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A methodology for using players’ chat content for dynamic difficulty adjustment in metaverse multiplayer games Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-12 Mohammad Mahdi Rezapour, Afsaneh Fatemi, Mohammad Ali Nematbakhsh
Personalization of game difficulty is a critical task in leveraging artificial intelligence (AI) technologies to enhance player engagement in virtual worlds like metaverse. One of the key challenges in this area is developing methods for assessing a player’s perception of game difficulty. This information can be used to dynamically adjust the game difficulty to match the player’s skill level and preferences
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Determination of best renewable energy sources in India using SWARA-ARAS in confidence level based interval-valued Fermatean fuzzy environment Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-12 Mijanur Rahaman Seikh, Prayosi Chatterjee
The requirement for renewable energy sources arises from the depletion of fossil fuels and the increasing energy demand. A case study in India has been conducted in this context to identify the most promising renewable energy sources. A novel multi-attribute group decision-making (MAGDM) method integrating stepwise weight assessment ratio analysis (SWARA) and additive ratio assessment (ARAS) has been
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Classification Assessment Tool: a program to measure the uncertainty of classification models in terms of class-level metrics Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-12 Szilárd Szabó, Imre J. Holb, Vanda Éva Abriha-Molnár, Gábor Szatmári, Sudhir Kumar Singh, Dávid Abriha
Accuracy assessments are important steps of classifications and get higher relevance with the soar of machine and deep learning techniques. We provided a method for quick model evaluations with several options: calculate the class level accuracy metrics for as many models and classes as needed; calculate model stability using random subsets of the testing data. The outputs are single calculations,
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Rethinking superpixel segmentation from biologically inspired mechanisms Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-12 TingYu Zhao, Bo Peng, Yuan Sun, DaiPeng Yang, ZhenGuang Zhang, Xi Wu
Recently, advancements in deep learning-based superpixel segmentation methods have brought about improvements in both the efficiency and the performance of segmentation. However, a significant challenge remains in generating superpixels that strictly adhere to object boundaries while conveying rich visual significance, especially with limited training data, leading to the generation of blunt superpixel
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Retraction notice to “On-line system identification of complex systems using Chebyshev neural networks” [Appl. Soft Comput. 7(2007) 364–372] Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-12 S. Purwar, I.N. Kar, A.N. Jha
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A design of fuzzy rule-based classifier optimized through softmax function and information entropy Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-11 Xiaoyu Han, Xiubin Zhu, Witold Pedrycz, Almetwally M. Mostafa, Zhiwu Li
Takagi–Sugeno–Kang (TSK) classifiers have achieved great success in many applications due to their interpretability and transparent model reliability for users. At present, however, how to evaluate classification results is still an unsolved issue for TSK classifiers. This study designs a fuzzy rule-based classifier based on TSK classifiers, the outputs of which for an instance can be considered as
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Enhanced Predictive Modeling of Rotating Machinery Remaining Useful Life by using Separable Convolution Backbone Networks Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-11 Li Zou, Cong Ma, Jun Hu, Zechuan Yu, Kejia Zhuang
Accurate prediction of Remaining Useful Life (RUL) is a critical aspect in the field of prognostics health management (PHM). Striking a balance between prediction precision and model complexity is a substantial challenge when deploying deep learning (DL) methods in PHM. In response to this challenge, the present study introduces a novel approach called the Separable Convolution Backbone Network (SCBNet)
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Deep Convolutional Neural Networks with Genetic Algorithm-Based Synthetic Minority Over-Sampling Technique for Improved Imbalanced Data Classification Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-11 Suja A. Alex, J. Jesu Vedha Nayahi, Sanaa Kaddoura
