Internet of Things (IoT) devices within smart cities, require innovative detection methods. This paper addresses this critical challenge by introducing a deep learning-based approach for the ...detection of network traffic attacks in IoT ecosystems. Leveraging the Kaggle dataset, our model integrates Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) to capture both spatial and sequential features in network traffic data. We trained and evaluated our model over ten epochs, achieving an impressive overall accuracy rate of 99%. The classification report reveals the model’s proficiency in distinguishing various attack categories, including ‘Normal’, ‘DoS’ (Denial of Service), ‘Probe’, ‘U2R’ (User to Root), and ‘Sybil’. Additionally, the confusion matrix offers valuable insights into the model’s performance across these attack types. In terms of overall accuracy, our model achieves an impressive accuracy rate of 99% across all attack categories. The weighted- average F1-score is also 99%, showcasing the model’s robust performance in classifying network traffic attacks in IoT devices for smart cities. This advanced architecture exhibits the potential to fortify IoT device security in the complex landscape of smart cities, effectively contributing to the safeguarding of critical infrastructure
The template matching technique is one of the most applied methods to find patterns in images, in which a reduced-size image, called a target, is searched within another image that represents the ...overall environment. In this work, template matching is used via a co-design system. A hardware coprocessor is designed for the computationally demanding step of template matching, which is the calculation of the normalized cross-correlation coefficient. This computation allows invariance in the global brightness changes in the images, but it is computationally more expensive when using images of larger dimensions, or even sets of images. Furthermore, we investigate the performance of six different swarm intelligence techniques aiming to accelerate the target search process. To evaluate the proposed design, the processing time, the number of iterations, and the success rate were compared. The results show that it is possible to obtain approaches capable of processing video images at 30 frames per second with an acceptable average success rate for detecting the tracked target. The search strategies based on PSO, ABC, FFA, and CS are able to meet the processing time of 30 frame/s, yielding average accuracy rates above 80% for the pipelined co-design implementation. However, FWA, EHO, and BFOA could not achieve the required timing restriction, and they achieved an acceptance rate around 60%. Among all the investigated search strategies, the PSO provides the best performance, yielding an average processing time of 16.22 ms coupled with a 95% success rate.
The early detection of brain tumor is crucial for effective treatment and improved patient prognosis in Industrial Information Systems. This research introduces a novel computational model employing ...a three-layer Convolutional Neural Network (CNN) for the identification of brain tumors in Industrial Information Systems. Leveraging advanced computational techniques, this proposed model can autonomously detect intricate patterns and features from medical imaging data, resulting in more accurate and expedited diagnoses. With an impressive 90 % precision rate, our model demonstrates the potential to serve as a valuable tool for medical professionals working in the field of neuroimaging. By presenting a dependable and precise computational model, this study contributes to the advancement of brain tumor identification within the domain of medical imaging. We anticipate that our methodology will aid healthcare providers in making more accurate diagnoses, thereby leading to enhanced patient outcomes. Potential avenues for future research encompass refining the model's fundamental architecture and exploring real-time therapeutic applications.
•Leveraging advanced computational techniques, this proposed model can detect intricate patterns and features from medical imaging data.•With 90 % precision rate, our model demonstrates the potential to serve as a valuable tool for medical professionals.•This study contributes to the advancement of brain tumor identification within the domain of medical imaging.•We anticipate that our methodology will aid healthcare providers in making more accurate diagnoses.
Voice is an essential component of human communication, serving as a fundamental medium for expressing thoughts, emotions, and ideas. Disruptions in vocal fold vibratory patterns can lead to voice ...disorders, which can have a profound impact on interpersonal interactions. Early detection of voice disorders is crucial for improving voice health and quality of life. This research proposes a novel methodology called VDDMFS voice disorder detection using MFCC (Mel-frequency cepstral coefficients), fundamental frequency and spectral centroid which combines an artificial neural network (ANN) trained on acoustic attributes and a long short-term memory (LSTM) model trained on MFCC attributes. Subsequently, the probabilities generated by both the ANN and LSTM models are stacked and used as input for XGBoost, which detects whether a voice is disordered or not, resulting in more accurate voice disorder detection. This approach achieved promising results, with an accuracy of 95.67%, sensitivity of 95.36%, specificity of 96.49% and f1 score of 96.9%, outperforming existing techniques.
As Intelligent Transport Systems (ITS) continue to evolve, the quest for improving road safety and transportation efficiency has gained renewed emphasis. One of the pivotal aspects in this endeavor ...is the detection and analysis of driver behavior. Recognizing signs of fatigue, distraction, or inattentiveness is critical in enhancing road safety and optimizing traffic flow. In this paper, we present a pioneering approach to driver behavior detection within the realm of ITS using deep learning models in the Cyber-Physical Systems (CPS) framework. Our research focuses on the discernment of critical behaviors such as eye closure, open-eye state, yawning, and non-yawning instances. With an unwavering commitment to road safety and transportation efficiency, we've harnessed the power of deep learning to design, develop, and train an exceptionally accurate model. Through rigorous evaluation, we achieved an impressive 94% accuracy. Our findings unveil the potential of CPS-based solutions for real-time driver behavior monitoring, providing a foundation for safer roadways and more streamlined traffic management. The proposed deep learning model offers robust and accurate predictions, enabling timely responses to various driving conditions. This research significantly advances the field of driver behavior analysis within the context of intelligent transportation systems, with broad implications for road safety and traffic management.
