International Journal of Communication Networks and Information Security (IJCNIS)
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1021 research outputs found
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Improving Spectrum Efficiency in 5G Networks via Collaborative Spectrum Sharing for MIMO-NOMA Enhancement
Get PDFThis research utilises two innovative ways to improve the spectrum efficiency of the 5G Downlink Non-Orthogonal Multiple Access (NOMA) power domain. Enhancements are achieved by a Cooperative Cognitive Radio Network (CCRN). Single-Input Single-Output (SISO), Multiple-Input Multiple-Output (MIMO), and Massive Multiple-Input Multiple-Output (M-MIMO) configurations are evaluated within a single cell of a communication network. NOMA users initially compete for CCRN common control channels. NOMA customers are given high-priority dedicated control channels during the second approach. The proposed approaches are assessed using MATLAB for three parameters: distance, power localization coefficient, and transmission power scenarios. Simulation involves four users utilizing 80 MHz bandwidths and Quadrature Phase-Shift Keying (QPSK) modulation. We examine successive interference cancellation and channel instability assuming that Rayleigh signal fades with frequency. User 4 attained the best Spectral efficiency compared to the other four users, achieving 3.9 bps/Hz/cell for SISO Downlink NOMA, 5.1 for CCRN using common channels, and 7.2 for dedicated control channels. The findings were achieved at a transmit power of 40 dBm. User 4, the top performer, attained a spectral efficiency of 51% utilising a 64 x 64 MIMO Downlink NOMA system. At 40 dB transmit power, common control channels and dedicated control channels improved spectral efficiency performance by 64% and 65% respectively compared to SISO Downlink NOMA. Moreover, 128 × 128 M-MIMO Downlink NOMA improved spectral efficiency performance by 79% for the highest-ranked U4 user. When compared to SISO Downlink NOMA at 40 dB transmit power, The CCRN combining common control channels and dedicated control channels improved spectral efficiency performance by 85% and 86%, respectively. According to the study, the second suggested choice, dedicated control channels with Cooperative Cognitive Radio NOMA (CCR-NOMA), provides clients with the maximum spectrum efficiency. MIMO and M-MIMO enhance spectrum efficiency
Automated Screening of Brain Disorders: A Machine Learning Model for MRI Classification
Get PDFThis study investigated the potential of using convolutional neural networks (CNNs) for diagnosing brain diseases based on MRI scans. The aim was to compare the accuracy of CNNs to clinician diagnoses and explore their limitations. In the course of the research, the following theoretical methods were used (literature analysis, generalisation); diagnostic (anamnestic survey, the use of MRI); empirical (study of the experience of medical organisations, regulatory documentation); methods of mathematical statistics and deep machine learning. A high-performing CNN model was developed, exhibiting excellent accuracy for specific diseases such as dementia with Lewy bodies. However, challenges were identified with distinguishing meningiomas and ependymomas, suggesting the need for further training data and refinement. These results, together with the conclusions of the works of other authors, continue the path to the implementation of quality education and artificial intelligence in clinical settings. The possibilities of using AI in neurosurgery and neurology are expanding more and more. The main areas of application are diagnostics, models of outcomes and treatment. Further research should focus on improving AI techniques, increasing databases and involving more patients for each of the diseases, including a larger control group
Blockchain Framework for Digital Learning and Information and Communications Technology
Get PDFAt present, the economic ties between countries worldwide are getting closer and closer. In a world where the internet industry is developing rapidly, Digital learning and ICT applications in blockchain have gradually matured. This paper takes digital learning and ICT blockchain application in e-commerce as the main research object, The rapid development of e-commerce has been promoted through the extensive application of digital learning and information and communication technology blockchain in e-commerce. Digital learning and information and communication technology solve the problems of e-commerce payment with encryption characteristics and security and openness in blockchain; At the same time, the information can be traced and cannot be tampered with to solve the quality problem of e-commerce goods. In a real sense to promote the sustainable development of the field of e-commerce, this study provides new ideas and guidance for the blockchain framework of e-learning and ICT in e-commerce
Smart Garden Management System Based on the combination of Internet of Things and Geographic Information System Technology
