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Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective

Section 2 shows the motivation and scope of this work along with the systematic reviews and meta-analysis process. Section 3 depicts the definition of m-health and its schematic representation along with the mobile sensors and their applications in m-health. Section 4 explains a detailed review about the applications of AI in m-health along with the performance measurement indicators used to examine the quality of m-healthcare. Section 5 presents the applications of big data analytics in m-health followed by the additional summary of its applications in the healthcare sector. Section 6 presents the proposed model based on the AI and big data analytics for m-health.

After completing the process of searching the article, the authors concealed the titles and abstracts of the retrieved articles using an inclusion and exclusion criteria. At last, 106 articles were obtained and kept for the review process.

Various concepts of analytics such as data mining and AI can be used to analyze the obtained data. These analytical approaches in big data can be used to identify the anomalies by analyzing a huge amount of data from various datasets and their sources. Figure 4 shows an example of the smartphone-based m-health model with the combination of AI and big data analytics.

The authors proposed a framework for the organizations in healthcare in providing intelligence-based smart services. Their detailed research depicts a novel framework for the smart healthcare system based on big data and also makes the research directions interdisciplinary. In fact, the proposed framework is the combination of three technical streams such as the AI, agent-based systems, and data mining along with the smart health.

These are combined to convey the perception of enabling a decision-making process in real time. Various concepts of analytics such as data mining and AI are used to analyze the obtained data from a patient. The AI-based engine comprises two modules such as the stream analysis module and the AI-based report management tool.

It is also used as a platform for the disease control, treatment, and diagnosis tool. It also detects the irregular records which are present in the EHR.

The big data analysis engine consists of two modules such as storage for big data and a statistical data analysis tool. The statistical data analysis tool retrieves the input data, processes it into queries, and then sends it to the AI-based engine.

The system can never be too accurate to replace the humans and their predictions. These systems have been made to ease out the health structure but they cannot be a substitute to human.

These m-health systems also make a user/patient to be dependent completely on them. If the user loses his or her mobile phone and user id/password, there is a possibility for all the information to be lost temporarily or even permanently. There might be a chance for various issues in the privacy and security of the health data present in it.

m-Health is a technique which uses mobile devices and technology for health interventions and is the biggest technological advancement of recent research. Similarly, the application of AI and the analytics of big data in healthcare are considered as one of the important achievements for the intelligent healthcare system. In this paper, a detailed review of the m-healthcare system is proposed based on the application of AI and big data analytics. Various advantages from this combination for the m-health perspective are presented. Particularly, all applications of relevant technological areas and the building blocks such as communications, sensors, and computing which are associated with mobile health are explained in detail. The role of various tools of machine learning within the current m-health model is also illustrated.

Artificial Intelligence in Medicine: Applications, implications, and limitations

However, while some algorithms can compete with and sometimes outperform clinicians in a variety of tasks, they have yet to be fully integrated into day-to-day medical practice.

Generally, the jobs AI algorithms can do are tasks that require human intelligence to complete, such as pattern and speech recognition, image analysis, and decision making. However, humans need to explicitly tell the computer exactly what they would look for in the image they give to an algorithm, for example.

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Advances in computational power paired with massive amounts of data generated in healthcare systems make many clinical problems ripe for AI applications.

Applications of AI in classical software engineering

The analysis results that major achievements and future potentials of AI are a) the automation of lengthy routine jobs in software development and testing using algorithms, e. AI thus contributes to speed up development processes, realize development cost reductions and efficiency gains.

The systematic review of prior empirical studies explicitly refers to experiences with AI application at the respective stages of the development life cycle. The review includes more than 60 publications in peer-reviewed journals and conference papers published between 2010 and 2020, to ensure topicality and academic quality of the results.

AI comprises several novel technologies and their development lines are still open.

As of today, AI indirectly enhances project planning mechanisms, according to participant 2. The analysis of data pools of earlier projects provides realistic estimates of failure quotas and iteration routines in earlier projects and locates potential areas of difficulties.

