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Статті в журналах з теми "Self-adaptation, Data-driven, Machine learning, Software architecture"
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Lopes, Rui Pedro, Bárbara Barroso, Leonel Deusdado, André Novo, Manuel Guimarães, João Paulo Teixeira, and Paulo Leitão. "Digital Technologies for Innovative Mental Health Rehabilitation." Electronics 10, no. 18 (September 14, 2021): 2260. http://dx.doi.org/10.3390/electronics10182260.
Повний текст джерелаАнотація:
Schizophrenia is a chronic mental illness, characterized by the loss of the notion of reality, failing to distinguish it from the imaginary. It affects the patient in life’s major areas, such as work, interpersonal relationships, or self-care, and the usual treatment is performed with the help of anti-psychotic medication, which targets primarily the hallucinations, delirium, etc. Other symptoms, such as the decreased emotional expression or avolition, require a multidisciplinary approach, including psychopharmacology, cognitive training, and many forms of therapy. In this context, this paper addresses the use of digital technologies to design and develop innovative rehabilitation techniques, particularly focusing on mental health rehabilitation, and contributing for the promotion of well-being and health from a holistic perspective. In this context, serious games and virtual reality allows for creation of immersive environments that contribute to a more effective and lasting recovery, with improvements in terms of quality of life. The use of machine learning techniques will allow the real-time analysis of the data collected during the execution of the rehabilitation procedures, as well as enable their dynamic and automatic adaptation according to the profile and performance of the patients, by increasing or reducing the exercises’ difficulty. It relies on the acquisition of biometric and physiological signals, such as voice, heart rate, and game performance, to estimate the stress level, thus adapting the difficulty of the experience to the skills of the patient. The system described in this paper is currently in development, in collaboration with a health unit, and is an engineering effort that combines hardware and software to develop a rehabilitation tool for schizophrenic patients. A clinical trial is also planned for assessing the effectiveness of the system among negative symptoms in schizophrenia patients.
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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.
Повний текст джерелаАнотація:
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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Park, Seunghyun, and Jin-Young Choi. "Malware Detection in Self-Driving Vehicles Using Machine Learning Algorithms." Journal of Advanced Transportation 2020 (January 17, 2020): 1–9. http://dx.doi.org/10.1155/2020/3035741.
Повний текст джерелаАнотація:
The recent trend for vehicles to be connected to unspecified devices, vehicles, and infrastructure increases the potential for external threats to vehicle cybersecurity. Thus, intrusion detection is a key network security function in vehicles with open connectivity, such as self-driving and connected cars. Specifically, when a vehicle is connected to an external device through a smartphone inside the vehicle or when a vehicle communicates with external infrastructure, security technology is required to protect the software network inside the vehicle. Existing technology with this function includes vehicle gateways and intrusion detection systems. However, it is difficult to block malicious code based on application behaviors. In this study, we propose a machine learning-based data analysis method to accurately detect abnormal behaviors due to malware in large-scale network traffic in real time. First, we define a detection architecture, which is required by the intrusion detection module to detect and block malware attempting to affect the vehicle via a smartphone. Then, we propose an efficient algorithm for detecting malicious behaviors in a network environment and conduct experiments to verify algorithm accuracy and cost through comparisons with other algorithms.
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Thembelihle, Dlamini, Michele Rossi, and Daniele Munaretto. "Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey." Wireless Communications and Mobile Computing 2017 (2017): 1–21. http://dx.doi.org/10.1155/2017/8618364.
Повний текст джерелаАнотація:
Future mobile networks (MNs) are required to be flexible with minimal infrastructure complexity, unlike current ones that rely on proprietary network elements to offer their services. Moreover, they are expected to make use of renewable energy to decrease their carbon footprint and of virtualization technologies for improved adaptability and flexibility, thus resulting in green and self-organized systems. In this article, we discuss the application of software defined networking (SDN) and network function virtualization (NFV) technologies towards softwarization of the mobile network functions, taking into account different architectural proposals. In addition, we elaborate on whether mobile edge computing (MEC), a new architectural concept that uses NFV techniques, can enhance communication in 5G cellular networks, reducing latency due to its proximity deployment. Besides discussing existing techniques, expounding their pros and cons and comparing state-of-the-art architectural proposals, we examine the role of machine learning and data mining tools, analyzing their use within fully SDN- and NFV-enabled mobile systems. Finally, we outline the challenges and the open issues related to evolved packet core (EPC) and MEC architectures.