Imbalanced data classification presents a challenge in machine learning, inducing biased model learning. Moreover, data dimensionality poses another challenge as it highly impacts classifier performance. This paper proposes a new deep-learning method that combines feature selection with oversampling to address these challenges. The proposed approach, GA-SMOTE-DCNN, integrates a genetic algorithm (GA)
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An adaptive large neighborhood search for the multi-vehicle profitable tour problem with flexible compartments and mandatory customers Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-11 Vincent F. Yu, Nabila Yuraisyah Salsabila, Aldy Gunawan, Anggun Nurfitriani Handoko
The home-refill delivery system is a business model that addresses the concerns of plastic waste and its impact on the environment. It allows customers to pick up their household goods at their doorsteps and refill them into their own containers. However, the difficulty in accessing customers’ locations and product consolidations are undeniable challenges. To overcome these issues, we introduce a new
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Mathematical Analysis of Big Data Analytics under Bipolar Complex Fuzzy Soft Information Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-11 Naeem Jan, Jeonghwan Gwak, Muhammet Deveci, Vladimir Simic, Jurgita Antucheviciene
The aim of this study is to develop new and efficient theories for handling complex and unreliable data in real-world scenarios. The proposed approach integrates two distinct theories: the Bipolar Complex Fuzzy Set (BCFS) and the Soft Set (SS), resulting in a novel and superior method compared to existing solutions. Furthermore, the value of big data analytics cannot be overstated as it provides businesses
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Vision Transformer-based overlay processor for Edge Computing Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-11 Fang Liu, Zimeng Fan, Wei Hu, Dian Xu, Min Peng, Jing He, Yanxiang He
Accelerating Visual Neural Networks in Edge Computing environments is crucial for processing image and video data. Visual Neural Networks, including Convolutional Neural Networks and Vision Transformers, are central to image recognition, video analysis, and object detection tasks. Deploying these networks on edge devices and accelerating them can significantly enhance data processing speed and efficiency
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Semi-supervised domain adaptation incorporating three-way decision for multi-view echocardiographic sequence segmentation Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-11 Shuxin Zhuang, Heye Zhang, Wanli Ding, Zhemin Zhuang, Jinglin Zhang, Zhifan Gao
Multi-view echocardiographic sequence segmentation is essential for the diagnosis of cardiac diseases in clinical practice. However, the variation in cardiac structures in different views and the lack of manual annotations make it challenging to establish a generalized segmentation model. In this paper, we propose a Bidirectional semi-supervised domain adaptation (BSDA) method based on the three-way
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Short-term global horizontal irradiance forecasting using weather classified categorical boosting Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-11 Ubaid Ahmed, Ahsan Raza Khan, Anzar Mahmood, Iqra Rafiq, Rami Ghannam, Ahmed Zoha
Accurate short-term solar irradiance (SI) forecasting is crucial for renewable energy integration to ensure unit commitment and economic load dispatch. However, hourly SI prediction is very challenging due to atmospheric conditions and weather fluctuations. This study proposes a hybrid approach using weather classification and boosting algorithms for short-term global horizontal irradiance (GHI) forecasting
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Adaptive Threshold Optimisation for Online Feature Selection using Dynamic Particle Swarm Optimisation in Determining Feature Relevancy and Redundancy Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-08 Ezzatul Akmal Kamaru Zaman, Azlin Ahmad, Azlinah Mohamed
In the era of data-driven decision-making, managing dynamic data streams characterised by evolving data distributions and high dimensionality presents a formidable challenge for online feature selection. This research addresses the challenge by developing innovative solutions in optimising Online Feature Selection (OFS) to manage feature irrelevancy and redundancy and rigorously validating the proposed
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Feature selection algorithm using neighborhood equivalence tolerance relation for incomplete decision systems Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-08 Shangzhi Wu, Litai Wang, Shuyue Ge, Zheng Xiong, Jie Liu
