Computational Intelligence最新文献

{"title":"Vision-Based UAV Detection and Tracking Using Deep Learning and Kalman Filter","authors":"Nancy Alshaer,&nbsp;Reham Abdelfatah,&nbsp;Tawfik Ismail,&nbsp;Haitham Mahmoud","doi":"10.1111/coin.70026","DOIUrl":"https://doi.org/10.1111/coin.70026","url":null,"abstract":"<p>The rapid increase in unmanned aerial vehicles (UAVs) usage across various sectors has heightened the need for robust detection and tracking systems due to safety and security concerns. Traditional methods like radar and acoustic sensors face limitations in noisy environments, underscoring the necessity for advanced solutions such as deep learning-based detection and tracking. Hence, this article proposes a two-stage platform designed to address these challenges by detecting, classifying, and tracking various consumer-grade UAVs. The tracking efficacy of the proposed system is assessed using a combination of deep learning and Kalman filter techniques. Specifically, we evaluate models such as YOLOv3, YOLOv4, YOLOv5, and YOLOx to identify the most efficient detector for the initial detection stage. Moreover, we employ both the Kalman filter and the Extended Kalman filter for the tracking stage, enhancing the system's robustness and enabling real-time tracking capabilities. To train our detector, we construct a dataset comprising approximately 10,000 records that capture the diverse environmental and behavioural conditions experienced by UAVs during their flight. We then present both visual and analytical results to assess and compare the performance of our detector and tracker. Our proposed system effectively mitigates cumulative detection errors across consecutive video frames and enhances the accuracy of the target's bounding boxes.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.70026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143439186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TransPapCanCervix: An Enhanced Transfer Learning-Based Ensemble Model for Cervical Cancer Classification","authors":"Barkha Bhavsar,&nbsp;Bela Shrimali","doi":"10.1111/coin.70027","DOIUrl":"https://doi.org/10.1111/coin.70027","url":null,"abstract":"<div>\u0000 \u0000 <p>Cervical cancer, like many other cancers, is most treatable when detected at an early stage. Using classification methods helps find early signs of cancer and small tumors. This allows doctors to act quickly and offer treatments that might cure the cancer. This paper presents a comprehensive approach to the classification of squamous cell carcinoma (SCC) leveraging a dataset comprising 1140 single-cell images sourced from Herlev. In addition to that, in this work, a new ensemble model based on the transfer-learning (TL) technique is developed on various deep learning models, including DenseNet121, DenseNet169, InceptionResNet, XceptionNet, ResNet50, and ResNet101 to demonstrate their efficacy in classifying diverse cellular features. To evaluate our proposed approach's performance, the ensemble approach's results are compared with some transfer learning models such as DenseNet121, DenseNet169, InceptionResNet, XceptionNet, ResNet50, and ResNet101. The experimental results demonstrate that transfer learning-based deep neural networks combined with ensemble methods enhance the diagnostic accuracy of SCC classification systems, achieving 98% accuracy across various cell types. This further validates the effectiveness of the proposed approach. A comprehensive investigation yields a precise and efficient model for SCC classification, offering detailed insights into both normal and abnormal cell types.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Implementation of Adaptive Multi-CNN Feature Fusion Model With Attention Mechanism With Improved Heuristic Algorithm for Kidney Stone Detection","authors":"Gunasekaran Kulandaivelu,&nbsp;M Suchitra,&nbsp;R Pugalenthi,&nbsp;Ruchika Lalit","doi":"10.1111/coin.70028","DOIUrl":"https://doi.org/10.1111/coin.70028","url":null,"abstract":"<div>\u0000 \u0000 <p>Nowadays, most people have been admitted to emergencies with severe pain caused by kidney stones worldwide. In this case, diverse imaging approaches are aided in the detection process of stones in the kidney. Moreover, the specialist acquires better diagnosis and interpretation of this image. Here, computer-aided techniques are considered the practical techniques, which it is used as the auxiliary tool for the process of diagnosis. Most urologists have failed to train the type of kidney stone identification effectively and it is operator-dependent. Concerning the surgical operation, there is a requirement for accurate as well as adequate detection of stone position in the kidney. Thus, it has made the detection process even more difficult. To overcome the challenging issues, an effective detection model for kidney stones using classifiers. Initially, the input images are collected