International Journal of Communication Networks and Information Security (IJCNIS)
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1021 research outputs found
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Non-Negative Matrix Factorization Based Single Channel Source Separation
Get PDFThe significance of speech recognition systems is widespread, encompassing applications like speech translation, robotics, and security. However, these systems often encounter challenges arising from noise and source mixing during signal acquisition, leading to performance degradation. Addressing this, cutting-edge solutions must effectively incorporate temporal dependencies spanning longer periods than a single time frame. To tackle this issue, this study introduces a novel model employing non-negative matrix factorization (NMF) modelling. This technique harnesses the scattering transform, involving wavelet filters and pyramid scattering, to compute sources and mitigate undesired signals. Once signal estimation is achieved, a source separation algorithm is devised, employing an optimization process grounded in training and testing approaches. By quantifying performance metrics, a comparative analysis is conducted between existing methods and the proposed model. Results indicate the superior performance of the suggested approach, underscored by these metrics. This signifies that the NMF and scattering transform-based model adeptly addresses the challenge of effectively utilizing temporal dependencies spanning more than a single time frame, ultimately enhancing speech recognition system efficacy
Research on the Application of Data Mining Technology in the Influence of ESG Rating on Corporation Financial Performance
Get PDFAiming at the analysis of corporation financial performance, this paper puts forward a data mining method to judge the impact of ESG rating on corporation financial performance. Firstly, the corporation’s financial performance data set is established according to ESG rating to test the wireless transmission capability and broadband transmission integrity of the data; Then, wireless transmission analysis is carried out for each ESG rating; Finally, with the help of data mining technology, the abnormal financial performance indicators of corporations are found, and the transmission indicators are changed in time. The results show that data mining technology has a high effect on ESG rating promotion, wireless transmission effect, mining depth, and economic benefits, with an improvement of 45%, the accuracy of evaluation results is over 90%, and the stability of results is over 80%. Therefore, under the condition of ESG rating, data mining technology can meet the evaluation requirements of a corporation’s financial performance impact and improve the effectiveness of performance data transmission
Study on Network Virtual Printing Sculpture Design using Artificial Intelligence
Get PDFSculptures are visionaries of a country’s culture from time immemorial. Chinese sculptures hold an aesthetic value in the global market, catalysed by opening the country's gates. On the other hand, this paved the way for many duplicates and replicates of the original sculptures, defaming the entire artwork. This work proposes a defrauding model that deploys a Siamese-based Convolutional Neural Network (S-CNN) that effectively detects the mimicked sculpture images. Nevertheless, adversarial attacks are gaining momentum, compromising the deep learning models to make predictions for faked or forged images. The work uses a Simplified Graph Convolutional Network (SGCN) to misclassify the adversarial images generated by the Fast Gradient Sign Method (FGSM) to combat this attack. The model's training is done with adversarial images of the Imagenet dataset. By transfer learning, the model is rested for its efficacy in identifying the adversarial examples of the Chinese God images dataset. The results showed that the proposed model could detect the generated adversarial examples with a reasonable misclassification rate
Design of Digital Museum System Based on Optimized Virtual Reality Technology
Get PDFAlthough China's cultural characteristics were diverse, its museum supply is limited. Traditional institutions would be unable to meet the people's needs for culture dissemination, historic preservation, cultural exchange, and science and research in history's era of the internet. By utilizing VR Technology in the field of furniture decorating, a new viewpoint and method for the development of a virtual museum are unveiled. Using optimum VR Technology in museum exhibition design based on the ideas of architecture, atmospheric art, light settings, coloring style, and ecological design, humans may be presented with natural and cultural heritages. The advancement of completely separate HTML text languages, QuickTime Virtual Reality innovation, Interactive Virtual Model-based Linguistic, three-dimensional (3D) applications, and data interaction systems for the exhibition has done result from an inquiry into virtual reality's history, definition, application, and present state. Ultimately, the planned work's effectiveness is analyzed and compared to other related projects to maximize its efficacy. Using the Origins software, the results of this study are shown
The Use of 5G Network Technology to Reform and Innovate the Culture of Opening Ceremony in Chinese Winter Olympics
Get PDFThe application of 5G network technology in the opening ceremony of the Winter Olympic Games has changed the form of cultural display and made form change and innovation a research hotspot. The original cultural display method cannot meet the requirements of change and innovation, and the innovation effect after the change is poor. To this end, this paper proposes a reform and innovation model based on 5G network technology to improve the form of cultural display. First, wireless self- organization and sensors are used to obtain data in the form of cultural display, and data transformation is carried out through 5G network technology, and the form is changed according to the characteristics of cultural data, and irrelevant change content is abandoned. Then, according to the ultra-wide communication technology, the change rate and display effect of the special effect are analyzed, and compared with the actual reception effect, the parameters and indicators of the cultural display form are adjusted. The innovative design results show that under the condition of 5G network transmission, the transformation and innovation mode can improve the realization effect of cultural display, and the improvement rate is greater than the actual design requirements, which can meet the needs of innovative design
Daily Activities of Elder Adults Using Optimized Deep Learning Model in China