Participant 3 esteems predictive analysis equipment which as of today accesses large online data pools to predict trends and outcomes of new applications. Predictive analyses enable software designers to plan their products more proactively and adjust to new technological trends in their emergence.

AI, according to participant 1, provides structured access to immense amounts of data which are retrieved from earlier similar projects, for instance. The number of expected bugs and their location is reliably predicted on that basis and error avoidance routines are established more effectively. AI has sped up the design speed of software projects, according to participant 2, by enabling programs to execute routine tasks, which previously had to be done by human developers.

The tool has reduced software development times and improves output quality.

Participant 3 is experienced with automated code compliers, which support the transformation of high-level programming language codes in machine-executable instructions.

Participant 5 agrees that developers can foresee and advocate for change, while AI routines can only apply and process existing knowledge.

Table 2 summarizes technologies, achievements, limitations and future development potentials of AI for the six stages of the software engineering life cycle as available from previous studies and the interviews.

The review has shown that the basic principles and technologies underlying AI supported software engineering are similar across the life cycle stages. However, AI comes to its limits when novel insights are sought and new problem sets are meant to be discovered and, innovative routines have to be developed. These fundamental activities so far remain at the hands of human designers and developers. Future AI routines could become more self-reliant if they could compose new tasks and solutions without human support.

Artificial Intelligence and Its Applications

In the paper entitled “A wavelet-based robust relevance vector machine based on sensor data scheduling control for modeling mine gas gushing forecasting on virtual environment,” W. present a wavelet-based robust relevance vector machine based on sensor data scheduling control for modeling mine gas gushing forecasting. Morlet wavelet function can be used as the kernel function of robust relevance vector machine. Mean percentage error has been used to measure the performance of the proposed method in this study. As the mean prediction error of mine gas gushing of the WRRVM model is less than 1.5% and the mean prediction error of mine gas gushing of the RVM model is more than 2.

Virtually in CFSO3, just the initial values of positions and velocities of the swarm members have to be randomly assigned.

In the paper entitled “Research on the production scheduling optimization for virtual enterprises,” M. An improved genetic algorithm is proposed in the model to solve the time complexity of virtual enterprise production scheduling.

In the paper entitled “Interesting activities discovery for moving objects based on collaborative filtering,” G. propose a method of interesting activities discovery based on collaborative filtering. First, the interesting degree of the objects’ activities is calculated comprehensively. Then, combined with the newly proposed hybrid collaborative filtering, similar objects can be computed and all kinds of interesting activities can be discovered.

The presented method predicts general context based on probability theory through a novel graphical data structure, which is a kind of weighted directed multigraphs. They also consider the periodic property of context data and devise a good solution to context data with such property.

In the paper entitled “Study on semi-parametric statistical model of safety monitoring of cracks in concrete dams,” C. consider that cracks are one of the hidden dangers in concrete dams. The study on safety monitoring models of concrete dam cracks has always been difficult. Previous projects show that the semiparametric statistical model has a stronger fitting effect and has a better explanation for cracks in concrete dams than the parametric statistical model. However, when used for forecast, the forecast capability of the semiparametric statistical model is equivalent to that of the parametric statistical model.

In the paper entitled “Efficient secure multiparty computation protocol for sequencing problem over insecure channel,” Y. believe that secure multiparty computation is more and more popular in electronic bidding, anonymous voting, and online auction, as a powerful tool in solving privacy preserving cooperative problems. Privacy preserving sequencing problem that is an essential link is regarded as the core issue in these applications. However, due to the difficulties of solving multiparty privacy preserving sequencing problem, related secure protocol is extremely rare. In order to break this deadlock, their paper presents an efficient secure multiparty computation protocol for the general privacy-preserving sequencing problem based on symmetric homomorphic encryption.

In the paper entitled “Nighttime fire/smoke detection system based on a support vector machine,” C. If smoke appears within the monitoring zone created from the diffusion or scattering of light in the projected path, the camera sensor receives a corresponding signal. Characterization of smoke is carried out by a nonlinear classification method using a support vector machine, and this is applied to identify the potential fire/smoke location.