Background: Prostate cancer is the 4th most common type of cancer. To reduce the workload of medical personnel in the medical diagnosis of prostate cancer and increase the diagnostic accuracy in ...noisy images, a deep learning model is desired for prostate cancer detection. Methods: A multi-scale denoising convolutional neural network (MSDCNN) model was designed for prostate cancer detection (PCD) that is capable of noise suppression in images. The model was further optimized by transfer learning, which contributes domain knowledge from the same domain (prostate cancer data) but heterogeneous datasets. Particularly, Gaussian noise was introduced in the source datasets before knowledge transfer to the target dataset. Results: Four benchmark datasets were chosen as representative prostate cancer datasets. Ablation study and performance comparison between the proposed work and existing works were performed. Our model improved the accuracy by more than 10% compared with the existing works. Ablation studies also showed average improvements in accuracy using denoising, multi-scale scheme, and transfer learning, by 2.80%, 3.30%, and 3.13%, respectively. Conclusions: The performance evaluation and comparison of the proposed model confirm the importance and benefits of image noise suppression and transfer of knowledge from heterogeneous datasets of the same domain.
The smart city vision has driven the rapid development and advancement of interconnected technologies using the Internet of Things (IoT) and cyber-physical systems (CPS). In this paper, various ...aspects of IoT and CPS in recent years (from 2013 to May 2023) are surveyed. It first begins with industry standards which ensure cost-effective solutions and interoperability. With ever-growing big data, tremendous undiscovered knowledge can be mined to be transformed into useful applications. Machine learning algorithms are taking the lead to achieve various target applications with formulations such as classification, clustering, regression, prediction, and anomaly detection. Notably, attention has shifted from traditional machine learning algorithms to advanced algorithms, including deep learning, transfer learning, and data generation algorithms, to provide more accurate models. In recent years, there has been an increasing need for advanced security techniques and defense strategies to detect and prevent the IoT and CPS from being attacked. Research challenges and future directions are summarized. We hope that more researchers can conduct more studies on the IoT and on CPS.
Sustainable entrepreneurship refers to the practice of creating and managing a business venture in a manner that is environmentally and socially responsible while still maintaining financial ...profitability. Social media has become essential for entrepreneurs to build and promote their businesses. It allows them to connect with potential customers, build brand awareness, and reach a wider audience. In recent years, there has been increasing interest in exploring the relationship between sustainable entrepreneurship and social media and how social media can impact sustainable entrepreneurship development. This paper aims to analyze social media's impact on sustainable entrepreneurship development. The study uses data collected from Scopus to identify keywords related to social media, entrepreneurship, and sustainability. Coupling analysis identifies the most frequent keyword groups and clusters and measures their centrality, impact, and color. The results show that social media and entrepreneurship are strongly linked and that social media can positively impact sustainable entrepreneurship development. The study also highlights the importance of ecosystem thinking in promoting sustainable entrepreneurship and the potential of machine learning and sentiment analysis in studying the impact of social media on entrepreneurship. The findings of this study can be useful for policymakers, entrepreneurs, and researchers interested in promoting sustainable entrepreneurship through social media.
With the current COVID‐19 pandemic, sophisticated epidemiological surveillance systems are more important than ever because conventional approaches have not been able to handle the scope and ...complexity of this global emergency. In response to this challenge, the authors present the state‐of‐the‐art SEIR‐Driven Semantic Integration Framework (SDSIF), which leverages the Internet of Things (IoT) to handle a variety of data sources. The primary innovation of SDSIF is the development of an extensive COVID‐19 ontology, which makes unmatched data interoperability and semantic inference possible. The framework facilitates not only real‐time data integration but also advanced analytics, anomaly detection, and predictive modelling through the use of Recurrent Neural Networks (RNNs). By being scalable and flexible enough to fit into different healthcare environments and geographical areas, SDSIF is revolutionising epidemiological surveillance for COVID‐19 outbreak management. Metrics such as Mean Absolute Error (MAE) and Mean sqḋ Error (MSE) are used in a rigorous evaluation. The evaluation also includes an exceptional R‐squared score, which attests to the effectiveness and ingenuity of SDSIF. Notably, a modest RMSE value of 8.70 highlights its accuracy, while a low MSE of 3.03 highlights its high predictive precision. The framework's remarkable R‐squared score of 0.99 emphasises its resilience in explaining variations in disease data even more.
SEIR‐Driven Semantic Integration Framework (SDSIF) is a cutting‐edge solution designed to handle diverse data sources, empowered by the Internet of Things (IoT). SDSIF's core innovation is creating a comprehensive COVID‐19 ontology, enabling unparalleled data interoperability and semantic inference. Beyond real‐time data integration, the framework supports advanced analytics, anomaly detection and predictive modelling with Recurrent Neural Networks (RNNs).
