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Diabetic Retinopathy (DR) is a disorder of the eye and refers to the damage to the blood vessels of the retina as a result of high blood sugar levels in the body. This condition is the most common cause of blindness among working-age people. Vision impairment may result from DR and is regarded as a serious diabetes complication all over the globe. This paper evaluates the efficacy of two deep learning models, DenseNet-121 and ResNet-50, which have a widespread application in performing automated analysis of retinal images and detecting the presence of DR. DenseNet utilizes dense connectivity in order to efficiently reuse features, while ResNet uses residual connections to enhance the training of deep networks. The experiments were conducted on both models using an open-sourced DR dataset and their performance was evaluated with respect to accuracy, sensitivity, specificity and computational efficiency. The results of the analysis suggest that DenseNet is superior to ResNet in terms of accuracy and parameter efficiency, and therefore it is the best method in dealing with DR in a clinical setting. This information may assist the physicians in determining the appropriate models which should be employed for diabetic retinopathy detection in clinics.

turing test polikar mind/umer idiot

Coronary artery disease (CAD) is the most ubiquitous and protuberant cause of fatal death. The hit in mortality rate is because of certain lifestyle variables including unhealthy diet, usage of tobaccos and drugs, physical inactivity, and environmental pollution. Traditional screening tests including computed tomography, angiography, electrocardiography, and magnetic resonance imaging are employed for diagnosis and would necessitate more manpower. Machine learning (ML) has been utilized in healthcare to create early predictions from massive volumes of data. The Scopus, Web of Science databases were exhaustively searched utilizing a search strategy that comprised CAD prediction, cardiac illness detection, and heart disease categorization. After applying the inclusion and exclusion criteria to the 99 articles obtained, the population of the study was composed of 30 articles. This review study offers an organized look at the articles published in ML based CAD detection and classification models that include clinical variables. The use of ML could produce amazing results in CAD detection, as evidenced by the classifiers random forest, decision tree, and k-nearest-neighbour with accuracy being >90%. The use of ML in CAD diagnosis lowers false-positive, and false-negative errors, and presents a special opportunity by providing patients quick, and affordable diagnostic services.

In paediatrics, bone age is a crucial indicator of how a child's skeleton is developing. They have had great success ever since the creation of deep learning (DL)-based bone age prediction tools. Deep features learning, however, has a significant computing overhead problem. Deep convolution layers are used in this technique to learn representative features in the small yet useful regions that are extracted for feature learning. This work suggests using an extreme learning machine algorithm as the fundamental architecture in the final bone age assessment study to realise the rapid computation speed and feature interaction. The viability and efficacy of the suggested strategy have been verified by experiments using data that is openly accessible. The suggested model is explicitly trained using a cutting-edge end-to-end learning architecture employing bone scans to extract the most discriminative patches from the original high-resolution image. The bone picture is the foundation of the procedure. Our main objective is to categorize individuals by age using convolution neural network (CNN) classification models, such as the Xception and Mobile Net models. As a result, we have achieved results that are 90% and 94% accurate in classifying people by age using CNN models.

Diabetes mellitus (DM) is a serious worldwide health issue, and its prevalence is rapidly growing. It is a spectrum of metabolic illnesses defined by perpetually increased blood glucose levels. Undiagnosed diabetes can lead to a variety of problems, including retinopathy, nephropathy, neuropathy, and other vascular abnormalities. In this context, machine learning (ML) technologies may be particularly useful for early disease identification, diagnosis, and therapy monitoring. The core idea of this study is to identify the strong ML algorithm to predict it. For this several ML algorithms were chosen i.e., support vector machine (SVM), Naïve Bayes (NB), K nearest neighbor (KNN), random forest (RF), logistic regression (LR), and decision tree (DT), according to studied work. Two, Pima Indian diabetic (PID) and Germany diabetes datasets were used and the experiment was performed using Waikato environment for knowledge analysis (WEKA) 3.8.6 tool. This article discussed about performance matrices and error rates of classifiers for both datasets. The results showed that for PID database (PIDD), SVM works better with an accuracy of 74% whereas for Germany KNN and RF work better with 98.7% accuracy. This study can aid healthcare facilities and researchers in comprehending the value and application of ML algorithms in predicting diabetes at an early stage.

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Machine learning is recognized as a tool for improving the performance of many kinds of systems, yet most machine learning systems themselves are not well equipped to improve their own learning performance. By emphasizing the role of domain knowledge, learning systems can be crafted as knowledge directed systems, and with the addition of a knowledge store for organizing and maintaining knowledge to assist learning, a learning machine learning (L-ML) algorithm is possible. The necessary components of L-ML systems are presented along with several case descriptions of existing machine learning systems that possess limited L-ML capabilities. Keywords: Algorithms, Artificial intelligence. (SDW)

Malaria continues to be a major public health challenge in sub-Saharan Africa, where accurate and timely diagnosis is often hindered by limitations in traditional diagnostic methods. This review evaluated the role of machine learning (ML) algorithms in improving malaria diagnosis in primary healthcare settings. Specifically, it explores applications of ML in microscopic image analysis, rapid diagnostic test (RDT) optimization, and predictive modeling, with a focus on their potential to enhance diagnostic accuracy and decision-making in resource-limited environments. ML techniques, such as convolutional neural networks (CNNs) for image analysis and data-driven models for optimizing RDT interpretation, have shown promise in addressing inter-observer variability and improving test sensitivity and specificity. Furthermore, predictive modeling integrating clinical, demographic, and environmental data can help prioritize malaria cases and guide healthcare providers in making accurate diagnoses. Despite these advancements, challenges such as data limitations, infrastructure gaps, and ethical considerations remain significant barriers to widespread adoption. The methodology utilized in this review involved a comprehensive synthesis of current literature, examining empirical studies on ML applications in malaria diagnosis and assessing their feasibility in primary healthcare contexts. To overcome these challenges, the article suggested policy recommendations, including investments in data infrastructure, capacity building, and public-private partnerships. Ultimately, ML offers a promising solution to enhance malaria diagnostic capabilities, contributing to better health outcomes in endemic regions. 

