Applied Soft Computing

Solar photovoltaic (SPV) energy exhibits a potential role in the world for generating electricity and avoiding the block out in the power system. However, the hardest task in the PV system is to track the global maxima power (GPP) instead of ...

• Configuration of 4 ×1 PV system is designed & investigated under PSC.
• A new CSA algorithm is proposed to attain maximum power under PSC's.
• The effectiveness of the proposed CSA is compared to SSA, MFO, IGWO, CS, and PSO.
• The ...

Over the past few years, the E-commerce industry has grown tremendously for selling products to consumers. Here, the consumer can easily purchase the products from their residing seats and gets the products at the door step. Also, the image ...

• To implement the enhanced classification model with incremental learning in fashion E-commerce sector.
• To develop improved YoloV3 with Hyper-parameter Optimization (II-Yolov3-HO) and used training and testing phase.
• To design ...

Although vibration-based fault diagnosis methods have achieved remarkable results in rotating machinery, they are still limited by various factors, such as environment noise and additional sensor installation. Meanwhile, due to inevitable ...

• An enhanced domain mapping method which can reduce the effect of domain shift is proposed.
• An enhanced deep fuzzy echo state network based on wise-layer fuzzy tuning paradigm is proposed.
• Current signals can be collected without ...

As a result of the ever increasing complexity of configuring and fine-tuning machine learning models, the field of automated machine learning (AutoML) has emerged over the past decade. However, software implementations like Auto-WEKA and Auto-...

• Multi-target prediction (MTP) in an umbrella term that covers many different sub-areas.
• DeepMTP was already proposed as a single approach that is compatible with the most MTP problem settings to date.
• DeepMTP already automates many ...

The use of multiple fixed-wing unmanned aerial vehicles in search and rescue missions after natural disasters has become of great interest as they can search large areas and find survivors as quickly as possible. This paper discusses a minimum ...

• A minimum time search algorithm was developed to use multiple fixed-wing UAVs for search and rescue missions.
• By applying the Dubins curve, the kinematic limits of fixed-wing UAVs were considered in an optimal search approach.
• A ...

In graph theory, a set consisting of vertices of a graph that are incident to at least one of the edges is called a vertex covering set for that fuzzy graph. Facility location problems are represented as fuzzy graphs, and a model is designed with ...

• Introducing coverage impact, time, speed, cover-break cost of a vertex covering set.
• Reliability analysis to use covering concept of fuzzy graphs with PSO algorithm.
• Developing programming problems (objectives and restrictions) ...

In the Metaverse, 3D modeling techniques and autoencoders offer a novel approach for handling 2D portraits of Non-Fungible Tokens (NFTs). These techniques have significant applications in the metaverse, a virtual, shared, and persistently online ...

• We proposed an autoencoder method for 3D reconstruction of 2D NFT portraits in the metaverse, inferring 3D structure and texture.
• We designed techniques for consistent 3D NFT portraits under various lighting conditions, enhancing their ...

This work addresses the problem of scheduling multiple hoists (robots), which arises on real automated electroplating lines. Several hoists operate on a shared track and should not collide with each other. The processing time for each operation ...

• A Hoist Scheduling Problem with multiple hoists is formulated and successfully solved.
• To solve the problem with uncertain data, the fuzzy numbers are used.
• The fuzzy approach optimizes the line performance and operation quality.

In this paper, a single-objective redundancy allocation problem (RAP) is considered for a series system with k-out-of-n subsystems. In each subsystem, the components are binary, homogeneous, and repairable. Component failure and repair processes ...

• The Redundancy Allocation Problem (RAP) in a series system was investigated.
• Binary, homogeneous, and repairable components were considered.
• An analysis of the impact of cold, warm, and hot standby on system availability was ...

Dynamic multiobjective optimization problems (DMOPs) typically involve multiple conflicting time-varying objectives that require optimization algorithms to quickly track the changing Pareto-optimal front (POF). To this end, several methods have ...

