Journal of Computational and Cognitive Engineering

Energy Efficient Real-Time E-Healthcare System Based on Fog Computing

2023-06-23T13:52:09+08:00

The rapid development of Internet of Things (IoT)-enabled systems in public and private spaces offers consumers numerous conveniences. Among different Internet-connected systems, the use of e-health systems is growing rapidly. The utilization of IoT devices and cloud-fog network technologies has made e-healthcare provision more convenient. While providing valuable services to the healthcare sector, like any other IoT-enabled systems it is putting pressure on energy, an essential element of life. Therefore, it is imperative to know the energy consumption model of e-health systems. Considering the importance of energy consumption in IoT based systems, this article develops a cloud-fog-based e-health system and makes it energy efficient by understanding energy consumption at different layers of communication. Moreover, how fog integration with the cloud reduces energy consumption and delays at different stages of communication is discussed.

Received: 15 May 2023 | Revised: 23 June 2023 | Accepted: 9 July 2023

Conflicts of Interest
The authors declare that they have no conflicts of interest to this work.

Data Availability Statement
Data available on request from the corresponding author upon reasonable request.

Prediction of Mutual Interdependencies Among the Drivers of Blockchain for Enhancing the Supply Chain Dynamics

2022-11-01T19:04:42+08:00

Nowadays, demand for customized products is increasing in the era of globalization and competitiveness. But this has burdened the supply chain (SC) performance and demands for the digitalization of its operation to enhance the transparency within its operation. As SCs are operating across regional boundaries, their dynamics need to be aligned with the paradigm of industry 4.0 technologies. Blockchain is one among those, which promises to enhance product traceability and bring transparency to its operation. But, the adoption of blockchain is not getting much attention, which indicates the need of an analysis of its drivers. As the avenues of information technology are getting attention, drivers of blockchain adoption are identified in this study. Furthermore, to reveal the mutual interrelationships between the drivers interpretive structural modelling-Cross-Impact Matrix Multiplication Applied to Classification (ISM-MICMAC) analysis is exercised. Driving factors are further analyzed by the Neutrosophic-based robust ranking, resulting in the primacy of the drivers. The outcomes of the present work substantially outrank the drivers, relative to its impact in the adoption of blockchain operations in SC performance systems. It provides a structured approach to managers for aligning SC operations with blockchain technology.

Received: 29 September 2022 | Revised: 1 November 2022 | Accepted: 14 November 2022

Conflicts of Interest
The authors declare that they have no conflicts of interest to this work.

Data Availability Statement
Data available on request from the corresponding author upon reasonable request.

Online Adaptive Asset Tracking Algorithm with Ordinal Information

2023-11-17T09:31:29+08:00

In recent years, an increasing number of researchers have applied machine learning techniques to online portfolio selection (OLPS), aiming to improve the efficiency and effectiveness of portfolio management in the digital field. In this study, we design and implement a novel OLPS algorithm called “online adaptive asset tracking algorithm” (OAAT). Compared to the peak price tracking (PPT) algorithm, it complements more historical information of assets in the investment portfolio and provides a more effective solution for parameter selection of the PPT algorithm. Firstly, the OAAT algorithm updates investment proportions by tracking the historical information of assets, which includes recent peak prices, historical returns, and historical volatility. Secondly, the OAAT algorithm optimizes parameters through online learning. The initial parameters are selected based on the minimum sum principle of the ordinal information. After each phase of trading, the parameters are optimized through the gradient descent algorithm, and the average values of the optimal parameters in the last 5 days are used as the parameters of the next phase. Finally, with the optimized parameters and the tracked asset information, the fast error backpropagation algorithm outputs the investment ratio through gradient projection. Compared with the benchmarks, follow-the-winner, follow-the-loser, and pattern-matching-based algorithms under four Hong Kong stock index constituents data sets and three US stock index constituents data sets, the empirical comparative analysis and statistical test show that the OAAT algorithm can effectively determine the investment proportion to balance return and risk.

Received: 29 August 2023 | Revised: 17 November 2023 | Accepted: 29 November 2023

Conflicts of Interest
The authors declare that they have no conflicts of interest to this work.