from the standard resources. Further, the input images are subjected to the adaptive multi-convolutional neural network with attention mechanism (AMC-AM) feature fusion model, in which, the pertinent features are extracted from the three networks: Visual Geometry Group16 (VGG16), Residual Network (ResNet), and Inception net. Thus, the three distinct features are obtained for the feature fusion procedure. Finally, the resultant features are subjected as input to the final layer of CNN. In the proposed network, the model is integrated with the attention mechanism and also the parameter tuning is done by proposing the modified social distance of coronavirus mask protection algorithm (MSD-CMPA). Therefore, the performance is examined using different metrics and compared with other baseline models. Hence, the proposed model overwhelms the outstanding results in detecting the kidney stones that aid the individual in getting rid of kidney disorders.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Reinforcement Learning Based Flow Aware-QoS Provisioning in SD-IoT for Precision Agriculture","authors":"Mohammed J. F. Alenazi,&nbsp;Mahmoud Ahmad Al-Khasawneh,&nbsp;Saeedur Rahman,&nbsp;Zaid Bin Faheem","doi":"10.1111/coin.70023","DOIUrl":"https://doi.org/10.1111/coin.70023","url":null,"abstract":"<div>\u0000 \u0000 <p>To meet the demands of modern technologies such as 5G, big data, edge computing, precision, and sustainable agriculture, the combination of Internet-of-Things (IoT) with software-defined networking (SDN) known as SD-IoT is suggested to automate the network by leveraging the programmable and centralized SDN interfaces. The previous literature has suggested quality-of-service (QoS) aware flow processing using manual strategies or heuristic algorithms, however, these schemes proposed with white-box approaches do not provide effective results as the network scales or dynamic changes are happening. This article proposes a novel QoS provision strategy using deep reinforcement learning (DRL) to calculate the optimal routes autonomously for SD-IoT traffic. To satisfy the different demands of flows in the SD-IoT network the flows are divided into two types. Hence, based on their service demand the routes are generated for them as per service request. The scenario is explained with precision agriculture based on SD-IoT and results are compared with benchmark strategies. A real internet topology is used for the evaluation of results. The results indicated that the proposed method gives improvements for QoS such as delay, throughput, packet loss rate, and jitter compared with benchmark models.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143404406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning and X-Ray Imaging Innovations for Pneumonia Infection Diagnosis: Introducing DeepPneuNet","authors":"Sanjay Chakraborty,&nbsp;Tirthajyoti Nag,&nbsp;Saroj Kumar Pandey,&nbsp;Jayasree Ghosh,&nbsp;Lopamudra Dey","doi":"10.1111/coin.70029","DOIUrl":"https://doi.org/10.1111/coin.70029","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper aims to develop a new deep learning model (DeepPneuNet) and evaluate its performance in predicting Pneumonia infection diagnosis based on patients' chest x-ray images. We have collected 5856 chest x-ray images that are labeled as either “pneumonia” or “normal” from a public forum. Before applying the DeepPneuNet model, a necessary feature extraction and feature mapping have been done on the input images. Conv2D layers with a 1 × 1 kernel size are followed by ReLU activation functions to make up the model. These layers are in charge of recognizing important patterns and features in the images. A MaxPooling 2D procedure is applied to minimize the spatial size of the feature maps after every two Conv2D layers. The sparse categorical cross-entropy loss function trains the model, and the Adam optimizer with a learning rate of 0.001 is used to optimize it. The DeepPneuNet provides 90.12% accuracy for diagnosis of the Pneumonia infection for a set of real-life test images. With 9,445,586 parameters, the DeepPneuNet model exhibits excellent parameter efficiency. DeepPneuNet is a more lightweight and computationally efficient alternative when compared to the other pre-trained models. We have compared accuracies for predicting Pneumonia diagnosis of our proposed DeepPneuNet model with some state-of-the-art deep learning models. The proposed DeepPneuNet model is more advantageous than the existing state-of-the-art learning models for Pneumonia diagnosis with respect to accuracy, precision, recall, <i>F</i>-score, training parameters, and training execution time.