Displaying publications 1 - 20 of 252 in total

  1. Mohamad Zamani NS, Wan Zaki WMD, Abd Hamid Z, Baseri Huddin A
    PeerJ, 2022;10:e14513.
    PMID: 36573241 DOI: 10.7717/peerj.14513
    BACKGROUND AND AIMS: A microscopic image has been used in cell analysis for cell type identification and classification, cell counting and cell size measurement. Most previous research works are tedious, including detailed understanding and time-consuming. The scientists and researchers are seeking modern and automatic cell analysis approaches in line with the current in-demand technology.

    OBJECTIVES: This article provides a brief overview of a general cell and specific stem cell analysis approaches from the history of cell discovery up to the state-of-the-art approaches.

    METHODOLOGY: A content description of the literature study has been surveyed from specific manuscript databases using three review methods: manuscript identification, screening, and inclusion. This review methodology is based on Prism guidelines in searching for originality and novelty in studies concerning cell analysis.

    RESULTS: By analysing generic cell and specific stem cell analysis approaches, current technology offers tremendous potential in assisting medical experts in performing cell analysis using a method that is less laborious, cost-effective, and reduces error rates.

    CONCLUSION: This review uncovers potential research gaps concerning generic cell and specific stem cell analysis. Thus, it could be a reference for developing automated cells analysis approaches using current technology such as artificial intelligence and deep learning.

    Matched MeSH terms: Artificial Intelligence*
  2. Hoang YN, Chen YL, Ho DKN, Chiu WC, Cheah KJ, Mayasari NR, et al.
    JAMA Netw Open, 2023 Dec 01;6(12):e2350367.
    PMID: 38150258 DOI: 10.1001/jamanetworkopen.2023.50367
    Matched MeSH terms: Artificial Intelligence*
  3. Yang XS, Chien SF, Ting TO
    ScientificWorldJournal, 2014;2014:425853.
    PMID: 25610904 DOI: 10.1155/2014/425853
    Matched MeSH terms: Artificial Intelligence*
  4. Kalatehjari R, Rashid AS, Ali N, Hajihassani M
    ScientificWorldJournal, 2014;2014:973093.
    PMID: 24991652 DOI: 10.1155/2014/973093
    Over the last few years, particle swarm optimization (PSO) has been extensively applied in various geotechnical engineering including slope stability analysis. However, this contribution was limited to two-dimensional (2D) slope stability analysis. This paper applied PSO in three-dimensional (3D) slope stability problem to determine the critical slip surface (CSS) of soil slopes. A detailed description of adopted PSO was presented to provide a good basis for more contribution of this technique to the field of 3D slope stability problems. A general rotating ellipsoid shape was introduced as the specific particle for 3D slope stability analysis. A detailed sensitivity analysis was designed and performed to find the optimum values of parameters of PSO. Example problems were used to evaluate the applicability of PSO in determining the CSS of 3D slopes. The first example presented a comparison between the results of PSO and PLAXI-3D finite element software and the second example compared the ability of PSO to determine the CSS of 3D slopes with other optimization methods from the literature. The results demonstrated the efficiency and effectiveness of PSO in determining the CSS of 3D soil slopes.
    Matched MeSH terms: Artificial Intelligence*
  5. Tsigaris P, Kendall G, Teixeira da Silva JA
    J Prof Nurs, 2023;49:188-189.
    PMID: 38042556 DOI: 10.1016/j.profnurs.2023.08.002
    The debate surrounding "predatory publishing" continues to be unable to find entirely effective solutions to dealing with this problem, despite fervent efforts by many academics and policy makers around the world. Given this situation, we were interested in appreciating whether ChatGPT would be able to offer insight and solutions, to complement current human-based efforts.
    Matched MeSH terms: Artificial Intelligence*
  6. Olayiwola Babarinsa, Hailiza Kamarulhaili
    MATEMATIKA, 2019;35(1):25-38.
