Leveraging transfer learning with deep learning for crime prediction.

Crime remains a crucial concern regarding ensuring a safe and secure environment for the public. Numerous efforts have been made to predict crime, emphasizing the importance of employing deep learning approaches for precise predictions. However, sufficient crime data and resources for training state-of-the-art deep learning-based crime prediction systems pose a challenge. To address this issue, this study adopts the transfer learning paradigm. Moreover, this study fine-tunes state-of-the-art statistical and deep learning methods, including Simple Moving Averages (SMA), Weighted Moving Averages (WMA), Exponential Moving Averages (EMA), Long Short Term Memory (LSTM), Bi-directional Long Short Term Memory (BiLSTMs), and Convolutional Neural Networks and Long Short Term Memory (CNN-LSTM) for crime prediction. Primarily, this study proposed a BiLSTM based transfer learning architecture due to its high accuracy in predicting weekly and monthly crime trends. The transfer learning paradigm leverages the fine-tuned BiLSTM model to transfer crime knowledge from one neighbourhood to another. The proposed method is evaluated on Chicago, New York, and Lahore crime datasets. Experimental results demonstrate the superiority of transfer learning with BiLSTM, achieving low error values and reduced execution time. These prediction results can significantly enhance the efficiency of law enforcement agencies in controlling and preventing crime.

Hybrid of deep learning and exponential smoothing for enhancing crime forecasting accuracy.

The continued urbanization poses several challenges for law enforcement agencies to ensure a safe and secure environment. Countries are spending a substantial amount of their budgets to control and prevent crime. However, limited efforts have been made in the crime prediction area due to the deficiency of spatiotemporal crime data. Several machine learning, deep learning, and time series analysis techniques are exploited, but accuracy issues prevail. Thus, this study proposed a Bidirectional Long Short Term Memory (Bi-LSTM) and Exponential Smoothing (ES) hybrid for crime forecasting. The proposed technique is evaluated using New York City crime data from 2010-2017. The proposed approach outperformed as compared to state-of-the-art Seasonal Autoregressive Integrated Moving Averages (SARIMA) with low Mean Absolute Percentage Error (MAPE) (0.3738, 0.3891, 0.3433,0.3964), Root Mean Square Error (RMSE)(13.146, 13.669, 13.104, 13.77), and Mean Absolute Error (MAE) (9.837, 10.896, 10.598, 10.721). Therefore, the proposed technique can help law enforcement agencies to prevent and control crime by forecasting crime patterns.

Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications.

The remarkable advancements in biotechnology and public healthcare infrastructures have led to a momentous production of critical and sensitive healthcare data. By applying intelligent data analysis techniques, many interesting patterns are identified for the early and onset detection and prevention of several fatal diseases. Diabetes mellitus is an extremely life-threatening disease because it contributes to other lethal diseases, i.e., heart, kidney, and nerve damage. In this paper, a machine learning based approach has been proposed for the classification, early-stage identification, and prediction of diabetes. Furthermore, it also presents an IoT-based hypothetical diabetes monitoring system for a healthy and affected person to monitor his blood glucose (BG) level. For diabetes classification, three different classifiers have been employed, i.e., random forest (RF), multilayer perceptron (MLP), and logistic regression (LR). For predictive analysis, we have employed long short-term memory (LSTM), moving averages (MA), and linear regression (LR). For experimental evaluation, a benchmark PIMA Indian Diabetes dataset is used. During the analysis, it is observed that MLP outperforms other classifiers with 86.08% of accuracy and LSTM improves the significant prediction with 87.26% accuracy of diabetes. Moreover, a comparative analysis of the proposed approach is also performed with existing state-of-the-art techniques, demonstrating the adaptability of the proposed approach in many public healthcare applications.

Early Initiation Of Beta Blockers Following Primary Endoscopic Therapy For Bleeding Oesophageal Varices In Cirrhotics.

BACKGROUND AND AIMS: Beta-blockers provide secondary prophylaxis following endoscopic therapy for variceal bleeding. Guidelines recommend starting beta-blockers 6 days after endoscopy to prevent masking hemodynamic signs of re-bleeding. We aimed to see safety of earlier initiation of betablockers. METHODS: Cirrhotic patients with upper GI bleed were given intravenous vasoactive agents until undergoing endoscopy. Patients with only oesophageal varices as source of bleed were recruited. Vasoactive agents were discontinued following variceal banding. The patients were observed for 12-18 hours, discharged on oral carvedilol 6.25 mg BID and monitored for 6 weeks for re-bleeding and mortality. RESULTS: Fifty patients were included, 27 (54%) male and 23 (46%) female. Average age was 43±3 years. Aetiology of cirrhosis was HCV in 42 (84%), HBV in 6 (12%), HCV & HBV in 2 (4%) and indeterminate in 1 (2%) patient. Seventeen (34%) patients had Child A, 22 (44%) Child B and 11 (22%) had Child C disease. Hospital stay was under 24 hours in 24 (48%), 24-48 hours in 15 (30%) and 48-72 hours in 11 (22%) patients. Five (10%) patients underwent EGD within 6 hours of admission, 28 (56%) within 12 hours, 14 (28%) within 24 hours and 3 (6%) within 36 hours. No re-bleeding, mortality or drug related adverse effects were noted during 6 weeks after discharge. CONCLUSIONS: Our study proves possibility of shorter management of variceal bleeding by having a 12-18 hour monitoring after endoscopic banding, followed by beta-blocker initiation and discharge. This will safely reduce physical and financial burden on health services.