Learning to rank is a supervised learning problem where the output space is the space of rankings but the supervision space is the space of relevance scores. We make theoretical contributions to the learning to rank problem both in the online and batch settings. First, we propose a perceptron-like algorithm for learning a ranking function in an online setting. Our algorithm is an extension of the classic perceptron algorithm for the classification problem. Second, in the setting of batch learning, we introduce a sufficient condition for convex ranking surrogates to ensure a generalization bound that is independent of number of objects per query. Our bound holds when linear ranking functions are used: a common practice in many learning to rank algorithms. En route to developing the online algorithm and generalization bound, we propose a novel family of listwise large margin ranking surrogates. Our novel surrogate family is obtained by modifying a well-known pairwise large margin ranking surrogate and is distinct from the listwise large margin surrogates developed using the structured prediction framework. Using the proposed family, we provide a guaranteed upper bound on the cumulative NDCG (or MAP) induced loss under the perceptron-like algorithm. We also show that the novel surrogates satisfy the generalization bound condition.

There are several confounding factors that can reduce the accuracy of gait recognition systems. These factors can reduce the distinctiveness, or alter the features used to characterise gait, they include variations in clothing, lighting, pose and environment, such as the walking surface. Full invariance to all confounding factors is challenging in the absence of high-quality labelled training data. We introduce a simulation-based methodology and a subject-specific dataset which can be used for generating synthetic video frames and sequences for data augmentation. With this methodology, we generated a multi-modal dataset. In addition, we supply simulation files that provide the ability to simultaneously sample from several confounding variables. The basis of the data is real motion capture data of subjects walking and running on a treadmill at different speeds. Results from gait recognition experiments suggest that information about the identity of subjects is retained within synthetically generated examples. The dataset and methodology allow studies into fully-invariant identity recognition spanning a far greater number of observation conditions than would otherwise be possible.

This article is from Frontiers in Public Health , volume 2 . Abstract Background: Active travel is an important area in physical activity research, but objective measurement of active travel is still difficult. Automated methods to measure travel behaviors will improve research in this area. In this paper, we present a supervised machine learning method for transportation mode prediction from global positioning system (GPS) and accelerometer data.Methods: We collected a dataset of about 150 h of GPS and accelerometer data from two research assistants following a protocol of prescribed trips consisting of five activities: bicycling, riding in a vehicle, walking, sitting, and standing. We extracted 49 features from 1-min windows of this data. We compared the performance of several machine learning algorithms and chose a random forest algorithm to classify the transportation mode. We used a moving average output filter to smooth the output predictions over time.Results: The random forest algorithm achieved 89.8% cross-validated accuracy on this dataset. Adding the moving average filter to smooth output predictions increased the cross-validated accuracy to 91.9%.Conclusion: Machine learning methods are a viable approach for automating measurement of active travel, particularly for measuring travel activities that traditional accelerometer data processing methods misclassify, such as bicycling and vehicle travel.

This is an outstanding book by Prof. David MacKay (of U. of Cambridge). It is downloadable from author's web page: http://www.inference.phy.cam.ac.uk/mackay/ . Please spread the word, and tell your profs to use this free book in their courses. This is an e-book free to read and share electronically as indicated so by the author. However, you are not allowed to print the whole book, instead you should order it from the publisher. Copyright Cambridge University Press 2003. On-screen viewing permitted. Printing not permitted. http://www.cambridge.org/0521642981 You can buy this book for 30 pounds or $50. See http://www.inference.phy.cam.ac.uk/mackay/itila/ for links. Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v 1 Introduction to Information Theory . . . . . . . . . . . . . 3 2 Probability, Entropy, and Inference . . . . . . . . . . . . . . 22 3 More about Inference . . . . . . . . . . . . . . . . . . . . . 48 I Data Compression . . . . . . . . . . . . . . . . . . . . . . 65 4 The Source Coding Theorem . . . . . . . . . . . . . . . . . 67 5 Symbol Codes . . . . . . . . . . . . . . . . . . . . . . . . . 91 6 Stream Codes . . . . . . . . . . . . . . . . . . . . . . . . . . 110 7 Codes for Integers . . . . . . . . . . . . . . . . . . . . . . . 132 II Noisy-Channel Coding . . . . . . . . . . . . . . . . . . . . 137 8 Dependent Random Variables . . . . . . . . . . . . . . . . . 138 9 Communication over a Noisy Channel . . . . . . . . . . . . 146 10 The Noisy-Channel Coding Theorem . . . . . . . . . . . . . 162 11 Error-Correcting Codes and Real Channels . . . . . . . . . 177 III Further Topics in Information Theory . . . . . . . . . . . . . 191 12 Hash Codes: Codes for Efficient Information Retrieval . . 193 13 Binary Codes . . . . . . . . . . . . . . . . . . . . . . . . . 206 14 Very Good Linear Codes Exist . . . . . . . . . . . . . . . . 229 15 Further Exercises on Information Theory . . . . . . . . . . 233 16 Message Passing . . . . . . . . . . . . . . . . . . . . . . . . 241 17 Communication over Constrained Noiseless Channels . . . 248 18 Crosswords and Codebreaking . . . . . . . . . . . . . . . . 260 19 Why have Sex? Information Acquisition and Evolution . . 269 IV Probabilities and Inference . . . . . . . . . . . . . . . . . . 281 20 An Example Inference Task: Clustering . . . . . . . . . . . 284 21 Exact Inference by Complete Enumeration . . . . . . . . . 293 22 Maximum Likelihood and Clustering . . . . . . . . . . . . . 300 23 Useful Probability Distributions . . . . . . . . . . . . . . . 311 24 Exact Marginalization . . . . . . . . . . . . . . . . . . . . . 319 25 Exact Marginalization in Trellises . . . . . . . . . . . . . . 324 26 Exact Marginalization in Graphs . . . . . . . . . . . . . . . 334 27 Laplace’s Method . . . . . . . . . . . . . . . . . . . . . . . 341 28 Model Comparison and Occam’s Razor . . . . . . . . . . . 343 29 Monte Carlo Methods . . . . . . . . . . . . . . . . . . . . . 357 30 Efficient Monte Carlo Methods . . . . . . . . . . . . . . . . 387 31 Ising Models . . . . . . . . . . . . . . . . . . . . . . . . . . 400 32 Exact Monte Carlo Sampling . . . . . . . . . . . . . . . . . 413 33 Variational Methods . . . . . . . . . . . . . . . . . . . . . . 422 34 Independent Component Analysis and Latent Variable Mod- elling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437 35 Random Inference Topics . . . . . . . . . . . . . . . . . . . 445 36 Decision Theory . . . . . . . . . . . . . . . . . . . . . . . . 451 37 Bayesian Inference and Sampling Theory . . . . . . . . . . 457 V Neural networks . . . . . . . . . . . . . . . . . . . . . . . . 467 38 Introduction to Neural Networks . . . . . . . . . . . . . . . 468 39 The Single Neuron as a Classifier . . . . . . . . . . . . . . . 471 40 Capacity of a Single Neuron . . . . . . . . . . . . . . . . . . 483 41 Learning as Inference . . . . . . . . . . . . . . . . . . . . . 492 42 Hopfield Networks . . . . . . . . . . . . . . . . . . . . . . . 505 43 Boltzmann Machines . . . . . . . . . . . . . . . . . . . . . . 522 44 Supervised Learning in Multilayer Networks . . . . . . . . . 527 45 Gaussian Processes . . . . . . . . . . . . . . . . . . . . . . 535 46 Deconvolution . . . . . . . . . . . . . . . . . . . . . . . . . 549 VI Sparse Graph Codes . . . . . . . . . . . . . . . . . . . . . 555 47 Low-Density Parity-Check Codes . . . . . . . . . . . . . . 557 48 Convolutional Codes and Turbo Codes . . . . . . . . . . . . 574 49 Repeat–Accumulate Codes . . . . . . . . . . . . . . . . . . 582 50 Digital Fountain Codes . . . . . . . . . . . . . . . . . . . . 589 VII Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . 597 A Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 598 B Some Physics . . . . . . . . . . . . . . . . . . . . . . . . . . 601 C Some Mathematics . . . . . . . . . . . . . . . . . . . . . . . 605 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 620