Rough set is an important method for dealing with incomplete information systems. In incomplete information systems, the most common way to determine the relation between two samples is the tolerance relation. However, the condition for the tolerance relation to determine those samples may belong to the same category is very lenient, which makes the reduction rate low when using the rough set generated
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A consensus-based single valued neutrosophic model for selection of educational vendors under metaverse with extended reality Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-08 Abhijit Saha, Renuka Kolandasamy, Prasenjit Chatterjee, Jurgita Antucheviciene
Selecting the right educational vendors in the Metaverse, particularly those utilizing extended reality (XR), is crucial for creating an engaging and immersive learning environment. Careful vendor selection ensures the delivery of high-quality XR educational content, considering factors such as seamless integration with different XR devices and addressing associated challenges, which can affect the
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How to select plan in emergency decision-making? A two-stage method with case-based reasoning and prospect theory Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-08 Wenbo Zhang, Xi Chen, Jie Mao, Feng Ke, Haiming Liang
Emergency events characterized by high uncertainty and complexity bring tremendous pressure and challenges to our society. Emergency decision-making (EDM) is an effective way to mitigate the losses caused by emergency events. The generation of alternatives and the selection of the best emergency plan are crucial to the successful management of an emergency event. To improve the efficiency of EDM, this
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Neutrosophic fusion of multimodal brain images: Integrating neutrosophic entropy and feature extraction Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-08 K.G. Lavanya, P. Dhanalakshmi, M. Nandhini
Due to the rapid growth of imaging modalities in clinical analysis and the indispensable requirement of brain images from various imaging modalities for diagnosing a disease, multi-modal brain image fusion has become an intriguing problem among researchers. Thus, the main motive of this paper is to obtain all the necessary information about the source images in a single fused image of high contrast
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Implementation of Caputo type fractional derivative chain rule on back propagation algorithm Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-07 Mücahid Candan, Mete Çubukçu
Fractional gradient computation is a challenging task for neural networks. In this study, the brief history of fractional derivation is abstracted, and the core framework of the Faà di Bruno formula is implemented to the fractional gradient computation problem. As an analytical approach to solve the chain rule problem of fractional derivatives, the use of the Faà di Bruno formula for the Caputo-type
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Reinforcement learning based bilevel real-time pricing strategy for a smart grid with distributed energy resources Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-07 Jingqi Wang, Yan Gao, Renjie Li
The integration of flexible loads, distributed energy resources, and other technologies is becoming common in advance power and energy systems. However, the integration also presents significant challenges due to the increasing complexity and uncertainty. To effectively manage these resources, an adaptive pricing mechanism is needed that can account for their variable availability. Based on this, we
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An intelligent framework of upgraded CapsNets with massive transmissibility data for identifying damage in bridges Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-07 Shuai Li, Maosen Cao, Mahmoud Bayat, Dragoslav Sumarac, Jie Wang
Structural monitoring systems installed on bridges are capable of capturing large-scale dynamic responses online and in real-time. The response data of the bridge under different loading conditions is used for condition assessment of the bridge. There is a chasm between monitoring data and damage assessment due to the difficulty in revealing the relationship between the massive monitoring data and
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A frequency-domain approach with learnable filters for image classification Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-07 José Augusto Stuchi, Natalia Gil Canto, Romis Ribeiro de Faissol Attux, Levy Boccato
Machine learning applied to computer vision and signal processing is achieving results comparable to the human brain due to the great improvements brought by deep neural networks (DNN). The majority of state-of-the-art architectures are DNN-related, but only a few explicitly explore the frequency domain to extract useful information and improve the results. This paper presents a new approach for exploring
