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Journal articles on the topic 'Machine Learning as a Service'

Author: Grafiati
Published: 6 September 2023

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1

Joseph Poulose, Travis, and S. Ganesh Kumar. "Service identification using k-NN machine learning." International Journal of Engineering & Technology 7, no. 2.4 (March 10, 2018): 182. http://dx.doi.org/10.14419/ijet.v7i2.4.13035.

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Web service categorization is a daunting task since it requires semantic descriptions of those services which are not provided to the majority of those websites. The proposal of a Semantic based automated service discovery requires a request from the user that can be analyzed which then provides the user with a list of related webs services based on the request that instigated the search. The problem with these service categorizations listed in the Universal description Discovery and Integration (UDDI) is the way the information is related to one another. The relations follow a syntactic method. Semantic based service descriptions is necessary for accurate web categorization. With the help of machine learning we can also predict the user’s service request automatically based on previous searches and also select the best web service for a particular request that the user has made using a k-nearest neighbor algorithm. By doing this we can distinguish between the various types of user requests, provide services that are suitable for that particular request as well as suggest other services that might potentially suit the needs of the user.
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WANG, Xinyue, Nobutada FUJII, Toshiya KAIHARA, and Daisuke KOKURYO. "SERVICE DESIGN WITH MACHINE LEARNING BASED ON USER ACTION HISTORY." Acta Electrotechnica et Informatica 20, no. 2 (June 30, 2020): 11–18. http://dx.doi.org/10.15546/aeei-2020-0008.

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Bhoite, Sayee N., Vaishnavi D. Gadekar, Shashank V. Kapadnis, and Priyanka R. Ghuge. "Churn Prediction using Machine Learning Models." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 2233–36. http://dx.doi.org/10.22214/ijraset.2023.52101.

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Abstract: The market is expanding quickly across all sectors, giving service providers access to a larger user base. Better offers have led to increased competition, creative new business ideas, and rising costs for acquiring new customers. Service providers understand how crucial it is to keep clients on-site in such a brief setup. Service providers must therefore prevent churn, a condition that occurs when a customer decides not to use a company's services any longer. This study examines the most widely used machine learning algorithms for churn prediction, not just in the banking industry but also in other businesses that place a high value on customer engagement.
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Točelovskis, Andrejs, and Artis Teilāns. "MACHINE LEARNING SERVICE FOR STUDENT REGISTRATION." HUMAN. ENVIRONMENT. TECHNOLOGIES. Proceedings of the Students International Scientific and Practical Conference, no. 24 (April 22, 2020): 103–9. http://dx.doi.org/10.17770/het2020.24.6759.

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Zuev, Dmitry, Alexey Kalistratov, and Andrey Zuev. "Machine Learning in IT Service Management." Procedia Computer Science 145 (2018): 675–79. http://dx.doi.org/10.1016/j.procs.2018.11.063.

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Zimal, Sudarshan, Chirag Shah, Shivam Borhude, Amit Birajdar, and Prof Shreedhar Patil. "Customer Churn Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 2 (February 28, 2023): 872–83. http://dx.doi.org/10.22214/ijraset.2023.49142.

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bstract: Rapid technology growth has affected corporate practices. With more items and services to select from, client churning has become a big challenge and threat to all firms. We offer a machine learning-based churn prediction model for a B2B subscription-based service provider. Our research aims to improve churn prediction. We employed machine learning to iteratively create and evaluate the resulting model using accuracy, precision, recall, and F1- score. The data comes from a financial administration subscription service. Since the given dataset is mostly non-churners, we analyzed SMOTE, SMOTEENN, and Random under Sampler to balance it. Our study shows that machine learning can anticipate client attrition. Ensemble learners perform better than single base learners, and a balanced training dataset should increase classifier performance.
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Yin, Hexiao. "Role of Artificial Intelligence Machine Learning in Deepening the Internet Plus Social Work Service." Mathematical Problems in Engineering 2021 (November 6, 2021): 1–10. http://dx.doi.org/10.1155/2021/6915568.

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The traditional social work services are mainly visits which have some problems such as inconvenient information circulation, unreasonable resource allocation, and low service efficiency. To improve these problems, Internet plus is used to reform social work services and form an Internet plus social work service mode. Although this model has a very good improvement effect on social work service, with the rapid increase of the number of social work services and the rapid growth of the number of volunteers, this model has limitations in the arrangement of social work services and volunteer management. Therefore, based on this model, with the help of machine learning, the Internet plus social work service mode can be deepened by using machine learning to manage social services and volunteers. Internet plus social work service is the main problem in this paper. The Internet plus social work service mode is formed. Then, the deepening role of machine learning in Internet + social work service is discussed, and some problems in Internet plus social work service mode are improved. Internet plus social work service mode can better improve the problems in traditional social work service. The paper also uses machine learning to further optimize the mode of Internet plus social work service, which has a good application in social work service prospects.
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Kumaran, N., Purandhar Sri Sai, and Lokesh Manikanta. "Web Phishing Detection using Machine Learning." International Journal of Innovative Technology and Exploring Engineering 11, no. 4 (March 30, 2022): 56–59. http://dx.doi.org/10.35940/ijitee.c9804.0311422.

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A web service is one of the most important Internet communications software services. Using fraudulent methods to get personal information is becoming increasingly widespread these days. However, it makes our lives easier, it leads to numerous security vulnerabilities to the Internet’s private structure. Web phishing is just one of the many security risks that web services face. Phishing assaults are usually detected by experienced users however, security is a primary concern for system users who are unaware of such situations. Phishing is the act of portraying malicious web runners as genuine web runners to obtain sensitive information from the end-user. Phishing is currently regarded as one of the most dangerous threats to web security. Vicious Web sites significantly encourage Internet criminal activity and inhibit the growth of Web services. As a result, there has been a tremendous push to build a comprehensive solution to prevent users from accessing such websites. We suggest a literacy-based strategy to categorize Web sites into three categories: benign, spam, and malicious. Our technology merely examines the Uniform Resource Locator (URL) itself, not the content of Web pages. As a result, it removes run-time stillness and the risk of drug users being exposed to cyber surfer-based vulnerabilities. When compared to a blacklisting service, our approach performs better on generality and content since it uses learning techniques.
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Liu, Ningbo. "An Empirical Study on Machine Learning for Enterprise Cloud Computing Service Management." Asian Journal of Economics, Business and Accounting 23, no. 9 (March 28, 2023): 48–57. http://dx.doi.org/10.9734/ajeba/2023/v23i9963.