    The proposed modified methods of Cramer's rule consider the column vector as well as the coefficient matrix concurrently in the linear system. The modified methods can be applied since Cramer's rule is typically known for solving the linear systems in $WZ$ factorization to yield Z-matrix. Then, we presented our results to show that there is no tangible difference in performance time between Cramer's rule and the modified methods in the factorization from improved versions of MATLAB. Additionally, the Frobenius norm of the modified methods in the factorization is better than using Cramer's rule irrespective of the version of MATLAB used.
    Matched MeSH terms: Artificial Intelligence
  7. Nurhafizah Jamain, Ismail Musirin, Mohd Helmi Mansor, Muhammad Murtadha Othman, Siti Aliyah Mohd Salleh
    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
  8. Asteris PG, Gandomi AH, Armaghani DJ, Tsoukalas MZ, Gavriilaki E, Gerber G, et al.
    J Cell Mol Med, 2024 Feb;28(4):e18105.
    PMID: 38339761 DOI: 10.1111/jcmm.18105
    Complement inhibition has shown promise in various disorders, including COVID-19. A prediction tool including complement genetic variants is vital. This study aims to identify crucial complement-related variants and determine an optimal pattern for accurate disease outcome prediction. Genetic data from 204 COVID-19 patients hospitalized between April 2020 and April 2021 at three referral centres were analysed using an artificial intelligence-based algorithm to predict disease outcome (ICU vs. non-ICU admission). A recently introduced alpha-index identified the 30 most predictive genetic variants. DERGA algorithm, which employs multiple classification algorithms, determined the optimal pattern of these key variants, resulting in 97% accuracy for predicting disease outcome. Individual variations ranged from 40 to 161 variants per patient, with 977 total variants detected. This study demonstrates the utility of alpha-index in ranking a substantial number of genetic variants. This approach enables the implementation of well-established classification algorithms that effectively determine the relevance of genetic variants in predicting outcomes with high accuracy.
    Matched MeSH terms: Artificial Intelligence
  9. Jawahar N, Ponnambalam SG, Sivakumar K, Thangadurai V
    ScientificWorldJournal, 2014;2014:458959.
    PMID: 24790568 DOI: 10.1155/2014/458959
    Products such as cars, trucks, and heavy machinery are assembled by two-sided assembly line. Assembly line balancing has significant impacts on the performance and productivity of flow line manufacturing systems and is an active research area for several decades. This paper addresses the line balancing problem of a two-sided assembly line in which the tasks are to be assigned at L side or R side or any one side (addressed as E). Two objectives, minimum number of workstations and minimum unbalance time among workstations, have been considered for balancing the assembly line. There are two approaches to solve multiobjective optimization problem: first approach combines all the objectives into a single composite function or moves all but one objective to the constraint set; second approach determines the Pareto optimal solution set. This paper proposes two heuristics to evolve optimal Pareto front for the TALBP under consideration: Enumerative Heuristic Algorithm (EHA) to handle problems of small and medium size and Simulated Annealing Algorithm (SAA) for large-sized problems. The proposed approaches are illustrated with example problems and their performances are compared with a set of test problems.
    Matched MeSH terms: Artificial Intelligence*
  10. Yigitcanlar T, Butler L, Windle E, Desouza KC, Mehmood R, Corchado JM
    Sensors (Basel), 2020 May 25;20(10).
    PMID: 32466175 DOI: 10.3390/s20102988
    In recent years, artificial intelligence (AI) has started to manifest itself at an unprecedented pace. With highly sophisticated capabilities, AI has the potential to dramatically change our cities and societies. Despite its growing importance, the urban and social implications of AI are still an understudied area. In order to contribute to the ongoing efforts to address this research gap, this paper introduces the notion of an artificially intelligent city as the potential successor of the popular smart city brand-where the smartness of a city has come to be strongly associated with the use of viable technological solutions, including AI. The study explores whether building artificially intelligent cities can safeguard humanity from natural disasters, pandemics, and other catastrophes. All of the statements in this viewpoint are based on a thorough review of the current status of AI literature, research, developments, trends, and applications. This paper generates insights and identifies prospective research questions by charting the evolution of AI and the potential impacts of the systematic adoption of AI in cities and societies. The generated insights inform urban policymakers, managers, and planners on how to ensure the correct uptake of AI in our cities, and the identified critical questions offer scholars directions for prospective research and development.
