scholarly journals Video Object Detection for Police Surveillance using Deep Learning

2021 ◽  
Vol 10 (3) ◽  
pp. 1-4
Keyword(s):  
Deep Learning ◽  
Urban Areas ◽  
Detection System ◽  
Confusion Matrix ◽  
Human Vision ◽  
Video Object ◽  
Television System ◽  
Public And Private ◽  
Human Operators ◽  
Develop System

Human vision is incredibly excellent and complex. In the previous years, people made significantly more leaps to expanding this visual capacity to machines. Cameras have been used as the eyes of computers.In response to increasing anxieties about crime and its threat to security and safety, the utilization of substantial numbers of closed-circuit television system (CCTV) in both public and private spaces have been considered a necessity. The use of these significant video footages is essential to incident investigations.But as the number of these systems rises, so as the need for human operator monitoring tasks.Unfortunately, many actionable incidents are utterly undetected in this manual systemdue to inherent limitations from deploying solely human operators eye-balling CCTV screens.As a result, surveillance footages are often used merely as passive records or as evidence for post-event investigations. This study aimed to develop a real-time firearm detection using deep learning embedded in CCTV cameras that pushes alert notifications to both iOS and Android mobile devices.This research used a descriptive design and asked IT experts to evaluate the develop system based on its compliance to ISO 25010 standard. Moreover, confusion matrix and intersection over union (IoU) were used to evaluate the performanceof the system.The detection system was found to be highly recommended in urban areas particularly for CCTVs found in barangay streets and establishments.

scholarly journals Deep Learning System for Vehicular Re-Routing and Congestion Avoidance

2019 ◽  
Vol 9 (13) ◽  
pp. 2717 ◽  
Author(s):  
Pedro Perez-Murueta ◽  
Alfonso Gómez-Espinosa ◽  
Cesar Cardenas ◽  
Miguel Gonzalez-Mendoza
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Urban Areas ◽  
Transportation Systems ◽  
Learning System ◽  
Traffic Network ◽  
Public And Private ◽  
Traffic Jam ◽  
Private Transportation ◽  
Alternative Routes

Delays in transportation due to congestion generated by public and private transportation are common in many urban areas of the world. To make transportation systems more efficient, intelligent transportation systems (ITS) are currently being developed. One of the objectives of ITS is to detect congested areas and redirect vehicles away from them. However, most existing approaches only react once the traffic jam has occurred and, therefore, the delay has already spread to more areas of the traffic network. We propose a vehicle redirection system to avoid congestion that uses a model based on deep learning to predict the future state of the traffic network. The model uses the information obtained from the previous step to determine the zones with possible congestion, and redirects the vehicles that are about to cross them. Alternative routes are generated using the entropy-balanced k Shortest Path algorithm (EBkSP). The proposal uses information obtained in real time by a set of probe cars to detect non-recurrent congestion. The results obtained from simulations in various scenarios have shown that the proposal is capable of reducing the average travel time (ATT) by up to 19%, benefiting a maximum of 38% of the vehicles.

scholarly journals Deep Learning to Detect and Classify the Purity Level of Luwak Coffee Green Beans

2021 ◽  
Vol 30 (1) ◽  
pp. 1-18
Author(s):  
Yusuf Hendrawan ◽  
Shinta Widyaningtyas ◽  
Muchammad Riza Fauzy ◽  
Sucipto Sucipto ◽  
Retno Damayanti ◽  
...  
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Low Cost ◽  
Confusion Matrix ◽  
Optimization Technique ◽  
Learning Rate ◽  
Production Costs ◽  
Green Beans ◽  
Initial Learning ◽  
Palm Civet

Luwak coffee (palm civet coffee) is known as one of the most expensive coffee in the world. In order to lower production costs, Indonesian producers and retailers often mix high-priced Luwak coffee with regular coffee green beans. However, the absence of tools and methods to classify Luwak coffee counterfeiting makes the sensing method’s development urgent. The research aimed to detect and classify Luwak coffee green beans purity into the following purity categories, very low (0-25%), low (25-50%), medium (50-75%), and high (75-100%). The classifying method relied on a low-cost commercial visible light camera and the deep learning model method. Then, the research also compared the performance of four pre-trained convolutional neural network (CNN) models consisting of SqueezeNet, GoogLeNet, ResNet-50, and AlexNet. At the same time, the sensitivity analysis was performed by setting the CNN parameters such as optimization technique (SGDm, Adam, RMSProp) and the initial learning rate (0.00005 and 0.0001). The training and validation result obtained the GoogLeNet as the best CNN model with optimizer type Adam and learning rate 0.0001, which resulted in 89.65% accuracy. Furthermore, the testing process using confusion matrix from different sample data obtained the best CNN model using ResNet-50 with optimizer type RMSProp and learning rate 0.0001, providing an accuracy average of up to 85.00%. Later, the CNN model can be used to establish a real-time, non-destructive, rapid, and precise purity detection system.

