scholarly journals Medicine Delivery Bot Using Time Series and Object Detection

2021 ◽  
pp. 286-290
Author(s):  
Kartikeya Bajpai ◽  
Prachi Jain
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Object Detection ◽  
Learning Algorithms ◽  
Research Paper ◽  
Traffic Light ◽  
Lapse Time ◽  
Manual Work ◽  
Traffic Lights ◽  
Present Context

Nowadays, delivery is mainly done by humans which includes a lot of manual work. The existing way is good but lacks faster deliveries. In the present context the deliveries are not possible 24*7 by humans, especially in the case of medicines, customers often require immediate deliveries for maintaining their course of medication. Since, in many other fields AI has contributed to decreasing a lot of manual work and time. In this research paper, we have proposed the idea of a delivery bot which uses deep learning algorithms to detect traffic lights and classify the color of the traffic light. On the basis of which the lapse time will be calculated in between the two traffic lights and hence maps the route for delivery with the help of geocoding accordingly which helps in more secure and faster deliveries.

scholarly journals An Insight into Traffic Light Discernment and Cognizance using Support Vector Machine, Multi Class Learning and Deep Learning Concepts

2020 ◽  
Vol 9 (1) ◽  
pp. 2526-2534
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Support Vector ◽  
Traffic Light ◽  
Full Data ◽  
Traffic Lights ◽  
Detection Algorithms ◽  
Bounding Box ◽  
Area Unit ◽  
Multiclass Classifier

This paper principally combines ideas of laptop vision, machine learning and deep learning for correct detection of traffic lights and their classifications. It checks for each circular and arrow stoplight cases. Color filtering and blob discover ion area unit principally to detect the candidates (traffic lights) [6]. Then, a PCA network is employed as a multiclass classifier which provides the result sporadically. MOT will used for more trailing method and prediction filters out false positives. Sometimes, vote theme can even be used rather than MOT. This method will be simply fitted into ADAS vehicles once hardware thinks about. Recognition is as vital as detective work the traffic lights. While not recognition, no full data will be transmitted [2]. Many complicated TLR’s will give advance functions like observing the most the most for a specific route (when there's quite one) and the way removed from the driving force [3]. Deep learning is additionally one among the rising techniques for analysis areas [7]. Object detection comes as associate integral a part of laptop vision. Object detection will be best utilized in create estimation, vehicle detection, police work etc. In detection algorithms, we tend to incline to draw a bounding box round the object of interest to find it among the image. Also, the drawing of the bounding box isn't distinctive and might hyperbolically looking on the need [9].

scholarly journals Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6218
Author(s):  
Rodrigo Carvalho Barbosa ◽  
Muhammad Shoaib Ayub ◽  
Renata Lopes Rosa ◽  
Demóstenes Zegarra Rodríguez ◽  
Lunchakorn Wuttisittikulkij
Keyword(s):  
Deep Learning ◽  
Traffic Control ◽  
Detection System ◽  
Lightweight Design ◽  
Vehicle Detection ◽  
Activation Function ◽  
Urban Traffic ◽  
Vehicle Classification ◽  
Traffic Light ◽  
Traffic Lights

Minimizing human intervention in engines, such as traffic lights, through automatic applications and sensors has been the focus of many studies. Thus, Deep Learning (DL) algorithms have been studied for traffic signs and vehicle identification in an urban traffic context. However, there is a lack of priority vehicle classification algorithms with high accuracy, fast processing, and a lightweight solution. For filling those gaps, a vehicle detection system is proposed, which is integrated with an intelligent traffic light. Thus, this work proposes (1) a novel vehicle detection model named Priority Vehicle Image Detection Network (PVIDNet), based on YOLOV3, (2) a lightweight design strategy for the PVIDNet model using an activation function to decrease the execution time of the proposed model, (3) a traffic control algorithm based on the Brazilian Traffic Code, and (4) a database containing Brazilian vehicle images. The effectiveness of the proposed solutions were evaluated using the Simulation of Urban MObility (SUMO) tool. Results show that PVIDNet reached an accuracy higher than 0.95, and the waiting time of priority vehicles was reduced by up to 50%, demonstrating the effectiveness of the proposed solution.

scholarly journals INTEGRATED SMART TRAFFIC CONTROL SYSTEM MENUJU PEKANBARU SEBAGAI SMART CITY

JURTEKSI ◽  
2020 ◽  
Vol 7 (1) ◽  
pp. 67-74
Author(s):  
Reny Medikawati Taufiq ◽  
Sunanto Sunanto ◽  
Yoze Rizki
Keyword(s):  
Deep Learning ◽  
Control System ◽  
Object Detection ◽  
Traffic Control ◽  
Integrated System ◽  
Design Solution ◽  
Traffic Light ◽  
Traffic Control System ◽  
Smart Mobility ◽  
Smart Traffic

