International Journal of Cognitive Computing in Engineering最新文献

{"title":"Trie-PMS8: A trie-tree based robust solution for planted motif search problem","authors":"Mohammad Hasan ,&nbsp;Abu Saleh Musa Miah ,&nbsp;Md. Humaun Kabir ,&nbsp;Mahmudul Alam","doi":"10.1016/j.ijcce.2024.07.004","DOIUrl":"10.1016/j.ijcce.2024.07.004","url":null,"abstract":"<div><p>Finding patterns in biological sequences is a crucial and intriguing task. This paper explores the (Ɩ, d) motif search problem, also known as Planted Motif Search (PMS), and discusses its challenging nature as an NP-hard problem. PMS and (Ɩ, d) motif search algorithms are believed to represent the next generation of tools for motif discovery. In this context, PMS deals with n biological sequences and two parameters, Ɩ and d, to identify sequences of Ɩ length that occur in all input strings with, at most, d mismatches. Many existing exact PMS algorithms exhibit exponential time complexity in worst-case scenarios. This paper introduces an innovative algorithm that focuses on improving the efficiency of the sample-driven portion of the process. Specifically, dynamic programming techniques are employed to avoid redundant calculations in frequently used subtrees. Furthermore, this paper presents novel approaches to enhance algorithm performance, such as utilizing a trie tree that significantly reduces the time for the “sort rows by size” step. It has also reduced the spaces that take linked lists on LL-PMS8 (<span><span>Hasan et al., Jun., 2022</span></span>) or reduced the number of l-mers. Using trie tree as the main way to speed things up gives a much better result than older versions of PMS methods like LL-PMS8 (<span><span>Hasan et al., Jun., 2022</span></span>). Overall time complexity reduced than the previous method is 26.17 % and 16.48 % for real-world and generated datasets (<span><span>Hasan et al., 2020</span></span>).</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 332-342"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307424000251/pdfft?md5=846137dd18a119f7d1c056597efd317f&pid=1-s2.0-S2666307424000251-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141962802","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":"Gorilla troops optimization with deep learning based crop recommendation and yield prediction","authors":"A. Punitha ,&nbsp;V. Geetha","doi":"10.1016/j.ijcce.2024.09.006","DOIUrl":"10.1016/j.ijcce.2024.09.006","url":null,"abstract":"<div><div>Agriculture plays a vital role in the Indian economy. Crop recommendation for a specific region is a tedious process as it can be affected by various variables such as soil type and climatic parameters. At the same time, crop yield prediction was based on several features like area, irrigation type, temperature, etc. The latest breakthroughs in Machine Learning (ML) and Artificial Intelligence (AI) technologies pave the way to designing effective crop recommendation and prediction models. Despite the significant advancements of Deep Learning (DL) models in crop recommendation, hyperparameter tuning using metaheuristic algorithms becomes essential for enhanced performance. This tool allows users to anticipate appropriate crops and their expected yields for a provided year, assisting agriculturalists in choosing crops suitable for their area and period and anticipating productivity. This article introduces a Gorilla Troops Optimization with Deep Learning-based Crop Recommendation and Yield Prediction model (GTODL-CRYPM). The proposed GTODL-CRYPM model mainly focuses on two processes, namely, crop recommendation and crop prediction. Firstly, the GTO with Long Short-Term Memory (LSTM) technique is employed to make efficient crop recommendations. Besides, the GTO model is applied to adjust the LSTM parameters optimally. Next, the Deep Belief Network (DBN) technique was executed to predict crop yield accurately. A wide range of experiments have been conducted to report the improved performance of the GTODL-CRYPM model. The outcomes are examined under the Crop Recommendation Dataset and Crop Yield Prediction Dataset. Experimentation outcomes highlighted the significant performance of the GTODL-CRYPM approach on the compared approaches, with a maximum accuracy of 99.88% and an R2 score of 99.14%.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 494-504"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419724","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":"GNN-RM: A trajectory completion algorithm based on graph neural networks and regeneration modules","authors":"Jiyuan Zhang ,&nbsp;Zhenjiang Zhang ,&nbsp;Lin Hui","doi":"10.1016/j.ijcce.2024.07.001","DOIUrl":"10.1016/j.ijcce.2024.07.001","url":null,"abstract":"<div><p>Data about vehicle trajectories assumes a crucial role in applications such as intelligent connected vehicles. However, missing values resulting from sensors and other factors frequently affect real trajectory data. Currently, it is challenging to utilize trajectory completion methods to generate accurate real-time results at an affordable computing cost. This paper proposes GNN-RM, a trajectory completion algorithm based on graph neural networks and regeneration modules, encompassing feature extraction, subgraph construction, spatial interaction graph, and trajectory regeneration modules. The feature extraction algorithm extracts influential data as feature vectors based on certain conditions and organizes these feature vectors into different subgraphs according to categories. The spatial interaction graph constructed through graph neural networks extracts spatial interaction features between vehicles and the environment, while the regeneration modules constructed by multi-head attention mechanisms extract temporal features of vehicles, thereby completing the missing trajectories. The experimental results demonstrate that GNN-RM can achieve higher trajectory completion accuracy with fewer input parameters than multiple baseline models.