Machine Deep Learning Algorithms Research Articles

Quantum Approach for Contextual Search, Retrieval, and Ranking of Classical Information.

Quantum-inspired algorithms represent an important direction in modern software information technologies that use heuristic methods and approaches of quantum science. This work presents a quantum approach for document search, retrieval, and ranking based on the Bell-like test, which is well-known in quantum physics. We propose quantum probability theory in the hyperspace analog to language (HAL) framework exploiting a Hilbert space for word and document vector specification. The quantum approach allows for accounting for specific user preferences in different contexts. To verify the algorithm proposed, we use a dataset of synthetic advertising text documents from travel agencies generated by the OpenAI GPT-4 model. We show that the "entanglement" in two-word document search and retrieval can be recognized as the frequent occurrence of two words in incompatible query contexts. We have found that the user preferences and word ordering in the query play a significant role in relatively small sizes of the HAL window. The comparison with the cosine similarity metrics demonstrates the key advantages of our approach based on the user-enforced contextual and semantic relationships between words and not just their superficial occurrence in texts. Our approach to retrieving and ranking documents allows for the creation of new information search engines that require no resource-intensive deep machine learning algorithms.

Optical Image Processing using Eulerian Amplification

Recent advancements in digital photography, particularly through mobile phone cameras, microscopes, and satellite imaging, have made high-resolution image capture increasingly accessible. However, these captured images often contain subtle changes in color resolution and density that are challenging to observe with the naked eye. This study explores the application of Eulerian amplification as a method to detect and enhance such small-amplitude variations, making them visible for improved analysis. Unlike traditional approaches, Eulerian methods are particularly effective for processing smooth structures and high-quality printed materials where minor amplifications in color can reveal significant grading details. Using a pixel-by-pixel Eulerian path analysis, this method allows for a detailed comparison of color intensity values on a fine 8-bit scale, which ranges from 0 to 255. Through this analysis, color distribution patterns are visualized across the spatial structure of the image, which is particularly beneficial for identifying specific variations in quality in printed materials, such as highly graded cards. To manage large image structures, spatial decomposition using a multi-level Gaussian pyramid technique is employed. By applying temporal filtering within select frequency bands (0.4–4 Hz) at a coarse pyramid level, the algorithm effectively pools spatial data, enabling the detection of color density shifts that may indicate image defects. The amplified image data is further processed using a classifier neural network, which provides high sensitivity and specificity in detecting edge defects, image centering issues, scuffs, and breakages. The system demonstrates promising results in identifying true positives while maintaining low false-negative rates, thus confirming the reliability of deep machine learning algorithms in high-sensitivity defect detection. This paper presents the significance of Eulerian amplification in optical image processing, offering a robust tool for precise, automated defect detection across a variety of high-resolution image types.

Predicting the closing price of cryptocurrency Ethereum

Given that cryptocurrencies are now involved in nearly every financial transaction due to their widespread acceptance as an alternative method of payment and currency exchange, researchers and economists have increased opportunities to analyze cryptocurrency prices. Over time, predicting the daily closing price of Ethereum has been challenging for investors, traders, and investment banks because of its significant price volatility. The daily closing price of cryptocurrency is crucial for trading or investing in Ethereum. This report aims to conduct a comparative analysis of the predictive performance of deep machine learning algorithms within a stacking ensemble modeling framework, utilizing daily historical price data of Ethereum from Coindesk, tweets from Twitter spanning from August 1, 2022, to August 8, 2022, and five additional covariates (closing price lag1, closing price lag2, noltrend, daytype, and month) derived from Ethereum's closing price. Seven models are employed to forecast the daily closing price of Ethereum: recurrent neural network, ensemble stacked recurrent neural network, gradient boosting machine, generalized linear model, distributed random forest, deep neural networks, and a stacked ensemble of gradient boosting machine, generalized linear model, distributed random forest, and deep neural networks. The primary evaluation metric is the mean absolute error (MAE). Based on MAE, the RNN forecasts outperform the other models in this study, achieving an MAE of 0.0309.