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Editorial for Special Issue on “Expert decision making for data analytics with applications” Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-06 Kevin Kam Fung Yuen, Jenq-Shiou Leu, Alessio Ishizaka, Hissam Tawfik, Frans Coenen
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Multi-task supervised contrastive learning for chest X-ray diagnosis: A two-stage hierarchical classification framework for COVID-19 diagnosis Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-06 Guan-Ying Chen, Chih-Ting Lin
Global pandemics have posed great challenges, such as limited samples and the scarcity of carefully curated datasets, in creating reliable models for chest X-ray (CXR) diagnosis. A common approach is to leverage pre-trained deep learning models using large datasets and then fine-tune model parameters on the target CXR dataset. However, in the presence of data bias, it is prone to shortcut learning
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Minimizing the searching time of multiple targets in uncertain environments with multiple UAVs Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-06 Sara Pérez-Carabaza, Eva Besada-Portas, José A. López-Orozco
The focus of this paper is the use of Unmanned Aerial Vehicles (UAVs) for searching multiple targets under uncertain conditions in the minimal possible time. The problem, known as Minimum Time Search (MTS), belongs to the Probabilistic Search (PS) field and addresses critical missions, such as search & rescue, and military surveillance. These operations, characterized by complex and uncertain environments
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Association mining based deep learning approach for financial time-series forecasting Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-05 Tanya Srivastava, Ishita Mullick, Jatin Bedi
Stock market plays a vital role in a country’s economy, serving as a platform for companies to raise capital and enabling investors to share in their growth and success. The market is very unpredictable, characterized by non-linear variations and sudden fluctuations driven by a multitude of external factors. In the past, several traditional, deep learning, machine learning-based, and hybrid solutions
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Influence measure-based large-scale group decision making with linear uncertain preference relations Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-05 Kaixin Gong, Weimin Ma, Wenjing Lei, Mark Goh, Zitong Ren
This research focuses on the problem of large-scale group decision-making (LSGDM) based on influence measure under linear uncertain preferences. The value of this research is that it improves the performance of the current clustering algorithms, increases the efficiency of consensus reaching for major decision-making events, thus reducing the cost of feedback adjustments, and at the same time reflecting
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Diversity enforced Genetic Algorithm (GA) for Binary Decision Diagram (BDD) reordering Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-05 Baker Abdalhaq, Amjad Hawash, Ahmed Awad
Binary Decision Diagrams (BDDs) have become the state-of-art data structures in numerous fields, wherein, small-sized BDDs are required to reduce the companion cost. Since BDD size is sensitive to the order of variables for the Boolean function in use, evolutionary algorithms have been extensively exploited to solve the BDD reordering problem which is provably an NP-hard problem. However, getting trapped
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A data-driven implicit deep adaptive neuro-fuzzy inference system capable of manifold learning for function approximation Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-04 Armin Salimi-Badr
Fuzzy Neural Networks (FNN) have the ability of decision-making based on constructing semi-ellipsoidal clusters in the input space as the antecedent parts of their fuzzy rules. To determine the output value for each input instance, FNNs consider its membership degree to different sub-regions of the input space. However, forming such meaningful sub-regions is not possible in all applications due to
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An enhanced decision making model for industrial robotic selection using three factors: Positive, abstained, and negative grades of membership Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-04 Daekook Kang, Michael Sandra, Samayan Narayanamoorthy, Krishnan Suvitha, Dragan Pamucar, Vladimir Simic
Traditional packaging industries that lack automation often grapple with a spectrum of challenges that impede operational efficiency, productivity and overall competitiveness. To maintain quality and safety, the food industry must transition from manual to robotic packaging processes. The most suitable robot for executing such task is identified via a new hybrid fuzzy Stratified Multi-Attribute Decision-Making
