scholarly journals Inside-out Vision for Treatment Identification in Dental Setup using Machine Learning

2021 ◽  
Author(s):  
Shaheena Noor ◽  
Humera Noor Minhas ◽  
Muhammad Imran Saleem ◽  
Vali Uddin ◽  
Najma Ismat
Keyword(s):  
Machine Learning ◽  
Dental Treatment ◽  
Multinomial Logistic Regression ◽  
Human Machine Interaction ◽  
Machine Learning Applications ◽  
In The Wild ◽  
Recognition Of Objects ◽  
Dental Applications ◽  
Machine Interaction ◽  
Inside Out

Abstract Smart clinics have gained much popularity due to the technological advancements in areas like computer vision. The recognition of objects and activities and overall perceiving the environment lies at the core of such systems. This is essential not just for the eco-independent systems, but also for Human-Machine-Interaction - specially in scenarios with small work-areas like dental treatment. In this paper, we compare a number of machine learning mod- els (including Multinomial Logistic Regression, Lazy Instance-based Learning (IBk), Sequential Minimal Optimization (SMO), Hoe ding Tree and Random Tree) for robustly identifying dental treatments. We take the objects-focussed as input, which covers parameters like material, symptoms of the patient teeth and tools used by the dentist. We take advantage of the fact that the issue of identifying a particular treatment can be solved by recognizing the objects seen during an activity. We collected a dental dataset in-the-wild and ran our tests to find that integrating different parameters improves accuracy relative to using each one separately. However, we also noted that in certain cases using the symptoms stand-alone gave better results. Also, with respect to RMS error convergence, symptoms showed to have lower error compared to combined. Finally, we noticed that the combined approach led to longer build and test times for the machine learning models. This shows that in machine learning applications in general and in medical/dental applications in particular, adding more parameters does not always lead to improved results. Rather it depends on the ML tool used, the parameters considered and the data given as input.

scholarly journals IOT based Advanced Medicine Dispenser Integrated with an Interactive Web Application

2018 ◽  
Vol 7 (4.10) ◽  
pp. 46
Author(s):  
Nanda Kishor Panda ◽  
Shubham Bhardwaj ◽  
H. Bharadwaj ◽  
Rohil Singhvi
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
The Internet ◽  
Human Machine Interaction ◽  
Everyday Objects ◽  
Front End ◽  
Healthcare Monitoring ◽  
User Friendly ◽  
Machine Interaction ◽  
Interactive User

Internet of Things (IOT) is a development of the internet which plays a  major role in integrating human-machine interaction by allowing everyday objects to send and receive data in a variety of applications. Using IOT in healthcare monitoring provides an avenue for doctors and patients to interact and to track the dosage of medication administered. The paper presents an interactive, user friendly network integrated with an automated medicine dispenser which uses IOT, cloud computing and machine learning. The network was built on a python tornado framework with a front end developed using materialise CSS. The feasibility of this approach was validated by building a prototype and conducting a survey.  

scholarly journals Machine Vision Enabled Bot for Object Tracking

2020 ◽  
Vol 1 (1) ◽  
pp. 15-26
Author(s):  
Rupali Patil ◽  
Adhish Velingkar ◽  
Mohammad Nomaan Parmar ◽  
Shubham Khandhar ◽  
Bhavin Prajapati
Keyword(s):  
Machine Learning ◽  
Object Tracking ◽  
Cloud Storage ◽  
Object Motion ◽  
Human Machine Interaction ◽  
Camera Control ◽  
Live Feed ◽  
Detection And Tracking ◽  
Left And Right ◽  
Machine Interaction

Object detection and tracking are essential and testing undertaking in numerous PC vision appliances. To distinguish the object first find a way to accumulate information. In this design, the robot can distinguish the item and track it just as it can turn left and right position and afterward push ahead and in reverse contingent on the object motion. It keeps up the consistent separation between the item and the robot. We have designed a webpage that is used to display a live feed from the camera and the camera can be controlled by the user efficiently. Implementation of machine learning is done for detection purposes along with open cv and creating cloud storage. The pan-tilt mechanism is used for camera control which is attached to our 3-wheel chassis robot through servo motors. This idea can be used for surveillance purposes, monitoring local stuff, and human-machine interaction.

