SEMCON

This paper presents a novel concept enrichment objective metric combining contextual and semantic information of terms extracted from the domain documents. The proposed metric is called SEMCON which stands for semantic and contextual objective metric. It employs a hybrid learning approach utilizing functionalities from statistical and linguistic ontology learning techniques. The metric also introduced for the first time two statistical features that have shown to improve the overall score ranking of highly relevant terms for concept enrichment. Subjective and objective experiments are conducted in various domains. Experimental results (F1) from computer domain show that SEMCON achieved better performance in contrast to tf*idf, and LSA methods, with 12.2%, 21.8%, and 24.5% improvement over them respectively. Additionally, an investigation into how much each of contextual and semantic components contributes to the overall task of concept enrichment is conducted and the obtained results suggest that a balanced weight gives the best performance.

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A Hybrid Concept Learning Approach to Ontology Enrichment

Innovations, Developments, and Applications of Semantic Web and Information Systems - Advances in Web Technologies and Engineering ◽

10.4018/978-1-5225-5042-6.ch004 ◽

2018 ◽

pp. 85-119

Author(s):

Zenun Kastrati ◽

Ali Shariq Imran ◽

Sule Yildirim Yayilgan

Keyword(s):

Concept Learning ◽

Future Research ◽

Learning Approach ◽

Learning Approaches ◽

New Concepts ◽

Learning Techniques ◽

Proposed Model ◽

Hybrid Concept ◽

Future Research Directions ◽

Linguistic Ontology

The wide use of ontology in different applications has resulted in a plethora of automatic approaches for population and enrichment of an ontology. Ontology enrichment is an iterative process where the existing ontology is continuously updated with new concepts. A key aspect in ontology enrichment process is the concept learning approach. A learning approach can be a linguistic-based, statistical-based, or hybrid-based that employs both linguistic as well as statistical-based learning approaches. This chapter presents a concept enrichment model that combines contextual and semantic information of terms. The proposed model called SEMCON employs a hybrid concept learning approach utilizing functionalities from statistical and linguistic ontology learning techniques. The model introduced for the first time two statistical features that have shown to improve the overall score ranking of highly relevant terms for concept enrichment. The chapter also gives some recommendations and possible future research directions based on the discussion in following sections.

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Multi-agent based data mining aggregation approaches using machine learning techniques

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.9631 ◽

2018 ◽

Vol 7 (3) ◽

pp. 1136

Author(s):

V Devasekhar ◽

P Natarajan

Keyword(s):

Machine Learning ◽

Data Mining ◽

Optimization Algorithm ◽

Experimental Results ◽

Machine Learning Techniques ◽

Statistical Features ◽

Agent Based ◽

Cluster Data ◽

Learning Techniques ◽

Multi Agent

Data Mining is an extraction of important knowledge from the various databases using different kinds of approaches. In the multi agent, distributed mining the knowledge aggregation is one of challenging task. This paper tries to optimize the problem of aggregation and boils down into the solution, which is derived based on the machine learning statistical features of each agents. However, in this paper a novel optimization algorithm called Multi-Agent Based Data Mining Aggregation (MABDA) is used for present day’s scenarios. The MBADA algorithm has agents which collect extracted knowledge and summarizes the various levels of agent’s cluster data into an aggregation with maximum accuracies. To prove the effectiveness of the proposed algorithm, the experimental results are compared with relatively existing methods.

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Nuclear Morphology Optimized Deep Hybrid Learning (NUMODRIL): A novel architecture for accurate diagnosis/prognosis of Ovarian Cancer

10.1101/2020.11.23.393660 ◽

2020 ◽

Author(s):

Duhita Sengupta ◽

Sk Nishan Ali ◽

Aditya Bhattacharya ◽

Joy Mustafi ◽

Asima Mukhopadhyay ◽

...

Keyword(s):

Ovarian Cancer ◽

Deep Learning ◽

Cancer Cells ◽

Hybrid Learning ◽

Nuclear Morphology ◽

Learning Approach ◽

Diagnosis And Prognosis ◽

Learning Techniques ◽

Wide Range ◽

Cancer Tissues

AbstractNuclear morphological features are potent determining factors for clinical diagnostic approaches adopted by pathologists to analyse the malignant potential of cancer cells. Considering the structural alteration of nucleus in cancer cells, various groups have developed machine learning techniques based on variation in nuclear morphometric information like nuclear shape, size, nucleus-cytoplasm ratio and various non-parametric methods like deep learning have also been tested for analysing immunohistochemistry images of tissue samples for diagnosing various cancers. Our aim is to study the morphometric distribution of nuclear lamin proteins as a specific parameter in ovarian cancer tissues. Besides being the principal mechanical component of the nucleus, lamins also present a platform for binding of proteins and chromatin thereby serving a wide range of nuclear functions like maintenance of genome stability, chromatin regulation. Altered expression of lamins in different subtypes of cancer is now evident from data across the world. It has already been elucidated that in ovarian cancer, extent of alteration in nuclear shape and morphology can determine degree of genetic changes and thus can be utilized to predict the outcome of low to high form of serous carcinoma. In this work, we have performed exhaustive imaging of ovarian cancer versus normal tissue and introduced a novel Deep Hybrid Learning approach on the basis of the distribution of lamin proteins. Although developed with ovarian cancer datasets in view, this architecture would be of immense importance in accurate and fast diagnosis and prognosis of all types of cancer associated with lamin induced morphological changes and would perform across small/medium to large datasets with equal efficiency.Significance StatementWe have developed a novel Deep Hybrid Learning approach based on nuclear morphology to classify normal and ovarian cancer tissues with highest possible accuracy and speed. Ovarian cancer cells can be easily distinguished from their enlarged nuclear morphology as is evident from lamin A & B distribution pattern. This is the first report to invoke specific nuclear markers like lamin A & B instead of classical haematoxylin-eosin staining in an effort to build parametric datasets. Our approach has been shown to outperform the existing deep learning techniques in training and validation of datasets over a wide range. Therefore this method could be used as a robust model to predict malignant transformations of benign nuclei and thus be implemented in the diagnosis and prognosis of ovarian cancer in future. Most importantly, this method can be perceived as a generalized approach in the diagnosis for all types of cancer.

