Classification Of Services Research Articles

Publicly Verifiable and Secure SVM Classification for Cloud-Based Health Monitoring Services

Publicly Verifiable and Secure SVM Classification for Cloud-Based Health Monitoring Services

Synthesizing Recent Trends in Interventions and Key Ecosystem Services in Indonesian Peatland

This study conducted a systematic literature review focusing on peatlands studies in Southeast Asia, specifically in Jambi, South Sumatra, and the Riau province of Indonesia, covering the period from 2001 to 2023. To ensure the quality and rigor of the analyzed articles, a critical process and systematic review were employed. Journal articles were extracted using reputable resources, including Google Scholar and Scopus, to enhance the validity and reliability of the research results. We identified significant research topics based on region, province, and sector. Additionally, we synthesized the existing classification of ecosystem services, drawing on previous studies conducted in Indonesia. These services were categorized as provisioning, regulating, cultural, and supporting services. We also reviewed the classification of ecosystem service types based on peatland degradation and restoration. This study identified evidence of peatland intervention to evaluate ecosystem services in Indonesia. We found that large-scale cultivation and production of palm oil, local policies, and forest fires were the main intervening factors in Indonesian peatlands. Furthermore, Indonesian peatlands have undergone conversion to oil palm, timber, and crop plantations. It is imperative to substantiate the effectiveness of future peatland restoration plans and further refine the quantification of services provided by peatland ecosystems through cooperative projects.

An efficient starling murmuration-based secure web service model for smart city application using DBN

The advent of IoT devices increased internet usage more than smartphones and personal computers. The manual analysis of the Web Service Description Language (WSDL) document is quite expensive and time-consuming, hence this paper proposes a novel deep learning architecture to overcome the issue associated with web service classification. A structural self-organized deep belief network (SSODBN) is used for real-time web service classification in different fields such as Education, Smart electricity, Intelligent road networks, Health and social care, and Sports, water, and gas distribution. The SSODBN architecture utilizes a dropout strategy to minimize the interrelationship between the feature detectors and a regularized reinforced transfer function to eliminate the irrelevant weights. The main advantage offered by the S-DBN architecture is the improved preprocessing with feature selection. The Starling Murmuration Optimizer (SMO) is utilized in this paper to minimize the reconstruction error of the S-DBN architecture. The security of the smart city architecture is mainly improved via the blockchain defined network (BDN) using user-authenticated blocks. The experimental results revealed that the proposed method managed the scalability, latency, and centralization issues with superior data transmission.

Knowledge distillation representation and DCNMIX quality prediction‐based Web service recommendation

SummaryWeb service recommendation as an emerging topic attracts increasing attention due to its important practical significance. As the number of available Web services continues to grow, users face the challenge of searching the most suitable services that meet their specific needs. Quality of service (QoS)‐based service recommendation becomes a popular approach to address this issue. However, existing QoS‐based service recommendation methods are inability to effectively capture valuable content and structural information from services. These methods often rely solely on low‐order explicit feature intersections in QoS information, do not fully utilize the high‐order implicit feature intersections, and ignore the rich semantic information existing in service descriptions and user preferences. To address this problem, this paper proposes a Web service recommendation method via combining knowledge distillation representation and DCNMIX quality prediction. This method combines content‐based and structure‐based service classification and service prediction based on multi‐dimensional service quality information. First, it builds a service relationship network using semantic features extracted from service descriptions. Second, it designs a graph neural network knowledge distillation framework. The teacher model extracts the knowledge of the graph neural network model, and the student model learns the structure‐based and feature‐based prior knowledge of the service relationship network. Then the student model is used to learn the knowledge of the teacher model, classify Web services, and obtain service representations. Finally, based on service representations and multi‐dimensional QoS information, it exploits the DCNMIX model to learn the explicit and implicit features intersections of Web services and obtain the prediction score and ranking of Web services. The experimental results on the ProgrammableWeb dataset show that the proposed method outperforms the state‐of‐the‐art baselines in terms of Recall, F1, Logloss, and AUC_ROC.

