Computational Intelligence
Mobasshira Zaman
Abstract
This research paper presents a comprehensive SWOT analysis of ChatGPT in healthcare, examining its strengths, weaknesses, opportunities, and threats. The paper highlights the potential benefits of ChatGPT, such as improved patient engagement and support for medical education, as well as its limitations, ...
Read More
This research paper presents a comprehensive SWOT analysis of ChatGPT in healthcare, examining its strengths, weaknesses, opportunities, and threats. The paper highlights the potential benefits of ChatGPT, such as improved patient engagement and support for medical education, as well as its limitations, including the risk of inaccurate data and inability to summarize non-text reports. The paper also identifies opportunities for ChatGPT, such as enabling personalized healthcare delivery and supporting remote patient monitoring. However, the paper also highlights potential threats, such as self-treatment among patients and the risk of an AI-driven infodemic. The significance of this research paper lies in its valuable insights into the ethical and safe use of ChatGPT in healthcare, providing healthcare professionals and policymakers with important considerations for its use. The SWOT analysis also serves as a framework for future research and development of ChatGPT and other large language models in healthcare. This research paper is a significant contribution to the ongoing discussion on the use of ChatGPT in healthcare and its potential impact on patient care and public health.
Computational Intelligence
Alper Kiraz; Enes Furkan Erkan; Onur Canpolat; Onur Kökümer
Abstract
In welded constructions, there should be no defects in the welding seams, or defects should have in an acceptable range for obtaining more reliable welding operations. An undercut is one of the most important welding defects occurring on the workpieces produced by butt welding. Determining the correct ...
Read More
In welded constructions, there should be no defects in the welding seams, or defects should have in an acceptable range for obtaining more reliable welding operations. An undercut is one of the most important welding defects occurring on the workpieces produced by butt welding. Determining the correct value of the stress concentration factor (SCF) allows deciding whether it accepts welding defects, in which case. Many characteristics and ranges influence SCF, making it challenging to calculate a more precise SCF. In this study, six different artificial neural networks (ANN) models are developed for predicting SCF. These models differ in terms of the training dataset used (70%-90%) and the number of neurons (5-10-20) in the hidden layer. Developed ANN models consist of three input variables the ratio of Undercut depth (h) and Undercut deep Radius (r), Reinforcement angle (Q1), deep angle of welding seam (Q2), and an output variable as SCF. The prediction performance of 6 developed ANN models in different specifications is compared. The model with a 90% training set and five neurons in the hidden layer performed the best with an accuracy of 0.9834. According to the ANN model with these features, MAE, MAPE, and RMSE values are calculated as 0.0094, 2.50%, and 0.0129, respectively.
Computational Intelligence
N. Prasad; B. Rajpal; K. K. R. Mangalore; R. Shastri; N. Pradeep
Abstract
Face recognition has always been one of the most searched and popular applications of object detection, starting from the early seventies. Facial recognition is used for access control, authentication, fraud detection, surveillance, and by individuals to unlock their devices. The less intrusive and robustness ...
Read More
Face recognition has always been one of the most searched and popular applications of object detection, starting from the early seventies. Facial recognition is used for access control, authentication, fraud detection, surveillance, and by individuals to unlock their devices. The less intrusive and robustness of the face detection systems, make it better than the fingerprint scanner and iris scanner. The frontal face can be easily detected, but multi-view face detection remains a difficult task, due to various factors like illumination, various poses, occlusions, and facial expressions. In this paper, we propose a Deep Neural Network (DNN) based approach to improve the accuracy of detection of the face. We show that Deep Neural Networks algorithms have better accuracy than traditional face detection algorithms for multi-view face detection. The Deep Neural Network (DNN) gives more precise and accurate results, as the DNN model is trained with large datasets and, the model learns the best features from the dataset.
Computational Intelligence
K Shweta Ranjan; B. Singh; D. Aggarwal
Abstract
In this Pandemic situation after dealing with contactless delivery, we introduce a "contactless menu", this feature help restaurants to gain the trust and confidence of customers in their safety and hygiene measures post-lockdown. The contactless menu will have two main components and, in the future, ...
Read More
In this Pandemic situation after dealing with contactless delivery, we introduce a "contactless menu", this feature help restaurants to gain the trust and confidence of customers in their safety and hygiene measures post-lockdown. The contactless menu will have two main components and, in the future, we are planning to include contactless payment.
Computational Intelligence
N. Pradeep; K. K. Rao Mangalore; B. Rajpal; N. Prasad; R. Shastri
Abstract
Recommendation based systems can be used for recommending different web page, books, restaurants, tv shows, movies etc. The aim of movie recommendation system is to recommend movies to different users based on their interests. This helps the user to save time browsing the internet looking for movies ...
Read More
Recommendation based systems can be used for recommending different web page, books, restaurants, tv shows, movies etc. The aim of movie recommendation system is to recommend movies to different users based on their interests. This helps the user to save time browsing the internet looking for movies from the thousand already existing ones. Content-based recommendation system describes the items that may be recommended to the user. Based on a data set, it predicts what movies a user will like considering the attributes present in the previously liked movies. Recommendation systems can recommend movies based on one or a combination of two or more attributes. While designing a movie recommendation system various factors are considered such as the genre of the movie, the director or the actors present in it. In this paper, the recommendation system has been built on cast, keywords, crew, and genres. A single column is created which will be the sum of all the 4 attributes, and it acts as a dominant factor for this movie recommender system.
