Intelligent Systems with Applications最新文献

{"title":"WOA-FCM-CNN-WNN-informer: An advanced hybrid deep learning model for ultra-accurate PV power forecasting in electric mobility","authors":"Lazhar Manai ,&nbsp;Walid Mchara ,&nbsp;Mohamed Abdellatif Khalfa ,&nbsp;Monia Raissi ,&nbsp;Wissem Dimassi","doi":"10.1016/j.iswa.2026.200630","DOIUrl":"10.1016/j.iswa.2026.200630","url":null,"abstract":"<div><div>Effective prediction of photovoltaic (PV) power generation is essential for enhancing energy management in solar-powered electric vehicles. This study introduces an innovative hybrid forecasting framework that combines Fuzzy C-Means (FCM) clustering, Convolutional Neural Networks (CNN), Wavelet Neural Networks (WNN), the Informer architecture, and the Whale Optimization Algorithm (WOA) to improve prediction accuracy. This approach introduces a condition-aware, end-to-end FCM-CNN-WNN-Informer pipeline tailored for PV dynamics, where: (i) <em>similar-day</em> fuzzy clustering normalizes weather heterogeneity <em>before</em> learning; (ii) wavelet-based multi-scale features are injected into a long-horizon Informer; (iii) a global, cross-module hyperparameter search via Whale Optimization Algorithm (WOA) jointly tunes all stages; (iv) a <em>Generalization Index (GI)</em> is proposed for robust model selection; and (v) Monte-Carlo dropout quantifies predictive uncertainty for practical deployment.</div><div>The proposed WOA-FCM-CNN-WNN-Informer model is evaluated on a comprehensive dataset of 70,080 hourly PV power recordings gathered over eight years in Tunisia. Results show superior performance compared to standard deep learning models like LSTM and BiLSTM. The framework reduces Mean Absolute Percentage Error (MAPE) by as much as 98.52% and Root Mean Squared Error (RMSE) by 93.84%, while maintaining a high coefficient of determination (<span><math><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>0</mn><mo>.</mo><mn>98</mn></mrow></math></span>) across varying meteorological conditions. These outcomes underscore the model’s robustness and its promise for advancing energy utilization, refining charging strategies, and supporting intelligent route planning in solar-electric transportation systems.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"29 ","pages":"Article 200630"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Musipainter: A music-conditioned generative architecture for artistic image synthesis","authors":"Alfredo Baione ,&nbsp;Giuseppe Rizzo ,&nbsp;Luca Barco ,&nbsp;Angelica Urbanelli ,&nbsp;Luigi Di Biasi ,&nbsp;Genoveffa Tortora","doi":"10.1016/j.iswa.2025.200611","DOIUrl":"10.1016/j.iswa.2025.200611","url":null,"abstract":"<div><div>Generative art is a challenging area of research in deep generative modeling. Exploring AI’s role in human–machine co-creative processes requires understanding machine learning’s potential in the arts. Building on this premise, this paper presents Musipainter, a cross-modal generative framework adapted to create artistic images that are historically and stylistically aligned with 30-second musical inputs, with a focus on creative and semantic coherence. To support this goal, we introduce Museart, a dataset designed explicitly for this research, and GIILS, a creativity-oriented metric that enables us to assess both artistic-semantic consistency and diversity in the generated outputs. The results indicate that Musipainter, supported by the Museart dataset and the exploratory GIILS metric, can offer a foundation for further research on AI’s role in artistic generation, while also highlighting the need for systematic validation and future refinements.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"29 ","pages":"Article 200611"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Video anomaly detection for edge-based IoT systems: A survey of input modalities and real-time applications","authors":"Hoangcong Le,&nbsp;Cheng-Kai Lu,&nbsp;Chen-Chien Hsu","doi":"10.1016/j.iswa.2026.200635","DOIUrl":"10.1016/j.iswa.2026.200635","url":null,"abstract":"<div><div>With the vast amount of video data generated daily, researchers have become increasingly interested in extracting meaningful information, particularly for analyzing abnormal events. This growing interest has accelerated progress in video anomaly detection (VAD) as a specialized subfield of computer vision, attracting considerable attention due to its potential applications in real-time scenarios such as elderly care, smart homes, and intelligent surveillance. To provide a comprehensive understanding of this rapidly evolving field, several systematic reviews have been conducted to help new researchers enter the field and assist experienced groups in keeping pace with recent advancements. However, existing surveys lack a focused analysis of how different input data modalities impact the performance of VAD systems, particularly from a privacy-preserving perspective. Understanding the effectiveness of various data modalities and data collection strategies is essential for protecting personal information in computer vision applications. Furthermore, the feasibility of deploying VAD models in real-time Internet of Things (IoT) environments remains underexplored, where low latency, limited resources, and strict privacy requirements are critical considerations. Although edge computing has been increasingly adopted to address these challenges, most studies overlook the deployment of VAD frameworks on resource-constrained devices. Integrating edge-based VAD systems with federated learning algorithms represents a promising direction for enabling privacy-aware and scalable real-world systems. Rather than providing a method-centric summary, this survey reorganizes the VAD literature from a deployment-oriented viewpoint, highlighting how input modality choices fundamentally affect privacy preservation and real-time feasibility on edge-based IoT systems. This work specifically reviews studies published between 2020 and 2025.