LCPCWSC: a Web service classification approach based on label confusion and priori correction

Purpose With the increasing number of Web services, correct and efficient classification of Web services is crucial to improve the efficiency of service discovery. However, existing Web service classification approaches ignore the class overlap in Web services, resulting in poor accuracy of classification in practice. This paper aims to provide an approach to address this issue. Design/methodology/approach This paper proposes a label confusion and priori correction-based Web service classification approach. First, functional semantic representations of Web services descriptions are obtained based on BERT. Then, the ability of the model is enhanced to recognize and classify overlapping instances by using label confusion learning techniques; Finally, the predictive results are corrected based on the label prior distribution to further improve service classification effectiveness. Findings Experiments based on the ProgrammableWeb data set show that the proposed model demonstrates 4.3%, 3.2% and 1% improvement in Macro-F1 value compared to the ServeNet-BERT, BERT-DPCNN and CARL-NET, respectively. Originality/value This paper proposes a Web service classification approach for the overlapping categories of Web services and improve the accuracy of Web services classification.

A Developed Multi-Level Deep Learning Model for Network Slicing Classification in 5G Network

5G is considered as a key contributor and infrastructure supplier in the communication technology industry, capable of supporting a wide range of services such as virtual reality, driverless automobiles, e-health, and a variety of intelligent applications. Network slicing is designed to support the diversity of services applications with increased performance and flexibility needs by dividing the physical network into many logical networks. Service classification allows 5G service providers to accurately select the network slices for each service. We propose a Network Slicing classifier that uses a Multi-level Deep learning Model. First, we created a dataset of 5G network slicing that contain attributes connected with various network services. Next, we performed a multi-level model that consist of a set of Machine learning and deep learning model (such as deep Neural Network, Random Forest and Decision Tree) as a first level followed by next level that which is represent Attentive Interpretable Tabular Learning model. The outcomes of the experiment showed that the proposed model was able to exceed the normal models with high performance results.

Nebraska Department of Correctional Services Classification and Crowding Project Report

Nebraska Department of Correctional Services Classification and Crowding Project Report

Mapping the Technology Transfer Offices in Greece: Initial Outcomes Concerning Medical and Health Technologies and Next Steps

AbstractWhile the academic debate on how to transfer technology from universities and research centres to the society is not absent in Greece, yet the study of the institutions mediating in this process is not that advanced. Here, we present the results from our first survey on the Technology Transfer Offices of 12 major Greek universities and research centres. We analyse their structure, regulations and classification of services, and report on their industrial (patents, trademarks, etc.) and intellectual (copyright, digital content, etc.) property outputs. Furthermore, we explore potential patterns of collaboration. With a few notable exceptions in a limited number of aspects that formulate the everyday workings and performance indicators of the examined TTOs, findings indicate an underdeveloped ecosystem with significant room for improvement. The research presented here is part of a broader project, which aims to map the entire ecosystem of the Greek higher education institutions and research centres.

Groundwater Ecosystem Services: Redefining and Operationalizing the Concept

In the last decade, the publication of research studies in the field of groundwater ecosystem services and their classification has increased dramatically. Several academic and other institutions have developed ecosystem service classification systems for use in environmental policy research. However, the existing classification systems need to be optimized in the following areas: (a) the weak correlation between concepts and the definitions assigned, which creates double counting in economic valuation, (b) the absence of correlation of the concept of use value with the ecosystem service, (c) the non-categorization of final and intermediate services, and (d) the overlapping of wetland, soil, and groundwater regulating ecosystem services. Our research aims, through the results of a literature analysis and the synthesis of the results of an expert consultation process, to develop a unified categorization system for groundwater ecosystem services. In the context of the conceptualization of groundwater ecosystem services, this research implemented an expert judging elicitation process where subject discussions and targeted interviews were performed, combined with a literature review analysis. Through the completion of a specific questionnaire and expert interviews, a new groundwater ecosystem services classification system, namely GROUNDWES, was established.

