Application Of Artificial Intelligence Techniques Research Articles

Applications of artificial intelligence for disaster management

Natural hazards have the potential to cause catastrophic damage and significant socioeconomic loss. The actual damage and loss observed in the recent decades has shown an increasing trend. As a result, disaster managers need to take a growing responsibility to proactively protect their communities by developing efficient management strategies. A number of research studies apply artificial intelligence (AI) techniques to process disaster-related data for supporting informed disaster management. This study provides an overview of current applications of AI in disaster management during its four phases: mitigation, preparedness, response, and recovery. It presents example applications of different AI techniques and their benefits for supporting disaster management at different phases, as well as some practical AI-based decision support tools. We find that the majority of AI applications focus on the disaster response phase. This study also identifies challenges to inspire the professional community to advance AI techniques for addressing them in future research.

Artificial Intelligence in Egyptian Tourism Companies: Implementation and Perception

Artificial intelligence (AI) is considered one of the most important innovative technology. AI has been used in several industries and has already accomplished notable impacts. The tourism industry is one of several sectors that is affected by AI tools; chatbots, personalized service recommendations, smart solutions are used in travel agencies. This paper aims to explore the implementation of AI techniques in the Egyptian tourism companies and investigate the employees’ perceptions towards using AI tools in tourism operations. This paper applies the quantitative approach; an online questionnaire was distributed to tourism companies, only 320 responses were valid for statistical analysis. Concerning applying AI tools in tourism operations, the results revealed significant differences among tourism companies which provide full tourism services. Regarding the size of tourism companies, large and medium-scale tourism companies apply AI techniques more than small and micro-scale ones. This research found two fundamental employees’ views of applying AI: advantages of AI (enthusiastic) and disadvantages of AI (suspicious). From a managerial standpoint, this research shows the AI techniques that are applied in tourism as well as identifies the importance of their implementation, which may help managers to draw policies and strategies to improve their technological infrastructure and skills, as well as apply the most beneficial AI tools. This, in turn, enhances their performance and saves time and money.

Advanced Learning Analytics in Academic Education

The integration of innovative data mining and decision-making techniques in the context of higher education is a bold initiative towards enhanced performance. Predictive and descriptive analytics add interesting insights for significant aspects the education. The purpose of this article is to summarize a novel approach for the adoption of artificial intelligence (AI) techniques towards forecasting of academic performance. The added value of applying AI techniques for advanced decision making in education is the realization that the scientific approach to standard problems in academia, like the enhancement of academic performance is feasible. For the purpose of this research the authors promote a research in Saudi Arabia. The vision of the Knowledge Society in the Kingdom of Saudi Arabia is a critical milestone towards digital transformation. The human capital and the integration of industry and academia has to be based on holistic approaches to skills and competencies management. One of the main objectives of an academic decision maker is to ensure that academic resources are adequately planned and that students are properly advised. To achieve such an objective, an extensive analysis of large volumes of data may be required. This research develops a decision support system (DSS) that is based on an artificial neural network (ANN) model that can be deployed for effective academic planning and advising. The system is based on evaluating academic metrics against academic performance for students. The model integrates inputs from relevant academic data sources into an autonomous ANN. A simulation of real data on an ANN is conducted to validate the system's accuracy. Moreover, an ANN is compared with different mathematical approaches. The system enables the quality assurance of planning, advising, and the monitoring of academic decisions. The overall contribution of this work is a novel approach to the deployment of Artificial Intelligent for advanced decision making in higher education. In future work this model is integrated with big data and analytics research for advanced visualizations

Application of artificial intelligence techniques in prediction of cyclic resistance ratio (CRR) of clean sands

Liquefaction study by in-situ tests like SPT and CPT are very complicated and time consuming. Cyclic Resistance Ratio (CRR) of a soil is controlled by various properties of the soil. Artificial intelligence techniques can identify relationship between various parameters which influence the liquefaction phenomenon from sufficiently large data set to generate models connecting those parameters. Models for prediction of cyclic resistance ratio (CRR) of clean sand is generated using MGGP, GPR and M5’ model tree in the present study using data from cyclic triaxial test and cyclic direct shear test. Using 346 data points, divided in 50% train to 50%test ratio, sufficiently accurate models were generated through the algorithms considered. These algorithms were compared by means of the Root Mean Square Error (RMSE), Coefficient of correlation (R2) and Maximum absolute Error in prediction (MAE). An equation connecting the CRR with other input parameters was developed using the MGGP algorithm, which also showed the maximum R2 value of 0.96 for the test data. The AI algorithms were observed to satisfactorily model the relation between the input parameters and the CRR without any prior knowledge of the same.

