Application Of Artificial Intelligence Techniques Research Articles

Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging.

The scientific community has joined forces to mitigate the scope of the current COVID-19 pandemic. The early identification of the disease, as well as the evaluation of its evolution is a primary task for the timely application of medical protocols. The use of medical images of the chest provides valuable information to specialists. Specifically, chest X-ray images have been the focus of many investigations that apply artificial intelligence techniques for the automatic classification of this disease. The results achieved to date on the subject are promising. However, some results of these investigations contain errors that must be corrected to obtain appropriate models for clinical use. This research discusses some of the problems found in the current scientific literature on the application of artificial intelligence techniques in the automatic classification of COVID-19. It is evident that in most of the reviewed works an incorrect evaluation protocol is applied, which leads to overestimating the results.

Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review.

BackgroundSARS-CoV-2, the novel coronavirus responsible for COVID-19, has caused havoc worldwide, with patients presenting a spectrum of complications that have pushed health care experts to explore new technological solutions and treatment plans. Artificial Intelligence (AI)–based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize these technologies in response to the challenges posed by the COVID-19 pandemic.ObjectiveThe objective of this study was to conduct a systematic review of the literature on the role of AI as a comprehensive and decisive technology to fight the COVID-19 crisis in the fields of epidemiology, diagnosis, and disease progression.MethodsA systematic search of PubMed, Web of Science, and CINAHL databases was performed according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to identify all potentially relevant studies published and made available online between December 1, 2019, and June 27, 2020. The search syntax was built using keywords specific to COVID-19 and AI.ResultsThe search strategy resulted in 419 articles published and made available online during the aforementioned period. Of these, 130 publications were selected for further analyses. These publications were classified into 3 themes based on AI applications employed to combat the COVID-19 crisis: Computational Epidemiology, Early Detection and Diagnosis, and Disease Progression. Of the 130 studies, 71 (54.6%) focused on predicting the COVID-19 outbreak, the impact of containment policies, and potential drug discoveries, which were classified under the Computational Epidemiology theme. Next, 40 of 130 (30.8%) studies that applied AI techniques to detect COVID-19 by using patients’ radiological images or laboratory test results were classified under the Early Detection and Diagnosis theme. Finally, 19 of the 130 studies (14.6%) that focused on predicting disease progression, outcomes (ie, recovery and mortality), length of hospital stay, and number of days spent in the intensive care unit for patients with COVID-19 were classified under the Disease Progression theme.ConclusionsIn this systematic review, we assembled studies in the current COVID-19 literature that utilized AI-based methods to provide insights into different COVID-19 themes. Our findings highlight important variables, data types, and available COVID-19 resources that can assist in facilitating clinical and translational research.

Application of Artificial Intelligence Techniques in Operating Mode of Professors’ Academic Governance in American Research Universities

Artificial intelligence technology is an important transformative force for teaching innovation in the intelligent era. It is being widely used in American school teaching, including the design of intelligent tutoring systems to achieve precise problem solving, the machine learning technology to ensure personalized activity design, the creation of intelligent virtual reality to promote classroom teaching contextualization, and the development of intelligent evaluation systems to ensure the scientific evaluation of capabilities. In the process of advancing the teaching and application of artificial intelligence technology, the United States has built a linkage mechanism of federal leadership, university follow‐up, and social collaboration and implemented the smart technology in school teaching and professors’ academic governance. This paper is aimed at studying the professors’ academic governance of American research universities by Internet data mining, historical analysis method, documentary method, survey method, and other methods. Professors’ academic governance is a vital part of the modern university system that causes the institutional reform of the internal governance structure of modern universities. The United States is a powerful country in higher education, and professors in American research universities have always participated in university academic governance for centuries. By studying the definition, history, and development and mode of operation of professors’ academic governance in American research universities, the results indicate a clear division of power and responsibility between the professors and administrators based on an artificial intelligence decision system in American research universities. Also, there is a good communication platform based on artificial intelligence environment for professors to discuss their opinions on academic affairs. Third, professors exercise academic power under the guarantee of diversified guaranteed systems based on the artificial intelligence evaluation system and the ideology of mutual respect based on the artificial intelligence management and service system. Studying the application of artificial intelligence techniques in operating mode and enlightenment of professors’ academic governance in an American research university is of great significance to promote the construction of other modern universities’ professors’ academic governance system.

