International Journal of Cognitive Computing in Engineering最新文献

{"title":"Fake review detection using transformer-based enhanced LSTM and RoBERTa","authors":"Rami Mohawesh ,&nbsp;Haythem Bany Salameh ,&nbsp;Yaser Jararweh ,&nbsp;Mohannad Alkhalaileh ,&nbsp;Sumbal Maqsood","doi":"10.1016/j.ijcce.2024.06.001","DOIUrl":"10.1016/j.ijcce.2024.06.001","url":null,"abstract":"<div><p>Internet reviews significantly influence consumer purchase decisions across all types of goods and services. However, fake reviews can mislead both customers and businesses. Many machine learning (ML) techniques have been proposed to detect fake reviews, but they often suffer from poor accuracy due to their focus on linguistic features rather than semantic content. This paper presents a novel semantic- and linguistic-aware model for fake review detection that improves accuracy by leveraging advanced transformer architecture. Our model integrates RoBERTa with an LSTM layer, enabling it to capture intricate patterns within fake reviews. Unlike previous methods, our approach enhances the robustness of fake review detection and authentic behavior profiling. Experimental results on semi-real benchmark datasets show that our model significantly outperforms state-of-the-art methods, achieving 96.03 % accuracy on the OpSpam dataset and 93.15 % on the Deception dataset. To further enhance transparency and credibility, we utilize Shapley Additive Explanations (SHAP) and attention techniques to clarify our model's classifications. The empirical findings indicate that our proposed model can offer rational explanations for classifying specific reviews as fake.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 250-258"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307424000196/pdfft?md5=7517a3a38ec51cfca56bbb57aabb26b5&pid=1-s2.0-S2666307424000196-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141415954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based approaches for abusive content detection and classification for multi-class online user-generated data","authors":"Simrat Kaur,&nbsp;Sarbjeet Singh,&nbsp;Sakshi Kaushal","doi":"10.1016/j.ijcce.2024.02.002","DOIUrl":"https://doi.org/10.1016/j.ijcce.2024.02.002","url":null,"abstract":"<div><p>With the rapid growth of social media culture, the use of offensive or hateful language has surged, which necessitates the development of effective abusive language detection models for online platforms. This paper focuses on developing a multi-class classification model to identify different types of offensive language. The input data is taken in the form of labeled tweets and is classified into offensive language detection, offensive language categorization, and offensive language target identification. The data undergoes pre-processing, which removes NaN value and punctuation, as well as performs tokenization followed by the generation of a word cloud to assess data quality. Further, the tf-idf technique is used for the selection of features. In the case of classifiers, multiple deep learning techniques, namely, bidirectional gated recurrent unit, multi-dense long short-term memory, bidirectional long short-term memory, gated recurrent unit, and long short-term memory, are applied where it has been found that all the models, except long short-term memory, achieved a high accuracy of 99.9 % for offensive language target identification. Bidirectional LSTM and multi-dense LSTM obtained the lowest loss and RMSE values of 0.01 and 0.1, respectively. This research provides valuable insights and contributes to the development of effective abusive language detection methods to promote a safe and respectful online environment. The insights gained can aid platform administrators in efficiently moderating content and taking appropriate actions against offensive language.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 104-122"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307424000068/pdfft?md5=b671af6be3039a34479959213a86aa16&pid=1-s2.0-S2666307424000068-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139945135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NeuroRF FarmSense: IoT-fueled precision agriculture transformed for superior crop care","authors":"Tarun Vats ,&nbsp;Shrey Mehra ,&nbsp;Uday Madan ,&nbsp;Amit Chhabra ,&nbsp;Akashdeep Sharma ,&nbsp;Kunal Chhabra ,&nbsp;Sarabjeet Singh ,&nbsp;Utkarsh Chauhan","doi":"10.1016/j.ijcce.2024.09.002","DOIUrl":"10.1016/j.ijcce.2024.09.002","url":null,"abstract":"<div><p>In light of the ongoing global hunger crisis, it is imperative to improve food production in accordance with Sustainable Development Goal 2.0, which aims to eliminate hunger while promoting sustainable agricultural practices. This research presents a novel Internet of Things (IoT)-driven crop management system, NeuroRF FarmSense, specifically designed for precision agriculture. By utilizing soil sensors and a robust IoT framework, this system enables effective data collection across vast and remote agricultural areas. The study utilizes an extensive Crop Recommendation Dataset obtained from Kaggle, which includes 2,200 entries and seven critical attributes essential for crop selection: phosphorus, humidity, potassium, temperature, nitrogen, pH, and rainfall. This dataset provides a detailed methodology for crop recommendations, revealing more than 22 alternative crops based on varying characteristics. For agricultural forecasting, the NeuroRF FarmSense system employs the NeuroRF Classifier, which integrates neural networks (NN) with the Random Forest Classifier, achieving an unprecedented