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
Decision-making techniques in supplier selection: Recent accomplishments and what lies ahead
تکنیک های تصمیم گیری در انتخاب تأمین کننده: دستاوردهای اخیر و آنچه پیش رو است-2020
Supplier selection (SS) is considered a sophisticated, application-oriented, decision-making (DM) problem and has received considerable attention. In the past two decades, DM theories and techniques continue to be incorporated into and contribute to the development of SS applications. Maintaining the pace of the rapid transitions in this field, this paper systematically reviews the relevant articles published be- tween 2013 and 2018. Articles that orient various DM techniques are selected and analyzed under a well- established framework. State-of-the-art developments in the adoption of DM techniques are summarized in a SS process. We pay particular attention to promising directions that can dominate future research in this field. This paper further extends the history of several interacting fields, including big data and eco- nomic theories, toward methodological rather than application dimensions. The potential of such fields for SS is discussed from an interdisciplinary perspective.
Keywords: Supplier selection | Decision making | Big data | Multiple criteria | Artificial intelligence | Literature review
Artificial Intelligence in Aortic Surgery: The Rise of the Machine
هوش مصنوعی در جراحی آئورت: ظهور ماشین-2020
The first concept of Artificial Intelligence (AI) came into attention during 1920s and currently it is rapidly being integrated in our daily clinical practice. The use of AI has evolved from basic image-based analysis into complex decisions related to different surgical procedure. AI has been very widely used in the cardiology field, however the use of such machine-led decisions has been limited and explored at slower pace in surgical practice. The use of AI in cardiac surgery is still at its infancy but growing dramatically to reflect the changes in the clinical decision making process for better patient outcomes. The machine-led but human controlled algorithms will soon be taking over most of the decision making processes in cardiac surgery. This review article focuses on the practice of AI in aortic surgery and the future of such technology-led decision making pathways on patient outcomes, surgeon’s learning skills and adaptability.
Keywords: Big data | Machine learning | Artificial intelligence | Aortic surgery
The digital surgeon: How big data, automation, and artificial intelligence will change surgical practice
جراح دیجیتال: داده های بزرگ ، اتوماسیون و هوش مصنوعی چقدر عمل جراحی را تغییر می دهند-2020
Exponential growth in computing power, data storage, and sensing technology has led to a world in which we can both capture and analyze incredibleamounts of data. The evolution of machine learning has further advanced the ability of computers to develop insights from massive data sets that are beyond the capacity of human analysis. The convergence of computational power, data storage, connectivity, and Artificial Intelligence (AI) has led to health technologies that, to date, have focused on diagnostic areas such as radiology and pathology. The question remains how the digital revolution will translate in the realm of surgery. There are three main areas where the authors believe that AI could impact surgery in the near future: enhancement of trainingmodalities, cognitive enhancement of the surgeon, and procedural automation.While the promise of Big Data, AI, and Automation is high, there have been unanticipated missteps in the use of such technologies that are worth considering as we evaluate how such technologies could/should be adopted in surgical practice. Surgeons must be prepared to adopt smarter training modalities, supervise the learning of machines that can enhance cognitive function, and ultimately oversee autonomous surgery without allowing for a decay in the surgeon’s operating skills.
Key words: Future pediatric surgery | Automation and artificial intelligence in | pediatric surgery
Revisiting the value of polysomnographic data in insomnia: more than meets the eye
بازنگری ارزش داده سندرم آپنه در بی خوابی: بیش از ملاقات چشم-2020
Background: Polysomnography (PSG) is not recommended as a diagnostic tool in insomnia. However, this consensual approach might be tempered in the light of two ongoing transformations in sleep research: big data and artificial intelligence (AI). Method: We analyzed the PSG of 347 patients with chronic insomnia, including 59 with Sleep State Misperception (SSM) and 288 without (INS). 89 good sleepers (GS) were used as controls. PSGs were compared regarding: (1) macroscopic indexes derived from the hypnogram, (2) mesoscopic indexes extracted from the electroencephalographic (EEG) spectrum, (3) sleep microstructure (slow waves, spindles). We used supervised algorithms to differentiate patients from GS. Results: Macroscopic features illustrate the insomnia conundrum, with SSM patients displaying similar sleep metrics as GS, whereas INS patients show a deteriorated sleep. However, both SSM and INS patients showed marked differences in EEG spectral components (meso) compared to GS, with reduced power in the delta band and increased power in the theta/alpha, sigma and beta bands. INS and SSM patients showed decreased spectral slope in NREM. INS and SSM patients also differed from GS in sleep microstructure with fewer and slower slow waves and more and faster sleep spindles. Importantly, SSM and INS patients were almost indistinguishable at the meso and micro levels. Accordingly, unsupervised classifiers can reliably categorize insomnia patients and GS (Cohens k ¼ 0.87) but fail to tease apart SSM and INS patients when restricting classifiers to micro and meso features (k¼0.004). Conclusion: AI analyses of PSG recordings can help moving insomnia diagnosis beyond subjective complaints and shed light on the physiological substrate of insomnia.