• Dynamic multiobjective optimization evolutionary algorithm is designed.
• The multi-directional difference model is applied to predict the initial population.
• POS estimation strategy improves the quality of historically obtained ...

In this study, the overall aerodynamic performance of the airfoils with variable amplitude geometric structure for lift-type vertical axis wind turbine has been analyzed and optimized using Fuzzy Analytic Hierarchy Process (AHP)-Fuzzy COmbinative ...

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• Overall performance of airfoils was determined by using Taguchi, Fuzzy AHP-CODAS.
• The hybrid fuzzy decision making based Taguchi approach was proposed for the first time.
• The importance weights of the responses were obtained by ...

Feature selection is a crucial data preprocessing technique extensively employed in machine learning and image processing. However, feature selection encounters significant challenges when addressing high-dimensional data due to the huge and ...

• HMOFA is proposed to solve high-dimensional feature selection tasks.
• A clustering initialization method is introduced to reduce redundant features and improve the quality of initial population.
• The population updates its position ...

Crop disease recognition plays a crucial role in agricultural production. However, disease images are large in scale and have a lot of redundant information, which reduces the effectiveness of deep neural networks in extracting diseases. To ...

• Coordinate attention mechanism was used to design a lightweight CA-AnchorNet.
• A lightweight CA-AnchorNet and PatchNet constitute a two-stage framework.
• A patch localization algorithm was designed through a class activation map.

Pronunciation feedback is essential for teaching languages to children, urging the need to create computer-assisted pronunciation training (CAPT) systems to automate this process. Most of the current CAPT systems for Arabic Language examined a ...

• Proposed AUMD-Child system as first Arabic CAPT to detect children mispronunciation.
• Exclusively utilize Vision Transformer in Arabic CAPT, fused with transfer learning.
• Conducted comparative experimental evaluation with state-of-...

Recently, the applications and artificial intelligences used for image manipulation have become quite successful. In this case, the manipulation of personal data can lead to problems of insurmountable magnitude. Such problems not only put ...

• IFDwT is a model developed to detect and localize manipulations made on images.
• The model is based on the state-of-the-art Visual Transformer architecture.
• The model can make predictions at all image sizes regardless of image size.

Ensemble models have emerged as a powerful technique for improving robustness in medical image segmentation. However, traditional ensembles suffer from limitations such as under-confidence and over-reliance on poor performing models. In this work,...

• Adaptive uncertainty-based ensemble model (AUE) proposed for tumor segmentation.
• AUE outperformed traditional ensemble models by a significant margin.
• Utilizing uncertainty estimates enhances segmentation performance.

This study proposes a novel approach combining Machine Learning (ML) techniques and Genetic Algorithms (GA) for predicting High-Performance Computing (HPC) job run times. The objective is to create a prediction method universally applicable to ...

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• Proposed an innovative method for predicting HPC job run times combining Machine Learning (ML) and Genetic Algorithms (GA).
• Replaced run time estimations by users with runtime classes defined by GA, enabling users to select from ...

Machine failures often arise from cumulative aging and anomalies, with early-warning indicators hidden in sensor-collected data. When high-quality labeled data are scarce for determining machine conditions, such failures can lead to performance ...

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• The unlabeled battery dataset is processed using MLP and CNN techniques.
• A novel neural network architecture is specifically engineered for fault diagnosis.
• Empirical validation robustly confirms the efficacy of the proposed ...

Fetal phonocardiography (FPCG) is a non-invasive Fetal Heart Rate (FHR) monitoring technique that can detect vibrations and murmurs in heart sounds. However, acquiring fetal heart sounds from a wearable FPCG device is challenging due to noise and ...

• Chebyshev filter and EC2EMDAN-PS-MODWT reduce low and high frequency noises.
• MA-DRL and optimization algorithms reduce complexity during classification.
• Machine learning spectrogram conversion to capture time, frequency, and ...

