A Novel Lifecycle Model for Web-based Application Development in Small and Medium Enterprises

Software engineering＇s lifecycle models havc proven to be very important for traditional software development. However, can these models be applied to the development of Web-based applications as well？ In recent years, Web-based applications have become more and more complicated and a lot of efforts have been placed on introducing new technologies such as J2EE, PhP, and .NET, etc., which have been universally accepted as the development technologies for Web-based applications. However, there is no universally accepted process model for the development of Web-based applications. Moreover, shaping the process model for small medium-sized enterprises （SMEs）, which have limited resources, has been relatively neglected. Based on our previous work, this paper presents an expanded lifecycle process model for the development of Web-based applications in SMEs. It consists of three sets of processes, i.e., requirement processes, development processes, and evolution processes. Particularly, the post-delivery evolution processes are important to SMEs to develop and maintain quality web applications with limited resources and time.

Feasibility Study of a National Web-Based GIS Application to Assess the Risk of Pesticide Drift in the U.S.

Agricultural pollution is a major issue in the United States (U.S.) and the world. Biotic and abiotic farming byproducts adversely affect the ecosystem and human health. While pesticides and fertilizers are the primary sources of agricultural pollution, organic agriculture can help remediate the negative effects on humans and the ecosystem. However, many factors like chemical drift can limit this advantage. This paper presents a feasibility study of a web-based (Geografic Information System) GIS application which can model and predict the areas affected by agricultural chemicals drift. Other applications exist with limited assumptions that make their outcomes far from reality. A root definition and a rich picture are developed as well as a Strengths Weaknesses Opportunities and Threats (SWOT) analysis. Because of the huge geographical context, data requirements and analyses requirements are expected to be massive. Nonetheless, despite the expected challenges, the advantages of the proposed application outweigh the risks.

AWeb Application Fingerprint Recognition Method Based on Machine Learning

Web application fingerprint recognition is an effective security technology designed to identify and classify web applications,thereby enhancing the detection of potential threats and attacks.Traditional fingerprint recognition methods,which rely on preannotated feature matching,face inherent limitations due to the ever-evolving nature and diverse landscape of web applications.In response to these challenges,this work proposes an innovative web application fingerprint recognition method founded on clustering techniques.The method involves extensive data collection from the Tranco List,employing adjusted feature selection built upon Wappalyzer and noise reduction through truncated SVD dimensionality reduction.The core of the methodology lies in the application of the unsupervised OPTICS clustering algorithm,eliminating the need for preannotated labels.By transforming web applications into feature vectors and leveraging clustering algorithms,our approach accurately categorizes diverse web applications,providing comprehensive and precise fingerprint recognition.The experimental results,which are obtained on a dataset featuring various web application types,affirm the efficacy of the method,demonstrating its ability to achieve high accuracy and broad coverage.This novel approach not only distinguishes between different web application types effectively but also demonstrates superiority in terms of classification accuracy and coverage,offering a robust solution to the challenges of web application fingerprint recognition.

PDBSCAN: Parallel DBSCAN for Large-Scale Clustering Applications

Density-based algorithm for discovering clusters in large spatial databases with noise(DBSCAN) is a classic kind of density-based spatial clustering algorithm and is widely applied in several aspects due to good performance in capturing arbitrary shapes and detecting outliers. However, in practice, datasets are always too massive to fit the serial DBSCAN. And a new parallel algorithm-Parallel DBSCAN(PDBSCAN) was proposed to solve the problem which DBSCAN faced. The proposed parallel algorithm bases on MapReduce mechanism. The usage of parallel mechanism in the algorithm focuses on region query and candidate queue processing which needed substantive computation resources. As a result, PDBSCAN is scalable for large-scale dataset clustering and is extremely suitable for applications in E-Commence, especially for recommendation.

Application of Cluster Analysis in hydrochemical composition classifications-case study of middle reaches of Yinma River

Chemical composition of groundwater reflects the historical evolvement of groundwater in an area. The Cluster Analysis method is introduced in this paper with descriptions of the principle for using this method, and introducing the classification process for chemical types of groundwater by using this method. The classification by cluster analysis indicated that the results considered all chemical components of groundwater, which, therefore, is a good effect.

