scholarly journals Human Activity Recognition with Deep Learning: Methods, Progress & Possibilities

2021 ◽  
Author(s):  
Pranjal Kumar
Keyword(s):  
Deep Learning ◽  
Older People ◽  
Mobile Phones ◽  
Cell Phones ◽  
Detailed Review ◽  
Video Cameras ◽  
Advantages And Disadvantages ◽  
The Past ◽  
Key Aspects ◽  
Method Group

Over the past decade, recognition of human activities (HAR) has become a vibrant field of research, in particular, the spread in our everyday lives of electronics such as mobile phones, smart cell phones, and video cameras. Furthermore, the advancement in the field of deep methodologies and other paradigms have enabled scientists to enable HAR in many areas, consisting of activities in fitness and wellness. For instance, HAR is one of many resorts to support older people through day-to-day activities to support their cognition and physicality. This study is centered on the key aspects deep learning plays in the development of HAR applications. Although numerous HAR examination studies were carried out previously, there have been no overall studies on this subject, in all the earlier studies there were only specific HAR-related subjects. A detailed review covering all the main subjects in this area is therefore essential. This study discusses the latest developments and works in HAR. It separates the methods and the advantages and disadvantages of each method group. This paper finally addresses many problems in the current HAR subject and provides recommendations for potential study.

scholarly journals Perceptions and Opinions towards Cell Phone Use as a Risk Factor of Brain Cancer among University Students in Malaysia

2013 ◽  
Vol 4 (1) ◽  
pp. 1-4
Author(s):  
Redhwan Ahmed Al-Naggar ◽  
Yuri V Bobryshev
Keyword(s):  
University Students ◽  
Mobile Phones ◽  
Brain Cancer ◽  
Cell Phone ◽  
Cell Phones ◽  
Medical Sciences ◽  
Cell Phone Use ◽  
The Past ◽  
Carcinogenic Effects ◽  
Study Participants

The worldwide use of cell phones has rapidly increased over the past decades. With the increasing use of mobile phones, concern has been raised about the possible carcinogenic effects as a result of exposure to radiofrequency electromagnetic fields. The objective of this study was to explore the perceptions and opinions towards brain cancer related to cell phone use among university students in Malaysia. The study revealed that the majority of the study participants believe that there is no relationship between brain cancer and hand phone use.DOI: http://dx.doi.org/10.3126/ajms.v4i1.7808 Asian Journal of Medical Sciences 4(2013) 1-4

scholarly journals Human Activity Recognition with Deep Learning: Overview, Challenges & Possibilities

2021 ◽  
Author(s):  
Pranjal Kumar
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Well Being ◽  
Human Activity Recognition ◽  
Research Field ◽  
Daily Lives ◽  
Video Cameras ◽  
Advantages And Disadvantages

Human Activity Recognition (HAR) has become a vibrant research field over the last decade, especially because of the spread of electronic devices like mobile phones, smart cell phones, and video cameras in our daily lives. In addition, the progress of deep learning and other algorithms has made it possible for researchers to use HAR in many fields including sports, health, and well-being. HAR is, for example, one of the most promising resources for helping older people with the support of their cognitive and physical function through day-to-day activities. This study focuses on the key role machine learning plays in the development of HAR applications. While numerous HAR surveys and review articles have previously been carried out, the main/overall HAR issue was not taken into account, and these studies concentrate only on specific HAR topics. A detailed review paper covering major HAR topics is therefore essential. This study analyses the most up-to-date studies on HAR in recent years and provides a classification of HAR methodology and demonstrates advantages and disadvantages for each group of methods. This paper finally addresses many problems in the current HAR subject and provides recommendations for potential study.

The Science of Cell Phone Use

2013 ◽  
Vol 3 (1) ◽  
pp. 7-18 ◽  
Author(s):  
Zheng Yan ◽  
Quan Chen ◽  
Chengfu Yu
Keyword(s):  
Cell Phone ◽  
Literature Search ◽  
Review Paper ◽  
Cell Phones ◽  
Short Introduction ◽  
Future Directions ◽  
Cell Phone Use ◽  
Detailed Review ◽  
The Past ◽  
History Of

Cell phones are becoming the most ubiquitous technology. Researchers in various other disciplines in behavioral sciences have been extensively examining how people use cell phones and what influences cell phone use have on people’s lives for more than 20 years. This review paper provides an overall picture of the science of cell phone use by sketching the past, present, and future of this emerged field of study. After a short introduction, it presents an overview of the literature search methods used in this study and a brief history of the science of cell phone use, provides a detailed review of five major areas and six specific topics of the field, and ends with an outline of future directions of research.

scholarly journals Human Activity Recognition with Deep Learning: Overview, Challenges & Possibilities

2021 ◽  
Author(s):  
Pranjal Kumar
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Well Being ◽  
Human Activity Recognition ◽  
Research Field ◽  
Daily Lives ◽  
Video Cameras ◽  
Advantages And Disadvantages

Human Activity Recognition (HAR) has become a vibrant research field over the last decade, especially because of the spread of electronic devices like mobile phones, smart cell phones, and video cameras in our daily lives. In addition, the progress of deep learning and other algorithms has made it possible for researchers to use HAR in many fields including sports, health, and well-being. HAR is, for example, one of the most promising resources for helping older people with the support of their cognitive and physical function through day-to-day activities. This study focuses on the key role machine learning plays in the development of HAR applications. While numerous HAR surveys and review articles have previously been carried out, the main/overall HAR issue was not taken into account, and these studies concentrate only on specific HAR topics. A detailed review paper covering major HAR topics is therefore essential. This study analyses the most up-to-date studies on HAR in recent years and provides a classification of HAR methodology and demonstrates advantages and disadvantages for each group of methods. This paper finally addresses many problems in the current HAR subject and provides recommendations for potential study.

