Crime Prediction with Historical Crime and Movement Data of Potential Offenders Using a Spatio-Temporal Cokriging Method

Crime prediction using machine learning and data fusion assimilation has become a hot topic. Most of the models rely on historical crime data and related environment variables. The activity of potential offenders affects the crime patterns, but the data with fine resolution have not been applied in the crime prediction. The goal of this study is to test the effect of the activity of potential offenders in the crime prediction by combining this data in the prediction models and assessing the prediction accuracies. This study uses the movement data of past offenders collected in routine police stop-and-question operations to infer the movement of future offenders. The offender movement data compensates historical crime data in a Spatio-Temporal Cokriging (ST-Cokriging) model for crime prediction. The models are implemented for weekly, biweekly, and quad-weekly prediction in the XT police district of ZG city, China. Results with the incorporation of the offender movement data are consistently better than those without it. The improvement is most pronounced for the weekly model, followed by the biweekly model, and the quad-weekly model. In sum, the addition of offender movement data enhances crime prediction, especially for short periods.

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Spatio-Temporal Data Fusion for Satellite Images Using Hopfield Neural Network

Remote Sensing ◽

10.3390/rs11182077 ◽

2019 ◽

Vol 11 (18) ◽

pp. 2077 ◽

Cited By ~ 8

Author(s):

Fung ◽

Wong ◽

Chan

Keyword(s):

Neural Network ◽

Data Fusion ◽

Satellite Images ◽

Hopfield Neural Network ◽

Temporal Data ◽

Fusion Model ◽

Resolution Image ◽

Coarse Resolution ◽

Spatio Temporal ◽

Fine Resolution

Spatio-temporal data fusion refers to the technique of combining high temporal resolution from coarse satellite images and high spatial resolution from fine satellite images. However, data availability remains a major limitation in algorithm development. Existing spatio-temporal data fusion algorithms require at least one known image pair between the fine and coarse resolution image. However, data which come from two different satellite platforms do not necessarily have an overlap in their overpass times, hence restricting the application of spatio-temporal data fusion. In this paper, a new algorithm named Hopfield Neural Network SPatio-tempOral daTa fusion model (HNN-SPOT) is developed by utilizing the optimization concept in the Hopfield neural network (HNN) for spatio-temporal image fusion. The algorithm derives a synthesized fine resolution image from a coarse spatial resolution satellite image (similar to downscaling), with the use of one fine resolution image taken on an arbitrary date and one coarse image taken on a predicted date. The HNN-SPOT particularly addresses the problem when the fine resolution and coarse resolution images are acquired from different satellite overpass times over the same geographic extent. Both simulated datasets and real datasets over Hong Kong and Australia have been used in the evaluation of HNN-SPOT. Results showed that HNN-SPOT was comparable with an existing fusion algorithm, the spatial and temporal adaptive reflectance fusion model (STARFM). HNN-SPOT assumes consistent spatial structure for the target area between the date of data acquisition and the prediction date. Therefore, it is more applicable to geographical areas with little or no land cover change. It is shown that HNN-SPOT can produce accurate fusion results with >90% of correlation coefficient over consistent land covers. For areas that have undergone land cover changes, HNN-SPOT can still produce a prediction about the outlines and the tone of the features, if they are large enough to be recorded in the coarse resolution image at the prediction date. HNN-SPOT provides a relatively new approach in spatio-temporal data fusion, and further improvements can be made by modifying or adding new goals and constraints in its HNN architecture. Owing to its lower demand for data prerequisites, HNN-SPOT is expected to increase the applicability of fine-scale applications in remote sensing, such as environmental modeling and monitoring.

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Machine Learning Prediction Model for Acute Renal Failure After Acute Aortic Syndrome Surgery

Frontiers in Medicine ◽

10.3389/fmed.2021.728521 ◽

2022 ◽

Vol 8 ◽

Author(s):

Jinzhang Li ◽

Ming Gong ◽

Yashutosh Joshi ◽

Lizhong Sun ◽

Lianjun Huang ◽

...