Get PDFThe health and happiness of seniors can be influenced by the design of public spaces and buildings. Planning for age-friendly communities requires taking into account the wide variety of daily activities and public facility consumption among older persons. Yet, conventional approaches fall short when it comes to providing comprehensive, objective measures of the actions of the elderly. This research proposes an attention-based recognition approach by decomposing neural network outputs into class-dependent features using dictionary learning to improve discrimination performance. This research aimed to develop an empirical typology of activity of daily living (ADL) and its connection to health in China's ageing population. A deep learning-based model was rummaged to categorise the contributors in the Chinese Longitudinal Healthy Longevity Study (CLHLS) into subgroups based on their abilities with ADL. To be more precise, the study trains a category-based recognition network (CRN) by superimposing a basic but effective specific recognition encoding (SRE) unit on top of convolutional layers. In order to encode attention maps for each class, the SRE module uses the feature maps produced by the Convolutional Neural Networks (CNN) to learn a class-specific vocabulary. Class-wise adaptive feature refining is achieved by multiplying these attention maps by the input features. In this case, a gazelle optimization algorithm (GOA) handles the hyper-parameter tuning process of the proposed model. This case study is the first of its kind in a dense metropolitan region, and it uses objective measurements to evaluate the activity spaces of the elderly. This research provides empirical data for the promotion of healthy ageing in urban areas
IoT-Based Data Size Minimization Using Cluster-Based-Similarity- Elimination
Get PDFThis paper proposes a new redundancy reduction approach for continuous data flow from IoT devices based on minimizing the size of IoT data using a novel cluster-based-similarity-elimination algorithm. The continuously flowing data from IoT devices are characterized by the existence of redundant records. This redundancy not only leads to the overfitting of models but also requires a large processing power because of the large number of records. Feature selection is a technique used to partially reduce the data and thus redundancy, however, this is not sufficient. Removing redundant data is considered of utmost importance because as smart city scenarios are implemented, flow data generation requires more advanced analytics to deal with the evolution and regrowth of the IoT environment. Thus, this study aims to minimize processing time while maintaining the best accuracy by minimizing data similarity, therefore addressing the overfitting problem, and saving time. The proposed approach minimizes the data size, considering the number of tuples. The effectiveness of the proposed approach was validated using various classification algorithms and evaluation metrics. The results show a significant improvement compared with traditional approaches, resulting in a reduction in the real-time classification execution time to only 9% of the original time. This approach can be used to optimize data size and achieve accurate results with a fast execution time while also addressing overfitting issues
A Grey Wolf Optimization-Based Clustering Approach for Energy Efficiency in Wireless Sensor Networks
Get PDFIn the realm of Wireless Sensor Networks, the longevity of a sensor node's battery is pivotal, especially since these nodes are often deployed in locations where battery replacement is not feasible. Heterogeneous networks introduce additional challenges due to varying buffer capacities among nodes, necessitating timely data transmission to prevent loss from buffer overflows. Despite numerous attempts to address these issues, previous solutions have been deficient in significant respects. Our innovative strategy employs Grey Wolf Optimization for Cluster Head selection within heterogeneous networks, aiming to concurrently optimise energy efficiency and buffer capacity. We conducted comprehensive simulations using Network Simulator 2, with results analysed in MATLAB, focusing on metrics such as energy depletion rates, remaining energy, node-to-node distance, node count, packet delivery, and average energy in the cluster head selection process. Our approach was benchmarked against leading protocols like LEACH and PEGASIS, considering five key performance indicators: energy usage, network lifespan, the survival rate of nodes over time, data throughput, and remaining network energy. The simulations demonstrate that our Grey Wolf Optimisation method outperforms conventional protocols, showing a 9% reduction in energy usage, a 12% increase in node longevity, a 9.8% improvement in data packet delivery, and a 12.2% boost in data throughput
Visualizing and Understanding the Customized Convolutional Neural Networks to Identify Hand Written Odia Characters and its Pattern Using Generative AI
No full textCharacter recognition is one of the on the field of deep learning. In the field of pattern matching and pattern recognition research based on deep learning model like ResNet and InceptionV3.Here we have used deep learning methods for our own Mendeley published dataset as OHCD_GIETV1.In this paper we selected some sample images of Simple character OHCD_GIETV1 and divided into different groups like Vowels, Voiceless, Voiced and Unstructured dataset .At tbe beginning we have used two popular methods of deep learning like ResNet and InceptionV3 to recognize Handwritten Odia characters .Among them Inceptionv3 gives better results. However despite leveraging the power of the model we found that the accuracy was not satisfactory. To overcome this limitation we implemented Customized Convolutional Neural Networks with MaxConv2D layer.It combines the operation of maxpooling and Conv2D.We increased our hidden layer up to 7, increased our epoch and achieved an accuracy of vowels 95.7%, Voiceless 94.0%, voiced 93.97% and Unstructured 94.24%.We suggested that our customized CNN is more effective across various subset of the dataset and make its robust solution for our Odia Handwritten Characters. To further enhance our capabilities in pattern recognition we implemented Generative AI with Hugging face and GRU with LSTM by using python library streamlit. Meanwhile GRULSTM gave their effectiveness in sequence modelling and obtained as a result of 95% probability in pattern matching .It indicate our model gives accurate and confident its prediction.Our research work is not only contribute the field of machine learning but also provides an valuable resources for our future research to other Indian language
An Optimized Deep Learning Based Optimization Algorithm for the Detection of Colon Cancer Using Deep Recurrent Neural Networks
Get PDFColon cancer is the second leading dreadful disease-causing death. The challenge in the colon cancer detection is the accurate identification of the lesion at the early stage such that mortality and morbidity can be reduced. In this work, a colon cancer classification method is identified out using Dragonfly-based water wave optimization (DWWO) based deep recurrent neural network. Initially, the input cancer images subjected to carry a pre-processing, in which outer artifacts are removed. The pre-processed image is forwarded for segmentation then the images are converted into segments using Generative adversarial networks (GAN). The obtained segments are forwarded for attribute selection module, where the statistical features like mean, variance, kurtosis, entropy, and textual features, like LOOP features are effectively extracted. Finally, the colon cancer classification is solved by using the deep RNN, which is trained by the proposed Dragonfly-based water wave optimization algorithm. The proposed DWWO algorithm is developed by integrating the Dragonfly algorithm and water wave optimization