Displaying publications 41 - 60 of 261 in total

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  1. Irrigation intelligence-enabling a cloud-based Internet of Things approach for enhanced water management in agriculture
    Al Mashhadany Y, Alsanad HR, Al-Askari MA, Algburi S, Taha BA
    Environ Monit Assess, 2024 Apr 09;196(5):438.
    PMID: 38592580 DOI: 10.1007/s10661-024-12606-1
    Advanced sensor technology, especially those that incorporate artificial intelligence (AI), has been recognized as increasingly important in various contemporary applications, including navigation, automation, water under imaging, environmental monitoring, and robotics. Data-driven decision-making and higher efficiency have enabled more excellent infrastructure thanks to integrating AI with sensors. The agricultural sector is one such area that has seen significant promise from this technology using the Internet of Things (IoT) capabilities. This paper describes an intelligent system for monitoring and analyzing agricultural environmental conditions, including weather, soil, and crop health, that uses internet-connected sensors and equipment. This work makes two significant contributions. It first makes it possible to use sensors linked to the IoT to accurately monitor the environment remotely. Gathering and analyzing data over time may give us valuable insights into daily fluctuations and long-term patterns. The second benefit of AI integration is the remote control; it provides for essential activities like irrigation, pest management, and disease detection. The technology can optimize water usage by tracking plant development and health and adjusting watering schedules accordingly. Intelligent Control Systems (Matlab/Simulink Ver. 2022b) use a hybrid controller that combines fuzzy logic with standard PID control to get high-efficiency performance from water pumps. In addition to monitoring crops, smart cameras allow farmers to make real-time adjustments based on soil moisture and plant needs. Potentially revolutionizing contemporary agriculture, this revolutionary approach might boost production, sustainability, and efficiency.
    Matched MeSH terms: Artificial Intelligence*
  2. Fulltext Enhancing business intelligence by means of suggestive reviews
    Qazi A, Raj RG, Tahir M, Cambria E, Syed KB
    ScientificWorldJournal, 2014;2014:879323.
    PMID: 25054188 DOI: 10.1155/2014/879323
    Appropriate identification and classification of online reviews to satisfy the needs of current and potential users pose a critical challenge for the business environment. This paper focuses on a specific kind of reviews: the suggestive type. Suggestions have a significant influence on both consumers' choices and designers' understanding and, hence, they are key for tasks such as brand positioning and social media marketing. The proposed approach consists of three main steps: (1) classify comparative and suggestive sentences; (2) categorize suggestive sentences into different types, either explicit or implicit locutions; (3) perform sentiment analysis on the classified reviews. A range of supervised machine learning approaches and feature sets are evaluated to tackle the problem of suggestive opinion mining. Experimental results for all three tasks are obtained on a dataset of mobile phone reviews and demonstrate that extending a bag-of-words representation with suggestive and comparative patterns is ideal for distinguishing suggestive sentences. In particular, it is observed that classifying suggestive sentences into implicit and explicit locutions works best when using a mixed sequential rule feature representation. Sentiment analysis achieves maximum performance when employing additional preprocessing in the form of negation handling and target masking, combined with sentiment lexicons.
    Matched MeSH terms: Artificial Intelligence*
  3. Biosensing human blood clotting factor by dual probes: Evaluation by deep long short-term memory networks in time series forecasting
    Gopinath SCB, Ismail ZH, Shapiai MI, Sobran NMM
    Biotechnol Appl Biochem, 2021 Apr 09.
    PMID: 33835514 DOI: 10.1002/bab.2164
    Artificial intelligence of things (AIoT) has become a potential tool for use in a wide range of fields, and its use is expanding in interdisciplinary sciences. On the other hand, in a clinical scenario, human blood-clotting disease (Royal disease) detection has been considered an urgent issue that has to be solved. This study uses AIoT with deep long short-term memory networks for biosensing application and analyzes the potent clinical target, human blood clotting factor IX, by its aptamer/antibody as the probe on the microscaled fingers and gaps of the interdigitated electrode. The earlier results by the current-volt measurements have shown the changes in the surface modification. The limit of detection (LOD) was noticed as 1 pM with the antibody as the probe, whereas the aptamer behaved better with the LOD at 100 fM. The time-series predictions from the AIoT application supported the obtained results with the laboratory analyses using both probes. This application clearly supports the results obtained from the interdigitated electrode sensor as aptamer to be the better option for analyzing the blood clotting defects. The current study supports a great implementation of AIoT in sensing application and can be followed for other clinical biomarkers.
