Application Of Computational Intelligence Research Articles

Investigating underlying molecular mechanisms, signaling pathways, emerging therapeutic approaches in pancreatic cancer.

Pancreatic adenocarcinoma, a clinically challenging malignancy constitutes a significant contributor to cancer-related mortality, characterized by an inherently poor prognosis. This review aims to provide a comprehensive understanding of pancreatic adenocarcinoma by examining its multifaceted etiologies, including genetic mutations and environmental factors. The review explains the complex molecular mechanisms underlying its pathogenesis and summarizes current therapeutic strategies, including surgery, chemotherapy, and emerging modalities such as immunotherapy. Critical molecular pathways driving pancreatic cancer development, including KRAS, Notch, and Hedgehog, are discussed. Current therapeutic strategies, including surgery, chemotherapy, and radiation, are discussed, with an emphasis on their limitations, particularly in terms of postoperative relapse. Promising research areas, including liquid biopsies, personalized medicine, and gene editing, are explored, demonstrating the significant potential for enhancing diagnosis and treatment. While immunotherapy presents promising prospects, it faces challenges related to immune evasion mechanisms. Emerging research directions, encompassing liquid biopsies, personalized medicine, CRISPR/Cas9 genome editing, and computational intelligence applications, hold promise for refining diagnostic approaches and therapeutic interventions. By integrating insights from genetic, molecular, and clinical research, innovative strategies that improve patient outcomes can be developed. Ongoing research in these emerging fields holds significant promise for advancing the diagnosis and treatment of this formidable malignancy.

A brief review on quantum computing based drug design

AbstractDesign and development of new drug molecules are essential for the survival of human society. New drugs are designed for therapeutic purposes to combat new diseases. Besides treating new diseases, new drug development is also needed to treat pre‐existing diseases more effectively and reduce the existing drugs' side effects. The design of drugs involves several steps, from the discovery of the drug molecule to its commercialization in the market. One of the most critical steps in drug design is to find the molecular interactions between the target (infected) molecule and the drug molecule. Several complex chemical equations need to be solved to determine the molecular interactions. In the late 20th Century, the advancement of computational technologies has made the solution of chemical equations relatively easier and faster. Moreover, the design of drug molecules involves multi‐criteria optimization. Classical computational methodologies have been used for drug design since the end of the 20th Century. However, nowadays, more advanced computational methodologies are inevitable in designing drugs for new diseases and drugs with fewer side effects. In this context, the quantum computing paradigm has proved beneficial in drug design due to its advanced computational capabilities. This paper presents a state‐of‐the‐art comprehensive review of the quantum computing‐based methodologies involved in drug design. A comparative study is made about the different quantum‐aided drug design methods, stating each methodology's merits and demerits. The review work presented in this manuscript will help new researchers assess the present state‐of‐the‐art concept of quantum‐based drug design.This article is categorized under: Technologies > Structure Discovery and Clustering Technologies > Computational Intelligence Application Areas > Health Care

Current Applications of Computational Intelligence in Education: A Systematic Review

Current Applications of Computational Intelligence in Education: A Systematic Review

An application of Computational Intelligence in group formation for digital drama education

Unveiling the intersection of technology and art, this research aims to explore the vital role of Computational Intelligence (CI) techniques in shaping digital theater performances within virtual worlds by managing student populations engaged in these productions. The realm of digital theater offers opportunities for creative expression and collaboration. This necessitates the integration of Computational Intelligence (CI) to establish cohesive teams in digital theater. By doing so, effective collaboration is promoted, and creativity is dynamically enhanced in virtual cultural environments and digital worlds. Central to this study is the use of a CI algorithm called Flying Fox Optimizer, designed to form homogeneous student groups, and optimize theater dynamics. The algorithm utilizes fuzzy logic, eliminating the requirement for fine-tuning by the user. The results reveal that through this mechanism, theater educators of the future can effortlessly create student groups that foster seamless collaboration in virtual theater education.

New Applications of Computational Intelligence for Constructing Predictive or Optimal Statistical Decisions under Parametric Uncertainty

New Applications of Computational Intelligence for Constructing Predictive or Optimal Statistical Decisions under Parametric Uncertainty

Special issue on “Recent advances on computational intelligence techniques and applications in big data”

Special issue on “Recent advances on computational intelligence techniques and applications in big data”

