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1

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T-PCCE: Twitter Personality based Communicative Communities Extraction System for Big Data

Kafeza, Eleanna ; Kanavos, Andreas ; Makris, Christos ; [et al.]

Institute of Electrical and Electronics Engineers (IEEE) ; 2020

In: IEEE Transactions on Knowledge and Data Engineering Vol. 32, No. 8 ( 2020-8-1), p. 1625-1638

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In: IEEE Transactions on Knowledge and Data Engineering, Institute of Electrical and Electronics Engineers (IEEE), Vol. 32, No. 8 ( 2020-8-1), p. 1625-1638

Type of Medium: Online Resource

ISSN: 1041-4347 , 1558-2191 , 2326-3865

URL: Journal

URL: Article

DOI: 10.1109/TKDE.69

DOI: 10.1109/TKDE.2019.2906197

RVK:

SA 5571

Language: Unknown

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Publication Date: 2020

detail.hit.zdb_id: 1001468-8

detail.hit.zdb_id: 2026620-0

SSG: 24,1

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2

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Classifying Hindi News Using Various Machine Learning and Deep Learning Techniques

Chhabra, Anusha ; Arora, Monika ; Sharma, Arpit ; [et al.]

World Scientific Pub Co Pte Ltd ; 2024

In: International Journal on Artificial Intelligence Tools Vol. 33, No. 02 ( 2024-03)

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In: International Journal on Artificial Intelligence Tools, World Scientific Pub Co Pte Ltd, Vol. 33, No. 02 ( 2024-03)

Abstract: Text classification involves organizing textual information into predefined classes, a task which is particularly useful in domains like sentiment analysis, spam detection, and content labeling. In India, where a massive amount of information is generated daily through newspapers and social media, Hindi is one of the most widely used and spoken languages. However, there is limited research on Hindi text classification and, particularly, regarding Hindi news classification. This paper presents a research study to classify Hindi news articles published in Hindi-language newspapers in India by using and comparing various Machine Learning (ML) and Deep Learning (DL) algorithms. To prepare the textual news data for classification, pre-processing and feature engineering techniques, such as count vectorizer, Tf-Idf vectorizer and Doc2Vec, were used and applied to convert texts into vectors. This pre-processing step on the textual data was very challenging due to the presence of multimodal words, conjunctions, punctuation, and special characters in Hindi texts. The study considered Hindi news headlines from predetermined categories (Science, Sports, Entertainment and Business) and, among the different ML and DL models tested and evaluated, Linear Regression with Doc2Vec vectorizer and SGD classifier with Tf-Idf vectorizer produced best accuracies of 97.04% and 96.59%, respectively. The best performing DL model was found to be the Bi-LSTM with an accuracy of approximately 97% on the testing data.

Type of Medium: Online Resource

ISSN: 0218-2130 , 1793-6349

URL: Article

DOI: 10.1142/S0218213023500641

Language: English

Publisher: World Scientific Pub Co Pte Ltd

Publication Date: 2024

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3

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Multiclass sentiment analysis on COVID-19-related tweets using deep learning models

Vernikou, Sotiria ; Lyras, Athanasios ; Kanavos, Andreas

Springer Science and Business Media LLC ; 2022

In: Neural Computing and Applications Vol. 34, No. 22 ( 2022-11), p. 19615-19627

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In: Neural Computing and Applications, Springer Science and Business Media LLC, Vol. 34, No. 22 ( 2022-11), p. 19615-19627

Type of Medium: Online Resource

ISSN: 0941-0643 , 1433-3058

URL: Article

DOI: 10.1007/s00521-022-07650-2

Language: English

Publisher: Springer Science and Business Media LLC

Publication Date: 2022

detail.hit.zdb_id: 1136944-9

detail.hit.zdb_id: 1480526-1

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4

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Topic Categorization of Biomedical Abstracts

Kanavos, Andreas ; Makris, Christos ; Theodoridis, Evangelos

World Scientific Pub Co Pte Ltd ; 2015

In: International Journal on Artificial Intelligence Tools Vol. 24, No. 01 ( 2015-02), p. 1540004-

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In: International Journal on Artificial Intelligence Tools, World Scientific Pub Co Pte Ltd, Vol. 24, No. 01 ( 2015-02), p. 1540004-

