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Displaying publications 101 - 120 of 282 in total

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Automatic colonic polyp detection using integration of modified deep residual convolutional neural network and ensemble learning approaches

Liew WS, Tang TB, Lin CH, Lu CK

Comput Methods Programs Biomed, 2021 Jul;206:106114.
PMID: 33984661 DOI: 10.1016/j.cmpb.2021.106114

BACKGROUND AND OBJECTIVE: The increased incidence of colorectal cancer (CRC) and its mortality rate have attracted interest in the use of artificial intelligence (AI) based computer-aided diagnosis (CAD) tools to detect polyps at an early stage. Although these CAD tools have thus far achieved a good accuracy level to detect polyps, they still have room to improve further (e.g. sensitivity). Therefore, a new CAD tool is developed in this study to detect colonic polyps accurately.
METHODS: In this paper, we propose a novel approach to distinguish colonic polyps by integrating several techniques, including a modified deep residual network, principal component analysis and AdaBoost ensemble learning. A powerful deep residual network architecture, ResNet-50, was investigated to reduce the computational time by altering its architecture. To keep the interference to a minimum, median filter, image thresholding, contrast enhancement, and normalisation techniques were exploited on the endoscopic images to train the classification model. Three publicly available datasets, i.e., Kvasir, ETIS-LaribPolypDB, and CVC-ClinicDB, were merged to train the model, which included images with and without polyps.
RESULTS: The proposed approach trained with a combination of three datasets achieved Matthews Correlation Coefficient (MCC) of 0.9819 with accuracy, sensitivity, precision, and specificity of 99.10%, 98.82%, 99.37%, and 99.38%, respectively.
CONCLUSIONS: These results show that our method could repeatedly classify endoscopic images automatically and could be used to effectively develop computer-aided diagnostic tools for early CRC detection.

Matched MeSH terms: Artificial Intelligence
Fulltext BeSafe B2.0 Smart Multisensory Platform for Safety in Workplaces

Márquez-Sánchez S, Campero-Jurado I, Robles-Camarillo D, Rodríguez S, Corchado-Rodríguez JM

Sensors (Basel), 2021 May 12;21(10).
PMID: 34066186 DOI: 10.3390/s21103372

Wearable technologies are becoming a profitable means of monitoring a person's health state, such as heart rate and physical activity. The use of the smartwatch is becoming consolidated, not only as a novelty but also as a very useful tool for daily use. In addition, other devices, such as helmets or belts, are beneficial for monitoring workers and the early detection of any anomaly. They can provide valuable information, especially in work environments, where they help reduce the rate of accidents and occupational diseases, which makes them powerful Personal Protective Equipment (PPE). The constant monitoring of the worker's health can be done in real-time, through temperature, falls, noise, impacts, or heart rate meters, activating an audible and vibrating alarm when an anomaly is detected. The gathered information is transmitted to a server in charge of collecting and processing it. In the first place, this paper provides an exhaustive review of the state of the art on works related to electronics for human activity behavior. After that, a smart multisensory bracelet, combined with other devices, developed a control platform that can improve operators' security in the working environment. Artificial Intelligence and the Internet of Things (AIoT) bring together the information to improve safety on construction sites, power stations, power lines, etc. Real-time and historic data is used to monitor operators' health and a hybrid system between Gaussian Mixture Model and Human Activity Classification. That is, our contribution is also founded on the use of two machine learning models, one based on unsupervised learning and the other one supervised. Where the GMM gave us a performance of 80%, 85%, 70%, and 80% for the 4 classes classified in real time, the LSTM obtained a result under the confusion matrix of 0.769, 0.892, and 0.921 for the carrying-displacing, falls, and walking-standing activities, respectively. This information was sent in real time through the platform that has been used to analyze and process the data in an alarm system.

Matched MeSH terms: Artificial Intelligence
Application of artificial intelligence methods for monsoonal river classification in Selangor river basin, Malaysia

Wong YJ, Shimizu Y, Kamiya A, Maneechot L, Bharambe KP, Fong CS, et al.

