The issue of illegal migration dominates political and media discourse, particularly on social media, generating interest in collecting and analyzing textual data from platforms like Twitter, and Facebook. Named Entit...
详细信息
The expanding adoption of Internet of Things (IoT) applications has amplified the challenge of identifying optimal solutions for resource allocation while balancing multiple competing objectives. As devices and applic...
详细信息
ISBN:
(数字)9798331533816
ISBN:
(纸本)9798331533823
The expanding adoption of Internet of Things (IoT) applications has amplified the challenge of identifying optimal solutions for resource allocation while balancing multiple competing objectives. As devices and applications proliferate, the complexity of resource management in fog computing escalates, necessitating advanced optimization techniques capable of adapting to evolving conditions and requirements. This paper presents a comparative analysis of two optimization strategies: Pareto-based multi-objective methods, exemplified by FP-NSGA-II (Fog Placement NSGA-II), a customized version of the Non-dominated Sorting Genetic Algorithm II tailored for task placement in fog environments, and weighted sum objective approaches, represented by Time Cost aware Scheduling (TCaS) and Weighted Sum-Genetic Algorithm (WS-GA). Through simulations and performance evaluations, this study compares these approaches in terms of trade-off management, scalability, and solution quality across varying system workloads. The experimental results highlight the strengths of the Pareto-based method, emphasizing its superior scalability and solution quality compared to weighted objective methods, along with its enhanced ability to balance multiple objectives while maintaining solution quality. The findings indicate that the Pareto-based approach is particularly effective in scenarios with competing objectives, as it provides a diverse set of solutions that can be tailored to specific application requirements.
The issue of illegal migration dominates political and media discourse, particularly on social media, generating interest in collecting and analyzing textual data from platforms like Twitter, and Facebook. Named Entit...
详细信息
ISBN:
(数字)9798350372632
ISBN:
(纸本)9798350372649
The issue of illegal migration dominates political and media discourse, particularly on social media, generating interest in collecting and analyzing textual data from platforms like Twitter, and Facebook. Named Entity Recognition (NER) is a natural language processing technique used to extract information from unstructured text, including identifying named entities such as people, organizations, and locations. This study aims to contribute to the identification of entities within collected textual data from Twitter related to illegal migration associated with Libya. The study will employ data mining tools, particularly pre-trained NER models to achieve its objective. The research is significant as there is currently no Libyan study that has addressed this specific subject using this technique. NER can help researchers, policymakers, law enforcement agencies, and humanitarian organizations to better understand the scope, dynamics, and impact of this complex issue.
Online news articles, blogs, sites are a rich source of diverse text-based data. However, the data contained in these sources cannot be manually extricated, recorded, and listed because it comes in colossal size. Accu...
详细信息
Online news articles, blogs, sites are a rich source of diverse text-based data. However, the data contained in these sources cannot be manually extricated, recorded, and listed because it comes in colossal size. Accurate mapping of precise news into their corresponding category is challenging in these times. Several methods have been proposed over time for news classification when training documents for each predefined class are present readily, however such methods were tried and tested upon a small dataset. With the underlying research, the aim is to propose a method that can be used when lakhs and lakhs of instances are present. This research analysis involves the task of news classification using multiclass classifiers - OneVsRest and OneVsOne classifiers over the Linear Support Vector Classification to learn the performance of multiclass news categorization. The proposed methodology “Keyword Based Classification Technique (KBCT)” in this study was executed and concluded using Python and deployed using Google Colaboratory. The result was expressed using four distinguished news classes over a multivariate dataset of 422419 instances from the uci-news-aggregator dataset. The OneVsRestClassifier's accuracy was computed to be 95.76% that was 0.09% more than the OneVsOneClassifier's accuracy of 95.67%. The proposed prototype was compared with some of the related studies and algorithms, and the outcomes produced by the OneVsRest model were the most optimum in terms of accuracy.
Aiming at the defects of the traditional fire detection methods,which are caused by false positives and false negatives in large space buildings,a fire identification detection method based on video images is *** algo...
详细信息
Aiming at the defects of the traditional fire detection methods,which are caused by false positives and false negatives in large space buildings,a fire identification detection method based on video images is *** algorithm first uses the hybrid Gaussian background modeling method and the RGB color model to perform fire prejudgment on the video image,which can eliminate most non-fire ***,the traditional regional growth algorithm is improved and the fire image segmentation effect is effectively ***,based on the segmented image,the dynamic and static features of the fire flame are further analyzed and extracted in the area of the suspected fire ***,the dynamic features of the extracted fire flame images were fused and classified by improved fruit fly optimization support vector machine,and the recognition results were *** video-based fire detection method proposed in this paper greatly improves the accuracy of fire detection and is suitable for fire detection and identification in large space scenarios.
As an Industrial Wireless Sensor Network(IWSN)is usually deployed in a harsh or unattended environment,the privacy security of data aggregation is facing more and more ***,the data aggregation protocols mainly focus o...
详细信息
As an Industrial Wireless Sensor Network(IWSN)is usually deployed in a harsh or unattended environment,the privacy security of data aggregation is facing more and more ***,the data aggregation protocols mainly focus on improving the efficiency of data transmitting and aggregating,alternately,the aim at enhancing the security of *** performances of the secure data aggregation protocols are the trade-off of several metrics,which involves the transmission/fusion,the energy efficiency and the security in Wireless Sensor Network(WSN).Unfortunately,there is no paper in systematic analysis about the performance of the secure data aggregation protocols whether in IWSN or in *** consideration of IWSN,we firstly review the security requirements and techniques in WSN data aggregation in this ***,we give a holistic overview of the classical secure data aggregation protocols,which are divided into three categories:hop-by-hop encrypted data aggregation,end-to-end encrypted data aggregation and unencrypted secure data *** this way,combining with the characteristics of industrial applications,we analyze the pros and cons of the existing security schemes in each category qualitatively,and realize that the security and the energy efficiency are suitable for ***,we make the conclusion about the techniques and approach in these categories,and highlight the future research directions of privacy preserving data aggregation in IWSN.
As an effective error correction technology,the Low Density Parity Check Code(LDPC)has been researched and applied by many ***,LDPC codes have some prominent performances,which involves close to the Shannon limit,achi...
详细信息
As an effective error correction technology,the Low Density Parity Check Code(LDPC)has been researched and applied by many ***,LDPC codes have some prominent performances,which involves close to the Shannon limit,achieving a higher bit rate and a fast ***,whether these excellent characteristics are suitable for the resource-constrained Wireless Sensor Network(WSN),it seems to be seldom *** this article,we review the LDPC code’s structure ***,and them classify and summarize the LDPC codes’construction and decoding algorithms,finally,analyze the applications of LDPC code for *** believe that our contributions will be able to facilitate the application of LDPC code in WSN.
暂无评论