Enhancing Multilingual Environment Services at the University of Derby: A Reflective Commentary and Marketing Analysis

This research focuses on the implementation of Multilingual Environment services at the University of Derby, aimed at enhancing customer satisfaction and organizational development. The study encompasses a reflective commentary and marketing analysis, evaluating the features, servuction, services theatre, and promotion strategies of the new services. Through the application of Lovelock’s classification of services and the servuction Framework, the study analyzes the intangibility, inseparability, variability, perishability, and rental/access factors of the Multilingual Environment services. Anticipated potential problems are identified, and solutions are proposed to address staff emotional issues and ensure effective service delivery. Additionally, a comprehensive marketing mix strategy, including product, price, place, promotion, people, process, and physical evidence, is developed to promote the new services. Furthermore, a reflective commentary highlights the necessary skills and knowledge for effective management, emphasizing the importance of time management, communication, critical thinking, and problem-solving abilities. Overall, this research provides valuable insights into the implementation of Multilingual Environment services, offering recommendations for improvement and emphasizing the potential for enhancing customer satisfaction and organizational success at the University of Derby.

Occupation classification model based on DistilKoBERT: using the 5th and 6th Korean Working Condition Surveys.

Accurate occupation classification is essential in various fields, including policy development and epidemiological studies. This study aims to develop an occupation classification model based on DistilKoBERT. This study used data from the 5th and 6th Korean Working Conditions Surveys conducted in 2017 and 2020, respectively. A total of 99,665 survey participants, who were nationally representative of Korean workers, were included. We used natural language responses regarding their job responsibilities and occupational codes based on the Korean Standard Classification of Occupations (7th version, 3-digit codes). The dataset was randomly split into training and test datasets in a ratio of 7:3. The occupation classification model based on DistilKoBERT was fine-tuned using the training dataset, and the model was evaluated using the test dataset. The accuracy, precision, recall, and F1 score were calculated as evaluation metrics. The final model, which classified 28,996 survey participants in the test dataset into 142 occupational codes, exhibited an accuracy of 84.44%. For the evaluation metrics, the precision, recall, and F1 score of the model, calculated by weighting based on the sample size, were 0.83, 0.84, and 0.83, respectively. The model demonstrated high precision in the classification of service and sales workers yet exhibited low precision in the classification of managers. In addition, it displayed high precision in classifying occupations prominently represented in the training dataset. This study developed an occupation classification system based on DistilKoBERT, which demonstrated reasonable performance. Despite further efforts to enhance the classification accuracy, this automated occupation classification model holds promise for advancing epidemiological studies in the fields of occupational safety and health.

The Integrated Atlas of Dementia Care in the Australian Capital Territory: A Collective Case Study of Local Service Provision.

This study evaluates the dementia care system in a local area and aimed to include all specialised services designed to provide health and social services to people with dementia or age-related cognitive impairment, as well as general services with a high or very high proportion of clients with dementia. The study used an internationally standardised service classification instrument called Description and Evaluation of Services and DirectoriEs for Long Term Care (DESDE-LTC) to identify and describe all services providing care to people with dementia in the Australian Capital Territory (ACT). A total of 47 service providers were eligible for inclusion. Basic information about the services was collected from their websites, and further information was obtained through interviews with the service providers. Of the 107 services offered by the 47 eligible providers, 27% (n = 29) were specialised services and 73% (n = 78) were general services. Most of the services were residential or outpatient, with a target population mostly of people aged 65 or older, and 50 years or older in the case of Aboriginal and Torres Strait Islander Australians. There were government supports available for most types of care through various programmes. Dementia care in the ACT relies heavily on general services. More widespread use of standardised methods of service classification in dementia will facilitate comparison with other local areas, allow for monitoring of changes over time, permit comparison with services provided for other health conditions and support evidence-informed local planning.

A Semi-Supervised Learning Approach to Quality-Based Web Service Classification

A Semi-Supervised Learning Approach to Quality-Based Web Service Classification

Are local retail services an amenity or a nuisance?