We demonstrate that it is possible to measure metallicity from the SDSS five-band photometry to better than 0.1 dex using supervised machine learning algorithms. Using spectroscopic estimates of metallicity as ground truth, we build, optimize and train several estimators to predict metallicity. We use the observed photometry, as well as derived quantities such as stellar mass and photometric redshift, as features, and we build two sample data sets at median redshifts of 0.103 and 0.218 and median r-band magnitude of 17.5 and 18.3 respectively. We find that ensemble methods, such as Random Forests of Trees and Extremely Randomized Trees, and Support Vector Machines all perform comparably well and can measure metallicity with a Root Mean Square Error (RMSE) of 0.081 and 0.090 for the two data sets when all objects are included. The fraction of outliers (objects for which |Z_true - Z_pred| > 0.2 dex) is 2.2 and 3.9%, respectively and the RMSE decreases to 0.068 and 0.069 if those objects are excluded. Because of the ability of these algorithms to capture complex relationships between data and target, our technique performs better than previously proposed methods that sought to fit metallicity using an analytic fitting formula, and has 3x more constraining power than SED fitting-based methods. Additionally, this method is extremely forgiving of contamination in the training set, and can be used with very satisfactory results for training sample sizes of just a few hundred objects. We distribute all the routines to reproduce our results and apply them to other data sets.

This study evaluated the nature-inspired optimization algorithms to improve classification involving imbalanced class problems. The particle swarm optimization (PSO) and grey wolf optimizer (GWO) were used to adaptively balance the distribution and then four supervised machine learning classifiers artificial neural network (ANN), support vector machine (SVM), extreme gradient-boosted tree (XGBoost), and random forest (RF) were applied to maximize the classification performance for electricity fraud prediction. The imbalance data was balanced using random undersampling (RUS) and two nature-inspired algorithm techniques (PSO and GWO). Results showed that for the data balanced using random undersampling, ANN (Sentest = 50.31%), and XGBoost (Sentest = 66.32%) has better sensitivity than SVM (Sentest = 23.61%), while RF exhibits overfitting (Sentrain = 100%, Sentest = 71.25%). The classification performance of RF model hybrid with PSO improved tremendously (AccTest = 96.98%, Sentest = 94.87%, Spectest = 99.16%, Pretest = 99.14%, F1 Score = 96.96%, and area under the curve (AUC) = 0.989). This was closely followed by hybrid of XGBoost with PSO. Moreover, RF and XGBoost hybrid with GWO also showed an improvement and promising results. This study has showed that nature-inspired optimization algorithms (PSO and GWO) are effective methods in addressing imbalanced dataset.

This is a companion note to our recent study of the weak convergence properties of constrained emphatic temporal-difference learning (ETD) algorithms from a theoretic perspective. It supplements the latter analysis with simulation results and illustrates the behavior of some of the ETD algorithms using three example problems.

Pairwise learning usually refers to a learning task which involves a loss function depending on pairs of examples, among which most notable ones include ranking, metric learning and AUC maximization. In this paper, we study an online algorithm for pairwise learning with a least-square loss function in an unconstrained setting of a reproducing kernel Hilbert space (RKHS), which we refer to as the Online Pairwise lEaRning Algorithm (OPERA). In contrast to existing works \cite{Kar,Wang} which require that the iterates are restricted to a bounded domain or the loss function is strongly-convex, OPERA is associated with a non-strongly convex objective function and learns the target function in an unconstrained RKHS. Specifically, we establish a general theorem which guarantees the almost surely convergence for the last iterate of OPERA without any assumptions on the underlying distribution. Explicit convergence rates are derived under the condition of polynomially decaying step sizes. We also establish an interesting property for a family of widely-used kernels in the setting of pairwise learning and illustrate the above convergence results using such kernels. Our methodology mainly depends on the characterization of RKHSs using its associated integral operators and probability inequalities for random variables with values in a Hilbert space.

In this thesis, we propose two new machine learning schemes, a subband-based Independent Component Analysis scheme and a hybrid Independent Component Analysis/Support Vector Machine scheme, and apply them to the problems of blind acoustic signal separation and face detection. Based on a linear model, classical Independent Component Analysis (ICA) provides a method of representing data as independent components. In contrast to Principal Component Analysis (PCA), which decorrelates the data based on its covariance matrix, ICA uses higher-order statistics of the data to minimize the dependence between the components of the system output. An important application of ICA is blind source separation. However, classical ICA algorithms do not work well for separation in the presence of noise or when performed on-line. Inspired by the psychoacoustic discovery that humans perceive and process acoustic signals in different frequency bands independently, we propose a new algorithm, subband-based ICA, that integrates ICA with time-frequency analysis to separate mixed signals. In subband-based ICA, the separations are performed in parallel in several frequency bands. Wavelet decomposition and best basis selection in wavelet/DCT packets can be incorporated into this algorithm.