Environ Monit Assess, 2021 Jun 22;193(7):438.
PMID: 34159431 DOI: 10.1007/s10661-021-09202-y

Rivers in Malaysia are classified based on water quality index (WQI) that comprises of six parameters, namely, ammoniacal nitrogen (AN), biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), pH, and suspended solids (SS). Due to its tropical climate, the impact of seasonal monsoons on river quality is significant, with the increased occurrence of extreme precipitation events; however, there has been little discussion on the application of artificial intelligence models for monsoonal river classification. In light of these, this study had applied artificial neural network (ANN) and support vector machine (SVM) models for monsoonal (dry and wet seasons) river classification using three of the water quality parameters to minimise the cost of river monitoring and associated errors in WQI computation. A structured trial-and-error approach was applied on input parameter selection and hyperparameter optimisation for both models. Accuracy, sensitivity, and precision were selected as the performance criteria. For dry season, BOD-DO-pH was selected as the optimum input combination by both ANN and SVM models, with testing accuracy of 88.7% and 82.1%, respectively. As for wet season, the optimum input combinations of ANN and SVM models were BOD-pH-SS and BOD-DO-pH with testing accuracy of 89.5% and 88.0%, respectively. As a result, both optimised ANN and SVM models have proven their prediction capacities for river classification, which may be deployed as effective and reliable tools in tropical regions. Notably, better learning and higher capacity of the ANN model for dataset characteristics extraction generated better predictability and generalisability than SVM model under imbalanced dataset.

Matched MeSH terms: Artificial Intelligence
Fulltext Path finding of static indoor mobile robot via AOR iterative method using harmonic potentials

A'qilah Ahmad Dahalan, Azali Saudi, Jumat Sulaiman

ZULFAQAR International Journal of Defence Science, Engineering & Technology, 2020;3(2):18-24.
MyJurnal

Mobile robots often have to discover a path of collision-free towards a specific goal point in their environment. We are trying to resolve the mobile robot problem iteratively by means of numerical technique. It is built on a method of potential field that count on the use of Laplace’s equation in the mobile robot’s configuration space to constrain/which reduces the generation of a potential function over regions. This paper proposed an iterative approach in solving robot path finding problem known as Accelerated Over-Relaxation (AOR). The experiment shows that these suggested approach can establish a smooth path between the starting and goal points by engaging with a finite-difference technique. The simulation results also show that a more rapidly solution with smoother path than the previous work is achieved via this numerical approach.

Matched MeSH terms: Artificial Intelligence
Fulltext Neovascularization Detection and Localization in Fundus Images Using Deep Learning

Tang MCS, Teoh SS, Ibrahim H, Embong Z

Sensors (Basel), 2021 Aug 06;21(16).
PMID: 34450766 DOI: 10.3390/s21165327

Proliferative Diabetic Retinopathy (PDR) is a severe retinal disease that threatens diabetic patients. It is characterized by neovascularization in the retina and the optic disk. PDR clinical features contain highly intense retinal neovascularization and fibrous spreads, leading to visual distortion if not controlled. Different image processing techniques have been proposed to detect and diagnose neovascularization from fundus images. Recently, deep learning methods are getting popular in neovascularization detection due to artificial intelligence advancement in biomedical image processing. This paper presents a semantic segmentation convolutional neural network architecture for neovascularization detection. First, image pre-processing steps were applied to enhance the fundus images. Then, the images were divided into small patches, forming a training set, a validation set, and a testing set. A semantic segmentation convolutional neural network was designed and trained to detect the neovascularization regions on the images. Finally, the network was tested using the testing set for performance evaluation. The proposed model is entirely automated in detecting and localizing neovascularization lesions, which is not possible with previously published methods. Evaluation results showed that the model could achieve accuracy, sensitivity, specificity, precision, Jaccard similarity, and Dice similarity of 0.9948, 0.8772, 0.9976, 0.8696, 0.7643, and 0.8466, respectively. We demonstrated that this model could outperform other convolutional neural network models in neovascularization detection.

