scholarly journals An Approach of CA with M-RIPPER for Heart Disease Prediction

2021 ◽  
Author(s):  
Manimurugan S ◽  
Saad Almutairi ◽  
Majed Mohammed Aborokbah ◽  
Narmatha C ◽  
Subramaniam Ganesan ◽  
...  
Keyword(s):  
Heart Disease ◽  
Learning Algorithm ◽  
Context Aware ◽  
Matlab Simulation ◽  
Proposed Model ◽  
The Everyday ◽  
Healthcare Model ◽  
Patient’S Information ◽  
Context Aware System ◽  
Present World

Abstract In the present world, mobile computing devices are popular and are identified in each aspect of life. This combination among computing and the present world is not restricted to the everyday life. The medical field was similarly concerned, where care is given in a wide scope of areas and conditions. The medical domain is continually being immersed with new kinds of innovations, including context-aware system and application. In this research, a context aware healthcare model based on IoT application is proposed. The smart medical devices are used to measure the data from the patients and store it in database. From the database, the patient’s information and medical records are considered as context aware data. For analyzing and classifying the data, the MRIPPER (Modified Repeated Incremental Pruning to Produce ErroR) algorithm is used. This algorithm is a rule-based machine learning algorithm. By using this algorithm, the rules are framed for the analysis of dataset for the prediction of heart disease. The performance analysis of the proposed model is experimented in MATLAB simulation tool. Further, the performance of the proposed model is compared with other existing models like J48, random forest, CART, OneR, and JRip algorithms. The proposed algorithm has achieved 98.89% accuracy, Precision is 96.76%, recall or sensitivity is 99.05%, specificity is 94.35%, and f-score is 97.60%. Overall, the proposed model has obtained 97.38% accuracy in predicting normal class and 97.93% in predicting abnormal class subjects.

UML based Model of a Context Aware System

2015 ◽  
Vol 7 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Umar Mahmud
Keyword(s):  
Object Oriented ◽  
Class Diagram ◽  
Use Case ◽  
Context Aware ◽  
Sequence Diagrams ◽  
Proposed Model ◽  
Software Product ◽  
Ck Metrics ◽  
Context Aware System ◽  
Use Case Diagram

Context Awareness is the mechanism through which systems can adapt to the needs of a user by monitoring the context. Context includes environment, spatial, temporal, etc information that is used to infer the current activity. UML is used to design a context aware system. The context aware system is viewed as an Object Oriented software product. The UML model is generated through ArgoUML, a free UML modelling tool. The Use Case Diagram, the Sequence Diagrams and the Class Diagram are modelled using this tool. The Class Diagram is subjected to CK metrics to identify the strengths and weaknesses of the design. The measurements show that the proposed model is within the recommended range.

Algorithms for Rule Generation and Matchmaking in Context-Aware System

2009 ◽  
Vol 20 (10) ◽  
pp. 2655-2666 ◽  
Author(s):  
Dong LIU ◽  
Xiang-Wu MENG ◽  
Jun-Liang CHEN ◽  
Ya-Mei XIA
Keyword(s):  
Context Aware ◽  
Rule Generation ◽  
Context Aware System

scholarly journals Data mining techniques with machine learning algorithm to predict patients of heart disease

2021 ◽  
Vol 1088 (1) ◽  
pp. 012035
Author(s):  
Mulyawan ◽  
Agus Bahtiar ◽  
Githera Dwilestari ◽  
Fadhil Muhammad Basysyar ◽  
Nana Suarna
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Heart Disease ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Data Mining Techniques

Enhanced context-aware recommendation using topic modeling and particle swarm optimization

2021 ◽  
pp. 1-16
Author(s):  
Ibtissem Gasmi ◽  
Mohamed Walid Azizi ◽  
Hassina Seridi-Bouchelaghem ◽  
Nabiha Azizi ◽  
Samir Brahim Belhaouari
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
State Of The Art ◽  
Weighting Function ◽  
Contextual Factors ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Pso Algorithm ◽  
Context Aware ◽  
Proposed Model

Context-Aware Recommender System (CARS) suggests more relevant services by adapting them to the user’s specific context situation. Nevertheless, the use of many contextual factors can increase data sparsity while few context parameters fail to introduce the contextual effects in recommendations. Moreover, several CARSs are based on similarity algorithms, such as cosine and Pearson correlation coefficients. These methods are not very effective in the sparse datasets. This paper presents a context-aware model to integrate contextual factors into prediction process when there are insufficient co-rated items. The proposed algorithm uses Latent Dirichlet Allocation (LDA) to learn the latent interests of users from the textual descriptions of items. Then, it integrates both the explicit contextual factors and their degree of importance in the prediction process by introducing a weighting function. Indeed, the PSO algorithm is employed to learn and optimize weights of these features. The results on the Movielens 1 M dataset show that the proposed model can achieve an F-measure of 45.51% with precision as 68.64%. Furthermore, the enhancement in MAE and RMSE can respectively reach 41.63% and 39.69% compared with the state-of-the-art techniques.

scholarly journals An Investigation into the Application of Deep Learning in the Detection and Mitigation of DDOS Attack on SDN Controllers

Technologies ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 14
Author(s):  
James Dzisi Gadze ◽  
Akua Acheampomaa Bamfo-Asante ◽  
Justice Owusu Agyemang ◽  
Henry Nunoo-Mensah ◽  
Kwasi Adu-Boahen Opare
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Learning Algorithm ◽  
Single Point ◽  
Denial Of Service ◽  
Specific Work ◽  
Learning Models ◽  
Ddos Attack ◽  
Deep Learning Algorithm ◽  
Proposed Model

