International Journal of Cognitive Computing in Engineering最新文献

{"title":"Enhancing personalized learning: AI-driven identification of learning styles and content modification strategies","authors":"Md. Kabin Hasan Kanchon,&nbsp;Mahir Sadman,&nbsp;Kaniz Fatema Nabila,&nbsp;Ramisa Tarannum,&nbsp;Riasat Khan","doi":"10.1016/j.ijcce.2024.06.002","DOIUrl":"https://doi.org/10.1016/j.ijcce.2024.06.002","url":null,"abstract":"<div><p>In the rapidly advancing era of educational technology, customized learning materials have the potential to enhance individuals’ learning capacities. This research endeavors to devise an effective method for detecting a learner’s preferred learning style and subsequently adapting the learning content to align with that style, utilizing artificial intelligence AI techniques. Our investigation finds that analyzing learners’ web tracking logs for activity classification and categorizing individual responses for feedback classification are highly effective methods for identifying a learner’s learning styles, such as visual, auditory, and kinesthetic. A custom dataset has been constructed in this research comprising approximately 506 samples and 22 features utilizing the Moodle learning management system (LMS), successfully categorizing students into their respective learning styles. Furthermore, decision tree, random forest, support vector machine (SVM), logistic regression, XGBoost, blending ensemble, and convolutional neural network (CNN) algorithms with corresponding optimized hyperparameters and synthetic minority oversampling technique (SMOTE) have been applied for learning behavior classification. The blending ensemble technique with the XGBoost meta-learning model accomplished the best performance for learning style detection with an accuracy of 97.56%. Next, the text content of the electronic documents is modified by employing different natural language processing (NLP) techniques, including named entity recognition of spaCy, knowledge graph, generative pre-trained transformer 3 (GPT-3), and text-to-text transfer transformer (T5) model, to accommodate diverse learning styles. Various approaches, such as color coding, audio scripts, mind maps, flashcards, etc., are implemented to adapt the content effectively for the detected categories of learners. The spaCy NLP-based named entity recognition (NER) model demonstrates a 94.16% F1 score and 0.92 exact match ratio for color coding text generation of ten electronic documents comprising 790 distinct individual words. These modifications aim to cater to the unique preferences of learners, fostering a more personalized and engaging educational experience. To the best of our knowledge, this is the first time an integrated learning style detection and content modification system has been developed in this work utilizing efficient AI techniques and a private dataset.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 269-278"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307424000184/pdfft?md5=695b8fb7c258779641d99fce8531d7a5&pid=1-s2.0-S2666307424000184-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141583230","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":"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":"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":"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":"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":"An effective object detection and tracking using automated image annotation with inception based faster R-CNN model","authors":"K. Vijiyakumar (Assistant Professor) ,&nbsp;V. Govindasamy (Associate Professor) ,&nbsp;V. Akila (Assistant Professor)","doi":"10.1016/j.ijcce.2024.07.006","DOIUrl":"10.1016/j.ijcce.2024.07.006","url":null,"abstract":"<div><p>The present study advances object detection and tracking techniques by proposing a novel model combining Automated Image Annotation with Inception v2-based Faster RCNN (AIA-IFRCNN). The research methodology utilizes the DCF-CSRT model for image annotation, Faster RCNN for object detection, and the inception v2 model for feature extraction, followed by a softmax layer for image classification. The proposed AIA-IFRCNN model is evaluated on three benchmark datasets: Bird (Dataset 1), UCSDped2 (Dataset 2), and Under Water (Dataset 3), to determine prediction accuracy, annotation time, Center Location Error (CLE), and Overlap Rate (OR). The experimental results indicate that the AIA-IFRCNN model outperformed existing models regarding detection accuracy and tracking performance. Notably, it achieved a maximum detection accuracy of 95.62 % on Dataset 1, outperforming other models. Additionally, it achieved minimum average CLE values of 4.16, 5.78, and 3.54, and higher overlap rates of 0.92, 0.90, and 0.94 on the respective datasets (1, 2 and 3). Hence, this research work on object detection and tracking using the AIA-IFRCNN model is essential for improving system efficiency and fostering innovation in the field of computer vision and object tracking.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 343-356"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307424000275/pdfft?md5=c89eb23204a378c89f4401b7c58b2cd7&pid=1-s2.0-S2666307424000275-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964412","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}