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A three-way confirmatory approach to formal concept analysis in classification Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-04 Mengjun Hu, Zhen Wang
Formal concept analysis (FCA) has demonstrated its effectiveness in classification through various studies. A few types of FCA-based classifiers, such as rule-based, concept-cognitive-learning-based, and hypothesis-based models, have been introduced for different purposes and distinct contexts. Nevertheless, these diverse models share fundamental principles that underlie the construction of effective
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An effective diagnosis of schizophrenia using kernel ridge regression-based optimized RVFL classifier Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-04 S.A. Varaprasad, Tripti Goel, M. Tanveer, R. Murugan
Schizophrenia (SCZ) is a severe mental and debilitating neuropsychiatric disorder that disrupts a person’s thought processes, emotions, and behavior. Due to misdiagnosis, self-denial, and social stigma, many SCZ cases go untreated. Magnetic resonance imaging (MRI) is an excellent noninvasive tool for soft tissue contrast imaging because it provides crucial data on tissue structure size, position, and
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Online semi-supervised active learning ensemble classification for evolving imbalanced data streams Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-04 Yinan Guo, Jiayang Pu, Botao Jiao, Yanyan Peng, Dini Wang, Shengxiang Yang
Concept drift is a core challenge in classification tasks of data streams. Although many drift adaptation methods have been presented, most of them assume that labels of all data are available, which is impractical in many real-world applications. Additionally, the absence of label makes the imbalance ratio of an imbalanced data stream difficultly being obtained in time, providing the inaccurate guidance
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An artificial fish swarm optimization algorithm for the urban transit routing problem Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-04 Vasileios Kourepinis, Christina Iliopoulou, Ioannis Tassopoulos, Grigorios Beligiannis
The Urban Transit Routing Problem (UTRP) is an NP-hard discrete problem that deals with the design of routes for public transport systems. It is a highly complex, multiply constrained problem, while the evaluation of candidate route sets can prove both challenging and time-consuming, with many potential solutions rejected on the grounds of infeasibility. Due to its difficulty, metaheuristic methods
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Efficient traffic-based IoT device identification using a feature selection approach with Lévy flight-based sine chaotic sub-swarm binary honey badger algorithm Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-04 Boxiong Wang, Hui Kang, Geng Sun, Jiahui Li
Internet of Things (IoT) refers to the various devices connected to the Internet, enabling them to communicate and transmit data with each other. The rapid development of the IoT also brings security and other problems in cyberspace. In this case, device identification is a crucial tool for IoT security issues, which can detect and prevent cyber-attacks. However, device identification has some challenges
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A local rough set method for feature selection by variable precision composite measure Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-04 Kehua Yuan, Weihua Xu, Duoqian Miao
Feature selection using variable precision neighborhood rough sets (VPNRS) has garnered considerable attention in data mining and knowledge discovery. Nevertheless, the positive region of VPNRS may not be strictly divided due to the introduction of variable parameters, which could reduce the credibility of feature significance. Meanwhile, the calculation of approximate space is also complex and expensive
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Scalable fuzzy multivariate outliers identification towards big data applications Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-02 Huda Mohammed Touny, Ahmed Shawky Moussa, Ali S. Hadi
Data outliers is intrinsically a fuzzy concept and should be treated as such. This paper is a continuation of a research on fuzzy outliers. Extending the BACON algorithm, FBACON1 and FBACON2 have been proposed as fuzzy solutions to the crisp decision boundary of BACON. This paper investigates the scalability potentials and drawbacks of FBACON1 and FBACON2 in Big Data. The investigation concluded that