scholarly journals Human-aided artificial intelligence: Or, how to run large computations in human brains? Toward a media sociology of machine learning

2019 ◽  
Vol 22 (10) ◽  
pp. 1868-1884 ◽  
Author(s):  
Rainer Mühlhoff
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cognitive Abilities ◽  
Human Machine Interaction ◽  
Technological Transformation ◽  
Media Sociology ◽  
Media Technologies ◽  
Media Philosophy ◽  
Human Brains ◽  
Machine Interaction

Today, artificial intelligence (AI), especially machine learning, is structurally dependent on human participation. Technologies such as deep learning (DL) leverage networked media infrastructures and human-machine interaction designs to harness users to provide training and verification data. The emergence of DL is therefore based on a fundamental socio-technological transformation of the relationship between humans and machines. Rather than simulating human intelligence, DL-based AIs capture human cognitive abilities, so they are hybrid human-machine apparatuses. From a perspective of media philosophy and social-theoretical critique, I differentiate five types of “media technologies of capture” in AI apparatuses and analyze them as forms of power relations between humans and machines. Finally, I argue that the current hype about AI implies a relational and distributed understanding of (human/artificial) intelligence, which I categorize under the term “cybernetic AI.” This form of AI manifests in socio-technological apparatuses that involve new modes of subjectivation, social control, and digital labor.

scholarly journals Self-Powered Intelligent Human-Machine Interaction for Handwriting Recognition

Research ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hang Guo ◽  
Ji Wan ◽  
Haobin Wang ◽  
Hanxiang Wu ◽  
Chen Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Human Computer Interaction ◽  
Supervised Machine Learning ◽  
Triboelectric Nanogenerator ◽  
Human Machine Interaction ◽  
The Neural Network ◽  
Interaction Interface ◽  
Machine Interaction ◽  
Computer Interaction

Handwritten signatures widely exist in our daily lives. The main challenge of signal recognition on handwriting is in the development of approaches to obtain information effectively. External mechanical signals can be easily detected by triboelectric nanogenerators which can provide immediate opportunities for building new types of active sensors capable of recording handwritten signals. In this work, we report an intelligent human-machine interaction interface based on a triboelectric nanogenerator. Using the horizontal-vertical symmetrical electrode array, the handwritten triboelectric signal can be recorded without external energy supply. Combined with supervised machine learning methods, it can successfully recognize handwritten English letters, Chinese characters, and Arabic numerals. The principal component analysis algorithm preprocesses the triboelectric signal data to reduce the complexity of the neural network in the machine learning process. Further, it can realize the anticounterfeiting recognition of writing habits by controlling the samples input to the neural network. The results show that the intelligent human-computer interaction interface has broad application prospects in signature security and human-computer interaction.

scholarly journals Prediction of state anxiety by machine learning applied to photoplethysmography data

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10448
Author(s):  
David Perpetuini ◽  
Antonio Maria Chiarelli ◽  
Daniela Cardone ◽  
Chiara Filippini ◽  
Sergio Rinella ◽  
...  
Keyword(s):  
Machine Learning ◽  
State Anxiety ◽  
Affective Computing ◽  
Pulse Wave ◽  
Learning Algorithm ◽  
Nervous Activity ◽  
Human Machine Interaction ◽  
Promising Tool ◽  
Custom Made ◽  
Machine Interaction

Background As the human behavior is influenced by both cognition and emotion, affective computing plays a central role in human-machine interaction. Algorithms for emotions recognition are usually based on behavioral analysis or on physiological measurements (e.g., heart rate, blood pressure). Among these physiological signals, pulse wave propagation in the circulatory tree can be assessed through photoplethysmography (PPG), a non-invasive optical technique. Since pulse wave characteristics are influenced by the cardiovascular status, which is affected by the autonomic nervous activity and hence by the psychophysiological state, PPG might encode information about emotional conditions. The capability of a multivariate data-driven approach to estimate state anxiety (SA) of healthy participants from PPG features acquired on the brachial and radial artery was investigated. Methods The machine learning method was based on General Linear Model and supervised learning. PPG was measured employing a custom-made system and SA of the participants was assessed through the State-Trait Anxiety Inventory (STAI-Y) test. Results A leave-one-out cross-validation framework showed a good correlation between STAI-Y score and the SA predicted by the machine learning algorithm (r = 0.81; p = 1.87∙10−9). The preliminary results suggested that PPG can be a promising tool for emotions recognition, convenient for human-machine interaction applications.