Keyword(s):

Machine Learning ◽

Renal Failure ◽

Prediction Model ◽

Prediction Models ◽

External Validation ◽

Scoring Systems ◽

Acute Aortic Syndrome ◽

Internal Validation ◽

Medical Centers ◽

Better Than

BackgroundAcute renal failure (ARF) is the most common major complication following cardiac surgery for acute aortic syndrome (AAS) and worsens the postoperative prognosis. Our aim was to establish a machine learning prediction model for ARF occurrence in AAS patients.MethodsWe included AAS patient data from nine medical centers (n = 1,637) and analyzed the incidence of ARF and the risk factors for postoperative ARF. We used data from six medical centers to compare the performance of four machine learning models and performed internal validation to identify AAS patients who developed postoperative ARF. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to compare the performance of the predictive models. We compared the performance of the optimal machine learning prediction model with that of traditional prediction models. Data from three medical centers were used for external validation.ResultsThe eXtreme Gradient Boosting (XGBoost) algorithm performed best in the internal validation process (AUC = 0.82), which was better than both the logistic regression (LR) prediction model (AUC = 0.77, p < 0.001) and the traditional scoring systems. Upon external validation, the XGBoost prediction model (AUC =0.81) also performed better than both the LR prediction model (AUC = 0.75, p = 0.03) and the traditional scoring systems. We created an online application based on the XGBoost prediction model.ConclusionsWe have developed a machine learning model that has better predictive performance than traditional LR prediction models as well as other existing risk scoring systems for postoperative ARF. This model can be utilized to provide early warnings when high-risk patients are found, enabling clinicians to take prompt measures.

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Is Artificial Intelligence Better Than Human Clinicians in Predicting Patient Outcomes? (Preprint)

10.2196/preprints.19918 ◽

2020 ◽

Author(s):

Joon Lee

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Patient Outcome ◽

Deep Learning ◽

Patient Outcomes ◽

Outcome Prediction ◽

Prediction Models ◽

Challenging Problem ◽

Imaging Diagnostics ◽

Better Than

UNSTRUCTURED In contrast with medical imaging diagnostics powered by artificial intelligence (AI), in which deep learning has led to breakthroughs in recent years, patient outcome prediction poses an inherently challenging problem because it focuses on events that have not yet occurred. Interestingly, the performance of machine learning–based patient outcome prediction models has rarely been compared with that of human clinicians in the literature. Human intuition and insight may be sources of underused predictive information that AI will not be able to identify in electronic data. Both human and AI predictions should be investigated together with the aim of achieving a human-AI symbiosis that synergistically and complementarily combines AI with the predictive abilities of clinicians.

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An Ensemble Prediction Model for Potential Student Recommendation Using Machine Learning

Symmetry ◽

10.3390/sym12050728 ◽

2020 ◽

Vol 12 (5) ◽

pp. 728 ◽

Cited By ~ 2

Author(s):

Lijuan Yan ◽

Yanshen Liu

Keyword(s):

Machine Learning ◽

Student Performance ◽

Prediction Models ◽

Characteristic Curve ◽

Machine Learning Algorithms ◽

Ensemble Prediction ◽

Support Vector ◽

Proposed Model ◽

Importance Analysis ◽

Better Than

Student performance prediction has become a hot research topic. Most of the existing prediction models are built by a machine learning method. They are interested in prediction accuracy but pay less attention to interpretability. We propose a stacking ensemble model to predict and analyze student performance in academic competition. In this model, student performance is classified into two symmetrical categorical classes. To improve accuracy, three machine learning algorithms, including support vector machine (SVM), random forest, and AdaBoost are established in the first level and then integrated by logistic regression via stacking. A feature importance analysis was applied to identify important variables. The experimental data were collected from four academic years in Hankou University. According to comparative studies on five evaluation metrics (precision, recall, F1, error, and area under the receiver operating characteristic curve ( AUC ) in this analysis, the proposed model generally performs better than compared models. The important variables identified from the analysis are interpretable, they can be used as guidance to select potential students.