    Matched MeSH terms: Artificial Intelligence
  4. Fulltext Adaptive particle swarm optimisation for solving non-convex economic dispatch problems
    Nurhafizah Jamain, Ismail Musirin, Mohd Helmi Mansor, Muhammad Murtadha Othman, Siti Aliyah Mohd Salleh
    Pertanika Journal of Science & Technology, 2017;25(103):275-286.
    MyJurnal
    This paper presents adaptive particle swarm optimization for solving non-convex economic dispatch problems. In this study, a new technique was developed known as adaptive particle swarm optimization (APSO), to alleviate the problems experienced in the traditional particle swarm optimisation (PSO). The traditional PSO was reported that this technique always stuck at local minima. In APSO, economic dispatch problem are considered with valve point effects. The search efficiency was improved when a new parameter was inserted into the velocity term. This has achieved local minima. In order to show the effectiveness of the proposed technique, this study examined two case studies, with and without contingency.
    Matched MeSH terms: Artificial Intelligence
  5. EEG/ERP adaptive noise canceller design with controlled search space (CSS) approach in cuckoo and other optimization algorithms
    Ahirwal MK, Kumar A, Singh GK
    IEEE/ACM Trans Comput Biol Bioinform, 2013 Nov-Dec;10(6):1491-504.
    PMID: 24407307 DOI: 10.1109/TCBB.2013.119
    This paper explores the migration of adaptive filtering with swarm intelligence/evolutionary techniques employed in the field of electroencephalogram/event-related potential noise cancellation or extraction. A new approach is proposed in the form of controlled search space to stabilize the randomness of swarm intelligence techniques especially for the EEG signal. Swarm-based algorithms such as Particles Swarm Optimization, Artificial Bee Colony, and Cuckoo Optimization Algorithm with their variants are implemented to design optimized adaptive noise canceler. The proposed controlled search space technique is tested on each of the swarm intelligence techniques and is found to be more accurate and powerful. Adaptive noise canceler with traditional algorithms such as least-mean-square, normalized least-mean-square, and recursive least-mean-square algorithms are also implemented to compare the results. ERP signals such as simulated visual evoked potential, real visual evoked potential, and real sensorimotor evoked potential are used, due to their physiological importance in various EEG studies. Average computational time and shape measures of evolutionary techniques are observed 8.21E-01 sec and 1.73E-01, respectively. Though, traditional algorithms take negligible time consumption, but are unable to offer good shape preservation of ERP, noticed as average computational time and shape measure difference, 1.41E-02 sec and 2.60E+00, respectively.
    Matched MeSH terms: Artificial Intelligence
  6. Fulltext Artificial intelligence enabled healthcare: A hype, hope or harm
    Bhattacharya S, Pradhan KB, Bashar MA, Tripathi S, Semwal J, Marzo RR, et al.
    J Family Med Prim Care, 2019 Nov;8(11):3461-3464.
    PMID: 31803636 DOI: 10.4103/jfmpc.jfmpc_155_19
    In this paper, we have described the health care problem (maldistribution of doctors) in India. Later, we have introduced the concept of artificial intelligence and we have described this technology with various examples, how it is rapidly changing the health care scenario across the world. We have also described the various advantages of artificial intelligence technology. At the end of the paper, we have raised some serious concerns regarding complete replacement of human based health care technology with artificial intelligence technology. Lastly, we concluded that we have to use artificial intelligent technology to prevent human sufferings/health care problems with proper caution.
    Matched MeSH terms: Artificial Intelligence
  7. A new hybrid intelligent system for accurate detection of Parkinson's disease
    Hariharan M, Polat K, Sindhu R
    Comput Methods Programs Biomed, 2014 Mar;113(3):904-13.