Secure, privacy-preserving and federated machine learning in medical imaging

The broad application of artificial intelligence techniques in medicine is currently hindered by limited dataset availability for algorithm training and validation, due to the absence of standardized electronic medical records, and strict legal and ethical requirements to protect patient privacy. In medical imaging, harmonized data exchange formats such as Digital Imaging and Communication in Medicine and electronic data storage are the standard, partially addressing the first issue, but the requirements for privacy preservation are equally strict. To prevent patient privacy compromise while promoting scientific research on large datasets that aims to improve patient care, the implementation of technical solutions to simultaneously address the demands for data protection and utilization is mandatory. Here we present an overview of current and next-generation methods for federated, secure and privacy-preserving artificial intelligence with a focus on medical imaging applications, alongside potential attack vectors and future prospects in medical imaging and beyond. Medical imaging data is often subject to privacy and intellectual property restrictions. AI techniques can help out by offering tools like federated learning to bridge the gap between personal data protection and data utilisation for research and clinical routine, but these tools need to be secure.

Data Science Techniques, Tools and Predictions

Almighty created human being with numerous wants and needs which makes them associated with their own data, choices and preferences. To grow and develop any business or organizations it is very obligatory to know their clients requests or customer needs based on their data. The evolving role of data makes it very vital element in any organization and carried with convinced operations. In this paper we are going to present a study of Data Science and its relevance with Artificial Intelligence, machine learning and deep learning. The incorporation of these intellectual sciences in data science is useful for perming numerous operations in our research we tried to demonstrate the data science operations like data cleaning, data processing, data modeling, data visualization and data presentations techniques. To grow any business it is mandatory to know their customer needs and satisfy their future expectations by smart decision makings. The intellectual algorithms or data operations in the data science make the data to be more effective in decision making and decision polices. We also focus on how data science incorporates mathematical & statistical methods, logical reasoning with applications of Artificial Intelligence techniques. We also focus on various data operations tools which exists in the market like python, SAS, R and many others. At last we focusses on how data science field going to meet the future expectations of many businesses. This research paper may become as successful reference for the people to carry out their research and meet the expectations of data science field with business growing decisions.

Applications of Artificial Intelligence Techniques to Enhance Sustainability of Industry 4.0: Design of an Artificial Neural Network Model as Dynamic Behavior Optimizer of Robotic Arms

Industrial robots have a great impact on increasing the productivity and reducing the time of the manufacturing process. To serve this purpose, in the past decade, many researchers have concentrated to optimize robotic models utilizing artificial intelligence (AI) techniques. Gimbal joints because of their adjustable mechanical advantages have been investigated as a replacement for traditional revolute joints, especially when they are supposed to have tiny motions. In this research, the genetic algorithm (GA), a well-known evolutionary technique, has been adopted to find optimal parameters of the gimbal joints. Since adopting the GA is a time-consuming process, an artificial neural network (ANN) architecture has been proposed to model the behavior of the GA. The result shows that the proposed ANN model can be used instead of the complex and time-consuming GA in the process of finding the optimal parameters of the gimbal joint.

Welcome to 2020 and the Future of Control

Welcome to 2020 and the Future of Control

Population Data Centre Profile: The Manitoba Centre for Health Policy

ObjectiveTo profile the Manitoba Centre for Health Policy (MCHP), a population health data centre located at the University of Manitoba in Winnipeg, Canada.
 ApproachWe describe how MCHP was established and funded, and how it continues to operate based on a foundation of trust and respect between researchers at the University of Manitoba and stakeholders in the Manitoba Government’s Department of Health. MCHP’s research priorities are jointly determined by its scientists’ own research interests and by questions put forward from Manitoba government ministries. Data governance, data privacy, data linkage processes and data access are discussed in detail. We also provide three illustrative examples of the MCHP Data Repository in action, demonstrating how studies using a variety of Repository datasets have had an impact on health and social policies and programs in Manitoba.
 DiscussionMCHP has experienced tremendous growth over the last three decades. We discuss emerging research directions as the capacity for innovation at MCHP continues to expand, including a focus on natural language processing and other applications of artificial intelligence techniques, a leadership role in the new SPOR Canadian Data Platform, and a foray into social policy evaluation and analysis. With these and other exciting opportunities on the horizon, the future at MCHP looks exceptionally bright.