A deep learning model employing Bi-LSTM architecture to predict Martian ionosphere electron density using data from the Mars Global Surveyor mission

Planetary scientists and space agencies are highly intrigued by the Martian ionosphere due to its significant impact on upcoming Mars missions. Given its potential influence on satellite communication and navigation systems, measuring the altitude distribution of electron density (ED) values in the Martian ionosphere becomes a vital parameter to consider. The parameter of electron density in the Martian ionosphere serves as a tool to represent and predict the impacts of severe space weather events and solar activity on the Martian ionosphere. This study presents the development of a Bi-directional Long Short-Term Memory (Bi-LSTM) model, a deep machine learning algorithm to predict the Mars ionospheric ED values. To train and evaluate the model’s performance, we employ data samples from the Mars Global Surveyor (MGS) mission. For the preparation of input variables in the Bi-LSTM model, approximately 5600 ED altitude distribution of electron density profiles logged by the MGS between the years 1998 to 2005, which corresponded to the major period of the 23rd solar cycle, are taken into consideration. The performance of the proposed model in predicting both quiet and disturbed times is evaluated using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics. The significant minimum values of MAE (0.56 × 109) and RMSE (0.67 × 109) for the quiet day considered and MAE (0.27 × 109) and RMSE (0.31 × 109) for the solar flare disturbed day considered indicate the fair performance of the Bi-LSTM based prediction model. This research’s findings are vital for upcoming Mars missions, as precise predictions of ED of Mars ionosphere are crucial for reliable navigation and communication systems.

A novel deep machine learning algorithm with dimensionality and size reduction approaches for feature elimination: thyroid cancer diagnoses with randomly missing data.

Thyroid cancer incidences endure to increase even though a large number of inspection tools have been developed recently. Since there is no standard and certain procedure to follow for the thyroid cancer diagnoses, clinicians require conducting various tests. This scrutiny process yields multi-dimensional big data and lack of a common approach leads to randomly distributed missing (sparse) data, which are both formidable challenges for the machine learning algorithms. This paper aims to develop an accurate and computationally efficient deep learning algorithm to diagnose the thyroid cancer. In this respect, randomly distributed missing data stemmed singularity in learning problems is treated and dimensionality reduction with inner and target similarity approaches are developed to select the most informative input datasets. In addition, size reduction with the hierarchical clustering algorithm is performed to eliminate the considerably similar data samples. Four machine learning algorithms are trained and also tested with the unseen data to validate their generalization and robustness abilities. The results yield 100% training and 83% testing preciseness for the unseen data. Computational time efficiencies of the algorithms are also examined under the equal conditions.

An efficient intrusion detection technique for traffic pattern learning

Efficient intrusion detection algorithms are required for network traffic learning patterns in order to protect advanced network communication channels. These systems can be used to detect normal and unusual patterns, signatures, and rule violations. In recent years, conventional and deep machine learning algorithms have been utilized in the field of network intrusion detection for network traffic learning systems. The use of machine learning opens up new attack surfaces that are very intriguing to investigate. Attackers can introduce noisy data into training data to influence testing patterns in computer networks. The goal of this work is to create an efficient intrusion detection solution for network traffic learning patterns using a supervised and unsupervised technique. We developed an effective intrusion detection system (IDs) using an appropriate NSLKDD dataset for network traffic patterns. The model was trained and evaluated using the Genetic Optimization Algorithm (GOA) and the Niave Bayesian technique to recognize usual and unexpected network traffic patterns. We created a strategy that begins with a random population and subsequent iterates through the fitness function, returning the best parents with high detection accuracy. The best parents were determined using the n-parameters iterated by the crossover and mutation procedures. A cross over function was created to combine genes from two fitness parents by randomly selecting portions from each parent. The individual components of the crossover offsprings are randomly flipped to achieve the mutation. The fitness of the previous generation was obtained to generate a new generation, and this process was repeated n times. This was created to detect network intrusions using Nave Bayes' binary categorization problem and evolutionary algorithms. We accomplished this task by aggregating noise into training set before broadcasting the average number, and it is critical not to have that public average too frequently. The experimental results reveal that our proposed GA fared better than the NB technique, with a detection accuracy of 95.0% versus a recommendable detection accuracy of 53.0%.

Characterizing damage evolution in fiber reinforced composites using in-situ X-ray computed tomography, deep machine learning and digital volume correlation (DVC)