Get PDFThe Internet of Things (IoT) technology is inevitably forging ahead in a number of areas all among them being the agricultural sector and in particular the affected environment. This paper utilizes the farm's IoT where through an effective analysis, the smart garden is designed and managed. It blends field research, project implementations and theoretical analysis to improve the case implementation. It can be considered as more rigorous and practical. The study intends, first and foremost, to look at agricultural IoT based technologies and how IoT changes the way farmers work and agricultural landscapes look. Different theories have been used in the implementation of these concepts which include agriculture, tourism, and landscape design. The technical side will integrate the Internet of Things technology, the adaptation theory of agricultural industrialization, the formation of the theory of ecotourism, the application of tourism psychological theory in tourism discipline, the ecology theory of landscape and aesthetics theory, and landscape gardening planning and design theory. A comprehensive examination of the smart park has been conducted, including relevant theories and the application of planning and design principles. This systematic research aims to provide scientific direction for the agricultural aspects of the smart park. In order to offer scientific direction for the planning and design of agricultural Internet of Things (IoT) in smart gardens, this study presents a theoretical framework for the planning and design of smart gardens that incorporate agricultural IoT. The framework is comprehensively explained, encompassing its concept, distinguishing features, relevant theories, and guidance approaches
The Development of Internet Intelligent Platform in Art Education and Emotional Interaction Therapy for High School Students in Shanghai
Get PDFHuman sentiment represents a complex aspect of psychology and consistently attracts attention across various scientific disciplines. This type of communication, inherently embedded in human interaction, conveys not just explicit messages but also a wealth of underlying emotional elements. This multifaceted nature of emotional communication is leveraged extensively in the study of emotions. In modern society, the unprecedented development of social economy and the unprecedented fierce competition for human survival have destroyed the harmony of human itself, between human beings and between human beings and nature. In the final analysis, it breaks the harmony of people's emotions, which leads to the negative development of emotions, so emotional cultivation is put on the agenda. Especially the emotional problems of senior high school students are particularly prominent. Based on the Internet intelligence platform, this paper analyzes the development of art education and emotional interaction therapy of senior high school students in Shanghai, and analyzes it through BP (Back-Propagation) neural network and DNN (Deep Neural Networks) Model. The results of the experimental evaluation indicate that the mean accuracy for identifying emotions stands at 61.4%. Within the spectrum of error assessment, the algorithm introduced in this study successfully mitigates 75.4% of potential inaccuracies. Therefore, a 60.2% improvement in the unweighted recognition rate of discrete emotions is quite meaningful, and also handles the unweighted case well. On the other hand, the recognition rate of emotional features under different samples has an average analysis strength of 67.4%
Clustering Methods for Network Data Analysis in Programming
Get PDFIn the modern world, data volumes are constantly increasing, and clustering has become an essential tool for identifying patterns and regularities in large datasets. The relevance of this study is associated with the growing need for effective data analysis methods in programming. The objective is to evaluate different clustering techniques within the programming domain and explore their suitability for analysing a wide range of datasets. Inductive and deductive methodologies, concrete illustrations, and visual techniques were employed. The clustering techniques were implemented using RStudio and Matlab tools. The study's findings facilitated the identification of crucial attributes of clustering techniques, including hierarchical structure, cluster quantity, and similarity metrics. The application of several data analysis and visualisation approaches, including k-means, c-means, hierarchical, least spanning tree, and linked component extraction, was illustrated. This study elucidated the clustering approaches that may be optimally employed in various contexts, resulting in enhanced precision in analyses and data-informed decision-making. The study's practical significance is in enhancing programmers' methodological toolset with tools for data analysis and processing
Secure Cloud Computing based Energy Analytics Framework in Construction of Residential Buildings
Get PDFThe buildings are emanating a massive producer of data amidst being massive consumers of energy resources. Electrification of a region is seen as a breakthrough in fostering the economic development of the region. However, rapid urbanization has paved the way for the construction of huge buildings which is home to a large amount of population, which directly or indirectly contributes to energy consumption. Energy analytics is a form of energy conservation, especially in residential buildings, which is generally harnessed through cutting-edge computing technologies. This work proposed a comprehensive framework with five layers that collects data from the energy monitoring edge devices to build energy analytics by processing the data in the cloud platform. In addition to this, the framework uses a security score to monitor the illegitimate access of the cloud source by tracking the registered devices. This is a robust and generic framework that has the scope to include AI-based strategies that can be orchestrated in the cloud computing platform
