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24th International Arab Conference on Information Technology, ACIT 2023

作者： Moubayed, Abdallah Injadat, MohammadNoor Alhindawi, Nouh Samara, Ghassan Abuasal, Sara Alazaidah, Raed School of Computing and Augmented Intelligence Arizona State University GilbertAZ United States Lebanese American University Electrical and Computer Engineering Department Byblos Lebanon Western University Electrical and Computer Engineering Department LondonON Canada Zarqa University Faculty of Information Technology Data Science and Artificial Intelligence Department Zarqa Jordan Jadara University Faculty of Science and Information Technology Irbid Jordan Zarqa University Faculty of Information Technology Computer Science Department Zarqa Jordan Zarqa University Faculty of Information Technology Software Engineering Department Zarqa Jordan

ISBN: (纸本)9798350384307

Analyzing and evaluating students' progress in any learning environment is stressful and time consuming if done using traditional analysis methods. This is further exasperated by the increasing number of students due to the shift of focus toward integrating the Internet technologies in education and the focus of academic institutions on moving toward e-Learning, blended, or online learning models. As a result, the topic of student performance prediction has become a vibrant research area in recent years. To address this, machine learning and data mining techniques have emerged as a viable solution. To that end, this work proposes the use of deep learning techniques (CNN and RNN-LSTM) to predict the students' performance at the midpoint stage of the online course delivery using three distinct datasets collected from three different regions of the world. Experimental results show that deep learning models have promising performance as they outperform other optimized traditional ML models in two of the three considered datasets while also having comparable performance for the third dataset. © 2023 IEEE.

关键词： Deep Learning e-Learning Online Courses Student Performance Prediction

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A Dynamic Binding Method for Situation-aware IoT Services Targeting Proactive BPM 26

A Dynamic Binding Method for Situation-aware IoT Services Ta...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Li, Xiuxian Wang, Guiling Shi, Yongpeng Yu, Jian Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Beijing China North China University of Technology School of Information Science and Technology Beijing China Auckland University of Technology Department of Computer Science Auckland New Zealand

ISBN: (纸本)9798350331684

By leveraging IoT data, Business Process Management (BPM) systems can sense the physical world situation and make more accurate decisions proactively, but the current BPM systems lack the effective method to take good use of IoT data. In this paper, a situation-aware dynamic binding method for IoT services targeting proactive BPM is proposed to meet this requirement. The method first establishes a prediction model to predict the bindable IoT services under given situations by constructing an encoder-decoder structure network model using bi-directional gated recurrent units (Bi-GRU) and incorporating an attention mechanism. Then our method creates a Dynamic IoT Service Task (DIT) model for BPM. This enables the BPM system to integrate the prediction model with dynamic switching of IoT services and a parallel running architecture for multiple IoT services. A prototype system is developed and a case study is conducted to evaluate the performance of the method. The study focuses on the safety supervision scenario for the transportation of hazardous Liquefied Natural Gas (LNG) by sea. Results show that the prediction model proposed in this paper outperforms other models on multiple indicators. Also, the case study and experimental results verify the effectiveness of the method. © 2023 IEEE.

关键词： Dynamics

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Mathematical Information Retrieval Search and Question Answering

arXiv

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arXiv 2024年

作者： Zanibbi, Richard Mansouri, Behrooz Agarwal, Anurag Department of Computer Science Rochester Institute of Technology United States Department of Computer Science University of Southern Maine United States School of Mathematics & Statistics Rochester Institute of Technology United States

Mathematical information is essential for technical work, but its creation, interpretation, and search are challenging. To help address these challenges, researchers have developed multimodal search engines and mathematical question answering systems. This book begins with a simple framework characterizing the information tasks that people and systems perform as we work to answer math-related questions. The framework is used to organize and relate the other core topics of the book, including interactions between people and systems, representing math formulas in sources, and evaluation. We close by addressing some key questions and presenting directions for future work. This book is intended for students, instructors, and researchers interested in systems that help us find and use mathematical information. © 2024, CC BY-NC-SA.

关键词： Question answering

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Extremely Greedy Equivalence Search

arXiv

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arXiv 2025年

作者： Nazaret, Achille Blei, David Department of Computer Science Columbia University New York United States Department of Statistics Columbia University New York United States

The goal of causal discovery is to learn a directed acyclic graph from data. One of the most well-known methods for this problem is Greedy Equivalence Search (GES). GES searches for the graph by incrementally and greedily adding or removing edges to maximize a model selection criterion. It has strong theoretical guarantees on infinite data but can fail in practice on finite data. In this paper, we first identify some of the causes of GES’s failure, finding that it can get blocked in local optima, especially in denser graphs. We then propose eXtremely Greedy Equivalent Search (XGES), which involves a new heuristic to improve the search strategy of GES while retaining its theoretical guarantees. In particular, XGES favors deleting edges early in the search over inserting edges, which reduces the possibility of the search ending in local optima. A further contribution of this work is an efficient algorithmic formulation of XGES (and GES). We benchmark XGES on simulated datasets with known ground truth. We find that XGES consistently outperforms GES in recovering the correct graphs, and it is 10 times faster. XGES implementations in Python and C++ are available at https://***/ANazaret/XGES. © 2025, CC BY.