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A machine learning method is proposed to aim at the problems of large data processing, complex data, and limited system resources in cloud computing service management. First, multi-department analysis is carried out on cloud service management data, and relevant management data is standardized to form a standardized management data collection; Machine learning is used to classify, mine, and extract service management data, and corresponding management measures are taken promptly to improve the management level of cloud computing services. MATLAB simulation shows that machine learning can improve the level of cloud computing service management, simplify the process of cloud computing service management, shorten the management time of cloud computing services, and meet the actual needs of service management.
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Han, Bo, and Rongli Zhang. "Virtual Machine Allocation Strategy Based on Statistical Machine Learning." Mathematical Problems in Engineering 2022 (July 5, 2022): 1–6. http://dx.doi.org/10.1155/2022/8190296.

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At present, big data cloud computing has been widely used in many enterprises, and it serves tens of millions of users. One of the core technologies of big data cloud service is computer virtualization technology. The reasonable allocation of virtual machines on available hosts is of great significance to the performance optimization of cloud computing. We know that with the continuous development of information technology and the increasing number of computer users, different virtualization technologies and the increasing number of virtual machines in the network make the effective allocation of virtualization resources more and more difficult. In order to solve and optimize this problem, we propose a virtual machine allocation algorithm based on statistical machine learning. According to the resource requirements of each virtual machine in cloud service, the corresponding comprehensive performance analysis model is established, and the reasonable virtual machine allocation algorithm description of the host in the resource pool is realized according to the virtualization technology type or mode provided by the model. Experiments show that this method has the advantages of overall performance, load balancing, and supporting different types of virtualization.
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Thu, Thuy Nguyen Thi. "Machine Learning Solution in Peer to Peer Loan Service in Vietnamese Banks." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 1636–41. http://dx.doi.org/10.5373/jardcs/v12sp7/20202268.

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Giuliano, Romeo, and Eros Innocenti. "Machine Learning Techniques for Non-Terrestrial Networks." Electronics 12, no. 3 (January 28, 2023): 652. http://dx.doi.org/10.3390/electronics12030652.

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Traditionally, non-terrestrial networks (NTNs) are used for a limited set of applications, such as TV broadcasting and communication support during disaster relief. Nevertheless, due to their technological improvements and integration in the 5G 3GPP standards, NTNs have been gaining importance in the last years and will provide further applications and services. 3GPP standardization is integrating low-Earth orbit (LEO) satellites, high-altitude platform stations (HAPSs) and unmanned aerial systems (UASs) as non-terrestrial elements (NTEs) in the NTNs within the terrestrial 5G standard. Considering the NTE characteristics (e.g., traffic congestion, processing capacity, oscillation, altitude, pitch), it is difficult to dynamically set the optimal connection based also on the required service to properly steer the antenna beam or to schedule the UE. To this aim, machine learning (ML) can be helpful. In this paper, we present novel services supported by the NTNs and their architectures for the integration in the terrestrial 5G 3GPP standards. Then, ML techniques are proposed for managing NTN connectivity as well as to improve service performance.
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Palanivel, K. "Machine Learning Architecture to Financial Service Organizations." International Journal of Computer Sciences and Engineering 7, no. 11 (November 30, 2019): 85–104. http://dx.doi.org/10.26438/ijcse/v7i11.85104.

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Chelmis, Charalampos, Wenting Qi, Wonhyung Lee, and Stephanie Duncan. "Smart Homelessness Service Provision with Machine Learning." Procedia Computer Science 185 (2021): 9–18. http://dx.doi.org/10.1016/j.procs.2021.05.002.

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Lee, Hyung Ho, Hak Jae Lee, Tae Su Kim, and Mi Hyun Kim. "Curation Service Implementation using Machine Learning Algorithm." Korean Institute of Smart Media 9, no. 4 (December 30, 2020): 118–25. http://dx.doi.org/10.30693/smj.2020.9.4.118.

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16

Hesamifard, Ehsan, Hassan Takabi, Mehdi Ghasemi, and Rebecca N. Wright. "Privacy-preserving Machine Learning as a Service." Proceedings on Privacy Enhancing Technologies 2018, no. 3 (June 1, 2018): 123–42. http://dx.doi.org/10.1515/popets-2018-0024.

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Abstract Machine learning algorithms based on deep Neural Networks (NN) have achieved remarkable results and are being extensively used in different domains. On the other hand, with increasing growth of cloud services, several Machine Learning as a Service (MLaaS) are offered where training and deploying machine learning models are performed on cloud providers’ infrastructure. However, machine learning algorithms require access to the raw data which is often privacy sensitive and can create potential security and privacy risks. To address this issue, we present CryptoDL, a framework that develops new techniques to provide solutions for applying deep neural network algorithms to encrypted data. In this paper, we provide the theoretical foundation for implementing deep neural network algorithms in encrypted domain and develop techniques to adopt neural networks within practical limitations of current homomorphic encryption schemes. We show that it is feasible and practical to train neural networks using encrypted data and to make encrypted predictions, and also return the predictions in an encrypted form. We demonstrate applicability of the proposed CryptoDL using a large number of datasets and evaluate its performance. The empirical results show that it provides accurate privacy-preserving training and classification.
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Mamdouh, Maged, Mostafa Ezzat, and Hesham A. Hefny. "Airport resource allocation using machine learning techniques." Inteligencia Artificial 23, no. 65 (May 15, 2020): 19–32. http://dx.doi.org/10.4114/intartif.vol23iss65pp19-32.

