Iman Soltani^1,*, Farzin Fardinfar², Farhad Shahnia^3,*

Energy Engineering, Vol.123, No.10, 2026, DOI:10.32604/ee.2026.081627 - 30 August 2026

Abstract This paper presents a probabilistic optimization framework for enhancing the photovoltaic hosting capacity of distribution networks through the coordinated operation of smart photovoltaic-static synchronous compensator (PV-STATCOM) inverters. High penetration of photovoltaic systems introduces significant operational challenges, including voltage rise, increased power losses, and reduced system reliability, particularly under uncertain load demand and intermittent solar generation. To address these challenges, a multi-objective optimization problem is formulated to simultaneously maximize the photovoltaic hosting capacity while minimizing voltage deviation and power losses, subject to network operational constraints. The proposed approach employs the Fire Hawk optimizer, a recently developed… More > Graphic Abstract

Bai Xiao^1,*, Jingjun Bu¹, Binbin Du², Yulin Ge², Jian Gao²

Energy Engineering, Vol.123, No.10, 2026, DOI:10.32604/ee.2026.081408 - 30 August 2026

Abstract In order to solve the problem of the mismatch between the supply of electric vehicle charging stations and charging demand of electric vehicle charging stations caused by the rapid growth of electric vehicles, and the difficulty in determining the optimal site and capacity of electric vehicle charging stations, an optimisation method for the siting and sizing of electric vehicle charging stations considering the accessibility of user charging was proposed. Firstly, the road network topology structure is established in the GIS environment, and the shortest time path of the user is planned by establishing the road… More >

Attilio Barretta^1,2,*, Angelo Mottaran¹, Nicolas Carl², Francesco Prata^2,3, Sara Tamburini^1,2, Edoardo Beatrici², Mario De Angelis^2,4, Francesco Cei^2,4, Natali Rodriguez Peñaranda², Francesco Pepillo², Alessio Guidotti², Vincenzo Cavarra², Claudio Brancelli², Pietro Pasquini², Pietro Piazza¹, Cristian Vincenzo Pultrone¹, Hussam Dababneh¹, Lorenzo Bianchi¹, Alessandro Larcher⁴, Alexandre Mottrie², Rocco Papalia³, Riccardo Schiavina¹

Canadian Journal of Urology, Vol.33, No.4, pp. 783-793, 2026, DOI:10.32604/cju.2026.078354 - 21 August 2026

Abstract Objectives: Complex cystic renal lesions pose a significant diagnostic challenge in the preoperative assessment of malignancy. Although percutaneous renal mass biopsy is well established for solid tumours diagnosis, its role in cystic lesions remains controversial. This systematic review aims to evaluate the diagnostic performance, safety, and clinical impact of percutaneous sampling—fine-needle aspiration (FNA) and core needle biopsy (CNB)—in Bosniak III–IV renal cysts. Methods: A systematic review and meta-analysis were conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [International Prospective Register of Systematic Reviews (PROSPERO) ID CRD420251124563]. PubMed/MEDLINE (Medical Literature… More >

Ren-Jieh Kuo^1,*, Muhammad Rizki¹, Ferani Eva Zulvia², Eddy Roflin³

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084436 - 13 August 2026

Abstract Imbalanced data remain a critical challenge in classification, as skewed distributions bias models toward majority classes and diminish sensitivity to minority classes, which are often the most critical. To address this issue, this paper proposes the Information Filtered Hybrid Algorithm (IF-HA), a novel entropy-based sampling method that integrates undersampling and oversampling guided by information theory. IF-HA quantifies instance importance through an instance-wise difference statistic. In the undersampling stage, majority of instances with low difference statistics in the border area are eliminated, while in the oversampling stage, synthetic samples are generated from two minority core points… More >

Zhengshun Fei^1,*, Qiao Sun¹, Chuang Yang², Siranee Nuchitprasitchai³, Yongping Zheng¹, Xinjian Xiang^1,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.078696 - 23 July 2026

Abstract Path planning is a critical component for enabling autonomous navigation in mobile robots. Sampling-based planners are widely adopted due to their strong generality, yet they rely heavily on uniform sampling, which often leads to unstable performance and high computational cost in complex environments. To address this issue, recent studies feed free-space point clouds into neural networks to infer a set of guidance states near the optimal path, thereby enabling non-uniform sampling; however, the accuracy of the guidance set becomes a key bottleneck for further improvement. In this paper, we propose an improved point-cloud neural RRT*… More >