Abstract: Nowadays, people frequently use search engines in order to find the information they need on the Web. Especially, Web search constitutes of a basic tool used by million researchers in their everyday work. A very popular indexing engine, concerning life sciences and biomedical research, is PubMed. PubMed is a free database accessing primarily the MEDLINE database of references and abstracts on life sciences and biomedical topics. The present search engines usually return search results in a global ranking, making it difficult for the users to browse in different topics or subtopics. In this work we propose a novel system to address the issues of clustering biomedical search engine results according to their topic. A methodology that exploits semantic text clustering techniques in order to group biomedical document collections in homogeneous topics, is presented and evaluated. In order to provide more accurate clustering results, we utilize various biomedical ontologies, likeMeSH and GeneOntology. Finally, we embed the proposed methodology in an online system that post-processes the PubMed online database so as to provide users the retrieved results according to well formed topics. So as to expose our method as an online real-time service with reasonable response times, we performed a wide range of engineering optimizations along with experimentation on preprocessing time to precision of results trade off.

Type of Medium: Online Resource

ISSN: 0218-2130 , 1793-6349

URL: Article

DOI: 10.1142/S0218213015400047

Language: English

Publisher: World Scientific Pub Co Pte Ltd

Publication Date: 2015

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5

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Defining and evaluating Twitter influence metrics: a higher-order approach in Neo4j

Drakopoulos, Georgios ; Kanavos, Andreas ; Mylonas, Phivos ; [et al.]

Springer Science and Business Media LLC ; 2017

In: Social Network Analysis and Mining Vol. 7, No. 1 ( 2017-12)

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In: Social Network Analysis and Mining, Springer Science and Business Media LLC, Vol. 7, No. 1 ( 2017-12)

Type of Medium: Online Resource

ISSN: 1869-5450 , 1869-5469

URL: Article

DOI: 10.1007/s13278-017-0467-9

Language: English

Publisher: Springer Science and Business Media LLC

Publication Date: 2017

detail.hit.zdb_id: 2595306-0

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6

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Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopathy

Mohanty, Cheena ; Mahapatra, Sakuntala ; Acharya, Biswaranjan ; [et al.]

MDPI AG ; 2023

In: Sensors Vol. 23, No. 12 ( 2023-06-19), p. 5726-

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In: Sensors, MDPI AG, Vol. 23, No. 12 ( 2023-06-19), p. 5726-

Abstract: Diabetic retinopathy (DR) is a common complication of long-term diabetes, affecting the human eye and potentially leading to permanent blindness. The early detection of DR is crucial for effective treatment, as symptoms often manifest in later stages. The manual grading of retinal images is time-consuming, prone to errors, and lacks patient-friendliness. In this study, we propose two deep learning (DL) architectures, a hybrid network combining VGG16 and XGBoost Classifier, and the DenseNet 121 network, for DR detection and classification. To evaluate the two DL models, we preprocessed a collection of retinal images obtained from the APTOS 2019 Blindness Detection Kaggle Dataset. This dataset exhibits an imbalanced image class distribution, which we addressed through appropriate balancing techniques. The performance of the considered models was assessed in terms of accuracy. The results showed that the hybrid network achieved an accuracy of 79.50%, while the DenseNet 121 model achieved an accuracy of 97.30%. Furthermore, a comparative analysis with existing methods utilizing the same dataset revealed the superior performance of the DenseNet 121 network. The findings of this study demonstrate the potential of DL architectures for the early detection and classification of DR. The superior performance of the DenseNet 121 model highlights its effectiveness in this domain. The implementation of such automated methods can significantly improve the efficiency and accuracy of DR diagnosis, benefiting both healthcare providers and patients.

Type of Medium: Online Resource

ISSN: 1424-8220

URL: Article

DOI: 10.3390/s23125726

Language: English

Publisher: MDPI AG

Publication Date: 2023

detail.hit.zdb_id: 2052857-7

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7

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A Regularization-Based Big Data Framework for Winter Precipitation Forecasting on Streaming Data

Kanavos, Andreas ; Trigka, Maria ; Dritsas, Elias ; [et al.]

MDPI AG ; 2021

In: Electronics Vol. 10, No. 16 ( 2021-08-04), p. 1872-

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In: Electronics, MDPI AG, Vol. 10, No. 16 ( 2021-08-04), p. 1872-

Abstract: In the current paper, we propose a machine learning forecasting model for the accurate prediction of qualitative weather information on winter precipitation types, utilized in Apache Spark Streaming distributed framework. The proposed model receives storage and processes data in real-time, in order to extract useful knowledge from different sensors related to weather data. In following, the numerical weather prediction model aims at forecasting the weather type given three precipitation classes namely rain, freezing rain, and snow as recorded in the Automated Surface Observing System (ASOS) network. For depicting the effectiveness of our proposed schema, a regularization technique for feature selection so as to avoid overfitting is implemented. Several classification models covering three different categorization methods namely the Bayesian, decision trees, and meta/ensemble methods, have been investigated in a real dataset. The experimental analysis illustrates that the utilization of the regularization technique could offer a significant boost in forecasting performance.

