Computers & Electrical Engineering最新文献_第4页

Optimized deep learning with Grad-CAM for automated cardamom classification: A multispectral imaging approach for real-time mobile deployment 优化深度学习与Grad-CAM自动豆蔻分类：多光谱成像方法的实时移动部署

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2026-04-01 Epub Date: 2026-02-05 DOI: 10.1016/j.compeleceng.2026.110992

Reny Jose, K. Balasubramanian

{"title":"Optimized deep learning with Grad-CAM for automated cardamom classification: A multispectral imaging approach for real-time mobile deployment","authors":"Reny Jose, K. Balasubramanian","doi":"10.1016/j.compeleceng.2026.110992","DOIUrl":"10.1016/j.compeleceng.2026.110992","url":null,"abstract":"<div><div>Classification of cardamom pods remains a challenging task due to changes in lighting, inconsistencies in texture, and spectral features that overlap with existing methods, which lack the attributes of robustness, interpretability, and real-time usability. This research addresses the aforementioned issues by introducing an advanced dual-stream deep learning model, Di-stream improved Divine Religions Planarian ConvNeXt (DDRPCNeXt), which combines deep spatial features with handcrafted descriptors for enhanced discrimination of spectral and textural features. Supported by the Improved Divine Religions Algorithm (IDRA) metaheuristic optimizer, the model gets to enjoy fast convergence and stable training. Gradient-weighted Class Activation Mapping (Grad-CAM) guarantees that predictions are transparent and interpretable by showing visual heatmaps. The system is made available via a Kotlin cross-platform mobile application, which allows for on-device real-time classification. When tested with a dataset consisting of 1000 high-resolution cardamom images, the framework demonstrated outstanding performance, recording 99.25% accuracy, 99.09% precision, and 99.15% recall and F1-score. Such results validate the suggested solution as a tool for automated agricultural quality control that is accurate, interpretable, and ready to be used in the field.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"132 ","pages":"Article 110992"},"PeriodicalIF":4.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Short-term scheduling strategy of renewable integrated hydrothermal system using chaotic chimp-sine cosine algorithm considering wind uncertainty and grid stability 考虑风力不确定性和电网稳定性的混沌黑猩猩-正弦余弦算法的可再生综合热液系统短期调度策略

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2026-04-01 Epub Date: 2026-02-09 DOI: 10.1016/j.compeleceng.2026.111025

Shahid A. Iqbal , Saurav Raj , Chandan Kumar Shiva

{"title":"Short-term scheduling strategy of renewable integrated hydrothermal system using chaotic chimp-sine cosine algorithm considering wind uncertainty and grid stability","authors":"Shahid A. Iqbal , Saurav Raj , Chandan Kumar Shiva","doi":"10.1016/j.compeleceng.2026.111025","DOIUrl":"10.1016/j.compeleceng.2026.111025","url":null,"abstract":"<div><div>The integration of renewable energy sources (RES) is vital for achieving long-term sustainability in power systems; however, it introduces significant operational challenges owing to the inherent variability of wind and solar generation. Existing research often neglects uncertainties or fails to address them within the realistic constraints of hydrothermal systems. This study proposes a robust and comprehensive short-term scheduling framework for renewable-integrated hydrothermal systems, accounting for the stochastic nature of RES. The proposed chaotic chimp sine cosine algorithm (C-CHOA-SC) effectively tackles the non-convex hydrothermal wind solar scheduling problem by enhancing exploration via chaotic dynamics and avoiding local optima. In this model, wind uncertainty is modelled using the Weibull distribution, and solar generation is capped at 30% of the load demand to maintain transient stability. Numerical simulations revealed that C-CHOA-SC reduced fuel costs by 9.5% compared to differential evolution (DE), 3.1% compared to particle swarm optimization (PSO), and 1.5% compared to grey wolf optimization (GWO). Emission reductions were even more pronounced, being 92% lower than DE, 21% lower than PSO, and 97% lower than GWO. Sensitivity analyses confirmed the algorithm's robustness and adaptability to real-world variations in RES availability. Overall, this framework offers an efficient and practical solution for smart grid operators and policymakers aiming to optimize economic performance, reduce emissions, and enhance operational reliability in renewable-dominated power systems.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"132 ","pages":"Article 111025"},"PeriodicalIF":4.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A low-latency deep learning approach for human action recognition in medical internet of things applications 医疗物联网应用中人体动作识别的低延迟深度学习方法

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2026-04-01 Epub Date: 2026-02-06 DOI: 10.1016/j.compeleceng.2026.111005

