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2022, Zahra Kodia, Lamjed Ben Said
Stock market prediction of Nifty 50 index applying machine learning techniques
Applied Artificial Intelligence, 36:1, 2111134, 2022
Abstract
The stock market is viewed as an unpredictable, volatile, and
competitive market. The prediction of stock prices has been
a challenging task for many years. In fact, many analysts are
highly interested in the research area of stock price prediction.
Various forecasting methods can be categorized into linear and
non-linear algorithms. In this paper, we offer an overview of the
use of deep learning networks for the Indian National Stock
Exchange time series analysis and prediction. The networks
used are Recurrent Neural Network, Long Short-Term Memory
Network, and Convolutional Neural Network to predict future
trends of NIFTY 50 stock prices. Comparative analysis is done
using different evaluation metrics. These analysis led us to
identify the impact of feature selection process and hyperparameter optimization on prediction quality and metrics used in the prediction of stock market performance and prices. The performance of the models was quantified using MSE metric.
These errors in the LSTM model are found to be lower compared
to RNN and CNN models. -
2021, ,
Context-Aware Service Recommendation Based on Knowledge Graph Embedding
International Conference on Data and Knowledge Engineering, 2021
Abstract
Over two decades, context awareness has been incorporated into recommender systems in order to provide, not only the top-rated items to consumers but also the ones that are suitable to the user context. As a class of context-aware systems, context-aware service recommendation (CASR) aims to bind high-quality services to users, while taking into account their context requirements, including invocation time, location, social profiles, connectivity, and so on. However, current CASR approaches are not scalable with the huge amount of service data (QoS and context information, users reviews and feedbacks). In addition, they lack a rich representation of contextual information, as they adopt a simple matrix view. Moreover, current CASR approaches adopt the traditional user-service relation and they do not allow for multi-relational interactions between users and services in different contexts. To offer a scalable and context-sensitive service recommendation with great analysis and learning capabilities, we provide a rich and multi-relational representation of the CASR knowledge, based on the concept of knowledge graph. The constructed context-aware service knowledge graph (C-SKG) is, then, transformed into a low-dimensional vector space to facilitate its processing. For this purpose, we adopt Dilated Recurrent Neural Networks to propose a context-aware knowledge graph embedding, based on the principles of first-order and subgraph-aware proximity. Finally, a recommendation algorithm is defined to deliver the top-rated services according to the target user’s context. Experiments have proved the accuracy and scalability of our solution, compared to state-of-the-art CASR approaches.
, ,A Quantum-Inspired Neural Network Model for Predictive BPaaS Management
International Conference on Database and Expert Systems Applications (DEXA), 2021
Abstract
Nowadays, companies are more and more adopting cloud technologies in the management of their business processes rising, then, the Business Process as a Service (BPaaS) model. In order to guarantee the consistency of the provisioned BPaaS, cloud providers should ensure a strategical management (e.g., allocation, migration, etc.) of their available resources (e.g., computation, storage, etc.) according to services requirements. Existing researches do not prevent resource provision problems before they occur. Rather, they conduct a real-time allocation of cloud resources. This paper makes use of historical resource usage information for providing enterprises and BPaaS providers with predictions of cloud availability zones’ states. For that, we first propose a Neural Network-based prediction model that exploits the superposition power of Quantum Computing and the evolutionary nature of the Genetic Algorithm, in order to optimize the accuracy of the predicted resource utilization. Second, we define a placement algorithm that, based on the prediction results, chooses the optimal cloud availability zones for each BPaaS fragment, i.e. under-loaded servers. We evaluated our approach using real cloud workload data-sets. The obtained results confirmed the effectiveness and the performance of our NNQGA approach, compared to traditional techniques.
, , ,Towards big services: a synergy between service computing and parallel programming
Computing, 2021
Abstract
Over the last years, cloud computing has emerged as a natural choice to host, manage, and provide various kinds of virtualized resources (e.g., software, business processes, databases, platforms, mobile and social applications, etc.) as on-demand services. This “servicelization” across various domains has produced a huge volume of data, leading to the emergence of a new service model, called big service. This latter consists of the encapsulation, abstraction and the processing of big data, allowing then to hide their complexity. However, this promising approach still lacks management facilities and tools. Indeed, due to the highly dynamic and uncertain nature of their hosting cloud environments, big services together with their accessed data need continuous management operations, so that to maintain a moderate state and high quality of their execution. In this context, frameworks for designing, composing, executing and managing big services become a major need. The purpose of this paper is to provide an understanding of the new emerging big service model from the lifecycle management phases’ point of view. We also study the role of big data frameworks and multi-cloud strategies in the provisioning of big services. A research road map on this topic will be summarized at the end of this paper.
,A Survey of Service Placement in Cloud Environments
Grid Computing, 2021
Abstract
Cloud computing is largely adopted by the current computing industry. Not only users can benefit from cloud scalability, but also businesses are more and more attracted by its flexibility. In addition, the number of offered cloud services (e.g., SaaS, BPaaS, mobile services, etc.) is continuously growing. This raises a question about how to effectively arrange and place them in the cloud, in order to offer high-performance services. Indeed, companies’ and providers’ benefits are strongly related to the optimal placement and management of cloud services, together with their related data. This produces various challenges, including the heterogeneity and dynamicity of hosting cloud zones, the cloud/service -specific placement constraints, etc. Recent cloud service placement approaches have dealt with these issues through different techniques, and by fixing various criteria to optimize. Moreover, researchers have considered other specificities, like the cloud environment type, the deployment model and the placement mode. This paper provides a comprehensive survey on service placement schemes in the cloud. We also identify the current challenges for different cloud service models and environments, and we provide our future directions.