Matched MeSH terms: Artificial Intelligence
Fulltext Detection of Cattle Using Drones and Convolutional Neural Networks

Rivas A, Chamoso P, González-Briones A, Corchado JM

Sensors (Basel), 2018 Jun 27;18(7).
PMID: 29954080 DOI: 10.3390/s18072048

Multirotor drones have been one of the most important technological advances of the last decade. Their mechanics are simple compared to other types of drones and their possibilities in flight are greater. For example, they can take-off vertically. Their capabilities have therefore brought progress to many professional activities. Moreover, advances in computing and telecommunications have also broadened the range of activities in which drones may be used. Currently, artificial intelligence and information analysis are the main areas of research in the field of computing. The case study presented in this article employed artificial intelligence techniques in the analysis of information captured by drones. More specifically, the camera installed in the drone took images which were later analyzed using Convolutional Neural Networks (CNNs) to identify the objects captured in the images. In this research, a CNN was trained to detect cattle, however the same training process could be followed to develop a CNN for the detection of any other object. This article describes the design of the platform for real-time analysis of information and its performance in the detection of cattle.

Matched MeSH terms: Artificial Intelligence
Prediction of tool life in end milling of Ti-6A1-4V alloy using artificial neural network and multiple regression models

SALAH AL-ZUBAIDI, JAHARAH A. GHANI, CHE HASSAN CHE HARON

Sains Malaysiana, 2013;42:1735-1741.

Tool life of the cutting tools is considered as one of the factors which has effects on machining costs and the quality of machined parts. The topic of tool life prediction has been an interesting and important research topic attracting the attention of a wide number of researchers in this particular area. In terms of the suitable methods used in this research topic, it is stated that both statistical and artificial intelligence (AI) approaches can be employed to model tool life. For further justifying the capability of the ANN model in predicting tool life, the current study was based on conducting experimental work for collecting the experimental data. After carrying out the experiment, 17 data sets were collected and they were divided into two subsets; the first one for training and the second for testing. Since the data sets seemed to be lower than the number of data sets used in previous studies, we attempted to make verification of the ability of the ANN model in learning and adapting with low training and testing data. Diverse topologies accompanied with single and two hidden layers were created for modeling the tool life. For choosing the best and most effective network, the study adopted the mean square error function as criteria for the evaluation of the network selection. Thus, based on the data generated from the same experiment, a regression model (RM) was constructed employing the SPSS software. A comparison between the ANN model and RMs in terms of their accuracy was carried out and the findings revealed that the accuracy of the ANN was higher than that of the RM.

Matched MeSH terms: Artificial Intelligence
Fulltext Retrofitting ancient timber glulam mortise & tenon construction joints through computer-aided laser cutting

Zhan Z, Wang C, Yap JBH, Loi MS

Heliyon, 2020 Apr;6(4):e03671.
PMID: 32382668 DOI: 10.1016/j.heliyon.2020.e03671

This study is aimed to rationalise and demonstrate the efficacy of utilising laser cutting technique in the fabrication of glulam mortise & tenon joints in timber frame. Trial-and-error experiments aided by laser cutter were conducted to produce 3D timber mortise & tenon joints models. The two main instruments used were 3D modelling software and the laser cutter TH 1390/6090. Plywood was chosen because it could produce smooth and accurate cut edges whereby the surface could remain crack-free, and it could increase stability due to its laminated nature. Google SketchUp was used for modelling and Laser CAD v7.52 was used to transfer the 3D models to the laser cutter because it is compatible with AI, BMP, PLT, DXF and DST templates. Four models were designed and fabricated in which the trial-and-error experiments proved laser cutting could speed up the manufacturing process with superb quality and high uniformity. Precision laser cutting supports easy automation, produces small heat-affected zone, minimises deformity, relatively quiet and produces low amount of waste. The LaserCAD could not process 3D images directly but needed 2D images to be transferred, so layering and unfolding works were therefore needed. This study revealed a significant potential of rapid manufacturability of mortise & tenon joints with high-quality and high-uniformity through computer-aided laser cutting technique for wide applications in the built environment.