Data Leakage Detection System based on Deep Learning

2016 ◽  
Vol 4 (2) ◽  
pp. 13-18
Author(s):  
Min-Ji Seo ◽  
◽  
Myung-Ho Kim ◽  
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Leakage Detection

Urban Transport

2018 ◽  
Author(s):  
José van
Keyword(s):  
Urban Areas ◽  
Urban Transport ◽  
Transport Sector ◽  
Public And Private ◽  
Transport Services ◽  
Ride Sharing ◽  
The World ◽  
Private And Public ◽  
Selection Mechanisms

Platformization affects the entire urban transport sector, effectively blurring the division between private and public transport modalities; existing public–private arrangements have started to shift as a result. This chapter analyzes and discusses the emergence of a platform ecology for urban transport, focusing on two central public values: the quality of urban transport and the organization of labor and workers’ rights. Using the prism of platform mechanisms, it analyzes how the sector of urban transport is changing societal organization in various urban areas across the world. Datafication has allowed numerous new actors to offer their bike-, car-, or ride-sharing services online; selection mechanisms help match old and new complementors with passengers. Similarly, new connective platforms are emerging, most prominently transport network companies such as Uber and Lyft that offer public and private transport options, as well as new platforms offering integrated transport services, often referred to as “mobility as a service.”

scholarly journals Deep learning for vision‐based fall detection system: Enhanced optical dynamic flow

2020 ◽  
Author(s):  
Sagar Chhetri ◽  
Abeer Alsadoon ◽  
Thair Al‐Dala'in ◽  
P. W. C. Prasad ◽  
Tarik A. Rashid ◽  
...  
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Fall Detection ◽  
Dynamic Flow

scholarly journals Semantic segmentation of PolSAR image data using advanced deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rajat Garg ◽  
Anil Kumar ◽  
Nikunj Bansal ◽  
Manish Prateek ◽  
Shashi Kumar
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Urban Area ◽  
Urban Areas ◽  
Learning Algorithms ◽  
Semantic Segmentation ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Deep Learning Model

AbstractUrban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

scholarly journals Equity in healthcare utilisation and cost of treatment in Western India

2021 ◽  
Author(s):  
Anil Gumber
Keyword(s):  
Urban Areas ◽  
Healthcare Utilisation ◽  
Morbidity Rate ◽  
Western India ◽  
Health Seeking ◽  
Public Provision ◽  
Cost Of Treatment ◽  
Public And Private ◽  
Burden Of Treatment ◽  
The Poor

AbstractThe paper compares the morbidity and healthcare utilisation scenario prevalent in Gujarat and Maharashtra as well as for all − India over the last 35 years by exploring the National Sample Surveys data for 1980–81, 1986–87, 1995–96, 2004, and 2014. The differentials and trends in morbidity rate, health seeking behaviour, use of public and private providers for inpatient and outpatient care and associated cost, and burden of treatment are analysed by population groups. Changes in people’s demand for health services are correlated with the supply factors i.e. expansion of public and private health infrastructure. Rising cost and burden of treatment on the poor are examined through receipt of free inpatient and outpatient services as well as the extent of financial protection under the health insurance schemes received by them. Overtime, morbidity rates have gone up, with several folds increase in select states; the reliance on public provision has gone down substantially despite being cheaper than the private sector; and cost of treatment at constant prices increased considerably even for the poor. Hospitalisation costs were higher among insured than the non-insured households in several states irrespective of whether resident in rural or urban areas (Haryana, Maharashtra, Himachal Pradesh, and Assam have reported that insured households ended-up paying almost double the hospitalisation expenses in 2014). Leaving aside Kerala (where insured households have paid just a half of the cost of the non-insured), this clearly reflects the widespread prevalence of moral hazard and insurance collusion in India.

scholarly journals An Efficient DenseNet-Based Deep Learning Model for Malware Detection

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 344
Author(s):  
Jeyaprakash Hemalatha ◽  
S. Abijah Roseline ◽  
Subbiah Geetha ◽  
Seifedine Kadry ◽  
Robertas Damaševičius
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Malware Detection ◽  
Class Imbalance ◽  
Learning Model ◽  
Computational Time ◽  
Learning Approaches ◽  
Security Threat ◽  
Static And Dynamic Analysis ◽  
Deep Learning Model

Recently, there has been a huge rise in malware growth, which creates a significant security threat to organizations and individuals. Despite the incessant efforts of cybersecurity research to defend against malware threats, malware developers discover new ways to evade these defense techniques. Traditional static and dynamic analysis methods are ineffective in identifying new malware and pose high overhead in terms of memory and time. Typical machine learning approaches that train a classifier based on handcrafted features are also not sufficiently potent against these evasive techniques and require more efforts due to feature-engineering. Recent malware detectors indicate performance degradation due to class imbalance in malware datasets. To resolve these challenges, this work adopts a visualization-based method, where malware binaries are depicted as two-dimensional images and classified by a deep learning model. We propose an efficient malware detection system based on deep learning. The system uses a reweighted class-balanced loss function in the final classification layer of the DenseNet model to achieve significant performance improvements in classifying malware by handling imbalanced data issues. Comprehensive experiments performed on four benchmark malware datasets show that the proposed approach can detect new malware samples with higher accuracy (98.23% for the Malimg dataset, 98.46% for the BIG 2015 dataset, 98.21% for the MaleVis dataset, and 89.48% for the unseen Malicia dataset) and reduced false-positive rates when compared with conventional malware mitigation techniques while maintaining low computational time. The proposed malware detection solution is also reliable and effective against obfuscation attacks.

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