Intelligent Data Science Technologies and Applications (IDSTA), International Conference on
2024 Fifth International Conference on Intelligent Data Science Technologies and Applications (IDSTA)
24-27 Sept. 2024
Showing 1-25 of 44
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The proceedings of this conference will be available for purchase through Curran Associates.
62194 - IDSTA, 2024 (PRT)
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Table of Contents
AI Should be Augmented Intelligence
Data and Control in 5G Mobility Applications
Federated Learning: Catalyzing the Next AI Breakthrough
Parameter estimation of a method for noise reduction fusing a normal microphone and a bone conduction microphone
Publication Year: 2024,Page(s):4 - 11
This study proposes a method for setting a parameter used in a noise reduction method fusing a typical microphone and a bone conduction microphone. A bone conduction microphone is a microphone that captures sound by measuring vibrations of the objects directly. When human voice is detected, it is usually attached around the neck. Due to its feature, bone conduction microphones can record the sound...Show More
Optimizing Multi-Class Classification in Educational Data with Ensemble Learning and Data Balancing Techniques
Mohammad F. Al-Hammouri;Ziad Akram Ali Hammouri;Islam T. Almalkawi;Ansam Lafee
Publication Year: 2024,Page(s):12 - 17
The field of education is increasingly embracing AI tools to improve student outcomes. This work aims to reduce academic failure in higher education by employing machine learning techniques to identify at-risk students early in their educational journey, enabling the implementation of supportive strategies to assist them. This study examines a dataset from a higher education institution and utiliz...Show More
TAGIFY: LLM-powered Tagging Interface for Improved Data Findability on OGD portals
Publication Year: 2024,Page(s):18 - 27
Efforts directed towards promoting Open Government Data (OGD) have gained significant traction across various governmental tiers since the mid-2000s. As more datasets are published on OGD portals, finding specific data becomes harder, leading to information overload and so-called “dark data”. Complete and accurate documentation of datasets, including association of proper tags with datasets is key...Show More
Applying Long Short-Term Memory Networks to Model Elliott Wave Patterns for Improved Risk Management in High-Frequency Trading
Publication Year: 2024,Page(s):28 - 33
Predicting financial markets remains a critical yet challenging task due to their complex and dynamic nature. This paper introduces a novel approach that combines Elliott Wave Theory (EWT) with Long Short-Term Memory (LSTM) networks to enhance the accuracy and reliability of financial market predictions. Elliott Wave Theory, which hypothesizes that market prices unfold in recognizable patterns dri...Show More
Machine Learning-Driven Framework for Sentiment Analysis of Tweets
Publication Year: 2024,Page(s):34 - 43
Sentiment analysis has become increasingly pivotal across diverse fields such as politics, marketing, and social sciences, driven by the profound influence of public opinion on decision-making processes. This study advances sentiment analysis for Arabic, a language marked by its rich morphological structure and high surface shape variability, which poses significant challenges in text analysis. Em...Show More
Preventing customer churn, i.e., termination of business commitments, is essential for companies operating in saturated markets, especially for subscription-based models such as telecommunication. Knowing when customers decide to terminate services is instrumental to effective churn prevention. In this study, we investigate how churn prediction performs in practice when training models on differen...Show More
It is important to analyze online data whose characteristic changes over time, such as financial and coronavirus infection data. Many studies have been conducted. In general, ensemble-based learning methods perform well as analysis methods, and methods such as SEA, DWM, and ARF have been proposed. In addition, the change in characteristics over time is called concept drift, and its classification ...Show More
A Hybrid Surrogate Deep Learning Model for Actual Evapotranspiration Prediction
Amir Aieb;Alexander Jacob;Antonio Liotta;Muhammad Usman Liaqat
Publication Year: 2024,Page(s):60 - 67