In-situ X-ray computed tomography (CT) techniques enable to examine microstructural evolution of composite materials continuously and nondestructively under loading for better understanding the initiation and propagation of microscopic damages. This study explores the tensile damage behavior of notched woven carbon/epoxy composites under successive loading process. Since the image contrast of different composite material phases is normally low and conventional image segmentation techniques are hard to identify microstructures and defects in different directions distinctively, a U-Net image segmentation model based on deep machine learning algorithms is employed to characterize microstructures and material damages precisely and consistently. Digital volume correlation (DVC) approach is proposed here tocharacterize the three-dimensional (3D) deformation fields of carbon/epoxy composites at different loading steps, and the DVC results are used to predict location of damage initiation and propagation. It is found that the damage modes of notched carbon/epoxy composites mainly include fiber breakage, longitudinal cracks in warp fiber tows, and transverse cracks in weft fiber tows. The individual fiber breakage occurs in warp fiber tows at the intermediate loading level, and longitudinal and transverse cracks appear in further loading levels. Longitudinal cracks and fiber breakage events take place in a region tangent to the notch tip and exhibit a strong interaction. The development of longitudinal cracks promotes the initiation and propagation of transverse cracks. In addition, the strain concentration regions in 3D deformation fields match well with the microscopic cracks segmented by the U-Net deep learning model. This study demonstrated that the integration of X-ray computed tomography, digital volume correlation and image semantic segmentation enables to identify damage initiation, evolution, extent and mechanism.

Transparent deep learning to identify autism spectrum disorders (ASD) in EHR using clinical notes.

Machine learning (ML) is increasingly employed to diagnose medical conditions, with algorithms trained to assign a single label using a black-box approach. We created an ML approach using deep learning that generates outcomes that are transparent and in line with clinical, diagnostic rules. We demonstrate our approach for autism spectrum disorders (ASD), a neurodevelopmental condition with increasing prevalence. We use unstructured data from the Centers for Disease Control and Prevention (CDC) surveillance records labeled by a CDC-trained clinician with ASD A1-3 and B1-4 criterion labels per sentence and with ASD cases labels per record using Diagnostic and Statistical Manual of Mental Disorders (DSM5) rules. One rule-based and three deep ML algorithms and six ensembles were compared and evaluated using a test set with 6773 sentences (N = 35 cases) set aside in advance. Criterion and case labeling were evaluated for each ML algorithm and ensemble. Case labeling outcomes were compared also with seven traditional tests. Performance for criterion labeling was highest for the hybrid BiLSTM ML model. The best case labeling was achieved by an ensemble of two BiLSTM ML models using a majority vote. It achieved 100% precision (or PPV), 83% recall (or sensitivity), 100% specificity, 91% accuracy, and 0.91 F-measure. A comparison with existing diagnostic tests shows that our best ensemble was more accurate overall. Transparent ML is achievable even with small datasets. By focusing on intermediate steps, deep ML can provide transparent decisions. By leveraging data redundancies, ML errors at the intermediate level have a low impact on final outcomes.

Telemedicine Screening for Diabetic Retinopathy Using Digital Technology: Foreign Experience

Background. Timely detection and treatment of diabetic retinopathy (DR) could prevent the development of blindness. However, large number of patients are diagnosed at late stages when effective treatment becomes impossible. In this context, active screening for DR plays an important role. Various screening programs have been developed, including programs using digital technologies.Objective. To analyze and summarize the literature data on digitalization of DR screening.Materials and methods. The study was carried out based on the results of a search in the PubMed / Medline bibliographic database and the Google system. The used keywords were “diabetes”, “retinopathy”, “telemedi-cine”, “digital technologies”, “artificial intelligence” and others. Relevant publications were selected by exam-ining the titles and abstracts of articles.Results. The study addressed the approaches to organizing the telemedicine programs of DR screening. The examples of innovative digital technologies for performing diagnostic studies as well as the characteristics of various electronic systems and devices are presented. It was noted that the DR screening could be performed in a local center with data transfer to a regional specialized diagnostic (analytical) center where telemedicine analysis is done. All diagnostic information is available to the diabetologist who prescribes and adjusts ther-apy. This operating principle facilitates the interaction between various specialists and thus increases the efficiency of screening.Discussion. Although the telemedicine screening for diabetic retinopathy has already been introduced in a number of countries, it is still at its early stages of development. Many modern technologies such as portable mobile devices could be adapted and used in the implementation of telemedicine programs. Deep machine learning algorithms and other artificial intelligence-based approaches demonstrate excellent results. Some systems have already received regulatory approval. Conclusions. The early diagnostics of diabetic retinopathy could be improved by integrating innovative screening technologies in a unified digital platform implemented in routine clinical practice.

