As one of the commonly used vehicles for underwater detection, underwater robots are facing a series of problems. The real underwater environment is large-scale, complex, real-time and dynamic, and many unknown obstacles may exist in the underwater environment. Under such complex conditions and lack of prior knowledge, the existing path planning methods are difficult to plan, therefore they cannot effectively meet the actual demands. In response to these problems, a three-dimensional marine environment including multiple obstacles is established with the real ocean current data in this paper, which is consistent with the actual application scenarios. Then, we propose an N-step Priority Double DQN (NPDDQN) path planning algorithm, which potently realizes obstacle avoidance in the complex environment. In addition, this study proposes an experience screening mechanism, which screens the explored positive experience and improves its reuse rate, thus efficiently improving the algorithm stability in the dynamic environment. This paper verifies the better performance of reinforcement learning compared with a variety of traditional methods in three-dimensional underwater path planning. Underwater robots based on the proposed method have good autonomy and stability, which provides a new method for path planning of underwater robots. Note to Practitioners&#x2014;The goal of this study is to provide a new solution for obstacle avoidance in path planning of underwater robots, which is consistent with the dynamic and real-time demands of the real environment. Existing underwater path planning researches lack a consistent environment with the actual application, and therefore we firstly construct a three-dimensional ocean environment with real ocean current data to provide support for the algorithms. Additionally, most of the algorithms are pre-planning methods or require long-time calculation, and there is little research on obstacle avoidance. In the face of obstacle changes, underwater robots with poor adaptability will cause performance decline and even economic losses. The proposed algorithm learns through interaction with the environment, and therefore it does not require any prior experience, and has good adaptability as well as fast inference speed. Especially, in the dynamic environment, algorithm performance is difficult to guarantee due to less positive experience in exploration. The proposed experience screening mechanism improves the stability of the algorithm, so that the underwater robot maintains stable performance in different dynamic environments.

Intelligent Path Planning of Underwater Robot Based on Reinforcement Learning

Research in Remote Patient Monitoring Systems (RPMS) is highly valued since it directly impacts people's health and wellbeing. The number of cases being treated with RPMS has increased since the beginning of the pandemic. However, there are still challenges to be met, such as mobility, heterogeneous networks, standardisation of RPMSs, automation, and Quality of Service (QoS), despite the rise in their use. The incorporation of artificial intelligence (AI) into remote patient monitoring (RPM) is causing a revolution in the healthcare industry by improving the quality of care provided to patients, boosting operational effectiveness, and making it possible to intervene earlier. RPM makes use of technology to monitor the health of patients in a remote location, hence minimising the frequency of in-person visits that are required. The addition of AI algorithms further enhances the potential of RPM by analysing huge volumes of patient data to identify patterns, abnormalities, and potential problems. In this study, we will investigate the most potentially game-changing applications of AI in remote patient monitoring in 2023 and beyond.

Enhancing Healthcare Delivery: AI Applications In Remote Patient Monitoring

In today’s fast-paced world, everything around us is automatized and customized as per our needs. Many researchers forecast that by 2025 we’ll be seeing approximately 8 million autonomous or semi-autonomous vehicles on the road. The agricultural sector is one of the vital issues that demand focus in the current scenario. These autonomous vehicles will have a high impact in the field of agriculture. One of the time-consuming tasks i.e. the seed sowing process needs to be automated more efficiently to reduce the human effort and increase the yield. These seeds get destroyed because of insects and other tiny animals who feed on them. Spraying pesticides manually would consume a lot of time and create health hazards. Using automation, we can mark the land and concentrate each crop on the dot. Machine Learning is now becoming the promising trend to levitate autonomous vehicles into reality. In this paper, we discussed the advantages and limitations of various vision-based algorithms and techniques that have been implemented in Autonomous Ground Vehicles (AGV) possible. Finally, a detailed analysis is tabulated as a summary of the techniques which were used and what are the pros and cons of each technique that helps in future improvements. 