Abstract: Pekanbaru still using conventional traffic light control system. Pekanbaru as the capital of Riau Province is predicted  udergo the  increased of urban population by 54.5% in 2025. It is important for Pekanbaru to immediately implement smart and efficient traffic management system, so that traffic congestion can be resolved quickly. This research paper provides a design solution for smart traffic light management (Smart Traffic Control System), based on object detection technology that uses deep learning to detect the number and type of vehicles. The number of vehicle is the basis for determining the green light timer automatically. The Smart Traffic Control System (STCS) is integrated with a web based geographic information system (smart map) that can display the current condition  (picture, the number of vehicle, congestion level) of each STCS location. This integrated system has been tested on a traffic light prototype, using a mini computer and a miniature vehicle. This integrated system is able to detect 9 out of 12 vehicles, and able to send data regularly to the smart map.  Keywords: deep learning; smart mobility; smart traffic control system Abstrak: Pengaturan lampu lalu lintas di Kota Pekanbaru masih dilakukan secara  konvensional. Pekanbaru sebagai ibukota Provinsi Riau diprediksikan akan mengalami peningkatan jumlah penduduk  perkotaan sebesar 54,5% pada tahun 2025. Dengan melihat predikisi ini, penting bagi kota Pekanbaru untuk segera memiliki tata kelola lalu lintas yang cerdas dan efisien agar kemacetan dapat ditanggulangi dengan cepat. Penelitian ini memberikan rancangan solusi untuk tata kelola  lampu lalu lintas cerdas (Smart Traffic Control System), berbasis teknologi object detection  yang menggunakan deep learning untuk mendeteksi jumlah dan jenis kendaraan. Jumlah kendaraan menjadi dasar penentuan timer lampu hijau secara otomatis. Smart Traffic Control System (STCS) terintegrasi dengan sistem informasi geografis berbasis web (smart map) yang secara kontinu menerima informasi kepadatan (gambar terkini, jumlah kendaraan, level kepadatan), kemudian menampilkannya diatas peta Kota Pekanbaru. Solusi sistem terintegrasi ini telah diujikan pada sebuah prototipe lampu lalu lintas, menggunakan komputer mini  dan  miniatur kendaraan. Sistem terintegrasi ini mampu mendeteksi 9 dari 12 kendaraan, dan mampu mengirimkan data secara berkala kepada smart map. Kata kunci: deep learning; smart mobility; smart traffic control system

scholarly journals A Case Study on Hospital Readmission Prediction Using Deep Learning Algorithms on EHRs

2021 ◽  
Vol 12 (3) ◽  
pp. 4267-4273
Author(s):  
B. Sushrith Et.al
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Financial Risk ◽  
Time Series Data ◽  
Learning Algorithms ◽  
Series Data ◽  
Critical Areas ◽  
Avoidable Readmission ◽  
Low Dimensional ◽  
Medical Concepts

In this paper, focus is made on predicting the patients who are going to be re-admitted back in the hospital before discharge using latest deep-learning algorithms is applied on the electronic health records of patients which is a time-series data. To begin with the study of the data and its analysis this project deployed the conventional supervised ML algorithms like the Logistic Regression, Naïve Bayes, Random Forest and SVM and compared their performances on different portion sizes of dataset. The final model built uses deep-learning architectures such as RNN and LSTM to improve the prediction results taking advantage of the time series data. Another feature added has been of low dimensional descriptions of medical concepts as the input to the model. Ultimately, this work tests, validates, and explains the developed system using the MIMIC-III dataset, which contains around 38000 patient’s information and about 61,155 patient’s data who admitted in ICU, duration of 10 years. The support from this exhaustive dataset is used to train the models that provide healthcare workers with proper information regarding their discharge and readmission in hospitals. These ML and deep learning models are used to know about the patient who is getting to be readmitted in the ICU before his discharge will help the hospital to allocate resources properly and also reduce the financial risk of patients. In order to reduce ICU readmission that can be avoided, hospitals have to be able to recognize patients who have a higher risk of ICU readmission. Those patients can then continue to stay in the ICU so that they will not have the risk of getting admit back to the hospital. Also, the resources of hospitals that were required for avoidable readmission can be re-allocated to more critical areas in the hospital that need them. A more effective model of predicting readmission system can play an important role in helping hospitals and ICU doctors to find the patients who are going to be readmitted before discharge. To build this system here we use different ML and deep-learning algorithms. Predictive models based on huge amounts of data are made to predict the patients who are going to be admitted back in the hospital after discharge.

