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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: Yadav, Ravindra Kumar | Bhadoria, Vikas Singh | Hrisheekesha, P.N.
Article Type: Research Article
Abstract: The increasing demand for electrical energy is a result of advancing technologies and changing lifestyles worldwide. Meeting this escalating energy need poses a substantial challenge, especially the difficulty in constructing new conventional power plants due to limited fossil fuel resources. To address this, demand-side management (DSM) in smart grid (SG), integrated with solar photovoltaic energy (SPE) have emerged as a crucial tool for effectively managing electricity demand, ensuring flexibility and reliability. DSM achieves optimal electricity utilization by rescheduling the operation schedules of consumer appliances and carefully adjusting their demand profiles. Integrating DSM into a smart grid framework is highly advantageous …for the power industry’s pursuit of sustainable energy goals. While various heuristic-based optimization techniques have been employed for DSM, the focus on SPE has been limited to small-scale residential loads. This study utilizes the Ant Colony Optimization (ACO) algorithm to tackle a day ahead DSM minimization problem, considering SPE in areas with large number of appliances. The DSM minimization problem falls into the category of discrete combinatorial problems, making it well-suited for ACO optimization. The self-healing, self-protection, and self-organizing attributes of ACO make it particularly effective for DSM solutions. Residential, commercial, and industrial loads, with and without SPE integration, are considered to demonstrate the efficacy of the proposed ACO algorithm. Simulation results are compared with other studies in the literature, including Evolutionary Algorithm (EA), Moth Flame Optimization (MFO), and Bacterial Foraging Optimization (BFO), in terms of reducing consumer’s cost of energy (CCE) and utility peak load (UPL). The findings indicate that the proposed ACO algorithm outperforms the other algorithms considered in the current context. Show more
Keywords: Demand side management, ant colony optimization, solar photovoltaic energy, utility peak load, consumer’s cost of energy
DOI: 10.3233/JIFS-234281
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7627-7642, 2024
Authors: Wang, Caichuan | Li, Jiajun
Article Type: Research Article
Abstract: With the continuous changes and development of financial markets, it has brought many difficulties to investment decision-making. For the multi-objective investment decision-making problem, the improved Ant colony optimization algorithms was used to improve the effectiveness and efficiency of the multi-objective investment decision-making. Therefore, based on intelligent Fuzzy clustering algorithm and Ant colony optimization algorithms, this paper studied a new multi-objective investment decision model, and proved the advantages of this method through comparative analysis of experiments. The experimental results showed that the improved Ant colony optimization algorithms has significantly reduced the system’s construction costs, operating costs and financial costs, all of …which were controlled below 41%. Compared with the traditional Ant colony optimization algorithms, this method had lower values in policy risk, technical risk and market risk, and can effectively control risks. Meanwhile, the environmental, economic, and social benefits of this method were all above 58%, and the average absolute return rate and success rate in this experiment were 21.5450% and 69.4083%, respectively. Therefore, from the above point of view, the multi-objective investment decision model based on intelligent Fuzzy clustering algorithm and the improved Ant colony optimization algorithms can effectively help decision-makers to find the best investment decision-making scheme, and can improve the accuracy and stability of decision-making. This research can provide reference significance for other matters in the field of investment decision-making. Show more
Keywords: Multi-objective investment, investment decision model, improved ant colony algorithm, intelligent fuzzy clustering algorithm, traditional ant colony algorithm
DOI: 10.3233/JIFS-234704
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7643-7657, 2024
Authors: Li, Zhongliang | Tu, Xuezhen | Gao, Hong | Huang, Shiyue | Ma, Zongmin
Article Type: Research Article
Abstract: With the development of artificial intelligence, deep-learning-based log anomaly detection proves to be an important research topic. In this paper, we propose LogCSS, a novel log anomaly detection framework based on the Context-Semantics-Statistics Convolutional Neural Network (CSSCNN). It is the first model that uses BERT (Bidirectional Encoder Representation from Transformers) and CNN (Convolutional Neural Network) to extract the semantic, temporal, and correlational features of the logs. We combine the features with the statistic information of log templates for the classification model to improve the accuracy. We also propose a technique, DOOT (Deals with the Out-Of-Templates), for online template matching. The …experimental research shows that our framework improves the average F1 score of the six best algorithms in the industry by more than 5% on the open-source dataset HDFS, and improves the average F1 score of the six best algorithms in the industry by more than 8% on the BGL dataset, LogCSS also performs better than other similar methods on our own constructed dataset. Show more
Keywords: Anomaly detection, convolutional neural network, intelligent operation and maintenance, data mining
DOI: 10.3233/JIFS-235801
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7659-7676, 2024
Authors: Byeon, Haewon | Tammina, Manoj Ram | Soni, Mukesh | Kuzieva, Nargiza | Jindal, Latika | Keshta, Ismail | Kulkarni, Mrunalini Harish
Article Type: Research Article
