Darwin: Convolutional Neural Network based Intelligent Health Assistant

Rai, Siddhant; Raut, Akshayanand; Savaliya, Akash; Shankarmani, Radha

doi:10.1109/iceca.2018.8474861

Cited by 17 publications

(10 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generative neural networks are also being used in medication development [24]. Matching diverse kinds of medications is a difficult undertaking, but generative neural networks [25] have simplified the process. They are utilized to combine various ingredients, which is the basis for medicine development.…”

Section: Medical Diagnosis and Health Carementioning

confidence: 99%

See 1 more Smart Citation

The role of artificial neural network and machine learning in utilizing spatial information

Goel

Kumar

2022

Spat. Inf. Res.

View full text Add to dashboard Cite

show abstract

Section: Medical Diagnosis and Health Carementioning

confidence: 99%

“…Because the majority of the autopilot operations are automated, it's critical to ensure that they feel maximized security. ANNs have been used to model the quality of the surface alloys for aerospace applications which are based on the wire electrical discharge machining (WEDM) process [32] and structural components [25].…”

Section: Aerospacementioning

confidence: 99%

The role of artificial neural network and machine learning in utilizing spatial information

Goel

Kumar

2022

Spat. Inf. Res.

View full text Add to dashboard Cite

show abstract

“…As representative examples of contextaware and generative-based conversational agents, we highlight a generic emotionally aware chatbot for user engagement through open-conversation [143] and an open-domain question-answering chatbot for user support [102]. Complementary, some approaches use convolutional neural networks (CNN) not only for intent/entity classification [104], but also in the context of image processing [113] as a complementary interaction mechanism with users for an extension of the knowledge base of the conversational agent.…”

Section: Technical Implementation Specifications (F6)mentioning

confidence: 99%

“…Although most examples are healthcare-related, they also report commerce and generic-purpose conversational agents. In alignment with Dsouza et al [27], healthcare-focused primary studies use public or private medical knowledge repositories like scrapped data from medical-related forums [11] or disease-symptom mapping knowledge databases [104]. Additionally, two additional insights can be extracted from these examples.…”

Section: Training Testing and Evaluation (Sq4)mentioning

confidence: 99%

Conversational Agents in Software Engineering: Survey, Taxonomy and Challenges

Motger¹,

Franch²,

Marco³

2021

Preprint

View full text Add to dashboard Cite

The use of natural language interfaces in the field of human-computer interaction is undergoing intense study through dedicated scientific and industrial research. The latest contributions in the field, including deep learning approaches like recurrent neural networks, the potential of context-aware strategies and user-centred design approaches, have brought back the attention of the community to software-based dialogue systems, generally known as conversational agents or chatbots. Nonetheless, and given the novelty of the field, a generic, context-independent overview on the current state of research of conversational agents covering all research perspectives involved is missing. Motivated by this context, this paper reports a survey of the current state of research of conversational agents through a systematic literature review of secondary studies. The conducted research is designed to develop an exhaustive perspective through a clear presentation of the aggregated knowledge published by recent literature within a variety of domains, research focuses and contexts. As a result, this research proposes a holistic taxonomy of the different dimensions involved in the conversational agents' field, which is expected to help researchers and to lay the groundwork for future research in the field of natural language interfaces. CCS Concepts: • General and reference → Surveys and overviews; • Human-centered computing → Natural language interfaces.

show abstract

“…Fooling neural networks is an important subject because machine learning models are widely used in medicine for automating many processes and for helping with diagnosis. For example, Rai et al proposed a convolutional neural network for healthcare assistant 29 while Rastgar-Jazi and Fernando used neural networks for detecting heart abnormalities from electrocardiogram (ECG) data. 30 Similarly, authors of 31 used neural networks for prediction and prevention of heart attacks from ECG data while Murugesan and Sukanesh used neural networks for detecting brain tumours in electroencephalograms (EEG) signals.…”

Section: Introductionmentioning

confidence: 99%

Model Fooling Attacks Against Medical Imaging: A Short Survey

Sipola¹,

Puuska²,

Kokkonen³

2020

ISIJ

View full text Add to dashboard Cite

This study aims to find a list of methods to fool artificial neural networks used in medical imaging. We collected a short list of publications related to machine learning model fooling to see if these methods have been used in the medical imaging domain. Specifically, we focused our interest to pathological whole slide images used to study human tissues. While useful, machine learning models such as deep neural networks can be fooled by quite simple attacks involving purposefully engineered images. Such attacks pose a threat to many domains, including the one we focused on since there have been some studies describing such threats.

show abstract

Darwin: Convolutional Neural Network based Intelligent Health Assistant

Cited by 17 publications

References 4 publications

The role of artificial neural network and machine learning in utilizing spatial information

The role of artificial neural network and machine learning in utilizing spatial information

Conversational Agents in Software Engineering: Survey, Taxonomy and Challenges

Model Fooling Attacks Against Medical Imaging: A Short Survey

Contact Info

Product

Resources

About