Design of Collaborative Platform on Clustering Applications for Governments

Collaborative platform on clustering applications for governments consists of six large-scale systems, including the clustering Government Internet portal system, clustering public-mailboxes collaboration system, clustering government affairs portal system, clustering emergency information collaboration system, clustering office automation collaboration system, and clustering messages collaboration systems. The appli-cation and technology architectures of the collaborative platform are elaborated in this paper,and the major key technologies on the platform are also expounded, which includes realization of many governments ap-plications’ scale integration and collaborative application, business model driven software development plat-form based on SOA, SSO, tans-departmental and cross-level multi-engine clustering protocol. Based on the "clustering application"design, to maximize the utilization of hardware, software resources and administra-tive resources of the provincial government collaborative platform, rural districts and counties can build their own platforms based on the provincial platform. The platform having been running for over 2 years shows that planning of urban and rural e-governments’ construction and maintenance is achieved, thus reducing costs greatly and improving governments’ functions.

Research and Discussion on Project Cluster Teaching Mode based on the Enterprise Level Application

Currently, the country promotes with great effort the university should the application specific education, speeds up constructing to take getting employed as the guidance modern vocational education system. In order to strengthen the vocational skill ability of student and enhance the employment competitiveness, this article proposes enterprise application-based project colony educational model. In the teaching process, the school subject knowledge education and business skills needs of the enterprise integration, the use of enterprise program teaching, so that students can not only receive professional knowledge of the system education, but also the ability of professional application of formal training and training, after graduation the students can quickly adapt to the work of the business requirements, to achieve the purpose of application-oriented teaching.

Performance Analysis of Round Robin Scheduling for Session-based Applications on Clusters

Nowadays session-based applications are one of the typical applications in the Internet,and people build such applications on clusters on concern of scalability. Scheduling in such a cluster is a key technology since system performance depends on it. In this paper,we investigate the Round-Robin algorithm in the context of Session-based applications. An analyzing model for such sys-tems is proposed. Through both theoretical analysis and simulation,we find the main factor for system performance. And the result also shows that this algorithm shows up with significantly different performance under various conditions.

Classification of Meteorological Satellite Ground System Applications

Meteorological satellite ground application system carries a large number of applications. These applications deal with a variety of tasks. In order to classify these applications according to the resource consumption and improve the rational allocation of system resources, this paper introduces several application analysis algorithms. Firstly, the requirements are abstractly described, and then analyzed by hierarchical clustering algorithm. Finally, the benchmark analysis of resource consumption is given. Through the benchmark analysis of resource consumption, we will give a more accurate meteorological satellite ground application system.

Acupoint selection rules for acupoint application in lung diseases in the post-epidemic era

Objective:To summarize the rules of acupoint selection of acupoint application to prevent and treat lung diseases under the background the post-epidemic era using data-mining technology.Method:The CNKI,Wanfang database,and VIP database were searched for clinical study articles on lung diseases treated by acupoint application published in the past 5 years.Data-eligible papers were extracted to establish a database of acupoint application for lung disease using Microsoft Excel 2019,with the goal of analyzing the frequency of acupoints,acupoint-meridian association,acupoint-location association,specific acupoint frequency,and cluster analysis.Association rules,consisting of acupoints with an application frequency of≥10,were devised by the Apriori algorithm to explore the correlation between acupoint groups and to analyze the rules of the compatibility of acupoint prescriptions.Results:A total of 229 eligible papers met our inclusion criteria.Forty-seven acupoints were applied,for a total frequency of acupoints of 1,035 times.Among these,acupoints for lung diseases were primarily distributed in the back-and-waist and chest-and-abdomen areas.From the analysis of the association rules,we obtained four groups of acupoint association rules based on acupoint clusters with a frequency≥10 and found that Feishu(BL 13),Tiantu(CV 22),Dazhui(GV 14),Dingchuan(EX-B1),and Danzhong(CV 17)constitute the core acupoints of prescriptions for clinical acupoint application to prevent and treat lung diseases.Conclusion:It is clearly shown that the core acupoints are relatively concentrated and that the selected acupoints were mainly locally selected,which could be a matching reference for the long-term prevention and treatment of lung diseases,including COVID-19.