    Matched MeSH terms: Artificial Intelligence*
  11. 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*
  12. Mak KK, Pichika MR
    Drug Discov Today, 2019 03;24(3):773-780.
    PMID: 30472429 DOI: 10.1016/j.drudis.2018.11.014
    Artificial intelligence (AI) uses personified knowledge and learns from the solutions it produces to address not only specific but also complex problems. Remarkable improvements in computational power coupled with advancements in AI technology could be utilised to revolutionise the drug development process. At present, the pharmaceutical industry is facing challenges in sustaining their drug development programmes because of increased R&D costs and reduced efficiency. In this review, we discuss the major causes of attrition rates in new drug approvals, the possible ways that AI can improve the efficiency of the drug development process and collaboration of pharmaceutical industry giants with AI-powered drug discovery firms.
    Matched MeSH terms: Artificial Intelligence*
  13. Dikshit A, Pradhan B
    Sci Total Environ, 2021 Dec 20;801:149797.
    PMID: 34467917 DOI: 10.1016/j.scitotenv.2021.149797
    Accurate prediction of any type of natural hazard is a challenging task. Of all the various hazards, drought prediction is challenging as it lacks a universal definition and is getting adverse with climate change impacting drought events both spatially and temporally. The problem becomes more complex as drought occurrence is dependent on a multitude of factors ranging from hydro-meteorological to climatic variables. A paradigm shift happened in this field when it was found that the inclusion of climatic variables in the data-driven prediction model improves the accuracy. However, this understanding has been primarily using statistical metrics used to measure the model accuracy. The present work tries to explore this finding using an explainable artificial intelligence (XAI) model. The explainable deep learning model development and comparative analysis were performed using known understandings drawn from physical-based models. The work also tries to explore how the model achieves specific results at different spatio-temporal intervals, enabling us to understand the local interactions among the predictors for different drought conditions and drought periods. The drought index used in the study is Standard Precipitation Index (SPI) at 12 month scales applied for five different regions in New South Wales, Australia, with the explainable algorithm being SHapley Additive exPlanations (SHAP). The conclusions drawn from SHAP plots depict the importance of climatic variables at a monthly scale and varying ranges of annual scale. We observe that the results obtained from SHAP align with the physical model interpretations, thus suggesting the need to add climatic variables as predictors in the prediction model.
    Matched MeSH terms: Artificial Intelligence*
  14. Tahir GA, Loo CK
    Comput Biol Med, 2021 12;139:104972.
    PMID: 34749093 DOI: 10.1016/j.compbiomed.2021.104972
    Food recognition systems recently garnered much research attention in the relevant field due to their ability to obtain objective measurements for dietary intake. This feature contributes to the management of various chronic conditions. Challenges such as inter and intraclass variations alongside the practical applications of smart glasses, wearable cameras, and mobile devices require resource-efficient food recognition models with high classification performance. Furthermore, explainable AI is also crucial in health-related domains as it characterizes model performance, enhancing its transparency and objectivity. Our proposed architecture attempts to address these challenges by drawing on the strengths of the transfer learning technique upon initializing MobiletNetV3 with weights from a pre-trained model of ImageNet. The MobileNetV3 achieves superior performance using the squeeze and excitation strategy, providing unequal weight to different input channels and contrasting equal weights in other variants. Despite being fast and efficient, there is a high possibility for it to be stuck in the local optima like other deep neural networks, reducing the desired classification performance of the model. Thus, we overcome this issue by applying the snapshot ensemble approach as it enables the M model in a single training process without any increase in the required training time. As a result, each snapshot in the ensemble visits different local minima before converging to the final solution which enhances recognition performance. On overcoming the challenge of explainability, we argue that explanations cannot be monolithic, since each stakeholder perceive the results', explanations based on different objectives and aims. Thus, we proposed a user-centered explainable artificial intelligence (AI) framework to increase the trust of the involved parties by inferencing and rationalizing the results according to needs and user profile. Our framework is comprehensive in terms of a dietary assessment app as it detects Food/Non-Food, food categories, and ingredients. Experimental results on the standard food benchmarks and newly contributed Malaysian food dataset for ingredient detection demonstrated superior performance on an integrated set of measures over other methodologies.
