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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Liu, Qi
Article Type: Research Article
Abstract: In the era of advanced technology, integrating and distributing data are crucial in smart grid-connected systems. However, as energy loads continue to increase, practical implementation of these systems faces challenges in resource allocation and lacks efficient data collaboration. In this study, the ant colony optimization algorithm is further investigated for stochastic crossover systems and cluster nodes in intelligent path planning management. To improve the pheromone setting method in smart grid-connected systems, we propose an adaptive intelligent ant colony optimization algorithm called the Group Allocation Optimization Algorithm (GAOA). This algorithm expands the pheromone transmission rate of network nodes, establishes a multi-constrained …adaptive model with data mining as the pheromone target, and analyzes the accuracy of resource allocation to import the optimal scheme for smart grid-connected systems. Through experimental results, we demonstrate that the optimized adaptive ant colony algorithm leads to effective improvements in grid-connected systems, pheromone evaluation, data throughput, convergence speed, and data load distribution. These findings provide evidence that the optimized ant colony algorithm is both feasible and effective for resource allocation in smart grid-connected systems. Show more
Keywords: Smart Grid-connected system, data-driven allocation, ant colony algorithm, group allocation optimization algorithm
DOI: 10.3233/JIFS-235091
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6795-6805, 2024
Authors: Pandey, Raksha | Kushwaha, Alok Kumar Singh | Kumar, Vinay
Article Type: Research Article
Abstract: Video forgery, a prevalent concern in today’s digital age, involves the deliberate manipulation of video content, often carried out using sophisticated video editing software. In response to this challenge, the need for an automated approach to detect forged video footage has become increasingly pressing. Our proposed methodology addresses this need by employing a multi-faceted strategy. It begins with the classification of video frames as either originating from genuine sources or having undergone manipulation. To assess the authenticity, the Δ r ¯ s metric is applied to evaluate the coherence of frame sequences. …Additionally, we’ve harnessed the power of machine learning, training a model on a diverse dataset, namely the VIFFD dataset. This robust machine learning approach, particularly the suggested Support Vector Machine (SVM) method, consistently achieves an impressive average accuracy of 94.4%, showcasing its potential as a dependable and effective solution for video forgery detection. In an era where the trustworthiness of video content is of paramount importance, our method emerges as a pivotal safeguard, contributing significantly to the preservation of the integrity and credibility of visual media. Show more
Keywords: Correlation coefficient, forgery detection, interframe video forgery, machine learning, video forensic
DOI: 10.3233/JIFS-235818
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6807-6820, 2024
Authors: Ma, Mengyuan | Huang, Huiling | Han, Jun | Feng, Yanbing | Yang, Yi
Article Type: Research Article
Abstract: Semantic segmentation is a pivotal task in the field of computer vision, encompassing diverse applications and undergoing continuous development. Despite the growing dominance of deep learning methods in this field, many existing network models suffer from trade-offs between accuracy and computational cost, or between speed and accuracy. In essence, semantic segmentation aims to extract semantic information from deep features and optimize them before upsampling output. However, shallow features tend to contain more detailed information but also more noise, while deep features have strong semantic information but lose some spatial information. To address this issue, we propose a novel mutual optimization …strategy based on shallow spatial information and deep semantic information, and construct a details and semantic mutual optimization network (DSMONet). This effectively reduces the noise in the shallow features and guides the deep features to reconstruct the lost spatial information, avoiding cumbersome side auxiliary or complex decoders. The Mutual Optimization Module (MOM) includes Semantic Adjustment Details Module (SADM) and Detail Guided Semantic Module (DGSM), which enables mutual optimization of shallow spatial information and deep semantic information. Comparative evaluations against other methods demonstrate that DSMONet achieves a favorable balance between accuracy and speed. On the Cityscapes dataset, DSMONet achieves performances of 79.3% mean of class-wise intersection-over-union (mIoU)/44.6 frames per second (FPS) and 78.0% mIoU/102 FPS. The code is available at https://github.com/m828/DSMONet . Show more
Keywords: Semantic segmentation, real time, deep learning, mutual optimization, accuracy
DOI: 10.3233/JIFS-235929
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6821-6834, 2024
Authors: Zhang, Zhifei | Wang, Shenmin
Article Type: Research Article