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Alexander, Francis J., James Ang, Jenna A. Bilbrey, Jan Balewski, Tiernan Casey, Ryan Chard, Jong Choi, et al. "Co-design Center for Exascale Machine Learning Technologies (ExaLearn)." International Journal of High Performance Computing Applications 35, no. 6 (September 27, 2021): 598–616. http://dx.doi.org/10.1177/10943420211029302.
Повний текст джерелаАнотація:
Rapid growth in data, computational methods, and computing power is driving a remarkable revolution in what variously is termed machine learning (ML), statistical learning, computational learning, and artificial intelligence. In addition to highly visible successes in machine-based natural language translation, playing the game Go, and self-driving cars, these new technologies also have profound implications for computational and experimental science and engineering, as well as for the exascale computing systems that the Department of Energy (DOE) is developing to support those disciplines. Not only do these learning technologies open up exciting opportunities for scientific discovery on exascale systems, they also appear poised to have important implications for the design and use of exascale computers themselves, including high-performance computing (HPC) for ML and ML for HPC. The overarching goal of the ExaLearn co-design project is to provide exascale ML software for use by Exascale Computing Project (ECP) applications, other ECP co-design centers, and DOE experimental facilities and leadership class computing facilities.
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Kondratenko, Yuriy, Igor Atamanyuk, Ievgen Sidenko, Galyna Kondratenko, and Stanislav Sichevskyi. "Machine Learning Techniques for Increasing Efficiency of the Robot’s Sensor and Control Information Processing." Sensors 22, no. 3 (January 29, 2022): 1062. http://dx.doi.org/10.3390/s22031062.
Повний текст джерелаАнотація:
Real-time systems are widely used in industry, including technological process control systems, industrial automation systems, SCADA systems, testing, and measuring equipment, and robotics. The efficiency of executing an intelligent robot’s mission in many cases depends on the properties of the robot’s sensor and control systems in providing the trajectory planning, recognition of the manipulated objects, adaptation of the desired clamping force of the gripper, obstacle avoidance, and so on. This paper provides an analysis of the approaches and methods for real-time sensor and control information processing with the application of machine learning, as well as successful cases of machine learning application in the synthesis of a robot’s sensor and control systems. Among the robotic systems under investigation are (a) adaptive robots with slip displacement sensors and fuzzy logic implementation for sensor data processing, (b) magnetically controlled mobile robots for moving on inclined and ceiling surfaces with neuro-fuzzy observers and neuro controllers, and (c) robots that are functioning in unknown environments with the prediction of the control system state using statistical learning theory. All obtained results concern the main elements of the two-component robotic system with the mobile robot and adaptive manipulation robot on a fixed base for executing complex missions in non-stationary or uncertain conditions. The design and software implementation stage involves the creation of a structural diagram and description of the selected technologies, training a neural network for recognition and classification of geometric objects, and software implementation of control system components. The Swift programming language is used for the control system design and the CreateML framework is used for creating a neural network. Among the main results are: (a) expanding the capabilities of the intelligent control system by increasing the number of classes for recognition from three (cube, cylinder, and sphere) to five (cube, cylinder, sphere, pyramid, and cone); (b) increasing the validation accuracy (to 100%) for recognition of five different classes using CreateML (YOLOv2 architecture); (c) increasing the training accuracy (to 98.02%) and testing accuracy (to 98.0%) for recognition of five different classes using Torch library (ResNet34 architecture) in less time and number of epochs compared with Create ML (YOLOv2 architecture); (d) increasing the training accuracy (to 99.75%) and testing accuracy (to 99.2%) for recognition of five different classes using Torch library (ResNet34 architecture) and fine-tuning technology; and (e) analyzing the effect of dataset size impact on recognition accuracy with ResNet34 architecture and fine-tuning technology. The results can help to choose efficient (a) design approaches for control robotic devices, (b) machine-learning methods for performing pattern recognition and classification, and (c) computer technologies for designing control systems and simulating robotic devices.