Abstract We test whether local retail services are considered a nuisance or an amenity and how this distinction is capitalized into residential property values. Using a rich, micro-spatial dataset on property sales transactions and business activity in New York City, we estimate the impact of access to neighborhood retail services on residential sales prices. We construct two instruments to channel supply-side drivers of retail change and to address concerns of endogeneity between changes in retail activity and property values. Results show that retail services that are more frequently consumed and experiential, and are located in relatively more mixed-use neighborhoods are positively capitalized into property values. Residents also pay more to be closer to more diverse retail clusters, and relatively less to be closer to chains. This is true across smaller 1-to-4 family homes as well as larger condos/coops and multi-family rental buildings. The price effects from certain classifications of retail, like restaurants and personal services, are mixed depending on the kind of residential property and the local concentration of the retail. Therefore, the relative strength of the amenity or nuisance effect is very much conditioned on the type of service and the localized neighborhood context.

Sustainability Factor for the Cost–Benefit Analysis of Building-Integrated Greenery Systems

Building-integrated greenery (BIG) systems, which include green roofs and green facades, are well-established nature-based solutions (NBS) with proven scientific benefits. However, initial costs and economic apprehensions stemming from potential negative outcomes act as adoption barriers. Furthermore, the lack of standardized indicators and assessment methodologies for evaluating the city-level impacts of BIG systems presents challenges for investors and policy makers. This paper addresses these issues by presenting a comprehensive set of indicators derived from widely accepted frameworks, such as the Common International Classification of Ecosystem Services (CICES) and the NBS impact evaluation handbook. These indicators contribute to the creation of a ‘sustainability factor’, which facilitates cost–benefit analyses for BIG projects using locally sourced data. The practical application of this factor to a 3500 m2 green roof in Lleida, Catalonia (Spain) demonstrates that allocating space for urban horticultural production (i.e., food production), CO2 capture, and creating new recreational areas produces benefits that outweigh the costs by a factor value of nine during the operational phase of the green roof. This cost–benefit analysis provides critical insights for investment decisions and public policies, especially considering the significant benefits at the city level associated with the implementation of BIG systems.

The final ecosystem goods and services Voltron: the power of tools together.

Environmental decision-making benefits from considering ecosystem services to ensure that aspects of the environment that people rely upon are fully evaluated. By focusing consideration of ecosystem services on final ecosystem goods and services (FEGS), the aspects of the environment directly enjoyed, used, or consumed by humans, these analyses can be more streamlined and effective. The U.S. Environmental Protection Agency has developed a set of tools to facilitate this consideration. The central feature of FEGS is that ecosystems are viewed through the diverse ways people directly benefit from them. The National Ecosystem Services Classification System (NESCS) Plus provides a framework for describing and identifying FEGS consistently. The standardization made available by NESCS Plus allows other tools and databases to interact using the NESCS Plus architecture and taxonomy, providing diverse insights for decision makers. Here, we examine the synergy of using the following four tools together: (1) the FEGS Scoping Tool; (2) the FEGS Metrics Report; (3) the EnviroAtlas; and (4) the EcoService Models Library. The FEGS Scoping Tool helps users determine what ecosystem services are relevant to a decision by harnessing FEGS understanding to enable communities to identify the relative importance of beneficiaries relevant to a decision and biophysical aspects of the environment of direct relevance to those beneficiaries. The FEGS Metrics Report can guide which metrics to monitor or model to represent those priority services. The EnviroAtlas, a powerful tool containing geospatial data and other resources related to ecosystem services, chemical and non-chemical stressors, and human health, and the EcoService Models Library, a database of ecosystem models, are two tools that support users in mapping and modeling endpoints relevant to priority services. While each of these tools is valuable on its own, together, they provide a powerful approach to easily incorporate and operationalize ecosystem services efforts into different parts of decision-making processes across different types of decisions. We illustrate how these integrated tools can be used together with a hypothetical example of a complex environmental management case study and the combined benefit of using the FEGS tools together.