Recently, a class of second-order neurodynamic approaches with convergence rates of O ( 1 t ) or O ( 1 t 2 ) has been developed to address the sparse signal reconstruction problem. In this paper, we propose a second-order projection neurodynamic ...

• Second-order projection neurodynamic approach with exponential convergence.
• Excellent convergence performance for sparse signal reconstruction.
• Applied to real signal and real image reconstruction.

A multiobjective optimization problem (MOP) returns a set of non-dominated points, the so-called Pareto front. Since this set is usually infinite, it is impossible to generate it completely in practice. Therefore, a discrete approximation of the ...

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• Two modified Pascoletti-Serafini scalarization approach are proposed.
• Six well-known test problems with convex and non-convex Pareto fronts are applied to show effectiveness of the algorithms.
• The presented algorithms have ...

The complexity and variability of high-resolution remote sensing data, such as high intra-class variability and inter-class similarity, pose significant challenges to model segmentation. To address the problem, this paper constructs a lightweight ...

• We present a lightweight self-supervised learning segmentation model for variable and complex high-resolution remote sensing images. It progressively captures contextual features across different scales and integrates local and global ...

Existing portfolio optimization models cannot well capture the real position allocation requirement, leading to limited impact in practice. To overcome this challenge, we propose a three-level nested portfolio optimization model with position ...

• We simultaneously consider stock selection and position allocation.
• We propose a three-level nested nonlinear portfolio optimization model.
• We give a few investment suggestions from new findings in the A-share market.

Machine learning algorithms have been widely applied and researched in the field of medical image classification. Most of the current algorithms are designed based on the assumption of data balance. However, in practical applications, medical ...

• We propose a solution to address the problem of intra-class mode collapse in GAN.
• We take into account the impact of boundary samples on classification performance.
• We propose a generate sample evaluation and filtering scheme based ...

Classic high-accuracy semantic segmentation models typically come with a large number of parameters, making them unsuitable for deployment on driverless platforms with limited computational power. To strike a balance between accuracy and limited ...

• We propose a new lightweight network (MSA-Net) for semantic segmentation from urban scenes.
• MSA-Net is the first to combine UNet and ShuffleNetv2, creating a deeper and lighter encoder–decoder architecture.
• MSA-Net designs enhanced ...

Feature representation plays a pivotal role in the decoding of motor imagery electroencephalograph (MI-EEG) signals. Conventional spatial representations are often hindered by the operational time-frequency and noise interferences of MI-EEG. In ...

• The selected regularized CSP representations based on supervised relevance and redundancy is applied for MI-EEG signals.
• A sparse and collaborative alternating cascade model is utilized to classify selected spatial representations.

Android OS is a widely-used platform for mobile devices. However, with the increasing number of Android applications and ongoing advancements in application development, there is a need for flexible and scalable malware detection methods that can ...

• Generating two label-homogeneous application networks.
• Utilizing static analysis and bipartite projection to create application network.
• Analyzing application networks using centrality measures.
• Optimizing networks by weighting ...

Many real-world engineering problems, characterized by high-dimensionality, nonlinearity, nonconvexity, and multi-modality, demand advanced optimization methods. Traditional algorithms may struggle with these challenges. Prairie dog optimization (...

• An improved optimization approach is developed for identifying PSS parameters.
• A refraction-based learning strategy is employed to diversify solutions‎.
• A neighborhood-based spiral mutation strategy mitigates stagnation issue.‎

Market intelligence, which collects and analyzes market trends and competitive landscape, is crucial for business success in the market. In particular, defining market scope is important because the market analysis outcomes vary depending on ...

• Siamese BERT-Networks (Sentence-RoBERTa) are adopted for market information analysis.
• Sentence-RoBERTa is fine-tuned with 26,248,771 data on product relationships.
• Market intelligence is retrieved using the proposed model.
• A ...