Preface

The 2023 2nd International Conference on the Energy Internet and Energy Interactive Technology (EIEIT 2023) took place via virtual form in Nanjing, China from May 26th to 28th, 2023, with the attendance of about 50 people.EIEIT 2023 focused on research areas such as Renewable Energy, Power Quality and Power Experience, Regional Energy Internet, Energy Management Technology, etc. The main goal was to bring together scientists, researchers and developers engaged in fields concerning energy to bridge the gap between research needs and advanced technological solutions.EIEIT 2023 provided an opportunity for researchers and related personnels to present their recent theoretical developments as well as practical applications, discuss unsolved problems and find solutions to them. It fostered research and development in energy internet and energy interactive technology.The program of the Conference was divided into three parts. First day of the Conference was devoted to registration for all the participants. The second day was focused on opening ceremony, keynote speech, oral and poster presentation, as well as discussion on the speeches and the whole meeting. Academic investigation took place during the third day of the Conference.We would like to highlight the main developments presented at the Conference and introduce the prominent speakers. Keynote speaker of the Conference Professor Ismail Musirin (Universiti Teknologi MARA, Malaysia) presented a report on Computational Intelligence Applications in Power System Load Shedding. He mainly dealt with researches on the algorithms and techniques development in solving problems in Power System involving voltage stability, economic power dispatch, renewable energy optimization, and application of artificial intelligence in power system, and he has got 22 years of research experience in the field of Power System Engineering. Associate Professor Norzanah Rosmin (Universiti Teknologi Malaysia, Malaysia) described Hybrid Energy Storage for Virtual Inertial Support in Wind Application System. Associate Professor Nor Asiah Muhamad (Universiti Teknologi Brunei, Brunei) demonstrated her speech on Harmonic Components Analysis of Emitted Ultraviolet Signals of Aged Transmission Line Insulators under Different Surface Discharge Intensities.Finally, we would like to thank the members of the Technical Program Committee and the Local Committee for giving such an opportunity to share scientific ideas and new developments at the Conference and Universiti Putra Malaysia, Malaysia for its friendly support. Last but not the least, our special acknowledgement also goes to the editors and staff of Journal of Physics: Conference Series, for their kindness and assistance in publishing this paper volume.The Committee of EIEIT 2023List of Committee Member is available in this pdf.

A Survey on Design, Applications and Limitations of Computational Intelligence

A Survey on Design, Applications and Limitations of Computational Intelligence

Application of Computational Intelligence in Describing Dust Emissions in Different Soil Tillage Applications in Middle Anatolia

Soil degradation is an increasing problem in Turkey, especially in the Middle Anatolia region where the annual precipitation is approximately 300 mm, resulting from conventional farming methods. To address this issue, the artificial neural networks (ANNs) are used, as they are flexible mathematical tools that capture data. This study aims to investigate the relationships between dust emission (PM10) and the mean weight diameter, shear stress, and stubble amount of the soil, which were measured in eight different tillage practices (conventional tillage, six types of reduced tillage, and direct seeding). The results show that the mean weight diameter, shear stress, and stubble amount of the soil varied between 4.89 and 14.17 mm, 0.40–1.23 N·cm−2, and 30.5–158 g·m−2, respectively, depending on the type of tillage works. Additionally, dust emissions generated during different tillage applications ranged from 27.73 to 153.45 mg·m−3. The horizontal shaft rototiller produced the highest dust emission, approximately 150% higher than those of disc harrow and winged chisel plows. The impact of tillage practices on dust emission was statistically significant (p < 0.01). A sophisticated 3-(7-7)-1 ANNs model using a backpropagation learning algorithm was developed to predict the concentration of dust, which outperformed the traditional statistical models. The model was based on the values of mean weight diameter, shear stress, and stubble amount of the soil after tillage. The best result was obtained from the ANN model among the polynomial and ANN models. In the ANN model, the coefficient of determination, root mean square error, and mean error were found to be 0.98, 6.70, and 6.11%, respectively. This study demonstrated the effectiveness of ANNs in predicting the levels of dust concentration based on soil tillage data, and it highlighted the importance of adopting alternative tillage practices to reduce soil degradation and dust emissions.

Application of Machine Learning Methods for Data Analytics in Social Sciences

This article addresses the challenges in the application of artificial intelligence methods such as machine learning, computational intelligence and/or soft computing methods in social sciences. The literature review is performed in order to give a review of different approaches and methods that have been applied so far. The most used method in social sciences and management is the SWOT method, for the identification of strengths, weaknesses, opportunities, and threats when making strategic decisions. Two fundamental characteristics of previous approaches are the development of numerical models of utility functions and the possibility of upgrading these models by formalizing the intuition of strategic decision-makers. There are several shortcomings of the existing approaches. The application of computational intelligence and machine learning methods in social sciences is identified as one of the most challenging and promising areas, which could overcome identified shortcomings. The principles of one popular machine learning method, the decision tree, are explained and a demonstration is performed on the case study of churn prediction. Benchmarking data set from the publicly available repository is used to demonstrate the suggested approach Evaluation results measured through model accuracy and reliability gave promising results for further analysis. A developed predictive model could serve as a standalone tool or as support for decision-making in social sciences.