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Akbari, Ali, Jonathan Martinez, and Roozbeh Jafari. "Facilitating Human Activity Data Annotation via Context-Aware Change Detection on Smartwatches." ACM Transactions on Embedded Computing Systems 20, no. 2 (March 2021): 1–20. http://dx.doi.org/10.1145/3431503.
Повний текст джерелаАнотація:
Annotating activities of daily living (ADL) is vital for developing machine learning models for activity recognition. In addition, it is critical for self-reporting purposes such as in assisted living where the users are asked to log their ADLs. However, data annotation becomes extremely challenging in real-world data collection scenarios, where the users have to provide annotations and labels on their own. Methods such as self-reports that rely on users’ memory and compliance are prone to human errors and become burdensome since they increase users’ cognitive load. In this article, we propose a light yet effective context-aware change point detection algorithm that is implemented and run on a smartwatch for facilitating data annotation for high-level ADLs. The proposed system detects the moments of transition from one to another activity and prompts the users to annotate their data. We leverage freely available Bluetooth low energy (BLE) information broadcasted by various devices to detect changes in environmental context. This contextual information is combined with a motion-based change point detection algorithm, which utilizes data from wearable motion sensors, to reduce the false positives and enhance the system's accuracy. Through real-world experiments, we show that the proposed system improves the quality and quantity of labels collected from users by reducing human errors while eliminating users’ cognitive load and facilitating the data annotation process.
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Nawrocki, Piotr, and Bartlomiej Sniezynski. "Adaptive Context-Aware Energy Optimization for Services on Mobile Devices with Use of Machine Learning." Wireless Personal Communications 115, no. 3 (August 13, 2020): 1839–67. http://dx.doi.org/10.1007/s11277-020-07657-9.
Повний текст джерелаАнотація:
AbstractIn this paper we present an original adaptive task scheduling system, which optimizes the energy consumption of mobile devices using machine learning mechanisms and context information. The system learns how to allocate resources appropriately: how to schedule services/tasks optimally between the device and the cloud, which is especially important in mobile systems. Decisions are made taking the context into account (e.g. network connection type, location, potential time and cost of executing the application or service). In this study, a supervised learning agent architecture and service selection algorithm are proposed to solve this problem. Adaptation is performed online, on a mobile device. Information about the context, task description, the decision made and its results such as power consumption are stored and constitute training data for a supervised learning algorithm, which updates the knowledge used to determine the optimal location for the execution of a given type of task. To verify the solution proposed, appropriate software has been developed and a series of experiments have been conducted. Results show that as a result of the experience gathered and the learning process performed, the decision module has become more efficient in assigning the task to either the mobile device or cloud resources.
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Kretsis, Aristotelis, Ippokratis Sartzetakis, Polyzois Soumplis, Katerina Mitropoulou, Panagiotis Kokkinos, Petros Nicopolitidis, Georgios Papadimitriou, and Emmanouel Varvarigos. "ARMONIA: A Unified Access and Metro Network Architecture." Applied Sciences 10, no. 23 (November 24, 2020): 8318. http://dx.doi.org/10.3390/app10238318.
Повний текст джерелаАнотація:
We present a self-configured and unified access and metro network architecture, named ARMONIA. The ARMONIA network monitors its status, and dynamically (re-)optimizes its configuration. ARMONIA leverages software defined networking (SDN) and network functions virtualization (NFV) technologies. These technologies enable the access and metro convergence and the joint and efficient control of the optical and the IP equipment used in these different network segments. Network monitoring information is collected and analyzed utilizing machine learning and big data analytics methods. Dynamic algorithms then decide how to adapt and dynamically optimize the unified network. The ARMONIA network enables unprecedented resource efficiency and provides advanced virtualization services, reducing the capital expenditures (CAPEX) and operating expenses (OPEX) and lowering the barriers for the introduction of new services. We demonstrate the benefits of the ARMONIA network in the context of dynamic resource provisioning of network slices. We observe significant spectrum and equipment savings when compared to static overprovisioning.