Breast cancer is the one common cause of death in both developed worlds and the most death-causing disease diagnosed among women. Early recognition of this condition can help to minimize death rates. The breast problem statement, in brief, is not reliable for accuracy recognition. They have a high degree of classification accuracy as well as diagnostic capabilities. The most common classifications are normal, benign cancer, and malignant cancer. Machine learning (ML) techniques are now widely used in the classification of breast cancer. In this paper, some machine learning technics have been investigated to diagnose breast cancer (BC) on magnetic resonance imaging (MRI) images using multi-step processes. The first step has been to take the MRI image as an input image and have been pre-processing an image, then use feature extraction by using (scaleinvariant feature transform (SIFT), histogram of oriented gradient (HOG), local binary patterns (LBP), bag of words (BoW), and edge-oriented histogram (EOH)). Next step we implement the classifying algorithms (KNN, decision tree (DT), naïve Bayes, ANN, SVM, RF, AdaBoost), have been used to detect and classify the normal or breast cancer region for this purpose datasets like ACRIN-Contralateral-Breast-MRI, In and breast cancer MRI dataset) has been collected our breast cancer MRI images from Erbil and Sulaymaniyah hospital the results was 91.9%, the result of ACRIN was 97% and the results Breast Cancer was 92.3%.

Recently, machine learning techniques have gained popularity for the medical diagnosis. Medical professionals use this approach to learn and detect the abnormalities of life-threatening chronic diseases. The increasing use of ML approaches may be due in part to better disease diagnosis enabled through improved symptom detection. The current study deployed different machine learning algorithms, including Decision Trees (DT), K-Nearest Neighbors (KNN), classifiers Multilayer Perceptron (MP), Support Vector Machines (SVM), and Random Forest (RF) for early predictions and symptoms of the disease. These models were capable of differentiating between benign and harmful cancer cells Benign tumours, which were non-cancerous and in most cases, non-lethal were mostly confined to the area from where they originated, however, it was observed that malignant cancer can start with abnormal cell growth in the human body. This abnormal cell growth can quickly spread to nearby tissues, which can cause infiltration of adjacent cells, resulting in a potentially fatal condition. Thereby, it was observed that Multilayer Perceptron (MLP) model provided the highest accuracy percentage of 86% when compared with all the other techniques in association with the accuracy rate of the models

Recently, machine learning techniques have gained popularity for the medical diagnosis. Medical professionals use this approach to learn and detect the abnormalities of life-threatening chronic diseases. The increasing use of ML approaches may be due in part to better disease diagnosis enabled through improved symptom detection. The current study deployed different machine learning algorithms, including Decision Trees (DT), K-Nearest Neighbors (KNN), classifiers Multilayer Perceptron (MP), Support Vector Machines (SVM), and Random Forest (RF) for early predictions and symptoms of the disease. These models were capable of differentiating between benign and harmful cancer cells Benign tumours, which were non-cancerous and in most cases, non-lethal were mostly confined to the area from where they originated, however, it was observed that malignant cancer can start with abnormal cell growth in the human body. This abnormal cell growth can quickly spread to nearby tissues, which can cause infiltration of adjacent cells, resulting in a potentially fatal condition. Thereby, it was observed that Multilayer Perceptron (MLP) model provided the highest accuracy percentage of 86% when compared with all the other techniques in association with the accuracy rate of the models

Learning a causal effect from observational data is not straightforward, as this is not possible without further assumptions. If hidden common causes between treatment $X$ and outcome $Y$ cannot be blocked by other measurements, one possibility is to use an instrumental variable. In principle, it is possible under some assumptions to discover whether a variable is structurally instrumental to a target causal effect $X \rightarrow Y$, but current frameworks are somewhat lacking on how general these assumptions can be. A instrumental variable discovery problem is challenging, as no variable can be tested as an instrument in isolation but only in groups, but different variables might require different conditions to be considered an instrument. Moreover, identification constraints might be hard to detect statistically. In this paper, we give a theoretical characterization of instrumental variable discovery, highlighting identifiability problems and solutions, the need for non-Gaussianity assumptions, and how they fit within existing methods.

An important use of private data is to build machine learning classifiers. While there is a burgeoning literature on differentially private classification algorithms, we find that they are not practical in real applications due to two reasons. First, existing differentially private classifiers provide poor accuracy on real world datasets. Second, there is no known differentially private algorithm for empirically evaluating the private classifier on a private test dataset. In this paper, we develop differentially private algorithms that mirror real world empirical machine learning workflows. We consider the private classifier training algorithm as a blackbox. We present private algorithms for selecting features that are input to the classifier. Though adding a preprocessing step takes away some of the privacy budget from the actual classification process (thus potentially making it noisier and less accurate), we show that our novel preprocessing techniques significantly increase classifier accuracy on three real-world datasets. We also present the first private algorithms for empirically constructing receiver operating characteristic (ROC) curves on a private test set.

Aim: In this study, we aimed to evaluate the effectiveness of artificial intelligence for the histopathological diagnosis of prostatic adenocarcinoma by analyzing the digitized pathology slides. Materials and Methods: After the approval of the research project by the Ethics Committee of the University of Lahore - Islamabad Campus, a total of eight hundred and two (802) images were obtained from the anonymized slides stained with hematoxylin and eosin, which included 337 anonymized images of prostatic adenocarcinoma and 465 anonymized images of nodular hyperplasia of prostate. Eighty percent (80%) of the total digital images were used for training and 20% for testing. Three ResNet architectures ResNet-18, ResNet-34, and ResNet-50 were employed for the analysis of these images. Results: In the present study, the analysis of pathology images by convolutional neural network architecture ResNet-50 has revealed the diagnostic accuracy of 99.5 %, while the other convolutional neural network architectures ResNet-18 and ResNet-34 showed the diagnostic accuracy of 97.1% and 98 %, respectively. Discussion: The findings of the present study suggest that an intelligent vision system is possibly a worthwhile tool for the histopathological evaluation of prostatic tissue to differentiate between benign and malignant disorders. The application of deep learning for the histological diagnosis of malignant tumors could be quite a helpful tool for better patient care.

