Processing, analyzing and continuously monitoring big data streams in real time is crucial for the security and efficiency of organizations and individuals. While machine learning and deep learning have greatly improved anomaly detection, they also have drawbacks, such as reliance on offline data and poor performance, especially with scarce or fluctuating data. To address this problem, we propose two enhanced methods for anomaly detection in streaming data: Gaussian Space Trees (GSTrees) and the Gaussian Weighted Adwin AutoEncoder (GWAAE). The effectiveness of these methods was evaluated on real datasets (ECG5000, Credit Card Fraud Detection, SMTP) processed as streaming data. For the ECG5000 dataset, GSTrees performed excellently on all metrics, consistently achieving over 94%, while GWAAE excelled with an ROC-AUC of over 89% and over 84% on all other metrics. For the SMTP dataset, the ROC-AUC of both proposed methods is above 80%. GSTrees and GWAAE successfully identified anomalies with a recall of over 82% and a ROC-AUC of over 94% and maintained this success with remarkably low false negative and false positive rates in the Credit Card Fraud Detection dataset. These results emphasize the robustness and applicability of GSTrees and GWAAE for real-time anomaly detection in dynamic environments.

The motive of this work is to provide the numerical performances of the monkeypox transmission mathematical model by using a novel deep neural network process with eleven and twenty-two neurons in the hidden layers. The purpose to provide the deep neural network stochastic process is to obtain more accurate solutions of the monkeypox transmission mathematical system. This process is enhanced by using an activation radial basis function in both layers for solving the monkeypox transmission mathematical model along with the implementation of the Bayesian regularization optimization scheme. The presentation of the mathematical dynamical model has two categories, human and rodent. The human dynamics is classified into, susceptible, exposed, infectious, clinically ill human and recovered individuals. The rodent is divided into three forms, susceptible, exposed, and infected. A dataset is presented with the Adam approach that is processed using the training, testing, and certification procedure by taking the data as 0.13, 0.12 and 0.15. The correctness is observed through the matching of the results and the statistical plots are plotted using the regression, state transition, error histograms and correlation.

Summary Cloud computing has the advantage of providing flexibility, high‐performance, pay‐as‐you‐use, and on‐demand service. One of the important research issues in cloud computing is task scheduling. The purpose of scheduling is to assign tasks to available resources while providing optimization on some objectives. Tasks have diversified characteristics, and resources are heterogeneous. These properties make task scheduling an NP‐complete problem. In this study, metaheuristic and hybrid metaheuristic algorithms are developed for task scheduling problems in cloud computing environments. We have developed genetic algorithm (GA), differential evolution (DE), and simulated annealing (SA) based metaheuristic algorithms, which are also combined with a greedy approach (GR). In addition to this, we have developed hybrid metaheuristics algorithms, called DE‐SA and GA‐SA, which are also combined with a greedy approach. The proposed approaches are evaluated in terms of completion time and load balancing of virtual machines. In terms of average completion time, as the number of tasks increases, it has been observed that the DESA algorithm outperforms the solely used DE and SA algorithms. In addition, experiments show that hybrid algorithms improve both the average completion time and the average standard deviation of virtual machine loads for some task groups.

Abstract The importance of string matching is due to its applications in many fields, such as medicine and bioinformatics. Various string matching algorithms are developed to speed up the search. Especially, hash‐based exact string matching algorithms are among the most time‐efficient ones. The efficiency of hash‐based approaches depends on the hash function. Hence, perfect hashing plays an essential role in hash‐based string matching. In this study, two q‐frame hash comparison‐based exact string matching algorithms, Hq‐QF and HqBM‐QF, are proposed. We have used a collision‐free perfect hash function for DNA sequences in the proposed algorithms. In the first approach, after hash values match for the last qcharacters, the character comparisons in the Hash‐q algorithm are replaced with q‐frame hash comparison. In the second approach, we improved the first approach by utilizing the shift size indicated at the th entry in the good suffix shift table. Since the number of character comparisons is minimized, the worst‐case time complexity of the proposed algorithms is . In both approaches, q‐frame hash comparisons replace most character comparisons as a trade‐off. The results show that the proposed approaches are more efficient than the Hash‐q algorithm in terms of runtime efficiency and the number of character comparisons.

