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Fulltext Future stem cell analysis: progress and challenges towards state-of-the art approaches in automated cells analysis

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*
Fulltext Consistency and Accuracy of Artificial Intelligence for Providing Nutritional Information

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*
Fulltext Computational intelligence and metaheuristic algorithms with applications

Yang XS, Chien SF, Ting TO

ScientificWorldJournal, 2014;2014:425853.
PMID: 25610904 DOI: 10.1155/2014/425853
Matched MeSH terms: Artificial Intelligence*
Fulltext The contribution of particle swarm optimization to three-dimensional slope stability analysis

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*
What does ChatGPT advise about predatory publishing?

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*
Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer

Sharma V, Singh A, Chauhan S, Sharma PK, Chaudhary S, Sharma A, et al.

Curr Drug Deliv, 2024;21(6):870-886.
PMID: 37670704 DOI: 10.2174/1567201821666230905090621

Drug discovery and development (DDD) is a highly complex process that necessitates precise monitoring and extensive data analysis at each stage. Furthermore, the DDD process is both timeconsuming and costly. To tackle these concerns, artificial intelligence (AI) technology can be used, which facilitates rapid and precise analysis of extensive datasets within a limited timeframe. The pathophysiology of cancer disease is complicated and requires extensive research for novel drug discovery and development. The first stage in the process of drug discovery and development involves identifying targets. Cell structure and molecular functioning are complex due to the vast number of molecules that function constantly, performing various roles. Furthermore, scientists are continually discovering novel cellular mechanisms and molecules, expanding the range of potential targets. Accurately identifying the correct target is a crucial step in the preparation of a treatment strategy. Various forms of AI, such as machine learning, neural-based learning, deep learning, and network-based learning, are currently being utilised in applications, online services, and databases. These technologies facilitate the identification and validation of targets, ultimately contributing to the success of projects. This review focuses on the different types and subcategories of AI databases utilised in the field of drug discovery and target identification for cancer.

Matched MeSH terms: Artificial Intelligence*
Fulltext Evaluating the Potential and Pitfalls of AI-Powered Conversational Agents as Humanlike Virtual Health Carers in the Remote Management of Noncommunicable Diseases: Scoping Review

Anisha SA, Sen A, Bain C

J Med Internet Res, 2024 Jul 16;26:e56114.
PMID: 39012688 DOI: 10.2196/56114

BACKGROUND: The rising prevalence of noncommunicable diseases (NCDs) worldwide and the high recent mortality rates (74.4%) associated with them, especially in low- and middle-income countries, is causing a substantial global burden of disease, necessitating innovative and sustainable long-term care solutions.
OBJECTIVE: This scoping review aims to investigate the impact of artificial intelligence (AI)-based conversational agents (CAs)-including chatbots, voicebots, and anthropomorphic digital avatars-as human-like health caregivers in the remote management of NCDs as well as identify critical areas for future research and provide insights into how these technologies might be used effectively in health care to personalize NCD management strategies.
METHODS: A broad literature search was conducted in July 2023 in 6 electronic databases-Ovid MEDLINE, Embase, PsycINFO, PubMed, CINAHL, and Web of Science-using the search terms "conversational agents," "artificial intelligence," and "noncommunicable diseases," including their associated synonyms. We also manually searched gray literature using sources such as ProQuest Central, ResearchGate, ACM Digital Library, and Google Scholar. We included empirical studies published in English from January 2010 to July 2023 focusing solely on health care-oriented applications of CAs used for remote management of NCDs. The narrative synthesis approach was used to collate and summarize the relevant information extracted from the included studies.
RESULTS: The literature search yielded a total of 43 studies that matched the inclusion criteria. Our review unveiled four significant findings: (1) higher user acceptance and compliance with anthropomorphic and avatar-based CAs for remote care; (2) an existing gap in the development of personalized, empathetic, and contextually aware CAs for effective emotional and social interaction with users, along with limited consideration of ethical concerns such as data privacy and patient safety; (3) inadequate evidence of the efficacy of CAs in NCD self-management despite a moderate to high level of optimism among health care professionals regarding CAs' potential in remote health care; and (4) CAs primarily being used for supporting nonpharmacological interventions such as behavioral or lifestyle modifications and patient education for the self-management of NCDs.
CONCLUSIONS: This review makes a unique contribution to the field by not only providing a quantifiable impact analysis but also identifying the areas requiring imminent scholarly attention for the ethical, empathetic, and efficacious implementation of AI in NCD care. This serves as an academic cornerstone for future research in AI-assisted health care for NCD management.
TRIAL REGISTRATION: Open Science Framework; https://doi.org/10.17605/OSF.IO/GU5PX.

Matched MeSH terms: Artificial Intelligence*
ChatGPT in pathology

Tan GC, Wong YP

Malays J Pathol, 2024 Aug;46(2):231-232.
PMID: 39207000

No abstract available.

Matched MeSH terms: Artificial Intelligence*
Modified Cramer’s rule and its application to solve linear systems in WZ factorization

Olayiwola Babarinsa, Hailiza Kamarulhaili

MATEMATIKA, 2019;35(1):25-38.
MyJurnal

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
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
Fulltext Genetic justification of COVID-19 patient outcomes using DERGA, a novel data ensemble refinement greedy algorithm

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
Fulltext Heuristics for multiobjective optimization of two-sided assembly line systems

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*
Fulltext Can Building "Artificially Intelligent Cities" Safeguard Humanity from Natural Disasters, Pandemics, and Other Catastrophes? An Urban Scholar's Perspective

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*
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*
Artificial intelligence in drug development: present status and future prospects

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*
Interpretable and explainable AI (XAI) model for spatial drought prediction

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*
Explainable deep learning ensemble for food image analysis on edge devices

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*
Fulltext Explainable AI in Scene Understanding for Autonomous Vehicles in Unstructured Traffic Environments on Indian Roads Using the Inception U-Net Model with Grad-CAM Visualization

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*
Fulltext Predicting the natural history of artificial intelligence in travel medicine

Flaherty GT, Piyaphanee W

J Travel Med, 2023 Feb 18;30(1).
PMID: 36208173 DOI: 10.1093/jtm/taac113
Matched MeSH terms: Artificial Intelligence*
Modeling of carbon dioxide fixation by microalgae using hybrid artificial intelligence (AI) and fuzzy logic (FL) methods and optimization by genetic algorithm (GA)

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*

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