Abstract: Online health consultations are becoming more popular as a result of technological improvements. Patients routinely look for information about medical disorders online, which could jeopardize the privacy of medical records and increase the workload of healthcare professionals. Nonetheless, academics continue to be extremely concerned about issues related to the quality characteristics that relate to the current architectural models, such as energy consumption, latency, resource utilization, scalability, and packet loss. This method, however, also results in a significant strain being placed on medical experts who must sort through vast amounts of medical records to extract certain information. This paper presents a …novel ciphertext policy attribute-based encryption method coupled with fuzzy logic to overcome these issues. This solution uses a hybrid structure of IPFS and blockchain to store data and enables complex bidirectional access control. Before being added to IPFS, medical records are encrypted. To ensure data integrity, related IPFS hash indexes are then added to the blockchain. Utilizing attribute-based technology, users’ data is encrypted to give them fine-grained bidirectional access control. A thorough security analysis proves the system’s resilience, especially when faced with chosen plaintext assaults inside the random oracle model. Tests for this study were conducted using 10–50 attribute sets. This paper’s technique solely makes use of a hash operation. All things considered; the study demonstrates that the proposed design is more efficient than earlier schemes. Thus, from the comparison study above, it can be concluded that the system presented in this work is more efficient. Results from simulations provide additional support for the suggested methodology by highlighting the improved computing efficiency of users as compared to baseline conventional systems. This study demonstrates how technological advancement and healthcare requirements can coexist harmoniously, paving the way for secure and effective online medical consultations that are powered by fuzzy logic. Show more
Keywords: Fuzzy logic, data analysis, online health consultation, advanced encryption system
DOI: 10.3233/JIFS-235893
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7677-7695, 2024
Authors: Luan, Fei | Tang, Biao | Li, Ye | Liu, Shi Qiang | Yang, Xueqin | Masoud, Mahmoud | Feng, Baoyu
Article Type: Research Article
Abstract: As environmental contamination becomes more and more severe, enterprises need to consider optimizing environmental criteria while optimizing production criteria. In this study, a multi-objective green flexible job shop scheduling problem (MO_GFJSP) is established with two objective functions: the makespan and the carbon emission. To effectively solve the MO_GFJSP, an improved chimp optimization algorithm (IChOA) is designed. The proposed IChOA has four main innovative aspects: 1) the fast non-dominated sorting (FDS) method is introduced to compare the individuals with multiple objectives and strengthen the solution accuracy. 2) a dynamic convergence factor (DCF) is introduced to strengthen the capabilities of exploration and …exploitation. 3) the position weight (PW) is used in the individual position updating to enhance the search efficiency. 4) the variable neighborhood search (VNS) is developed to strengthen the capacity to escape the local optimum. By executing abundant experiments using 20 benchmark instances, it was demonstrated that the developed IChOA is efficient to solve the MO_GFJSP and effective for reducing carbon emission in the flexible job shop. Show more
Keywords: Multi-objective green flexible job shop scheduling, meta-heuristics, improved chimp optimization algorithm, variable neighborhood search
DOI: 10.3233/JIFS-236157
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7697-7710, 2024
Authors: Xiao, Yongxia | Tang, Xiao
Article Type: Research Article
Abstract: In the interval-valued intuitionistic fuzzy environment, a new multi-attribute three-way decision-making model is proposed to address the problems that the relative loss function in the existing multi-attribute three-way decision-making model does not consider the degree of hesitancy, and the alternative conditional probabilities are given subjectively by the authors, which lacks objectivity. First, three types of ideal solutions are introduced, and the correlation coefficients between the evaluated values and ideal solutions are utilized to construct alternative relative loss functions. Second, the ELECTRE-I method is generalized to the interval-valued intuitionistic fuzzy environment to establish the outranking relation and a method for estimating …the conditional probability of alternatives is given. Finally, the model is used to experimentally analyze examples to illustrate the effectiveness and rationality of the model. Show more
Keywords: Interval-valued intuitionistic fuzzy sets, multi-attribute decision-making, three-way decision, correlation coefficient, outranking relation
DOI: 10.3233/JIFS-236356
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7711-7725, 2024
Authors: Wang, Zhongan | Li, Honghai | Pang, Minghao | Wu, Yingna | Yang, Rui | Wu, Zhiwei | Cai, Guoshuang
Article Type: Research Article
Abstract: Detection and classification methods for the melt pool state in laser direct energy deposition (L-DED) can significantly help predict defects and mechanical properties of L-DED metal parts. Although traditional machine learning algorithms based on physical modeling methods and convolutional neural networks have recently been introduced into melt pool state identification, these methods rely on complex artificially designed features or cannot simultaneously detect defects in multiple dimensions. In this paper, a novel bilateral stream neural network was designed for melt pool identification, which performs defect identification in two label dimensions simultaneously. Two sets of single-channel experiments were designed to collect the …dataset captured by a high-speed camera. By cutting the metal parts and marking them with professional equipment operated by professionals, the dataset was labeled according to the bonding condition and dilution rate criteria. Without an additive model structure, the model achieved 95.2% accuracy in identifying defects in the bonding condition and 92.8% in determining deficiencies in the dilution rate. In order to explain the identification mechanism of the model, the CAM method was utilized for the visual display of the model recognition process, which provides a potential application solution for the online monitoring method of the L-DED. Show more