Abstract Real‐time stream data processing has gained high importance with the rapid rise of big data trends in different areas such as social media, finance, business, science, and bioinformatics. Stream data can be characterized as fast, unstable, and big data sets. Due to these properties of stream data, it cannot be processed effectively with traditional algorithms. Just like stream data processing, clustering is also a difficult task. However, researchers have attempted to classify stream data by modifying traditional algorithms or designing new ones. So, in previous studies, incremental methods were used for clustering the stream data. This paper highlights the need to develop an efficient real‐time clustering algorithm for data streams in the presence of concept high drift and an adaptive algorithm for different dimensions. The proposed clustering algorithm, SubtStream, combines decremental (subtractive property) and incremental (additivity property) strategies to overcome the high drift. It also introduces a new dimension‐based approach to adopt the dimension change. We use three radius parameters, Predefined User Parameter, Proactive Adaptive Parameter, and Reactive Adaptive Parameter, to achieve adaptability. The proposed method, SubtStream, showed better performance on synthetic and real data sets.

Summary In this study, ratio estimators are proposed by utilizing some robust techniques to get the maximum benefit of the auxiliary variable for the estimation of the population mean in simple random sampling. The expressions for mean squared error are derived for the first degree of approximation. Theoretical comparisons demonstrate that the suggested estimators having robust regression estimates perform better than the existing estimators under certain conditions. Theoretical findings are supported with the aid of the original dataset in an application. In addition, a simulation study is also conducted to evaluate the performance of the suggested estimators.

Due to the rapidly increasing amount of biomedical publications, it has become challenging to follow scientific articles and new developments. Keywords in scientific articles provide a quick understanding and summarize the important points of the context. When keywords are not used in some biomedical articles or are not sufficient to express the content of the text, automatic keyword extraction systems are needed. This paper addresses the keyword extraction problem as a sequence labeling task where words are represented as deep contextual embeddings. We predict the keyword tags identified in sequence labeling by fine-tuning XLNET and BERT-based models such as BERT, BioBERT, SCIBERT, and RoBERTa. Our proposed method does not need extra dictionaries required by rule-based methods and feature extraction as in traditional machine learning methods. Performance evaluation on the benchmark dataset for biomedical keyword extraction shows that domain-specific contextualized embeddings (BioBERT, SciBERT) achieve state-of-the-art results compared to the general domain embeddings (BERT, RoBERTa, XLNET) and unsupervised methods.

Phrase-based models are among the best performing statistical machine translation (SMT) systems. These systems make translations phrase-by-phrase at a time. The decoding process is done locally in these systems. In addition, neural machine translation (NMT) systems have become very popular for the past four or five years with essential features such as more fluent translations. However, sometimes NMT systems give up accuracy for fluent translations due to the nature of the decoding technique they use. In this study, we aim to develop a hybrid system by guiding NMT decoding using the output sentences of the phrase-based SMT systems. According to the two-way translation experiments, German-to-English and English-to-German, and the results obtained in terms of two popular machine translation evaluation metrics: BLEU and METEOR, our method improves the quality of NMT system translations.

As an emerging technology, Vehicular ad hoc networks (VANETs) aim to increase safety and comfort of driving in highways and urban streets. The conventional measures taken have a positive effect on the decline of casualties and injuries incurred by the accidents. But the number of incidents in the traffic still remains stable. VANETs intend to overcome this problem by providing additional information to the driver, thus allowing him to react faster and better in adverse situations. In order to achieve this, VANETs require efficient routing protocols for message transmission among vehicles. In this paper, we explore the performances of four prominent non-delay tolerant routing protocols, namely GPSR, GPSR+AGF, GSR, and GPSRJ+. Although relatively outdated, these protocols are initial inspiration to many routing methods and commonly used as comparison benchmark when a new routing protocol is to be introduced. The evaluation metrics that are used are Packet Delivery Ratio, Average Delay, Traffic Control Overhead, and Average Hop Count.