Matched MeSH terms: Artificial Intelligence
Edge AI-Based Automated Detection and Classification of Road Anomalies in VANET Using Deep Learning

Bibi R, Saeed Y, Zeb A, Ghazal TM, Rahman T, Said RA, et al.

Comput Intell Neurosci, 2021;2021:6262194.
PMID: 34630550 DOI: 10.1155/2021/6262194

Road surface defects are crucial problems for safe and smooth traffic flow. Due to climate changes, low quality of construction material, large flow of traffic, and heavy vehicles, road surface anomalies are increasing rapidly. Detection and repairing of these defects are necessary for the safety of drivers, passengers, and vehicles from mechanical faults. In this modern era, autonomous vehicles are an active research area that controls itself with the help of in-vehicle sensors without human commands, especially after the emergence of deep learning (DNN) techniques. A combination of sensors and DNN techniques can be useful for unmanned vehicles for the perception of their surroundings for the detection of tracks and obstacles for smooth traveling based on the deployment of artificial intelligence in vehicles. One of the biggest challenges for autonomous vehicles is to avoid the critical road defects that may lead to dangerous situations. To solve the accident issues and share emergency information, the Intelligent Transportation System (ITS) introduced the concept of vehicular network termed as vehicular ad hoc network (VANET) for achieving security and safety in a traffic flow. A novel mechanism is proposed for the automatic detection of road anomalies by autonomous vehicles and providing road information to upcoming vehicles based on Edge AI and VANET. Road images captured via camera and deployment of the trained model for road anomaly detection in a vehicle could help to reduce the accident rate and risk of hazards on poor road conditions. The techniques Residual Convolutional Neural Network (ResNet-18) and Visual Geometry Group (VGG-11) are applied for the automatic detection and classification of the road with anomalies such as a pothole, bump, crack, and plain roads without anomalies using the dataset from different online sources. The results show that the applied models performed well than other techniques used for road anomalies identification.

Matched MeSH terms: Artificial Intelligence
Global variations in diagnostic guidelines for Barrett's esophagus

Kusano C, Singh R, Lee YY, Soh YSA, Sharma P, Ho KY, et al.

Dig Endosc, 2022 Nov;34(7):1320-1328.
PMID: 35475586 DOI: 10.1111/den.14342

Endoscopic diagnosis of gastroesophageal junction and Barrett's esophagus is essential for surveillance and early detection of esophageal adenocarcinoma and esophagogastric junction cancer. Despite its small size, the gastroesophageal junction has many inherent problems, including marked differences in diagnostic methods for Barrett's esophagus in international guidelines. To define Barrett's esophagus, gastroesophageal junction location should be clarified. Although gastric folds and palisade vessels are landmarks for identifying this junction, they are sometimes difficult to observe due to air entry or reflux esophagitis. The possibility of diagnosing a malignancy associated with Barrett's esophagus <1 cm, identified using palisade vessels, should be re-examined. Nontargeted biopsies of Barrett's esophagus are commonly used to detect intestinal metaplasia, dysplasia, and cancer as described in the Seattle protocol. Barrett's esophagus with intestinal metaplasia has a high risk of becoming cancerous. Furthermore, the frequency of cancer in patients with Barrett's esophagus without intestinal metaplasia is high, and the guidelines differ on whether to include the presence of intestinal metaplasia in the diagnosis of Barrett's esophagus. Use of advanced imaging technologies, including narrow-band imaging with magnifying endoscopy and linked color imaging, is reportedly valid for diagnosing Barrett's esophagus. Furthermore, artificial intelligence has facilitated the diagnosis of Barrett's esophagus through its deep learning and image recognition capabilities. However, it is necessary to first use the endoscopic definition of the gastroesophageal junction, which is common in all countries, and then elucidate the characteristics of Barrett's esophagus in each region, for example, length differences in the risk of carcinogenesis with and without intestinal metaplasia.