Keywords: Laser direct energy deposition, melt pool state, bilateral stream neural network
DOI: 10.3233/JIFS-236589
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7727-7738, 2024
Authors: Deng, Xiangyu | Hu, Yiman | Yang, Yahan
Article Type: Research Article
Abstract: With the development of artificial intelligence technology, the digital transformation of student-oriented education becomes particularly important. How to promote real-time interaction between teachers and students in the classroom is an urgent issue which is needed to pay attention to. Based on the facial expression features of students in a classroom, this paper analyzes the changes in angles between facial expression feature points using Dlib. Additionally, this paper proposes a novel algorithm for extracting variable scale template edge trend features. The algorithm adaptively processes the template based on the edge trend features of expression feature points, and use the proposed template …slope normalization algorithm to achieve multi-scale template edge trend extraction. Then, DNN are used to recognize different listening expressions. The experimental results show that the proposed algorithm has faster recognition speed and better robustness when applied to classroom expression recognition. By identifying students’ class status to remind teachers to adjust their class progress, the goal of improving classroom learning effectiveness is achieved Show more
Keywords: Dlib face recognition, learning effectiveness, expression recognition, DNN prediction, feature extraction
DOI: 10.3233/JIFS-237143
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7739-7750, 2024
Authors: Wan, Yifei | Huang, Qi | Wu, Yin | Li, Songling
Article Type: Research Article
Abstract: By designing a digital power grid multi-source data security collaborative management platform, the system configuration problem of the OMS system and the power grid management platform for the main distribution network of the power grid is solved. A design method for the digital power grid multi-source data security collaborative management platform based on discrete particle swarm optimization algorithm is proposed. Based on the design concept of SOA, realize the overall design framework of the platform according to the design method of multi-layer technical system in the business presentation layer, business process and composition layer, service layer, component layer and resource …layer, realize the basic layer design of the system management platform through the basic application platform design scheme of XML configuration, implement the query, processing and output representation of the grid’s multivariate data using B/S architecture protocol, and use the Spring Framework The platform software architecture is implemented using J2EE technology and multi module component design scheme. The discrete particle swarm optimization algorithm is used for the fusion and scheduling of multi-source data in the digital power grid. The interface design and functional construction of the power grid management platform are implemented in the OMS system of the power grid main distribution network, and the logical model of the transformation project is constructed to achieve platform optimization and construction. Tests have shown that the designed digital power grid multi-source data security collaborative management platform has good human-machine interaction, strong data fusion scheduling ability, reduced resource and subsystem coupling, and supports the flexibility of physical deployment and maintenance. Show more
Keywords: Discrete particle swarm optimization algorithm, digital power grid, multi source data, safety, collaborative management platform, main distribution network OMS system of the power grid
DOI: 10.3233/JIFS-237849
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7751-7761, 2024
Authors: Bin, Chenzhong | Liu, Wenqiang | Ding, Hantao | Wen, Yimin
Article Type: Research Article
Abstract: Existing POI recommendation methods often fail to capture the fine-grained preferences of users and face the challenge of modeling multiple relationships. Moreover, knowledge graph-based recommendation methods are limited in storing dynamic user trajectories, making them unsuitable for POI recommendation scenarios. In this paper, we propose a Multi-View Heterogeneous Knowledge learning model that utilizes techniques for heterogeneous knowledge representation learning and multi-view context modeling. Our model comprehensively models user preferences and the relationships between users and POIs by utilizing information from users’ visiting sequences and POI attributes knowledge graph. Specifically, we design a heterogeneous knowledge embedding method to learn the representation …of users and POIs using POI attribute knowledge and users’ visiting sequences. Additionally, we constructed a user trajectory similarity graph and a POI attribute similarity graph to explore potential relations between users and between POIs. The former measures the similarity of user behaviors based on user visit sequences, and the latter quantifies the similarity between different POIs through a novel feature mapping method. Finally, we propose a multi-view hybrid learning method that combines unsupervised and supervised learning paradigms to model complex relationships, improving the overall recommendation performance. Extensive experiments on real-world datasets validate the effectiveness of our method. Show more
Keywords: POI recommendations, heterogeneous knowledge learning, multi-view learning, multiple context modeling, knowledge graph
DOI: 10.3233/JIFS-232792
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7763-7777, 2024
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