An Emergent Technology: Offline Web Application

Web-based application has been complained a lot because of some serious problems, such as the limitations of browsers, security problems and the lack of offline access. To solve these, online applications are set to go offline and free from the browser. Some organization has already gain achievements on this area. As the first mover, Google is expected to use Google Gears to offer offline functionality for web applications, and Firefox3.0 are expected to support it. Microsoft's Silverlight and Adobe's Air technologies allow online applications to run independently of the browser. (Nuttal, 2007) Organizations willing to launch this technology should not only pay attention on the SWOT of this technology itself, but also take a look of their information and business strategies and figure out the right way they utilize this technology. Therefore they need to take advantage of the strengths and opportunities to meet their business strategies and achieve objectives.

K-means Find Density Peaks in Molecular Conformation Clustering

Performing cluster analysis on molecular conformation is an important way to find the representative conformation in the molecular dynamics trajectories.Usually,it is a critical step for interpreting complex conformational changes or interaction mechanisms.As one of the density-based clustering algorithms,find density peaks(FDP)is an accurate and reasonable candidate for the molecular conformation clustering.However,facing the rapidly increasing simulation length due to the increase in computing power,the low computing efficiency of FDP limits its application potential.Here we propose a marginal extension to FDP named K-means find density peaks(KFDP)to solve the mass source consuming problem.In KFDP,the points are initially clustered by a high efficiency clustering algorithm,such as K-means.Cluster centers are defined as typical points with a weight which represents the cluster size.Then,the weighted typical points are clustered again by FDP,and then are refined as core,boundary,and redefined halo points.In this way,KFDP has comparable accuracy as FDP but its computational complexity is reduced from O(n^(2))to O(n).We apply and test our KFDP method to the trajectory data of multiple small proteins in terms of torsion angle,secondary structure or contact map.The comparing results with K-means and density-based spatial clustering of applications with noise show the validation of the proposed KFDP.

ALPHA系统TRUcluster集群的应用

Cluster技术发展已经很多年了,在铁路信息系统中使用了多种集群技术和产品,TRUcluster是其中非常有代表性的一种,由于其强大的功能而在TMIS中使用,主要对集群系统、TRUcluster技术特色作基本的概述,并论述铁路计算机系统建设的选择原则及未来的展望。

A heuristic clustering algorithm based on high density-connected partitions

Clustering data with varying densities and complicated structures is important,while many existing clustering algorithms face difficulties for this problem. The reason is that varying densities and complicated structure make single algorithms perform badly for different parts of data. More intensive parts are assumed to have more information probably,an algorithm clustering from high density part is proposed,which begins from a tiny distance to find the highest density-connected partition and form corresponding super cores,then distance is iteratively increased by a global heuristic method to cluster parts with different densities. Mean of silhouette coefficient indicates the cluster performance. Denoising function is implemented to eliminate influence of noise and outliers. Many challenging experiments indicate that the algorithm has good performance on data with widely varying densities and extremely complex structures. It decides the optimal number of clusters automatically.Background knowledge is not needed and parameters tuning is easy. It is robust against noise and outliers.

Lens-Oppositional Wild Geese Optimization Based Clustering Scheme for Wireless Sensor Networks Assists Real Time Disaster Management

Recently,wireless sensor networks(WSNs)find their applicability in several real-time applications such as disaster management,military,surveillance,healthcare,etc.The utilization of WSNs in the disaster monitoring process has gained significant attention among research communities and governments.Real-time monitoring of disaster areas using WSN is a challenging process due to the energy-limited sensor nodes.Therefore,the clustering process can be utilized to improve the energy utilization of the nodes and thereby improve the overall functioning of the network.In this aspect,this study proposes a novel Lens-Oppositional Wild Goose Optimization based Energy Aware Clustering(LOWGO-EAC)scheme for WSN-assisted real-time disaster management.The major intention of the LOWGO-EAC scheme is to perform effective data collection and transmission processes in disaster regions.To achieve this,the LOWGOEAC technique derives a novel LOWGO algorithm by the integration of the lens oppositional-based learning(LOBL)concept with the traditional WGO algorithm to improve the convergence rate.In addition,the LOWGO-EAC technique derives a fitness function involving three input parameters like residual energy(RE),distance to the base station(BS)(DBS),and node degree(ND).The proposed LOWGO-EAC technique can accomplish improved energy efficiency and lifetime of WSNs in real-time disaster management scenarios.The experimental validation of the LOWGO-EAC model is carried out and the comparative study reported the enhanced performance of the LOWGO-EAC model over the recent approaches.