Deep self-supervised machine learning algorithms with a novel feature elimination and selection approaches for blood test-based multi-dimensional health risks classification

BackgroundBlood test is extensively performed for screening, diagnoses and surveillance purposes. Although it is possible to automatically evaluate the raw blood test data with the advanced deep self-supervised machine learning approaches, it has not been profoundly investigated and implemented yet.ResultsThis paper proposes deep machine learning algorithms with multi-dimensional adaptive feature elimination, self-feature weighting and novel feature selection approaches. To classify the health risks based on the processed data with the deep layers, four machine learning algorithms having various properties from being utterly model free to gradient driven are modified.ConclusionsThe results show that the proposed deep machine learning algorithms can remove the unnecessary features, assign self-importance weights, selects their most informative ones and classify the health risks automatically from the worst-case low to worst-case high values.

Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology.

The mammalian cell is a complex entity, with membrane-bound and membrane-less organelles playing vital roles in regulating cellular homeostasis. Organellar protein niches drive discrete biological processes and cell functions, thus maintaining cell equilibrium. Cellular processes such as signaling, growth, proliferation, motility, and programmed cell death require dynamic protein movements between cell compartments. Aberrant protein localization is associated with a wide range of diseases. Therefore, analyzing the subcellular proteome of the cell can provide a comprehensive overview of cellular biology. With recent advancements in mass spectrometry, imaging technology, computational tools, and deep machine learning algorithms, studies pertaining to subcellular protein localization and their dynamic distributions are gaining momentum. These studies reveal changing interaction networks because of "moonlighting proteins" and serve as a discovery tool for disease network mechanisms. Consequently, this review aims to provide a comprehensive repository for recent advancements in subcellular proteomics subcontexting methods, challenges, and future perspectives for method developers. In summary, subcellular proteomics is crucial to the understanding of the fundamental cellular mechanisms and the associated diseases.

Target Image Processing Based on Super-resolution Reconstruction and Deep Machine Learning Algorithm

In dictionary-based single-frame image reconstruction algorithms, dictionaries rely on the design of artificial shallow features and are limited in their ability to represent image features. Therefore, this paper proposes a high-accuracy reconstruction method based on deep learning feature dictionary. This algorithm first uses a deep network to learn high-resolution and low-resolution training example images with deep features; Then co-train the feature dictionary under the super dense framework of the sparse dictionary; Finally, a single low-resolution image can be input and a super-resolution reconstruction can be performed using a dictionary. From the theoretical analysis, the introduction of deep network to extract the deep-level features of the image and its use in dictionary training is more beneficial to complement the high-frequency information in the low-resolution image. Experiments show that the proposed method achieves the best results in terms of both the peak signal-to-noise ratio and the gradient energy function of the reconstructed images. This shows that compared with traditional interpolation methods and some deep learning methods, the proposed method can recover image details to a high degree while preserving the original image damage information. This proves that the subjective visual and objective evaluation indicators of the algorithm presented in this article are higher than those of the comparative algorithm.

Predicting groundwater level using traditional and deep machine learning algorithms

This research aims to evaluate various traditional or deep machine learning algorithms for the prediction of groundwater level (GWL) using three key input variables specific to Izeh City in the Khuzestan province of Iran: groundwater extraction rate (E), rainfall rate (R), and river flow rate (P) (with 3 km distance). Various traditional and deep machine learning (DML) algorithms, including convolutional neural network (CNN), recurrent neural network (RNN), support vector machine (SVM), decision tree (DT), random forest (RF), and generative adversarial network (GAN), were evaluated. The convolutional neural network (CNN) algorithm demonstrated superior performance among all the algorithms evaluated in this study. The CNN model exhibited robustness against noise and variability, scalability for handling large datasets with multiple input variables, and parallelization capabilities for fast processing. Moreover, it autonomously learned and identified data patterns, resulting in fewer outlier predictions. The CNN model achieved the highest accuracy in GWL prediction, with an RMSE of 0.0558 and an R2 of 0.9948. It also showed no outlier data predictions, indicating its reliability. Spearman and Pearson correlation analyses revealed that P and E were the dataset’s most influential variables on GWL. This research has significant implications for water resource management in Izeh City and the Khuzestan province of Iran, aiding in conservation efforts and increasing local crop productivity. The approach can also be applied to predicting GWL in various global regions facing water scarcity due to population growth. Future researchers are encouraged to consider these factors for more accurate GWL predictions. Additionally, the CNN algorithm’s performance can be further enhanced by incorporating additional input variables.

Climate variability impacts on crop yields and agriculture contributions to gross domestic products in the Nile basin (1961–2016): What did deep machine learning algorithms tell us?

Climate variability impacts on crop yields and agriculture contributions to gross domestic products in the Nile basin (1961–2016): What did deep machine learning algorithms tell us?

