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Authors: Mishra, P.; Advisor: -; Participants: Varadharajan, V.; Tupakula, U.; Pilli, E. S. (2019) - Intrusion detection is one of the important security problems in todays cyber world. A significant number of techniques have been developed which are based on machine learning approaches. However, they are not very successful in identifying all types of intrusions. In this paper, a detailed investigation and analysis of various machine learning techniques have been carried out for finding the cause of problems associated with various
machine learning techniques in detecting intrusive activities. Attack classification and mapping of the attack features is provided corresponding to each attack. Issues which are related to detecting low-frequency attacks using network attack dataset are al...
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Authors: Benda, Frank; Advisor: -; Participants: Braune, Roland; Doerner, Karl F.; Hartl, Richard F. (2019) - In proposing a machine learning approach for a flow shop scheduling problem with alternative resources, sequence-dependent setup times, and blocking, this paper seeks to generate a tree-based priority rule in terms of a well-performing decision tree (DT) for dispatching jobs. Furthermore, generating a generic DT and RF that yields competitive results for instance scenarios that structurally differ from the training instances was another goal of our research. The proposed DT relies on high quality solutions, obtained using a constraint programming (CP) formulation. Novel aspects include a unified representation of job sequencing and machine assignment decisions, as well as the generation...
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Authors: Fleming, S. W.; Advisor: -; Participants: Goodbody, A. G. (2019) - Hydroelectric power generation, water supplies for municipal, agricultural, manufacturing, and service industry uses including technology-sector requirements, dam safety, ood control, recreational uses, and ecological and legal constraints, all place simultaneous, competing demands on the heavily stressed water management infrastructure of the mostly arid American West. Optimally managing these resources depends on predicting water availability. We built a probabilistic nonlinear regression water supply forecast (WSF) technique for the US Department of Agriculture, which runs the largest stand-alone WSF system in the US West. Design criteria included improved accuracy over the existi...
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Authors: Ji, Y.; Advisor: -; Participants: Wang, Q.; Li, X.; Liu, J. (2019) - This survey gives a comprehensive overview of tensor techniques and applications in machine learning. Tensor represents higher order statistics. Nowadays, many applications based on machine learning algorithms require a large amount of structured high-dimensional input data. As the set of data increases, the complexity of these algorithms increases exponentially with the increase of vector size. Some scientists found that using tensors instead of the original input vectors can effectively solve these high-dimensional problems. This survey introduces the basic knowledge of tensor, including tensor operations, tensor decomposition, some tensor-based algorithms, and some applications of ...
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Authors: Sartzetakis, I.; Advisor: -; Participants: Christodoulopoulos, K. K.; Varvarigos, E. M. (2019) - In optical transport networks the quality of transmission (QoT) is estimated before provisioning new
connections or upgrading existing ones. Traditionally, a physical layer model (PLM) is used for QoT estimation
coupled with high margins to account for the model inaccuracy and the uncertainty in the evolving physical layer
conditions. Reducing the margins increases network efficiency but requires accurate QoT estimation. We present
two machine learning (ML) approaches to formulate such an accurate QoT estimator. We gather physical layer feedback, by monitoring the QoT of existing connections, to understand the actual physical conditions of the network. These data are used to train...
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Authors: Gu, H.Y.; Advisor: -; Participants: - (2016) - Geographic Object-Based Image Analysis (GEOBIA) techniques have become increasingly popular in recent years and are able to incorporate and develop ontology model within the classification process. They have been claimed to represent a paradigm shift in remote sensing interpretation. Nevertheless, it is lack of formal expression and objective modelling of the whole process of GEOBIA, and lack of the study of semantic classification method using ontology. A major reason is the complexity of the process of GEOBIA. The study has put forward an object-based semantic classification method of high resolution satellite imagery using ontology that aims to fully exploit the advantages of ontol...
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Authors: Bui,Thi Thu Hoa; Advisor: -; Participants: - (2021) - In order to study the water user behaviors, most of the previous studies have been using the traditional statistical approach, however, the development of model analytical techniques like machine learning approach which will help analysts to get more results. In this article, both the traditional statistical analysis and machine learning (ML) approach is used to study the behavior of irrigation water consumption with a logistic regression model. 235 households in Nam Dinh, Thai Nguyen, and Phu Tho province were surveyed. The research results show that the regression coefficients between the two approaches are quite similar. However, by the machine learning approach, more specific rese...
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Authors: Cremer, J. L.; Advisor: -; Participants: Konstantelos, I. (2019) - Various supervised machine learning approaches have been used in the past to assess the power system security (also known as reliability). This is typically done by training a classifier on a large number of operating points whose postfault status (stable or unstable) has been determined via time-domain simulations.