Review on Vision-Based Control Using Artificial Intelligence in Autonomous Ground Vehicle

Introduction: Artificial intelligence (AI), particularly ChatGPT developed by OpenAI, has shown the potential to improve diagnostic accuracy and efficiency in emergency department (ED) triage. This study aims to evaluate the diagnostic performance and safety of ChatGPT in prioritizing patients based on urgency in ED settings. Methods: A systematic review and meta-analysis were conducted following PRISMA guidelines. Comprehensive literature searches were performed in Scopus, Web of Science, PubMed, and Embase. Studies evaluating ChatGPT's diagnostic performance in ED triage were included. Quality assessment was conducted using the QUADAS-2 tool. Pooled accuracy estimates were calculated using a random-effects model, and heterogeneity was assessed with the I² statistic. Results: Fourteen studies with a total of 1,412 patients or scenarios were included. ChatGPT 4.0 demonstrated a pooled accuracy of 0.86 (95% CI: 0.64-0.98) with substantial heterogeneity (I² = 93%). ChatGPT 3.5 showed a pooled accuracy of 0.63 (95% CI: 0.43-0.81) with significant heterogeneity (I² = 84%). Funnel plots indicated potential publication bias, particularly for ChatGPT 3.5. Quality assessments revealed varying levels of risk of bias and applicability concerns. Conclusion: ChatGPT, especially version 4.0, shows promise in improving ED triage accuracy. However, significant variability and potential biases highlight the need for further evaluation and enhancement.

Diagnostic Accuracy of ChatGPT for Patients’ Triage; a Systematic Review and Meta-Analysis

Abstract This research focuses on the performance of the obstacle avoidance feature implemented on a wheeled mobile robot by using Sugeno Fuzzy Inference System (FIS). The test was done using a simulation software Webots and the layout was first draft out using walls, floors, and objects in order to set a boundary and obstacles for the simulation. To test the effectiveness and efficiency in terms of time and distance, the simulation time and trajectory of the Khepera implementing the Sugeno FIS was recorded and compared with the Khepera without implementing the Sugeno FIS. The inputs of the ultrasonic sensors were also recorded in order to analyze whether if the Khepera detects incoming objects or if Khepera follows the rules set in the algorithm. 

A Wheeled Mobile Robot Obstacles Avoidance for Navigation Control in a Static and Dynamic Environments

Now in these days, artificial intelligence (AI) is playing a major role in healthcare. It has many applications in diagnosis, robotic surgeries, and research, powered by the growing availability of healthcare facts and brisk improvement of analytical techniques. AI is launched in such a way that it has similar knowledge as a human but is more efficient. A robot has the same expertise as a surgeon; even if it takes a longer time for surgery, its sutures, precision, and uniformity are far better than the surgeon, leading to fewer chances of failure. To make all these things possible, AI needs some sets of algorithms. In Artificial Intelligence, there are two key categories: machine learning (ML) and natural language processing (NPL), both of which are necessary to achieve practically any aim in healthcare. The goal of this study is to keep track of current advancements in science, understand technological availability, recognize the enormous power of AI in healthcare, and encourage scientists to use AI in their related fields of research. Discoveries and advancements will continue to push the AI frontier and expand the scope of its applications, with rapid developments expected in the future.

/pdf/applications-of-artificial-intelligence-in-healthcare-2zsekmsx.pdf

Applications of Artificial Intelligence in Healthcare

Navigation technology is conceivably an important requirement for real time application of Autonomous vehicle. This paper focuses on the application of one of the soft computing technique i.e. a fuzzy logic based controller on the autonomous vehicle for the challenging job of obstacle avoidance. The proposed fuzzy logic controller is having two input and one output. The distances from left and right sensor have been considered as the input, whereas, the rotation angle is considered as the output. A total of 36 set of rule base have been used in designing the fuzzy logic controllers with Graphical User Interface (GUI), that provides the information in which the vehicle moves.

Static Obstacles Avoidance in Autonomous Ground Vehicle using Fuzzy Logic Controller

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of {limit} columns in the table. Remove at least 1 column to add or create another one.

Manjari Singh

Author Tools

Chat about Author

Papers

Applications of Artificial Intelligence in Healthcare

Static Obstacles Avoidance in Autonomous Ground Vehicle using Fuzzy Logic Controller