accuracy of 99.82%, exceeding prior records. This integrative approach harnesses the advantages of NN’s ReLU activation and dropout regularization alongside the robustness of RF. By utilizing NN predictions as input features for RF training and refining RF through grid search with cross-validation, the ensemble model produces highly precise predictions, facilitating strategic crop cultivation for optimal yields across diverse environmental conditions. This innovative methodology signifies a strong solution for classification challenges in precision agriculture. By merging IoT technology with machine learning algorithms, smart farming is poised to enter a transformative phase, providing a scalable response to the pressing issues of global food security. This research aspires to advance precision agriculture in harmony with global sustainability objectives.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 425-435"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307424000330/pdfft?md5=7ff194a6519af25daa7963c696f01eef&pid=1-s2.0-S2666307424000330-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142233056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ECDSA-based tamper detection in medical data using a watermarking technique","authors":"Rupa Ch ,&nbsp;Naga Vivek K ,&nbsp;Gautam Srivastava ,&nbsp;Reddy Gadekallu","doi":"10.1016/j.ijcce.2024.01.003","DOIUrl":"10.1016/j.ijcce.2024.01.003","url":null,"abstract":"<div><p>Telemedicine is a form of healthcare delivery that employs communication technology to provide medical care to patients remotely. The use of telemedicine has seen a significant increase in recent years, presenting challenges such as patient privacy, data security, the need for reliable communication technology, and the potential for misdiagnosis without a physical examination. Digital Watermarking can assist in addressing such issues by incorporating a unique identifier into an image that can be used to authenticate its validity. To tackle these issues, this study proposes a robust digital watermarking approach tailored to brain medical images, combining hashing, the Elliptic Curve Digital Signature Algorithm (ECDSA), and the Integer Wavelet Transform-Discrete Cosine Transform (IWT-DCT). This method utilizes the Secure Hash Algorithm (SHA-256) to first segment the brain's Region of Interest (RoI). Subsequently, the hashed RoI, along with an ECDSA signature, is embedded into the high-frequency sub-bands of the medical image using IWT-DCT. The embedding process strategically alters the coefficients of the high-frequency sub-bands to accommodate the signature while minimizing perceptual distortion. The technique leverages the robustness of transformed-domain image watermarking techniques against various attacks and combines it with SHA-256 for integrity and ECDSA for authentication purposes. The results demonstrate that the suggested approach is robust to a variety of image processing techniques, including noise addition, filtering, and compression while maintaining high levels of imperceptibility. Key metrics such as the Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Structural Similarity Index (SSIM) were used to evaluate performance. The suggested strategy exhibited a substantial improvement over existing methods. The PSNR increased to 68.67, indicating higher image quality, while the MSE reduced to 0.96, demonstrating closer pixel values to the original image. Moreover, the SSIM reached 0.98, denoting a nearly perfect resemblance between the watermarked and original images. The suggested approach also demonstrated quick embedding and extraction speeds, as well as tamper detection capabilities.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 78-87"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307424000044/pdfft?md5=d958e970445c6af70fac2c663bd5da1f&pid=1-s2.0-S2666307424000044-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139631511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Twitter sentiment analysis using conditional generative adversarial network","authors":"V. Mahalakshmi ,&nbsp;P. Shenbagavalli ,&nbsp;S. Raguvaran ,&nbsp;V. Rajakumareswaran ,&nbsp;E. Sivaraman","doi":"10.1016/j.ijcce.2024.03.002","DOIUrl":"10.1016/j.ijcce.2024.03.002","url":null,"abstract":"<div><p>Sentiment analysis, which aims to extract information from textual data indicating people's ideas or attitudes about a particular problem, has developed into one of the most exciting study issues in natural language processing (NLP) with the development of social media. Twitter is a social network with an extensive audience that expresses their thoughts and opinions clearly and readily. Due to the prevalence of slang phrases and incorrect spellings in short phrase styles, Twitter data analysis is more challenging than data analysis from other social networks. Automated feature selection still has several limitations, such as higher computing costs that rise with the number of characteristics. Deep learning, which is self-learned and more accurate at processing vast amounts of data, is utilized to overcome these challenges. This paper introduces a conditional generative adversarial network (GAN) for Twitter sentiment analysis, whereas a convolutional neural network (CNN) has been used to extract traits from Twitter data. Compared to existing works, the proposed work has outperformed in accuracy, recall, precision, and F1 score. The suggested method is the most accurate, with a classification accuracy of 93.33 %.