Application of artificial intelligence techniques for identifying rock mass quality in an underground tunnel

The horizontal probe drilling data from the Pahang-Selangor raw water transfer tunnel (PSRWT) was used to develop intelligence models that could be used to identify the rock mass quality ahead of the excavation. In this study, two common artificial intelligence techniques; artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were used to achieve this aim. The input variables include rock quality designation, discontinuity spacing, groundwater inflow, discontinuity conditions, and penetration rate. The target variable was the rock mass rating index. Coefficient of determination (R2), root mean squared error (RMSE), and variance accounted for (VAF) were calculated to assess the developed models. The R2, VAF, and RMSE values of 0.922, 92.08%, and 2.284 respectively for ANN indicates a lower prediction output than the ANFIS model with R2, VAF, and RMSE values of 0.925, 92.27%, and 2.054 respectively. The results show that ANFIS outperformed ANN in predicting rock mass rating.

Application of artificial intelligence techniques for identifying rock mass quality in an underground tunnel

Application of artificial intelligence techniques for identifying rock mass quality in an underground tunnel

Evaluating Deep and Statistical Machine Learning Models in the Classification of Breast Cancer from Digital Mammograms

The application of artificial intelligence techniques in computer aided detection and diagnosis problems has been among the most promising areas with interest from the scientific community and healthcare industry. Recently, deep learning has become the prime tool for such application with many studies focusing on developing variants that optimize diagnostic performance. Despite the widely accepted success of this class of techniques in this application by the scientific community, it is not prudent to consider it as the only tool available for such purpose. In particular, statistical machine learning offers a variety of techniques that can also be applied at a much lower computational cost. Unfortunately, the results from both strategies cannot be directly compared due to the differences in experimental setups and datasets used in available research studies. Therefore, we focus in this study on this direct comparison using the same dataset and similar data preprocessing as the input to both. We compare statistical machine learning to deep learning in the context of computer-aided detection of breast cancer from mammographic images. The results are compared using diagnostic performance metrics and suggest that simpler statistical machine learning techniques may provide better performance with simpler architectures that allow explanation of results.

Automating and Optimizing Software Testing using Artificial Intelligence Techniques

The final product of software development process is a software system and testing is one of the important stages in this process. The success of this process can be determined by how well it accomplishes its goal. Due to the advancement of technology, various software testing tools have been introduced in the software engineering discipline. The use of software is increasing day-by-day and complexity of software functions are challenging and there is need to release the software within the short quality evaluation period, there is a high demand in adopting automation in software testing. Emergence of automatic software testing tools and techniques helps in quality enhancement and reducing time and cost in the software development activity. Artificial Intelligence (AI) techniques are widely applied in different areas of Software engineering (SE). Application of AI techniques can help in achieving good performance in software Testing and increase the productivity of the software development firms. This paper briefly presents the state of the art in the field of software testing by applying AI techniques in software testing.

Advances of Artificial Intelligence Application in Medical Imaging of Ovarian Cancers.

Ovarian cancer is one of the three most common gynecological cancers in the world, and is regarded as a priority in terms of women's cancer. In the past few years, many researchers have attempted to develop and apply artificial intelligence (AI) techniques to multiple clinical scenarios of ovarian cancer, especially in the field of medical imaging. AI-assisted imaging studies have involved computer tomography (CT), ultrasonography (US), and magnetic resonance imaging (MRI). In this review, we perform a literature search on the published studies that using AI techniques in the medical care of ovarian cancer, and bring up the advances in terms of four clinical aspects, including medical diagnosis, pathological classification, targeted biopsy guidance, and prognosis prediction. Meanwhile, current status and existing issues of the researches on AI application in ovarian cancer are discussed.