    PMID: 24485390 DOI: 10.1016/j.cmpb.2014.01.004
    Elderly people are commonly affected by Parkinson's disease (PD) which is one of the most common neurodegenerative disorders due to the loss of dopamine-producing brain cells. People with PD's (PWP) may have difficulty in walking, talking or completing other simple tasks. Variety of medications is available to treat PD. Recently, researchers have found that voice signals recorded from the PWP is becoming a useful tool to differentiate them from healthy controls. Several dysphonia features, feature reduction/selection techniques and classification algorithms were proposed by researchers in the literature to detect PD. In this paper, hybrid intelligent system is proposed which includes feature pre-processing using Model-based clustering (Gaussian mixture model), feature reduction/selection using principal component analysis (PCA), linear discriminant analysis (LDA), sequential forward selection (SFS) and sequential backward selection (SBS), and classification using three supervised classifiers such as least-square support vector machine (LS-SVM), probabilistic neural network (PNN) and general regression neural network (GRNN). PD dataset was used from University of California-Irvine (UCI) machine learning database. The strength of the proposed method has been evaluated through several performance measures. The experimental results show that the combination of feature pre-processing, feature reduction/selection methods and classification gives a maximum classification accuracy of 100% for the Parkinson's dataset.
    Matched MeSH terms: Artificial Intelligence
  8. Fulltext Sagittal Jaw Relationship of Different Types of Cleft and Non-cleft Individuals
    Alam MK, Alfawzan AA, Haque S, Mok PL, Marya A, Venugopal A, et al.
    Front Pediatr, 2021;9:651951.
    PMID: 34026687 DOI: 10.3389/fped.2021.651951
    To investigate whether the craniofacial sagittal jaw relationship in patients with non-syndromic cleft differed from non-cleft (NC) individuals by artificial intelligence (A.I.)-driven lateral cephalometric (Late. Ceph.) analysis. The study group comprised 123 subjects with different types of clefts including 29 = BCLP (bilateral cleft lip and palate), 41 = UCLP (unilateral cleft lip and palate), 9 = UCLA (unilateral cleft lip and alveolus), 13 = UCL (unilateral cleft lip) and NC = 31. The mean age was 14.77 years. SNA, SNB, ANB angle and Wits appraisal was measured in lateral cephalogram using a new innovative A.I driven Webceph software. Two-way ANOVA and multiple-comparison statistics tests were applied to see the differences between gender and among different types of clefts vs. NC individuals. A significant decrease (p < 0.005) in SNA, ANB, Wits appraisal was observed in different types of clefts vs. NC individuals. SNB (p > 0.005) showed insignificant variables in relation to type of clefts. No significant difference was also found in terms of gender in relation to any type of clefts and NC group. The present study advocates a decrease in sagittal development (SNA, ANB and Wits appraisal) in different types of cleft compared to NC individuals.
    Matched MeSH terms: Artificial Intelligence
  9. Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems
    Singh NK, Yadav M, Singh V, Padhiyar H, Kumar V, Bhatia SK, et al.
    Bioresour Technol, 2023 Feb;369:128486.
    PMID: 36528177 DOI: 10.1016/j.biortech.2022.128486
    Artificial intelligence (AI) and machine learning (ML) are currently used in several areas. The applications of AI and ML based models are also reported for monitoring and design of biological wastewater treatment systems (WWTS). The available information is reviewed and presented in terms of bibliometric analysis, model's description, specific applications, and major findings for investigated WWTS. Among the applied models, artificial neural network (ANN), fuzzy logic (FL) algorithms, random forest (RF), and long short-term memory (LSTM) were predominantly used in the biological wastewater treatment. These models are tested by predictive control of effluent parameters such as biological oxygen demand (BOD), chemical oxygen demand (COD), nutrient parameters, solids, and metallic substances. Following model performance indicators were mainly used for the accuracy analysis in most of the studies: root mean squared error (RMSE), mean square error (MSE), and determination coefficient (DC). Besides, outcomes of various models are also summarized in this study.
    Matched MeSH terms: Artificial Intelligence*
  10. A computational method for reliable gait event detection and abnormality detection for feedback in rehabilitation
    Senanayake C, Senanayake SM
    Comput Methods Biomech Biomed Engin, 2011 Oct;14(10):863-74.
    PMID: 20924859 DOI: 10.1080/10255842.2010.499866
    In this paper, a gait event detection algorithm is presented that uses computer intelligence (fuzzy logic) to identify seven gait phases in walking gait. Two inertial measurement units and four force-sensitive resistors were used to obtain knee angle and foot pressure patterns, respectively. Fuzzy logic is used to address the complexity in distinguishing gait phases based on discrete events. A novel application of the seven-dimensional vector analysis method to estimate the amount of abnormalities detected was also investigated based on the two gait parameters. Experiments were carried out to validate the application of the two proposed algorithms to provide accurate feedback in rehabilitation. The algorithm responses were tested for two cases, normal and abnormal gait. The large amount of data required for reliable gait-phase detection necessitate the utilisation of computer methods to store and manage the data. Therefore, a database management system and an interactive graphical user interface were developed for the utilisation of the overall system in a clinical environment.