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 161-169"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266630742400010X/pdfft?md5=161eadd262cd948d92791bdc68e39146&pid=1-s2.0-S266630742400010X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140268403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From predictive analytics to emotional recognition–The evolving landscape of cognitive computing in animal welfare","authors":"Suresh Neethirajan","doi":"10.1016/j.ijcce.2024.02.003","DOIUrl":"https://doi.org/10.1016/j.ijcce.2024.02.003","url":null,"abstract":"<div><p>This paper explores the fusion of data science and cognitive techniques in deciphering the behaviors and emotions of farm animals. The focus is on the strategic application of digital imaging and artificial intelligence to discern subtle behavioral patterns and micro-expressions in livestock, offering a predictive window into their emotional states. The significance of acoustic vocalization analysis in interpreting complex communicative signals and emotional subtleties is highlighted. The work extends to cognitive evaluations, such as mirror tests and bias assessments, revealing higher levels of self-awareness and cognitive abilities in farm animals than previously recognized. Emphasizing the need for a synergistic approach, the paper advocates for melding technological advancements with a deep understanding of animal psychology and behavior. This ensures that technology enhances rather than supplants traditional observational methods in animal welfare. The discussion delves into various methodologies and algorithms that measure cognition, underscoring the pivotal role of cognitive computing in advancing animal welfare. A cautious and informed application of these technologies is proposed, emphasizing their role in augmenting, not undermining, the essential human-animal bond. Ultimately, this critical review calls for an ethical, empathetic, and scientifically grounded integration of cognitive computing into animal welfare practices.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 123-131"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266630742400007X/pdfft?md5=6311c74c3230c98e7ad3c1a04fa5d3fd&pid=1-s2.0-S266630742400007X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140030732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyze the impact of feature selection techniques in the early prediction of CKD","authors":"K Hema ,&nbsp;K. Meena ,&nbsp;Ramaraj Pandian","doi":"10.1016/j.ijcce.2023.12.002","DOIUrl":"10.1016/j.ijcce.2023.12.002","url":null,"abstract":"<div><h3>Background</h3><p>Chronic renal disease, often known as Chronic Kidney Disease (CKD), is an illness that causes a steady decline in kidney function. As per the World Health Organization survey, the incidence of CKD may increase from 10% to 13% by 2030. Because of the lack of symptoms in the initial phase, diagnosing CKD early on may be difficult. The key objective of this study is to develop a forecasting model for the early detection of chronic renal disease.</p></div><div><h3>Methods</h3><p>In medical science, Machine Learning (ML) Techniques play a significant role in disease prediction despite numerous studies conducted to categorize CKD in patients using machine learning tools. Most researchers need to analyze the impact of feature selection techniques, yielding high-quality and reliable results. The efficiency of any Techniques/Algorithms depends on feature selection, feature extraction, and classifiers. In this work, the impact of feature selection is experimented with using the Exhaustive Feature Selection (EFS) method. For the early prediction of CKD, a comparative examination of machine learning classifiers, including Gradient Boost (GB), XGBoost, Decision Tree (DT), Random Forest (RF), and KNN (k-nearest neighbors), are utilized.</p></div><div><h3>Results</h3><p>Two types of datasets, standard (New Model) &amp; real-time data sets collected from the dialysis unit of a reputed hospital in Chennai, are used to carry out extensive experiment analysis. Various metrics, including Accuracy, Precision, Recall, and F1-score, are used to tabulate the results of experiments conducted to measure the performance of the proposed approach for various combinations of test and training data.</p></div><div><h3>Conclusion</h3><p>CKD is an irreversible and silent disease; it might have a high impact on many people and begin to manifest themselves at an early age in life. This research paper analyses the effect of feature selection techniques on early CKD prediction.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 66-77"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307423000426/pdfft?md5=137c3649d2841b5bf30476563074d493&pid=1-s2.0-S2666307423000426-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139638598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge mapping analysis of situational awareness and aviation: A bibliometric study","authors":"Nuno Moura Lopes,&nbsp;Manuela Aparicio,&nbsp;Fátima Trindade Neves","doi":"10.1016/j.ijcce.2024.06.003","DOIUrl":"10.1016/j.ijcce.2024.06.003","url":null,"abstract":"<div><p>This study was motivated by the observed growth and increased significance of situation awareness (SA) in recent years. Despite its acknowledged importance, a notable gap exists in the literature regarding comprehensive systematic reviews of SA within the aviation sector. This gap spurred a meticulous analysis of 754 articles from the Web of Science (WoS) core database for bibliometric knowledge mapping. The primary aim was to fill this gap and acquire a holistic understanding of SA in aviation. This analysis highlighted the USA as the primary contributor