Evaluation of Artificial Intelligence Techniques Applied in Watson and AlphaGo

Artificial intelligence (AI) and software engineering are two important areas in computer science. In recent years, researchers are trying to apply AI techniques in various stages of software development to improve the overall quality of software products. Moreover, there are also some researchers who focus on the intersection between software engineering and AI. In fact, the relationship between software engineering and AI is very weak; however, methods and techniques in one area have been adopted in another area. More and more software products are capable of performing intelligent behavior like human beings. In this research project, two cases studies which are IBM Watson and Google AlphaGo that use different AI techniques in solving real-world challenging problems have been analyzed, evaluated and compared. Based on the analysis of both case studies, using AI techniques such as deep learning and machine learning in software systems contributes to intelligent systems. Watson adopts ’decision making support’ strategy to help humans make decisions; whereas AlphaGo uses ’self-decision making’ to choose operations that contribute to the best outcome. In addition, Watson learns from man-made resources such as paper; AlphaGo, on the other hand, learns from massive online resources such as photos. AlphaGo uses neural networks and reinforcement learning to mimic human brain, which might be very useful in medical research for diagnosis and treatment. However, there is still a long way to go if we want to reproduce human brain in machine and view computers as thinkers, because human brain and machines are intrinsically different. It would be more promising to see whether computers and software systems will become more and more intelligent to help with real world challenging problems that human beings cannot do.

Application of Artificial Intelligence in Incremental Sheet Metal Forming: A Review

Artificial Intelligence (AI) has been widely used in manufacturing, healthcare, sports, finance, and other areas to model nonlinearities and make reliable predictions. In manufacturing, AI has been applied to improve processes, reduce costs, and increase reliability. A novel manufacturing process that has been augmented with AI is Incremental Sheet Forming (ISF), a technology that applies a step-by-step incremental feed to a sheet metal or polymer blank using a CNC machine. The quality of the produced part is affected by parameters related to four process elements: the blank, the blank holder, the forming tool, and the CNC machine (applied force). The ISF process is greatly affected by forming process parameters, material property parameters, and geometric parameters. Numerous research efforts have correlated the relationship between the ISF parameters to final product attributes using analytical, experimental, and numerical techniques. However, these techniques are not efficient due to the nonlinearities and complexities of the relationships, the time-consuming nature of the process, and extensive computational time needed to simulate the process. To compensate for these shortcomings, researchers have started to apply AI techniques in analysis of ISF. The aim of this paper is to review the application of AI in the ISF process in forming of metal sheets in order to summarize the contributions of prior research efforts and identify potential opportunities for future research.

A Head-to Head Comparison of Machine Learning Algorithms for Identification of Implanted Cardiac Devices

Application of artificial intelligence techniques in medicine has rapidly expanded in recent years. Two algorithms for identification of cardiac implantable electronic devices using chest radiography were recently developed: The PacemakerID algorithm, available as a mobile phone application (PIDa) and a web platform (PIDw) and The Pacemaker Identification with Neural Networks (PPMnn), available via web platform. In this study, we assessed the relative accuracy of these algorithms. The machine learning algorithms (PIDa, PIDw, PPMnn) were used to predict device manufacturer using chest X-rays for patients with implanted devices. Each prediction was considered correct if predicted certainty was >75%. For comparative purposes, accuracy of each prediction was compared to the result using the CARDIA-X algorithm. 500 X-rays were included from a convenience sample. Raw accuracy was PIDa 89%, PIDw 73%, PPMnn 71% and CARDIA-X 85%. In conclusion, machine learning algorithms for identification of cardiac devices are accurate at determining device manufacturer, have capacity for improved accuracy with additional training sets and can utilize simple user interfaces. These algorithms have clinical utility in limiting potential infectious exposures and facilitate rapid identification of devices as needed for device reprogramming.

Model-Based Reasoning of Clinical Diagnosis in Integrative Medicine: Real-World Methodological Study of Electronic Medical Records and Natural Language Processing Methods