9 Mesmerizing Examples Of artificial intelligence examples applications

present “Robust quadratic regression and its application to energy-growth consumption problem.” The paper proposes a robust quadratic regression model to handle the statistics inaccuracy. First, they give a solvable equivalent semidefinite programming for the robust least square model with ball uncertainty set. Then the result is generalized to robust models under – and -norm criteria with general ellipsoid uncertainty sets. In addition, they establish a robust regression model for per capita GDP and energy consumption in the energy-growth problem under the conservation hypothesis.

In the paper “Identification of code-switched sentences and words using language modeling approaches,” L. A code-switched sentence is detected on the basis of whether it contains words or phrases from another language. Once the code-switched sentences are identified, the positions of the code-switched words in the sentences are then identified. Experimental results show that the language modeling approach achieved an F-measure of 80. For the identification of code-switched words, the word-based and POS-based models achieved F-measures of 41.

In the paper entitled “Matching cost filtering for dense stereo correspondence,” Y. propose a new cost-aggregation module to compute the matching responses for all the image pixels at a set of sampling points generated by a hierarchical clustering algorithm. The complexity of this implementation is linear both in the number of image pixels and in the number of clusters. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art local methods in terms of both accuracy and speed.

3% of image SIFT keypoints, uSee exceeded prior literature results by achieving an accuracy of 99.

In the paper entitled “Solving the balanced academic curriculum problem using the ACO metaheuristic,” J. consider that the balanced academic curriculum problem consists in the assignation of courses to academic periods satisfying all the load limits and prerequisite constraints. They present the design of a solution to the balanced academic curriculum problem based on the ACO metaheuristic, in particular via the best-worst ant system.

In the paper entitled “Hybrid functional-neural approach for surface reconstruction,” A. Gálvez introduce a new hybrid functional-neural approach for surface reconstruction. The approach is based on the combination of two powerful artificial intelligence paradigms: on one hand, they apply the popular Kohonen neural network to address the data parameterization problem. On the other hand, they introduce a new functional network, called NURBS functional network, whose topology is aimed at reproducing faithfully the functional structure of the NURBS surfaces. These neural and functional networks are applied in an iterative fashion for further surface refinement.

In the paper “Optimum performance-based seismic design using a hybrid optimization algorithm,” S. present a hybrid optimization method to optimum seismic design of steel frames considering four performance levels. These performance levels are considered to determine the optimum design of structures to reduce the structural cost. A pushover analysis of steel building frameworks subject to equivalent-static earthquake loading is utilized.

We would like to express our gratitude to all of the authors for their contributions and the reviewers for their effort providing constructive comments and feedback.

Applications of AI in classical software engineering

The analysis results that major achievements and future potentials of AI are a) the automation of lengthy routine jobs in software development and testing using algorithms, e. AI thus contributes to speed up development processes, realize development cost reductions and efficiency gains.

The systematic review of prior empirical studies explicitly refers to experiences with AI application at the respective stages of the development life cycle. The review includes more than 60 publications in peer-reviewed journals and conference papers published between 2010 and 2020, to ensure topicality and academic quality of the results.

AI comprises several novel technologies and their development lines are still open.

As of today, AI indirectly enhances project planning mechanisms, according to participant 2. The analysis of data pools of earlier projects provides realistic estimates of failure quotas and iteration routines in earlier projects and locates potential areas of difficulties.

Participant 3 esteems predictive analysis equipment which as of today accesses large online data pools to predict trends and outcomes of new applications. Predictive analyses enable software designers to plan their products more proactively and adjust to new technological trends in their emergence.

AI, according to participant 1, provides structured access to immense amounts of data which are retrieved from earlier similar projects, for instance. The number of expected bugs and their location is reliably predicted on that basis and error avoidance routines are established more effectively. AI has sped up the design speed of software projects, according to participant 2, by enabling programs to execute routine tasks, which previously had to be done by human developers.

The tool has reduced software development times and improves output quality.

Participant 3 is experienced with automated code compliers, which support the transformation of high-level programming language codes in machine-executable instructions.

