scholarly journals Obstacle Detection and Safely Navigate the Autonomous Vehicle from Unexpected Obstacles on the Driving Lane

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4719
Author(s):  
Malik Haris ◽  
Jin Hou
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicle ◽  
Research Area ◽  
Visual Navigation ◽  
Obstacle Detection ◽  
The Road ◽  
Markov Random ◽  
Active Research ◽  
On The Road ◽  
Active Research Area

Nowadays, autonomous vehicle is an active research area, especially after the emergence of machine vision tasks with deep learning. In such a visual navigation system for autonomous vehicle, the controller captures images and predicts information so that the autonomous vehicle can safely navigate. In this paper, we first introduced small and medium-sized obstacles that were intentionally or unintentionally left on the road, which can pose hazards for both autonomous and human driving situations. Then, we discuss Markov random field (MRF) model by fusing three potentials (gradient potential, curvature prior potential, and depth variance potential) to segment the obstacles and non-obstacles into the hazardous environment. Since the segment of obstacles is done by MRF model, we can predict the information to safely navigate the autonomous vehicle form hazardous environment on the roadway by DNN model. We found that our proposed method can segment the obstacles accuracy from the blended background road and improve the navigation skills of the autonomous vehicle.

scholarly journals Impact of Deep Learning in Medical Imaging: a Systematic New Proposed Model

2019 ◽  
Vol 8 (3S2) ◽  
pp. 112-118
Keyword(s):  
Deep Learning ◽  
Medical Imaging ◽  
Learning Algorithms ◽  
Brain Lesion ◽  
Research Area ◽  
Magnetic Resonance Images ◽  
Machine Learning Algorithms ◽  
Proposed Model ◽  
Active Research ◽  
Active Research Area

For years’ radiologist and clinician continues to employs various approaches, machine learning algorithms included to detect, diagnose, and prevent diseases using medical imaging. Recent advances in deep learning made medical imaging analysis and processing an active research area, various algorithms for segmentation, detection, and classification have been proposed. In this survey, we describe the trends of deep learning algorithms use in medical imaging, their architecture, hardware, and software used are all discussed. We concluded with the proposed model for brain lesion segmentation and classification using Magnetic Resonance Images (MRI).

scholarly journals A Study on Building a “Real-Time Vehicle Accident and Road Obstacle Notification Model” Using AI CCTV

2021 ◽  
Vol 11 (17) ◽  
pp. 8210
Author(s):  
Chaeyoung Lee ◽  
Hyomin Kim ◽  
Sejong Oh ◽  
Illchul Doo
Keyword(s):  
Deep Learning ◽  
Traffic Safety ◽  
Research Direction ◽  
Detection Algorithm ◽  
Obstacle Detection ◽  
Video Data ◽  
Future Research ◽  
The Road ◽  
Detection Model ◽  
On The Road

This research produced a model that detects abnormal phenomena on the road, based on deep learning, and proposes a service that can prevent accidents because of other cars and traffic congestion. After extracting accident images based on traffic accident video data by using FFmpeg for model production, car collision types are classified, and only the head-on collision types are processed by using the deep learning object-detection algorithm YOLO (You Only Look Once). Using the car accident detection model that we built and the provided road obstacle-detection model, we programmed, for when the model detects abnormalities on the road, warning notification and photos that captures the accidents or obstacles, which are then transferred to the application. The proposed service was verified through application notification simulations and virtual experiments using CCTVs in Daegu, Busan, and Gwangju. By providing services, the goal is to improve traffic safety and achieve the development of a self-driving vehicle sector. As a future research direction, it is suggested that an efficient CCTV control system be introduced for the transportation environment.

scholarly journals "Chitty-Chitty-Chat Bot": Deep Learning for Conversational AI

2018 ◽  
Author(s):  
Rui Yan
Keyword(s):  
Deep Learning ◽  
Research Area ◽  
Data Driven ◽  
Massive Data ◽  
Interaction Interface ◽  
Active Research ◽  
Chat Bot ◽  
Task Oriented ◽  
Active Research Area

Conversational AI is of growing importance since it enables easy interaction interface between humans and computers. Due to its promising potential and alluring commercial values to serve as virtual assistants and/or social chatbots, major AI, NLP, and Search & Mining conferences are explicitly calling-out for contributions from conversational studies. It is an active research area and of considerable interest. To build a conversational system with moderate intelligence is challenging, and requires abundant dialogue data and interdisciplinary techniques. Along with the Web 2.0, the massive data available greatly facilitate data-driven methods such as deep learning for human-computer conversations. In general, conversational systems can be categorized into 1) task-oriented systems which aim to help users accomplish goals in vertical domains, and 2) social chat bots which can converse seamlessly and appropriately with humans, playing the role of a chat companion. In this paper, we focus on the survey of non-task-oriented chit-chat bots.