Matched MeSH terms: Artificial Intelligence
Resource recovery from hydroponic wastewaters using microalgae-based biorefineries: A circular bioeconomy perspective

Ajeng AA, Rosli NSM, Abdullah R, Yaacob JS, Qi NC, Loke SP

J Biotechnol, 2022 Dec 10;360:11-22.
PMID: 36272573 DOI: 10.1016/j.jbiotec.2022.10.011

As the world's population grows, it is necessary to rethink how countries throughout the world produce food in order to replace the conventional and unsustainable agricultural techniques. Microalgae cultivation using a nutrient-rich solution from hydroponic systems not only presents a novel approach to solving problems pertaining to the impact of the discharges on the natural environment but also provides a plethora of other biotechnological applications particularly in the productions of high value-added products and plants growth stimulants, which can be potentially assimilated into the circular bioeconomy (CBE) in the hydroponic sector. In this review, the potential and practicability of microalgae to be merged into hydroponics CBE are reviewed. Overall, the integration of microalgal biorefineries in hydroponics systems can be realized after considering their Technology Readiness Level and System Readiness Level beforehand. Several suggestions on strains and hydroponics system improvement using existing biotechnological tools, Artificial Intelligence (AI) and nanobiotechnology in support of the CBE will be covered.

Matched MeSH terms: Artificial Intelligence
Computational Approaches in the Discovery and Development of Therapeutic and Prophylactic Agents for Viral Diseases

Gaurav A, Agrawal N, Al-Nema M, Gautam V

Curr Top Med Chem, 2022;22(26):2190-2206.
PMID: 36278463 DOI: 10.2174/1568026623666221019110334

Over the last two decades, computational technologies have played a crucial role in antiviral drug development. Whenever a virus spreads and becomes a threat to global health, it brings along the challenge of developing new therapeutics and prophylactics. Computational drug and vaccine discovery has evolved quickly over the years. Some interesting examples of computational drug discovery are anti-AIDS drugs, where HIV protease and reverse transcriptase have been targeted by agents developed using computational methods. Various computational methods that have been applied to anti-viral research include ligand-based methods that rely on known active compounds, i.e., pharmacophore modeling, machine learning or classical QSAR; structure-based methods that rely on an experimentally determined 3D structure of the targets, i.e., molecular docking and molecular dynamics and methods for the development of vaccines such as reverse vaccinology; structural vaccinology and vaccine epitope prediction. This review summarizes these approaches to battle viral diseases and underscores their importance for anti-viral research. We discuss the role of computational methods in developing small molecules and vaccines against human immunodeficiency virus, yellow fever, human papilloma virus, SARS-CoV-2, and other viruses. Various computational tools available for the abovementioned purposes have been listed and described. A discussion on applying artificial intelligence-based methods for antiviral drug discovery has also been included.

Matched MeSH terms: Artificial Intelligence
Fulltext Detection of Colorectal Polyps from Colonoscopy Using Machine Learning: A Survey on Modern Techniques

ELKarazle K, Raman V, Then P, Chua C

Sensors (Basel), 2023 Jan 20;23(3).
PMID: 36772263 DOI: 10.3390/s23031225

Given the increased interest in utilizing artificial intelligence as an assistive tool in the medical sector, colorectal polyp detection and classification using deep learning techniques has been an active area of research in recent years. The motivation for researching this topic is that physicians miss polyps from time to time due to fatigue and lack of experience carrying out the procedure. Unidentified polyps can cause further complications and ultimately lead to colorectal cancer (CRC), one of the leading causes of cancer mortality. Although various techniques have been presented recently, several key issues, such as the lack of enough training data, white light reflection, and blur affect the performance of such methods. This paper presents a survey on recently proposed methods for detecting polyps from colonoscopy. The survey covers benchmark dataset analysis, evaluation metrics, common challenges, standard methods of building polyp detectors and a review of the latest work in the literature. We conclude this paper by providing a precise analysis of the gaps and trends discovered in the reviewed literature for future work.