A Selective Survey Review of Computational Intelligence Applications in the Primary Subdomains of Civil Engineering Specializations

Artificial intelligence is the branch of computer science that attempts to model cognitive processes such as learning, adaptability and perception to generate intelligent behavior capable of solving complex problems with environmental adaptation and deductive reasoning. Applied research of cutting-edge technologies, primarily computational intelligence, including machine/deep learning and fuzzy computing, can add value to modern science and, more generally, to entrepreneurship and the economy. Regarding the science of civil engineering and, more generally, the construction industry, which is one of the most important in economic entrepreneurship both in terms of the size of the workforce employed and the amount of capital invested, the use of artificial intelligence can change industry business models, eliminate costly mistakes, reduce jobsite injuries and make large engineering projects more efficient. The purpose of this paper is to discuss recent research on artificial intelligence methods (machine and deep learning, computer vision, natural language processing, fuzzy systems, etc.) and their related technologies (extensive data analysis, blockchain, cloud computing, internet of things and augmented reality) in the fields of application of civil engineering science, such as structural engineering, geotechnical engineering, hydraulics and water resources. This review examines the benefits and limitations of using computational intelligence in civil engineering and the challenges researchers and practitioners face in implementing these techniques. The manuscript is targeted at a technical audience, such as researchers or practitioners in civil engineering or computational intelligence, and also intended for a broader audience such as policymakers or the general public who are interested in the civil engineering domain.

Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence

Convolutional neural networks (CNNs) have received widespread attention due to their powerful modeling capabilities and have been successfully applied in natural language processing, image recognition, and other fields. On the other hand, traditional CNN can only deal with Euclidean spatial data. In contrast, many real-life scenarios, such as transportation networks, social networks, reference networks, and so on, exist in graph data. The creation of graph convolution operators and graph pooling is at the heart of migrating CNN to graph data analysis and processing. With the advancement of the Internet and technology, graph convolution network (GCN), as an innovative technology in artificial intelligence (AI), has received more and more attention. GCN has been widely used in different fields such as image processing, intelligent recommender system, knowledge-based graph, and other areas due to their excellent characteristics in processing non-European spatial data. At the same time, communication networks have also embraced AI technology in recent years, and AI serves as the brain of the future network and realizes the comprehensive intelligence of the future grid. Many complex communication network problems can be abstracted as graph-based optimization problems and solved by GCN, thus overcoming the limitations of traditional methods. This survey briefly describes the definition of graph-based machine learning, introduces different types of graph networks, summarizes the application of GCN in various research fields, analyzes the research status, and gives the future research direction.

Technique for Automatic Algorithm Selection for Computational Intelligence in Cloud-Based Computing Environments

This research presents an Auto-Algorithm selection technique for Computational Intelligence in Cloud-Based computing environment. By leveraging the capabilities of a network information processing platform, users can effortlessly and intelligently build Computational Intelligence models tailored to their specific problems without the need for manually configuring the Computational Intelligence environment, selecting algorithms, or adjusting complex Computational Intelligence functions and parameters. The proposed procedure allows users to simply upload sample data through a web interface, freeing Computational Intelligence applications from environmental constraints and taking advantage of the network information processing platform's capabilities. This approach transparently handles the model building process, significantly reducing the barriers to entry for utilizing Computational Intelligence. By addressing the issues of unpredictable model selection, manual parameter adjustment, and the challenges faced by common users, this auto selection procedure empowers the practical application of Computational Intelligence in various domains.

Using the integrated application of computational intelligence for landslide susceptibility modeling in East Azerbaijan Province, Iran

Using the integrated application of computational intelligence for landslide susceptibility modeling in East Azerbaijan Province, Iran

Application of algorithmic models of machine learning to the freight transportation process

The results of the analysis of algorithmic models of machine learning application to the freight transportation process are given in this paper. Analysis of existing research allowed discovering a range of advantages in the application of computational intelligence in logistic systems, including increasing the accuracy of forecasting, reduction of transport costs, increasing the efficiency of cargo delivery, risks reduction, and search for key performance factors. In the research process, the main directions of application of algorithmic models of machine learning were determined. They are vehicle routing, choice of cargo type, transportation type and vehicle type; forecasting fuel consumption by vehicles, disruptions in transportation, transport costs, duration of the order fulfillment; evaluation of the rolling stock fleet and the efficiency of carrying out the transport task. Based on the researched publications, the most common algorithmic models of machine learning in freight transportation were identified, and their effectiveness was analyzed. Linear and logistic regression models are simple enough; however, they do not always provide high simulation results. Deep learning models are quite widely applied to all identified areas. Decision tree and random forest models often show the highest simulation performance. Models of k-nearest neighbors and support vectors should be used both in classification tasks, for example, in choosing the type of cargo and type of transportation, and for forecasting the fuel consumption and the duration of the transport process.