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personalized Recommendation Method Based on Rating Matrix and Review Text","authors":"Shiru Wang,&nbsp;Wenna Du,&nbsp;Amran Bhuiyan,&nbsp;Zehua Chen","doi":"10.1111/coin.70024","DOIUrl":"https://doi.org/10.1111/coin.70024","url":null,"abstract":"<div>\u0000 \u0000 <p>In recent years, the algorithm based on review text has been widely used in recommendation systems, which can help mitigate the effect of sparsity in rating data within recommender algorithms. Existing methods typically employ a uniform model for capturing user and item features, but they are limited to the shallow feature level, and the user's personalized preferences and deep features of the item have not been fully explored, which may affect the relationship between the two representations learned by the model. The deeper relationship between them will affect the prediction results. Consequently, we propose a personalized recommendation method based on the rating matrix and review text denoted PRM-RR, which is used to deeply mine user preferences and item characteristics. In the process of processing the comment text, we employ ALBERT to obtain vector representations for the words present in the review text firstly. Subsequently, taking into account that significant words and reviews bear relevance not solely to the review text but also to the user's individualized preferences, the proposed personalized attention module synergizes the user's personalized preference information with the review text vector, thereby engendering an enriched review-based user representation. The fusion of the user's review representation and rating representation is accomplished through the feature fusion module using cross-modal attention, yielding the final user representation. Lastly, we employ a factorization machine to predict the user's rating for the item, thereby facilitating the recommendation process. Experimental results on three benchmark datasets show that our method outperforms the baseline algorithm in all cases, demonstrating that our method effectively improves the performance of recommendations. The code is available at https://github.com/ZehuaChenLab/PRM-RR.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi IRS-Aided Low-Carbon Power Management for Green Communication in 6G Smart Agriculture Using Deep Game Theory","authors":"Fahad Masood,&nbsp;Jawad Ahmad,&nbsp;Alanoud Al Mazroa,&nbsp;Nada Alasbali,&nbsp;Abdulwahab Alazeb,&nbsp;Mohammed S. Alshehri","doi":"10.1111/coin.70022","DOIUrl":"https://doi.org/10.1111/coin.70022","url":null,"abstract":"<div>\u0000 \u0000 <p>Power consumption management is vital in achieving sustainable and low-carbon green communication goals in 6G smart agriculture. This research aims to provide a low-power consumption measurement framework designed specifically for critical data handling in smart agriculture application networks. Deep Q-learning combined with game theory is proposed to allow network entities such as Internet of Things (IoT) devices, Intelligent Reflecting Surfaces (IRSs), and Base Stations (BS) to make intelligent decisions for optimal resource allocation and energy and power consumption. The learning capabilities of DQL with strategic reasoning of game theory, a hybrid framework, have been developed to realize an adaptive routing plan that emphasizes energy-conscious communication protocols and underestimates the environment. It further enables the investigation of multi-IRS performance through several key metrics assessments, such as reflected power consumption, energy efficiency, and Signal-to-Noise Ratio (SNR) improvement.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143117841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning Aided SID in Near-Field Power Internet of Things Networks With Hybrid Recommendation Algorithm","authors":"Chuangang Chen,&nbsp;Qiang Wu,&nbsp;Hangao Wang,&nbsp;Jing Chen","doi":"10.1111/coin.70021","DOIUrl":"https://doi.org/10.1111/coin.70021","url":null,"abstract":"<div>\u0000 \u0000 <p>In the realm of power Internet of Things (IoT) networks, secure inspection detection (SID) is paramount for maintaining system integrity and security. This paper presents a novel framework that leverages deep learning-based semi-autoencoders in conjunction with a hybrid recommendation algorithm to enhance SID tasks. Our proposed method utilizes the deep learning-based semi-autoencoder to effectively capture and learn complex patterns from high-dimensional power IoT data, facilitating the identification of anomalies indicative of potential security threats. The hybrid recommendation algorithm, which combines collaborative filtering and content-based filtering, further refines the detection process by cross-verifying the identified anomalies with historical data and contextual information, thereby improving the accuracy and reliability of the SID tasks. Through extensive simulations and practical data evaluations, our proposed framework demonstrates superior performance over conventional methods, achieving higher detection accuracy. In particular, the detection accuracy of the proposed scheme is more than 20% higher than that of the competing schemes.