Metaheuristics Based Node Localization Approach for Real-Time Clustered Wireless Networks

In recent times,real time wireless networks have found their applicability in several practical applications such as smart city,healthcare,surveillance,environmental monitoring,etc.At the same time,proper localization of nodes in real time wireless networks helps to improve the overall functioning of networks.This study presents an Improved Metaheuristics based Energy Efficient Clustering with Node Localization(IM-EECNL)approach for real-time wireless networks.The proposed IM-EECNL technique involves two major processes namely node localization and clustering.Firstly,Chaotic Water Strider Algorithm based Node Localization(CWSANL)technique to determine the unknown position of the nodes.Secondly,an Oppositional Archimedes Optimization Algorithm based Clustering(OAOAC)technique is applied to accomplish energy efficiency in the network.Besides,the OAOAC technique derives afitness function comprising residual energy,distance to cluster heads(CHs),distance to base station(BS),and load.The performance validation of the IM-EECNL technique is carried out under several aspects such as localization and energy efficiency.A wide ranging comparative outcomes analysis highlighted the improved performance of the IM-EECNL approach on the recent approaches with the maximum packet delivery ratio(PDR)of 0.985.

Internet of Things Enabled Energy Aware Metaheuristic Clustering for Real Time Disaster Management

Wireless Sensor Networks(WSNs)are a major element of Internet of Things(IoT)networks which offer seamless sensing and wireless connectivity.Disaster management in smart cities can be considered as a safety critical application.Therefore,it becomes essential in ensuring network accessibility by improving the lifetime of IoT assisted WSN.Clustering and multihop routing are considered beneficial solutions to accomplish energy efficiency in IoT networks.This article designs an IoT enabled energy aware metaheuristic clustering with routing protocol for real time disaster management(EAMCR-RTDM).The proposed EAMCR-RTDM technique mainly intends to manage the energy utilization of nodes with the consideration of the features of the disaster region.To achieve this,EAMCR-RTDM technique primarily designs a yellow saddle goatfish based clustering(YSGF-C)technique to elect cluster heads(CHs)and organize clusters.In addition,enhanced cockroach swarm optimization(ECSO)based multihop routing(ECSO-MHR)approach was derived for optimal route selection.The YSGF-C and ECSO-MHR techniques compute fitness functions using different input variables for achieving improved energy efficiency and network lifetime.The design of YSGF-C and ECSO-MHR techniques for disaster management in IoT networks shows the novelty of the work.For examining the improved outcomes of the EAMCR-RTDM system,a wide range of simulations were performed and the extensive results are assessed in terms of different measures.The comparative outcomes highlighted the enhanced outcomes of the EAMCRRTDM algorithm over the existing approaches.

On Clustering Algorithms for Biological Data

Age of knowledge explosion requires us not only to have the ability to get useful information which represented by data but also to find knowledge in information. Human Genome Project achieved large amount of such biological data, and people found clustering is a promising approach to analyze those biological data for knowledge hidden. The researches on biological data go to in-depth gradually and so are the clustering algorithms. This article mainly introduces current broad-used clustering algorithms, including the main idea, improvements, key technology, advantage and disadvantage, and the applications in biological field as well as the problems they solve. What’s more, this article roughly introduces some database used in biological field.

DYNAMIC REQUEST DISPATCHING ALGORITHM FOR WEB SERVER CLUSTER

Distributed architectures support increased load on popular web sites by dispatching client requests transparently among multiple servers in a cluster. Packet Single-Rewriting technology and client address hashing algorithm in ONE-IP technology which can ensure application-session-keep have been analyzed, an improved request dispatching algorithm which is simple, effective and supports dynamic load balance has been proposed. In this algorithm, dispatcher evaluates which server node will process request by applying a hash function to the client IP address and comparing the result with its assigned identifier subset; it adjusts the size of the subset according to the performance and current load of each server, so as to utilize all servers' resource effectively. Simulation shows that the improved algorithm has better performance than the original one.

3-Phase Fault Finding in Oil Field MV Distribution Network Using Fuzzy Clustering Techniques

This paper studies an existing 13.8 kilovolt distribution network which, serves an oil production field spread over an area of approximately 60 kilometers square, in order to locate any fault that may occur anywhere in the network using fuzzy c-mean classification techniques. In addition, Sections 5 and 6 introduce two different methods for normalizing data and selecting the optimum number of clusters in order to classify data. Results and conclusions are given to show the feasibility for the suggested fault location method. Suggestion for future related research has been provided in Section 8.