    Matched MeSH terms: Artificial Intelligence*
  15. Kolekar S, Gite S, Pradhan B, Alamri A
    Sensors (Basel), 2022 Dec 10;22(24).
    PMID: 36560047 DOI: 10.3390/s22249677
    The intelligent transportation system, especially autonomous vehicles, has seen a lot of interest among researchers owing to the tremendous work in modern artificial intelligence (AI) techniques, especially deep neural learning. As a result of increased road accidents over the last few decades, significant industries are moving to design and develop autonomous vehicles. Understanding the surrounding environment is essential for understanding the behavior of nearby vehicles to enable the safe navigation of autonomous vehicles in crowded traffic environments. Several datasets are available for autonomous vehicles focusing only on structured driving environments. To develop an intelligent vehicle that drives in real-world traffic environments, which are unstructured by nature, there should be an availability of a dataset for an autonomous vehicle that focuses on unstructured traffic environments. Indian Driving Lite dataset (IDD-Lite), focused on an unstructured driving environment, was released as an online competition in NCPPRIPG 2019. This study proposed an explainable inception-based U-Net model with Grad-CAM visualization for semantic segmentation that combines an inception-based module as an encoder for automatic extraction of features and passes to a decoder for the reconstruction of the segmentation feature map. The black-box nature of deep neural networks failed to build trust within consumers. Grad-CAM is used to interpret the deep-learning-based inception U-Net model to increase consumer trust. The proposed inception U-net with Grad-CAM model achieves 0.622 intersection over union (IoU) on the Indian Driving Dataset (IDD-Lite), outperforming the state-of-the-art (SOTA) deep neural-network-based segmentation models.
    Matched MeSH terms: Artificial Intelligence*
  16. Flaherty GT, Piyaphanee W
    J Travel Med, 2023 Feb 18;30(1).
    PMID: 36208173 DOI: 10.1093/jtm/taac113
    Matched MeSH terms: Artificial Intelligence*
  17. Kushwaha OS, Uthayakumar H, Kumaresan K
    Environ Sci Pollut Res Int, 2023 Feb;30(10):24927-24948.
    PMID: 35349067 DOI: 10.1007/s11356-022-19683-0
    In this study, we are reporting a novel prediction model for forecasting the carbon dioxide (CO2) fixation of microalgae which is based on the hybrid approach of adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA). The CO2 fixation rate of various algal strains was collected and the cultivation conditions of the microalgae such as temperature, pH, CO2 %, and amount of nitrogen and phosphorous (mg/L) were taken as the input variables, while the CO2 fixation rate was taken as the output variable. The optimization of ANFIS parameters and the formation of the optimized fuzzy model structure were performed by genetic algorithm (GA) using MATLAB in order to achieve optimum prediction capability and industrial applicability. The best-fitting model was figured out using statistical analysis parameters such as root mean square error (RMSE), coefficient of regression (R2), and average absolute relative deviation (AARD). According to the analysis, GA-ANFIS model depicted a greater prediction capability over ANFIS model. The RMSE, R2, and AARD for GA-ANFIS were observed to be 0.000431, 0.97865, and 0.044354 in the training phase and 0.00056, 0.98457, and 0.032156 in the testing phase, respectively, for the GA-ANFIS Model. As a result, it can be concluded that the proposed GA-ANFIS model is an efficient technique having a very high potential to accurately predict the CO2 fixation rate.