Naimul Hasan Shadesh^*, Zannatul Ferdous, Bipasha Iasmin

Journal on Artificial Intelligence, Vol.8, pp. 335-357, 2026, DOI:10.32604/jai.2026.078589 - 22 July 2026

Abstract Sentiment analysis is a core task in Natural Language Processing (NLP) that aims to identify opinions and sentiment polarity expressed in textual data. This study presents a systematic empirical evaluation of classical machine learning–based sentiment analysis methods using a unified experimental framework. Several supervised classifiers, including Decision Trees, Logistic Regression, Support Vector Machines (SVM), Random Forests, Naïve Bayes, and K-Nearest Neighbors (KNN), are evaluated on labeled text datasets collected from multiple domains such as product reviews, customer feedback, hotel reviews, and social media content. The experimental pipeline includes standard NLP preprocessing steps—text normalization, tokenization, stopword More >

Rubina Castro^1,2, Bruno Silva^1,3, Luiz Guerreiro Lopes^1,4, Fábio Mendonça^1,2,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.079979 - 15 June 2026

Abstract Path planning for autonomous underwater vehicles requires reliable and computationally efficient methods, particularly in cluttered environments. This work presents a comparative evaluation of representative approaches, including metaheuristic optimization methods (continuous genetic algorithm, particle swarm optimization, gray wolf optimizer, and Jaya), a sampling-based method (probabilistic roadmap with genetic refinement), a reactive strategy (artificial potential fields), and a control-based approach (model predictive control with control barrier functions). The algorithms are assessed in a controlled two-dimensional simulated workspace with randomly generated obstacles and systematically increasing obstacle density. Each configuration is evaluated across multiple independent trials using metrics such… More >

Abdullahi Abubakar Imam^1,*, Sahalu Balarabe Junaidu², Hussaini Mamman³, Ganesh Kumar³, Abdullateef Oluwagbemiga Balogun³, Sunder Ali Khowaja⁴, Shuib Basri³, Luiz Fernando Capretz⁵, Asmah Husaini⁶, Hanif Abdul Rahman⁶, Usman Ali¹, Fatoumatta Conteh¹

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.076187 - 30 March 2026

Abstract Artificial Intelligence (AI) in healthcare enables predicting diabetes using data-driven methods instead of the traditional ways of screening the disease, which include hemoglobin A1c (HbA1c), oral glucose tolerance test (OGTT), and fasting plasma glucose (FPG) screening techniques, which are invasive and limited in scale. Machine learning (ML) and deep neural network (DNN) models that use large datasets to learn the complex, nonlinear feature interactions, but the conventional ML algorithms are data sensitive and often show unstable predictive accuracy. Conversely, DNN models are more robust, though the ability to reach a high accuracy rate consistently on… More >

Long Li, Hengyang Wu^*, Na Wang

Journal on Artificial Intelligence, Vol.8, pp. 169-182, 2026, DOI:10.32604/jai.2026.077462 - 23 March 2026

Abstract Zero-shot learning as an emerging approach in image captioning techniques, has garnered significant attention from researchers in recent years due to its ability to accomplish tasks without requiring specific category training data. Existing zero-shot image captioning schemes largely rely on traditional language models, which exhibit low efficiency and suboptimal generation quality. To address this issue, this study proposes Hamiltonian Monte Carlo for Image Captioning (HMCIC). This method first models the image captioning task as a probabilistic sampling problem in parameter space, integrating semantic matching and syntactic coherence into an energy function to guide the generation… More >

Seunggyu Byeon¹, Jung-hun Lee², Jong-Deok Kim^3,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.074033 - 12 March 2026

Abstract This paper introduces a fuzzy C-means-based pooling layer for convolutional neural networks that explicitly models local uncertainty and ambiguity. Conventional pooling operations, such as max and average, apply rigid aggregation and often discard fine-grained boundary information. In contrast, our method computes soft memberships within each receptive field and aggregates cluster-wise responses through membership-weighted pooling, thereby preserving informative structure while reducing dimensionality. Being differentiable, the proposed layer operates as standard two-dimensional pooling. We evaluate our approach across various CNN backbones and open datasets, including CIFAR-10/100, STL-10, LFW, and ImageNette, and further probe small training set restrictions More >