BackgroundIntegrative medicine is a form of medicine that combines practices and treatments from alternative medicine with conventional medicine. The diagnosis in integrative medicine involves the clinical diagnosis based on modern medicine and syndrome pattern diagnosis. Electronic medical records (EMRs) are the systematized collection of patients health information stored in a digital format that can be shared across different health care settings. Although syndrome and sign information or relative information can be extracted from the EMR and content texts can be mapped to computability vectors using natural language processing techniques, application of artificial intelligence techniques to support physicians in medical practices remains a major challenge.ObjectiveThe purpose of this study was to investigate model-based reasoning (MBR) algorithms for the clinical diagnosis in integrative medicine based on EMRs and natural language processing. We also estimated the associations among the factors of sample size, number of syndrome pattern type, and diagnosis in modern medicine using the MBR algorithms.MethodsA total of 14,075 medical records of clinical cases were extracted from the EMRs as the development data set, and an external test data set consisting of 1000 medical records of clinical cases was extracted from independent EMRs. MBR methods based on word embedding, machine learning, and deep learning algorithms were developed for the automatic diagnosis of syndrome pattern in integrative medicine. MBR algorithms combining rule-based reasoning (RBR) were also developed. A standard evaluation metrics consisting of accuracy, precision, recall, and F1 score was used for the performance estimation of the methods. The association analyses were conducted on the sample size, number of syndrome pattern type, and diagnosis of lung diseases with the best algorithms.ResultsThe Word2Vec convolutional neural network (CNN) MBR algorithms showed high performance (accuracy of 0.9586 in the test data set) in the syndrome pattern diagnosis of lung diseases. The Word2Vec CNN MBR combined with RBR also showed high performance (accuracy of 0.9229 in the test data set). The diagnosis of lung diseases could enhance the performance of the Word2Vec CNN MBR algorithms. Each group sample size and syndrome pattern type affected the performance of these algorithms.ConclusionsThe MBR methods based on Word2Vec and CNN showed high performance in the syndrome pattern diagnosis of lung diseases in integrative medicine. The parameters of each group’s sample size, syndrome pattern type, and diagnosis of lung diseases were associated with the performance of the methods.Trial RegistrationClinicalTrials.gov NCT03274908; https://clinicaltrials.gov/ct2/show/NCT03274908

Application of artificial intelligence techniques for stuck pipe events classification during drilling phase

Application of artificial intelligence techniques for stuck pipe events classification during drilling phase

Application of artificial intelligence techniques in irrigation and crop health management for crop yield enhancement

Agriculture is the backbone of all developing countries. The livelihood of a huge majority of the Indian population is completely dependent on agriculture. However lack of basic requirement lead to poor development in this sector. The major factor required for the development of this sector is to improve water supply level. The lack of proper monsoon has resulted in poor agricultural yield. Based on the natural calamities we can predict the requirement of water for particular crop at different stages of growth and also enhance the yield. In this paper, we have chosen Thottiyam taluk as example located in the state of Tamilnadu, India. For that location we have collected the basic attributes required for good irrigation management for the months of December and January. Artificial Intelligence techniques are applied to predict the water level requirement and their performances can be compared.

Application of Artificial Intelligence Techniques for the Estimation of Basal Insulin in Patients with Type I Diabetes.

Artificial intelligence techniques have been positioned in the resolution of problems in various areas of healthcare. Clinical decision support systems developed from this technology have optimized the healthcare of patients with chronic diseases through mobile applications. In this study, several models based on this methodology have been developed to calculate the basal insulin dose in patients with type I diabetes using subcutaneous insulin infusion pumps. Methods. A pilot experimental study was performed with data from 56 patients with type 1 diabetes who used insulin infusion pumps and underwent continuous glucose monitoring. Several models based on artificial intelligence techniques were developed to analyze glycemic patterns based on continuous glucose monitoring and clinical variables in order to estimate the basal insulin dose. We used neural networks (NNs), Bayesian networks (BNs), support vector machines (SVMs), and random forests (RF). We then evaluated the agreement between predicted and actual values using several statistical error measurements: mean absolute error (MAE), mean square error (MSE), root-mean-square error (RMSE), Pearson's correlation coefficient (R), and determination coefficient (R2). Results. Twenty-four different models were obtained, one for each hour of the day, with each chosen technique. Correlation coefficients obtained with RF, SVMs, NNs, and BNs were 0.9999, 0.9921, 0.0303, and 0.7754, respectively. The error increased between 06:00 and 07:00 and between 13:00 and 17:00. Conclusions. The performance of the RF technique was excellent and got very close to the actual values. Intelligence techniques could be used to predict basal insulin dose. However, it is necessary to explore the validity of the results and select the target population. Models that allow for more accurate levels of prediction should be further explored.