Abstract: The focus of attention has shifted to land use and land cover changes as a result of the world’s fast urbanisation, and logical planning of urban land resources depends greatly on the forecast and analysis of these changes. In order to more precisely forecast and assess patterns of land use change, the study suggests a grey Markov land pattern analysis and prediction model that incorporates social aspects. The study builds a land pattern analysis and prediction model using a major city as the research object. The outcomes demonstrated the high accuracy and reliability of the grey Markov land pattern analysis …and prediction model incorporating social factors, which can more accurately reflect and predict the land use pattern of the study area, with an average relative error of less than 0.01, an accuracy of more than 98%, and an overall fit that has increased by more than 3%. The overall pattern of change is very consistent with the reality. The model predicts that the main trend of future land use in the study area is the continued expansion of urban land such as industrial land, land for transport facilities and land for settlements, while non-construction land such as agricultural land and forest land will continue to decrease. The optimized land pattern analysis and prediction model of the study has a good application environment. Show more
Keywords: Grey system theory, land use change, prediction model, socio-economic factors
DOI: 10.3233/JIFS-235965
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6835-6850, 2024
Authors: Arivanandhan, Rajesh | Ramanathan, Kalaivani | Chellamuthu, Senthilkumar
Article Type: Research Article
Abstract: Users possess the option to rent instances of various sorts, in a variety of regions, and a variety of availability zones, thanks to cloud service carriers like AWS, GCP, and Azure. In the cloud business right now, fixed price models are king when it comes to pricing. However, as the diversity of cloud providers and users grows, this approach is unable to accurately reflect the market’s current needs for cost savings. As a consequence, a dynamic pricing strategy has become a desirable tactic to better handle the erratic cloud demand. In this study, a deep learning model was used to …propose a dynamic pricing structure that ensures service providers are treated fairly in a multi-cloud context. The computational optimization of DL approaches can be severely hampered by the requirement for human hyperparameter selection. Traditional automated solutions to this issue have inadequate durability or fail in specific circumstances. To choose the hyper-parameters in the Dueling Deep Q-Network (DDQN), the hybrid DL approach in this study uses the concept-based wild horse optimization (WHO) method. A community of untamed horses is evolved, and the fitness of the population is evaluated concurrently to estimate the optimum hyper-parameters. The plan changes the price appropriately to promote the use of underutilized resources and discourage the use of overutilized resources. The evaluation’s findings demonstrated that the suggested strategy can lower end-user costs while conducting compute- and data-intensive activities in a multi-cloud environment. The research was concluded by comparing current models after the results were analyzed using various performance indicators. Show more
Keywords: Cloud providers, dynamic pricing scheme, Deep Learning, hyper-parameter selection, Oppositional-Based Learning, Wild Horse Optimization and Dueling Deep Q-Network
DOI: 10.3233/JIFS-236043
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6851-6865, 2024
Authors: Essaki Muthu, A. | Saravanan, K.
Article Type: Research Article
Abstract: Cataract, a common eye disease, causes lens opacification, which can lead to blindness. Early cataract detection in a privacy-preserving approach has led us to investigate the concept of Federated Learning (FL) and its prominent technique, known as Federated Averaging (FedAVG). Federated learning has the potential to solve the privacy issues by allowing data servers to train their models natively and distribute them without invading patient confidentiality. This research introduces an interactive federated learning framework that permits multiple medical institutions to screen cataract from split lamp images utilising convolutional neural network (CNN) without sharing patient data, as well as grade normal, …mild, moderate, and severe cataracts. The CNN is developed based on Modified-ResNet-50 and FedAVG technique could achieve relatively high accuracy. The experimental results demonstrate that the proposed modification reduces the processing time to a greater extent. Show more
Keywords: Federated learning, confidentiality, accuracy, CNN
DOI: 10.3233/JIFS-223465
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6867-6880, 2024
Authors: Wang, Haohao | Li, Wei | Yang, Bin
Article Type: Research Article
Abstract: Rosenfeld defined a fuzzy subgroup of a given group as a fuzzy subset with two special conditions and Mustafa Demirci proposed the idea of fuzzifying the operations on a group through a fuzzy equality and a fuzzy equivalence relation. This paper mainly focuses on fuzzy subsets and vague sets of monoids with several extended algebraic properties. Firstly, we generalize some algebraic properties of t -norms to fuzzy t -norms, this allows for a broader analysis and classification of fuzzy t -norms, enabling their wider application. Furthermore, we explore specific research on the properties of vague t -norms. Finally, selected conclusions …about fuzzy t -norms are extended to bounded lattices. Show more