Abstract We discuss the development and application of Web‐service‐based geographical information system (GIS) Grids. Following the WS‐I+ approach of building Grids on Web service standards, we have developed data Grid components for archival and real‐time data, map generating services that can be used to build user interfaces, information services for storing both stateless and stateful metadata, and service orchestration and management tools. Our goal is to support dynamically assembled Grid service collections that combine both GIS services with more traditional Grid capabilities such as file transfer and remote code execution. We are applying these tools to problems in earthquake modeling and forecasting, but we are attempting to build general purpose tools by using and extending appropriate standards. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract We discuss our message‐based approach to managing real‐time data streams and building higher level services to produce and consume them. Our messaging system acts as a substrate that can be used to provide qualities of service to various streaming applications ranging from audio–video collaboration systems to sensor Grids. The messaging substrates are composed of distributed, hierarchically arranged message broker networks. Services such as filters are deployed along the edges of the network. We discuss the role of management systems for both broker networks and filter services: broker network topologies must be created and maintained, and distributed filters must be arranged in appropriate sequences. These managed broker networks may be applied to a wide range of problems. We discuss applications to audio–video collaboration in some detail and also describe applications to streaming Global Positioning System data streams. These provide specific application filters that can transform and republish message streams to the broker system. Copyright © 2006 John Wiley & Sons, Ltd.

We designed eSports - a collaborative and synchronous video annotation platform, which is to be used in Internet scale cross-platform grid computing environment to facilitate computer supported cooperative work (CSCW) in education settings such as distance sport coaching, distance classroom etc. Different from traditional multimedia annotation systems, eSports provides the capabilities to collaboratively and synchronously play and archive real time live video, to take snapshots, to annotate video snapshots using whiteboard and to play back the video annotations synchronized with original video streams. eSports is designed based on the grid based collaboration paradigm $the shared event model using NaradaBrokering, which is a publish/subscribe based distributed message passing and event notification system. In addition to elaborate the design and implementation of eSports, we analyze the potential use cases of eSports under different education settings. We believed that eSports is very useful to improve the online collaborative coaching and education.

Abstract In order to build an integrated collaboration system over heterogeneous collaboration technologies, we propose a global multimedia collaboration system (Global‐MMCS) based on XGSP A/V Web‐Services framework. This system can integrate multiple A/V services, and support various collaboration clients and communities. Now the prototype is being developed and deployed across many universities in U.S.A. and China. Copyright © 2004 John Wiley & Sons, Ltd.

Abstract We describe an information system architecture for the ACES (Asia–Pacific Cooperation for Earthquake Simulation) community. It addresses several key features of the field—simulations at multiple scales that need to be coupled together; real‐time and archival observational data, which needs to be analyzed for patterns and linked to the simulations; a variety of important algorithms including partial differential equation solvers, particle dynamics, signal processing and data analysis; a natural three‐dimensional space (plus time) setting for both visualization and observations; the linkage of field to real‐time events both as an aid to crisis management and to scientific discovery. We also address the need to support education and research for a field whose computational sophistication is rapidly increasing and spans a broad range. The information system assumes that all significant data is defined by an XML layer which could be virtual, but whose existence ensures that all data is object‐based and can be accessed and searched in this form. The various capabilities needed by ACES are defined as grid services, which are conformant with emerging standards and implemented with different levels of fidelity and performance appropriate to the application. Grid Services can be composed in a hierarchical fashion to address complex problems. The real‐time needs of the field are addressed by high‐performance implementation of data transfer and simulation services. Further, the environment is linked to real‐time collaboration to support interactions between scientists in geographically distant locations. Copyright © 2002 John Wiley & Sons, Ltd.