Population Data Centre Profile: The Manitoba Centre for Health Policy.

ObjectiveTo profile the Manitoba Centre for Health Policy (MCHP), a population health data centre located at the University of Manitoba in Winnipeg, Canada.ApproachWe describe how MCHP was established and funded, and how it continues to operate based on a foundation of trust and respect between researchers at the University of Manitoba and stakeholders in the Manitoba Government’s Department of Health. MCHP’s research priorities are jointly determined by its scientists’ own research interests and by questions put forward from Manitoba government ministries. Data governance, data privacy, data linkage processes and data access are discussed in detail. We also provide three illustrative examples of the MCHP Data Repository in action, demonstrating how studies using a variety of Repository datasets have had an impact on health and social policies and programs in Manitoba.DiscussionMCHP has experienced tremendous growth over the last three decades. We discuss emerging research directions as the capacity for innovation at MCHP continues to expand, including a focus on natural language processing and other applications of artificial intelligence techniques, a leadership role in the new SPOR Canadian Data Platform, and a foray into social policy evaluation and analysis. With these and other exciting opportunities on the horizon, the future at MCHP looks exceptionally bright.

Application of Artificial Intelligence Techniques to Predict Survival in Kidney Transplantation: A Review.

A key issue in the field of kidney transplants is the analysis of transplant recipients’ survival. By means of the information obtained from transplant patients, it is possible to analyse in which cases a transplant has a higher likelihood of success and the factors on which it will depend. In general, these analyses have been conducted by applying traditional statistical techniques, as the amount and variety of data available about kidney transplant processes were limited. However, two main changes have taken place in this field in the last decade. Firstly, the digitalisation of medical information through the use of electronic health records (EHRs), which store patients’ medical histories electronically. This facilitates automatic information processing through specialised software. Secondly, medical Big Data has provided access to vast amounts of data on medical processes. The information currently available on kidney transplants is huge and varied by comparison to that initially available for this kind of study. This new context has led to the use of other non-traditional techniques more suitable to conduct survival analyses in these new conditions. Specifically, this paper provides a review of the main machine learning methods and tools that are being used to conduct kidney transplant patient and graft survival analyses.

Guest editorial: Artificial intelligence (AI)-driven spectrum management

Recent advances in communication and networking technologies are leading to a plethora of novel wireless services that range from unmanned aerial vehicle (UAV) communication to smart cognitive networks and massive Internet of Things (IoT) systems. Enabling these emerging applications over the fifth generation (5G) of wireless cellular systems requires meeting numerous challenges pertaining to spectrum sharing and management. In fact, most 5G applications will be highly reliant on intelligent spectrum management techniques, which should adapt to dynamic network environments while also guaranteeing high reliability and high quality-of-experience (QoE). In this context, the use of artificial intelligence (AI) techniques that include deep learning, convolutional neural networks, and reinforcement learning, among many others, is expected to play a very important role in paving the way towards truly AI-driven spectrum management, thus enabling tomorrow's smart city services. Therefore, it has become imperative to investigate and apply AI techniques to solve emerging spectrum management problems in various wireless networks. This includes leveraging AI to address a wide range of wireless networking challenges ranging from network management to dynamic spectrum sharing and resource management.

Application of Artificial Intelligence Techniques for Modeling and Simulation of Photovoltaic Arrays

A method for modeling photovoltaic (PV) arrays - based on artificial intelligence techniques, namely genetic algorithm (GA) and cuckoo optimization algorithm (COA) - is presented. COA and GA are used to obtain the parameters of the PV array model using the PV cell’s datasheet information. The adopted models – using GA and COA - are implemented on a simulation platforms using MATLAB 2020a environment for two-diode and single-diode models. The proposed optimization method fits the mathematical current-voltage (I-V) characteristic to the three (V, I) remarkable points without the need to guess or to estimate any other parameter. The obtained models are tested and validated with experimental data taken from the Mutah university’s PV power plant. The results show that for both of the employed optimization algorithms, the two-diode model is more accurate than the single-diode model. The results also disclose that, at different values of temperature and solar irradiance, the COA – compared to the GA – better handles the optimization problem with low iterations and better fitness value.

