SMS Spam Filtering on Multiple Background Datasets Using Machine Learning Techniques: A Novel Approach

As the number of satellite launches increases each year, it is only natural that an interest in the safety and monitoring of these systems would increase as well. However, as a system becomes more complex, generating a high-fidelity model that accurately describes the system becomes complicated. Therefore, imploring a data-driven method can provide to be more beneficial for such applications. This research proposes a novel approach for data-driven machine learning techniques on the detection and isolation of nonlinear systems, with a case-study for an in-orbit closed loop-controlled satellite with reaction wheels as actuators. High-fidelity models of the 3-axis controlled satellite are employed to generate data for both nominal and faulty conditions of the reaction wheels. The generated simulation data is used as input for the isolation method, after which the data is pre-processed through feature extraction from a temporal, statistical, and spectral domain. The pre-processed features are then fed into various machine learning classifiers. Isolation results are validated with cross-validation, and model parameters are tuned using hyperparameter optimization. To validate the robustness of the proposed method, it is tested on three characterized datasets and three reaction wheel configurations, including standard four-wheel, three-orthogonal, and pyramid. The results prove superior performance isolation accuracy for the system under study compared to previous studies using alternative methods (Rahimi & Saadat, 2019, 2020).

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Analysis of Naıve Bayes Algorithm for Email Spam Filtering

International Journal for Modern Trends in Science and Technology - RTT2020 ◽

10.46501/ijmtst0701002 ◽

2021 ◽

Vol 7 (01) ◽

pp. 5-9

Author(s):

RajKishore Sahni

Keyword(s):

Machine Learning ◽

Service Providers ◽

Machine Learning Techniques ◽

Research Trend ◽

Learning Approaches ◽

Spam Filtering ◽

Internet Service ◽

Learning Techniques ◽

Bayes Algorithm ◽

Email Spam

The upsurge in the volume of unwanted emails called spam has created an intense need for the development of more dependable and robust antispam filters. Machine learning methods of recent are being used to successfully detect and filter spam emails. We present a systematic review of some of the popular machine learning based email spam filtering approaches. Our review covers survey of the important concepts, attempts, efficiency, and the research trend in spam filtering. The preliminary discussion in the study background examines the applications of machine learning techniques to the email spam filtering process of the leading internet service providers (ISPs) like Gmail, Yahoo and Outlook emails spam filters. Discussion on general email spam filtering process, and the various efforts by different researchers in combating spam through the use machine learning techniques was done. Our review compares the strengths and drawbacks of existing machine learning approaches and the open research problems in spam filtering. We recommended deep learning and deep adversarial learning as the future techniques that can effectively handle the menace of spam emails

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Using Machine Learning for Decreasing State Uncertainty in Planning

Journal of Artificial Intelligence Research ◽

10.1613/jair.1.11567 ◽

2020 ◽

Vol 69 ◽

pp. 765-806

Author(s):

Senka Krivic ◽

Michael Cashmore ◽

Daniele Magazzeni ◽

Sandor Szedmak ◽

Justus Piater

Keyword(s):

Machine Learning ◽

Active Learning ◽

Learning Process ◽

The State ◽

Machine Learning Techniques ◽

Planning Problem ◽

Planning Techniques ◽

Novel Approach ◽

Learning Techniques ◽

State Uncertainty

We present a novel approach for decreasing state uncertainty in planning prior to solving the planning problem. This is done by making predictions about the state based on currently known information, using machine learning techniques. For domains where uncertainty is high, we define an active learning process for identifying which information, once sensed, will best improve the accuracy of predictions. We demonstrate that an agent is able to solve problems with uncertainties in the state with less planning effort compared to standard planning techniques. Moreover, agents can solve problems for which they could not find valid plans without using predictions. Experimental results also demonstrate that using our active learning process for identifying information to be sensed leads to gathering information that improves the prediction process.

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