A method is being developed that provides for an artificial-intelligence system to learn a user's preferences for sets of objects and to thereafter automatically select subsets of objects according to those preferences. The method was originally intended to enable automated selection, from among large sets of images acquired by instruments aboard spacecraft, of image subsets considered to be scientifically valuable enough to justify use of limited communication resources for transmission to Earth. The method is also applicable to other sets of objects: examples of sets of objects considered in the development of the method include food menus, radio-station music playlists, and assortments of colored blocks for creating mosaics. The method does not require the user to perform the often-difficult task of quantitatively specifying preferences; instead, the user provides examples of preferred sets of objects. This method goes beyond related prior artificial-intelligence methods for learning which individual items are preferred by the user: this method supports a concept of setbased preferences, which include not only preferences for individual items but also preferences regarding types and degrees of diversity of items in a set. Consideration of diversity in this method involves recognition that members of a set may interact with each other in the sense that when considered together, they may be regarded as being complementary, redundant, or incompatible to various degrees. The effects of such interactions are loosely summarized in the term portfolio effect. The learning method relies on a preference representation language, denoted DD-PREF, to express set-based preferences. In DD-PREF, a preference is represented by a tuple that includes quality (depth) functions to estimate how desired a specific value is, weights for each feature preference, the desired diversity of feature values, and the relative importance of diversity versus depth. The system applies statistical concepts to estimate quantitative measures of the user s preferences from training examples (preferred subsets) specified by the user. Once preferences have been learned, the system uses those preferences to select preferred subsets from new sets. The method was found to be viable when tested in computational experiments on menus, music playlists, and rover images. Contemplated future development efforts include further tests on more diverse sets and development of a sub-method for (a) estimating the parameter that represents the relative importance of diversity versus depth, and (b) incorporating background knowledge about the nature of quality functions, which are special functions that specify depth preferences for features.

Preeclampsia and eclampsia are the most common obstetric disorders associated with poor maternal and neonatal outcome. The study’s primary objective is to assess the accuracy of novel high-risk factors core using machine learning algorithms in predicting preeclampsia. The study included 400 pregnant women and used 27 novel high-risk factors to predict preeclampsia. The target variables for predicting preeclampsia are systolic and diastolic blood pressures. Various algorithms, including decision tree (DT), random forest (RF), gradient boosting, support vector machine (SVM), K-neighbors, light gradient boosting machine (LGBM), multi-layer perceptron (MLP), Adaboost classifier, and extra trees classifier are used in the analysis. The accuracy and precision of the LGBM classifier (0.85 and 0.9583 with F1 0.7188), support vector classifier (0.8417 and 0.92 with F1 0.7077), DT (0.825 and 0.913 with F1 0.6667), and extra trees (0.8167 and 0.9091 with F1 0.6452) are found to be better algorithms for prediction of preeclampsia. According to the novel high-risk factors score, 17.5% of pregnant women were identified as being at high risk for preeclampsia during the first trimester, which increased to 18.7% in 3rd trimester; in addition, 16% of pregnant women had a blood pressure of 140/90 mmHg and the above. Novel, high-risk scores and machine learning algorithms can effectively predict preeclampsia at an early period.

Due to the large number of legal cases, the processing of them by the courts is generally very slow. Among these cases, we find accidents cases, which require a great speed of judgment to compensate the victims of those accidents. To this end, we thought of exploiting the possibilities offered by machine learning in order to simulate the work of judges and contribute to speeding up the time of decision. Further, we applied different machine learning algorithms, such as linear regression, decision trees, and random forests. According to the results achieved, the Random Forest is the most perfect model for with the utmost accuracy about 91.05%. 

Overall, life-time prevalence rates of mental health problems are around 30-50% in many countries (Andrade et al., 2000; Kessler et al., 2007), imposing a heavy burden on individuals, families, and communities, accompanied with high health costs related to screening, prevention, and treatment (GBD 2019 Mental Disorders Collaborators, 2022). Previous studies have built prediction models to be able to increase detection and prevention success, and to increase knowledge on possible risk factors of mental illness (Dwyer et al., 2018; Macalli et al., 2021; Tate et al., 2022; H. Yang et al., 2010). Mental health, however, includes both mental illness and well-being. That is, well-being is not simply the absence of mental illness (Keyes, 2002). To assure that our society remains resilient it is therefore also important to develop optimal risk prediction models for well-being, to be able to predict who will thrive and understand why this is the case (Oparina et al., 2022). This information can be valuable for well-being interventions. Previous research on mental health issues have provided us with possible risk factors, that are also relevant for well-being. First, mental health in adulthood has its developmental origins in childhood and adolescence, as indicated by associations with childhood psychopathology, making the availability of longitudinal data crucial (Lahey et al., 2014; Rutter et al., 2006). Second, mental health traits (e.g., depression, life satisfaction, positive affect) are partly driven by thousands of genetic variants with many small but relevant effects, many of which are shared across disorders (Baselmans, van de Weijer, et al., 2019; Kim et al., 2022; Meng et al., 2022; Thorp et al., 2021). Third, many environmental exposures are associated with mental health, examples including socio-economic status, childhood maltreatment, substance use, urbanicity and life events (Uher & Zwicker, 2017). Just as is seen for genetic effects, environmental effects for mental health and well-being overlap. Finally, environmental factors interact with genetic effects on mental health (Assary et al., 2018; Dunn et al., 2016; Uher & Zwicker, 2017). Together, a complex picture of mental health development emerges. Optimal prediction thus likely requires a broad inclusion of possible and protective risk factors, which may lead to the identification of the most relevant factors associated with mental health. This in turn could lead to individualized prediction models for individuals’ future mental health status (Bzdok et al., 2021). Given the multitude of factors associated with mental health, accurate prediction requires appropriate methods that can deal with high complexity. The rise of big data has led to the development of machine learning methods that enable the inclusion of large numbers of variables, while accounting for their potential interactions, consistent with the consensus that mental health results from complex interactions between developmental, social, psychological, genetic, and environmental factors. Recent developments in digitalization and record linkage have further made it increasingly possible to expand our environmental scope by including more objective environmental exposures (e.g., air pollution, green spaces) in mental health models (van de Weijer et al., 2021). Previous machine learning studies on responses to anti-depressants (Taliaz et al., 2021), rehospitalization after depressive episodes (Cearns et al., 2019), and resilience after cancer diagnoses (Kourou et al., 2021) have indeed shown that models including different data modalities outperform models including a single set of predictors. In line with the multi-factorial nature of well-being, a recent study further found that expanding the set of predictive features increased the performance of the models for well-being considerably (Oparina et al., 2022). Recent developments have thus paved the way for more accurate predictions for mental health related traits (Dwyer et al., 2018). However, many studies are conducted using clinical samples, i.e., when treatment is already sought, limiting their external validity and practical usefulness, especially for prevention. In addition, most studies focused on mental illness, rather than on mental health and well-being (Macalli et al., 2021; Tate et al., 2022). At the same time, machine learning prediction studies in population samples largely failed to take an integrative approach meaning that either cross-sectional data were used, or environmental exposures and/or genetic data were limited or missing (Dwyer et al., 2018; Macalli et al., 2021; Oparina et al., 2022; Tate et al., 2022; H. Yang et al., 2010). This may explain why predictive accuracies have not reached the standards needed for clinical use yet (Runeson et al., 2017). In the current project, we will overcome these caveats by building prediction models for well-being with extensive longitudinal data on environmental and psychosocial factors, and genetic data. More specifically, by using an extensive set of predictors and utilizing novel machine learning methods that enable the combined use of multiple prediction models (stacked ensemble model; see below), we aim to build a highly generalizable, comprehensive prediction model for well-being, which can inform future models for clinical prediction and decision-making, hereby preparing society for future mental health challenges.