Cryptocurrencies and Artificial Intelligence: Challenges and Opportunities

Decentralized cryptocurrencies have gained a lot of attention over the last decade. Bitcoin was introduced as the first cryptocurrency to allow direct online payments without relying on centralized financial entities. The use of Bitcoin has vastly grown as a financial asset rather than just a tool for online payments. A lot of cryptocurrencies have been created since 2011 with Bitcoin dominating the cryptocurrencies’ market. With plenty of cryptocurrencies being used as financial assets and with millions of trades being executed through different exchange services, cryptocurrencies are susceptible to trading problems and challenges similar to those traditionally encountered in the financial domain. Price and trend prediction, volatility prediction, portfolio construction and fraud detection are some examples related to trading. In addition, there are other challenges that are specific to the domain of cryptocurrencies such as mining, cybersecurity, anonymity and privacy. In this paper, we survey the application of artificial intelligence techniques to address these challenges for cryptocurrencies with their vast amount of daily transactions, trades and news that are beyond human capabilities to analyze and learn from. This paper discusses the recent research work done in this emerging area and compares them in terms of used techniques and datasets. It also highlights possible research gaps and some potential areas for improvement.

Improving Software Development effort estimating using Support Vector Regression and Feature Selection

Accurate and reliable software development effort estimation (SDEE) is one of the main concerns for project managers. Planning and scheduling a software projects using and inaccurate estimate may cause severe risks to software project under development such as delayed delivery, poor quality software, missing features. Therefore, accurate prediction of software effort plays an important role in the minimization of these risks that can lead to projects failure. Nowadays, application of artificial intelligence techniques has grown dramatically for predicting software effort. The researchers found that these techniques are suitable tools for accurate prediction. In this study, an improved model is designed for estimating software effort using support vector regression (SVR) and two feature selection (FS) methods. Prior to building model step, a preprocessing stage is performed by random forest or Boruta feature selection methods to remove unimportant features. Next, the SVR model is tuned by a grid search approach. The performance of model is then evaluated over eight well-known datasets through 30%holdout validation method. To show the impact of feature selection on the accuracy of SVR model, the proposed model was compared with SVR model without feature selection. The results indicated that SVR with feature selection outperforms SVR without FS in terms of the three accuracy measures used in this empirical study.

An adaptive network-based fuzzy inference system to supply chain performance evaluation based on SCOR® metrics

Evaluating the performance of supply chains (SC) is a critical activity to enhance the outcomes of operations along the SC tiers. In order to support this evaluation process, several studies have proposed the application of artificial intelligence techniques combined with the performance metrics suggested by the SCOR® model (Supply Chain Operations Reference). However these propositions present some limitations. While the systems based on Mamdani fuzzy inference do not allow adaptation to the environment of use based on historical performance data, the systems based on artificial neural networks are not adequate to deal with imprecise data and qualitative metrics. In order to overcome these limitations, this paper presents a new approach to support SC performance evaluation based on the combination between the SCOR® metrics with an adaptive network-based fuzzy inference systems (ANFIS). In total, 56 candidate topologies were implemented and assessed using MATLAB. The random subsampling cross-validation method was applied to select the most appropriate topological parameters for each ANFIS model. The mean square error between the target values and the values estimated by each ANFIS model demonstrate its greater accuracy of prediction. In addition, results of the hypothesis tests based on paired samples indicate that the proposed ANFIS models are adequate to support SC performance evaluation. The proposed system can help managers to develop improvement actions plans based on the outcomes of the evaluation process. When compared to previous approaches, it presents advantages such as greater accuracy of prediction, learning ability based on historical data, suitability to support decision making under uncertainty, better interpretability of results, among others.

Computer-aided diagnosis in rheumatic diseases using ultrasound: an overview.