Matched MeSH terms: Artificial Intelligence
Smart systems in producing algae-based protein to improve functional food ingredients industries

Neo YT, Chia WY, Lim SS, Ngan CL, Kurniawan TA, Chew KW

Food Res Int, 2023 Mar;165:112480.
PMID: 36869493 DOI: 10.1016/j.foodres.2023.112480

Production and extraction systems of algal protein and handling process of functional food ingredients need to control several parameters such as temperature, pH, intensity, and turbidity. Many researchers have investigated the Internet of Things (IoT) approach for enhancing the yield of microalgae biomass and machine learning for identifying and classifying microalgae. However, there have been few specific studies on using IoT and artificial intelligence (AI) for production and extraction of algal protein as well as functional food ingredients processing. In order to improve the production of algal protein and functional food ingredients, the implementation of smart system is a must to have real-time monitoring, remote control system, quick response to sudden events, prediction and characterisation. Techniques of IoT and AI are expected to help functional food industries to have a big breakthrough in the future. Manufacturing and implementation of beneficial smart systems are important to provide convenience and to increase the efficiency of work by using the interconnectivity of IoT devices to have good capturing, processing, archiving, analyzing, and automation. This review investigates the possibilities of implementation of IoT and AI in production and extraction of algal protein and processing of functional food ingredients.

Matched MeSH terms: Artificial Intelligence
Enhancing the performance of data-driven models for monthly reservoir evaporation prediction

Allawi MF, Aidan IA, El-Shafie A

Environ Sci Pollut Res Int, 2021 Feb;28(7):8281-8295.
PMID: 33052565 DOI: 10.1007/s11356-020-11062-x

The accuracy level for reservoir evaporation prediction is an important issue for decision making in the water resources field. The traditional methods for evaporation prediction could encounter numerous obstacles owing to the effect of several parameters on the shape of the evaporation pattern. The current research presented modern model called the Coactive Neuro-Fuzzy Inference System (CANFIS). Modification for such model has been achieved for enhancing the evaporation prediction accuracy. Genetic algorithm was utilized to select the effective input combination. The efficiency of the proposed model has been compared with popular artificial intelligence models according to several statistical indicators. Two different case studies Aswan High Dam (AHD) and Timah Tasoh Dam (TTD) have been considered to explore the performance of the proposed models. It is concluded that the modified GA-CANFIS model is better than GA-ANFIS, GA-SVR, and GA-RBFNN for evaporation prediction for both case studies. GA-CANFIS attained minimum RMSE (15.22 mm month-1 for AHD, 8.78 mm month-1 for TTD), minimum MAE (12.48 mm month-1 for AHD, 5.11 mm month-1 for TTD), and maximum determination coefficient (0.98 for AHD, 0.95 for TTD).

Matched MeSH terms: Artificial Intelligence
Fulltext Next-generation nanophotonic-enabled biosensors for intelligent diagnosis of SARS-CoV-2 variants

Taha BA, Al Mashhadany Y, Al-Jubouri Q, Rashid ARBA, Luo Y, Chen Z, et al.

Sci Total Environ, 2023 Jul 01;880:163333.
PMID: 37028663 DOI: 10.1016/j.scitotenv.2023.163333

Constantly mutating SARS-CoV-2 is a global concern resulting in COVID-19 infectious waves from time to time in different regions, challenging present-day diagnostics and therapeutics. Early-stage point-of-care diagnostic (POC) biosensors are a crucial vector for the timely management of morbidity and mortalities caused due to COVID-19. The state-of-the-art SARS-CoV-2 biosensors depend upon developing a single platform for its diverse variants/biomarkers, enabling precise detection and monitoring. Nanophotonic-enabled biosensors have emerged as 'one platform' to diagnose COVID-19, addressing the concern of constant viral mutation. This review assesses the evolution of current and future variants of the SARS-CoV-2 and critically summarizes the current state of biosensor approaches for detecting SARS-CoV-2 variants/biomarkers employing nanophotonic-enabled diagnostics. It discusses the integration of modern-age technologies, including artificial intelligence, machine learning and 5G communication with nanophotonic biosensors for intelligent COVID-19 monitoring and management. It also highlights the challenges and potential opportunities for developing intelligent biosensors for diagnosing future SARS-CoV-2 variants. This review will guide future research and development on nano-enabled intelligent photonic-biosensor strategies for early-stage diagnosing of highly infectious diseases to prevent repeated outbreaks and save associated human mortalities.