Skin Cancer Prediction Model Based on Multi-Layer Perceptron Network
Get PDFMelanoma is acknowledged by the World Health Organization as the most severe type of skin cancer, significantly contributing to skin cancer-related deaths worldwide. This type of cancer manifests through noticeable changes in moles, including their size, shape, colour, or texture. In this study, we introduce an innovative and robust method for detecting and classifying melanoma in various image types, including both basic and clinical dermatological images. Our approach employs the HSV (Hue, Saturation, and Value) colour model, along with mathematical morphology and Gaussian filtering techniques. These methods are used to pinpoint the area of interest in an image and compute four key descriptors crucial for melanoma analysis: symmetry, border irregularity, colour variation, and dimension. Despite the prior usage of these descriptors over an extended period, the manner in which they are calculated in this proposal is a key factor contributing to the improvement of the outcomes. Following this, a multilayer perceptron is utilized for the purpose of categorizing malignant and benign melanoma. The study included three datasets consisting of basic and dermatological photographs that are frequently referenced in academic literature. These datasets were applied to both train and assess the effectiveness of the proposed technique. Based on the results obtained from k-fold cross-validation, it is evident that the proposed model surpasses three existing state-of-the-art approaches. In particular, the model demonstrates remarkable precision, with an accuracy rate of 98.5% for basic images and 98.6% for clinical dermatological images. It exhibits a high level of sensitivity, measuring 96.68% for simple images and 98.05% for dermatological images. Additionally, its specificity stands at 98.15% when analyzing basic images and 98.01% for dermatological images, indicating its effectiveness in both types of image analysis. The findings have demonstrated that the utilization of this gadget as an assistive tool for melanoma diagnosis would enhance levels of reliability in comparison to traditional methods
Data Transmission Security and Legal Regulation in Clinical Application of Human Gene Editing from Perspective of Big Data
Get PDFGene editing, as an emerging biotechnology, has enormous potential for application but also brings various risks. Considering the current development status of gene editing technology, the criminal regulation of gene editing is based on the theory of risk criminal law. Ethical safety should be protected as a legal interest, and specific criminalization standards should be used to distinguish gene editing for therapeutic purposes, human embryo gene editing, and other types of gene editing behavior. In view of the many problems currently existing in gene editing legislation, at the legislative level, it is necessary to balance the expansion of legal provisions brought about by risk criminal law theory and the exoneration brought about by allowed risk theory, with administrative legal norms in place, and the criminal law should exercise restraint on emerging technologies; At the judicial level, by referring to the understanding of judicial interpretations of similar crimes, corrections can be made to the elements of criminal composition, serious circumstances, and deficiencies in unit crimes
Application of Communication Technology and Neural Network Technology in Film and Television Creativity and Post-Production
Get PDFThe intersection of communication technology and neural network advancements in the realm of film and television creativity and post-production assumes a pivotal role, heralding a new era of content innovation. These technologies not only fuel the inventive spirit in film and television narratives, but they also elevate production efficiency and artistic quality to unprecedented heights. Enabled by high-speed data transmission and cloud storage solutions, global teams now collaborate in real-time, seamlessly sharing voluminous media files, thereby expediting the entire creative journey from script to screen. This not only mitigates the reliance on traditional manual labor but also enhances the realism and sophistication of visual effects. Illustratively, facial recognition and expression capture technologies accurately capture actors' expressions, transplanting them onto digital characters, thereby yielding realistic animations. Moreover, neural networks analyze vast audiences' data, providing producers with a nuanced understanding of audience preferences, guiding content creation, and aligning film and television works with market demands. In this manner, the integration of communication and neural network technologies ushers in a new dawn for film and television, one that is rich in innovation, efficiency, and artistic excellence