Deep-learning model for prenatal congenital heart disease screening generalizes to community setting and outperforms clinical detection.

Despite nearly universal prenatal ultrasound screening programs, congenital heart defects (CHD) are still missed, which may result in severe morbidity or even death. Deep machine learning (DL) can automate image recognition from ultrasound. The main aim of this study was to assess the performance of a previously developed DL model, trained on images from a tertiary center, using fetal ultrasound images obtained during the second-trimester standard anomaly scan in a low-risk population. A secondary aim was to compare initial screening diagnosis, which made use of live imaging at the point-of-care, with diagnosis by clinicians evaluating only stored images. All pregnancies with isolated severe CHD in the Northwestern region of The Netherlands between 2015 and 2016 with available stored images were evaluated, as well as a sample of normal fetuses' examinations from the same region and time period. We compared the accuracy of the initial clinical diagnosis (made in real time with access to live imaging) with that of the model (which had only stored imaging available) and with the performance of three blinded human experts who had access only to the stored images (like the model). We analyzed performance according to ultrasound study characteristics, such as duration and quality (scored independently by investigators), number of stored images and availability of screening views. A total of 42 normal fetuses and 66 cases of isolated CHD at birth were analyzed. Of the abnormal cases, 31 were missed and 35 were detected at the time of the clinical anatomy scan (sensitivity, 53%). Model sensitivity and specificity were 91% and 78%, respectively. Blinded human experts (n = 3) achieved mean ± SD sensitivity and specificity of 55 ± 10% (range, 47-67%) and 71 ± 13% (range, 57-83%), respectively. There was a statistically significant difference in model correctness according to expert-graded image quality (P = 0.03). The abnormal cases included 19 lesions that the model had not encountered during its training; the model's performance in these cases (16/19 correct) was not statistically significantly different from that for previously encountered lesions (P = 0.41). A previously trained DL algorithm had higher sensitivity than initial clinical assessment in detecting CHD in a cohort in which over 50% of CHD cases were initially missed clinically. Notably, the DL algorithm performed well on community-acquired images in a low-risk population, including lesions to which it had not been exposed previously. Furthermore, when both the model and blinded human experts had access to only stored images and not the full range of images available to a clinician during a live scan, the model outperformed the human experts. Together, these findings support the proposition that use of DL models can improve prenatal detection of CHD. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

DETERMINING THE PROPERTIES OF ROCK SAMPLES USING DEEP MACHINE LEARNING

Porosity, absolute permeability, and diffusion coefficient are crucial characteristics governing fluid flow in the porous media of geological formations. Determining these properties traditionally involves resource-intensive and time-consuming processes. However, with the advancement of deep learning methods in the last 3–4 years, artificial neural networks have gained significant traction in predicting the transport properties of the fluid-porous medium system and the geometric characteristics of porous samples based on their images. This approach allows for the rapid determination of these properties with acceptable accuracy. The aim of this article is to conduct a scientific review of literature from open sources on the deter-mination of absolute permeability, diffusion coefficient, and porosity from their images acquired through various scanning methods. Additionally, this article incorporates proprietary data, specifically images from four carbonate samples. Convolutional neural networks were examined as the method of choice.The results of this article comprise a scientific review of moderate depth regarding the effectiveness and applicability of the approach for determining important characteristics of porous media using deep machine learning methods based on sample images. In this article, we also present the results of predict-ing the open porosity of four carbonate samples based on their X-ray images using the convolutional neural network model we constructed. The conducted review has demonstrated that images (scans) of geological rock samples obtained through various scanning methods allow for the calculation of their transport properties with a high degree of accuracy using deep machine learning algorithms, and this can be achieved within a significantly short timeframe. This implies that deep machine learning can serve as a valuable alternative tool for estimating the properties of geological rock samples based on their images. The convolutional neural network model we constructed exhibited predictive capability for the porosity of three carbonate samples with a coefficient of determination ranging from 0.936 to 0.976.

Forecasting multistep daily stock prices for long-term investment decisions: A study of deep learning models on global indices

Deep machine learning algorithms play an important role in facilitating the development of predictive models for the stock market. However, most studies focus on predicting next-day stock prices or movements, limiting the usability of the predictive model for investors. This study extensively explores the ability of deep learning models to predict out-of-sample the daily prices of global stock indices over a long term, up to a year. The performance of six models, including Deep Neural Network (DNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN), are compared using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The models predict the long-term daily prices of five global stock indices, namely the Nifty, the Dow Jones Industrial Average (DJIA), the DAX performance index (DAX), the Nikkei 225 (NI225), and the Shanghai Stock Exchange composite Index (SSE). The results confirm the superiority of LSTM for predicting long-term daily prices. The Bi-LSTM does not improve the result of LSTM but performs better than other algorithms. CNN overfits the training data and poorly forecasts the long-term stock prices of global indices on the testing data. This research demonstrates the potential of deep learning models for long-term stock price forecasting, offering valuable insights for investors. Additionally, the patterns of predicted daily prices can be helpful in building trading and risk management decision systems.