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A spatiotemporal convolution recurrent neural network for pixel-level peripapillary atrophy prediction using sequential fundus images Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-02 Mengxuan Li, Weihang Zhang, He Zhao, Yubin Xu, Jie Xu, Huiqi Li
The progression of peripapillary atrophy (PPA) is closely associated with the development of retinal diseases such as myopia and glaucoma. PPA prediction employing longitudinal images to obtain its progress trend can facilitate personalized treatment. Although existing studies have attempted to predict the persistence of PPA, such studies cannot provide quantitative measurement for personalized treatment
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Discriminative elastic-net broad learning systems for visual classification Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-01 Yanting Li, Junwei Jin, Yun Geng, Yang Xiao, Jing Liang, C.L. Philip Chen
The broad learning system (BLS) has garnered significant attention in the realm of visual classification due to its exceptional balance between accuracy and efficiency. However, the supervision mechanism in BLS typically relies on strict binary labels, limiting the approximation freedom and failing to represent the data distribution adequately. Furthermore, the inadequacy of the guidance mechanism
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Interaction matters: Encrypted traffic classification via status-based interactive behavior graph Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-01 Yao Li, Xingshu Chen, Wenyi Tang, Yi Zhu, Zhenhui Han, Yawei Yue
Accurately classifying encrypted traffic is the indispensable cornerstone for network management and Quality of Service (QoS) improvement. Although existing works that learn from non-interaction features of communication behavior have achieved a satisfactory performance, there still remains an unsolved crux before practical application that current works fail to distinguish different encrypted traffic
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Anomaly detection in time-series data using evolutionary neural architecture search with non-differentiable functions Appl. Soft Comput. (IF 8.7) Pub Date : 2024-03-01 Santiago Gomez-Rosero, Miriam A.M. Capretz
Deep neural networks have become the benchmark in diverse fields such as energy consumption forecasting, speech recognition, and anomaly detection, owing to their ability to efficiently process and analyze data. However, they face challenges in managing the complexity and variability in time series data, often leading to increased model complexity and prolonged search duration during parameter tuning
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Modified genetic algorithm and fine-tuned long short-term memory network for intrusion detection in the internet of things networks with edge capabilities Appl. Soft Comput. (IF 8.7) Pub Date : 2024-02-28 Yakub Kayode Saheed, Oluwadamilare Harazeem Abdulganiyu, Taha Ait Tchakoucht
The emergence of smart cities is an example of how new technologies, such as the Internet of Things (IoT), have facilitated the creation of extensive interconnected and intelligent ecosystems. The widespread deployment of IoT devices has enabled the provision of constant environmental feedback, thereby facilitating the automated adaptation of associated systems. This has brought about a fundamental
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Robust maximum expert consensus model with adjustment path under uncertain environment Appl. Soft Comput. (IF 8.7) Pub Date : 2024-02-28 Yifan Ma, Ying Ji, Chethana Wijekoon
The maximum expert consensus model (MECM) is a commonly used consensus model in group decision making (GDM). In traditional MECM, the consensus constraints are not fully considered and the adjustment cost of decision maker (DM) is certain. Moreover, directing the DM’s opinion in a visual path is seldom considered in the consensus reaching process (CRP) of MECM. Inspired by these issues, this paper
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Temporal cluster-based local deep learning or signal processing-temporal convolutional transformer for daily runoff prediction? Appl. Soft Comput. (IF 8.7) Pub Date : 2024-02-27 Vahid Moosavi, Sahar Mostafaei, Ronny Berndtsson
Water scarcity poses a major obstacle to sustainable development, and precise discharge prediction plays a vital role in enabling effective water resource management. This study investigated improved prediction techniques for nonstationary time series. The study evaluated the effect of signal processing techniques and blending approaches on the performance of deep learning models for daily discharge
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Low-cost architecture performance evaluation strategy based on pixel difference degree contrast measurement Appl. Soft Comput. (IF 8.7) Pub Date : 2024-02-27 Rui Zhang, Peng-Yun Zhang, Mei-Rong Gao, Jian-Zhe Ma, Li-Hu Pan