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143117829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Enhanced Cross-Attention Based Multimodal Model for Depression Detection","authors":"Yifan Kou,&nbsp;Fangzhen Ge,&nbsp;Debao Chen,&nbsp;Longfeng Shen,&nbsp;Huaiyu Liu","doi":"10.1111/coin.70019","DOIUrl":"https://doi.org/10.1111/coin.70019","url":null,"abstract":"<div>\u0000 \u0000 <p>Depression, a prevalent mental disorder in modern society, significantly impacts people's daily lives. Recently, there have been advancements in developing automated diagnosis models for detecting depression. However, data scarcity, primarily due to privacy concerns, has posed a challenge. Traditional speech features have limitations in representing knowledge for depression diagnosis, and the complexity of deep learning algorithms necessitates substantial data support. Furthermore, existing multimodal methods based on neural networks overlook the heterogeneity gap between different modalities, potentially resulting in redundant information. To address these issues, we propose a multimodal depression detection model based on the Enhanced Cross-Attention (ECA) Mechanism. This model effectively explores text-speech interactions while considering modality heterogeneity. Data scarcity has been mitigated by fine-tuning pre-trained models. Additionally, we design a modal fusion module based on ECA, which emphasizes similarity responses and updates the weight of each modal feature based on the similarity information between modal features. Furthermore, for speech feature extraction, we have reduced the computational complexity of the model by integrating a multi-window self-attention mechanism with the Fourier transform. The proposed model is evaluated on the public dataset, DAIC-WOZ, achieving an accuracy of 80.0% and an average <i>F</i>1 value improvement of 4.3% compared with relevant methods.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Heuristic Strategy Assisted Deep Learning Models for Brain Tumor Classification and Abnormality Segmentation","authors":"Veesam Pavan Kumar,&nbsp;Satya Ranjan Pattanaik,&nbsp;V. V. Sunil Kumar","doi":"10.1111/coin.70018","DOIUrl":"https://doi.org/10.1111/coin.70018","url":null,"abstract":"<div>\u0000 \u0000 <p>Brain tumors are prevalent forms of malignant neoplasms that, depending on their type, location, and grade, can significantly reduce life expectancy due to their invasive nature and potential for rapid progression. Accordingly, brain tumors classification is an essential step that allows doctors to perform appropriate treatment. Many studies have been done in the sector of medical image processing by employing computational methods to effectively segment and classify tumors. However, the larger amount of information collected by healthcare images prohibits the manual segmentation process in a reasonable time frame, reducing error measures in healthcare settings. Therefore, automated and efficient techniques for segmentation are crucial. In addition, various visual information, noisy images, occlusion, uneven image textures, confused objects, and other features may impact the process. Therefore, the implementation of deep learning provides remarkable results in medicinal image processing, particularly in the segmentation and classification process. However, conventional deep learning-assisted methods struggle with complex structures and dimensional issues. Thus, this paper develops an effective technique for diagnosing brain tumors. The main aspect of the proposed system is to classify the brain tumor types by segmenting the affected regions of the raw images. This novel approach can be applied for various applications like diagnostic centers, decision-making tools, clinical trials, medical research institutes, disease prognosis, and so on. Initially, the requisite images are collected from standard datasets and further, it is subjected to the segmentation period. In this stage, the Multi-scale and Dilated TransUNet++ (MDTUNet++) model is employed to segment the abnormalities. Further, the segmented images are given into an Adaptive Dilated Dense Residual Attention Network (ADDRAN) to classify the brain tumor types. Here, to optimize the ADDRAN technique's parameters, an Improved Hermit Crab Optimizer (IHCO) is supported, which increases the accuracy rates of the overall network. Finally, the numerical examination is conducted to guarantee the robustness and usefulness of the designed model by contrasting it with other related techniques. For Dataset 1, the accuracy value attains 93.71 for the proposed work compared to 87.86 for CNN, 90.18 for DenseNet, and 89.56 and 90.96 for RAN and DRAN, respectively. Thus, supremacy has been achieved for the recommended system while detecting the brain tumor types.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}