• Synthetic data : revolutionizing the industrial metaverse 

  Nassif, Jimmy; Tekli, Joe; Kamradt, Marc (Springer Nature, 2024-01-03)

  The book concentrates on the impact of digitalization and digital transformation technologies on the Industry 4.0 and smart factories, how the factory of tomorrow can be designed, built, and run virtually as a digital twin ...
• How Visual Data Is Revolutionizing the Industry World 

  Unknown author (Springer, 2024-01-04)

  This chapter briefly describes how Industry 4.0 technologies have placed digital multimedia data at the center of organizations of all sizes and across many industries. It describes the potential of harnessing the power ...
• Fast Text Classification using Lean Gradient Descent Feed Forward Neural Network for Category Feature Augmentation 

  Attieh, Joseph; Tekli, Joe (IEEE, 2024-05-29)

  Text classification is a key task of the Natural Language Processing (NLP) field that aims at assigning predefined categories to textual documents. Performing text classification requires features that effectively represent ...
• Fuzzy Data Deduplication at Edge Nodes in Connected Environments 

  Yakhni, Sylvana; Tekli, Joe; Chbeir, Richard (Springer, 2023-08-03)

  The Internet of Things (IoT) is ushering-in the era of connected environments, i.e., networks of physical objects that are embedded with sensors and softwar, connecting and exchanging data with other devices and systems. ...
• Simulating Weather Events on a Real-world Map using Unity 3D 

  Noueihed, Hamza; Harb, Heba; Tekli, Joe (Springer, 2022-01)

  During the past decade, 3D simulation models have gained importance in the development of software solutions that aim to mimic real-world events and phenomena with increasing levels of accuracy and detail. In this paper, ...
• Low-Light Image Enhancement Using Image-to-Frequency Filter Learning 

  Al Sobbahi, Rayan; Tekli, Joe (Springer, 2022-05-17)

  Low-light image (LLI) enhancement techniques have recently demonstrated remarkable progress especially with the use of deep learning (DL) approaches. Yet most existing techniques adopt an image-to-image learning paradigm ...
• Unsupervised Extractive Text Summarization Using Frequency-Based Sentence Clustering 

  Hajjar, Ali; Tekli, Joe (Springer International, 2022-08-29)

  Large texts are not always entirely meaningful: they might include repetitions and useless details, and might not be easy to interpret by humans. Automatic text summarization aims to simplify text by making it shorter and ...
• Preprocessing Techniques for End-To-End Trainable RNN-Based Conversational System 

  Maziad, Hussein; Rammouz, Julie-Ann; El Asmar, Boulos; Tekli, Joe (Springer International, 2021-05-11)

  Spoken dialogue system interfaces are gaining increasing attention, with examples including Apple’s Siri, Amazon’s Alexa, and numerous other products. Yet most existing solutions remain heavily data-driven, and face ...
• Unsupervised Topical Organization of Documents using Corpus-based Text Analysis 

  Sarkissian, Sarkis; Tekli, Joe (The Association for Computing Machinery, 2021-11-09)

  This study aims at automating the process of topical keyword organization of set of documents in an input text corpus. It is conducted in the context of a larger project to investigate efficient unsupervised learning ...
• Almost Linear Semantic XML Keyword Search 

  Tekli, Joe; Tekli, Gilbert; Chbeir, Richard (The Association for Computing Machinery, 2021-11-09)

  Many efforts have been deployed by the IR community to extend free-text query processing toward semi-structured XML search. Most methods rely on the concept of Lowest Comment Ancestor (LCA) between two or multiple structural ...
• Depthwise Separable Convolutions and Variational Dropout within the context of YOLOv3 

  Chakar, Joseph; Al Sobbahi, Rayan; Tekli, Joe (Springer International, 2020-12-07)

  Deep learning algorithms have demonstrated remarkable performance in many sectors and have become one of the main foundations of modern computer-vision solutions. However, these algorithms often impose prohibitive levels ...
• Data Redundancy Management in Connected Environments 

  Mansour, Elio; Shahzad, Faisal; Tekli, Joe; Chbeir, Richard (The Association for Computing Machinery, 2020-11-16)

  Connected environments are typically defined as physical infrastructures (e.g., building) equipped with sensors that produce and exchange raw data. Although the sensed data is considered to contain useful and valuable ...
• Difficulties and Improvements to Graph-Based Lexical Sentiment Analysis Using LISA 

  Fares, Mireille; Moufarrej, Angela; Jreij, Eliane; Tekli, Joe; Grosky, William (IEEE, 2019-08-29)

  Lexical sentiment analysis (LSA) underlines a family of methods combining natural language processing, machine learning, or graph navigation techniques to identify the underlying sentiments or emotions carried in textual ...
• PIN Prototype for Intelligent Nutrition Assessment and Meal Planning 

  Salloum, George; Semaan, Elie; Tekli, Joe (IEEE, 2018-09-11)

  This paper briefly describes and evaluates Personal Intelligent Nutritionist (or PIN), a prototype system for intelligent nutrition assessment and meal planning. It aims to automate the two main services offered by a ...
• Upgraded SemIndex Prototype Supporting Intelligent Database Keyword Queries through Disambiguation, Query as You Type, and Parallel Search Algorithms 

  Tekli, Joe; Chbeir, Richard; Traina, Agma J.M.; Traina, Caetano; Yetongnon, Kokou; Ibanez, Carlos Raymundo (IEEE, 2018-09-11)

  This paper describes an upgraded version of the SemIndex prototype system for semantic-aware search in textual SQL databases. Semantic-aware querying has emerged as a required extension of the standard containment keyword-based ...
• MUSE Prototype for Music Sentiment Expression 

  Abboud, Ralph; Tekli, Joe (IEEE, 2018-09-11)

  This paper briefly describes and evaluates MUSE, a MUsical Sentiment Expression prototype system, taking as input a MIDI music file and producing as output a sentiment vector describing the 6 primary emotions (i.e., anger, ...
• Towards Multimedia Fragmentation 

  Saad, Samir; Tekli, Joe; Chbeir, Richard; Yetongnon, Kokou (Springer-Verlag, 2006)

  Database fragmentation is a process for reducing irrelevant data accesses by grouping data frequently accessed together in dedicated segments. In this paper, we address multimedia database fragmentation by extending existing ...
• Efficient XML Structural Similarity Detection using Sub-tree Commonalities 

  Tekli, Joe; Chbeir, Richard; Yetongnon, Kokou (Elsevier, 2007-01)

  Developing efficient techniques for comparing XML-based documents becomes essential in the database and information retrieval communities. Various algorithms for comparing hierarchically structured data, e.g. XML documents, ...
• Knowledge-based virtual outdoor weather event simulator using unity 3D 

  Noueihed, Hamza; Harb, Heba; Tekli, Joe (2022-01-24)

  During the past two decades, 3D simulation models have gained importance in the development of software solutions that aim to mimic real-world events and phenomena with increasing levels of accuracy and detail. In this ...
• Computer Science Education Facing Unconventional Odds: Case Studies from the Arab World 

  Aly, Sherif G.; Echihabi, Karima; Eldawlatly, Seif; Shuaib, Khaled; Tekli, Joe (2024-02-20)

  Computer Science has been rapidly evolving at unprecedented rates over the past decade. As curricular guidelines are being developed by the CS2023 ACM/IEEE/AAAI taskforce, this article attempts to emphasize how computer ...

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