The attainment of academic superiority relies heavily upon the accessibility of scholarly resources and the expression of research findings through faultless language usage. Although modern tools, such as the Publish or Perish software program, are proficient in sourcing academic papers based on specific keywords, they often fall short of extracting comprehensive content, including crucial references.The challenge of linguistic precision remains a prominent issue, particularly for research papers composed by non-native English speakers who may encounter word usage errors. This manuscript serves a twofold purpose: firstly, it reassesses the effectiveness of ChatGPT-4 in the context of retrieving pertinent references tailored to specific research topics. Secondly, it introduces a suite of language editing services that are skilled in rectifying word usage errors, ensuring the refined presentation of research outcomes. The article also provides practical guidelines for formulating precise queries to mitigate the risks of erroneous language usage and the inclusion of spurious references. In the ever evolving realm of academic discourse, leveraging the potential of advanced AI, such as ChatGPT-4,can significantly enhance the quality and impact of scientific publications.
Objective: Research in sports medicine and exercise science has experienced significant growth over recent years. With this expansion, there has been a concomitant rise in ethical challenges specific to these disciplines. While various ethical guidelines exist for numerous scientific fields, a comprehensive set tailored specifically for sports medicine and exercise science is lacking. Aiming to bridge this gap, this paper proposes a comprehensive, updated set of ethical guidelines specifically targeted at researchers in sports medicine and exercise science, providing them with a thorough framework to ensure research integrity. Methods: A collaborative approach was adopted, involving contributions from a diverse group of international experts in the field. A thorough review of existing ethical guidelines was conducted, followed by the identification and detailed examination of 15 specific ethical topics relevant to the discipline. Each topic was discussed in terms of its definition, consequences, and preventive measures. Results: The research in sports medicine and exercise science has grown significantly, bringing to the fore ethical challenges unique to these disciplines. Our comprehensive review identifies 15 key ethical challenges: plagiarism, data falsification, role of artificial intelligence chatbots in academic writing, overstating results, excessive/strategic self-citation, duplicate publications, non-disclosure of conflicts of interest, image manipulation, misuse of peer review, ghost and gift authorship, inadequate data retention, data fabrication, falsification of IRB approvals, lack of informed consent, and unethical human or animal experimentation. For each identified challenge, we propose practical solutions and best practices, enriched by the diverse perspectives of our collaborative international expert panel. This endeavor aims to offer a foundational set of ethical guidelines tailored to the nuanced needs of sports medicine and exercise science, ensuring research integrity and promoting ethical responsibility across these vital fields. Conclusion: This article represents a seminal contribution to the establishment of essential ethical guidelines specifically designed for the fields of sports medicine and exercise science. This article charts a clear course for researchers, clinicians, and policymakers by integrating these ethical principles at the heart of our scholarly and clinical activities. Consequently, it envisions a future where the principles of research integrity and ethical responsibility consistently inform every scientific discovery and every clinical engagement.
Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematically studied by exploring available studies of different metaheuristic algorithms used for FS to improve TC. This paper will contribute to the body of existing knowledge by answering four research questions (RQs): 1) What are the different approaches of FS that apply metaheuristic algorithms to improve TC? 2) Does applying metaheuristic algorithms for TC lead to better accuracy than the typical FS methods? 3) How effective are the modified, hybridized metaheuristic algorithms for text FS problems?, and 4) What are the gaps in the current studies and their future directions? These RQs led to a study of recent works on metaheuristic-based FS methods, their contributions, and limitations. Hence, a final list of thirty-seven (37) related articles was extracted and investigated to align with our RQs to generate new knowledge in the domain of study. Most of the conducted papers focused on addressing the TC in tandem with metaheuristic algorithms based on the wrapper and hybrid FS approaches. Future research should focus on using a hybrid-based FS approach as it intuitively handles complex optimization problems and potentiality provide new research opportunities in this rapidly developing field.