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Alwakeel, Lyan, and Kevin Lano. "Functional and Technical Aspects of Self-management mHealth Apps: Systematic App Search and Literature Review." JMIR Human Factors 9, no. 2 (May 25, 2022): e29767. http://dx.doi.org/10.2196/29767.
Повний текст джерелаАнотація:
Background Although the past decade has witnessed the development of many self-management mobile health (mHealth) apps that enable users to monitor their health and activities independently, there is a general lack of empirical evidence on the functional and technical aspects of self-management mHealth apps from a software engineering perspective. Objective This study aims to systematically identify the characteristics and challenges of self-management mHealth apps, focusing on functionalities, design, development, and evaluation methods, as well as to specify the differences and similarities between published research papers and commercial and open-source apps. Methods This research was divided into 3 main phases to achieve the expected goal. The first phase involved reviewing peer-reviewed academic research papers from 7 digital libraries, and the second phase involved reviewing and evaluating apps available on Android and iOS app stores using the Mobile Application Rating Scale. Finally, the third phase involved analyzing and evaluating open-source apps from GitHub. Results In total, 52 research papers, 42 app store apps, and 24 open-source apps were analyzed, synthesized, and reported. We found that the development of self-management mHealth apps requires significant time, effort, and cost because of their complexity and specific requirements, such as the use of machine learning algorithms, external services, and built-in technologies. In general, self-management mHealth apps are similar in their focus, user interface components, navigation and structure, services and technologies, authentication features, and architecture and patterns. However, they differ in terms of the use of machine learning, processing techniques, key functionalities, inference of machine learning knowledge, logging mechanisms, evaluation techniques, and challenges. Conclusions Self-management mHealth apps may offer an essential means of managing users’ health, expecting to assist users in continuously monitoring their health and encourage them to adopt healthy habits. However, developing an efficient and intelligent self-management mHealth app with the ability to reduce resource consumption and processing time, as well as increase performance, is still under research and development. In addition, there is a need to find an automated process for evaluating and selecting suitable machine learning algorithms for the self-management of mHealth apps. We believe that these issues can be avoided or significantly reduced by using a model-driven engineering approach with a decision support system to accelerate and ameliorate the development process and quality of self-management mHealth apps.
Дисертації з теми "Self-adaptation, Data-driven, Machine learning, Software architecture"
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VAIDHYANATHAN, KARTHIK. "Data-Driven Self-Adaptive Architecting Using Machine Learning." Doctoral thesis, Gran Sasso Science Institute, 2021. http://hdl.handle.net/20.500.12571/15976.
Повний текст джерелаАнотація:
The last decade has seen a significant evolution in software architecting practices as the process of managing and developing software is becoming more and more complex. This is especially true due to the heterogeneous composition of modern software systems
coupled with the run-time uncertainties. These include application downtime due to high CPU utilization, server outages, resource constraints, dynamic resource demands, etc. These can have a big impact on the Quality of Service (QoS) oered by the system,
thereby impacting the experience of the end-user. Self-adaptation is nowadays considered to be one of the best solutions to dynamically reconfigure a system in the occurrence of deviations from the expected QoS. However, one of the issues with the existing solutions is that most of them are reactive in nature, where adaptation is carried out in the event of uncertainties. Moreover, current adaptation methods: i) do not provide the systems the ability to proactively identify the need for adaptation with good accuracy; ii) may temporally overcome an impending failure, while not preventing the system from the state in the future. In essence, they do not exploit the knowledge gained from every adaptation performed. The use of machine learning techniques to aid self-adaptation has been proposed in the literature as a way to mitigate this problem based on the concept of self-adaptation through achieving, but not much work has been done in this area. Moreover, the challenges associated with learning bias and less accurate predictions also need to be handled while using machine learning techniques, which otherwise leads to sub-optimal adaptations. To this end, in this thesis, we develop a data-driven approach to architecting self-adaptive systems using machine learning techniques. The approach, in principle, shifts the focus from self-adaptive architectures to self-learning architectures. It achieves this by using a combination of deep neural networks and reinforcement learning (RL) techniques to ensure that the system continuously learns and improves the quality of adaptation performed over time. It further uses quantitative verification (QV) techniques to overcome learning bias and enable faster convergence towards optimal adaptations. More specifically the approach i) continuously monitors the system data; ii) uses deep neural networks to forecast any QoS uncertainties; iii) leverages the forecasts using RL techniques to identify the adaptation strategy; iv) it further uses QV techniques to verify the decision selected by RL; v) keeps improving the decisions based on the forecasts as well as the feedbacks obtained from QV; vi) continuously keeps performing the loop of learning, selection, verification, and adaptations to converge towards optimal adaptations, thereby enabling the architectures to learn and improve over time.