to publications, with NASA leading among the institutions in paper contributions. Human Factors and the International Journal of Aerospace Psychology were the leading journals in this domain. This bibliometric study underscored the key focus on healthcare, aviation, performance, workload, and safety through co-occurrence and co-citation analyses. A chronological examination of keywords revealed a central research trajectory centered on patient and crew safety and the impact of automation on human performance in dynamic flight scenarios. Burst keyword analysis pinpointed leading-edge research in SA within healthcare, model, and system design, and the implications of human factors. This study explored the research landscape of SA in aviation using a bibliometric approach. The outcomes shed light on the present research landscape and expedite scholars’ comprehension of advancements in this pivotal field. Finally, we derived a conceptual framework using the main components found in the literature. This framework will help researchers identify the main dimensions of SA.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 279-296"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307424000202/pdfft?md5=ba8360637582c4c744e3d3611a650653&pid=1-s2.0-S2666307424000202-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141622411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing real-time fuel classification with novel multi-scale and multi-level MHOG and light gradient boosting machine","authors":"Hemachandiran S. ,&nbsp;Ajit kumar ,&nbsp;Aghila G.","doi":"10.1016/j.ijcce.2024.08.005","DOIUrl":"10.1016/j.ijcce.2024.08.005","url":null,"abstract":"<div><p>Accurately classifying petrol and diesel fuel using an image processing method is crucial for fuel-related industries such as petrol pumps, refineries, and fuel storage facilities. However, distinguishing between these fuels using traditional methods can be challenging due to their similar visual characteristics. This article introduces a novel multi-scale and multi-level modified histogram of oriented gradients (MHOG) feature descriptors for robust classification of fuel images. Our proposed method involves extracting distinctive features from the images using the novel multi-scale and multi-level MHOG feature descriptor. These features are then utilized to train a range of machine learning classifiers with different hyperparameter settings for an ablation study. To the best of our knowledge, this is the first ablation study for this fuel classification application. To evaluate the effectiveness of our approach, we conduct experiments on a carefully labeled dataset consisting of petrol and diesel fuel images. The results demonstrate the high accuracy of our proposed method, achieving a classification accuracy of 98% using the light gradient boosting machine (LGBM). Furthermore, our method surpasses existing state-of-the-art techniques for fuel image classification. With its superior performance, this approach holds great potential for efficient and effective fuel classification in diverse fuel-related industries.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 398-405"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307424000329/pdfft?md5=87952451f193c05ac6edc135b07b75a4&pid=1-s2.0-S2666307424000329-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142020930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A decentralised federated learning scheme for heterogeneous devices in cognitive IoT","authors":"Huanhuan Ge ,&nbsp;Xingtao Yang ,&nbsp;Jinlong Wang ,&nbsp;Zhihan Lyu","doi":"10.1016/j.ijcce.2024.08.001","DOIUrl":"10.1016/j.ijcce.2024.08.001","url":null,"abstract":"<div><p>Cognitive Internet of Things (IoT) technologies typically rely on substantial data collected from edge devices for data analysis and decision-making. However this reliance often leads to the inadvertent exposure of private data from smart edge devices. Federated learning (FL) is a distributed machine learning framework that protects user privacy by performing collaborative training without uploading private data. Nevertheless, applying classical FL to cognitive IoT systems to preserve privacy preservation faces significant challenges, such as central server failure and communication burden. Furthermore, when edge devices with heterogeneous data and systems participate in federated training, the learning process becomes slow and the performance of edge devices is compromised. To address these challenges, we propose a decentralised FL framework for cognitive IoT, termed DFL–MKF. In DFL– MKF, the centralised server is eliminated and each edge device is dynamically connected. We initialised models for edge devices based on computational and storage capabilities to accommodate system heterogeneity. Edge devices learned from the knowledge of multiple neighbours via knowledge transfer, and knowledge fusion was employed to aggregate the knowledge of multiple neighbours, thereby improving the performance of local models, and addressing data heterogeneity. Comprehensive experiments were performed on three image classification tasks. The results of these experiments demonstrate that the proposed method achieved superior performance compared to various baselines and improved communication efficiency.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 357-366"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307424000299/pdfft?md5=e029201b31143d3e9d6e997ad3677fdc&pid=1-s2.0-S2666307424000299-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141979469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}