The estimation of hydrological components on a spatiotemporal scale poses a challenge for researchers as they develop data-driven tools that can be transferred to different regions with varying characteristics. In this study, we propose a hybrid architecture of a surrogate deep learning (DL) model based on the data obtained by the Wflow estimation. The choice of the target region is based on exten...Show More
Reliable Leukemia diagnosis and localization through Explainable Deep Learning
Marcello Di Giammarco;Benedetta Dukic;Fabio Martinelli;Mario Cesarelli;Fabrizio Ravelli;Antonella Santone;Francesco Mercaldo
Publication Year: 2024,Page(s):68 - 75
Acute lymphoblastic leukemia is a cancer of the blood and bone marrow, it is the most common form of childhood cancer. In the United States, approximately 75% of people under age 20 diagnosed with leukemia are diagnosed with acute lymphoblastic leukemia. An estimated 400 people ages 15 to 19 in the US are diagnosed with the disease each year. In this paper we propose a Deep Learning network-based ...Show More
Evaluating the Robustness of ASR Systems in Adverse Acoustic Conditions
Publication Year: 2024,Page(s):76 - 80
The effectiveness of automatic speech recognition (ASR) systems in environments with acoustic challenges directly influences their utility in a range of voice-activated applications. This paper focuses on an experimental analysis of the resilience of various ASR models to acoustic disturbances — specifically white noise, reverberation, time stretch, and pitch shift — within the context of the Ital...Show More
Detection of Conflicts, Contradictions and Inconsistencies in Regulatory Documents: A Literature Review
Publication Year: 2024,Page(s):81 - 88
Companies create regulatory documents, such as policies, standards, and guidelines, to define their processes and structures. These documents are used by employees to ensure the proper execution of processes and by auditors to check the actual compliance with the respective requirements. Especially in large organizations, these documents are constantly updated, which can lead to conflicts, inconsi...Show More
Line Simplification for Efficient Approximate Join Queries On Big Geospatial Data
Fatima Ahmed Alhammadi;Haya Almadhloum Alsuwaidi;Shooq Abdelrahman Alzarooni;Isam Mashhour Al Jawarneh
Publication Year: 2024,Page(s):89 - 94
Line simplification algorithms are often used to render high-resolution geographic features at appropriate resolutions when applied to polygons. They are generalization techniques in which selective vertices are removed from a line feature to eliminate details whilst preserving the line’s basic shape. In this paper, two different line simplification algorithms (Douglas-Peucker and Visvalingam-Whya...Show More
Hand-eye coordination is crucial for performing tasks like reaching for objects in both humans and robots. This paper investigates two methods—visual servoing and deep reinforcement learning (RL)—to achieve hand-eye coordination in robotic systems. Visual servoing typically leverages video tracking and online Jacobian learning to control the robot based on camera-robot geometry, while RL uses neur...Show More
Medical Image-to-Image Translation with Spatial Self-Attention for Radiotherapy in Federated Learning
Tariq Bdair;Hiba Saadeh;Ban Qaqish;Aya Sulaq;Majdi Rawashdeh
Publication Year: 2024,Page(s):103 - 110
Globally, cancer remains a leading cause of death, affecting millions of people each year. Accurate medical imaging is crucial for the effective planning of radiotherapy. However, repeated exposure to radiation from Computed Tomography (CT) scans during treatment planning can put patients at more risk. Fortunately, the recent improvement in automated image-to-image translation using deep learning ...Show More
Dynamic Threshold-Based Anomaly Detection in Photovoltaic Generation Time Series Using Statistical Methods
Michelle Melo Cavalcante;João Lucas de Souza Silva;Tárcio André Dos Santos Barros
Publication Year: 2024,Page(s):111 - 117
The efficient operation of photovoltaic (PV) plants requires continuous monitoring to identify and correct anomalies that may affect the performance and lifespan of the equipment. However, some challenges include defining which data can be collected and useful from the installation to identify anomalies, as well as which models can be applied. Therefore, this paper proposes an approach for anomaly...Show More
Proceedings
The proceedings of this conference will be available for purchase through Curran Associates.
62194 - IDSTA, 2024 (PRT)
- Print on DemandPurchase at Partner
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