Hoangcong Le, Cheng-Kai Lu

{"title":"A low-latency deep learning approach for human action recognition in medical internet of things applications","authors":"Hoangcong Le, Cheng-Kai Lu","doi":"10.1016/j.compeleceng.2026.111005","DOIUrl":"10.1016/j.compeleceng.2026.111005","url":null,"abstract":"<div><div>Real-time human action recognition (HAR) plays a vital role in healthcare monitoring, particularly for elderly care and assistive environments. However, existing HAR systems often struggle with high computational demands, large model sizes, and vulnerability to background noise, limiting their use on edge devices in the Internet of Medical Things (IoMT) settings. This study proposes Temporal-SpatialCNN, a lightweight framework built on a novel CNNBlock architecture that integrates convolution, batch normalization, and Spatial-Dropout to enhance both efficiency and generalization. The study systematically analyzes various layer arrangements within CNNBlock and identifies an optimal configuration that improves recognition performance while maintaining minimal computational overhead. The model incorporates diverse skeletal input modalities – joint coordinates, joint collection distances, slow motion, and velocity – to capture enriched spatio-temporal features. Extensive experiments on five well-known benchmark datasets validate the effectiveness of the proposed approach, achieving state-of-the-art accuracy (up to 99.66% on the Florence-3D dataset) with an inference time of 9.6 ms. To demonstrate real-world applicability, a real-time Save Our Soul (SOS) system was implemented on standard hardware, capable of detecting emergency gestures such as calls for assistance, thereby highlighting the model’s practical potential in real-time, resource-constrained healthcare scenarios.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"132 ","pages":"Article 111005"},"PeriodicalIF":4.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancements and challenges in deepfake medical imaging: generation and detection techniques 深度假医学成像的进展和挑战：生成和检测技术

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2026-04-01 Epub Date: 2026-02-12 DOI: 10.1016/j.compeleceng.2026.111038

Sayeli Dey, Smita Das, Penumala Nani

引用次数: 0

Early detection of mental health disorders using machine learning models: An analysis based on behavioral and voice data 使用机器学习模型早期检测精神健康障碍：基于行为和语音数据的分析

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2026-04-01 Epub Date: 2026-02-10 DOI: 10.1016/j.compeleceng.2026.110996

Prashant Vats , Tan Kuan Tak , Kamal Upreti , Shubham Mahajan , Pravin R. Kshirsagar , Govind Murari Upadhyay

{"title":"Early detection of mental health disorders using machine learning models: An analysis based on behavioral and voice data","authors":"Prashant Vats , Tan Kuan Tak , Kamal Upreti , Shubham Mahajan , Pravin R. Kshirsagar , Govind Murari Upadhyay","doi":"10.1016/j.compeleceng.2026.110996","DOIUrl":"10.1016/j.compeleceng.2026.110996","url":null,"abstract":"<div><div>Mental illnesses are to be detected promptly and correctly to intervene effectively and in time. In this paper, a multi-stage NeuroVibeNet model of early mental disorders detection based on multimodal behavioral and voice data is proposed. It starts with the preprocessing of data that is high-quality and consistent, such as mean imputation, min-max normalization, outlier detection, noise reduction, and short-time energy extraction. The majority of the advanced methods employed in extracting temporal, spectral, and complex features include multiscale entropy, soft dynamic time warping, spectral contrast analysis, formant frequency analysis, and a one-dimensional convolutional neural network autoencoder. The feature selection is done via a sparse autoencoder that is used to maximize relevance and minimize redundancy. The chosen features are fed into the NeuroVibeNet architecture, where TabNet is used to process behavioral data, and Capsule Networks are used to process voice data to allow learning representations with attention and hierarchy. Lastly, a voting-based ensemble classifier uses the two modalities to combine the predictions to make strong classification decisions. The structure is coded in Python and tested on three benchmark datasets with the accuracy of 0.9839, 0.9856, and 0.9855, which is better than the current approaches.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"132 ","pages":"Article 110996"},"PeriodicalIF":4.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-time phishing uniform resource locator detection based on hybrid embedding transformer and retraining-free inferencing 基于混合嵌入变压器和无需再训练推理的实时网络钓鱼统一资源定位器检测

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2026-04-01 Epub Date: 2026-02-02 DOI: 10.1016/j.compeleceng.2026.111001

