INTRODUCTION: Artificial intelligence is a branch of computer science capable of analysing complex medical data. Their potential to exploit meaningful relationship with in a data set can be used in the diagnosis, treatment and predicting outcome in many clinical scenarios. METHODS: Medline and internet searches were carried out using the keywords 'artificial intelligence' and 'neural networks (computer)'. Further references were obtained by cross-referencing from key articles. An overview of different artificial intelligent techniques is presented in this paper along with the review of important clinical applications. RESULTS: The proficiency of artificial intelligent techniques has been explored in almost every field of medicine. Artificial neural network was the most commonly used analytical tool whilst other artificial intelligent techniques such as fuzzy expert systems, evolutionary computation and hybrid intelligent systems have all been used in different clinical settings. DISCUSSION: Artificial intelligence techniques have the potential to be applied in almost every field of medicine. There is need for further clinical trials which are appropriately designed before these emergent techniques find application in the real clinical setting.

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Artificial intelligence in medicine.

handbook of medical image processing and analysis is available in our book collection an online access to it is set as public so you can download it instantly. Our book servers saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the handbook of medical image processing and analysis is universally compatible with any devices to read.

Handbook Of Medical Image Processing And Analysis

Survey of machine learning algorithms for breast cancer detection using mammogram images

https://www.tandfonline.com/doi/pdf/10.1080/16168658.2021.2019430?needAccess=true

A Hybrid Method for Recommendation Systems based on Tourism with an Evolutionary Algorithm and Topsis Model

The emergence of innovative technologies has an impact on the methods of teaching and learning. With the rapid development of artificial intelligence (AI) technology in recent years, using AI in education has become more and more apparent. This article first outlines the application of AI in the field of education, such as adaptive learning, teaching evaluation, virtual classroom, etc. And then analyzes its impact on teaching and learning, which has a positive meaning for improving teachers' teaching level and students' learning quality. Finally, it puts forward the challenges that AI applications may face in education in the future and provides references for AI to promote education reform.
 
Received: 16 January 2021 / Accepted: 24 March 2021 / Published: 10 May 2021

/pdf/a-review-on-artificial-intelligence-in-education-azd9rb2kp8.pdf

A Review on Artificial Intelligence in Education

eLearning has completely transformed the way in which learning is imparted to students. Relevance of the topic being taught is often found lacking in learning material. Students can access lot of contents or video for learning different topics but keeping track of its relevance for him is a tedious job. Recommendation system plays an important role in such situations. The system which provides recommendation is a reference system that would recommend an education work to a student based on the works already done. This paper reviews the main paradigms of recommendation systems using explicit and implicit feedback and the various methodologies that have been implemented to design recommender systems to enhance learning. The concepts of eLearning and recommendation systems are summarized. eLearning recommendation systems will be useful to enhance learning.

Recommender System in eLearning: A Survey

Conversational AI is a sub-domain of Artificial Intelligence that deals with speech-based or text-based AI agents that have the capability to simulate and automate conversations and verbal interactions. Conversational AI Agents like chatbots and voice assistants have proliferated due to two main developments. On one hand the methods required to develop highly accurate AI models i.e. Machine Learning, Deep Learning have seen a tremendous amount of advancement due to the increasing research interest in these fields accompanied by the progress in achieving higher computing power with the help of complex hardware architectures like GPUs and TPUs. Secondly, due to the Natural Language interface and the nature of their design, conversational agents have been seen as a natural fit in a wide array of applications like healthcare, customer care, ecommerce and education. This rise in the practical implementation and their demand has in turn made Conversational AI a ripe area for innovation and novel research. Newer and more complex models for the individual core components of a Conversational AI architecture are being introduced at a never before seen rate. This study is intended to shed light on such latest research in Conversational AI architecture development and also to highlight the improvements that these novel innovations have achieved over their traditional counterparts. This paper also provides a comprehensive account of some of the research opportunities in the Conversational AI domain and thus setting up the stage for future research and innovation in this field.

Conversational AI: An Overview of Methodologies, Applications & Future Scope

Diabetes mellitus is a chronic disease and a health challenge worldwide. According to the International Diabetes Federation, 451 million people across the globe have diabetes, with this number anticipated to rise up to 693 million people by 2045. It has been shown that 80% of the complications arising from type II diabetes can be prevented or delayed by early identification of the people who are at risk. Diabetes is difficult to diagnose in the early stages as its symptoms grow subtly and gradually. In a majority of the cases, the patients remain undiagnosed until they are admitted for a heart attack or begin to lose their sight. This paper analyzes the different classification algorithms based on a patient's health history to aid doctors identify the presence of as well as promote early diagnosis and treatment. The experiments were conducted on Pima Indian Diabetes data set. Various classifiers used include K Nearest Neighbors, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, Support Vector Machine and Neural Network. Results demonstrate that Random Forests performed well on the data set giving an accuracy of 79.7%.

/pdf/analysis-of-classifiers-for-prediction-of-type-ii-diabetes-14w2zv090b.pdf

Analysis of Classifiers for Prediction of Type II Diabetes Mellitus

Diabetic Retinopathy (DR) is a diabetes complication that affects vision. It is caused by damage to the blood vessels of retina. Early and accurate detection of DR is crucial to reduce likelihood of progression to proliferative retinopathy and blindness. This paper proposes a method for classifying the severity of DR using deep learning. Experiments were conducted by blending the members of EfficientNet for classification of the diabetic retinopathy image as no DR, mild, moderate, severe, or proliferative DR. The models have been trained using different datasets and best model achieved a quadratic kappa score of 0.924377 on the APTOS test dataset. The results are promising and warrant further investigation. The presented model has the potential aid in fast diagnosis for better early detection of DR.

Diabetic Retinopathy Classification using a Combination of EfficientNets

Diagnostic radiology struggles to maintain high interpretation accuracy. Retrieval of past similar cases would help the inexperienced radiologist in the interpretation process. Character n-gram model has been effective in text retrieval context in languages such as Chinese where there are no clear word boundaries. We propose the use of visual character n-gram model for representation of image for classification and retrieval purposes. Regions of interests in mammographic images are represented with the character ngram features. These features are then used as input to back-propagation neural network for classification of regions into normal and abnormal categories. Experiments on miniMIAS database show that character n-gram features are useful in classifying the regions into normal and abnormal categories. Promising classification accuracies are observed (83.33%) for fatty background tissue warranting further investigation. We argue that Classifying regions of interests would reduce the number of comparisons necessary for finding similar images from the database and hence would reduce the time required for retrieval of past similar cases.

http://www.airccse.org/journal/jma/6214ijma04.pdf

Pradnya Kulkarni

Papers

Recommender System in eLearning: A Survey

Conversational AI: An Overview of Methodologies, Applications & Future Scope

Analysis of Classifiers for Prediction of Type II Diabetes Mellitus

Diabetic Retinopathy Classification using a Combination of EfficientNets

Visual character n-grams for classification and retrieval of radiological images