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"29 ","pages":"Article 200635"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review of vision transformer and explainable AI advances in multimodal facial expression recognition","authors":"Ilya Kus ,&nbsp;Cemal Kocak ,&nbsp;Ayse Keles","doi":"10.1016/j.iswa.2025.200615","DOIUrl":"10.1016/j.iswa.2025.200615","url":null,"abstract":"<div><div>Facial expression is one of the most important indicators used to convey human emotions. Facial expression recognition is the process of automatically detecting and classifying these expressions by computer systems. Multimodal facial expression recognition aims to perform a more accurate and comprehensive emotion analysis by combining facial expressions with different modalities such as image, speech, Electroencephalogram (EEG), or text. This study systematically reviews research conducted between 2021 and 2025 on the Vision Transformer (ViT) based approaches and Explainable Artificial Intelligence (XAI) techniques in multimodal facial expression recognition, as well as the datasets employed in these studies. The findings indicate that ViT-based models outperform conventional Convolutional Neural Networks (CNNs) by effectively capturing long-range dependencies between spatially distant facial regions, thereby enhancing emotion classification accuracy. However, significant challenges remain, including data privacy risks arising from the collection of multimodal biometric information, data imbalance and inter-modality incompatibility, high computational costs hindering real-time applications, and limited progress in model explainability. Overall, this study highlights that integrating advanced ViT architectures with robust XAI and privacy-preserving techniques can enhance the reliability, transparency, and ethical deployment of multimodal facial expression recognition systems.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"29 ","pages":"Article 200615"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing decision-making: A comprehensive review of intelligent systems, applications, and challenges","authors":"Hussein A.A. Al-Khamees ,&nbsp;Ahmad AL Smadi ,&nbsp;Mutasem K. Alsmadi ,&nbsp;Abdulrahman A. Alkannad ,&nbsp;Ahed Abugabah ,&nbsp;Latifa Abdullah Almusfar ,&nbsp;Bashair Althani","doi":"10.1016/j.iswa.2026.200631","DOIUrl":"10.1016/j.iswa.2026.200631","url":null,"abstract":"<div><div>The rapid evolution of intelligent systems, powered by artificial intelligence and machine learning, has created a fragmented research landscape. While numerous studies exist on specific applications, a holistic synthesis of their architectures, taxonomies, applications, and challenges is absent. This paper will bridge this gap by providing a comprehensive systematic review that integrates these disparate elements. This paper conducts a systematic review of over 100 peer-reviewed scientific publications, following a structured process to identify, analyze, and synthesize the current state of intelligent systems research. The review encompasses a wide range of domains, including healthcare, cybersecurity, data mining, and industrial automation. Our analysis yields a unified taxonomy and clarifies the core architectural components of intelligent systems. We identify and categorize key application domains and demonstrate their transformative impact. The review also synthesizes prevailing challenges, such as data quality, scalability, and ethical concerns, and pinpoints emerging trends, including the rise of multimodal AI and hybrid intelligent systems. To the best of our knowledge, this is the first review to offer a consolidated framework that integrates the architecture, taxonomy, applications, and cross-domain challenges of intelligent systems into a single reference. This work serves as a foundational guide for researchers and practitioners, facilitating future advancements in the development of efficient, scalable, and context-aware intelligent systems.