Частини книг з теми "Self-adaptation, Data-driven, Machine learning, Software architecture"
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Burdescu, Dumitru Dan, and Marian Cristian Mihaescu. "Improvement of Self-Assessment Effectiveness by Activity Monitoring and Analysis." In Monitoring and Assessment in Online Collaborative Environments, 198–217. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-786-7.ch011.
Повний текст джерелаАнотація:
Self-assessment is one of the crucial activities within e-learning environments that provide learners with feedback regarding their level of accumulated knowledge. From this point of view, the authors think that guidance of learners in self-assessment activity must be an important goal of e-learning environment developers. The scope of the chapter is to present a recommender software system that runs along the e-learning platform. The recommender software system improves the effectiveness of self-assessment activities. The activities performed by learners represent the input data and the machine learning algorithms are used within the business logic of the recommender software system that runs along the e-learning platform. The output of the recommender software system is represented by advice given to learners in order to improve the effectiveness of self-assessment process. The methodology for obtaining improvement of self-assessment is based on embedding knowledge management into the business logic of the e-learning platform. Naive Bayes Classifier is used as machine learning algorithm for obtaining the resources (e.g., questions, chapters, and concepts) that need to be further accessed by learners. The analysis is accomplished for disciplines that are well structured according to a concept map. The input data set for the recommender software system is represented by student activities that are monitored within Tesys e-learning platform. This platform has been designed and implemented within Multimedia Applications Development Research Center at Software Engineering Department, University of Craiova. Monitoring student activities is accomplished through various techniques like creating log files or adding records into a table from a database. The logging facilities are embedded in the business logic of the e-learning platform. The e-learning platform is based on a software development framework that uses only open source software. The software architecture of the e-learning platform is based on MVC (model-view-controller) model that ensures the independence between the model (represented by MySQL database), the controller (represented by the business logic of the platform implemented in Java) and the view (represented by WebMacro which is a 100% Java open-source template language).
Тези доповідей конференцій з теми "Self-adaptation, Data-driven, Machine learning, Software architecture"
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Wu, Dazhong, Janis Terpenny, Li Zhang, Robert Gao, and Thomas Kurfess. "Fog-Enabled Architecture for Data-Driven Cyber-Manufacturing Systems." In ASME 2016 11th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/msec2016-8559.
Повний текст джерелаАнотація:
Over the past few decades, both small- and medium-sized manufacturers as well as large original equipment manufacturers (OEMs) have been faced with an increasing need for low cost and scalable intelligent manufacturing machines. Capabilities are needed for collecting and processing large volumes of real-time data generated from manufacturing machines and processes as well as for diagnosing the root cause of identified defects, predicting their progression, and forecasting maintenance actions proactively to minimize unexpected machine down times. Although cloud computing enables ubiquitous and instant remote access to scalable information and communication technology (ICT) infrastructures and high volume data storage, it has limitations in latency-sensitive applications such as high performance computing and real-time stream analytics. The emergence of fog computing, Internet of Things (IoT), and cyber-physical systems (CPS) represent radical changes in the way sensing systems, along with ICT infrastructures, collect and analyze large volumes of real-time data streams in geographically distributed environments. Ultimately, such technological approaches enable machines to function as an agent that is capable of intelligent behaviors such as automatic fault and failure detection, self-diagnosis, and preventative maintenance scheduling. The objective of this research is to introduce a fog-enabled architecture that consists of smart sensor networks, communication protocols, parallel machine learning software, and private and public clouds. The fog-enabled architecture will have the potential to enable large-scale, geographically distributed online machine and process monitoring, diagnosis, and prognosis that require low latency and high bandwidth in the context of data-driven cyber-manufacturing systems.