    Matched MeSH terms: Artificial Intelligence
  11. Fulltext Clinical Applications of Artificial Intelligence and Machine Learning in Children with Cleft Lip and Palate-A Systematic Review
    Huqh MZU, Abdullah JY, Wong LS, Jamayet NB, Alam MK, Rashid QF, et al.
    Int J Environ Res Public Health, 2022 Aug 31;19(17).
    PMID: 36078576 DOI: 10.3390/ijerph191710860
    OBJECTIVE: The objective of this systematic review was (a) to explore the current clinical applications of AI/ML (Artificial intelligence and Machine learning) techniques in diagnosis and treatment prediction in children with CLP (Cleft lip and palate), (b) to create a qualitative summary of results of the studies retrieved.

    MATERIALS AND METHODS: An electronic search was carried out using databases such as PubMed, Scopus, and the Web of Science Core Collection. Two reviewers searched the databases separately and concurrently. The initial search was conducted on 6 July 2021. The publishing period was unrestricted; however, the search was limited to articles involving human participants and published in English. Combinations of Medical Subject Headings (MeSH) phrases and free text terms were used as search keywords in each database. The following data was taken from the methods and results sections of the selected papers: The amount of AI training datasets utilized to train the intelligent system, as well as their conditional properties; Unilateral CLP, Bilateral CLP, Unilateral Cleft lip and alveolus, Unilateral cleft lip, Hypernasality, Dental characteristics, and sagittal jaw relationship in children with CLP are among the problems studied.

    RESULTS: Based on the predefined search strings with accompanying database keywords, a total of 44 articles were found in Scopus, PubMed, and Web of Science search results. After reading the full articles, 12 papers were included for systematic analysis.

    CONCLUSIONS: Artificial intelligence provides an advanced technology that can be employed in AI-enabled computerized programming software for accurate landmark detection, rapid digital cephalometric analysis, clinical decision-making, and treatment prediction. In children with corrected unilateral cleft lip and palate, ML can help detect cephalometric predictors of future need for orthognathic surgery.

    Matched MeSH terms: Artificial Intelligence
  12. Exploring the artificial intelligence and machine learning models in the context of drug design difficulties and future potential for the pharmaceutical sectors
    Shiammala PN, Duraimutharasan NKB, Vaseeharan B, Alothaim AS, Al-Malki ES, Snekaa B, et al.
    Methods, 2023 Nov;219:82-94.
    PMID: 37778659 DOI: 10.1016/j.ymeth.2023.09.010
    Artificial intelligence (AI), particularly deep learning as a subcategory of AI, provides opportunities to accelerate and improve the process of discovering and developing new drugs. The use of AI in drug discovery is still in its early stages, but it has the potential to revolutionize the way new drugs are discovered and developed. As AI technology continues to evolve, it is likely that AI will play an even greater role in the future of drug discovery. AI is used to identify new drug targets, design new molecules, and predict the efficacy and safety of potential drugs. The inclusion of AI in drug discovery can screen millions of compounds in a matter of hours, identifying potential drug candidates that would have taken years to find using traditional methods. AI is highly utilized in the pharmaceutical industry by optimizing processes, reducing waste, and ensuring quality control. This review covers much-needed topics, including the different types of machine-learning techniques, their applications in drug discovery, and the challenges and limitations of using machine learning in this field. The state-of-the-art of AI-assisted pharmaceutical discovery is described, covering applications in structure and ligand-based virtual screening, de novo drug creation, prediction of physicochemical and pharmacokinetic properties, drug repurposing, and related topics. Finally, many obstacles and limits of present approaches are outlined, with an eye on potential future avenues for AI-assisted drug discovery and design.
    Matched MeSH terms: Artificial Intelligence*
  13. Fulltext Modern Trends and Applications of Intelligent Methods in Biomedical Signal and Image Processing
    Kubicek J, Penhaker M, Krejcar O, Selamat A
    Sensors (Basel), 2021 Jan 27;21(3).
    PMID: 33513910 DOI: 10.3390/s21030847
    There are various modern systems for the measurement and consequent acquisition of valuable patient's records in the form of medical signals and images, which are supposed to be processed to provide significant information about the state of biological tissues [...].