Semantic Segmentation of Medical Images with Deep Learning: Overview

2020 ◽  
Vol 4 (3) ◽  
pp. 568-575
Author(s):  
Yamina Azzi ◽  
Abdelouahab Moussaoui ◽  
Mohand-Tahar Kechadi
Keyword(s):  
Deep Learning ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Semantic Segmentation ◽  
Research Area ◽  
Medical Image Segmentation ◽  
Challenging Tasks ◽  
Active Research ◽  
Preliminary Description ◽  
Active Research Area

Semantic segmentation is one of the biggest challenging tasks in computer vision, especially in medical image analysis, it helps to locate and identify pathological structures automatically. It is an active research area. Continuously different techniques are proposed. Recently Deep Learning is the latest technique used intensively to improve the performance in medical image segmentation. For this reason, we present in this non-systematic review a preliminary description about semantic segmentation with deep learning and the most important steps to build a model that deal with this problem.

scholarly journals Leaf Features Extraction for Plant Classification using CNN

2021 ◽  
pp. 148-154
Author(s):  
Mr. P. Siva Prasad ◽  
Dr. A. Senthilrajan
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Research Area ◽  
Network Approach ◽  
Neural Network Approach ◽  
Active Research ◽  
Active Research Area

Deep learning is now an active research area. Deep learning has done a success in computer vision and image recognition. It is a subset of the Machine Learning. In Deep learning, Convolutional Neural Network (CNN) is popular deep neural network approach. In this paper, we have addressed that how to extract useful leaf features automatically from the leaf dataset through Convolutional Neural Networks (CNN) using Deep Learning. In this paper, we have shown that the accuracy obtained by CNN approach is efficient when compared to accuracy obtained by the traditional neural network.

A Person-Centered Design Framework for Serious Games for Dementia

2020 ◽  
Vol 64 (1) ◽  
pp. 18-22
Author(s):  
Bella Yigong Zhang ◽  
Mark Chignell
Keyword(s):  
Theoretical Basis ◽  
Serious Games ◽  
Aging Population ◽  
Research Area ◽  
Design Framework ◽  
Strategic Design ◽  
Health And Wellbeing ◽  
Initial Design ◽  
Active Research ◽  
Active Research Area

With the rapidly aging population and the rising number of people living with dementia (PLWD), there is an urgent need for programming and activities that can promote the health and wellbeing of PLWD. Due to staffing and budgetary constraints, there is considerable interest in using technology to support this effort. Serious games for dementia have become a very active research area. However, much of the work is being done without a strong theoretical basis. We incorporate a Montessori approach with highly tactile interactions. We have developed a person-centered design framework for serious games for dementia with initial design recommendations. This framework has the potential to facilitate future strategic design and development in the field of serious games for dementia.

scholarly journals Digitization and Visualization of Folk Dances in Cultural Heritage: A Review

Inventions ◽  
2018 ◽  
Vol 3 (4) ◽  
pp. 72 ◽  
Author(s):  
Iris Kico ◽  
Nikos Grammalidis ◽  
Yiannis Christidis ◽  
Fotis Liarokapis
Keyword(s):  
Performance Evaluation ◽  
Cultural Heritage ◽  
Computer Science ◽  
Learning Process ◽  
Research Area ◽  
Active Research ◽  
Active Research Area

According to UNESCO, cultural heritage does not only include monuments and collections of objects, but also contains traditions or living expressions inherited from our ancestors and passed to our descendants. Folk dances represent part of cultural heritage and their preservation for the next generations appears of major importance. Digitization and visualization of folk dances form an increasingly active research area in computer science. In parallel to the rapidly advancing technologies, new ways for learning folk dances are explored, making the digitization and visualization of assorted folk dances for learning purposes using different equipment possible. Along with challenges and limitations, solutions that can assist the learning process and provide the user with meaningful feedback are proposed. In this paper, an overview of the techniques used for the recording of dance moves is presented. The different ways of visualization and giving the feedback to the user are reviewed as well as ways of performance evaluation. This paper reviews advances in digitization and visualization of folk dances from 2000 to 2018.

scholarly journals Enhancing Big Data Auditing

2018 ◽  
Vol 11 (1) ◽  
pp. 90
Author(s):  
Sara Alomari ◽  
Mona Alghamdi ◽  
Fahd S. Alotaibi
Keyword(s):  
Big Data ◽  
Research Area ◽  
Provable Data Possession ◽  
Integrity Verification ◽  
The Core ◽  
Outsourced Data ◽  
Active Research ◽  
Data Auditing ◽  
Active Research Area ◽  
Proof Of Retrievability

The auditing services of the outsourced data, especially big data, have been an active research area recently. Many schemes of remotely data auditing (RDA) have been proposed. Both categories of RDA, which are Provable Data Possession (PDP) and Proof of Retrievability (PoR), mostly represent the core schemes for most researchers to derive new schemes that support additional capabilities such as batch and dynamic auditing. In this paper, we choose the most popular PDP schemes to be investigated due to the existence of many PDP techniques which are further improved to achieve efficient integrity verification. We firstly review the work of literature to form the required knowledge about the auditing services and related schemes. Secondly, we specify a methodology to be adhered to attain the research goals. Then, we define each selected PDP scheme and the auditing properties to be used to compare between the chosen schemes. Therefore, we decide, if possible, which scheme is optimal in handling big data auditing.

scholarly journals E-Mail Usage in South Pacific Distance Education

2005 ◽  
pp. 1034-1039
Author(s):  
Jonathan Frank ◽  
Janet Toland ◽  
Karen D. Schenk
Keyword(s):  
Learning Styles ◽  
Research Area ◽  
Web Based ◽  
Group Interactions ◽  
Face To Face ◽  
Web Based Learning ◽  
Active Research ◽  
The Impact ◽  
E Mail ◽  
Active Research Area

The impact of cultural diversity on group interactions through technology is an active research area. Current research has found that a student’s culture appears to influence online interactions with teachers and other students (Freedman & Liu, 1996). Students from Asian and Western cultures have different Web-based learning styles (Liang & McQueen, 1999), and Scandinavian students demonstrate a more restrained online presence compared to their more expressive American counterparts (Bannon, 1995). Differences were also found across cultures in online compared to face-to-face discussions (Warschauer, 1996). Student engagement, discourse, and interaction are valued highly in “western” universities. With growing internationalization of western campuses, increasing use of educational technology both on and off campus, and rising distance learning enrollments, intercultural frictions are bound to increase.

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