The research performed under this grant investigated three primary areas. First, collective excitation and distributed winner-take-all dynamics were investigated in laterally connected networks to further characterize the properties of biological self-organization. Self-organization was further investigated as part of the k-means clustering algorithm, where the trade-off between learning of new exemplars and global efficiency was optimized. Finally, a cross-validation technique, referred to as Generalized Prediction Error (GPE), was investigated as a means of predicting generalization error after training.

The fundamental problem of multiple secondary users contending for opportunistic spectrum access over multiple channels in cognitive radio networks has been formulated recently as a decentralized multi-armed bandit (D-MAB) problem. In a D-MAB problem there are $M$ users and $N$ arms (channels) that each offer i.i.d. stochastic rewards with unknown means so long as they are accessed without collision. The goal is to design a decentralized online learning policy that incurs minimal regret, defined as the difference between the total expected rewards accumulated by a model-aware genie, and that obtained by all users applying the policy. We make two contributions in this paper. First, we consider the setting where the users have a prioritized ranking, such that it is desired for the $K$-th-ranked user to learn to access the arm offering the $K$-th highest mean reward. For this problem, we present the first distributed policy that yields regret that is uniformly logarithmic over time without requiring any prior assumption about the mean rewards. Second, we consider the case when a fair access policy is required, i.e., it is desired for all users to experience the same mean reward. For this problem, we present a distributed policy that yields order-optimal regret scaling with respect to the number of users and arms, better than previously proposed policies in the literature. Both of our distributed policies make use of an innovative modification of the well known UCB1 policy for the classic multi-armed bandit problem that allows a single user to learn how to play the arm that yields the $K$-th largest mean reward.

The fundamental problem of multiple secondary users contending for opportunistic spectrum access over multiple channels in cognitive radio networks has been formulated recently as a decentralized multi-armed bandit (D-MAB) problem. In a D-MAB problem there are $M$ users and $N$ arms (channels) that each offer i.i.d. stochastic rewards with unknown means so long as they are accessed without collision. The goal is to design a decentralized online learning policy that incurs minimal regret, defined as the difference between the total expected rewards accumulated by a model-aware genie, and that obtained by all users applying the policy. We make two contributions in this paper. First, we consider the setting where the users have a prioritized ranking, such that it is desired for the $K$-th-ranked user to learn to access the arm offering the $K$-th highest mean reward. For this problem, we present the first distributed policy that yields regret that is uniformly logarithmic over time without requiring any prior assumption about the mean rewards. Second, we consider the case when a fair access policy is required, i.e., it is desired for all users to experience the same mean reward. For this problem, we present a distributed policy that yields order-optimal regret scaling with respect to the number of users and arms, better than previously proposed policies in the literature. Both of our distributed policies make use of an innovative modification of the well known UCB1 policy for the classic multi-armed bandit problem that allows a single user to learn how to play the arm that yields the $K$-th largest mean reward.

Using quantum algorithms to improve deep learning models' capabilities is becoming increasingly popular as quantum computing develops. In this work, we investigate how quantum algorithms using quantum neural networks (QNNs) might enhance the effectiveness and performance of deep learning models. We examine the effects of quantum-inspired methods on tasks, including regression, sorting, and optimization, by thoroughly analyzing quantum algorithms and how they integrate with deep learning systems. We experiment with Estimator QNN and Sampler QNN implementations using Qiskit machine-learning, analyzing their forward and backward pass outcomes to assess the effectiveness of quantum algorithms in improving deep learning models. Our research clarifies the scope, intricacy, and scalability issues surrounding QNNs and offers insights into the possible advantages and difficulties of quantum-enhanced deep learning. This work adds to the continuing investigation of quantum computing's potential to advance machine learning and artificial intelligence paradigms by clarifying the interaction between quantum algorithms and deep learning systems.

Objective: Classifying the risk of policyholders based on observable characteristics can help insurance companies to reduce losses, identify customers more accurately, and prevent adverse selection in the insurance market. The purpose of this article is to examine the financial losses caused by third party insurance and to predict the risk of policyholders in the event of an accident. Methodology: using decision tree algorithms, support vector machine, Naive Bayes and neural network; The hidden data patterns have been discovered in order to classify third party insurance policyholders. Also, the unbalanced distribution of data in two groups of damaged and undamaged causes an important challenge in the application of machine learning and data mining methods, which is considered in this article. Findings: The data set belongs to one of the insurance companies and contains more than four hundred thousand samples registered in five years and includes four independent variables of car type, car group, license plate type and car age and a dependent and two-valued variable of financial damage. According to the obtained results, the best performance and prediction accuracy (with accuracy F1=0.72±0.01) is related to the decision tree model. Conclusion: The impact of variables on the occurrence of damage in order of priority are: car type, license plate type, car age and car group. The evaluation results show that more data related to the driver's characteristics is needed for more accurate prediction of damage and high-risk customers.

We provide a unified framework using which we design scalable dynamic adaptive video streaming algorithms based on index based policies (dubbed DAS-IP) to maximize the Quality of Experience (QoE) provided to clients using video streaming services. Due to the distributed nature of our algorithm, it is easily implementable. We begin by considering the simplest set-up of a one-hop wireless network in which an Access Point (AP) transmits video packets to multiple clients over a shared unreliable channel. The video file meant for each client has been fragmented into several packets, and the server maintains multiple copies (each of different quality) of the same video file. Clients maintain individual packet buffers in order to mitigate the effect of uncertainty on video iterruption. Streaming experience, or the Quality of Experience (QoE) of a client depends on several factors: i) starvation/outage probability, i.e., average time duration for which the client does not play video because the buffer is empty, ii) average video quality, iii) average number of starvation periods, iv) temporal variations in video quality etc. We pose the problem of making dynamic streaming decisions in order to maximize the total QoE as a Constrained Markov Decision Process (CMDP). A consideration of the associated dual MDP suggests us that the problem is vastly simplified if the AP is allowed to charge a price per unit bandwidth usage from the clients. More concretely, a "client-by-client" QoE optimization leads to the networkwide QoE maximization, and thus provides us a decentralized streaming algorithm. This enables the clients to themselves decide the optimal streaming choices in each time-slot, and yields us a much desired client-level adaptation algorithm. The optimal policy has an appealing simple threshold structure.