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Mobility Prediction-Based Optimisation and Encryption of Passenger Traffic-Flows Using Machine Learning

Sensors ◽

10.3390/s20092629 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2629

Author(s):

Syed Muhammad Asad ◽

Jawad Ahmad ◽

Sajjad Hussain ◽

Ahmed Zoha ◽

Qammer Hussain Abbasi ◽

...

Keyword(s):

Machine Learning ◽

Real Time ◽

Radio Frequency Identification ◽

City Planning ◽

Prediction Models ◽

Mobility Prediction ◽

Traffic Flows ◽

Passenger Traffic ◽

Passenger Flow ◽

Spatio Temporal

Information and Communication Technology (ICT) enabled optimisation of train’s passenger traffic flows is a key consideration of transportation under Smart City planning (SCP). Traditional mobility prediction based optimisation and encryption approaches are reactive in nature; however, Artificial Intelligence (AI) driven proactive solutions are required for near real-time optimisation. Leveraging the historical passenger data recorded via Radio Frequency Identification (RFID) sensors installed at the train stations, mobility prediction models can be developed to support and improve the railway operational performance vis-a-vis 5G and beyond. In this paper we have analysed the passenger traffic flows based on an Access, Egress and Interchange (AEI) framework to support train infrastructure against congestion, accidents, overloading carriages and maintenance. This paper predominantly focuses on developing passenger flow predictions using Machine Learning (ML) along with a novel encryption model that is capable of handling the heavy passenger traffic flow in real-time. We have compared and reported the performance of various ML driven flow prediction models using real-world passenger flow data obtained from London Underground and Overground (LUO). Extensive spatio-temporal simulations leveraging realistic mobility prediction models show that an AEI framework can achieve 91.17% prediction accuracy along with secure and light-weight encryption capabilities. Security parameters such as correlation coefficient (<0.01), entropy (>7.70), number of pixel change rate (>99%), unified average change intensity (>33), contrast (>10), homogeneity (<0.3) and energy (<0.01) prove the efficacy of the proposed encryption scheme.

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A spatio-temporal method for crime prediction using historical crime data and transitional zones identified from nightlight imagery

International Journal of Geographical Information Science ◽

10.1080/13658816.2020.1737701 ◽

2020 ◽

Vol 34 (9) ◽

pp. 1740-1764 ◽

Cited By ~ 2

Author(s):

Bo Yang ◽

Lin Liu ◽

Minxuan Lan ◽

Zengli Wang ◽

Hanlin Zhou ◽

...

Keyword(s):

Crime Data ◽

Crime Prediction ◽

Spatio Temporal ◽

Temporal Method

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Is Artificial Intelligence Better Than Human Clinicians in Predicting Patient Outcomes?

Journal of Medical Internet Research ◽

10.2196/19918 ◽

2020 ◽

Vol 22 (8) ◽

pp. e19918

Author(s):

Joon Lee

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Patient Outcome ◽

Deep Learning ◽

Patient Outcomes ◽

Outcome Prediction ◽

Prediction Models ◽

Challenging Problem ◽

Imaging Diagnostics ◽

Better Than

In contrast with medical imaging diagnostics powered by artificial intelligence (AI), in which deep learning has led to breakthroughs in recent years, patient outcome prediction poses an inherently challenging problem because it focuses on events that have not yet occurred. Interestingly, the performance of machine learning–based patient outcome prediction models has rarely been compared with that of human clinicians in the literature. Human intuition and insight may be sources of underused predictive information that AI will not be able to identify in electronic data. Both human and AI predictions should be investigated together with the aim of achieving a human-AI symbiosis that synergistically and complementarily combines AI with the predictive abilities of clinicians.