Clinical evaluation of rheumatic and musculoskeletal diseases through images is a challenge for the beginner rheumatologist since image diagnosis is an expert task with a long learning curve. The aim of this work was to present a narrative review on the main ultrasound computer-aided diagnosis systems that may help clinicians thanks to the progress made in the application of artificial intelligence techniques. We performed a literature review searching for original articles in seven repositories, from 1970 to 2019, and identified 11 main methods currently used in ultrasound computer-aided diagnosis systems. Also, we found that rheumatoid arthritis, osteoarthritis, systemic lupus erythematosus, and idiopathic inflammatory myopathies are the four musculoskeletal and rheumatic diseases most studied that use these innovative systems, with an overall accuracy of > 75%.

174P - Progression of computer aided diagnosis on gastric cancer

BackgroundApplication of computer and artificial intelligence (AI) has made revolutionary progress in medical fields. Computer-assisted diagnosis (CAD) on pathology is successful in diagnosis, classification, staging and predicting prognosis of cancer. MethodsIn this work, we used the technique of convolutional neural networks (CNN) for training of morphological images of gastric cancer, which is a promising application of artificial intelligence technique for medical image analysis, especially for cancer pathology. ResultsWe introduced successful works of AI-aided gastric cancer analysis for over 300 cases. AI-assisted pathology technique could help the pathologist in detecting and locating the abnormal area in images and to diagnose and classify benign or malignant tissues. Therefore, AI exactly improves the diagnostic status of gastric cancer, such as in interpretation of medical images and classification between benign and cancerous tissues. The complete workflow of AI diagnosis for gastric cancer is under construction. AI is more useful in releasing pathologists from repeated work, and improving efficiency. ConclusionsIn conclusions, there are still notable technical obstacles before this approach can be used to improve conventional clinical practice. Higher performance needs corrected marking images by experienced pathologist and require ‘training’ algorithms on tremendous data. We stressed the importance of proper algorithms for improving confidence of analytic results that is close to human experts. Additionally, we indicated the gaps in current research and principal resolutions for advancing the field. Legal entity responsible for the studyThe authors without further recourse to the authors. FundingHas not received any funding. DisclosureAll authors have declared no conflicts of interest.

Application of Artificial Intelligence Techniques to Predict the Well Productivity of Fishbone Wells

Fishbone multilateral wells are applied to enhance well productivity by increasing the contact area between the bottomhole and reservoir region. Fishbone wells are characterized by reduced operational time and a competitive cost in comparison to hydraulic fracturing operations. However, limited models are reported to determine the productivity of fishbone wells. In this paper, several artificial intelligence methods were applied to estimate the performance of fishbone wells producing from a heterogeneous and anisotropic gas reservoir. The well productivity was determined using an artificial neural network, a fuzzy logic system and a radial basis network. The models were developed and validated utilizing 250 data sets, with the inputs being the permeability ratio (Kh/Kv), flowing bottomhole pressure and lateral length. The results showed that the artificial intelligence models were able to predict the fishbone well productivity with an acceptable absolute error of 7.23%. Moreover, a mathematical equation was extracted from the artificial neural network, which is able to provide a simple and direct estimation of fishbone well productivity. Actual flow tests were used to evaluate the reliability of the developed model, and a very acceptable match was obtained between the predicted and actual flow rates, wherein an absolute error of 6.92% was achieved. This paper presents effective models for determining the well performance of complex multilateral wells producing from heterogeneous reservoirs. The developed models will help to reduce the uncertainty associated with numerical methods, and the extracted equation can be inserted into commercial software, thereby significantly reducing deviation between the actual data and simulated results.

A State-of-the-Art Review on Manufacturing and Additive Influences on Sand-Cast Components

Sand casting is the one of the oldest fabrication techniques, which has a number of important technological challenges with respect to improving productivity and quality of the castings. It is essential for the foundries to reduce or eliminate the casting defects if they need to survive in this highly competitive global market. This review arises from a strong industrial demand for understanding the physical and process parameters to improve the quality and soundness of the sand-cast products. The present article primarily reviews the investigations done by researchers on the usability of locally available silica sands and clays for casting both ferrous and non-ferrous alloys. The paper highlights the application of artificial intelligence techniques in predicting the sand mould quality and further optimizing the casting process parameters. This article also analyses the potentials and feasibility of three-dimensional printing in sand casting process. Further, the role of different additives on sand mould composition and different trace elements or inoculants on mechanical properties and microstructure of sand casting has been discussed. Finally, elaborations have been made about various types of hybrid sand casting processes along with metal matrix composites.