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The airport ground handling has a global trend to meet the Service Level Agreement (SLA) requirementsthat represents resource allocation with more restrictions according to flights. That can be achieved by predictingfuture resources demands. this research presents a comparison between the most used machine learning techniquesimplemented in many different fields for demand prediction and resource allocation. The prediction model nomi-nated and used in this research is the Support Vector Machine (SVM) to predict the required resources for eachflight, despite the restrictions imposed by airlines when contracting their services in the Service Level Agreement.The approach has been trained and tested using real data from Cairo International Airport. the proposed (SVM)technique implemented and explained with a varying accuracy of resource allocation prediction, showing thateven for variations accuracy in resource prediction in different scenarios; the Support Vector Machine techniquecan produce a good performance as resource allocation in the airport.
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Fragiadakis, George, Evangelia Filiopoulou, Christos Michalakelis, Thomas Kamalakis, and Mara Nikolaidou. "Applying Machine Learning in Cloud Service Price Prediction: The Case of Amazon IaaS." Future Internet 15, no. 8 (August 19, 2023): 277. http://dx.doi.org/10.3390/fi15080277.

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When exploring alternative cloud solution designs, it is important to also consider cost. Thus, having a comprehensive view of the cloud market and future price evolution allows well-informed decisions to choose between alternatives. Cloud providers offer various service types with different pricing policies. Currently, infrastructure-as-a-Service (IaaS) is considered the most mature cloud service, while reserved instances, where virtual machines are reserved for a fixed period of time, have the largest market share. In this work, we employ a machine-learning approach based on the CatBoost algorithm to explore a price-prediction model for the reserve instance market. The analysis is based on historical data provided by Amazon Web Services from 2016 to 2022. Early results demonstrate the machine-learning model’s ability to capture the underlying evolution patterns and predict future trends. Findings suggest that prediction accuracy is not improved by integrating data from older time periods.
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Pani, Akankshya, and Shrasti Mourya. "A REVIEW ON SMART AND INTELLIGENT E-GOVERNANCE." International Journal of Social Science and Economic Research 08, no. 04 (2023): 698–704. http://dx.doi.org/10.46609/ijsser.2023.v08i04.010.

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The use of information and communication technologies (ICTs) in the delivery of government services to citizens is referred to as e-governance. The implementation of e-governance has the potential to improve service delivery, citizen participation, and transparency in government operations. Smart e-governance is a relatively new concept in, which artificial intelligence (AI), machine learning, and data analytics are used in government processes. Smart e-governance systems with AI and machine learning models have the potential to transform public service delivery, allowing governments to provide citizens with more efficient and personalised services. Machine learning models can analyse enormous volumes of data, producing predictions and providing insights that might assist governments in identifying and addressing problems before they become significant. The purpose of this research is to study smart e- governance systems utilising machine learning, AI and data analytics models and to assess its effectiveness in improving public service delivery.
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Rodríguez-Gracia, Diego, José A. Piedra-Fernández, Luis Iribarne, Javier Criado, Rosa Ayala, Joaquín Alonso-Montesinos, and Capobianco-Uriarte Maria de las Mercedes. "Microservices and Machine Learning Algorithms for Adaptive Green Buildings." Sustainability 11, no. 16 (August 9, 2019): 4320. http://dx.doi.org/10.3390/su11164320.

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In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings.
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Kong, Yuqiang, and Yaoping He. "Customer Service System Design Based on Big Data Machine Learning." Journal of Physics: Conference Series 2066, no. 1 (November 1, 2021): 012017. http://dx.doi.org/10.1088/1742-6596/2066/1/012017.

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Abstract In recent years, with the rapid development of big data, traditional offline transactions have been moved to online in large numbers driven by the Internet. The virtual nature of online transactions has caused it to have problems such as difficulty in guaranteeing product quality and difficulty in user consultation. In addition, consumers are paying more and more attention to the quality of services, and the participation of customer service in the process of online transactions is very important. However, the current e-commerce market in our country is large and the number of online shopping users is extremely large. Customer service personnel are facing great work pressure. In addition, customer service has the characteristics of difficulty in recruiting, high labor costs, and high turnover rate. Such a dilemma is not conducive to our country. The sound development of e-commerce needs to be solved urgently. In order to solve these problems, it is a good method to apply related technologies to realize the automatic response of customer service. The purpose of this article is to design and research a customer service system based on big data machine learning. This article first through the understanding of the basic concepts of big data, and then extend the core technology of big data. Combining with the design ideas and concepts of contemporary customer service systems in our country, we will discuss the design and research of customer service systems based on big data machine learning. Research shows that traditional customer service in the era of big data can no longer meet people’s growing needs, and customer service systems based on big data machine learning are more efficient and convenient.
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Wang, Jiao, Jay Weitzen, Oguz Bayat, Volkan Sevindik, and Mingzhe Li. "Performance Model for Video Service in 5G Networks." Future Internet 12, no. 6 (June 8, 2020): 99. http://dx.doi.org/10.3390/fi12060099.

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Network slicing allows operators to sell customized slices to various tenants at different prices. To provide better-performing and cost-efficient services, network slicing is looking to intelligent resource management approaches to be aligned to users’ activities per slice. In this article, we propose a radio access network (RAN) slicing design methodology for quality of service (QoS) provisioning, for differentiated services in a 5G network. A performance model is constructed for each service using machine learning (ML)-based approaches, optimized using interference coordination approaches, and used to facilitate service level agreement (SLA) mapping to the radio resource. The optimal bandwidth allocation is dynamically adjusted based on instantaneous network load conditions. We investigate the application of machine learning in solving the radio resource slicing problem and demonstrate the advantage of machine learning through extensive simulations. A case study is presented to demonstrate the effectiveness of the proposed radio resource slicing approach.
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Akinje, Ayorinde O., and Abdulgalee Fuad. "Fraudulent Detection Model Using Machine Learning Techniques for Unstructured Supplementary Service Data." International Journal of Innovative Computing 11, no. 2 (October 31, 2021): 51–60. http://dx.doi.org/10.11113/ijic.v11n2.299.