Toward a cyber-physical manufacturing metrology model for industry 4.0

AbstractIndustry 4.0 represents high-level methodologies for the development of new generation manufacturing metrology systems, which are more intelligent (smart), autonomous, flexible, high-productive, and self-adaptable. One of the systems capable of responding to these challenges is a cyber-physical manufacturing metrology system (CP2MS) with techniques of artificial intelligence (AI). In general, CP2MS systems generate Big data, horizontally by integration [coordinate measuring machines (CMMs)] and vertically by control. This paper presents a cyber-physical manufacturing metrology model (CP3M) for Industry 4.0 developed by applying AI techniques such as engineering ontology (EO), ant-colony optimization (ACO), and genetic algorithms (GAs). Particularly, the CP3M presents an intelligent approach of probe configuration and setup planning for inspection of prismatic measurement parts (PMPs) on a CMM. A set of possible PMP setups and probe configurations is reduced to optimal number using developed GA-based methodology. The major novelty is the development of a new CP3M capable of responding to the requirements of an Industry 4.0 concept such as intelligent, autonomous, and productive measuring systems. As such, they respond to one smart metrology requirement within the framework of Industry 4.0, referring to the optimal number of PMPs setups and for each setup defines the configurations of probes. The main contribution of the model is productivity increase of the measuring process through the reduction of the total measurement time, as well as the elimination of errors due to the human factor through intelligent planning of probe configuration and part setup. The experiment was successfully performed using a PMP specially designed and manufactured for the purpose.

Condition Monitoring and Fault Diagnosis of Induction Motor

An induction motor is at the heart of every rotating machine and hence it is a very vital component. Almost in every industry, around 90% of the machines apply an induction motor as a prime mover. It is a very important driving unit of the machine. Hence, it is necessary to monitor its condition to avoid any catastrophic failure and stoppage of production. The breakdown of the induction motor would not be affordable due to remarkable financial loss, unpredicted shutdown, and the associated repair cost. Vibration is a manifestation of induction motor due to the issues in alignment, balancing, and clearances. Bearing, the most vulnerable to failure due to continuous working under fatigue loading leads to defects. These defects cause changes in the vibration signature over time. The vibration monitoring techniques helps to effectively diagnose mechanical faults such as bearing defect and stator rotor rub. The purpose of this review paper is to summarize the major faults in induction motor, recent diagnostics methods augmented with advanced signal processing techniques, and real-life applications in electric vehicles. It also discusses possible research gaps and opportunities to contribute based on the review findings in the field of condition monitoring. This article presents a detailed review of recent trends in the research of condition monitoring and fault diagnosis of the induction motor. The emphasis is given on the major faults in the induction motor covering time-domain, frequency-domain, and time–frequency domain methods along with an application of artificial intelligence techniques for fault detection. This article presents a comprehensive review of literature which highlights the development and new propositions by researchers in the field of diagnostic techniques for the different faults of induction motor in the last decade. Researchers documented applications of the different conventional methods, advanced signal processing techniques, and soft computing techniques for fault identification of induction motor. This review is carried out for fault identification of induction motor used in machines in general and in particular for identifying the faults in an induction motor of an electric vehicle. A dedicated discussion on the review findings, research gaps, future trends in the field of condition monitoring of induction motor is presented. Condition monitoring of the induction motor in an electric vehicle is also discussed in this paper. It is observed that the vibration-based techniques are reported to be effective for the identification of mechanical faults while motor current signature analysis is effective for electrical fault in an induction motor. The review presented to analyze the suitability of various condition monitoring techniques for the induction motor fault identification in general and particularly its application in an electric vehicle. It is observed that the diagnosis of faults at the incipient level without using the signal processing technique is challenging. Fault diagnosis of induction motor has witnessed the changes from traditional diagnosis techniques to advanced techniques with a hybrid application of signal processing and artificial intelligence techniques. Still, there is a potential of improvement in reliability, efficiency, robustness, computational time, and real-time diagnostics of faults in IM.