In the dynamic field of modern artificial intelligence, GPT-4 emerges as a key participant, addressing challenges similar to Big Data's 5Vs—Volume, Velocity, Variety, Veracity, and Value. This study explores the convergence of GPT-4's operational framework with the core aspects of Big Data, highlighting the model's flexibility and efficacy in handling intricate datasets. GPT-4 excels in managing extensive textual data, aligning with Big Data's voluminous nature, and demonstrates real-time processing capabilities to match the rapid evolution of Big Data. While initially text-oriented, GPT-4 expands into image recognition, enhancing versatility and aligning with Big Data's Variety aspect. The model's evolving proficiency in non-textual domains broadens its utility. Addressing Veracity, GPT-4 critically evaluates diverse training data, mirroring Big Data's challenges in ensuring accuracy. Its outputs, offering context and insights, contribute to actionable knowledge and align with Big Data's objectives. Despite differences, GPT-4 serves as a microcosm, providing scalable and accessible data processing capabilities, establishing itself as a crucial tool in the AI domain. This paper emphasizes the parallels and underscores GPT-4's adaptability in handling complex datasets.
Artificial intelligence (AI) has emerged as a transformative force in various sectors, including medicine and healthcare. Large language models like ChatGPT showcase AI’s potential by generating human-like text through prompts. ChatGPT’s adaptability holds promise for reshaping medical practices, improving patient care, and enhancing interactions among healthcare professionals, patients, and data. In pandemic management, ChatGPT rapidly disseminates vital information. It serves as a virtual assistant in surgical consultations, aids dental practices, simplifies medical education, and aids in disease diagnosis. A total of 82 papers were categorised into eight major areas, which are G1: treatment and medicine, G2: buildings and equipment, G3: parts of the human body and areas of the disease, G4: patients, G5: citizens, G6: cellular imaging, radiology, pulse and medical images, G7: doctors and nurses, and G8: tools, devices and administration. Balancing AI’s role with human judgment remains a challenge. A systematic literature review using the PRISMA approach explored AI’s transformative potential in healthcare, highlighting ChatGPT’s versatile applications, limitations, motivation, and challenges. In conclusion, ChatGPT’s diverse medical applications demonstrate its potential for innovation, serving as a valuable resource for students, academics, and researchers in healthcare. Additionally, this study serves as a guide, assisting students, academics, and researchers in the field of medicine and healthcare alike.
This article investigates the capabilities and limitations of ChatGPT, a natural language processing (NLP) tool, and large language models (LLMs), developed from advanced artificial intelligence (AI). Designed to help computers understand and produce text understandable by humans, ChatGPT is particularly aimed at general scientific writing and healthcare research applications. Our methodology involved searching the Scopus database for ’type 2 diabetes’ and ’T2 diabetes’ articles from reputable journals. After eliminating duplicates, we used ChatGPT to formulate conclusions for each selected article by inputting their structured abstracts, excluding the original conclusions. Additionally, we tested ChatGPT’s response to simple misuse scenarios. Our findings show that ChatGPT can accurately grasp context and concisely summarize primary research findings. Additionally, it helps individuals who are not as experienced in mathematical analysis by providing coding guidelines for mathematical analyses in a variety of computer languages and by demystifying difficult model results. In conclusion, even if ChatGPT and other AI technologies are revolutionizing scientific publishing and healthcare, their use should be strictly controlled by authoritative laws.
In general, multidimensional data (mobile application for example) contain a large number of unnecessary information. Web app users find it difficult to get the information needed quickly and effectively due to the sheer volume of data (big data produced per second). In this paper, we tend to study the data mining in web personalization using blended deep learning model. So, one of the effective solutions to this problem is web personalization. As well as, explore how this model helps to analyze and estimate the huge amounts of operations. Providing personalized recommendations to improve reliability depends on the web application using useful information in the web application. The results of this research are important for the training and testing of large data sets for a map of deep mixed learning based on the model of back-spread neural network. The HADOOP framework was used to perform a number of experiments in a different environment with a learning rate between -1 and +1. Also, using the number of techniques to evaluate the number of parameters, true positive cases are represent and fall into positive cases in this example to evaluate the proposed model.
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