Keywords: t-norm, t-conorm, uninorm, nullnorm, aggregation function, fuzzy monoid, vague monoid
DOI: 10.3233/JIFS-231401
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6881-6891, 2024
Authors: Du, Wen Sheng
Article Type: Research Article
Abstract: The geometric-arithmetic mean inequality is undoubtedly the most important one in the area of information aggregation. Recently, some q -rung orthopair fuzzy aggregation operators were proposed based on the Hamacher operations. In this paper, we give a detailed theoretical and practical analysis of the developed Hamacher arithmetic and geometric operators for q -rung orthopair fuzzy values. First, we investigate the monotonicity of these Hamacher aggregation operators on q -rung orthopair fuzzy values with respect to the parameter within Hamacher operations. Then, we discuss the limiting cases of these q -rung orthopair fuzzy Hamacher aggregation operators as the parameter therein approaches …to zero or infinity and give a new characterization of the boundedness of these aggregation operators. Subsequently, we establish the geometric-arithmetic mean inequality for q -rung orthopair fuzzy information based on Hamacher operations. Finally, we present a decision making method by use of these aggregation operators and apply it to the problem of enterprise resource planning system selection. Show more
Keywords: Aggregation operator, enterprise resource planning system, geometric-arithmetic mean inequality, Hamacher operation, q-rung orthopair fuzzy value
DOI: 10.3233/JIFS-231452
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6893-6910, 2024
Authors: Xu, Xiaohui
Article Type: Research Article
Abstract: In the new normal period, the trend changes and adjustments of the environment such as international trade, production capacity, labor supply and resource constraints have put forward new requirements for China’s industrial development, which have brought new challenges and given new opportunities. In the new normal stage where economic growth continues to decline, industrial growth is still an important support for economic growth. The advancement of industrial technology is the main driving force for improving the total factor productivity of the industrial industry. Therefore, the most important thing to promote industrial growth is to upgrade the level of industrial technology. …In response to the above-mentioned problems, this paper analyzed the relationship between industrial technology and industrial output in the new normal environment by using the BP neural network (BPNN) algorithm. The connection between the two has been found, which provided a clear direction for the functional adjustment of economic law. Experimental studies have shown that there is a positive relationship between industrial technological progress and industrial output. When other conditions are the same, and when the non-new normal is selected, industrial output increases by about 0.36% for every 1% increase in industrial technological progress. When choosing to be in the new normal, industrial technological progress has a higher impact on industrial output. For every 1% increase in technological progress, industrial output increases by about 0.39%. Show more
Keywords: Sustainable development, new industrial normal, economic law, functional adjustment, artificial neural network
DOI: 10.3233/JIFS-233251
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6911-6924, 2024
Authors: Wang, Fei
Article Type: Research Article
Abstract: Recently, there has been a lot of interest in using the wearable sensors for tracking the exercise progress because of the unbiased accuracy and precision they are provided throughout the continual monitoring. For those with physical impairments, the system’s non-intrusive, lightweight ways of the monitoring activity may ease their load and enhance the quality of their decision-making. As a different measuring unit measures the exercise activity levels recorded by the each wearable sensor, it is challenging to assess the monitoring system. Hence, this paper proposes a Hybridized Fuzzy Multi-Attribute for Exercise Monitoring System (HFMA-EMS) to address the uncertainty issues of …the wearable sensors. The Triangular Fuzzy membership function is proposed to begin classifying the observed values. Pair-wise attribute comparison and evaluator weighting in a T-spherical uncertain linguistic set setting utilizing the Techniques for Ordering of Preferences by Similarities to Ideal Solutions (TOPSIS). In the suggested method, a utility function is used to assess the merits of a model in which attribute the weights are calculated, followed by an exercise in which the attributes are ordered employing the Measurements of the Alternative and Ranking Compromise Solutions model (MARCOS). The performance is performed to analyze the proposed method’s accuracy, precision, recall, f1-score, and correct and incorrect exercise assessment by an accelerometer, gyroscope, and magnetic field sensor unit. The application scenario of the HFMA-EMS can be used in the clinical applications, healthcare management, and sports injury detection. Show more
Keywords: Exercise monitoring system, wearable sensors, disabled individuals, TOPSIS, MARCOS, fuzzy multi-attribute model
DOI: 10.3233/JIFS-235112
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6925-6938, 2024
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