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The increase in mobile phones accessibility and technological advancement in almost every corner of the world has shaped how banks offer financial service. Such services were extended to low-end customers without a smartphone providing Alternative Banking Channels (ABCs) service, rendering regular financial service same as those on smartphones. One of the services of this ABC’s is Unstructured Supplementary Service Data (USSD), two-way communication between mobile phones and applications, which is used to render financial services all from the bank accounts linked for this USSD service. Fraudsters have taken advantage of innocent customers on this channel to carry out fraudulent activities with high impart of fraudulent there is still not an implemented fraud detection model to detect this fraud activities. This paper is an investigation into fraud detection model using machine learning techniques for Unstructured Supplementary Service Data based on short-term memory. Statistical features were derived by aggregating customers activities using a short window size to improve the model performance on selected machine learning classifiers, employing the best set of features to improve the model performance. Based on the results obtained, the proposed Fraudulent detection model demonstrated that with the appropriate machine learning techniques for USSD, best performance was achieved with Random forest having the best result of 100% across all its performance measure, KNeighbors was second in performance measure having an average of 99% across all its performance measure while Gradient boosting was third in its performance measure, its achieved accuracy is 91.94%, precession is 86%, recall is 100% and f1 score is 92.54%. Result obtained shows two of the selected machine learning random forest and decision tree are best fit for the fraud detection in this model. With the right features derived and an appropriate machine learning algorithm, the proposed model offers the best fraud detection accuracy.
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Paruchuri, Harish. "Market Segmentation, Targeting, and Positioning Using Machine Learning." Asian Journal of Applied Science and Engineering 8, no. 1 (March 20, 2019): 7–14. http://dx.doi.org/10.18034/ajase.v8i1.7.

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The occurrence of numerous rivals and industrialists has created a lot of pressure between rivalry companies to go in search of new customers and at the same time working to maintain the existing ones. Owing to this, the necessity for a new customer service policy or strategy becomes essential irrespective of the business size. Moreover, the capability of any company that wants to remain in existence and growth as well needs to understand its clients and also delivers necessary customer support to make available targeted client services and develop branded clients’ service policy. This understanding is conceivable via organized client service. The respective segment devises consumers who share similar market potentials. Big data philosophies and machine learning devise appropriate means of promoting better recognition and approval of computerized client segmentation methods in good turn of outdated business analytics that frequently lead to any meaningful approach that can keep or source for a new customer while the client disreputable is very growing by the day. This study makes use of the k-means clustering algorithm for this determination. The Sklearn public library was established for the k-Means algorithm and the package is competent by means of a 100-model two-parameters dataset generated or collected from the retail trade. Features of an average figure of client buying and average figure of periodic consumers.
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Im, Euntack, Jina Lee, Sungbyeong An, and Gwangyong Gim. "A Study on Prediction Performance Measurement of Automated Machine Learning." International Journal of Software Innovation 11, no. 1 (January 1, 2023): 1–11. http://dx.doi.org/10.4018/ijsi.315656.

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In digital economics, where value creation using big data becomes important, the ability to analyze data using machine learning and deep learning technology is a key activity in corporate activities. Nevertheless, companies consider it difficult to introduce machine learning and artificial intelligence technologies because they need an understanding of the business as well as data and analysis algorithms. Accordingly, services such as automated machine learning have emerged for easy use of machine learning. In this study, the authors explored the automated machine learning service and compared the random forest and extreme gradient boosting analysis results using WiseProphet and Python. WiseProphet is used as a representative of automated machine learning solutions because it is a cloud-based service that anyone can easily access and can be used in various ways. It is contrasted with the model implemented by Python, which writes code with No coding. As a result of comparing the prediction performance, WiseProphet automatically outperformed the analysis result by parameter optimization.
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Khosravi, Habib Allah, and Yaghoub Farjami. "Machine Learning Based Classifier for Service Function Chains." Computing and Informatics 39, no. 3 (2020): 410–38. http://dx.doi.org/10.31577/cai_2020_3_410.

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Yang, Jie, and Xianzhong Zhou. "Semi-automatic Web Service Classification Using Machine Learning." International Journal of u- and e-Service, Science and Technology 8, no. 4 (April 30, 2015): 339–48. http://dx.doi.org/10.14257/ijunesst.2015.8.4.31.

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Yen, Hsueh-Chuan, Tai-Chang Hsia, and Ren-Chieh Liao. "Machine Learning in a Humanoid Intelligent Service Robot." Journal of Information and Optimization Sciences 35, no. 2 (March 4, 2014): 129–41. http://dx.doi.org/10.1080/02522667.2013.867737.

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Song, Yang. "Web service reliability prediction based on machine learning." Computer Standards & Interfaces 73 (January 2021): 103466. http://dx.doi.org/10.1016/j.csi.2020.103466.

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Mathew, Chris, Charan P, Sushanth S. Rao, BVS Datta Goutham, Dr Sheshappa S.N, and Prof Mr Manohar R. "Terms of Service Fairness Analyser using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 1814–23. http://dx.doi.org/10.22214/ijraset.2023.51970.