Dam Minh Linh , Huynh Trong Thua , Tran Cong Hung

{"title":"Real-time phishing uniform resource locator detection based on hybrid embedding transformer and retraining-free inferencing","authors":"Dam Minh Linh , Huynh Trong Thua , Tran Cong Hung","doi":"10.1016/j.compeleceng.2026.111001","DOIUrl":"10.1016/j.compeleceng.2026.111001","url":null,"abstract":"<div><div>Phishing attacks that evade traditional detection mechanisms by exploiting deceptive uniform resource locators (URLs) remain a significant cybersecurity threat. This study proposes an adaptive phishing URL detection framework that integrates Levenshtein distance-based string similarity, a hybrid embedding transformer (HET) encoder-based server-side verification mechanism, and a dynamically updated local blacklist. First, a rapid local lookup is executed to identify known phishing URLs. If the input URL is absent from the blacklist, the Levenshtein distance algorithm detects subtle character-level variations, identifying typosquatting and obfuscation effectively. For ambiguous cases, the HET-based module uses a lightweight post-hoc inference method that classifies URL embeddings via k-nearest neighbor voting based on Euclidean similarity in the latent space, thereby avoiding retraining and enabling real-time adaptation to emerging phishing threats. Confirmed phishing URLs are added iteratively to the local repository to improve detection continuously, enhancing future classification accuracy. Experimental evaluation on a large-scale dataset comprising 235,795 URLs revealed that the proposed method outperforms state-of-the-art approaches, achieving a detection accuracy of 99.8 %, with a false-positive rate of 0.441 % and false-negative rate of 0.0617 %. Additionally, real-time validation using a Chrome browser extension confirmed rapid processing, with an average processing time of 4.43–6.84 ms per URL on a dataset comprising 5,000 URLs. These results highlight the efficiency of the proposed framework in real-world cybersecurity contexts, enabling high detection accuracy, fast response times, and adaptability to evolving phishing strategies, and underscore the importance of proactive threat intelligence and real-time phishing mitigation in developing scalable, high-performance security infrastructures.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"132 ","pages":"Article 111001"},"PeriodicalIF":4.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An ultralightweight and reliable authentication protocol for secure communication in UAV-assisted IoAV systems 一种用于无人机辅助IoAV系统安全通信的超轻量级可靠认证协议

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2026-04-01 Epub Date: 2026-02-05 DOI: 10.1016/j.compeleceng.2026.110998

Sanjeev Kumar , Mohd Shariq , Gopal Singh Rawat , Muhammad Shafiq , Khalid Alsubhi , Mehedi Masud , Hossam Meshref

{"title":"An ultralightweight and reliable authentication protocol for secure communication in UAV-assisted IoAV systems","authors":"Sanjeev Kumar , Mohd Shariq , Gopal Singh Rawat , Muhammad Shafiq , Khalid Alsubhi , Mehedi Masud , Hossam Meshref","doi":"10.1016/j.compeleceng.2026.110998","DOIUrl":"10.1016/j.compeleceng.2026.110998","url":null,"abstract":"<div><div>The Internet of Vehicles (IoV) enables intelligent transportation systems in the efficient management of autonomous vehicles (AVs) by enabling real-time data exchange and message communication over the Internet. Rapid advances in autonomous systems, hardware sensors, software, and the integration of artificial intelligence have enabled the development of automated services and improved convenience for users. In the IoV, heavy traffic, connectivity issues, and frequent handoffs create significant challenges. Unmanned aerial vehicles (UAVs, sometimes called drones) offer a promising solution to these issues by helping to alleviate network congestion and improving overall network performance. Because UAVs are highly mobile devices, potential security breaches pose a significant challenge for communication between UAVs and autonomous vehicles. To address this, we propose a secure and efficient ultralightweight protocol that uses UAV technology in an IoV environment. Our protocol employs elliptic-curve cryptography and cryptographic operators such as exclusive-or operations and one-way hashing. A formal security analysis of the protocol using the Scyther simulation tool reveals that it is resilient against security attacks, while an informal security analysis shows that the protocol is secure against several known security and privacy threats. The computational and communication costs of the proposed protocol are lower than those of other existing protocols, while being efficient, outperforming other solutions in terms of security features.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"132 ","pages":"Article 110998"},"PeriodicalIF":4.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fuzzy-enhanced variable weight graph convolutional networks for recommender systems 推荐系统的模糊增强变权图卷积网络

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2026-03-01 Epub Date: 2026-01-08 DOI: 10.1016/j.compeleceng.2026.110970