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"29 ","pages":"Article 200631"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blind steganalysis-driven secure transmission validation using feature-based classification in JPEG images","authors":"Deepa D. Shankar ,&nbsp;Adresya Suresh Azhakath","doi":"10.1016/j.iswa.2025.200623","DOIUrl":"10.1016/j.iswa.2025.200623","url":null,"abstract":"<div><div>Information technology and digital media have significantly improved in recent years, facilitating the internet as an effective channel for communication and data transmission. Nevertheless, the rapid advancement of technology has rendered data a source of mismanagement and vulnerable to exploitation. Consequently, technologies such as data concealment were devised to mitigate exploitation. Steganalysis is a technique for data concealment. Various processes, including breaches of information security, can be mitigated by steganalysis. This work aims to encapsulate the notion of blind statistical steganalysis within image processing methodologies and ascertain the accuracy percentage of secure transmission. This work discusses the extraction of features that indicate a change during embedding. A specific percentage of text is integrated into a JPEG image of a predetermined size. The text embedding utilizes various steganographic techniques in both the spatial and transform domains. The steganographic techniques include LSB Matching, LSB Replacement, Pixel Value Differencing, and F5. Due to the blind nature of steganalysis, there are no cover images available for comparative analysis. An estimation of the cover image is obtained by a calibration concept. After embedding, the images are partitioned into 8 × 8 blocks, from which certain features are extraction for classification. This paper utilizes interblock dependent features and intrablock dependent features. Both dependencies are regarded as means to mitigate the shortcomings of each individually. The approach of machine learning is employed using a classifier to distinguish between the stego image and the cover image. This research does a comparative investigation of the classifiers SVM and SVM-PSO. Comparative research is frequently performed both with and without use cross-validation methodology. The study incorporates the concept of cross-validation for comparative analysis. There are six unique kernel functions and four sample methods for grouping. The embedding ratio employed in this investigation is 50%.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"29 ","pages":"Article 200623"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Authenticating devices based on audio feature selection with scene-specific tuning and landmark augmentation","authors":"Andi Bahtiar Semma ,&nbsp;Kusrini ,&nbsp;Arief Setyanto ,&nbsp;Bruno da Silva ,&nbsp;An Braeken","doi":"10.1016/j.iswa.2025.200593","DOIUrl":"10.1016/j.iswa.2025.200593","url":null,"abstract":"<div><div>Authentication methods have evolved significantly, transitioning from traditional passwords to biometric and multi-factor techniques, with audio-based systems now emerging as a promising frontier. These systems leverage ambient sounds or device-generated noise for continuous authentication but encounter challenges such as environmental noise, spoofing risks, and standardized audio feature selection. This study tackles these issues by focusing on robust handling of environmental variations and interference and optimizing audio feature selection for effective environmental audio authentication. A key innovation introduced is the concept of “audio landmarks,” randomly generated signals embedded into audio samples. These landmarks enhance device authentication by enriching feature representation and reducing sensitivity to noise, leading to significant improvements in precision, recall, and F1 scores across various scenarios. In some cases, features achieved a perfect F1 score 1.00 under ideal conditions. Among audio features analyzed, the Constant-Q Transform (CQT) excels, particularly in music or speech scenes. However, combining multiple features often introduces redundancy due to overlapping information and varying optimal thresholds, which may not constantly improve performance. Additionally, spectral centroids and spectral contrast, which are computationally lightweight at 9 ms and 10 ms, respectively, deliver excellent performance, making them ideal for real-time or resource-constrained applications, as tested on the Raspberry Pi 4. These findings provide practical guidelines for audio-based device authentication by leveraging cryptographic hashing for deterministic landmark generation and the balanced fusion of landmark and acoustic features. This enables robust authentication even in challenging scenarios where environmental sounds are insufficient.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200593"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond algorithms: Artificial intelligence driven talent identification with human insight","authors":"Tiago Jacob Fernandes França ,&nbsp;José Henrique Pereira São Mamede ,&nbsp;João Manuel Pereira Barroso ,&nbsp;Vítor Manuel Pereira Duarte dos Santos","doi":"10.1016/j.iswa.2025.200604","DOIUrl":"10.1016/j.iswa.2025.200604","url":null,"abstract":"<div><div>The rapid evolution of Artificial Intelligence (AI) is reshaping Human Resource Management (HRM), with growing interest in its role in talent identification. While AI has demonstrated effectiveness in analysing structured data, its limitations in assessing qualitative attributes such as creativity, adaptability, and emotional intelligence remain underexplored. This study addresses