Lung Cancer Diagnosis and Treatment Using AI and Mobile Applications

Cancer has become very common in this evolving world. Technology advancements, increased radiations have made cancer a common syndrome. Various types of cancers like Skin Cancer, Breast Cancer, Prostate Cancer, Blood Cancer, Colorectal cancer, Kidney Cancer and Lung Cancer exits. Among these various types of cancers, the mortality rate is high in lung cancer which is tough to diagnose and can be diagnosed only in advanced stages. Small cell lung cancer and non-small cell lung cancer are the two types in which non-small cell lung cancer (NSCLC) is the most common type which makes up to 80 to 85 percent of all cases [1]. Digital Image Processing and Artificial Intelligence advancements has helped a lot in medical image analysis and Computer Aided Diagnosis(CAD). Numerous research is carried out in this field to improve the detection and prediction of the cancerous tissues. In current methods, traditional image processing techniques is applied for image processing, noise removal and feature extraction. There are few good approaches that applies Artificial Intelligence and produce better results. However, no research has achieved 100% accuracy in nodule detection, early detection of cancerous nodules nor faster processing methods. Application of Artificial Intelligence techniques like Machine Learning, Deep Learning is very minimal and limited. In this paper [Figure 1], we have applied Artificial intelligence techniques to process CT (Computed Tomography) Scan image for data collection and data model training. The DICOM image data is saved as numpy file with all medical information extracted from the files for training. With the trained data we apply deep learning for noise removal and feature extraction. We can process huge volume of medical images for data collection, image processing, detection and prediction of nodules. The patient is made well aware of the disease and enabled with their health tracking using various mobile applications made available in the online stores for iOS and Android mobile devices.

ARTIFICIAL INTELLIGENCE: AID IN EARLY ORAL CANCER DIAGNOSIS AT THE DENTAL CLINIC

Early oral cancer diagnosis is the key to successful treatment. However, the literature continues showing cases that begin treatment in the advanced stages. In dental practice, a routine should be established: full mouth examination to identify changes that lead to early diagnosis. There are references that professionals and students are insecure in their diagnoses. This was the opportunity to develop an auxiliary device to the diagnostic exam, based on artificial intelligence (AI), identifying early signs. Computational diagnosis allows for the ability to detect changes that the untrained human eye cannot detect, thereby improving patient care. Applied AI techniques include artificial neural networks (ANNs), genetic algorithms (AGs), and fuzzy logic. AI is a computational system that mimics a natural system. ANNs, properly trained, can help in early diagnosis, especially in conditions with a multifactorial etiology. In this study, a prototype was tested for diagnostic aid during oral cavity examination. The sample used for the device training and validation showed 78.89% accuracy in diagnosis. This study demonstrated the high potential of the AI system as a diagnostic support for the dentist in general practice. Early oral cancer diagnosis is the key to successful treatment. However, the literature continues showing cases that begin treatment in the advanced stages. In dental practice, a routine should be established: full mouth examination to identify changes that lead to early diagnosis. There are references that professionals and students are insecure in their diagnoses. This was the opportunity to develop an auxiliary device to the diagnostic exam, based on artificial intelligence (AI), identifying early signs. Computational diagnosis allows for the ability to detect changes that the untrained human eye cannot detect, thereby improving patient care. Applied AI techniques include artificial neural networks (ANNs), genetic algorithms (AGs), and fuzzy logic. AI is a computational system that mimics a natural system. ANNs, properly trained, can help in early diagnosis, especially in conditions with a multifactorial etiology. In this study, a prototype was tested for diagnostic aid during oral cavity examination. The sample used for the device training and validation showed 78.89% accuracy in diagnosis. This study demonstrated the high potential of the AI system as a diagnostic support for the dentist in general practice.

A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength

Despite the extensive use of mortars materials in constructions over the last decades, there is not yet a reliable and robust method, available in the literature, which can estimate its strength based on its mix parameters. This limitation is due to the highly nonlinear relation between the mortar’s compressive strength and the mixed components. In this paper, the application of artificial intelligence techniques toward the prediction of the compressive strength of cement-based mortar materials with or without metakaolin has been investigated. Specifically, surrogate models (such as artificial neural network, ANN and adaptive neuro-fuzzy inference system, ANFIS models) have been developed to the prediction of the compressive strength of mortars trained using experimental data available in the literature. The comparison of the derived results with the experimental findings demonstrates the ability of both ANN and ANFIS models to approximate the compressive strength of mortars in a reliable and robust manner. Although ANFIS was able to obtain higher performance prediction to estimate the compressive strength of mortars compared to ANN model, it was found through the verification process of some other additional data, the ANFIS model has overfitted the data. Therefore, the developed ANN model has been introduced as the best predictive technique for solving problem of the compressive strength of mortars. Furthermore, using the optimum developed model an ambitious attempt to reveal the nature of mortar materials has been made.