scholarly journals Traffic Light and Back-light Recognition using Deep Learning and Image Processing with Raspberry Pi

2021 ◽  
Vol 8 (2) ◽  
pp. 15-19
Author(s):  
Julkar Nine ◽  
Rahul Mathavan
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Real Time ◽  
Network Architecture ◽  
Intelligent Vehicles ◽  
Driving Safety ◽  
Raspberry Pi ◽  
Traffic Light ◽  
Traffic Lights ◽  
Architecture Model

Traffic light detection and back-light recognition are essential research topics in the area of intelligent vehicles because they avoid vehicle collision and provide driver safety. Improved detection and semantic clarity may aid in the prevention of traffic accidents by self-driving cars at crowded junctions, thus improving overall driving safety. Complex traffic situations, on the other hand, make it more difficult for algorithms to identify and recognize objects. The latest state-of-the-art algorithms based on Deep Learning and Computer Vision are successfully addressing the majority of real-time problems for autonomous driving, such as detecting traffic signals, traffic signs, and pedestrians. We propose a combination of deep learning and image processing methods while using the MobileNetSSD (deep neural network architecture) model with transfer learning for real-time detection and identification of traffic lights and back-light. This inference model is obtained from frameworks such as Tensor-Flow and Tensor-Flow Lite which is trained on the COCO data. This study investigates the feasibility of executing object detection on the Raspberry Pi 3B+, a widely used embedded computing board. The algorithm’s performance is measured in terms of frames per second (FPS), accuracy, and inference time.

scholarly journals Trajectory Object Detection using Deep Learning Algorithms

2019 ◽  
Vol 8 (3) ◽  
pp. 7895-7898
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Large Scale ◽  
Smart Cities ◽  
Learning Algorithms ◽  
Big Data Analytics ◽  
Light Condition ◽  
Transport Systems ◽  
Video Footage ◽  
Vehicle Type

Video surveillance data in smart cities needs to analyze a large amount of video footage in order to locate the people who are violating the traffic rules. The fact is that it is very easy for the human being to recognize different objects in images and videos. For a computer program this is quite a difficult task. Hence there is a need for visual big data analytics which involves processing and analyzing large scale visual data such as images or videos. One major application of trajectory object detection is the Intelligent Transport Systems (ITS). Vehicle type detection, tracking and classification play an important role in ITS. In order to analyze huge amount of video footage deep learning algorithms have been deployed. The main phase of vehicle type detection includes annotating the data, training the model and validating the model. The problems and challenges in identifying or detecting type of vehicle are due to weather, shadows, blurring effect, light condition and quality of the data. In this paper deep learning algorithms such as Faster R CNN and Mask R CNN and Frameworks like YOLO were used for the object detection. Dataset (different types of vehicle pictures in video format) were collected both from in-house premises as well as from the Internet to detect and recognize the type of vehicles which are common in traffic systems. The experimental results show that among the three approaches used the Mask R CNN algorithm is found to be more efficient and accurate in vehicle type detection.

scholarly journals DiaMole: Mole Detection and Segmentation Software for Mobile Phone Skin Images

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shuai Liu ◽  
Zheng Chen ◽  
Huahui Zhou ◽  
Kunlin He ◽  
Meiyu Duan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Mobile Phone ◽  
Early Stage ◽  
Learning Algorithms ◽  
Skin Lesions ◽  
Detection Algorithm ◽  
Incidence And Mortality ◽  
Malignant Skin ◽  
User Friendly

Motivation. The worldwide incidence and mortality rates of melanoma are on the rise recently. Melanoma may develop from benign lesions like skin moles. Easy-to-use mole detection software will help find the malignant skin lesions at the early stage. Results. This study developed mole detection and segmentation software DiaMole using mobile phone images. DiaMole utilized multiple deep learning algorithms for the object detection problem and mole segmentation problem. An object detection algorithm generated a rectangle tightly surrounding a mole in the mobile phone image. Moreover, the segmentation algorithm detected the precise boundary of that mole. Three deep learning algorithms were evaluated for their object detection performance. The popular performance metric mean average precision (mAP) was used to evaluate the algorithms. Among the utilized algorithms, the Faster R-CNN could achieve the best mAP = 0.835, and the integrated algorithm could achieve the mAP = 0.4228. Although the integrated algorithm could not achieve the best mAP, it can avoid the missing of detecting the moles. A popular Unet model was utilized to find the precise mole boundary. Clinical users may annotate the detected moles based on their experiences. Conclusions. DiaMole is user-friendly software for researchers focusing on skin lesions. DiaMole may automatically detect and segment the moles from the mobile phone skin images. The users may also annotate each candidate mole according to their own experiences. The automatically calculated mole image masks and the annotations may be saved for further investigations.

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