The question why deep learning algorithms perform so well in practice has attracted increasing research interest. However, most of well-established approaches, such as hypothesis capacity, robustness or sparseness, have not provided complete explanations, due to the high complexity of the deep learning algorithms and their inherent randomness. In this work, we introduce a new approach~\textendash~ensemble robustness~\textendash~towards characterizing the generalization performance of generic deep learning algorithms. Ensemble robustness concerns robustness of the \emph{population} of the hypotheses that may be output by a learning algorithm. Through the lens of ensemble robustness, we reveal that a stochastic learning algorithm can generalize well as long as its sensitiveness to adversarial perturbation is bounded in average, or equivalently, the performance variance of the algorithm is small. Quantifying ensemble robustness of various deep learning algorithms may be difficult analytically. However, extensive simulations for seven common deep learning algorithms for different network architectures provide supporting evidence for our claims. Furthermore, our work explains the good performance of several published deep learning algorithms.

The question why deep learning algorithms perform so well in practice has attracted increasing research interest. However, most of well-established approaches, such as hypothesis capacity, robustness or sparseness, have not provided complete explanations, due to the high complexity of the deep learning algorithms and their inherent randomness. In this work, we introduce a new approach~\textendash~ensemble robustness~\textendash~towards characterizing the generalization performance of generic deep learning algorithms. Ensemble robustness concerns robustness of the \emph{population} of the hypotheses that may be output by a learning algorithm. Through the lens of ensemble robustness, we reveal that a stochastic learning algorithm can generalize well as long as its sensitiveness to adversarial perturbation is bounded in average, or equivalently, the performance variance of the algorithm is small. Quantifying ensemble robustness of various deep learning algorithms may be difficult analytically. However, extensive simulations for seven common deep learning algorithms for different network architectures provide supporting evidence for our claims. Furthermore, our work explains the good performance of several published deep learning algorithms.

The question why deep learning algorithms perform so well in practice has attracted increasing research interest. However, most of well-established approaches, such as hypothesis capacity, robustness or sparseness, have not provided complete explanations, due to the high complexity of the deep learning algorithms and their inherent randomness. In this work, we introduce a new approach~\textendash~ensemble robustness~\textendash~towards characterizing the generalization performance of generic deep learning algorithms. Ensemble robustness concerns robustness of the \emph{population} of the hypotheses that may be output by a learning algorithm. Through the lens of ensemble robustness, we reveal that a stochastic learning algorithm can generalize well as long as its sensitiveness to adversarial perturbation is bounded in average, or equivalently, the performance variance of the algorithm is small. Quantifying ensemble robustness of various deep learning algorithms may be difficult analytically. However, extensive simulations for seven common deep learning algorithms for different network architectures provide supporting evidence for our claims. Furthermore, our work explains the good performance of several published deep learning algorithms.

In this report we describe a tool for comparing the performance of causal structure learning algorithms implemented in the TETRAD freeware suite of causal analysis methods. Currently the tool is available as a package in the TETRAD source code (written in Java), which can be loaded up in an Integrated Development Environment (IDE) such as IntelliJ IDEA. Simulations can be done varying the number of runs, sample sizes, and data modalities. Performance on this simulated data can then be compared for a number of algorithms, with parameters varied and with performance statistics as selected, producing a publishable report. The order of the algorithms in the output can be adjusted to the user's preference using a utility function over the statistics. Data sets from simulation can be saved along with their graphs to a file and loaded back in for further analysis, or used for analysis by other tools.

A recent goal in the Reinforcement Learning (RL) framework is to choose a sequence of actions or a policy to maximize the reward collected or minimize the regret incurred in a finite time horizon. For several RL problems in operation research and optimal control, the optimal policy of the underlying Markov Decision Process (MDP) is characterized by a known structure. The current state of the art algorithms do not utilize this known structure of the optimal policy while minimizing regret. In this work, we develop new RL algorithms that exploit the structure of the optimal policy to minimize regret. Numerical experiments on MDPs with structured optimal policies show that our algorithms have better performance, are easy to implement, have a smaller run-time and require less number of random number generations.

Majority of Indian framers depend on rainfall for agriculture. Thus, in an agricultural country like India, rainfall prediction becomes very important. Rainfall causes natural disasters like flood and drought, which are encountered by people across the globe every year. Rainfall prediction over drought regions has a great importance for countries like India whose economy is largely dependent on agriculture. A sufficient data length can play an important role in a proper estimation drought, leading to a better appraisal for drought risk reduction. Due to dynamic nature of atmosphere statistical techniques fail to provide good accuracy for rainfall prediction. So, we are going to use Machine Learning algorithms like Multiple Linear Regression, Random Forest Regressor and AdaBoost Regressor, where different models are going to be trained using training data set and tested using testing data set. The dataset which we have collected has the rainfall data from 1901 2015, where across the various drought affected states. Nonlinearity of rainfall data makes Machine Learning algorithms a better technique. Comparison of different approaches and algorithms will increase an accuracy rate of predicting rainfall over drought regions. We are going to use Python to code for algorithms. Intention of this project is to say, which algorithm can be used to predict rainfall, in order to increase the countries socioeconomic status.  by Mylapalle Yeshwanth | Palla Ratna Sai Kumar | Dr. G. Mathivanan M.E., Ph.D "Comparative Study of Machine Learning Algorithms for Rainfall Prediction"  Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019,  URL: https://www.ijtsrd.com/papers/ijtsrd22961.pdf Paper URL: https://www.ijtsrd.com/computer-science/data-miining/22961/comparative-study-of-machine-learning-algorithms-for-rainfall-prediction/mylapalle-yeshwanth

We consider learning problems over training sets in which both, the number of training examples and the dimension of the feature vectors, are large. To solve these problems we propose the random parallel stochastic algorithm (RAPSA). We call the algorithm random parallel because it utilizes multiple parallel processors to operate on a randomly chosen subset of blocks of the feature vector. We call the algorithm stochastic because processors choose training subsets uniformly at random. Algorithms that are parallel in either of these dimensions exist, but RAPSA is the first attempt at a methodology that is parallel in both the selection of blocks and the selection of elements of the training set. In RAPSA, processors utilize the randomly chosen functions to compute the stochastic gradient component associated with a randomly chosen block. The technical contribution of this paper is to show that this minimally coordinated algorithm converges to the optimal classifier when the training objective is convex. Moreover, we present an accelerated version of RAPSA (ARAPSA) that incorporates the objective function curvature information by premultiplying the descent direction by a Hessian approximation matrix. We further extend the results for asynchronous settings and show that if the processors perform their updates without any coordination the algorithms are still convergent to the optimal argument. RAPSA and its extensions are then numerically evaluated on a linear estimation problem and a binary image classification task using the MNIST handwritten digit dataset.

