International Journal of Networks and Systems最新文献

{"title":"Violence Detection Using Deep Learning","authors":"","doi":"10.30534/ijns/2024/101312024","DOIUrl":"https://doi.org/10.30534/ijns/2024/101312024","url":null,"abstract":"Due to the increased risk of exposure to violent and harmful content brought about by the spread of online video content, robust systems for automatic detection and filtering have to be developed. This research suggests a novel method for deep learning-based violent content detection in videos. Our model examines both temporal and spatial characteristics in video frames by utilizing the power of recurrent neural networks (RNNs) and convolutional neural networks (CNNs).The suggested system uses a two-stream architecture, where one stream is used for temporal information using bidirectional LSTM (Long Short-Term Memory) networks to capture sequential dependencies, and the other stream is devoted to spatial analysis using 3D CNNs for frame-level understanding [1]. To ensure strong generalization, the model is additionally trained on a varied dataset that includes both violent and nonviolent content. Transfer learning is used with pre- trained deep learning models on large-scale datasets to improve the model's performance [5]. Comprehensive tests show how well the suggested method works to reliably identify violent content in videos of different genres and settings. The system demonstratesits potential for incorporation into online video platforms to give viewers a safer and more secure experience by achieving state-of-the-art outcomes in terms of precision, recall, and F1 score [4]. The suggested deep learning-based approach supports further initiatives to lessen the negative impacts of violent content in digital media and promote a safe and healthy online community [1]. Using Deep Learning to Address the Problem of Violent Video Detection: A Bright Future for Security and Safety. The proliferation of violent content is a key concern posed by the ever-increasing abundance of online video content. This puts personal safety, public safety, and platforms' capacity to properly filter information at risk. Presenting deep learning, a potent technique that presents a viable way to automatically identify violent content in videos [2]. To sum up, deep learning presents a potent and exciting way to address the pressing problem of violent video content. We can create a more secure online environment for everyone by utilizing this technology properly and resolving the issues it raises [5]. Further investigation into cross-modality learning and real-time detection shows promise for even higher efficiency and accuracy","PeriodicalId":516643,"journal":{"name":"International Journal of Networks and Systems","volume":"25 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140512661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pneumonia Detection Using Machine Learning","authors":"","doi":"10.30534/ijns/2024/051312024","DOIUrl":"https://doi.org/10.30534/ijns/2024/051312024","url":null,"abstract":"An enormous amount of morbidity and mortality cases are caused by pneumonia, which is still a major global health concern. Pneumonia must be accurately and quickly detected in order to manage patients effectively and achieve better results. Machine learning (ML) algorithms have become effective instruments in recent years for automating the detection and diagnosis of pneumonia from medical imaging data. The goal of this review paper is to give a thorough overview of recent developments in ML-based pneumonia detection. It includes the various ML algorithms used, the training and testing datasets, and the evaluation metrics used to rate the effectiveness of these models. Additionally, this review highlights the difficulties encountered in the field and suggests possible directions for improvement in order to create a more reliable and robust pneumonia detection system. Healthcare professionals place a high value on pneumonia detection, and machine learning (ML)-based automation of There's been a lot of attention paid to this process. The importance of pneumonia detection and the part that ML techniques play in automating this process are highlighted in the introduction to this review paper. In the following section, it examines different machine learning (ML) The various system used for the discernment of pneumonia. Such include supervised understanding algorithms like logistic statistics, vector machine and randomization. forests, and convolutional neural networks. The review also discusses pneumonia detection using unsupervised learning techniques like clustering, dimensionality reduction, and autoencoders. In order to develop them, an assessment of pneumonia detection models is essential. The study has examined several appraisal metrics which are commonly used for that purpose, such as sensitivity, specificity, precision and the operational status of receivers. characteristic (ROC) curve, recall, precision, and F1-score. The selection of suitable metrics, which considers specific requirements for pneumonia detection, is main factor to be taken into consideration. The main obstacles is that there are no annotation data. to creating reliable pneumonia detection models. Accurate ML algorithms must be trained on high-quality labelled datasets. However, since chest X-ray images must be annotated by qualified radiologists, obtaining a sizable annotated dataset for pneumonia is frequently challenging. The creation of efficient ML models for pneumonia detection is hampered by the limited availability of annotated data.","PeriodicalId":516643,"journal":{"name":"International Journal of Networks and Systems","volume":"32 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140512445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IoT and its Potential for Transforming Industries","authors":"","doi":"10.30534/ijns/2024/041312024","DOIUrl":"https://doi.org/10.30534/ijns/2024/041312024","url":null,"abstract":"The Internet of Things (IoT) includes connected devices that communicate over the Internet. This technology has the potential to change industries