State Services In The Republic Of Kazakhstan: The Concept And System

The relevance of the topic under study is primarily due to the formation and rapid development of the institute of public services. This, in turn, poses to the researcher the practical and theoretical task of establishing the correlation between the used concepts in public service activities. The article deals with the concept and basic characteristics of public services, the system of subjects of their provision. On the basis of doctrinal sources the correlation of the concepts of public, municipal, state, administrative, social, communal and informational services has been revealed. The author offers a classification of public services according to the most relevant criteria for the practice of Kazakhstan. Analysis of concepts and clarification of signs of public services allowed to form the author's definition of an administrative service. In conclusion, the author characterizes the system of public services and subjects of their provision in the Republic of Kazakhstan as one of the most dynamically developing areas requiring a systematic reform of the legislation of the Republic of Kazakhstan. The purpose of this article is to form a system of public services and subjects of their provision, as well as to identify the specifics of the activities of the NJSC «SC "Government for citizens".

Distributed Denial of Service Classification for Software-Defined Networking Using Grammatical Evolution

Software-Defined Networking (SDN) stands as a pivotal paradigm in network implementation, exerting a profound influence on the trajectory of technological advancement. The critical role of security within SDN cannot be overstated, with distributed denial of service (DDoS) emerging as a particularly disruptive threat, capable of causing large-scale disruptions. DDoS operates by generating malicious traffic that mimics normal network activity, leading to service disruptions. It becomes imperative to deploy mechanisms capable of distinguishing between benign and malicious traffic, serving as the initial line of defense against DDoS challenges. In addressing this concern, we propose the utilization of traffic classification as a foundational strategy for combatting DDoS. By categorizing traffic into malicious and normal streams, we establish a crucial first step in the development of effective DDoS mitigation strategies. The deleterious effects of DDoS extend to the point of potentially overwhelming networked servers, resulting in service failures and SDN server downtimes. To investigate and address this issue, our research employs a dataset encompassing both benign and malicious traffic within the SDN environment. A set of 23 features is harnessed for classification purposes, forming the basis for a comprehensive analysis and the development of robust defense mechanisms against DDoS in SDN. Initially, we compare GenClass with three common classification methods, namely the Bayes, K-Nearest Neighbours (KNN), and Random Forest methods. The proposed solution improves the average class error, demonstrating 6.58% error as opposed to the Bayes method error of 32.59%, KNN error of 18.45%, and Random Forest error of 30.70%. Moreover, we utilize classification procedures based on three methods based on grammatical evolution, which are applied to the aforementioned data. In particular, in terms of average class error, GenClass exhibits 6.58%, while NNC and FC2GEN exhibit average class errors of 12.51% and 15.86%, respectively.

FairNet: A Measurement Framework for Traffic Discrimination Detection on the Internet

Network neutrality is related to the nondiscriminatory treatment of packets on the Internet. Any deliberate discrimination of traffic of one application while favoring others violates the principle of neutrality. Many countries have enforced laws against such discrimination. One requires tools to detect any net neutrality violations to enforce such laws. However, detecting such violations is challenging as it is hard to separate any degradation in quality due to natural network effects and selective degradation. Also, legitimate traffic management and deliberate discrimination methods can be technically the same, making it challenging to distinguish them further. We developed an end-to-end measurement framework named FairNet to detect discrimination of traffic. It compares the performance of similar services. Our focus is on HTTPS streaming services which constitute a predominant portion of the Internet traffic. The effect of confounding factors (congestion, traffic management policy, dynamic rate adaptation) is made similar on the test services to ensure a fair comparison. FairNet framework uses a replay server and user-client that exchanges correctly identifiable traffic streams over the Internet. The Server Name Indication (SNI) field in the TLS handshake, which goes in plain text, ensures that the traffic from the replay server appears to network middle-boxes as that coming from its actual server. We validated that appropriate SNIs result in the correct classification of services using a commercial traffic shaper. FairNet uses two novel algorithms based on application-level throughput and connection status to detect traffic discrimination. We also validated the methodologys effectiveness by collecting network logs through mobile apps over the live Internet and analyzing them.

Classification of Services through Feature Selection and Machine Learning in 5G Networks

Classification of Services through Feature Selection and Machine Learning in 5G Networks