BACKGROUND AND OBJECTIVES: One of the criteria for deciding to invest in a listed company is the amount or changes in the stock price of the company in the future days and months. Various methods have been studied to predict the stock price and investment risk in a company. In most of these methods, the stock price is predicted as a continuous response variable. For this purpose, time series models are used in which assumptions such as the normality of disturbances or the linearity of the model are important. The purpose of this research is to introduce a two-category response variable based on the direction of share price movement in the next day and to introduce some statistical classification methods to predict it. These models do not have the limitations of the previous models, and for that reason they are of interest. The main objective of this article is to implement the studied methods and compare their accuracy in predicting the orientation of stock price movement of stock exchange insurance companies. Methodology: In the current research, we have predicted the direction of stock price movement by using K-nearest neighbors, decision tree and random forest algorithms, which are among the non-parametric classification methods of statistical learning. The data used in this research includes information on the stock price of one of the insurance companies during the years 2019 to 2020, which has a suitable and high share in the portfolio of the insurance industry. To determine the accuracy of the studied models, the data were randomly divided into two groups, training and testing. Then, the statistical learning models were implemented on training data and their validity was measured using experimental data. FINDINGS: The research results indicate the high accuracy of all three non-parametric models in predicting the stock price category of the insurance company. Likewise, among the studied models, the K-nearest neighbors algorithm performed better than other algorithms in predicting the direction of stock price movement. CONCLUSION: Considering the importance of the risk of investing in an insurance company for customers, attainment to a valid model for stock price classification and specifying the variables that increase or decrease the price can help customers and insurance companies make better decisions.

In machine learning, the choice of a learning algorithm that is suitable for the application domain is critical. The performance metric used to compare different algorithms must also reflect the concerns of users in the application domain under consideration. In this work, we propose a novel probability-based performance metric called Relevance Score for evaluating supervised learning algorithms. We evaluate the proposed metric through empirical analysis on a dataset gathered from an intelligent lighting pilot installation. In comparison to the commonly used Classification Accuracy metric, the Relevance Score proves to be more appropriate for a certain class of applications.

A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning.The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.

We consider the problem of finding stationary Nash equilibria (NE) in a finite discounted general-sum stochastic game. We first generalize a non-linear optimization problem from Filar and Vrieze [2004] to a $N$-player setting and break down this problem into simpler sub-problems that ensure there is no Bellman error for a given state and an agent. We then provide a characterization of solution points of these sub-problems that correspond to Nash equilibria of the underlying game and for this purpose, we derive a set of necessary and sufficient SG-SP (Stochastic Game - Sub-Problem) conditions. Using these conditions, we develop two actor-critic algorithms: OFF-SGSP (model-based) and ON-SGSP (model-free). Both algorithms use a critic that estimates the value function for a fixed policy and an actor that performs descent in the policy space using a descent direction that avoids local minima. We establish that both algorithms converge, in self-play, to the equilibria of a certain ordinary differential equation (ODE), whose stable limit points coincide with stationary NE of the underlying general-sum stochastic game. On a single state non-generic game (see Hart and Mas-Colell [2005]) as well as on a synthetic two-player game setup with $810,000$ states, we establish that ON-SGSP consistently outperforms NashQ ([Hu and Wellman, 2003] and FFQ [Littman, 2001] algorithms.

Learning an algorithm--a systematic sequence of operations for solving a problem with given input--is often difficult for students due to the abstract nature of the algorithms and the data they process. To help students understand the behavior of algorithms, a subfield in computing education research has focused on algorithm visualization--learning material showing the steps and data used by an algorithm. As the use of mobile devices has risen together with the capabilities of the devices, mobile learning is more important than ever. It also opens possibilities to contextualize the learning experience. In this paper, we present our work towards location-aware mobile learning of spatial algorithms that adapts the learning material to the location of the student. [For the full proceedings see ED562140.]

We study quantum learning algorithms for quantum measurements. The optimal learning algorithm is derived for arbitrary von Neumann measurements in the case of training with one or two examples. The analysis of the case of three examples reveals that, differently from the learning of unitary gates, the optimal algorithm for learning of quantum measurements cannot be parallelized, and requires quantum memories for the storage of information.

This 60th episode of Learning Machines 101 discusses how one can use novelty detection or anomaly detection machine learning algorithms to monitor the performance of other machine learning algorithms deployed in real world environments. The episode is based upon a review of a talk by Chief Data Scientist Ira Cohen of Anodot presented at the 2016 Berlin Buzzwords Data Science Conference. Check out: www.learningmachines101.com to hear the podcast or read a transcription of the podcast!

This study discusses the prediction model of Entrepreneurial Exit from Entrepreneurial Perceptions, acquired the data from the Global Entrepreneurship Monitor's (GEM) database in 2008-2019. Some essential indicators include Opportunity Perception, Fear of Failure, Capability Perception, Role Model, and Entrepreneurial Intention. Data mining results show that the exit reasons and entrepreneurial intention have a more significant impact on entrepreneurial exit than other variables. This research applies the Random Forest Algorithm to get a prediction model that shows the entrepreneurial exit. According to the Random Forest Algorithm results, accuracy, ROC-AUC score, AUC curve, precision, recall, and F1 score validate the classification method. The prediction model shows that the best accuracy predictor of entrepreneurial exit is 99 percent, and another criteria ROC_AUC score 96%. Consistent results demonstrate that the proposed method can consider a promisingly successful predictive model of entrepreneurial exit with excellent predictive performance. These results can predict the individuals' entrepreneurial exit possibility before the psychological and financial impact and loss of capital and failure.

Although the advantage of computational pathology is vast it does have challenges. Thereby, aim to evaluate the deep learning methods employed for digitized WSI histopathology image classification in cancers. Also, to elicit the challenges while employing DL and provide solutions specific to WSI digitalized pathology image analysis in cancers.