Software-Defined Networking (SDN) is a new paradigm that revolutionizes the idea of a software-driven network through the separation of control and data planes. It addresses the problems of traditional network architecture. Nevertheless, this brilliant architecture is exposed to several security threats, e.g., the distributed denial of service (DDoS) attack, which is hard to contain in such software-based networks. The concept of a centralized controller in SDN makes it a single point of attack as well as a single point of failure. In this paper, deep learning-based models, long-short term memory (LSTM) and convolutional neural network (CNN), are investigated. It illustrates their possibility and efficiency in being used in detecting and mitigating DDoS attack. The paper focuses on TCP, UDP, and ICMP flood attacks that target the controller. The performance of the models was evaluated based on the accuracy, recall, and true negative rate. We compared the performance of the deep learning models with classical machine learning models. We further provide details on the time taken to detect and mitigate the attack. Our results show that RNN LSTM is a viable deep learning algorithm that can be applied in the detection and mitigation of DDoS in the SDN controller. Our proposed model produced an accuracy of 89.63%, which outperformed linear-based models such as SVM (86.85%) and Naive Bayes (82.61%). Although KNN, which is a linear-based model, outperformed our proposed model (achieving an accuracy of 99.4%), our proposed model provides a good trade-off between precision and recall, which makes it suitable for DDoS classification. In addition, it was realized that the split ratio of the training and testing datasets can give different results in the performance of a deep learning algorithm used in a specific work. The model achieved the best performance when a split of 70/30 was used in comparison to 80/20 and 60/40 split ratios.

scholarly journals CAFD: Context-Aware Fault Diagnostic Scheme towards Sensor Faults Utilizing Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 617
Author(s):  
Umer Saeed ◽  
Young-Doo Lee ◽  
Sana Ullah Jan ◽  
Insoo Koo
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Diagnostic System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Context Aware ◽  
Sensor Faults ◽  
Training Time ◽  
Low Intensity ◽  
Fault Diagnostic

Sensors’ existence as a key component of Cyber-Physical Systems makes it susceptible to failures due to complex environments, low-quality production, and aging. When defective, sensors either stop communicating or convey incorrect information. These unsteady situations threaten the safety, economy, and reliability of a system. The objective of this study is to construct a lightweight machine learning-based fault detection and diagnostic system within the limited energy resources, memory, and computation of a Wireless Sensor Network (WSN). In this paper, a Context-Aware Fault Diagnostic (CAFD) scheme is proposed based on an ensemble learning algorithm called Extra-Trees. To evaluate the performance of the proposed scheme, a realistic WSN scenario composed of humidity and temperature sensor observations is replicated with extreme low-intensity faults. Six commonly occurring types of sensor fault are considered: drift, hard-over/bias, spike, erratic/precision degradation, stuck, and data-loss. The proposed CAFD scheme reveals the ability to accurately detect and diagnose low-intensity sensor faults in a timely manner. Moreover, the efficiency of the Extra-Trees algorithm in terms of diagnostic accuracy, F1-score, ROC-AUC, and training time is demonstrated by comparison with cutting-edge machine learning algorithms: a Support Vector Machine and a Neural Network.

scholarly journals Context-Aware Neural Machine Translation for Korean Honorific Expressions

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1589
Author(s):  
Yongkeun Hwang ◽  
Yanghoon Kim ◽  
Kyomin Jung
Keyword(s):  
Machine Translation ◽  
Deep Neural Networks ◽  
Contextual Information ◽  
Context Aware ◽  
Neural Machine Translation ◽  
Translation Quality ◽  
Sentence Level ◽  
Proposed Model ◽  
The Given ◽  
The Relationship

Neural machine translation (NMT) is one of the text generation tasks which has achieved significant improvement with the rise of deep neural networks. However, language-specific problems such as handling the translation of honorifics received little attention. In this paper, we propose a context-aware NMT to promote translation improvements of Korean honorifics. By exploiting the information such as the relationship between speakers from the surrounding sentences, our proposed model effectively manages the use of honorific expressions. Specifically, we utilize a novel encoder architecture that can represent the contextual information of the given input sentences. Furthermore, a context-aware post-editing (CAPE) technique is adopted to refine a set of inconsistent sentence-level honorific translations. To demonstrate the efficacy of the proposed method, honorific-labeled test data is required. Thus, we also design a heuristic that labels Korean sentences to distinguish between honorific and non-honorific styles. Experimental results show that our proposed method outperforms sentence-level NMT baselines both in overall translation quality and honorific translations.

scholarly journals Context-Aware Adaptation of Component-Based Systems: An Active Repository Approach

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Sindolfo Miranda Filho ◽  
Julio Melo ◽  
Luiz Eduardo Leite ◽  
Guido Lemos
Keyword(s):  
Context Aware ◽  
Current Configuration ◽  
Fuzzy Logic Algorithm ◽  
Running System ◽  
Component Based Software Engineering ◽  
Context Aware System ◽  
The Given ◽  
To Receive ◽  
Component Repository

Context-aware systems are able to monitor and automatically adapt their operation accordingly to the execution context in which they are introduced. Component-based software engineering (CBSE) focuses on the development and reuse of self-contained software assets in order to achieve better productivity and quality. In order to store and retrieve components, CBSE employs component repository systems to provide components to the system developers. This paper presents an active component repository that is able to receive the current configuration from the context-aware system and compute the components and the new architecture that better fit the given context. Since the repository has a wide knowledge of available components, it can better decide which configuration is more suitable to the running system. The repository applies Fuzzy logic algorithm to evaluate the adequacy level of the components and GRASP algorithm to mount the new system architecture. In order to verify the feasibility of our approach, we use a digital TV middleware case study to achieve experimental results.

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