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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: Meenakshi, A. | Bramila, M.
Article Type: Research Article
Abstract: Molecular structures are characterised by the Hosoya polynomial and Wiener index, ideas from mathematical chemistry and graph theory. The graph representation of a chemical compound that has atoms as vertices and chemical bonds as edges is called a molecular graph, and the Hosoya polynomial is a polynomial related to this graph. As a graph attribute that remains unchanged under graph isomorphism, the Hosoya polynomial is known as a graph invariant. It offers details regarding the quantity of distinct non-empty subgraphs within a specified graph. A topological metric called the Wiener index is employed to measure the branching complexity and size …of a molecular graph. For every pair of vertices in a molecular network, the Wiener index is the total of those distances. In this paper, discussed the Hosoya polynomial, Wiener index and Hyper-Wiener index of the Abid-Waheed graphs (AW)a 8 and (AW)a 10 . This graph is similar to Jahangir’s graph. Further, we have extended the research work on the applications of the described graphs. Show more
Keywords: Wiener index, Abid-Waheed, Hosoya polynomial, diameter, distance, connected graph
DOI: 10.3233/JIFS-236051
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2024
Authors: Lin, Jiayi
Article Type: Research Article
Abstract: At this stage, network communication technology is increasingly mature, and intelligent wearable products are also widely used in human daily life. Wearable products are popular with users because of their numerous types, complete functions and convenient services. Wearable products integrate interaction technology, and users can interact with products. However, how to improve the user’s interaction experience and reduce the user’s cognitive burden on the interaction interface is an urgent problem in the current product interaction design. Therefore, based on the analysis of the types and related technologies of wearable products, this paper made a specific analysis of the interaction design …of wearable products, and established an interaction design model. At the same time, the wearable fall detection system was also tested by machine learning algorithm. The experimental results showed that the average test result of the algorithm in this paper was 87.39%, while the average test result of the traditional algorithm was 83.79%. In terms of the missed alarm rate of fall detection, the average test result of this algorithm was 6.4%, while the average test result of the traditional algorithm was 12.33%. In terms of fall detection sensitivity, the average test result of this algorithm was 92.50%, while the average test result of the traditional algorithm was 88.24%. Compared with traditional algorithms, this method performs better, with lower missed detection rate and higher sensitivity. Innovative combination of machine learning algorithm, through three-dimensional coordinate system, differentiation and vector sum formula, improves the accuracy and reliability of fall detection. In conclusion, the algorithm in this paper can effectively optimize the relevant performance of the system, thus improving the accuracy of the system’s fall detection. Show more
Keywords: 5 G network communication technology, wearable products, interaction design, wearable fall detection system
DOI: 10.3233/JIFS-237837
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Li, Hongjun | Zhang, Jinlong
Article Type: Research Article
Abstract: This paper presents a sophisticated four-stage optimization and intelligent control algorithm tailored for two-way electric vehicle charging (EVC) stations integrated with advanced photovoltaic systems and fixed battery energy storage in commercial buildings. The primary objective is to minimize operating costs while prioritizing customer satisfaction within a dynamic and uncertain energy landscape. Our algorithm optimizes the scheduled charging and discharging of electric vehicles (EVs), local battery storage (BS) units, grid power supply, and deferred loads to balance instantaneous supply and demand. The first stage focuses on developing optimal energy management plans for the day ahead, considering factors such as projected energy …production, anticipated EVC demand, and building energy consumption patterns. Building on this foundation, the second stage introduces multilayer EV charging price structures and optimizes participation rewards for discharging, dynamically addressing EV charging patterns and price sensitivities. Approaching the commissioning timeline, the third stage refines energy management plans for the upcoming hours using real-time data and forecasts, adapting to evolving conditions for optimal resource allocation. The final stage involves real-time control and the implementation of optimized programs, dynamically adjusting charge/discharge processes, grid interactions, and load deferral to maintain supply-demand balance and minimize operating costs. Our algorithm enhances system resilience in unpredictable conditions, providing compelling incentives for active EV user participation. Coordinating the integrated system efficiently, including the commercial building’s energy load, ensures reliable service to customers while reducing costs. Extensive case studies and a comparative analysis validate the algorithm’s efficiency in significantly reducing operating costs and enhancing resilience to uncertainty. The paper concludes by highlighting the algorithm’s pioneering role in intelligent EV charging station (CHS) management, offering a cost-effective, customer-oriented, and dynamic energy control strategy for advancing global energy practices. Show more
Keywords: Electric vehicle charging, photovoltaic integration, battery energy storage, energy management optimization, commercial building integration
DOI: 10.3233/JIFS-241032
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Valadez-Godínez, Sergio | Sossa, Humberto | Santiago-Montero, Raúl
Article Type: Research Article