these gaps through an exploratory mixed-methods design, combining a global survey (<em>n</em> = 240) with semi-structured interviews of HR professionals. Quantitative analysis highlights patterns of association between key competencies, while qualitative findings provide contextual insights into perceptions of fairness, bias, and cultural resistance. The results suggest that AI can complement, but not replace, human judgement, supporting a Hybrid Evaluative Model that integrates algorithmic efficiency with human interpretation. The study contributes rare empirical evidence to a nascent field, highlights the ethical imperatives of bias mitigation and transparency, and underscores the importance of cultural context (collectivist versus individualist orientations) in shaping the acceptance and effectiveness of AI-enabled HR practices. These findings offer practical guidance for organisations and advance theory-building at the intersection of AI and HRM.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200604"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Liver cirrhosis prediction: The employment of the machine learning-based approaches","authors":"Genjuan Ma,&nbsp;Yan Li","doi":"10.1016/j.iswa.2025.200573","DOIUrl":"10.1016/j.iswa.2025.200573","url":null,"abstract":"<div><div>Early detection of liver cirrhosis remains problematic due to its asymptomatic onset and the inherent class imbalance in clinical data. This study conducts a comprehensive evaluation of machine learning models for predicting cirrhosis stages, with a focus on addressing these challenges. An approach employing Quadratic Discriminant Analysis (QDA) is benchmarked against seven other models, including powerful ensembles like Stacking and HistGradientBoosting, on a clinical dataset. Methodologies such as SMOTE oversampling, stratified data splitting, and class-specific covariance estimation were implemented to manage data complexity. The results demonstrate that a Stacking ensemble achieves the highest overall predictive performance with a micro-AUC of 0.80. The proposed QDA method also proves to be a highly effective and competitive model, achieving a robust AUC of 0.76 and outperforming several specialized imbalance-learning algorithms. Crucially, QDA offers this strong performance with exceptional computational efficiency. These findings show that while complex ensembles can yield top-tier accuracy, QDA’s capacity to model non-linear feature associations makes it a powerful and practical choice for the diagnosis of cirrhosis.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200573"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A corporate credit evaluation method considering strong feature privacy with non-private label: A vertical heterogeneous feature fusion approach","authors":"Xifeng Ning ,&nbsp;Chao Yang ,&nbsp;Hailu Sun ,&nbsp;Xinyuan Song ,&nbsp;Zifan Hu ,&nbsp;Yu Feng ,&nbsp;Jiawei Li ,&nbsp;Yifan Zhu","doi":"10.1016/j.iswa.2025.200603","DOIUrl":"10.1016/j.iswa.2025.200603","url":null,"abstract":"<div><div>In modern monitoring and operational management, whether in industrial systems, financial risk control, or infrastructure maintenance, decision-making increasingly relies on integrating heterogeneous data from multiple sources. However, due to data privacy regulations, distributed storage, communication constraints, and sensor failures, it is often difficult to centralize modeling when dealing with high-dimensional, incomplete datasets held by different institutions. Federated learning offers a privacy-preserving joint modeling solution, yet still faces challenges such as high communication overhead, low robustness to participant dropout, and risks of gradient leakage. In certain incomplete-data scenarios, not all data is private—labels such as equipment inspection results, fault reports, or corporate blacklists and whitelists published by authoritative bodies may be public—while feature data remains private and partially missing. To address this, we propose an innovative collaborative modeling framework tailored for incomplete-data monitoring and operations, in which each participant independently trains a model on its private features and exchanges only prediction results rather than gradients. Inspired by collective expert scoring, each “expert” evaluates based on its own data, then shares scores that are integrated into a comprehensive assessment. This approach offers multiple advantages: independent model training for each party, improved efficiency by migrating only prediction results, enhanced security by avoiding gradient transmission, and higher robustness since the failure of one participant does not halt others’ training. We present three variants of this prediction-result fusion method and evaluate them on representative datasets, including enterprise credit risk assessment as a case study, comparing against vertical federated logistic regression. Experimental results validate the effectiveness of the proposed approach, which can be widely applied to diverse monitoring and operational scenarios under incomplete data conditions.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200603"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}