The time and effort required to manually design deep neural architectures is extremely high, which has led to the development of neural architecture search technology as an automatic architecture design method. However, the neural architecture search convergence process is slow and expensive, and the process requires training a large number of candidate architectures to get the final result. If the
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Data and measurement mechanism integrated imaging method for electrical capacitance tomography Appl. Soft Comput. (IF 8.7) Pub Date : 2024-02-27 Jing Lei, Qibin Liu
This study presents a new imaging paradigm for overcoming the challenges limiting the improvement of the imaging quality in the electrical capacitance technique by reshaping imaging paradigms. The new imaging model enables the integration of measurement physics, sparsity-induced prior and physics-informed multi-fidelity learning prior (PIMFLP), as well as the synergy between data-driven and measurement
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A two-objective-optimization-driven group decision making model under the bipolarity of decision information Appl. Soft Comput. (IF 8.7) Pub Date : 2024-02-24 Ziqian Luo, Fang Liu, Qirui You, Witold Pedrycz
When building consensus in group decision making (GDM) under uncertainty, an important yet rarely studied issue is to find the Pareto solutions of multi-objective optimization model. This paper reports a two-objective (2Ob) optimization driven consensus model in GDM by describing the bipolarity of judgements through intuitionistic multiplicative preference relations (IMPRs). First, it is realized that
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An efficient approach based on a novel 1D-LBP for the detection of bearing failures with a hybrid deep learning method Appl. Soft Comput. (IF 8.7) Pub Date : 2024-02-24 Yılmaz Kaya, Melih Kuncan, Eyyüp Akcan, Kaplan Kaplan
Bearings serve as fundamental components in the transmission of motion for rotating machinery. The occurrence of mechanical wear and subsequent bearing failures within these rotating systems can lead to diminished operational efficiency and, if left unaddressed, may result in the complete cessation of the system's function. Hence, there exists a critical need for effective monitoring methodologies
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Fuzzy-based predictive deep reinforcement learning for robust and constrained optimal control of industrial solar thermal plants Appl. Soft Comput. (IF 8.7) Pub Date : 2024-02-24 Fitsum Bekele Tilahun
Integrating distributed solar fields (DSFs) into conventional heat and power plants (CHPs) of industries is mostly constrained by the availability of a real-time capable control scheme. Safe and efficient operation of industrial DSF requires the supply of a fluctuating and periodically available energy at the required temperature while reducing losses and ensuring operational constraint. Reinforcement
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Convolution smoothing and non-convex regularization for support vector machine in high dimensions Appl. Soft Comput. (IF 8.7) Pub Date : 2024-02-24 Kangning Wang, Junning Yang, Kemal Polat, Adi Alhudhaif, Xiaofei Sun
The support vector machine (SVM) is a well-known statistical learning tool for binary classification. One serious drawback of SVM is that it can be adversely affected by redundant variables, and research has shown that variable selection is crucial and necessary for achieving good classification accuracy. Hence some SVM variable selection studies have been devoted, and they have an unified “empirical
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Unsupervised feature selection using chronological fitting with Shapley Additive explanation (SHAP) for industrial time-series anomaly detection Appl. Soft Comput. (IF 8.7) Pub Date : 2024-02-24 Qixuan Li, Yangjian Ji, Mingrui Zhu, Xiaoyang Zhu, Linjin Sun
With the development of the industrial Internet of Things (IIOT), an amount of industrial multivariate time series (IMTS) data has been collected by various sensors. IMTS data anomaly detection plays an important role in industrial process monitoring and operation condition identification. Many real-world IMTS datasets usually have a large number of redundant features, which may lead to deviation of
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Graph neural networks based framework to analyze social media platforms for malicious user detection Appl. Soft Comput. (IF 8.7) Pub Date : 2024-02-24 Zafran Khan, Zeeshan Khan, Byung-Geun Lee, Hong Kook Kim, Moongu Jeon