Computational Intelligence
L. Kanagasabai
Abstract
Acridoidea Stirred Artificial Bee Colony (ASA) Algorithm is applied to solve the power loss reduction problem. In the projected algorithm natural Acridoidea jumping phenomenon has been imitated and the modeled design has been intermingled with artificial bee colony algorithm. In the proposed algorithm, ...
Read More
Acridoidea Stirred Artificial Bee Colony (ASA) Algorithm is applied to solve the power loss reduction problem. In the projected algorithm natural Acridoidea jumping phenomenon has been imitated and the modeled design has been intermingled with artificial bee colony algorithm. In the proposed algorithm, the position update has been done through the distance of jumping done by Acridoidea. The distance (D) is horizontal (h) with angle (θ), velocity (V) parameter amplifying the rate which is based on the gravity of Ballistic projectile. Normally, the angle will be 45◦ horizontal and it depends on the take-off velocity. ASA algorithm has been tested in standard IEEE 57 bus test system and results show that the proposed ASA algorithm reduced the real power loss effectively.
Computational Intelligence
R. Shastri; N. Pradeep; K. K. Rao Mangalore; B. Rajpal; N. Prasad
Abstract
Breast cancer has been the riskiest malignancy among ladies around the world. Nearly 2 million new cases were diagnosed in 2018. The main problem in the detection of breast cancer is to find how tumors turn into malignant or benign and we can do this with the help of machine learning techniques as they ...
Read More
Breast cancer has been the riskiest malignancy among ladies around the world. Nearly 2 million new cases were diagnosed in 2018. The main problem in the detection of breast cancer is to find how tumors turn into malignant or benign and we can do this with the help of machine learning techniques as they provide an appropriate result. According to research, an experienced physician can diagnose cancer with 79% accuracy while using machine learning techniques provides an accuracy of 91%. In this work, machine learning techniques have been applied which include K-Nearest Neighbors algorithm (KNN), Support Vector Machine (SVM), and Decision Tree Classifier (DT). To predict whether the cause is benign or malignant we have used the breast cancer dataset. The SVM classifier gives more accurate and precise results as compared to others, and this classifier is trained with the larger datasets.
Computational Intelligence
B. Singh; K Sh. Ranjan; D. Aggarwal
Abstract
There are currently two ways to vote in India. They are secret ballots and electronic vote machines, but these two processes have some limitations or disadvantages. The current system is also insecure. Many people miss the opportunity to vote simply because they need to go to the polling station and ...
Read More
There are currently two ways to vote in India. They are secret ballots and electronic vote machines, but these two processes have some limitations or disadvantages. The current system is also insecure. Many people miss the opportunity to vote simply because they need to go to the polling station and wait for several places to vote. In this paper, we proposed a voting method. In our method, the voting process has three security stages. The first stage is facial recognition, the second stage is Election ID (EID) number verification, and the third stage is One-Time-Password (OTP) verification using the user's mobile phone number registered.
Computational Intelligence
L. Kanagasabai
Abstract
This paper presents Augmented Monkey Optimization Algorithm (AMOA) applied to solve optimal reactive power problem. Communal behaviour of monkeys has been utilized to model the algorithm. Normally, group monkeys assess the distance from the source to food for foraging behaviour. Local leader renews its ...
Read More
This paper presents Augmented Monkey Optimization Algorithm (AMOA) applied to solve optimal reactive power problem. Communal behaviour of monkeys has been utilized to model the algorithm. Normally, group monkeys assess the distance from the source to food for foraging behaviour. Local leader renews its most excellent location inside the group, when the food source is not rationalized then the group will start probing in different directions for the food sources. Two most important control parameters are Global Leader Limit (GLlimit) and Local Leader Limit (LLlimit) which give appropriate way to global and local leaders correspondingly. Levy flight has been intermingled in the algorithm to enhance the search ability. Proposed AMOA accelerates the exploitation ability that has been tested in standard IEEE 14, 30, 57,118,300 bus test systems. The simulation results show the projected algorithm reduced the real power loss comprehensively.
Computational Intelligence
P. Pandey
Abstract
Graphical passwords are the ways in which user click on the image or user can select the image to authenticate themselves instead of giving passwords. This technique is more secure that textual password techniques. In this article, the shoulder surfing preventive mechanism of graphical password authentication ...
Read More
Graphical passwords are the ways in which user click on the image or user can select the image to authenticate themselves instead of giving passwords. This technique is more secure that textual password techniques. In this article, the shoulder surfing preventive mechanism of graphical password authentication is given. Finally the login password system is proposed to deal with such type of problems. First time, we are introducing a modified approach is given to resolve the shoulder surfing based on recall and recognition based concepts. Usually it is seen that the most common vulnerability of graphical password is shoulder surfing attack. This research aims to analyze the usability feature of recognition based and recall based graphical password methods and present a technique to apply an image based password that is safe from the shoulder surfing attack. In the similar context, the purpose of this paper is to present an alternative way to apply the recall and recognition based technique that will be protective for guess through shoulder surfing. And this graphical technique will be easy to memories the authentication password and process of authentication.