Wanna Cui, Hak-Keung Lam

{"title":"Fuzzy-enhanced variable weight graph convolutional networks for recommender systems","authors":"Wanna Cui, Hak-Keung Lam","doi":"10.1016/j.compeleceng.2026.110970","DOIUrl":"10.1016/j.compeleceng.2026.110970","url":null,"abstract":"<div><div>Recommender systems play an essential role in alleviating information overload by delivering personalized suggestions to users across domains such as e-commerce, restaurant services, and digital media. In recent years, graph-based approaches, particularly those leveraging graph convolutional networks (GCNs), have shown strong performance by modeling high-order connectivity. However, their effectiveness remains constrained by three critical challenges: the sparsity of user–item interactions, the presence of noisy or transient behaviors that distort preference modeling, and the underutilization of contextual information contained in reviews and product descriptions. To address these limitations, we propose a novel framework, termed fuzzy and variable weight graph convolutional network (FVW-GCN). The framework incorporates a fuzzy relation modeling module that enriches the adjacency structure by applying fuzzy C-means clustering to semantic embeddings extracted from pre-trained language models, thereby improving connectivity for sparse and long-tail items. In addition, a variable-weight GCN module is introduced, where a tuning GCN learns localized weight matrices from sampled subgraphs, which are then used by a tuned GCN to adaptively refine embeddings and suppress noisy signals. Through this combination, FVW-GCN effectively strengthens meaningful relations while reducing the influence of unreliable interactions. Extensive experiments conducted on benchmark datasets demonstrate that FVW-GCN consistently outperforms state-of-the-art baselines across several standard evaluation metrics, including recall, normalized discounted cumulative gain, and hit ratio. These results confirm the robustness and effectiveness of the proposed framework, highlighting its potential to support more accurate, diverse, and user-centric recommendation services in real-world applications.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110970"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A real-time smart energy management system for greenhouses using a hybrid optimization algorithm: Experimental implementation for efficient and sustainable operation 基于混合优化算法的温室实时智能能源管理系统：高效可持续运行的实验实现

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2026-03-01 Epub Date: 2026-01-12 DOI: 10.1016/j.compeleceng.2026.110948

Mohamed W. Haggag , Asmaa H. Rabie , Islam Ismael , Waleed Shaaban

{"title":"A real-time smart energy management system for greenhouses using a hybrid optimization algorithm: Experimental implementation for efficient and sustainable operation","authors":"Mohamed W. Haggag , Asmaa H. Rabie , Islam Ismael , Waleed Shaaban","doi":"10.1016/j.compeleceng.2026.110948","DOIUrl":"10.1016/j.compeleceng.2026.110948","url":null,"abstract":"<div><div>The increasing global demand for food and energy, along with climate change and resource scarcity, causes potential challenges to sustainable agriculture. Smart greenhouses create controlled environments that optimize crop production and minimize resource use, especially in arid regions. This paper introduces a Real-Time Smart Greenhouse Energy Management (SGEM) system that combines IoT-based sensing, renewable energy sources, and a Hybrid Optimization Algorithm (HOA). The HOA combines Particle Swarm Optimization (PSO) for global exploration with the Coati Optimization Algorithm (COA) for local exploitation. To optimize operating costs, battery State of Charge (SoC), and the use of renewable energy, the HOA dynamically gathers energy from photovoltaic panels (PV), battery storage, and the electrical grid. The system is designed as a hybrid PV-battery-grid configuration, validated through both simulation and experimental implementation, guaranteeing a steady supply of energy while minimizing grid dependency. Experimental validation shows that the SGEM system reduces costs by 49.98%, lowers daily grid consumption by 50.24%, cuts CO₂ emissions by 50.5%, and extends battery life by 14.7%. The results obtained demonstrate the system’s capability for adaptive, efficient, and sustainable greenhouse energy management, providing a scalable solution for modern smart agriculture.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110948"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A comprehensive review of computational techniques for obscenity detection: Past, present, and future 淫秽检测的计算技术的全面回顾：过去，现在和未来

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2026-03-01 Epub Date: 2026-01-12 DOI: 10.1016/j.compeleceng.2026.110960

Pundreekaksha Sharma , Dr. Vijay Kumar , Dr. Neeraj Kumar

{"title":"A comprehensive review of computational techniques for obscenity detection: Past, present, and future","authors":"Pundreekaksha Sharma , Dr. Vijay Kumar , Dr. Neeraj Kumar","doi":"10.1016/j.compeleceng.2026.110960","DOIUrl":"10.1016/j.compeleceng.2026.110960","url":null,"abstract":"<div><div>With the rapid proliferation of obscene content over the internet, detecting and preventing obscenity has become the most prominent way of maintaining a safe digital environment. The accessibility of obscene content has significant psychological, social, ethical, and technological impacts. To overcome these challenges, it is essential to develop an obscenity detection system using advanced artificial intelligence techniques, including social and ethical considerations that prevent the spread of obscenity. This research has presented a comprehensive literature analysis covering traditional to advanced computational techniques for obscenity detection. It also serves as a valuable resource for researchers improving obscenity detection techniques. Analyse computer vision techniques for obscenity detection, featuring hybrid deep learning methods including Transformers, vision transformers, diffusion models, and other techniques. Additionally, this research discusses the strengths and limitations of these techniques. Examines the mathematical formulations and equations of the models, and the impact of input and additional parameters. Compare the performance of models on various datasets and discuss how to develop a diverse dataset. A significant overview of social and ethical considerations included in obscenity detection. The research paper also highlights challenges and potential future research directions in obscenity detection. In conclusion, this research provides a gap analysis that helps researchers enhance computational techniques for obscenity detection.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110960"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0