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 297-306"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307424000226/pdfft?md5=74f56c06adad9bd22b32d9700a64c456&pid=1-s2.0-S2666307424000226-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141637802","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":"Integration of Artificial Intelligence and Wearable Internet of Things for Mental Health Detection","authors":"Wei Wang ,&nbsp;Jian Chen ,&nbsp;Yuzhu Hu ,&nbsp;Han Liu ,&nbsp;Junxin Chen ,&nbsp;Thippa Reddy Gadekallu ,&nbsp;Lalit Garg ,&nbsp;Mohsen Guizani ,&nbsp;Xiping Hu","doi":"10.1016/j.ijcce.2024.07.002","DOIUrl":"10.1016/j.ijcce.2024.07.002","url":null,"abstract":"<div><p>The integration of Artificial Intelligence (AI) and Wearable Internet of Things (WIoT) for mental health detection is a promising area of research with the potential to revolutionize mental health monitoring and diagnosis. Since early detection of mental diseases, i.e., depression, is of great importance for diagnosis and treatment, a fast and convenient way is urgently needed. Traditional diagnostic methods are time-consuming, laborious, over-subjective, and easily lead to misdiagnosis. The advance in information techniques and wearable devices brings innovation to mental disease detection. Therefore, this article first compares intelligent depression detection methods and traditional methods to illustrate the significance and then analyzes the opportunities of the wearable device. Then we provide specific psychophysiological data measured by wearable devices and introduce relevant datasets for depression detection. An illustrative example of depression detection with sleep data is presented and discussed and our proposed ensemble method has improved nearly 10% to baselines. Analytical results demonstrate the great potential of using wearable device-measured psychophysiological data to detect depression intelligently.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 307-315"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307424000238/pdfft?md5=99b396f78cea67ca4f3a330801e23bf8&pid=1-s2.0-S2666307424000238-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141699558","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":"Advanced Parkinson’s Disease Detection: A comprehensive artificial intelligence approach utilizing clinical assessment and neuroimaging samples","authors":"Nusrat Islam,&nbsp;Md. Shaiful Alam Turza,&nbsp;Shazzadul Islam Fahim,&nbsp;Rashedur M. Rahman","doi":"10.1016/j.ijcce.2024.05.001","DOIUrl":"10.1016/j.ijcce.2024.05.001","url":null,"abstract":"<div><p>Medical experts are utilizing neuroimaging and clinical assessments to enhance the early identification of Parkinson’s disease. The current research initiative offers ways to identify Parkinson’s disease using machine learning and transfer learning. To carry out this, we extracted 7500 MRI images from 2022 and 2023 and 12 clinical assessment records from 2010 to 2023 from the well-known Parkinson’s Progression Marker Initiative (PPMI) database. Then, we applied machine and transfer learning approaches using clinical assessment records and MRI images, respectively. To identify Parkinson’s Disease (PD) using samples from clinical assessments, four distinct resampling techniques were employed. Subsequently, three machine learning models were applied to train on these resample records, and the recall score was analyzed. A hybrid of SMOTE and ENN proved to be the most effective approach for handling all of the imbalanced data, according to the recall study. Later, four different feature selection methods were used to find the top 10 features using these new samples. Lastly, we trained and validated the model using nine machine-learning algorithms. We also used explainable AI techniques like LIME and SHAP to interpret clinical assessment records. The extra tree classifier outperformed the others in terms of accuracy, reaching 98.44% using the tree-based feature selection technique. In addition to examining clinical assessment samples, this study investigated Parkinson’s disease using neuroimaging data. In pursuit of this objective, four pre-trained architectures were employed to analyze MRI images through two distinct approaches. The first approach involved utilizing the convolutional layer while replacing the remaining two layers with a customized Artificial Neural Network (ANN). Subsequently, training and evaluation are performed using our MRI samples, followed by analyzing significant weights using a LIME interpretable explainer. The second approach employs an improvisational technique without directly replacing the last layer. Instead, we predicted the weights of our MRI samples using the knowledge of the pre-trained model and stored them. Finally, CNN architecture was utilized for Parkinson’s disease detection, achieving an optimal accuracy of 85.08% with the implementation of DenseNet169 and CNN.