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Spatio-temporal data fusion for fine-resolution subsidence estimation

Environmental Modelling & Software ◽

10.1016/j.envsoft.2021.104975 ◽

2021 ◽

Vol 137 ◽

pp. 104975

Author(s):

Hone-Jay Chu ◽

Muhammad Zeeshan Ali ◽

Thomas J. Burbey

Keyword(s):

Data Fusion ◽

Temporal Data ◽

Spatio Temporal ◽

Fine Resolution

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Spatiotemporal Prediction of Theft Risk with Deep Inception-Residual Networks

Smart Cities ◽

10.3390/smartcities4010013 ◽

2021 ◽

Vol 4 (1) ◽

pp. 204-216

Author(s):

Xinyue Ye ◽

Lian Duan ◽

Qiong Peng

Keyword(s):

New York ◽

New York City ◽

Emergency Service ◽

Prediction Models ◽

Smart Cities ◽

Low Level ◽

Fine Grained ◽

Crime Prediction ◽

High Level ◽

Better Than

Spatiotemporal prediction of crime is crucial for public safety and smart cities operation. As crime incidents are distributed sparsely across space and time, existing deep-learning methods constrained by coarse spatial scale offer only limited values in prediction of crime density. This paper proposes the use of deep inception-residual networks (DIRNet) to conduct fine-grained, theft-related crime prediction based on non-emergency service request data (311 events). Specifically, it outlines the employment of inception units comprising asymmetrical convolution layers to draw low-level spatiotemporal dependencies hidden in crime events and complaint records in the 311 dataset. Afterward, this paper details how residual units can be applied to capture high-level spatiotemporal features from low-level spatiotemporal dependencies for the final prediction. The effectiveness of the proposed DIRNet is evaluated based on theft-related crime data and 311 data in New York City from 2010 to 2015. The results confirm that the DIRNet obtains an average F1 of 71%, which is better than other prediction models.

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Evolutionary Learning-Derived Clinical-Radiomic Models for Predicting Early Recurrence of Hepatocellular Carcinoma after Resection

Liver Cancer ◽

10.1159/000518728 ◽

2021 ◽

pp. 1-11

Author(s):

I-Cheng Lee ◽

Jo-Yu Huang ◽

Ting-Chun Chen ◽

Chia-Heng Yen ◽

Nai-Chi Chiu ◽

...

Keyword(s):

Machine Learning ◽

Hepatocellular Carcinoma ◽

Surgical Resection ◽

Clinical Features ◽

Prediction Models ◽

Early Recurrence ◽

Evolutionary Learning ◽

Training Set ◽

Test Set ◽

Better Than

Background and Aims: Current prediction models for early recurrence of hepatocellular carcinoma (HCC) after surgical resection remain unsatisfactory. The aim of this study was to develop evolutionary learning-derived prediction models with interpretability using both clinical and radiomic features to predict early recurrence of HCC after surgical resection. Methods: Consecutive 517 HCC patients receiving surgical resection with available contrast-enhanced computed tomography (CECT) images before resection were retrospectively enrolled. Patients were randomly assigned to a training set (n = 362) and a test set (n = 155) in a ratio of 7:3. Tumor segmentation of all CECT images including noncontrast phase, arterial phase, and portal venous phase was manually performed for radiomic feature extraction. A novel evolutionary learning-derived method called genetic algorithm for predicting recurrence after surgery of liver cancer (GARSL) was proposed to design prediction models for early recurrence of HCC within 2 years after surgery. Results: A total of 143 features, including 26 preoperative clinical features, 5 postoperative pathological features, and 112 radiomic features were used to develop GARSL preoperative and postoperative models. The area under the receiver operating characteristic curves (AUCs) for early recurrence of HCC within 2 years were 0.781 and 0.767, respectively, in the training set, and 0.739 and 0.741, respectively, in the test set. The accuracy of GARSL models derived from the evolutionary learning method was significantly better than models derived from other well-known machine learning methods or the early recurrence after surgery for liver tumor (ERASL) preoperative (AUC = 0.687, p < 0.001 vs. GARSL preoperative) and ERASL postoperative (AUC = 0.688, p < 0.001 vs. GARSL postoperative) models using clinical features only. Conclusion: The GARSL models using both clinical and radiomic features significantly improved the accuracy to predict early recurrence of HCC after surgical resection, which was significantly better than other well-known machine learning-derived models and currently available clinical models.

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