Autoencoders in Deep Neural Network Architecture for Real Work Applications

2020 ◽  
pp. 214-236
Author(s):  
Houda Abouzid ◽  
Otman Chakkor
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Source Separation ◽  
Science Research ◽  
Learning To Learn ◽  
Human Machine Interaction ◽  
Machine Learning Applications ◽  
Pass Through

The most heard sound exists as a mixture of several audio sources. All human beings have the ability to concentrate on a single source of their interest and ignore the other sources as disturbing background noise. To apply this powerful gift to a machine, it must obligatory pass through the source separation process. If there is not enough information about the process of mixture of those sources and their nature as well, the problem is known by Blind Source Separation BSS. This thesis is dedicated to study the BSS as a solution for human machine interaction. The objective consists in recovering one or several source signals from a given mixture signal. Recently, the science research is towards artificial intelligence and machine learning applications. The proposed approach for the separation will be to apply a Deep Neural Network method based on Keras. Extracting features from the audio with signal processing techniques and machine learning to learn a representation from the audio for the compression tasks and the suppression of the noise will improve the state-of-the-art.

How Artificial Intelligence, Machine Learning and Deep Learning are Radically Different?

2018 ◽  
Vol 8 (2) ◽  
pp. 1
Author(s):  
Tanya Tiwari ◽  
Tanuj Tiwari ◽  
Sanjay Tiwari
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Analysis Software ◽  
Science Data ◽  
Human Machine Interaction ◽  
Learning Techniques ◽  
Nested Hierarchy ◽  
Further Development ◽  
Machine Interaction

There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). A computer system able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. Artificial Intelligence has made it possible. Deep learning is a subset of machine learning, and machine learning is a subset of AI, which is an umbrella term for any computer program that does something smart. In other words, all machine learning is AI, but not all AI is machine learning, and so forth. Machine Learning represents a key evolution in the fields of computer science, data analysis, software engineering, and artificial intelligence. Machine learning (ML)is a vibrant field of research, with a range of exciting areas for further development across different methods and applications. These areas include algorithmic interpretability, robustness, privacy, fairness, inference of causality, human-machine interaction, and security. The goal of ML is never to make “perfect” guesses, because ML deals in domains where there is no such thing. The goal is to make guesses that are good enough to be useful. Deep learning is a particular kind of machine learning that achieves great power and flexibility by learning to represent the world as nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones. This paper gives an overview of artificial intelligence, machine learning & deep learning techniques and compare these techniques.

scholarly journals Data Science on Industrial Data—Today’s Challenges in Brown Field Applications

Challenges ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 2
Author(s):  
Tilman Klaeger ◽  
Sebastian Gottschall ◽  
Lukas Oehm
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Data Science ◽  
Production Systems ◽  
Ground Truth ◽  
Data Sets ◽  
Human Machine Interaction ◽  
Automation Systems ◽  
Machine Learning Applications ◽  
Cyber Physical Production Systems

Much research is done on data analytics and machine learning for data coming from industrial processes. In practical approaches, one finds many pitfalls restraining the application of these modern technologies especially in brownfield applications. With this paper, we want to show state of the art and what to expect when working with stock machines in the field. The paper is a review of literature found to cover challenges for cyber-physical production systems (CPPS) in brownfield applications. This review is combined with our own personal experience and findings gained while setting up such systems in processing and packaging machines as well as in other areas. A major focus in this paper is on data collection, which tends be more cumbersome than most people might expect. In addition, data quality for machine learning applications is a challenge once leaving the laboratory and its academic data sets. Topics here include missing ground truth or the lack of semantic description of the data. A last challenge covered is IT security and passing data through firewalls to allow for the cyber part in CPPS. However, all of these findings show that potentials of data driven production systems are strongly depending on data collection to build proclaimed new automation systems with more flexibility, improved human–machine interaction and better process-stability and thus less waste during manufacturing.

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