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Abstract: Terms of Service (ToS) are fundamental factors in the creation of physical as well as online legally relevant relationships. Very often, they disregard the consumer protection law. In this perspective, a relevant issue is that public agencies in charge of control concretely lack the resources needed to effectively fight against such unlawful practices. We propose a definition of ToS unfairness and a novel machine learning-based approach to classify clauses in ToS, represented by using sentence embedding, into both categories and fairness classes. We use Naïve Bayes Classifier to achieve the desired categorization and result. Terms of Service (ToS) are fundamental factors in the creation of physical as well as online legally relevant relationships. They not only define mutual rights and obligations but also inform users about contract key issues that, in online settings, span from liability limitations to data management and processing conditions. Despite their crucial role, however, ToS are often neglected by users that frequently accept without even reading what they agree upon, representing a critical issue when there exist potentially unfair clauses. To enhance users’ awareness and uphold legal safeguards, we first propose a definition of ToS unfairness based on a novel unfairness measure computed counting the unfair clauses contained in a ToS, and therefore, weighted according to their direct impact on the customers concrete interests.
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Kishimoto, Tomoe, Junichi Tnaka, Tetsuro Mashimo, Ryu Sawada, Koji Terashi, Michiru Kaneda, Masahiko Saito, and Nagataka Matsui. "Anomaly detection using Unsupervised Machine Learning for Grid computing site operation." EPJ Web of Conferences 245 (2020): 07016. http://dx.doi.org/10.1051/epjconf/202024507016.

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A Grid computing site is composed of various services including Grid middleware, such as Computing Element and Storage Element. Text logs produced by the services provide useful information for understanding the status of the services. However, it is a time-consuming task for site administrators to monitor and analyze the service logs every day. Therefore, a support framework has been developed to ease the site administrator’s work. The framework detects anomaly logs using Machine Learning techniques and alerts site administrators. The framework has been examined using real service logs at the Tokyo Tier2 site, which is one of the Worldwide LHC Computing Grid sites. In this paper, a method of the anomaly detection in the framework and its performances at the Tokyo Tier2 site are reported.
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Philipp, Robert, Andreas Mladenow, Christine Strauss, and Alexander Voelz. "Revealing Challenges within the Application of Machine Learning Services – A Delphi Study." Journal of Data Intelligence 2, no. 1 (March 2021): 1–29. http://dx.doi.org/10.26421/jdi2.1-1.

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Over the past years, Machine Learning has been applied to an increasing number of problems across numerous industries. However, the steady rise in the application of Machine Learning has not come without challenges since companies often lack the expertise or infrastructure to build their own Machine Learning systems. These challenges led to the emergence of a new paradigm, called Machine Learning as a Service. Scientific literature has mainly analyzed this topic in the context of platform solutions that provide ready-to-use environments for companies. We recently have developed a platform-independent approach and labeled it Machine Learning Services. The aim of the present study is to identify and evaluate challenges and opportunities in the application of Machine Learning Services. To do so, we conducted a Delphi Study with a panel of machine learning experts. The study consisted of three rounds and was structured according to the five steps of the Data Science Lifecycle. A variety of challenges from the areas “Communication”, “Environment”, “Approach”, “Data”, “Retraining, Testing, Monitoring and Updating”, “Model Training and Evaluation” were identified. Subsequently, the challenges revealed by the Delphi Study were compared with previous work on Machine Learning as a Service, which resulted from a structured literature review. The identified areas serve as possible future research fields and give further implications for practice. Alleviating communication issues and assessing the business IT infrastructure prior to the machine learning project are among the key findings of our study.
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Amrish, R., K. Bavapriyan, V. Gopinaath, A. Jawahar, and C. Vinoth Kumar. "DDoS Detection using Machine Learning Techniques." March 2022 4, no. 1 (May 22, 2022): 24–32. http://dx.doi.org/10.36548/jismac.2022.1.003.

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A Distributed Denial of Service (DDoS) attack is a type of cyber-attack that attempts to interrupt regular traffic on a targeted server by overloading the target. The system under DDoS attack remains occupied with the requests from the bots rather than providing service to legitimate users. These kinds of attacks are complicated to detect and increase day by day. In this paper, machine learning algorithm is employed to classify normal and DDoS attack traffic. DDoS attacks are detected using four machine learning classification techniques. The machine learning algorithms are tested and trained using the CICDDoS2019 dataset, gathered by the Canadian Institute of Cyber Security. When compared against KNN, Decision Tree, and Random Forest, the Artificial Neural Network (ANN) generates the best results.
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Springer, Tom, Erik Linstead, Peiyi Zhao, and Chelsea Parlett-Pelleriti. "Towards QoS-Based Embedded Machine Learning." Electronics 11, no. 19 (October 6, 2022): 3204. http://dx.doi.org/10.3390/electronics11193204.

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Due to various breakthroughs and advancements in machine learning and computer architectures, machine learning models are beginning to proliferate through embedded platforms. Some of these machine learning models cover a range of applications including computer vision, speech recognition, healthcare efficiency, industrial IoT, robotics and many more. However, there is a critical limitation in implementing ML algorithms efficiently on embedded platforms: the computational and memory expense of many machine learning models can make them unsuitable in resource-constrained environments. Therefore, to efficiently implement these memory-intensive and computationally expensive algorithms in an embedded computing environment, innovative resource management techniques are required at the hardware, software and system levels. To this end, we present a novel quality-of-service based resource allocation scheme that uses feedback control to adjust compute resources dynamically to cope with the varying and unpredictable workloads of ML applications while still maintaining an acceptable level of service to the user. To evaluate the feasibility of our approach we implemented a feedback control scheduling simulator that was used to analyze our framework under various simulated workloads. We also implemented our framework as a Linux kernel module running on a virtual machine as well as a Raspberry Pi 4 single board computer. Results illustrate that our approach was able to maintain a sufficient level of service without overloading the processor as well as providing an energy savings of almost 20% as compared to the native resource management in Linux.
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Choudhary, Esha. "Spam SMS Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 6868–76. http://dx.doi.org/10.22214/ijraset.2023.53235.