    Matched MeSH terms: Artificial Intelligence*
  18. Tang PM, Koopman J, Mai KM, De Cremer D, Zhang JH, Reynders P, et al.
    J Appl Psychol, 2023 Nov;108(11):1766-1789.
    PMID: 37307359 DOI: 10.1037/apl0001103
    The artificial intelligence (AI) revolution has arrived, as AI systems are increasingly being integrated across organizational functions into the work lives of employees. This coupling of employees and machines fundamentally alters the work-related interactions to which employees are accustomed, as employees find themselves increasingly interacting with, and relying on, AI systems instead of human coworkers. This increased coupling of employees and AI portends a shift toward more of an "asocial system," wherein people may feel socially disconnected at work. Drawing upon the social affiliation model, we develop a model delineating both adaptive and maladaptive consequences of this situation. Specifically, we theorize that the more employees interact with AI in the pursuit of work goals, the more they experience a need for social affiliation (adaptive)-which may contribute to more helping behavior toward coworkers at work-as well as a feeling of loneliness (maladaptive), which then further impair employee well-being after work (i.e., more insomnia and alcohol consumption). In addition, we submit that these effects should be especially pronounced among employees with higher levels of attachment anxiety. Results across four studies (N = 794) with mixed methodologies (i.e., survey study, field experiment, and simulation study; Studies 1-4) with employees from four different regions (i.e., Taiwan, Indonesia, United States, and Malaysia) generally support our hypotheses. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
    Matched MeSH terms: Artificial Intelligence*
  19. Qiu Y, Pan J, Ishak NA
    Comput Intell Neurosci, 2022;2022:1362996.
    PMID: 36193186 DOI: 10.1155/2022/1362996
    Several primary school students in Fujian Province have perceived studying mathematics as challenging. To deal with this issue, computer technology advancements, specifically artificial intelligence (AI), present an opportunity to evaluate individual students' learning challenges and give individualized support to optimize their success in mathematics classes. It is also possible to use virtual reality (VR) to assist learners in acquiring complex mathematical and logical ideas and to lessen learners' mistakes. As a result, researchers, particularly beginners, are missing out on a complete perspective of the study of AI in teaching mathematics. That is why we are exploring the role of AI in math education by developing a "fuzzy-based tweakable convolution neural network with a long short-term memory (FT-CNN-LSTM-AM)" method. For this investigation, the students' datasets are taken and educated by mathematical teaching via the application of AI. The proposed method is utilized to predict the students' performance in mathematical education. A grey wolf optimizer is employed to boost the effectiveness of the proposed method. Furthermore, the performance of the proposed method is analyzed and compared with existing approaches to gain the highest reliability.
    Matched MeSH terms: Artificial Intelligence*
  20. Menon S, Anand D, Kavita, Verma S, Kaur M, Jhanjhi NZ, et al.
    Sensors (Basel), 2023 Jul 04;23(13).
    PMID: 37447981 DOI: 10.3390/s23136132
    With the increasing growth rate of smart home devices and their interconnectivity via the Internet of Things (IoT), security threats to the communication network have become a concern. This paper proposes a learning engine for a smart home communication network that utilizes blockchain-based secure communication and a cloud-based data evaluation layer to segregate and rank data on the basis of three broad categories of Transactions (T), namely Smart T, Mod T, and Avoid T. The learning engine utilizes a neural network for the training and classification of the categories that helps the blockchain layer with improvisation in the decision-making process. The contributions of this paper include the application of a secure blockchain layer for user authentication and the generation of a ledger for the communication network; the utilization of the cloud-based data evaluation layer; the enhancement of an SI-based algorithm for training; and the utilization of a neural engine for the precise training and classification of categories. The proposed algorithm outperformed the Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system, the data fusion technique, and artificial intelligence Internet of Things technology in providing electronic information engineering and analyzing optimization schemes in terms of the computation complexity, false authentication rate, and qualitative parameters with a lower average computation complexity; in addition, it ensures a secure, efficient smart home communication network to enhance the lifestyle of human beings.
    Matched MeSH terms: Artificial Intelligence*
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