Data Availability Statement
Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Utilizing Artificial Intelligence and Finite Element Method to Simulate the Effects of New Tunnels on Existing Tunnel Deformation

2022-08-08T17:05:35+08:00

Buildings and infrastructure should be constructed while maintaining the safety of the existing infrastructure. Since tunnels are an essential component of urban infrastructure, building new tunnels next to existing ones may have unintended consequences. This study aims to examine the effects of building a new tunnel next to an existing tunnel under different conditions. Two-dimensional models have been developed using the finite element method (FEM) for the existing tunnel, with a diameter of 5 meters, and for the new tunnel, with a diameter that differs from the existing tunnel. Then, the effects of two factors, namely the distance between the two tunnels and the diameter of the new tunnel, were examined on the horizontal, vertical, and total deformation of the soil and the existing tunnel. For the first time, this study attempts to predict the effect of new tunnels on existing tunnels using artificial intelligence (AI) methods. Due to the importance of tunnels and the increasing number of tunnels being constructed in urban areas, this issue will be of great interest. For analyzing the feasibility of using mathematical methods to predict tunnel deformation, the multiple linear regression (MLR) method and an AI technique, namely classification and regression random forests (CRRFs), were used to utilize the generated database. Analyzing FEM results represented that by increasing the diameter of the new tunnel from 3 to 5, horizontal and vertical displacement of the existing tunnel increased by approximately 5 and 10 times, respectively. Further, by reducing the distance between the new tunnel and the existing tunnel from 11 meters to 6 meters, the intensity of horizontal and vertical deformation of the existing tunnel increased by about 2 and 3 times, respectively. Moreover, the results of mathematical models demonstrated that the CRRF method was more accurate than the MLR method, with R of 0.94 and mean absolute error of 2.89 for the testing database, which indicated its proper performance.

Received: 1 July 2022 | Revised: 5 August 2022 | Accepted: 12 August 2022

Conflicts of Interest
The authors declare that they have no conflicts of interest to this work.

Data Availability Statement
Data available on request from the corresponding author upon reasonable request.

Numerical Analysis of Differential Equation with Type-2 Fuzzy Number as Initial Condition

2022-11-17T16:49:40+08:00

The primary intention of this article is to study numerical solutions of differential equation with interval type-2 fuzzy number as the initial condition. The differential equation is first redrafted in the parametric form; then, it is restructured into three systems of linear differential equations. Each system includes two concurrent linear differential equations with respective initial conditions. The classical fourth-order Runge–Kutta method is developed for the above-derived systems. The ability of the method is corroborated by illustrating the problems.

Received: 5 September 2022 | Revised: 10 November 2022 | Accepted: 27 November 2022

Conflicts of Interest
The authors declare that they have no conflicts of interest to this work.

Data Availability Statement
Data available on request from the corresponding author upon reasonable request.

2-Tuple Linguistic Fermatean Fuzzy Decision-Making Method Based on COCOSO with CRITIC for Drip Irrigation System Analysis

2022-08-22T15:22:12+08:00

Given the increasing scarcity of water resources, especially climate change, the adoption of water-efficient irrigation systems (ISs) is becoming increasingly important. Drip irrigation systems (DISs) are the most successful method of saving water and increasing agricultural yields in water-efficient IS. DIS reduces not only the cost of water supply but also the cost of activities such as labor costs and other planting costs. DIS is the most reliable, profitable, and cost-effective agricultural irrigation technique for the vast majority of crops, and it could be a potential solution to the growing water crisis caused by climate change. The Hamacher operation is an extension of the algebraic and Einstein operations. The combination of 2-tuple linguistic Fermatean fuzzy (2TLFF) numbers and the Hamacher operation is more valuable and agile. The method based on the Combined Compromise Solution (CoCoSo) with Criteria Importance Through Intercriteria Correlation (CRITIC) is introduced to manage multiple attribute group decision-making (MAGDM) issues in a 2TLFF environment. Finally, a practical example is shown, followed by a comparison study that supports the unique approach’s efficacy and generalizability. The suggested method distinguishes itself by having no paradoxical instances and a powerful ability for recognizing the optimal choice.

Received: 2 August 2022 | Revised: 4 September 2022 | Accepted: 12 September 2022

Conflicts of Interest
Muhammad Akram is an Editorial Board Member for Journal of Computational and Cognitive Engineering, and was not involved in the editorial review or the decision to publish this article. The authors declare that they have no conflicts of interest to this work.

Data Availability Statement
Data available on request from the corresponding author upon reasonable request.