    Matched MeSH terms: Artificial Intelligence*
  14. Diagnostic approaches for dengue infection
    Thergarajan G, Sekaran SD
    Expert Rev Mol Diagn, 2023;23(8):643-651.
    PMID: 37417532 DOI: 10.1080/14737159.2023.2234815
    INTRODUCTION: Every year, a significant rise in dengue incidence observed is responsible for 10% of fever episodes in children and adolescents in endemic countries. Considering that the symptoms of dengue are similar to those of many other viruses, early diagnosis of the disease has long been difficult, and lack of sensitive diagnostic tools may be another factor contributing to a rise in dengue incidence.

    AREAS COVERED: This review will highlight dengue diagnostics strategies and discuss other possible targets for dengue diagnosis. Understanding the dynamics of the immune response and how it affects viral infection has enabled informed diagnosis. As more technologies emerge, precise assays that include some clinical markers need to be included.

    EXPERT OPINION: Future diagnostic strategies will require the use both viral and clinical markers in a serial manner with the use of artificial intelligence technology to determine from the first point of illness to better determine severity status and management. A definitive endpoint is not in the horizon as the disease as well as the virus is constantly evolving and hence many developed assays need to be constantly changing some of their reagents periodically as newer genotypes and probably too serotypes emerge.

    Matched MeSH terms: Artificial Intelligence
  15. New insights in improving sustainability in meat production: opportunities and challenges
    Kumar P, Abubakar AA, Verma AK, Umaraw P, Adewale Ahmed M, Mehta N, et al.
    Crit Rev Food Sci Nutr, 2023 Nov;63(33):11830-11858.
    PMID: 35821661 DOI: 10.1080/10408398.2022.2096562
    Treating livestock as senseless production machines has led to rampant depletion of natural resources, enhanced greenhouse gas emissions, gross animal welfare violations, and other ethical issues. It has essentially instigated constant scrutiny of conventional meat production by various experts and scientists. Sustainably in the meat sector is a big challenge which requires a multifaced and holistic approach. Novel tools like digitalization of the farming system and livestock market, precision livestock farming, application of remote sensing and artificial intelligence to manage production and environmental impact/GHG emission, can help in attaining sustainability in this sector. Further, improving nutrient use efficiency and recycling in feed and animal production through integration with agroecology and industrial ecology, improving individual animal and herd health by ensuring proper biosecurity measures and selective breeding, and welfare by mitigating animal stress during production are also key elements in achieving sustainability in meat production. In addition, sustainability bears a direct relationship with various social dimensions of meat production efficiency such as non-market attributes, balance between demand and consumption, market and policy failures. The present review critically examines the various aspects that significantly impact the efficiency and sustainability of meat production.
    Matched MeSH terms: Artificial Intelligence*
  16. Fulltext IoT-based smart wheelchair system for physically impaired person
    Muhammad Afiq Mohd Aizam, Nor Shahanim Mohamad Hadis, Samihah Abdullah
    ESTEEM Academic Journal, 2020;16(1):59-73.
    MyJurnal
    Disabled persons usually require an assistant to help them in their daily routines especially for their mobility. The limitation of being physically impaired affects the quality of life in executing their daily routine especially the ones with a wheelchair. Pushing a wheelchair has its own side effects for the user especially the person with hands and arms impairments. This paper aims to develop a smart wheelchair system integrated with home automation. With the advent of the Internet of Things (IoT), a smart wheelchair can be operated using voice command through the Google assistant Software Development Kit (SDK). The smart wheelchair system and the home automation of this study were powered by Raspberry Pi 3 B+ and NodeMCU, respectively. Voice input commands were processed by the Google assistant Artificial Intelligence Yourself (AIY) to steer the movement of wheelchair. Users were able to speak to Google to discover any information from the website. For the safety of the user, a streaming camera was added on the wheelchair. An improvement to the wheelchair system that was added on the wheelchair is its combination with the home automation to help the impaired person to control their home appliances through Blynk application.
    Observations on three voice tones (low, medium and high) of voice command show that the minimum voice intensity for this smart wheelchair system is 68.2 dB. Besides, the user is also required to produce a clear voice command to increase the system accuracy.