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Abstract: As the popularity of mobile phone devices has increased, Short Message Service (SMS) has grown into a multi-billion dollars industry. At the same time, reduction in the cost of messaging services has resulted in growth in unsolicited commercial advertisements (spams) being sent to mobile phones. In parts of Asia, up to 30% of text messages were spam in 2012. Lack of real databases for SMS spams, short length of messages and limited features, and their informal language are the factors that may cause the established email filtering algorithms to underperform in their classification. In this project, a dataset of real SMS Spams from UCI Machine Learning repository is used, and after pre-processing and vectorization, different machine learning algorithms are applied to the dataset. Finally, the results are compared and the best algorithm for spam filtering for text messaging is introduced and converted into an open-source website. The SMS spam collection set is used for testing the method. After collecting the various supervised learning algorithms, we find that the Multinomial Naïve Bayes algorithm gives us 97.1% Accuracy and 100% Precision
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Kadiri, Padmaja, and Seshadri Ravala. "Kernel-Based Machine Learning Models to Predict Mitigation Time During Cloud Security Attacks." International Journal of e-Collaboration 17, no. 4 (October 2021): 75–88. http://dx.doi.org/10.4018/ijec.2021100106.

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Security threats are unforeseen attacks to the services provided by the cloud service provider. Depending on the type of attack, the cloud service and its associated features will be unavailable. The mitigation time is an integral part of attack recovery. This research paper explores the different parameters that will aid in predicting the mitigation time after an attack on cloud services. Further, the paper presents machine learning models that can predict the mitigation time. The paper presents the kernel-based machine learning models that can predict the average mitigation time during security attacks. The analysis of the results shows that the kernel-based models show 87% accuracy in predicting the mitigation time. Furthermore, the paper explores the performance of the kernel-based machine learning models based on the regression-based predictive models. The regression model is used as a benchmark model to analyze the performance of the machine learning-based predictive models in the prediction of mitigation time in the wake of an attack.
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Ullal, Mithun S., Pushparaj M. Nayak, Ren Trevor Dais, Cristi Spulbar, and Ramona Birau. "INVESTIGATING THE NEXUS BETWEEN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING TECHNOLOGIES IN THE CASE OF INDIAN SERVICES INDUSTRY." Business: Theory and Practice 23, no. 2 (September 12, 2022): 323–33. http://dx.doi.org/10.3846/btp.2022.15366.

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The future in the services industry belongs to Artificial Intelligence (AI) driven machines, which is a major source of worry for the job market in India. Over 50% of India’s GDP constitutes services, and it is a major source of employment for the skilled manpower of India. The research measures the impact of AI on service jobs in India based on qualitative parameters such as logical, natural, physical, and compassion; and finds which aspect serves the jobs better between machines and humans. The jobs taken over by AI are primarily at the task level more than the job level and for the basic tasks predominantly. The replacement starts with the basic tasks involved in providing service and then it grows to perform all the tasks involved in services. The research finds out that the logical aspects of the service will slowly reduce in the coming 5–10 years as AI will perform all the logic-related tasks leaving more emotional tasks such as compassion for humans. Finally, even these emotional-related tasks will be taken over by the AI which provides us with a very interesting combination of man and machine in the Indian scenario still threatening human employment.
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Igried, Bashar, Atalla Fahed Al-Serhan, Ayoub Alsarhan, Mohammad Aljaidi, and Amjad Aldweesh. "Machine Learning Failure-Aware Scheme for Profit Maximization in the Cloud Market." Future Internet 15, no. 1 (December 20, 2022): 1. http://dx.doi.org/10.3390/fi15010001.

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A successful cloud trading system requires suitable financial incentives for all parties involved. Cloud providers in the cloud market provide computing services to clients in order to perform their tasks and earn extra money. Unfortunately, the applications in the cloud are prone to failure for several reasons. Cloud service providers are responsible for managing the availability of scheduled computing tasks in order to provide high-level quality of service for their customers. However, the cloud market is extremely heterogeneous and distributed, making resource management a challenging problem. Protecting tasks against failure is a challenging and non-trivial mission due to the dynamic, heterogeneous, and largely distributed structure of the cloud environment. The existing works in the literature focus on task failure prediction and neglect the remedial (post) actions. To address these challenges, this paper suggests a fault-tolerant resource management scheme for the cloud computing market in which the optimal amount of computing resources is extracted at each system epoch to replace failed machines. When a cloud service provider detects a malfunctioning machine, they transfer the associated work to new machinery.
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Talwani, Suruchi, Jimmy Singla, Gauri Mathur, Navneet Malik, N. Z. Jhanjhi, Mehedi Masud, and Sultan Aljahdali. "Machine-Learning-Based Approach for Virtual Machine Allocation and Migration." Electronics 11, no. 19 (October 9, 2022): 3249. http://dx.doi.org/10.3390/electronics11193249.

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Due to its ability to supply reliable, robust and scalable computational power, cloud computing is becoming increasingly popular in industry, government, and academia. High-speed networks connect both virtual and real machines in cloud computing data centres. The system’s dynamic provisioning environment depends on the requirements of end-user computer resources. Hence, the operational costs of a particular data center are relatively high. To meet service level agreements (SLAs), it is essential to assign an appropriate maximum number of resources. Virtualization is a fundamental technology used in cloud computing. It assists cloud providers to manage data centre resources effectively, and, hence, improves resource usage by creating several virtualmachine (VM) instances. Furthermore, VMs can be dynamically integrated into a few physical nodes based on current resource requirements using live migration, while meeting SLAs. As a result, unoptimised and inefficient VM consolidation can reduce performance when an application is exposed to varying workloads. This paper introduces a new machine-learning-based approach for dynamically integrating VMs based on adaptive predictions of usage thresholds to achieve acceptable service level agreement (SLAs) standards. Dynamic data was generated during runtime to validate the efficiency of the proposed technique compared with other machine learning algorithms.
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Preciado-Velasco, Jorge E., Joan D. Gonzalez-Franco, Caridad E. Anias-Calderon, Juan I. Nieto-Hipolito, and Raul Rivera-Rodriguez. "5G/B5G Service Classification Using Supervised Learning." Applied Sciences 11, no. 11 (May 27, 2021): 4942. http://dx.doi.org/10.3390/app11114942.