Participant 5 agrees that developers can foresee and advocate for change, while AI routines can only apply and process existing knowledge.

Table 2 summarizes technologies, achievements, limitations and future development potentials of AI for the six stages of the software engineering life cycle as available from previous studies and the interviews.

The review has shown that the basic principles and technologies underlying AI supported software engineering are similar across the life cycle stages. However, AI comes to its limits when novel insights are sought and new problem sets are meant to be discovered and, innovative routines have to be developed. These fundamental activities so far remain at the hands of human designers and developers. Future AI routines could become more self-reliant if they could compose new tasks and solutions without human support.

Artificial intelligence for structural glass engineering applications — overview, case studies and future potentials

6 are discussed at this point, as the conduction of every step is essential for building a sensible AI/ML application. This step may take minutes to months in dependence of the problem and the data structure of the respective environment. It is advisable to consider standardization protocols for this step in order to guarantee data consistency within a company.

As shown in Fig 16, the trained U-Net is well suited to create a mask image from the original image without the need for further human interaction. A slight improvement of the mask images created by AI could be achieved by the cut-off condition or binary prediction. The presented NN for predicting the cut glass edge is therefore very accurate and saves a significant amount of time in the prediction and production of mask images. In addition, the mask images can be further processed, for example to make statistical analyses of the break structure of the cut glass edge.

Providing this AI-based method delivers remarkable economic and ecological advantages.

The careful study of the organization learned by each model revealed the existence of a deeper bias, or architectural style.

To summarize this section, AI has the potential to accelerate design and structural verification processes to a great demand while customization wishes may enter more naturally and affordably. The authors are currently at a stage, where first knowledge and experiences are gathered with these ideas.

Artificial Intelligence Applications in Dermatology: Where Do We Stand?

Although most applications involve analyzing and classifying images, there are other tools such as risk assessment calculators. The most progress thus far has taken place in the field of melanoma diagnosis, followed by ulcer and psoriasis assessment tools, then followed by numerous less frequently studied applications. However, critical barriers and literature gaps exist that significantly limit AI’s applicability to clinical practice at this time. For the less common applications, technological papers and commentaries are needed to improve capabilities and provoke interest. For the more saturated topics, there is a larger need for clinical trials providing evidence of clinical efficacy, while successfully overcoming the identified barriers.

Artificial Intelligence

This section outlines four application domains that will be developed in examples throughout the book.

Opportunities and Challenges for Artificial Intelligence Applications in Infrastructure Management During the Anthropocene

National Science and Technology Council says in its 2016 report, “There is no single definition of AI that is universally accepted by practitioners. Some define AI loosely as a computerized system that exhibits behavior that is commonly thought of as requiring intelligence. Others define AI as a system capable of rationally solving complex problems or taking appropriate actions to achieve its goals in whatever real world circumstances it encounters.” Herein, we use “AI” to include big data and analytics dimensions, but ultimately describe the leadership and intelligence capabilities that are needed to replace or augment people.

How is Bayes’ Theorem used in artificial intelligence and machine learning?

Bayesian network formalism was invented to allow efficient representation of, and rigorous reasoning with, uncertain knowledge. This approach largely overcomes many problems of the probabilistic reasoning systems to the 1960s and 70s; it now dominates AI research on uncertain reasoning and expert systems.

Like predicting a particular disease based on the symptoms and physical condition of the patient. There are many algorithms currently in use that are based on this theorem, like binary and multi-class classifier, for example, email spam filters.

Since you are a highschool student I will try to express it easier. It is a problem for a machine to make a decision if you haven’t given that information to it before. But sometimes there can be so many cases, here Data Mining, Neural Networks, Fuzzy Logic etc are used withing AI.

Advanced Applications of Neural Networks and Artificial Intelligence: A Review

Neural Networks can result in such devices which are able to detect faults in those areas where fault detection is difficult for human beings. Eg: fault detection in tracks of Railways, Metros and Roller coasters.Neural networks can detect patterns so it can detect faults when the railway track donot resemble its original shape i. having distorted shapes or having gaps, cracks or bents in it. These neural can be combined with a gps to locate their position as well as the position of crack. Also Thehealth of tracks can be checked by measuring their width, inclination and condition of screws. This in turn can be of great use to avoid accidents.