Mobile Phone Behavior: An Emerged Discipline of Research

2015 ◽  
pp. 399-409 ◽  
Author(s):  
Zheng Yan ◽  
Quan Chen
Keyword(s):  
Mobile Phone ◽  
Mobile Phones ◽  
Future Research ◽  
Behavioral Sciences ◽  
Mobile Phone Use ◽  
Detailed Review ◽  
Ubiquitous Technology ◽  
The Past

Mobile phones are becoming the most ubiquitous technology in the history. Researchers in various disciplines of behavioral sciences have been extensively examining how people use mobile phones and what influences mobile phone use have on people's lives for nearly 25 years. This chapter attempts to provide an overall picture of the science of mobile phone behavior by describing the past, present, and future of this emerged discipline of study in behavioral sciences. It provides a detailed review of five major areas and six specific topics of mobile phone behavior research and a brief outline of three directions of future research.

Remaking Retirement

2020 ◽  
Keyword(s):  
Interest Rates ◽  
Older Workers ◽  
Older Persons ◽  
The United States ◽  
Older Population ◽  
The Past ◽  
Cost Of Education ◽  
The World ◽  
Low Interest Rates ◽  
Key Aspects

Around the world, people nearing and entering retirement are holding ever-greater levels of debt than in the past. This is not a benign situation, as many pre-retirees and retirees are stressed about their indebtedness. Moreover, this growth in debt among the older population may render retirees vulnerable to financial shocks, medical care bills, and changes in interest rates. Contributors to this volume explore key aspects of the rise in debt across older cohorts, drill down into the types of debt and reasons for debt incurred by the older population, and review policies to remedy some of the financial problems facing older persons, in the United States and elsewhere. The authors explore which groups are most affected by debt, and they also identify the factors causing this important increase in leverage at older ages. It is clear that the economic and market environments are influential when it comes to saving and debt. Access to easy borrowing, low interest rates, and the rising cost of education have had important impacts on how much people borrow, and how much debt they carry at older ages. In this environment, the capacity to manage debt is ever more important as older workers lack the opportunity to recover for mistakes.

Power information network intrusion detection based on deep learning and cloud computing

2021 ◽  
pp. 1-9
Author(s):  
Xiangbing Zhao ◽  
Jianhui Zhou
Keyword(s):  
Cloud Computing ◽  
Deep Learning ◽  
Intrusion Detection ◽  
High Speed ◽  
Information Networks ◽  
Network Intrusion Detection ◽  
Information Network ◽  
Advantages And Disadvantages ◽  
Network Intrusion ◽  
Capture Mechanism

With the advent of the computer network era, people like to think in deeper ways and methods. In addition, the power information network is facing the problem of information leakage. The research of power information network intrusion detection is helpful to prevent the intrusion and attack of bad factors, ensure the safety of information, and protect state secrets and personal privacy. In this paper, through the NRIDS model and network data analysis method, based on deep learning and cloud computing, the demand analysis of the real-time intrusion detection system for the power information network is carried out. The advantages and disadvantages of this kind of message capture mechanism are compared, and then a high-speed article capture mechanism is designed based on the DPDK research. Since cloud computing and power information networks are the most commonly used tools and ways for us to obtain information in our daily lives, our lives will be difficult to carry out without cloud computing and power information networks, so we must do a good job to ensure the security of network information network intrusion detection and defense measures.

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

scholarly journals Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

2021 ◽  
Vol 26 (1) ◽  
pp. 200-215
Author(s):  
Muhammad Alam ◽  
Jian-Feng Wang ◽  
Cong Guangpei ◽  
LV Yunrong ◽  
Yuanfang Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Natural Scene ◽  
Remote Sensing Images ◽  
Advantages And Disadvantages ◽  
Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

scholarly journals Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3046
Author(s):  
Shervin Minaee ◽  
Mehdi Minaei ◽  
Amirali Abdolrashidi
Keyword(s):  
Deep Learning ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Visualization Technique ◽  
Convolutional Network ◽  
The Past ◽  
Multiple Datasets ◽  
The Face ◽  
Traditional Approaches

Facial expression recognition has been an active area of research over the past few decades, and it is still challenging due to the high intra-class variation. Traditional approaches for this problem rely on hand-crafted features such as SIFT, HOG, and LBP, followed by a classifier trained on a database of images or videos. Most of these works perform reasonably well on datasets of images captured in a controlled condition but fail to perform as well on more challenging datasets with more image variation and partial faces. In recent years, several works proposed an end-to-end framework for facial expression recognition using deep learning models. Despite the better performance of these works, there are still much room for improvement. In this work, we propose a deep learning approach based on attentional convolutional network that is able to focus on important parts of the face and achieves significant improvement over previous models on multiple datasets, including FER-2013, CK+, FERG, and JAFFE. We also use a visualization technique that is able to find important facial regions to detect different emotions based on the classifier’s output. Through experimental results, we show that different emotions are sensitive to different parts of the face.

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