Abstract: The Associative Pattern Classifier (APC) was designed as an associative memory, focusing particularly on pattern classification. This implies that the training memory is constructed in a single operation and pattern classification also occurs in a single process. It is important to note that the APC translates the input patterns through a translation vector, which represents the average of all input patterns. Until now, there is no theoretical framework to explain the inner workings of the APC. Its relevance is inferred from the fact that several studies have been conducted using it as a foundation. This paper seeks to provide a …theoretical comprehension of the APC’s operation to facilitate future enhancements. We found the APC creates a system in static equilibrium through concurrent vectors at the origin (translation vector), resulting in a balanced separation of patterns. However, the APC cannot achieve complete pattern separation because of the presence of a neutral region. The neutral region is defined by all the points that define the separation hyperplanes. The points over the hyperplanes cannot be classified by the APC. Additionally, we discovered that the APC is unable to accurately classify the translation vector, which could be included as part of the input patterns. Our previous research showed that the APC is unsuccessful in achieving the linear separation of the AND function. In this research, we also broaden the examination of the AND function to illustrate that achieving linear separation is not feasible because the separation line represents a neutral region. The APC demonstrated exceptional performance when tested with artificial datasets where patterns were distributed over balanced regions, thus operating as an efficient multiclass and non-linear classifier. Nevertheless, the performance of the APC is lower when tested with real-world databases, making the APC inaccurate due to its restricted inner workings. Show more
Keywords: Classifier, pattern, associative memory, class, classification
DOI: 10.3233/JIFS-219347
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-23, 2024
Authors: Zhang, Wei | Zheng, Hongxuan | Zhang, Runyu
Article Type: Research Article
Abstract: In this paper, a self-organizing RBF (SORBF) neural network with an adaptive threshold is proposed based on improved particle swarm optimization (IPSO) and neural strength (NS). The parameters and structure of SORBF can be optimized simultaneously and dynamically. Moreover, the tiresome problem of threshold setting is solved. Firstly, the network size and parameters of SORBF are mapped into the particle information of PSO. Secondly, an IPSO algorithm, based on diversity inertia weight and elite knowledge guiding, is proposed to reduce the probability of the population falling into the local optimum. Then, IPSO is used for optimizing the parameters of SORBF. …Based on neuron growth intensity and competition intensity, SORBF can realize the hidden neuron addition and deletion adaptively. Moreover, the thresholds during the structure adjustment can be provided adaptively based on the network scale and neuron strength, which avoids the subjectivity setting and can improve the adaptive ability. Finally, the convergence analysis of IPSO is provided to ensure the performance of SORBF. Experiment results show that the proposed SORBF has good self-organizing ability and compact network structure compared with other methods. Show more
Keywords: RBF neural network, PSO, self-organization, neural strength, adaptive threshold
DOI: 10.3233/JIFS-239569
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Wei, Guangcun | Fu, Jihua | Pan, Zhifei | Fang, Qingge | Zhang, Zhi
Article Type: Research Article
Abstract: The text in natural scenes is often smaller compared to artificially designed text. Due to the small proportion of pixels, low resolution, less semantic information, and susceptibility to complex scenes, tiny text detection often results in many missed detections. To address this issue, this paper draws inspiration from small object detection methods and proposes TiTDet, a detection algorithm more suitable for tiny text. Due to the small proportion of pixels, low resolution, less semantic information, and susceptibility to complex scenes, tiny text detection often results in many missed detections. To address this issue, this paper draws inspiration from small object …detection methods and proposes TiTDet, a detection algorithm more suitable for tiny text. Firstly, this paper incorporates a context extraction module and an attention-guided module. These modules guide contextual information learning through a self attention mechanism, while eliminating the possible negative impact caused by redundant information. Regarding multi-scale feature fusion, this paper proposes a fine-grained effective fusion factor, making the fusion process emphasize small object learning more and highlight the feature expression of tiny texts. In terms of post-processing, this paper proposes a differentiable binarization module, incorporating the binarization process into model training. Leveraging the implicit information in the data to drive model improvement can enhance the post-processing effect. Lastly, this paper proposes a scale-sensitive loss, which can handle tiny texts more fairly, fully considering the positional relationship between the predicted and real regions, and better guiding the model training. This paper proves that TiTDet exhibits high sensitivity and accuracy in detecting tiny texts, achieving an 86.0% F1-score on ICDAR2015. The paper also compares the superiority of the method on CTW1500 and Total-Text. Show more
Keywords: Tiny text detection, context extraction module, attention-guided module, effective fusion factor, scale-sensitive loss
DOI: 10.3233/JIFS-236317
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Pandiyarajan, Abinaya | Jagatheesaperumal, Senthil Kumar | Thayanithi, Manonmani
Article Type: Research Article
