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This paper presents a new method of smart waste city management which makes the environment of the city clean with a low cost. In this approach, the sensor model detects, measures, and transmits waste volume data over the Internet. The collected data including trash bin's geolocation and the serial number is processed by using regression, classification and graph theory. Thenceforth a new method is...
In recent years, the importance of feature engineering has been confirmed by the exceptional performance of deep learning techniques, that automate this task for some applications. For others, feature engineering requires substantial manual effort in designing and selecting features and is often tedious and non-scalable. We present AutoLearn, a regression-based feature learning algorithm. Being data-driven,...
Lack of safety and efficacy are the two major reasons for the failures of drug candidates in drug discovery and development. Reliable prediction of blood-brain barrier permeation even before chemical synthesis still remains as one of the major challenges in drug discovery. New approaches and models that are reliable and can reduce experimental evaluations of pre-clinical candidates are in urgent need...
Hospital readmissions within 30 days after discharge are costly and it has been a prior for researchers to identify patients at risk of early readmission. Most of the reported hospital readmission prediction models have been built with historical data and thus can outdate over time. In this work, a self-adaptive 30-day diabetic hospital readmission prediction model has been developed. A diabetic inpatient...
Deep learning algorithms have recently produced state-of-the-art accuracy in many classification tasks, but this success is typically dependent on access to many annotated training examples. For domains without such data, an attractive alternative is to train models with light, or distant supervision. In this paper, we introduce a deep neural network for the Learning from Label Proportion (LLP) setting,...
Slowing cystic fibrosis (CF) lung disease progression is crucial to survival, but point-of-care technologies aimed at early detection — and possibly prevention — of rapid lung function decline are limited. This proof-of-principle study leverages a rich national patient registry and follow-up data on a local CF cohort to build an algorithm and prototype prognostic tool aimed at early detection of rapid...
Owning property is one of the most important investments that a person can make in their lifetime. Therefore, being able to accurately know the real-time value of any property is crucial for making wise sales and purchases. Since the online real estate database company Zillow first developed a machine learning system to predict property sale prices in real time, it has continually worked to improve...
We develop Bayesian algorithms to perform realtime positioning and uncertainty quantification on Global Positioning System (GPS) data. We test the algorithms on GPS data from several global locations and score their predictions using the log-score. The best algorithm is a Kalman filter that assumes an Ornstein-Uhlenbeck (OU) noise model. The OU model accounts for the observed autocorrelated process-noise...
This paper presents the modeling, simulation of a double-stage evaporation process to obtain Bioethanol from sugarcane juice; the model was validated with data from a plant located in Peru. So is the calculation of a Nonlinear Control and optimization following the Extended Prediction Self-Adaptive Control approach, because it has less computational load. The results achieved the control of juice...
The ever-growing population of smartphone» connected to mobile networks is changing the cellular traffic ecosystem. The traffic volumes and patterns generated by smartphone apps pose complex challenges to cellular network operators, particularly in terms of detection and diagnosis of network anomalies caused by specific apps. The high-dimensionality of network data provided by current network monitoring...
The latest video compression standards, such as the H.264/AVC and the High Efficiency Video Coding (HEVC), provide fast Motion Estimation (ME) algorithms in their reference software aiming at complexity reduction. Test Zone Search (TZS) is the state-of-the-art fast ME algorithm, currently deployed in the reference HEVC encoder due to its great coding efficiency. However, ME is still one of the main...
Accurately predicting driving service orders in different regions is an essential task for service companies, in order to improve the service quality. In this paper, a specific ensemble multi-view prediction framework is proposed to address this task. It ensembles several different multi-view-based models with a weighted linear combination. Specifically, we have designed three specific multi-view-based...
Selecting relevant features in data modeling is critical to ensure effective and accurate prediction of future effects. The problem becomes compounded when the relevance of previously selected features cannot be guaranteed due to changes in the underlying dataset. We propose an algorithm based on the statistical plaid model for the discovery and tracking of feature relevance scores in datasets that...
In wireless sensor networks, sensor nodes are often deployed intensively within the sensing area in order to achieve effective monitoring, resulting in a high degree of correlation between them. There is a certain variation rule between node acquisition data and time. The current time correlation will result in an abundance of redundant data within the sensing area, so eliminating data redundancy...
This document describes the problem presented at AAIA'17 Data Mining Challenge and my approach to solving it. In terms of reinforcement learning the task was to build an algorithm that predicts a value function for the game of Hearthstone: Heroes of Warcraft. I used an ensemble of 85 models trained on different features to build the final solution which scored the 36th place on the final leaderboard.
The following article is created as a result of the AAIA'17 Data Mining Challenge: Helping AI to Play Hearthstone. The Challenge goal was to correctly predict which bot would win a bot-vs-bot Hearthstone match based on what was known at the given time. Hearthstone is an online two-players card game with imperfect information (unlike chess and go, and like poker), where the goal of one player is to...
In practice, the data owners of source projects may need to share data without disclosing sensitive information. Therefore, privacy-preserving data-sharing becomes an important topic in cross-company defect prediction (CCDP). In this context, the challenge is how to achieve a high privacy-preserving level while ensuring the utility of the shared privatized data for CCDP. CLIFF&MORPH is a recently...
Recent meta-learning approaches are oriented towards algorithm selection, optimization or recommendation of existing algorithms. In this paper we show how Inductive algorithms constructed from building blocks on small data sub-sample can be scaled up to model large data sets. We demonstrate how one particular template (simple ensemble of fast sigmoidal regression models) outperforms state-of-the-art...
Election forecasting has attracted many research efforts. Election forecasting helps the government to optimize the rules of election and candidates to adjust their campaign strategy according to forecasting results, and it contributes to election campaigns. Based on the poll model, this paper proposes poll model improved by delegating and weighting operations applicable to the election of Hongkong...
Sometimes parents don't have resources or time to attend to their young ones as they have certain predispositions. This document demonstrates the process of construction of a web-service/module, defines the algorithm, procedure of construction of the algorithm and the analysis/results of the procedures performed. The market for this system is the working class nuclear families or single parents that...
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