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The classification of services in 5G/B5G (Beyond 5G) networks has become important for telecommunications service providers, who face the challenge of simultaneously offering a better Quality of Service (QoS) in their networks and a better Quality of Experience (QoE) to users. Service classification allows 5G service providers to accurately select the network slices for each service, thereby improving the QoS of the network and the QoE perceived by users, and ensuring compliance with the Service Level Agreement (SLA). Some projects have developed systems for classifying these services based on the Key Performance Indicators (KPIs) that characterize the different services. However, Key Quality Indicators (KQIs) are also significant in 5G networks, although these are generally not considered. We propose a service classifier that uses a Machine Learning (ML) approach based on Supervised Learning (SL) to improve classification and to support a better distribution of resources and traffic over 5G/B5G based networks. We carry out simulations of our proposed scheme using different SL algorithms, first with KPIs alone and then incorporating KQIs and show that the latter achieves better prediction, with an accuracy of 97% and a Matthews correlation coefficient of 96.6% with a Random Forest classifier.
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Jin, Yongchao, Dongmei Liu, Kenan Wang, Renfang Wang, and Xiaodie Zhuang. "Prediction Model of Elderly Care Willingness Based on Machine Learning." Mathematics 11, no. 3 (January 26, 2023): 606. http://dx.doi.org/10.3390/math11030606.

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At present, the problem of an aging population in China is severe. The integration of existing healthcare services with elderly care services is inefficient and cannot meet the needs of the elderly. As such, China urgently needs the concerted efforts of various social forces to cope with the increasingly serious problem of aging. In accordance with Andersen’s behavioral model, a survey was conducted in Tangshan City among seniors 60 years of age and older. Using logistic regression models, decision tree models, and random forest models, we examined the factors impacting senior people’s desire to choose the integrated medical care and nursing care model. The results of the three models displayed that the elderly’s propensity to choose the combined medical care and nursing care model is significantly influenced by the amount of insurance, life care needs, and healthcare needs. Moreover, the study found that the willingness of the elderly in Tangshan to improve the combined medical and nursing care service system is low. The government should appeal to the community to participate in multiple developments to improve the integrated medical and nursing service system.
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Koonsanit, Kitti, and Nobuyuki Nishiuchi. "Predicting Final User Satisfaction Using Momentary UX Data and Machine Learning Techniques." Journal of Theoretical and Applied Electronic Commerce Research 16, no. 7 (November 18, 2021): 3136–56. http://dx.doi.org/10.3390/jtaer16070171.

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User experience (UX) evaluation investigates how people feel about using products or services and is considered an important factor in the design process. However, there is no comprehensive UX evaluation method for time-continuous situations during the use of products or services. Because user experience changes over time, it is difficult to discern the relationship between momentary UX and episodic or cumulative UX, which is related to final user satisfaction. This research aimed to predict final user satisfaction by using momentary UX data and machine learning techniques. The participants were 50 and 25 university students who were asked to evaluate a service (Experiment I) or a product (Experiment II), respectively, during usage by answering a satisfaction survey. Responses were used to draw a customized UX curve. Participants were also asked to complete a final satisfaction questionnaire about the product or service. Momentary UX data and participant satisfaction scores were used to build machine learning models, and the experimental results were compared with those obtained using seven built machine learning models. This study shows that participants’ momentary UX can be understood using a support vector machine (SVM) with a polynomial kernel and that momentary UX can be used to make more accurate predictions about final user satisfaction regarding product and service usage.
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Dani, Himangi. "Review on Frameworks Used for Deployment of Machine Learning Model." International Journal for Research in Applied Science and Engineering Technology 10, no. 2 (February 28, 2022): 211–15. http://dx.doi.org/10.22214/ijraset.2022.40222.

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Abstract: According to the current scenario, the use of machine learning is increasing in a variety of web applications and services. A good visual experience, fast performance, and easy to use framework is critical for developing and deploying your model. Working on a machine learning model is one thing but deploying a machine learning model to production can be another. Creating a Machine Learning model is one thing but deploying the model in real-time is the real challenge. For that purpose, many different technologies are available in the field. The simplest way to deploy a machine learning model is to create a web service or application. In this paper, we will discuss different frameworks for the deployment of the machine learning model on web applications or services. In this paper we will discuss Flask framework, Streamlit framework, Django Framework. Keywords: Flask, Streamlite, Django Framework, Model deployment, Web Framework
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Satybaldina, D. Zh, N. K. Bisenbaeva, Ye N. Seitkulov, and A. K. Seksenbaeva. "Detecting and classifying network attacks with Splunk Machine Learning Toolkit." BULLETIN of the L N Gumilyov Eurasian National University MATHEMATICS COMPUTER SCIENCE MECHANICS Series 142, no. 1 (March 30, 2023): 21–34. http://dx.doi.org/10.32523/2616-7182/bulmathenu.2023/1.2.

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In modern conditions of digital technologies implementation in various sectors of the economy, the digitalization of public administration, healthcare, education, and science, the growth in the number of Internet services and mobile devices the issues of ensuring the security of cellular communication systems are becoming increasingly relevant. It is becoming increasingly difficult to detect multiple and complex cyber security threats as the sources and methods ofcyber-attacks evolve and expand. Classic network attack detection approaches that rely heavily on static matching, such as signature analysis, blacklisting, or regular expression patterns, are limited in flexibility and are ineffective for early anomaly detection and rapid response to information security incidents. To solve this problem, the use of machine learning (ML) algorithms is proposed. ML methods can provide new approaches and higher rates of detection of malicious activity on the network. In this work, the Splunk Enterprise data analysis platform and the Splunk Machine Learning Toolkit for creating, training, testing, and validating a network attack classifier are used. The performance of the proposed model was evaluatedby applying four machine learning algorithms such as a decision tree, a support vector machine, a random forest, and adouble random forest. Experimental results show that all used ML algorithms can be effectively used to detect network attacks, and the double random forest method has the best accuracy in detecting distributed denial-of-service attacks.
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Ismail, Essamlali, Bahnasse Ayoub, Khiat Azeddine, and Ouajji Hassan. "Machine learning in the service of a clean city." Procedia Computer Science 198 (2022): 530–35. http://dx.doi.org/10.1016/j.procs.2021.12.281.