, , ,Clustering-based data placement in cloud computing: a predictive approach
Cluster Computing, 2021
Abstract
Nowadays, cloud computing environments have become a natural choice to host and process a huge volume of data. The combination of cloud computing and big data frameworks is an effective way to run data-intensive applications and tasks. Also, an optimal arrangement of data partitions can improve the tasks executions, which is not the case in most big data frameworks. For example, the default distribution of data partitions in Hadoop-based clouds causes several problems, which are mainly related to the load balancing and the resource usage. In addition, most existing data placement solutions are static and lack precision in the placement of data partitions. To overcome these issues, we propose a data placement approach based on the prediction of the future resources usage. We exploit Kernel Density Estimation (KDE) and Fuzzy FCA techniques to, first, forecast the workers’ and tasks’ future resource consumption and, second, cluster data partitions and intensive jobs according to the estimated resource usage. Fuzzy FCA is also used to exclude partitions and jobs that require less resources, which will reduce the needless migrations. To allow monitoring and predicting the workers’ states and the data partitions’ consumption, we modeled the big data cluster as an autonomic service-based system. The obtained results have shown that our solution outperformed existing approaches in terms of migrations rate and resource consumption.
, ,Context-Aware Service Recommendation Based on Knowledge Graph Embedding
IEEE Transactions on Knowledge and Data Engineering, 2021
Abstract
Over two decades, context awareness has been incorporated into recommender systems in order to provide, not only the top-rated items to consumers but also the ones that are suitable to the user context. As a class of context-aware systems, context-aware service recommendation (CASR) aims to bind high-quality services to users, while taking into account their context requirements, including invocation time, location, social profiles, connectivity, and so on. However, current CASR approaches are not scalable with the huge amount of service data (QoS and context information, users reviews and feedbacks). In addition, they lack a rich representation of contextual information, as they adopt a simple matrix view. Moreover, current CASR approaches adopt the traditional user-service relation and they do not allow for multi-relational interactions between users and services in different contexts. To offer a scalable and context-sensitive service recommendation with great analysis and learning capabilities, we provide a rich and multi-relational representation of the CASR knowledge, based on the concept of knowledge graph. The constructed context-aware service knowledge graph (C-SKG) is, then, transformed into a low-dimensional vector space to facilitate its processing. For this purpose, we adopt Dilated Recurrent Neural Networks to propose a context-aware knowledge graph embedding, based on the principles of first-order and subgraph-aware proximity. Finally, a recommendation algorithm is defined to deliver the top-rated services according to the target user’s context. Experiments have proved the accuracy and scalability of our solution, compared to state-of-the-art CASR approaches.
,Security-aware multi-cloud service composition by exploiting rough sets and fuzzy FCA
Soft Computing, 2021
Abstract
With the emergence of cloud computing and to better satisfy users’ complex requirements in front of the huge number of cloud services, these latter may be combined while considering the virtualized environment’s constraints, including quality of services, security policies, resources availability, interoperability, etc. As composing services from multiple clouds was proved to be more beneficial than relying on services from one single cloud, the new approaches are now spanning multiple clouds. Despite the advantages of multi-cloud environments, there are always some security risks that mostly threaten the cloud consumers’ data, which makes the identification of suitable services a challenging task. In this work, we propose a security-aware multi-cloud service composition approach using fuzzy formal concept analysis (fuzzy FCA) and rough set theory (RS), which are two techniques with a strong mathematical background. To guarantee a high security level of the hosting clouds and the selected services, we exploit the fuzzy relations of fuzzy FCA and the approximation of RS. These techniques will help reducing the search space, by eliminating the disqualified clouds and insecure services. The experimental results proved the performance and the effectiveness of our approach.
Rahma Ferjani, Lilia Rejeb, Lamjed Ben SaidBelief eXtended Classifier System: A New Approach for Dealing with Uncertainty in Sleep Stages Classification
In International Conference on Hybrid Intelligent Systems (pp. 454-463). Cham: Springer International Publishing., 2021
Abstract
Sleep is an essential element that affects directly our daily life thus sleep analysis is a very interesting field. Sleep stages classification represents the base of all sleep analysis activities. However, the classification of sleep stages suffers from high uncertainty between its stages which could lead to degrade the performance of classification methods. To cope partially with this issue, we propose a new approach that deals with uncertainty especially with imprecision. Our method integrates the belief function theory in eXtended Classifier System (XCS). The proposed approach shows a good performance ability comparing to classical methods.
, Saoussen Bel Haj Kacem,The Behaviour of the Product T-Norm in Combination with Several Implications in Fuzzy PID Controller
In: Fujita, H., Selamat, A., Lin, J.CW., Ali, M. (eds) Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices. IEA/AIE 2021. Lecture Notes in Computer Science(), vol 12798. Springer, Cham. https://doi.org/10.1007/978-3-030-79457, 2021
Abstract
Fuzzy control is an intelligent software performed to tune a process and make it react in a desirable way. Nowadays, many researchers are interested in the Fuzzy Proportional-Integral-Derivative (FPID) controller because of its performance and simple structure. FPID controller, as fuzzy controller, is based on the Compositional Rule of Inference (CRI) that allows to infer with fuzzy data. As defined by Zadeh, the CRI contains two parameters: t-norm (T) and fuzzy implication (I). Because of the singleton representation of crisp inputs in fuzzy controllers, the t-norm is no longer considered in the CRI, which gives results based only on the fuzzy implication. In this study, we use non-singleton representation of the inputs, and we apply several implications in a fuzzy PID controller combined with the product t-norm. We study the behaviour of the fuzzy PID controller according to each combination (T,I) to evaluate its efficiency in term of quality and time of convergence. We finally compare the obtained results with the theoretical inference results and we find that they are consistent.