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A Novel Ensemble Learning Approach of Deep Learning Techniques to Monitor Distracted Driver Behaviour in Real Time

2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA) ◽

10.1109/caida51941.2021.9425243 ◽

2021 ◽

Author(s):

Hafiz Umer Draz ◽

Muhammad Zeeshan Khan ◽

Muhammad Usman Ghani Khan ◽

Amjad Rehman ◽

Ibrahim Abunadi

Keyword(s):

Deep Learning ◽

Real Time ◽

Ensemble Learning ◽

Learning Approach ◽

Driver Behaviour ◽

Learning Techniques

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RSSI-Based for Device-Free Localization Using Deep Learning Technique

Smart Cities ◽

10.3390/smartcities3020024 ◽

2020 ◽

Vol 3 (2) ◽

pp. 444-455

Author(s):

Abdul Syafiq Abdull Sukor ◽

Latifah Munirah Kamarudin ◽

Ammar Zakaria ◽

Norasmadi Abdul Rahim ◽

Sukhairi Sudin ◽

...

Keyword(s):

Deep Learning ◽

Machine Learning Algorithms ◽

Learning Approach ◽

Statistical Features ◽

Received Signal Strength Indicator ◽

Privacy Concerns ◽

Physical Discomfort ◽

Learning Technique ◽

Device Free ◽

The Internet Of Things

Device-free localization (DFL) has become a hot topic in the paradigm of the Internet of Things. Traditional localization methods are focused on locating users with attached wearable devices. This involves privacy concerns and physical discomfort especially to users that need to wear and activate those devices daily. DFL makes use of the received signal strength indicator (RSSI) to characterize the user’s location based on their influence on wireless signals. Existing work utilizes statistical features extracted from wireless signals. However, some features may not perform well in different environments. They need to be manually designed for a specific application. Thus, data processing is an important step towards producing robust input data for the classification process. This paper presents experimental procedures using the deep learning approach to automatically learn discriminative features and classify the user’s location. Extensive experiments performed in an indoor laboratory environment demonstrate that the approach can achieve 84.2% accuracy compared to the other basic machine learning algorithms.

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A Novel Concept Acquisition Approach Based on Formal Contexts

The Scientific World JOURNAL ◽

10.1155/2014/136324 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 9

Author(s):

Ting Qian ◽

Ling Wei

Keyword(s):

Data Analysis ◽

Formal Concept Analysis ◽

Real Life ◽

Experimental Results ◽

New Method ◽

Concept Acquisition ◽

Formal Concept ◽

Knowledge Processing ◽

Formal Concepts ◽

Novel Concept

As an important tool for data analysis and knowledge processing, formal concept analysis (FCA) has been applied to many fields. In this paper, we introduce a new method to find all formal concepts based on formal contexts. The amount of intents calculation is reduced by the method. And the corresponding algorithm of our approach is proposed. The main theorems and the corresponding algorithm are examined by examples, respectively. At last, several real-life databases are analyzed to demonstrate the application of the proposed approach. Experimental results show that the proposed approach is simple and effective.

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A High Capacity, Room Temperature, Hybrid Flow Battery Consisting of Liquid Na-Cs Anode and Aqueous NaI Catholyte

Batteries ◽

10.3390/batteries4040060 ◽

2018 ◽

Vol 4 (4) ◽

pp. 60 ◽

Cited By ~ 1

Author(s):

Caihong Liu ◽

Leon Shaw

Keyword(s):

Room Temperature ◽

Electrochemical Impedance ◽

Coulombic Efficiency ◽

High Capacity ◽

High Energy ◽

Flow Battery ◽

Membrane Interfaces ◽

Number Of Cycles ◽

Novel Concept ◽

First Time

In this study, we have proposed a novel concept of hybrid flow batteries consisting of a molten Na-Cs anode and an aqueous NaI catholyte separated by a NaSICON membrane. A number of carbonaceous electrodes are studied using cyclic voltammetry (CV) for their potentials as the positive electrode of the aqueous NaI catholyte. The charge transfer impedance, interfacial impedance and NaSICON membrane impedance of the Na-Cs ‖ NaI hybrid flow battery are analyzed using electrochemical impedance spectroscopy. The performance of the Na-Cs ‖ NaI hybrid flow battery is evaluated through galvanostatic charge/discharge cycles. This study demonstrates, for the first time, the feasibility of the Na-Cs ‖ NaI hybrid flow battery and shows that the Na-Cs ‖ NaI hybrid flow battery has the potential to achieve the following properties simultaneously: (i) An aqueous NaI catholyte with good cycle stability, (ii) a durable and low impedance NaSICON membrane for a large number of cycles, (iii) stable interfaces at both anode/membrane and cathode/membrane interfaces, (iv) a molten Na-Cs anode capable of repeated Na plating and stripping, and (v) a flow battery with high Coulombic efficiency, high voltaic efficiency, and high energy efficiency.

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