    Matched MeSH terms: Artificial Intelligence
  17. Fulltext A review of machine learning methods to predict the solubility of overexpressed recombinant proteins in Escherichia coli
    Habibi N, Mohd Hashim SZ, Norouzi A, Samian MR
    BMC Bioinformatics, 2014;15:134.
    PMID: 24885721 DOI: 10.1186/1471-2105-15-134
    Over the last 20 years in biotechnology, the production of recombinant proteins has been a crucial bioprocess in both biopharmaceutical and research arena in terms of human health, scientific impact and economic volume. Although logical strategies of genetic engineering have been established, protein overexpression is still an art. In particular, heterologous expression is often hindered by low level of production and frequent fail due to opaque reasons. The problem is accentuated because there is no generic solution available to enhance heterologous overexpression. For a given protein, the extent of its solubility can indicate the quality of its function. Over 30% of synthesized proteins are not soluble. In certain experimental circumstances, including temperature, expression host, etc., protein solubility is a feature eventually defined by its sequence. Until now, numerous methods based on machine learning are proposed to predict the solubility of protein merely from its amino acid sequence. In spite of the 20 years of research on the matter, no comprehensive review is available on the published methods.
    Matched MeSH terms: Artificial Intelligence*
  18. Fulltext Recombinant bromelain production in Escherichia coli: process optimization in shake flask culture by response surface methodology
    Muntari B, Amid A, Mel M, Jami MS, Salleh HM
    AMB Express, 2012;2:12.
    PMID: 22336426 DOI: 10.1186/2191-0855-2-12
    Bromelain, a cysteine protease with various therapeutic and industrial applications, was expressed in Escherichia coli, BL21-AI clone, under different cultivation conditions (post-induction temperature, L-arabinose concentration and post-induction period). The optimized conditions by response surface methodology using face centered central composite design were 0.2% (w/v) L-arabinose, 8 hr and 25°C. The analysis of variance coupled with larger value of R2 (0.989) showed that the quadratic model used for the prediction was highly significant (p < 0.05). Under the optimized conditions, the model produced bromelain activity of 9.2 U/mg while validation experiments gave bromelain activity of 9.6 ± 0.02 U/mg at 0.15% (w/v) L-arabinose, 8 hr and 27°C. This study had innovatively developed cultivation conditions for better production of recombinant bromelain in shake flask culture.
    Matched MeSH terms: Artificial Intelligence
  19. Fulltext A review for detecting gene-gene interactions using machine learning methods in genetic epidemiology
    Koo CL, Liew MJ, Mohamad MS, Salleh AH
    Biomed Res Int, 2013;2013:432375.
    PMID: 24228248 DOI: 10.1155/2013/432375
    Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs), support vector machine (SVM), and random forests (RFs) in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease.
    Matched MeSH terms: Artificial Intelligence*
  20. Fulltext A multinational study on the factors influencing university students' attitudes and usage of ChatGPT
    Abdaljaleel M, Barakat M, Alsanafi M, Salim NA, Abazid H, Malaeb D, et al.
    Sci Rep, 2024 Jan 23;14(1):1983.
    PMID: 38263214 DOI: 10.1038/s41598-024-52549-8
    Artificial intelligence models, like ChatGPT, have the potential to revolutionize higher education when implemented properly. This study aimed to investigate the factors influencing university students' attitudes and usage of ChatGPT in Arab countries. The survey instrument "TAME-ChatGPT" was administered to 2240 participants from Iraq, Kuwait, Egypt, Lebanon, and Jordan. Of those, 46.8% heard of ChatGPT, and 52.6% used it before the study. The results indicated that a positive attitude and usage of ChatGPT were determined by factors like ease of use, positive attitude towards technology, social influence, perceived usefulness, behavioral/cognitive influences, low perceived risks, and low anxiety. Confirmatory factor analysis indicated the adequacy of the "TAME-ChatGPT" constructs. Multivariate analysis demonstrated that the attitude towards ChatGPT usage was significantly influenced by country of residence, age, university type, and recent academic performance. This study validated "TAME-ChatGPT" as a useful tool for assessing ChatGPT adoption among university students. The successful integration of ChatGPT in higher education relies on the perceived ease of use, perceived usefulness, positive attitude towards technology, social influence, behavioral/cognitive elements, low anxiety, and minimal perceived risks. Policies for ChatGPT adoption in higher education should be tailored to individual contexts, considering the variations in student attitudes observed in this study.
    Matched MeSH terms: Artificial Intelligence*
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