Matched MeSH terms: Artificial Intelligence
Fulltext Medical image processing and COVID-19: A literature review and bibliometric analysis

Abumalloh RA, Nilashi M, Yousoof Ismail M, Alhargan A, Alghamdi A, Alzahrani AO, et al.

J Infect Public Health, 2022 Jan;15(1):75-93.
PMID: 34836799 DOI: 10.1016/j.jiph.2021.11.013

COVID-19 crisis has placed medical systems over the world under unprecedented and growing pressure. Medical imaging processing can help in the diagnosis, treatment, and early detection of diseases. It has been considered as one of the modern technologies applied to fight against the COVID-19 crisis. Although several artificial intelligence, machine learning, and deep learning techniques have been deployed in medical image processing in the context of COVID-19 disease, there is a lack of research considering systematic literature review and categorization of published studies in this field. A systematic review locates, assesses, and interprets research outcomes to address a predetermined research goal to present evidence-based practical and theoretical insights. The main goal of this study is to present a literature review of the deployed methods of medical image processing in the context of the COVID-19 crisis. With this in mind, the studies available in reliable databases were retrieved, studied, evaluated, and synthesized. Based on the in-depth review of literature, this study structured a conceptual map that outlined three multi-layered folds: data gathering and description, main steps of image processing, and evaluation metrics. The main research themes were elaborated in each fold, allowing the authors to recommend upcoming research paths for scholars. The outcomes of this review highlighted that several methods have been adopted to classify the images related to the diagnosis and detection of COVID-19. The adopted methods have presented promising outcomes in terms of accuracy, cost, and detection speed.

Matched MeSH terms: Artificial Intelligence
Fulltext COVID-19 Isolation Control Proposal via UAV and UGV for Crowded Indoor Environments: Assistive Robots in the Shopping Malls

Aslan MF, Hasikin K, Yusefi A, Durdu A, Sabanci K, Azizan MM

Front Public Health, 2022;10:855994.
PMID: 35734764 DOI: 10.3389/fpubh.2022.855994

Artificial intelligence researchers conducted different studies to reduce the spread of COVID-19. Unlike other studies, this paper isn't for early infection diagnosis, but for preventing the transmission of COVID-19 in social environments. Among the studies on this is regarding social distancing, as this method is proven to prevent COVID-19 to be transmitted from one to another. In the study, Robot Operating System (ROS) simulates a shopping mall using Gazebo, and customers are monitored by Turtlebot and Unmanned Aerial Vehicle (UAV, DJI Tello). Through frames analysis captured by Turtlebot, a particular person is identified and followed at the shopping mall. Turtlebot is a wheeled robot that follows people without contact and is used as a shopping cart. Therefore, a customer doesn't touch the shopping cart that someone else comes into contact with, and also makes his/her shopping easier. The UAV detects people from above and determines the distance between people. In this way, a warning system can be created by detecting places where social distance is neglected. Histogram of Oriented-Gradients (HOG)-Support Vector Machine (SVM) is applied by Turtlebot to detect humans, and Kalman-Filter is used for human tracking. SegNet is performed for semantically detecting people and measuring distance via UAV. This paper proposes a new robotic study to prevent the infection and proved that this system is feasible.

Matched MeSH terms: Artificial Intelligence
Fulltext Diagnosis of Schizophrenia Based on the Data of Various Modalities: Biomarkers and Machine Learning Techniques (Review)

Sharaev MG, Malashenkova IK, Maslennikova AV, Zakharova NV, Bernstein AV, Burnaev EV, et al.