Hybrid of deep recurrent network and long short term memory for rear-end collision detection in fog based internet of vehicles

The development and use of intelligent transportation systems as an emerging trend in the application of computational intelligence within the concept of internet of vehicles (IoV) is attracting attention in the academia and industries. The computational intelligence algorithms such as the deep learning is playing a significant role in the IoV. The detection of rear end collision in the IoV is a critical aspect of the IoV because of safety. Previous research applied genetically optimized artificial neural network (ANN) (GA-ANN) for the detection of rear end collision in the IoV. However, GA-ANN has limitations such as stretching of the entire network leading to reduced flexibility, deteriorating performance as data increases to very large size, lack of generalization power resulting from over-fitting of the training data and calling for additional effort for regularization functions to monitor the model’s complexity. In this research, as an alternative to GA-ANN for improving the accuracy of rear end collision detection in IoV, we propose a hybrid of Deep Recurrent Neural Network (DRNN) and Long Short Term Memory (LSTM) (DRNN-LSTM) for rear end collision detection in the IoV. The scenario for the IoV is created and simulated to generate the dataset. The propose DRNN-LSTM is applied to detect the rear end collision in IoV. The performance of the proposed DRNN-LSTM is compared with the constituent algorithms of the DRNN-LSTM, ANN and GA-ANN. The results show that the proposed DRNN-LSTM detects rear end collision in IoV better than the GA-ANN, LSTM, DRNN and ANN. Thus, our propose approach has potential to improve safety, and effectiveness of the overall vehicle mobility in the IoV environment. Keywords: Collision Detection; Internet of Vehicles; Long Short Term Memory; Deep Recurrent Neural Network; Deep Learning Algorithm.

Cybersecurity applications of computational intelligence

Cybersecurity applications of computational intelligence

Computational intelligence applied to cybersecurity

Computational intelligence applied to cybersecurity

The Application of Deep Learning Imputation and Other Advanced Methods for Handling Missing Values in Network Intrusion Detection

In intelligent information systems data play a critical role. The issue of missing data is one of the commonplace problems occurring in data collected in the real world. The problem stems directly from the very nature of data collection. In this paper, the notion of handling missing values in a real-world application of computational intelligence is considered. Two experimental campaigns were conducted, evaluating different approaches to the missing values imputation on Random Forest-based classifiers, trained using modern cybersecurity benchmarks datasets: CICIDS2017 and IoT-23. In result of the experiments it transpired that the chosen algorithm for data imputation has a severe impact on the results of the classifier used for network intrusion detection. It also comes to light that one of the most popular approaches to handling missing data — complete case analysis — should never be used in cybersecurity.

Computational Intelligence in Security of Digital Twins Big Graphic Data in Cyber-physical Systems of Smart Cities

This investigation focuses on the application of computational intelligence to the security of Digital Twins (DTs) graphic data of the Cyber-physical System (CPS). The intricate and diverse physical space of CPS in the smart city is mapped in virtual space to construct the DTs CPS in the smart city. Besides, Differential Privacy Frequent Subgraph-Big Multigraph (DPFS-BM) is employed to ensure data privacy security. Moreover, the analysis and prediction model for the DTs big graphic data (BGD) in the CPS is built based on Differential Privacy-AlexNet (DP-AlexNet). Alexnet successfully solves the gradient dispersion problem of the Sigmoid function of deep network structures. Finally, the comparative analysis approach is utilized to verify the performance of the model reported here by comparing it with Long Short-Term Memory, Convolutional Neural Network, Recurrent Neural Network, original AlexNet, and Multi-Layer Perceptron in a simulation experiment. Through the comparison in the root mean square error, the mean absolute error, the mean absolute percentage error, training time, and test time, the model proposed here outperforms other models regarding errors, time delay, and time consumption. In the same environment, the system performs better with multi-hop paths, extra relays, and a high fading index; in that case, the outage probability is minimal. Therefore, the DP-AlexNet model is suitable for processing BGD. Moreover, its speed acceleration is more apparent than that of other models, with a higher SpeedUp indicator. The research effectively combines data mining and data security, which is of significant value for optimizing the privacy protection technology of frequent subgraph mining on a single multi-graph. Besides, the constructed DTs of CPS can provide excellent accuracy and a prominent acceleration effect on the premise of low errors. In addition, the model reported here can provide reference for the intelligent and digital development of smart cities.