Online social media (OSM) has emerged as the most pertinent and readily available platform for individuals to effectively express their perspectives. Users connect seamlessly in an unstructured network, allowing information to flow within seconds. This interconnectedness, while enabling rapid information dissemination, also opens the door to significant challenges such as misinformation, disinformation
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An integrated quantum picture fuzzy rough sets with golden cuts for evaluating carbon footprint-based investment decision policies of sustainable industries Appl. Soft Comput. (IF 8.7) Pub Date : 2024-02-24 Gang Kou, Dragan Pamucar, Hasan Dinçer, Serhat Yüksel, Muhammet Deveci, Muhammad Umar
The purpose of this study is to make evaluation related to the significant determinants of the effectiveness of the carbon footprint-based investments while constructing a novel decision-making model. At the first stage, selected five determinants are evaluated with multi stepwise weight assessment ratio analysis (M-SWARA) methodology based on quantum picture fuzzy rough sets. In the second part, sustainable
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TSFIS-GWO: Metaheuristic-driven takagi-sugeno fuzzy system for adaptive real-time routing in WBANs Appl. Soft Comput. (IF 8.7) Pub Date : 2024-02-24 Saeideh Memarian, Navid Behmanesh-Fard, Pouya Aryai, Mohammad Shokouhifar, Seyedali Mirjalili, María del Carmen Romero-Ternero
Wireless body area network (WBAN) is an internet-of-things technology that facilitates remote patient monitoring and enables medical staff to administer timely treatments. One of the main challenges in designing WBANs is the routing problem, which is complicated due to dynamic changes in network topology and the limited resources of nodes. Several heuristic and metaheuristic methods have been presented
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A Swarm-Intelligence Based Formulation for Solving Nonlinear ODEs: γβII-(2+3)P method Appl. Soft Comput. (IF 8.7) Pub Date : 2024-02-23 Mehdi Babaei
The interconnectivity relationships between the weights of an important integration formula are discovered by swarm intelligence. These relationships make it possible to generate wide spectrum of -solving techniques in a continuous weighting space. So, against the conventional methods, new formulations would have variable weights which are tuned up by the weighting rules. Moreover, in an innovative
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A multi-objective Grey Wolf–Cuckoo Search algorithm applied to spatial truss design optimization Appl. Soft Comput. (IF 8.7) Pub Date : 2024-02-23 Nam Vo, Huy Tang, Jaehong Lee
A novel hybrid algorithm called Multi-Objective Hybrid Grey Wolf Cuckoo Search (MOGWOCS) is developed for spatial truss designs in this study. A new simple yet efficient mechanism to select the best candidates is proposed. Furthermore, harmonic averaging is employed to be a replacement for conventional arithmetic mean for higher effectiveness. Additionally, the Lévy flight in Cuckoo Search (CS) is
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A semi-supervised framework for computational fluid dynamics prediction Appl. Soft Comput. (IF 8.7) Pub Date : 2024-02-23 Xiao Wang, Yidao Dong, Shufan Zou, Laiping Zhang, Xiaogang Deng
Data-driven deep learning approach heavily relies on the diversity and quantity of data. Acquiring data in the computational fluid dynamics (CFD) domain is a time and computationally intensive process. This paper proposes a semi-supervised learning method called discriminative regression fitters (DRF) for aerodynamic prediction of airfoils. DRF utilizes neural networks’ memory property to dynamically
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Solving spatiotemporal partial differential equations with Physics-informed Graph Neural Network Appl. Soft Comput. (IF 8.7) Pub Date : 2024-02-23 Zixue Xiang, Wei Peng, Wen Yao, Xu Liu, Xiaoya Zhang
Physics-informed neural networks (PINNs) have recently gained considerable attention as a prominent deep learning technique for solving partial differential equations (PDEs). However, traditional fully connected PINNs often encounter slow convergence issues attributed to automatic differentiation in constructing loss functions. In addition, convolutional neural network (CNN)-based PINNs face challenges
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A combined interval prediction system based on fuzzy strategy and neural network for wind speed Appl. Soft Comput. (IF 8.7) Pub Date : 2024-02-23 Yunbo Niu, Jianzhou Wang, Ziyuan Zhang, Yannan Yu, Jingjiang Liu
Wind energy exhibits strong fluctuations and intermittencies. The accurate prediction of wind speed is of considerable significance for the operation and maintenance of wind farms and the safety of the power grid. However, previous studies have often ignored the impact of data noise on trend prediction, and lacked effective data pre-processing methods and adaptive interval prediction schemes, resulting