Methodology for Power Systems’ Emergency Control Based on Deep Learning and Synchronized Measurements

Modern electrical power systems place special demands on the speed and accuracy of transient and steady-state process control. The introduction of renewable energy sources has significantly influenced the amount of inertia and uncertainty of transient processes occurring in energy systems. These changes have led to the need to clarify the existing principles for the implementation of devices for protecting power systems from the loss of small-signal and transient stability. Traditional methods of developing these devices do not provide the required adaptability due to the need to specify a list of accidents to be considered. Therefore, there is a clear need to develop fundamentally new devices for the emergency control of power system modes based on adaptive algorithms. This work proposes to develop emergency control methods based on the use of deep machine learning algorithms and obtained data from synchronized vector measurement devices. This approach makes it possible to ensure adaptability and high performance when choosing control actions. Recurrent neural networks, long short-term memory networks, restricted Boltzmann machines, and self-organizing maps were selected as deep learning algorithms. Testing was performed by using IEEE14, IEEE24, and IEEE39 power system models. Two data samples were considered: with and without data from synchronized vector measurement devices. The highest accuracy of classification of the control actions’ value corresponds to the long short-term memory networks algorithm: the value of the accuracy factor was 94.31% without taking into account the data from the synchronized vector measurement devices and 94.45% when considering this data. The obtained results confirm the possibility of using deep learning algorithms to build an adaptive emergency control system for power systems.

A Game-Theoretical Approach to Clinical Decision Making with Immersive Visualisation

Cancer is a disease characterised by changes in combinations of genes within affected tumour cells. The deep understanding of genetic activity afforded to cancer specialists through complex genomics data analytics has advanced the clinical management of cancer by using deep machine learning algorithms and visualisation. However, most of the existing works do not integrate intelligent decision-making aids that can guide users in the analysis and exploration processes. This paper contributes a novel strategy that applies game theory within a VR-enabled immersive visualisation system designed as the decision support engine to mimic real-world interactions between stakeholders within complex relationships, in this case cancer clinicians. Our focus is to apply game theory to assist doctors in the decision-making process regarding the treatment options for rare-cancer patients. Nash Equilibrium and Social Optimality strategy profiles were used to facilitate complex analysis within the visualisation by inspecting which combination of genes and dimensionality reduction methods yields the best survival rate and by investigating the treatment protocol to form new hypotheses. Using a case simulation, we demonstrate the effectiveness of game theory in guiding the analyst with a patient cohort data interrogation system as compared to an analyst without a decision support system. Particularly, the strategy profile (t-SNE method and DNMT3B_ZBTB46_LAPTM4B gene) gains the highest payoff for the two doctors.

Artificial intelligence as a coming revolution in medicine

Introduction: The development of medicine and information technology in recent decades has undoubtedly contributed to improving public health. Artificial intelligence is a technology that has great potential to revolutionize the functioning of health care around the world. Appropriate use of the development of technology can revolutionize many areas of modern medicine, however, it should not be forgotten that this technology should be subjected to appropriate standardization and legal regulation.
 Objective: The purpose of this study is to review the available scientific literature in order to systematize the current knowledge on the use of artificial intelligence in the process of diagnosis and treatment. Ethical aspects related to the implementation of AI for use in health care are also analyzed.
 Results: Artificial intelligence uses deep machine learning algorithms. It is a technology that has been known for a long time, but recently the chances of its widespread use have increased significantly, although scientists still do not fully understand the operation of AI algorithms. Currently, there are attempts to use this technology in many medical fields such as cardiology, diagnostic imaging, gastroenterology, pathomorphology, ultrasound. Artificial intelligence can also be used to improve the functioning of patient service in health care.
 Summary: The development of artificial intelligence algorithms creates a huge opportunity to improve the quality of diagnostic and treatment processes. The current rapid development of the technology is revolutionizing many branches of medicine, improving treatment outcomes. However, the development of this technology requires the creation of an appropriate law governing AI in medicine.