The ability of recognising the patterns can be further extended to recognise 3D objects. Recognition of 3D objects can help us in finding the objects. We can implement this technique in robotics so that the robots could identify objects. It will be very helpful in industries where robots can work as assistants for humans. Using this technique we will be able to search useful objects from trashes. The same can also be used to flter the non recyclable waste from recyclable waste in garbage treatment plant. Robots which are able to search 3D objects can also search humans during a rescue mission.
Artificial Intelligence artificial intelligence examples applications

Artificial Intelligence with MIT App Inventor

MIT is building tools into App Inventor that will enable even beginning students to create original AI applications that would have been advanced research a decade ago.

26 Artificial Intelligence and Machine Learning

Classification algorithms are used when the outputs are restricted to a limited set of values. For an algorithm that identifies spam emails, the output would be the prediction of either “spam” or “not spam”, represented by the Boolean values true and false. Regression algorithms are named for their continuous outputs, meaning they may have any value within a range.

In unsupervised learning, the algorithm builds a mathematical model from a set of data which contains only inputs and no desired output labels. Unsupervised learning algorithms are used to find structure in the data, like grouping or clustering of data points. Unsupervised learning can discover patterns in the data, and can group the inputs into categories, as in feature learning.

INSPIRE standards as a framework for artificial intelligence applications: a landslide example

The similarity score between a given model and instance is used as a proxy of landslide susceptibility. A high similarity score between an instance and a landslide susceptibility model signals a high susceptibility to that type of landslide.

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From a data structure perspective, INSPIRE code lists cannot currently host multi-hierarchical taxonomies. This limits the nature of reasoning that can be brought to bear on them.

The extensibility of INSPIRE allows for domain-specific applications, like the approach presented in this paper, to fit within the INSPIRE framework. However, problems may also arise from the fact that INSPIRE is extensible. Extensibility allows greater precision in terminology and schema for a specific application, but this allows different public and private institutions to implement separate and eventually incompatible extensions. Much work remains at the level of thematic clusters to implement as many standardized vocabularies and schemas as possible.

Ontologies provide the formal structure for INSPIRE code lists to run algorithms similar to that applied here.

This study also illustrates that, in their current state of development, the INSPIRE standards are not sufficiently expressive to support complex landslide susceptibility mapping. We provided an example of how INSPIRE’s extension capabilities may be implemented to add the required expressivity. Through its Re3gistry register, this extension framework ensures that the expressivity extensions are documented and available to all interested parties for reuse.

This project was first presented at the Helsinki 2019 INSPIRE data challenge and won the first prize. The authors would also like to acknowledge Massimiliano Alvioli et al.avaflow code, and WeTransform GmbH for the Hale Connect and Hale Studio software licences. We would also like to thank the reviewers Ivan Marchesini and Omar F.

Oftentimes a business or organization may wish to do the same task over and over again, and there is a lot of data at its disposal. The lecture discusses differences between prediction systems and recommendation tasks, supplemented by examples from industry that include e-commerce applications, language modeling, and image analysis.

Artificial Intelligence in Clinical Health Care Applications: Viewpoint

They may also serve to facilitate communication between scientists involved in AI and medical doctors.

Chapter 13. AI, Visitor Experience, and Museum Operations: A Closer Look at the Possible

1 This concept has shifted over the last few decades and there have been fluctuations in the application of AI technologies.

There are multiple classifications of AI technologies2 and among the most common methods we find are computer vision, machine learning, robotics, and natural language processing. All of these methods offer a way to speed processes which would otherwise involve human labor and costs, such as language translation or image identification. Although we may be in another hype phase of the term “AI,” popularity of the tools are normalizing its usage in research and practice.

Big Data in Machine Learning

Many, large companies from the control and automation segment are already infected with the “ML virus”. But according to industry experts, the use of machine learning in industrial applications is still in its infancy.

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