, multilingual SED statistically demonstrate better performance than single-language SED, in terms of enhancing prediction accuracy and model robustness.
Keywords: Big data | Multilingual search engine index | Crude oil price forecasting | Google Trends | Artificial intelligence
STrategically Acquired Gradient Echo (STAGE) imaging, part III: Technical advances and clinical applications of a rapid multi-contrast multi-parametric brain imaging method
تصویربرداری گرادیان اکو (STAGE) استراتژیک ، بخش سوم: پیشرفت های فنی و برنامه های بالینی از یک روش تصویربرداری سریع مغزی چند پارامتری سریع با کنتراست-2020
One major thrust in radiology today is image standardization with a focus on rapidly acquired quantitative multi-contrast information. This is critical for multi-center trials, for the collection of big data and for the use of artificial intelligence in evaluating the data. Strategically acquired gradient echo (STAGE) imaging is one such method that can provide 8 qualitative and 7 quantitative pieces of information in 5 min or less at 3 T. STAGE provides qualitative images in the form of proton density weighted images, T1 weighted images, T2* weighted images and simulated double inversion recovery (DIR) images. STAGE also provides quantitative data in the form of proton spin density, T1, T2* and susceptibility maps as well as segmentation of white matter, gray matter and cerebrospinal fluid. STAGE uses vendors product gradient echo sequences. It can be applied from 0.35 T to 7 T across all manufacturers producing similar results in contrast and quantification of the data. In this paper, we discuss the strengths and weaknesses of STAGE, demonstrate its contrast-to-noise (CNR) behavior relative to a large clinical data set and introduce a few new image contrasts derived from STAGE, including DIR images and a new concept referred to as true susceptibility weighted imaging (tSWI) linked to fluid attenuated inversion recovery (FLAIR) or tSWI-FLAIR for the evaluation of multiple sclerosis lesions. The robustness of STAGE T1 mapping was tested using the NIST/NIH phantom, while the reproducibility was tested by scanning a given individual ten times in one session and the same subject scanned once a week over a 12-week period. Assessment of the CNR for the enhanced T1W image (T1WE) showed a significantly better contrast between gray matter and white matter than conventional T1W images in both patients with Parkinsons disease and healthy controls. We also present some clinical cases using STAGE imaging in patients with stroke, metastasis, multiple sclerosis and a fetus with ventriculomegaly. Overall, STAGE is a comprehensive protocol that provides the clinician with numerous qualitative and quantitative images.
Keywords: Quantitative magnetic resonance imaging | Susceptibility weighted imaging | T1 mapping | Quantitative susceptibility mapping | Multi-parametric magnetic resonance imaging | Strategically acquired gradient echo
Development of short chain fatty acid-based artificial neuron network tools applied to biohydrogen production
توسعه ابزارهای شبکه عصبی مصنوعی مبتنی بر اسیدهای چرب با زنجیره کوتاه استفاده شده برای تولید بیوهیدروژن-2020
The biological production of biohydrogen through dark fermentation is a very complex system where the use of an artificial neuron network (ANN) for prediction, controlling and monitoring has a great potential. In this study three ANN models based on volatile fatty acids (VFA) production and speciation were evaluated for their capacity to predict (i) accumulated H2 production, (ii) hydrogen production rate and (iii) H2 yield. Lab-scale biohydrogen and VFA production kinetics from a previous study were used for training and validation of the models. The input parameters studied were: time and acetate and butyrate concentrations (model 1), time and lactate, acetate, propionate and butyrate concentrations (model 2), time and the sum of all VFA (model 3) and time and butyrate/acetate (model 4). All models could predict biohydrogen accumulated production, hydrogen production rate and H2 yield with high accuracy (R2 > 0.987). VFAT is the input parameter indicated for processes using pure cultures, while for complex/mixed cultures a model based on acetate and butyrate is recommended.
Keywords: Volatile fatty acids | Artificial intelligence | Biofuel | Dark fermentation
Knowledge-based system for resolving design clashes in building information models
سیستم دانش بنیان برای حل اختلافات طراحی در ساخت مدلهای اطلاعاتی-2020
Although building information modelling (BIM) has revolutionized building design and construction management, it is still time-consuming for a BIM project team to coordinate with designers to resolve clashes during the pre-construction stages. During the construction stage, shop drawings frequently have to be revised because of cognitive differences between the designers and constructors. These two groups of people view the resolution of such clashes from their own perspectives because of differences in their inherent knowledge and experience. To effectively improve the project delivery time, one option is to reduce the number of model revisions during the construction stage. This could be done by providing a BIM model as a reference. In this model, design clashes can be resolved from the perspective of the constructor before discussions in design coordination meetings to find compromises. In this work, an artificial intelligence system for such design clash resolution was developed with machine learning and heuristic optimizing techniques. In the experiment, we present a real case of a student residence, in which the mechanical, electrical, and plumbing systems in the basement are used to validate the effectiveness of the proposed system. The experimental results show the feasibility and effectiveness of the proposed system.
Keywords: Building information modelling | Design clash resolution | Knowledge extraction | Heuristic optimization