by increasing productivity, reducing costs and improving efficiency. In manufacturing, IoT devices improve machine maintenance, supply chain management and inventory management. Healthcare uses IoT for drug tracking and patient tracking. Transportation can benefit from improved visibility and streamlining of operations. In the energy sector, IoT optimizes use and reduces waste. New IoT applications can be used in a variety of industries to increase productivity, efficiency and effectiveness.","PeriodicalId":516643,"journal":{"name":"International Journal of Networks and Systems","volume":"25 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140512062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"R-CNN Based Deep Learning Approach for Counting Animals in the Forest: A Survey","authors":"","doi":"10.30534/ijns/2024/011312024","DOIUrl":"https://doi.org/10.30534/ijns/2024/011312024","url":null,"abstract":"This review paper delves into the pivotal realm of animal classification using images obtained through diverse techniques in forest environments. A robust framework is introduced, employing Transfer Learning (TL) within a Convolutional Neural Network (CNN) and leveraging the power of the Region-based Convolutional Neural Network (R-CNN) model for the construction of an automated animal identification system. This innovative framework is adeptly applied to analyze and identify focal species within captured images, contributing to the advancement of wildlife monitoring technologies. The dataset under scrutiny comprises 6,203 camera trap images featuring 11 distinct species, including Wild pig, Barking deer, Chital, Elephant, Gaur, Hare, Jackal, Junglecat, Porcupine, Sambhar, and Sloth bear. The inclusion of this diverse set of species ensures the robustness and applicability of the proposed methodology across a broad spectrum of wildlife scenarios. The integration of Transfer Learning withinthe Region-based Convolutional Neural Network (R-CNN) emerges as a crucial element, showcasing outstanding performance in species classification.Notably, the proposed model achieves a remarkable accuracy rate of 96% on the test dataset after a mere 18 epochs, employing a batch size of 32. This breakthrough holds the potential to expedite research outcomes, foster the evolution of more efficient and dependable animal monitoring systems, and consequently, alleviate the time and effort invested by researchers.In line with ethical considerations, the authors maintain anonymity in theircontribution, focusing on the significant strides made in the classification andanalysis of camera trap images within the observed site. This paper positions itself as a noteworthy and impactful contribution to the broader field of wildlife research and technology","PeriodicalId":516643,"journal":{"name":"International Journal of Networks and Systems","volume":"73 1‐2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139640630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Secure Transactions in a Chip: A Contemporary Review of Smart Card Innovations","authors":"","doi":"10.30534/ijns/2024/031312024","DOIUrl":"https://doi.org/10.30534/ijns/2024/031312024","url":null,"abstract":"Smart card technology has emerged as a powerful tool in the field of secure identification, authentication, and transaction processing. This abstract provides a comprehensive overview of smart card technology, highlighting its key features, applications, and benefits. Smart cards, also known as integrated circuit cards, are portable devices that incorporate a microprocessor and memory to securely store and process information. These cards have revolutionized various industries by enabling secure access control, secure payment transactions, and secure storage of sensitive data. The abstract begins by exploring the fundamental components and architecture of smart cards. It delves into the different types of smart cards, such as contact-based and contactless cards, and explains the communication protocols employed in their operation. Furthermore, the abstract discusses the extensive range of applications where smart cards have found widespread adoption. These applications include identification cards, payment cards, healthcare cards, transportation cards, and more. The abstract highlights the advantages of using smart cards in each of these domains, such as enhanced security, convenience, and interoperability.","PeriodicalId":516643,"journal":{"name":"International Journal of Networks and Systems","volume":"24 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140512700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"5G's Integration with Edge Computing","authors":"","doi":"10.30534/ijns/2024/021312024","DOIUrl":"https://doi.org/10.30534/ijns/2024/021312024","url":null,"abstract":"This study addresses the transformative integration of 5G networks with Edge Computing and Mobile Edge Computing (MEC) and explores the collaborative standards established by industry associations such as ETSI and 3GPP. The article explores the multiple possibilities of this integration, encompassing consumer and operator services, and meeting the demands of new technologies such as augmented reality, virtual reality and the Internet of Things. The strategic coexistence of distributed MEC is explored, while the security and privacy challenges of MEC are explored, emphasizing layered security and blockchain technologies. The study highlights the role of 5G and MEC in reshaping the communications landscape, providing affordable and efficient computing at the network edge, and improving network performance and quality of experience (QoE). As the 5G and MEC ecosystem evolves, the paper predicts a transformative impact on connectivity, speed, reliability and responsiveness across industries, and emphasizes the continued importance of research and development in shaping the future of communications and computing.","PeriodicalId":516643,"journal":{"name":"International Journal of Networks and Systems","volume":"8 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140512200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}