A Machine Learning Approach in Predicting Student’s Academic Performance Using Artificial Neural Network

2022-12-28T00:53:14+08:00

The rate at which students succeed in their academic pursuits contributes significantly to the academic achievement of their educational institutions because it is used as a measure of the institution’s performance. Many factors could be responsible for students’ academic performance and student success. Quick understanding of weak students and providing solutions to improve their performance will significantly increase their academic success rate. Educational data mining using artificial neural network plays a crucial role in determining their likely performance and helps them to initiate measures that can reposition the students’ performance in the future. This study developed a model that predicts students’ failure and success rates with the aid of a machine learning algorithm. The study sampled 720 students from three selected tertiaries institutions in Adamawa State, Nigeria. Three hundred students were selected from Modibbo Adama University, Yola, 300 students were selected from Adamawa State University, Mubi, and 120 students were selected from Adamawa State Polytechnic, Yola. The research makes use of descriptive statistics to identify the variables that likely affect students’ academic performance. The collected data were preprocessed, cleaned, and modeled using Jupyter Notebook, a Python Anaconda development platform for artificial neural to build the student’s academic performance predictive model. The neural network is modeled with 12 input variables, two layers of hidden neurons, and one output layer. The dataset is trained using the backpropagation learning algorithm. The performance of the neural network is evaluated using k-fold cross-validation. The neural network model has achieved a good accuracy of 97.36%.

Received: 14 October 2022 | Revised: 15 December 2022 | Accepted: 29 December 2022

Conflicts of Interest
The authors declare that they have no conflicts of interest to this work.

Data Availability Statement
Data available on request from the corresponding author upon reasonable request.

A Taxonomy of AI Techniques for Security and Privacy in Cyber–Physical Systems

2024-01-03T10:22:13+08:00

This research paper addresses the concerns related to security and privacy in cyber–physical systems (CPS) and explores the role of artificial intelligence (AI) in addressing these concerns. This paper presents a comprehensive classification of various security and privacy threats in CPS, providing an organized overview of potential risks, economic loss, and enabling effective risk assessment. This paper highlights how AI can help address the security and privacy concerns in CPS by presenting a detailed flow chart that illustrates the stepby-step process of using AI and machine learning (ML) techniques to detect security and privacy issues. This integrated approach serves as a guide for designing ML-based secure CPS, enabling proactive defense mechanisms and improving incident response and recovery. Furthermore, the research explores the various AI techniques that can be employed to address security and privacy concerns in CPS. A taxonomy of ML techniques specifically relevant to security and privacy issues is provided, offering insights into the potential applications of these techniques. In conclusion, this research emphasizes the significance of addressing security and privacy concerns in CPS and highlights the role of AI in tackling these challenges.

Received: 23 August 2023 | Revised: 18 December 2023 | Accepted: 11 January 2024

Conflicts of Interest
The author declares that he has no conflicts of interest to this work.

Data Availability Statement
Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Implementation of Artificial Intelligence in Aquaculture and Fisheries: Deep Learning, Machine Vision, Big Data, Internet of Things, Robots and Beyond

2023-03-28T19:58:33+08:00

The aquaculture and fishery industry are multi-billion-dollar business across the globe, and the demand for aquatic species produce increases exponentially throughout these years. However, the depletion of aquaculture lands and aquatic pollution are some of the major worrying predicaments challenging the future of this industry. Sustainable growth strategies are the only way out, and they must come hand in hand with the implementation of artificial intelligence to achieve the desired outcome high throughput in short time periods. The intelligent fish farm and smart cage aquaculture management system are some of the fruits of this drive, and the system keeps improving to date. In this review, we provide recent updates over the past half-decade of artificial intelligence implementation in fishery and aquaculture in hope to provide highlights and future directions to push the industry to greater heights.

Received: 27 February 2023 | Revised: 28 March 2023 | Accepted: 14 April 2023

Conflicts of Interest
The author declares that he has no conflicts of interest to this work.

Data Availability Statement
Data available on request from the corresponding author upon reasonable request.

Assessment of Machine Learning Techniques and Traffic Flow: A Qualitative and Quantitative Analysis

2023-07-12T16:10:42+08:00

Traffic flow analysis is an interesting study topic in transportation studies. A better understanding of traffic flow is essential for more effective traffic reduction methods. Because managing traffic flow in cities is getting more complicated, we need more methodical ways to deal with these problems. Machine learning (ML) techniques have been suggested as a possible solution because they can process great amounts of data and give insights that can be used to help make decisions about how to manage traffic. The main objective of this research is to conduct a comprehensive examination of the quantitative and qualitative aspects of utilizing ML techniques in the management of traffic flow. Using the Web of Science platform, documents from January 2007 to April 2023 were assessed. The study found that traffic flow management has been using ML techniques more and more over the past few years. This study shows the different approaches and methods that were used, as well as the results and limits of these methods. The results recommend that ML can be a useful tool for managing traffic flow in cities, but further investigation is warranted to gain a complete comprehension of both the advantages and disadvantages of the subject under scrutiny.

Received: 11 May 2023 | Revised: 12 July 2023 | Accepted: 20 July 2023

Conflicts of Interest
The authors declare that they have no conflicts of interest to this work.

Data Availability Statement
Data available on request from the corresponding author upon reasonable request.