Abstract: This study explores how Electronic Health Records (EHR) might be transformed in the context of the rapid improvements in cloud computing and IoT technology. But worries about sensitive data security and access management when it moves to large cloud provider networks surface. Even if they are secure, traditional encryption techniques sometimes lack the granularity needed for effective data protection. We suggest the Secure Access Policy – Ciphertext Policy – Attribute-based Encryption (SAPCP-ABE) algorithm as a solution to this problem. This method ensures that only authorized users may access the necessary data while facilitating fine-grained encrypted data exchange. The three main …phases of SAPCP-ABE are retrieval and decoding, where the system verifies users’ access restrictions, secure outsourcing that prioritizes critical attributes, and an authenticity phase for early authentication. Performance tests show that SAPCP-ABE is a better scheme than earlier ones, with faster encryption and decryption speeds of 5 and 5.1 seconds for 512-bit keys, respectively. Security studies, numerical comparisons, and implementation outcomes demonstrate our suggested approach’s efficacy, efficiency, and scalability. Show more
Keywords: Attribute-based encryption, electronic health record, access policy, cloud providers, cloud computing
DOI: 10.3233/JIFS-240341
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Huang, Ying | Li, Lang | Li, Di | Li, Yongchao
Article Type: Research Article
Abstract: AND-Rotation-XOR (AND-RX) ciphers are known for its unique round function and excellent implementation performance. As a result, AND-RX ciphers are well suited for protecting sensitive information on resource-constrained devices. AND-RX ciphers need to be passed by rigorous cryptanalysis methods before practice. Integral cryptanalysis is one of the important cryptanalysis methods. MILP-based automated model is constructed to solve the integral cryptanalysis of AND-RX ciphers. The automated model usually consumes a long time when the block length and the number of round function components are large. In this paper, we design a neural distinguisher named IABC model for fast and efficient integral …cryptanalysis. The IABC model learns to distinguish between ciphertext multisets to construct an integral distinguisher for AND-RX cipher, which ciphertext multisets from plaintext or random plaintexts. The IABC model is used for SIMON, SIMECK and SAND ciphers, which validates the neural distinguisher for AND-RX ciphers. The experimental results show that the IABC model is capable of expanding the number of rounds of integral distinguishers for AND-RX ciphers with certain accuracy. Therefore, IABC model can be effectively used for integral cryptanalysis of AND-RX ciphers. In addition, we discover that a larger number of active bits in the plaintext multiset results in a more accurate IABC model. Show more
Keywords: AND-RX cipher, integral cryptanalysis, division property, neural distinguisher
DOI: 10.3233/JIFS-238122
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Ranjith, K. | Karthikeyan, K.
Article Type: Research Article
Abstract: The flow-shop scheduling problem (FSSP) has received a considerable amount of attention due to its wide-ranging applications. However, the omission of uncertainty significantly diminishes the practicality of scheduling results, underscoring its the necessity to address uncertainty in the flow shop problem. In this paper, a fuzzy two-machine flow-shop problem is considered and an effective algorithm with a fuzzy ranking method is proposed to minimize the total waiting time. The processing times are represented using trapezoidal membership functions. Furthermore, a two-stage flow shop scheduling problem is used in the proposed algorithm and various categories of fuzzy mean techniques. The experimental results …and statistical comparisons demonstrate that the proposed algorithm exhibits significant advantages in effectively solving the FFSSP (Fuzzy Flow-Shop Scheduling Problem). Show more
Keywords: Two-stage flow shop, trapezoidal fuzzy number, mean ranking techniques, waiting time
DOI: 10.3233/JIFS-235526
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Sageengrana, S. | Selvakumar, S.
Article Type: Research Article
Abstract: Distraction and fatigue are serious issues in online learning, and they directly impact educational outcomes. To achieve excellent academic achievement, students need to focus on their studies without being distracted or fatigued. Learners frequently overlook crucial information, directions, and concepts while they are passive and sleepy. They tend to miss important content, instructions, and concepts. Iris Angle Position (IAP) and electroencephalography (EEG) were used in this model to identify the behaviour of learners. Specifically, a Deep Convolutional Neural Network (DCNN) is constructed to extract IAP in order to accurately capture the learner’s facial area. EEG signals are effectively handled and …sorted using deep reinforcement learning (DRL). The learners’ facial landmarks are retrieved from a frame using the dlib toolbox. Only eye landmark points from face landmarks alone are focused on in order to determine the learner’s behaviour. When the learners EEG signals and Iris positions are monitored simultaneously, it’s helpful to identify the learner’s fatigue state (LFS) and the learner’s distraction state (LDS). The Brain Vision Algorithm (BVA) uses iris position and minimal facial landmarks, along with brain activity, to properly identify the learner’s level of distraction and exhaustion. When a student is detected as being preoccupied or sleepy, an alert goes off automatically, and the educator gets performance feedback. Iris position data and brain-computer interface-based EEG signal values are utilised to identify distraction and sleepiness. Comparative tests have demonstrated that this innovative method offers fast and high-accuracy student activity detection in virtual learning settings. Applying the suggested approach to different existing classifiers yields an F-Score of 91.92%, a recall of 93.87%, and a precision of 92.37% . The results showed that the detection rates for both distracted and sleepy phases were higher than those attained with other currently used techniques. Show more
Keywords: Drowsiness, online learning, iris position, EEG signals, distraction, brain vision algorithm
DOI: 10.3233/JIFS-237016
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
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