Type of Medium: Online Resource

ISSN: 2079-9292

URL: Article

DOI: 10.3390/electronics10161872

Language: English

Publisher: MDPI AG

Publication Date: 2021

detail.hit.zdb_id: 2662127-7

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8

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ITSS: An Intelligent Traffic Signaling System Based on an IoT Infrastructure

Rai, Satyananda Champati ; Nayak, Samaleswari Pr ; Acharya, Biswaranjan ; [et al.]

MDPI AG ; 2023

In: Electronics Vol. 12, No. 5 ( 2023-02-28), p. 1177-

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In: Electronics, MDPI AG, Vol. 12, No. 5 ( 2023-02-28), p. 1177-

Abstract: Recently, there has been a huge spike in the number of automobiles in the urban areas of many countries, particularly in India. The number of vehicles are increasing rapidly and with the existing infrastructure, the traffic systems stand still during peak hours. Some of the main challenges for traffic management are the movement of overloaded vehicles beyond their restricted zone and time, reckless driving, and overlooking road safety rules. This paper proposes an Internet of Things (IoT)-based real-time Intelligent Traffic Signal System (ITSS), which consists of inductive loops and a programmable micro-controller to determine traffic density. Inter-communication in the centralized control unit sets the timer of the traffic light and synchronizes with the traffic density in real-time for smooth mobility of vehicles with less delay. Additionally, to prioritize emergency vehicles over other vehicles in the same lane, a pre-emption mechanism has been integrated through infrared sensors. The result of traffic density determines the timer of the light post in real-time, which in result enhances the smooth flow of vehicles with reduced delay for travelers. Using its automatic on-demand traffic signaling system, the presented solution has advantages over fixed systems.

Type of Medium: Online Resource

ISSN: 2079-9292

URL: Article

DOI: 10.3390/electronics12051177

Language: English

Publisher: MDPI AG

Publication Date: 2023

detail.hit.zdb_id: 2662127-7

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9

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Evaluating Methods for Efficient Community Detection in Social Networks

Kanavos, Andreas ; Voutos, Yorghos ; Grivokostopoulou, Foteini ; [et al.]

MDPI AG ; 2022

In: Information Vol. 13, No. 5 ( 2022-04-19), p. 209-

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In: Information, MDPI AG, Vol. 13, No. 5 ( 2022-04-19), p. 209-

Abstract: Exploring a community is an important aspect of social network analysis because it can be seen as a crucial way to decompose specific graphs into smaller graphs based on interactions between users. The process of discovering common features between groups of users, entitled “community detection”, is a fundamental feature for social network analysis, wherein the vertices represent the users and the edges their relationships. Our study focuses on identifying such phenomena on the Twitter graph of posts and on determining communities, which contain users with similar features. This paper presents the evaluation of six established community-discovery algorithms, namely Breadth-First Search, CNM, Louvain, MaxToMin, Newman–Girvan and Propinquity Dynamics, in terms of four widely used graphs and a collection of data fetched from Twitter about man-made and physical data. Furthermore, the size of each community, expressed as a percentage of the total number of vertices, is identified for the six particular algorithms, and corresponding results are extracted. In terms of user-based evaluation, we indicated to some students the communities that were extracted by every algorithm, with a corresponding user and their tweets in the grouping and considered three different alternatives for the extracted communities: “dense community”, “sparse community” and “in-between”. Our findings suggest that the community-detection algorithms can assist in identifying dense group of users.

Type of Medium: Online Resource

ISSN: 2078-2489

URL: Article

DOI: 10.3390/info13050209

Language: English

Publisher: MDPI AG

Publication Date: 2022

detail.hit.zdb_id: 2599790-7

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10

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Graph communities in Neo4j : Four algorithms at work

Drakopoulos, Georgios ; Gourgaris, Panagiotis ; Kanavos, Andreas

Springer Science and Business Media LLC ; 2020

In: Evolving Systems Vol. 11, No. 3 ( 2020-09), p. 397-407

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In: Evolving Systems, Springer Science and Business Media LLC, Vol. 11, No. 3 ( 2020-09), p. 397-407

Type of Medium: Online Resource

ISSN: 1868-6478 , 1868-6486

URL: Article

DOI: 10.1007/s12530-018-9244-x

Language: English

Publisher: Springer Science and Business Media LLC

Publication Date: 2020

detail.hit.zdb_id: 2561630-4

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