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Song, Zhengyi, and Young Moon. "CyberManufacturing System: A Solution for Sustainable Manufacturing." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-86092.
Повний текст джерелаАнотація:
CyberManufacturing System (CMS) is emerging as a new manufacturing paradigm and an integrated management approach, and it is capable of providing on-demand, data-driven, highly-collaborative, knowledge-intensive and sustainability-oriented manufacturing solutions. The recent developments in the Internet of Things, Cloud Computing, Service-Oriented Technologies, and Machine Learning, all contribute to the development of CMS. In CMS, each manufacturer is able to package their resources and capabilities into services and make them available to customers through pay-per-use pricing strategy. Associated capabilities such as computing and simulation resources, application software, know-hows, and expertise also become accessible to worldwide users via the Internet. The manufacturing community is searching for sustainable manufacturing solutions to address environmental degradation and natural resource depletion issues. Sustainable manufacturing systems need to be socially and environmentally responsible as well as economically viable. CMS possesses advanced features — such as resource sharing, servitization and self-manage capabilities — suitable for addressing sustainability issues. This paper presents a framework of the CMS paradigm and performance analysis from the perspective of sustainability. An architecture is proposed to elaborate the constitutions of CMS and to make manufacturing operations transparent. Two case studies are used to illustrate (i) how initial manufacturing requests can be processed and met by a collection of production services and (ii) how the effectiveness of the proposed framework in addressing sustainability issues can be evaluated.
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Nagaraj, Guru, Prashanth Pillai, and Mandar Kulkarni. "Deep Similarity Learning for Well Test Model Identification." In SPE Middle East Oil & Gas Show and Conference. SPE, 2021. http://dx.doi.org/10.2118/204675-ms.
Повний текст джерелаАнотація:
Abstract Over the years, well test analysis or pressure transient analysis (PTA) methods have progressed from straight lines via type curve analysis to pressure derivatives and deconvolution methods. Today, analysis of the log-log (pressure and its derivative) response is the most used method for PTA. Although these methods are widely available through commercial software, they are not fully automated, and human interaction is needed for their application. Furthermore, PTA is described as an inverse problem, whose solution in general is non-unique, and several models (well, reservoir and boundary) can be found applicable to similar pressure-derivative response. This tends to always bring about confusion in choosing the correct model using the conventional approach. This results in multiple iterations that are time consuming and requires constant human interaction. Our approach automates the process of PTA using a Siamese neural network (SNN) architecture comprised of Convolutional neural network (CNN) and Long Short-Term Memory (LSTM) layers. The SNN model is trained on simulated experimental data created using a design of experiments (DOE) approach involving most common 14 interpretation scenarios across well, reservoir, and boundary model types. Across each model type, parameters such as permeability, horizontal well length, skin factor, and distance to the boundary were sampled to compute 560 different pressure derivative responses. SNN is trained using a self-supervised training strategy where the positive and negative pairs are generated from the training data. We use transformations such as compression and expansion to generate positive pairs and negative pairs for the well test model responses. For a given well test model response, similarity scores are computed against the candidates in each model class, and the best match from each class is identified. These matches are then ranked according to the similarity scores to identify optimal candidates. Experimental analysis indicated that the true model class frequently appeared among the top ranked classes. The model achieves an accuracy of 93% for the top one model recommendations when tested on 70 samples from the 14 interpretation scenarios. Prior information on the top ranked probable well test models, significantly reduces the manual effort involved in the analysis. This machine learning (ML) approach can be integrated with any PTA software or function as a standalone application in the interpreter's system. Current work using SNN with LSTM layers can be used to speed up the process of detecting the pressure derivative response explained by a certain combination of well, reservoir and boundary models and produce models with less user interaction. This methodology will facilitate the interpretation engineer in making the model recognition faster for detailed integration with additional information from sources such as geophysics, geology, petrophysics, drilling, and production logging.