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 199-220"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307424000147/pdfft?md5=8c784f6f904d96d32c9fa6405a58701d&pid=1-s2.0-S2666307424000147-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141143341","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":"Multi-Agent cubature Kalman optimizer: A novel metaheuristic algorithm for solving numerical optimization problems","authors":"Zulkifli Musa ,&nbsp;Zuwairie Ibrahim ,&nbsp;Mohd Ibrahim Shapiai","doi":"10.1016/j.ijcce.2024.03.003","DOIUrl":"https://doi.org/10.1016/j.ijcce.2024.03.003","url":null,"abstract":"<div><p>Optimization problems arise in diverse fields such as engineering, economics, and industry. Metaheuristic algorithms, including the Simulated Kalman Filter (SKF), have been developed to solve these problems. SKF, inspired by the Kalman Filter (KF) in control engineering, requires three parameters (initial error covariance <span><math><mrow><mi>P</mi><mo>(</mo><mn>0</mn><mo>)</mo></mrow></math></span>, measurement noise <span><math><mi>Q</mi></math></span>, and process noise <span><math><mi>R</mi></math></span>). However, studies have yet to focus on tuning these parameters. Furthermore, no significant improvement is shown by the parameter-less SKF (with randomized <span><math><mrow><mi>P</mi><mo>(</mo><mn>0</mn><mo>)</mo></mrow></math></span>, <span><math><mi>Q</mi></math></span>, and <span><math><mi>R</mi></math></span>). Randomly choosing values between 0 and 1 may lead to too small values. As an estimator, KF raises concerns with excessively small <span><math><mi>Q</mi></math></span> and <span><math><mi>R</mi></math></span> values, which can introduce numerical stability issues and result in unreliable outcomes. Tuning parameters for SKF is a challenging and time-consuming task. The Multi-Agent Cubature Kalman Filter (MACKO), inspired by the Cubature Kalman filter (CKF), was introduced in this work. The nature of the Cubature Kalman filter (CKF) allows the use of small values for parameters <span><math><mrow><mi>P</mi><mo>(</mo><mn>0</mn><mo>)</mo></mrow></math></span>, <span><math><mi>Q</mi></math></span>, and <span><math><mi>R</mi></math></span>. In the MACKO algorithm, Cubature Transformation Techniques (CTT) are employed. CTT can use small values for parameters <span><math><mrow><mi>P</mi><mo>(</mo><mn>0</mn><mo>)</mo></mrow></math></span>, <span><math><mi>Q</mi></math></span>, and <span><math><mi>R</mi></math></span>, so CKF was developed to overcome KF and other estimation algorithms. Moreover, in CTT, the term local neighborhoods is used to propagate the cubature point in local search, where the radius, <span><math><mi>δ</mi></math></span>, of local search is updated in every iteration to balance between the exploration and exploitation processes. MACKO is evaluated on the CEC 2014 benchmark suite with 30 optimization problems, and its performance is compared with nine existing metaheuristic algorithms. Simulation results demonstrate that MACKO is superior, outperforming the benchmark algorithms, as indicated by Friedman's test with a 5 % significance level.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 140-152"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307424000111/pdfft?md5=ee0644b08d3b1054f7635b0ee37851bc&pid=1-s2.0-S2666307424000111-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140162680","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 novel and secured email classification and emotion detection using hybrid deep neural network","authors":"Parthiban Krishnamoorthy ,&nbsp;Mithileysh Sathiyanarayanan ,&nbsp;Hugo Pedro Proença","doi":"10.1016/j.ijcce.2024.01.002","DOIUrl":"https://doi.org/10.1016/j.ijcce.2024.01.002","url":null,"abstract":"<div><p>Compared to other social media data, email data differs from it in various topic-specific ways, including extensive replies, formal language, significant length disparities, high levels of anomalies, and indirect linkages. In this paper, the creation of a potent and computationally effective classifier to categorize spam and ham email documents is proposed. To assess and validate spam texts, this paper employs a variety of data mining-based classification approaches. On the benchmark Enron dataset, which is open to the public, tests were run. The final 7 Enron datasets were created by combining the six different types of Enron datasets that we had acquired. We preprocess the dataset at an early stage to exclude any useless phrases. This method falls under several categories, including Logistic Regression (LR), Convolutional Neural Networks (CNN), Random Forests (RF), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and suggested Deep Neural Networks (DNN). Using Bidirectional Long Short-Term Memory (BiLSTM), email documents may be screened for spam and labeled as such. In performance comparisons, DNN-BiLSTM outperforms other classifiers in terms of accuracy on all seven Enron datasets. In comparison to other machine learning classifiers, the findings demonstrate that DNN-BiLSTM and Convolutional Neural Networks can categorize spam with 96.39 % and 98.69 % accuracy, respectively. The report also covers the dangers of managing cloud data and the security problems that might occur. To safeguard data in the cloud while maintaining privacy, hybrid encryption is examined in this white paper. In the AES-Rabit hybrid encryption system, the symmetric session key exchange-based Rabit technique is combined with the benefits of the AES algorithm for faster data encryption.