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Kumar, M. A. R., Abdullah Khan Mohammed, Siri Reddy Gundlapally, Tarun Ramavath, and Yadav Sujith. "Automatic car service recommendation system using machine learning techniques." i-manager’s Journal on Image Processing 9, no. 4 (2022): 46. http://dx.doi.org/10.26634/jip.9.4.19241.

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The automobile industry has been growing at a high rate in the past few decades, contributing about 7.5% to India's total Gross Domestic Product (GDP). As the number of vehicle owners are increasing the demand and need for automobile service is also high, but people are busy with their routines, hence failing to perform proper maintenance on their vehicles. This paper uses machine learning algorithms and object detection to come up with the idea to develop a web application that suggests users some offers and timing for their car maintenance by analyzing a car using computer vision without the owner's involvement. This project aims at both the owner's convenience and the growth of the service provider's business. Generally, we do not realize that multiple tasks can be done at a time, which results in incomplete tasks. This paper presents a machine learning-based automated car maintenance system with effective time utilization, by using the Internet of Things (IoT) device that could be installed at the parking's main gate in places where people tend to spend many hours, like offices or malls. This device consists of a camera that is responsible for detecting a car image from the live video. These images are then sent to the device, which uses pre-trained models to detect any damages or dirtiness in the vehicle.
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Alubaidan, Haya, Reem Alzaher, Maryam AlQhatani, and Rami Mohammed. "DDoS Detection in Software-Defined Network (SDN) Using Machine Learning." International Journal on Cybernetics & Informatics 12, no. 04 (July 27, 2023): 93–104. http://dx.doi.org/10.5121/ijci.2023.120408.

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In recent years, the concept of cloud computing and the software-defined network (SDN) have spread widely. The services provided by many sectors such as medicine, education, banking, and transportation are being replaced gradually with cloud-based applications. Consequently, the availability of these services is critical. However, the cloud infrastructure and services are vulnerable to attackers who aim to breach its availability. One of the major threats to any system availability is a Denial-of-Service (DoS) attack, which is intended to deny the legitimate user from accessing cloud resources. The Distributed Denial-of-Service attack (DDoS) is a type of DoS attack which is considerably more effective and dangerous. A lot of efforts have been made by the research community to detect DDoS attacks, however, there is still a need for further efforts in this germane field. In this paper, machine learning techniques are utilized to build a model that can detect DDoS attacks in Software-Defined Networks (SDN). The used ML algorithms have shown high performance in the earliest studies; hence they have been used in this study along with feature selection technique. Therefore, our model utilized these algorithms to detect DDoS attacks in network traffic. The outcome of this experiment shows the impact of feature selection in improving the model performance. Eventually, The Random Forest classifier has achieved the highest accuracy of 0.99 in detecting DDoS attack.
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S, Chandan, Lohith S, Yamini G B, Nithin Gowda, and Shruthi N. "Face Mask Detection Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 466–71. http://dx.doi.org/10.22214/ijraset.2022.43727.

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Abstract: COVID-19 pandemic has rapidly affected ourday-to-day life disrupting the world trade and movements. Wearing a protective face mask hasbecome a new normal. In the near future, many public service providers will ask the customers to wear masks correctly to avail of their services. Therefore, face maskdetection has become a crucial task to help global society. This paper presents a simplified approach to achieve this purpose using some basic Machine Learning packages like TensorFlow, Keras and OpenCV. The application of “machine learning” and “artificial intelligence” has become popular within the last decade. Both terms are frequently used in science and media, sometimes interchangeably, sometimes with different meanings. In this work, we specify the contribution of machine learning to artificial intelligence. We review relevant literature and present a conceptual framework which clarifies the role of machine learning to build (artificial) intelligent agents.The proposed method detects the face from the image correctly and then identifies if it has a mask on it or not. As a surveillance task performer, it can also detect a face along with a mask in motion. The method attains accuracy up to 95.77% and 94.58% respectively on two different datasets. We explore optimized values of parameters using the mobileNetV2 which is a Convolutional Neural Network architecture to detect the presence of masks correctly without causing over- fitting.
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Namicheishvili, Oleg, and Jujuna Gogiashvili. "Machine Intelligence in the Service of Human Intelligence." Works of Georgian Technical University, no. 1(527) (March 21, 2023): 38–49. http://dx.doi.org/10.36073/1512-0996-2023-1-38-49.

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Deep neural networks allow AI to achieve unprecedented levels of accuracy. For example, Alexa, Google Search and Google Photos are powered by deep learning, and the more we use these tools, the more effective they become. In healthcare, the diagnosis of cancerous tumors on MRI images using AI technologies (deep learning, image classification, object recognition) is as accurate as the findings of highly trained radiologists. AI makes it possible to get the most out of the data. With the advent of self-learning algorithms, the data itself becomes intellectual property. The data contains the answers you need – you just need to find them with the help of AI technology. As data is now more important than ever before, it can provide a competitive advantage. When using the same technology in a competitive environment, whoever has the most accurate data will win.
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Adhav, Dhanashri. "Measurement of Objective Video Quality in Social Cloud using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 6952–55. http://dx.doi.org/10.22214/ijraset.2023.53100.

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Abstract: This paper focuses on the objective of video quality analysis (VQA) and proposes a deep learning-based approach to measure quality degradation. The study involves conducting experiments on various social clouds (SCs) and low-quality videos. Specifically, the selected videos are uploaded to SCs to evaluate the differences in video service and quality. The average of all videos, denoted as Avg 100, is used to measure the quality, while the peak signal-to-noise ratio (PSNR) is found to have no significant impact on other indicators. By utilizing deep learning techniques, this research aims to provide optimal video quality and multimedia services that meet the standards of Quality of Service (QoS) and enhance the overall Quality of Experience (QoE) for users
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