Sovrem Tekhnologii Med, 2022;14(5):53-75.
PMID: 37181835 DOI: 10.17691/stm2022.14.5.06

Schizophrenia is a socially significant mental disorder resulting frequently in severe forms of disability. Diagnosis, choice of treatment tactics, and rehabilitation in clinical psychiatry are mainly based on the assessment of behavioral patterns, socio-demographic data, and other investigations such as clinical observations and neuropsychological testing including examination of patients by the psychiatrist, self-reports, and questionnaires. In many respects, these data are subjective and therefore a large number of works have appeared in recent years devoted to the search for objective characteristics (indices, biomarkers) of the processes going on in the human body and reflected in the behavioral and psychoneurological patterns of patients. Such biomarkers are based on the results of instrumental and laboratory studies (neuroimaging, electro-physiological, biochemical, immunological, genetic, and others) and are successfully being used in neurosciences for understanding the mechanisms of the emergence and development of nervous system pathologies. Presently, with the advent of new effective neuroimaging, laboratory, and other methods of investigation and also with the development of modern methods of data analysis, machine learning, and artificial intelligence, a great number of scientific and clinical studies is being conducted devoted to the search for the markers which have diagnostic and prognostic value and may be used in clinical practice to objectivize the processes of establishing and clarifying the diagnosis, choosing and optimizing treatment and rehabilitation tactics, predicting the course and outcome of the disease. This review presents the analysis of the works which describe the correlates between the diagnosis of schizophrenia, established by health professionals, various manifestations of the psychiatric disorder (its subtype, variant of the course, severity degree, observed symptoms, etc.), and objectively measured characteristics/quantitative indicators (anatomical, functional, immunological, genetic, and others) obtained during instrumental and laboratory examinations of patients. A considerable part of these works has been devoted to correlates/biomarkers of schizophrenia based on the data of structural and functional (at rest and under cognitive load) MRI, EEG, tractography, and immunological data. The found correlates/biomarkers reflect anatomic disorders in the specific brain regions, impairment of functional activity of brain regions and their interconnections, specific microstructure of the brain white matter and the levels of connectivity between the tracts of various structures, alterations of electrical activity in various parts of the brain in different EEG spectral ranges, as well as changes in the innate and adaptive links of immunity. Current methods of data analysis and machine learning to search for schizophrenia biomarkers using the data of diverse modalities and their application during building and interpretation of predictive diagnostic models of schizophrenia have been considered in the present review.

Matched MeSH terms: Artificial Intelligence
Fulltext Predicting compressive strength of eco-friendly plastic sand paver blocks using gene expression and artificial intelligence programming

Iftikhar B, Alih SC, Vafaei M, Javed MF, Rehman MF, Abdullaev SS, et al.

Sci Rep, 2023 Jul 27;13(1):12149.
PMID: 37500697 DOI: 10.1038/s41598-023-39349-2

Plastic sand paver blocks provide a sustainable alternative by using plastic waste and reducing the need for cement. This innovative approach leads to a more sustainable construction sector by promoting environmental preservation. No model or Equation has been devised that can predict the compressive strength of these blocks. This study utilized gene expression programming (GEP) and multi-expression programming (MEP) to develop empirical models to forecast the compressive strength of plastic sand paver blocks (PSPB) comprised of plastic, sand, and fibre in an effort to advance the field. The database contains 135 results for compressive strength with seven input parameters. The R2 values of 0.87 for GEP and 0.91 for MEP for compressive strength reveal a relatively significant relationship between predicted and actual values. MEP outperformed GEP by displaying a higher R2 and lower values for statistical evaluations. In addition, a sensitivity analysis was conducted, which revealed that the sand grain size and percentage of fibres play an essential part in compressive strength. It was estimated that they contributed almost 50% of the total. The outcomes of this research have the potential to promote the reuse of PSPB in the building of green environments, hence boosting environmental protection and economic advantage.

Matched MeSH terms: Artificial Intelligence

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