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 44-57"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307424000019/pdfft?md5=33188ffd49284bc48ef3e041ba0a1d10&pid=1-s2.0-S2666307424000019-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139434163","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":"Navigating the Nexus: A systematic review of the symbiotic relationship between the metaverse and gaming","authors":"Sahar Yousif Mohammed ,&nbsp;Mohammed Aljanabi ,&nbsp;Thippa Reddy Gadekallu","doi":"10.1016/j.ijcce.2024.02.001","DOIUrl":"https://doi.org/10.1016/j.ijcce.2024.02.001","url":null,"abstract":"<div><p>The advent of the metaverse has sparked profound interest in its integration within the gaming domain. Games, being intrinsic components of the metaverse, have attracted considerable attention, prompting inquiries into the transformative influence of the metaverse on the gaming industry and its communities within this immersive digital realm. This study aims to synthesize the literature by providing a comprehensive overview of recent studies on the complex interplay between video games and the metaverse, illuminating the benefits, difficulties, and uncharted territory of this field. Fifteen (15) relevant studies were reviewed in accordance with PRISMA criteria, supplemented with the LDA topic modeling to answer the developed research questions. The research showed that the metaverse has many impacts on the gaming industry; it serves as a platform for interactive games, a driver for collaborative gaming environments, a medium for immersive virtual reality (VR) experiences, and a source of useful information about the basic aspects and mechanisms that support the connected relationship between the metaverse and gaming. Furthermore, the evolution of these dimensions and processes across time was investigated. This systematic review makes a noteworthy addition to the ongoing scholarly discussion over the convergence of the metaverse and gaming. This statement highlights the significant capacity for transformation that arises from the integration of these elements, while also recognizing the intricate and demanding nature of this process. The ongoing transformation of the gaming industry and its communities by the metaverse prompts an investigation that lays the groundwork for future scholarly inquiry. This study aims to provide a detailed examination and enhanced comprehension of the intricate relationship between these two domains.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 88-103"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307424000056/pdfft?md5=3948c8774103b53ccac51a0d236f3398&pid=1-s2.0-S2666307424000056-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139936196","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":"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":"Novel Darknet traffic data synthesis using Generative Adversarial Networks enhanced with oscillatory Growing Cosine Unit activated convolution layers","authors":"Antony Pradeep C ,&nbsp;Geraldine Bessie Amali D ,&nbsp;Mathew Mithra Noel ,&nbsp;Muhammad Rukunuddin Ghalib ,&nbsp;Prabhakar Rontala Subramaniam ,&nbsp;Chitra Venugopal","doi":"10.1016/j.ijcce.2024.01.004","DOIUrl":"https://doi.org/10.1016/j.ijcce.2024.01.004","url":null,"abstract":"<div><p>The Darknet is an anonymous, encrypted collection of websites, with a passive listening nature - accepting incoming packets, while not supporting outgoing packets. Thus, it can potentially host criminal or malicious activity and software, becoming a cyber security threat. Network Detection Systems are effective in identifying dark net traffic and mitigating its ill effects. However, capturing and extracting data from raw network traffic for training these systems can be time-intensive and costly. Using the CIC-Darknet 2020 dataset, this paper proposes using a Novel Generative Adversarial Networks (GAN) Architecture generating the required training and testing data for these systems. This uses a combination of Growing Cosine Unit (GCU) activated convolution layers and Dense layers for the Generator. Feature selection with statistical correlation methods is used to select the most relevant features. An independently trained Evaluator network is used to evaluate the generated data. The proposed system is compared to other established Tabular data GANs like Conditional Tabular GAN (CTGAN) and Copula GAN with similar parameters and on the same data. The Proposed GAN architecture outperforms CTGAN and CopulaGAN by 20 % and 10 % in ters of accuracy while also taking 90 % and 30 % less time to train respectively. Results from measuring similarity of data using the Inverted Kolmogorov - Smirnov D statistic also show significantly better results for the Proposed GAN Architecture. This shows significant promise in using Generative models to reduce the time and effort costs associated with collecting and formatting data to use in research and for training detection systems.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 58-65"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307424000032/pdfft?md5=397991379654e2ab598cde6ef7f1a335&pid=1-s2.0-S2666307424000032-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139550089","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}