Keywords: Artificial intelligence | Machine learning | Insomnia | Polysomnography | REM | NREM sleep
Does artificial intelligence dream of non-terrestrial techno-signatures?
آیا هوش مصنوعی رویای امضاهای فنی غیر زمینی را می بیند؟-2020
Today, we live in the midst of a surge in the use of artificial intelligence in many scientific and technological applications, including the Search for Extraterrestrial Intelligence (SETI). However, human perception and decision-making is still the last part of the chain in any data analysis or interpretation of results or outcomes. One of the potential applications of artificial intelligence is not only to assist in big data analysis but to help to discern possible artificiality or oddities in patterns of either radio signals, megastructures or techno-signatures in general. In this study, we review the comparative results of an experiment based on geometric patterns reconnaissance and a perception task, performed by 163 human volunteers and an artificial intelligence convolutional neural network (CNN) computer vision model. To test the model, we used an image of the famous bright spots on the Occator crater on Ceres. We wanted to investigate how the search for techno-signatures or oddities might be influenced by our cognitive skills and consciousness, and whether artificial intelligence could help or not in this task. This article also discusses how unintentional human cognitive bias might affect the search for extraterrestrial intelligence and techno-signatures compared with artificial intelligence models, and how such artificial intelligence models might perform in this type of task. We discuss how searching for unexpected, irregular features might prevent us from detecting other nearside or in-plain-sight rare and unexpected signs. The results strikingly showed that a CNN trained to detect triangles and squares scored positive hits on these two geometric shapes as some humans did
Deep learning and artificial intelligence methods for Raman and surface-enhanced Raman scattering
یادگیری عمیق و روش های هوش مصنوعی برای پراکندگی رامان و سطح رو به افزایش رامان-2020
Machine learning is shaping up our lives in many ways. In analytical sciences, machine learning provides an unprecedented opportunity to extract information from complex or big datasets in chromatography, mass spectrometry, NMR, and spectroscopy, among others. This is especially the case in Raman and surface-enhanced Raman scattering (SERS) techniques where vibrational spectra of complex chemical mixtures are acquired as large datasets for the analysis or imaging of chemical systems. The classical linear methods of processing the information no longer suffice and thus machine learning methods for extracting the chemical information from Raman and SERS experiments have been implemented recently. In this review, we will provide a brief overview of the most common machine learning techniques employed in Raman, a guideline for new users to implement machine learning in their data analysis process, and an overview of modern applications of machine learning in Raman and SERS.
Keywords: Deep learning | Machine learning | Artificial intelligence | Artificial neural network | Raman | Surface enhanced Raman scattering | SERS | Sensors
Combination of a big data analytics resource system with an artificial intelligence algorithm to identify clinically actionable radiation dose thresholds for dysphagia in head and neck patients
ترکیبی از یک سیستم منبع تحلیلی داده های بزرگ با یک الگوریتم هوش مصنوعی برای شناسایی آستانه های دوز پرتودرمانی از نظر بالینی برای دیسفاژی در بیماران سر و گردن-2020
Purpose/Objective(s): We combined clinical practice changes, standardizations and technology to automate aggregation, integration and harmonization of comprehensive patient data from the multiple source systems used in clinical practice into a big data analytics resource system (BDARS). We then developed novel artificial intelligence (AI) algorithms, coupled to the BDARS, to identify structure DVH metrics associated with dysphagia. Materials/Methods: From the BDARS harmonized data of ≥ 22,000 patients, we identified 132 patients recently treated for head and neck cancer who also demonstrated dysphagia scores that worsened from base line to a maximum grade ≥ 2. We developed a method that used both physical and biologically corrected (α/β =2.5) DVH curves to test both absolute and percentage volume based DVH metrics. Combining a statistical categorization algorithm with machine learning (SCA-ML) the method provided more extensive detailing of response threshold evidence than either approach alone. A sensitivity guided, minimum input ML model was iteratively constructed to identify the key structure DVH metric thresholds. Results: Seven swallowing structures producing 738 candidate DVH metrics were ranked for association with dysphagia using SCA-ML scoring. Structures included superior pharyngeal constrictor (SPC), inferior pharyngeal constrictor (IPC), larynx, esophagus. Bilateral parotid and submandibular gland (SG) structures were categorized by relative mean dose (e.g SG_High, SG_Low) as a dose vs tumor centric analog to contra and ipsilateral designations. Structure – DVH metrics with high SCA-ML scores included SPC:D20%[EQD2Gy] ≥ 47.7, SPC:D25%[Gy] ≥ 50.4, IPC:D35%[Gy] ≥ 61.7, Parotid_Low:D60%[Gy] ≥ 13.2 and SG_High:D35%[Gy] ≥ 61.7. Larynx:D25%[Gy] ≥ 21.2 and SG_Low:D45%≥28.2 had high SCA-ML scores, but were segmented on fewer than 90% of plans. A model based on SPC:D20%[EQD2Gy] alone had sensitivity and AUC of 0.88 ±0.13 and 0.74 ± 0.17 respectively. Conclusion: This study provides practical demonstration of combining big data with AI to increase volume of evidence in clinical learning paradigms