The output of this training process can be expressed as a security rule that is used online to classify an operating point. A critical, and little-studied aspect of these approaches is the interpretability of the rules produced. The lack of interpretability is a well-known issue of some machine learning approaches, especially when dealing with difficult classification proble...
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Authors: Fang, H.; Advisor: -; Participants: Wang, X.; Tomasin,S. (2019) - The 5G and beyond wireless networks are critical to support diverse vertical applications by
connecting heterogeneous devices and machines, which directly increase vulnerability for various
spoofing attacks. Conventional cryptographic and physical layer authentication techniques are facing
some challenges in complex dynamic wireless environments, including significant security
overhead, low reliability, as well as difficulties in pre-designing a precise authentication model, providing
continuous protection, and learning time-varying attributes. In this article, we envision new authentication
approaches based on machine learning techniques by opportunistically leveraging physica...
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Authors: Liang, F.; Advisor: -; Participants: Hatcher, W. G.; Liao, W.; Gao, W.; Yu, W. (2019) - The advancement of the Internet of Things (IoT) has allowed for unprecedented data collection, automation, and remote sensing and actuation, transforming autonomous systems and bringing smart command and control into numerous cyber physical systems (CPS) that our daily lives depend on. Simultaneously, dramatic improvements in machine learning and deep neural network architectures have enabled unprecedented analytical capabilities, which we see in increasingly common applications and production technologies, such as self-driving vehicles and intelligent mobile applications. Predictably, these technologies have seen rapid adoption, which has left many implementations vulnerable to threa...
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Authors: Tonello, A. M.; Advisor: -; Participants: Letizia, N. A.; Righini, D.; Marcuzzi, F. (2019) - A great deal of attention has been recently given to Machine Learning (ML) techniques in many different application elds. This paper provides a vision of what ML can do in Power Line Communications (PLC). We rst and brie y describe classical formulations of the ML, and distinguish deterministic from statistical learning models with relevance to communications. We then discuss ML applications in PLC for each layer, namely, for characterization and modeling, for the development of physical layer algorithms, for media access control and networking. Finally, other applications of the PLC that can bene t from the usage of ML, as grid diagnostics, are analyzed. Illustrative numerical exam...
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Authors: Leonita, G.; Advisor: -; Participants: Kuffer, M.; Sliuzas, R. V.; Persello, C. (2018) - Results show that local acceptance for a remote sensing-based slum mapping approach varies among stakeholder groups. Therefore, a locally adapted framework is required to combine ground surveys with robust and consistent machine learning methods, for being able to deal with big data, and to allow the rapid extraction of consistent information on the dynamics of slums at a large scale.
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Authors: Nawaz, S. J.; Advisor: -; Participants: Sharma, S. K.; Wyne, S.; Patwary, M. N.; Asaduzzaman, M. (2019) - The upcoming fth generation (5G) of wireless networks is expected to lay a foundation of intelligent networks with the provision of some isolated arti cial intelligence (AI) operations. However, fully intelligent network orchestration and management for providing innovative services will only be realized in Beyond 5G (B5G) networks. To this end, we envisage that the sixth generation (6G) of wireless networks will be driven by on-demand self-recon guration to ensure a many-fold increase in the network performance and service types. The increasingly stringent performance requirements of emerging networks may nally trigger the deployment of some interesting new technologies, such as l...
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Authors: Ostermann, F. O.; Advisor: -; Participants: Garcia-Chapeton, G.; Kraak, M. J.; Zurita-Milla, R. (2018) - This paper presents a conceptual model and a proof-of-concept implementation of a novel approach to engage citizens in supervising the analysis of user-generated geographic content (UGGC).The complexity of some tasks and the increasing volume of UGGC often restrict the role of citizens to data providers. We propose a hybrid processing approach, which maps geographical problems into data mining and machine-learning tasks, presents analysis results to human supervisors, and uses the responses to improve the machine-learning and data mining. For the pilot study, we adapt an approach to find semantically distinct places in UGGC.
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Authors: Ho, Viet Hung; Advisor: -; Participants: - (2021) - In recent years, the application of the Machine Learning (ML) method in analyzing and studying hydrological problems is increasingly becoming common. The numerical models based on ML algorithms have been widely used for predicting river water levels or flowrate. This paper proposes a new approach using one of the applications of deep learning models to predict river water levels in irrigation systems. A predictive model has been developed based on the Long Short-Term Memory (LSTM) neural networks to forecast the water levels upstream of Tranh Culvert in the Bac Hung Hai irrigation system in Vietnam. The findings of this study indicate that although only a modest amount of data is requ...
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