关键词： Directed graphs

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A Novel Machine Learning Classifier Based on Genetic Algorithms and data Importance Reformatting

arXiv

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arXiv 2024年

作者： Alkhayyat, A.K. Hewahi, N.M. Big Data Science and Analytics Program College of Science University of Bahrain Bahrain Department of Computer Science College of Information Technology University of Bahrain Bahrain

In this paper, a novel classification algorithm that is based on data Importance (DI) reformatting and Genetic Algorithms (GA) named GADIC is proposed to overcome the issues related to the nature of data which may hinder the performance of the Machine Learning (ML) classifiers. GADIC comprises three phases which are data reformatting phase which depends on DI concept, training phase where GA is applied on the reformatted training dataset, and testing phase where the instances of the reformatted testing dataset are being averaged based on similar instances in the training dataset. GADIC is an approach that utilizes the exiting ML classifiers with involvement of data reformatting, using GA to tune the inputs, and averaging the similar instances to the unknown instance. The averaging of the instances becomes the unknown instance to be classified in the stage of testing. GADIC has been tested on five existing ML classifiers which are Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Logistic Regression (LR), Decision Tree (DT), and Naïve Bayes (NB). All were evaluated using seven open-source UCI ML repository and Kaggle datasets which are Cleveland heart disease, Indian liver patient, Pima Indian diabetes, employee future prediction, telecom churn prediction, bank customer churn, and tech students. In terms of accuracy, the results showed that, with the exception of approximately 1% decrease in the accuracy of NB classifier in Cleveland heart disease dataset, GADIC significantly enhanced the performance of most ML classifiers using various datasets. In addition, KNN with GADIC showed the greatest performance gain when compared with other ML classifiers with GADIC followed by SVM while LR had the lowest improvement. The lowest average improvement that GADIC could achieve is 5.96%, whereas the maximum average improvement reached 16.79%. © 2024, CC BY.

关键词： Adversarial machine learning

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SCMix: Stochastic Compound Mixing for Open Compound Domain Adaptation in Semantic Segmentation

arXiv

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arXiv 2024年

作者： Yao, Kai Tan, Zhaorui Su, Zixian Yang, Xi Sun, Jie Huang, Kaizhu Department of Intelligent Science Xi'an Jiaotong-Liverpool University China Department of Computer Science University of Liverpool United Kingdom Data Science Research Center Duke Kunshan University China

Open compound domain adaptation (OCDA) aims to transfer knowledge from a labeled source domain to a mix of unlabeled homogeneous compound target domains while generalizing to open unseen domains. Existing OCDA methods solve the intra-domain gaps by a divide-and-conquer strategy, which divides the problem into several individual and parallel domain adaptation (DA) tasks. Such approaches often contain multiple sub-networks or stages, which may constrain the model's performance. In this work, starting from the general DA theory, we establish the generalization bound for the setting of OCDA. Built upon this, we argue that conventional OCDA approaches may substantially underestimate the inherent variance inside the compound target domains for model generalization. We subsequently present Stochastic Compound Mixing (SCMix), an augmentation strategy with the primary objective of mitigating the divergence between source and mixed target distributions. We provide theoretical analysis to substantiate the superiority of SCMix and prove that the previous methods are sub-groups of our methods. Extensive experiments show that our method attains a lower empirical risk on OCDA semantic segmentation tasks, thus supporting our theories. Combining the transformer architecture, SCMix achieves a notable performance boost compared to the SoTA results. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Liver-Tumor Detection Using CNN ResUNet

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computers, Materials & Continua 2021年第5期67卷 1899-1914页

作者： Muhammad Sohaib Aslam Muhammad Younas Muhammad Umar Sarwar Muhammad Arif Shah Atif Khan MIrfan Uddin Shafiq Ahmad Muhammad Firdausi Mazen Zaindin Department of Computer Science Government College University FaisalabadFaisalabad38000Pakistan Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripur22620Pakistan Department of Computer Science Islamia College PeshawarPeshawarKPPakistan Institute of Computing Kohat University of Science and TechnologyKohatPakistan Department of Industrial Engineering College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia Department of Statistics and Operations Research College of ScienceKing Saud UniversityRiyadh11421Saudi Arabia

Liver tumor is the fifth most occurring type of tumor in men and the ninth most occurring type of tumor in women according to recent reports of Global cancer statistics *** are several imaging tests like Computed Tomography(CT),Magnetic Resonance Imaging(MRI),and ultrasound that can diagnose the liver tumor after taking the sample from the tissue of the *** tests are costly and *** paper proposed that image processing through deep learning Convolutional Neural Network(CNNs)ResUNet model that can be helpful for the early diagnose of tumor instead of conventional *** existing studies have mainly used the two Cascaded CNNs for liver segmentation and evaluation of Region Of Interest(ROI).This study uses ResUNet,an updated version of U-Net and ResNet Models that utilize the service of Residential *** apply over method on the 3D-IRCADb01 dataset that is based on CT slices of liver tumor affected *** results showed the True Value Accuracy around 99%and F1 score performance around 95%.This method will be helpful for early and accurate diagnose of the Liver tumor to save the lives of many patients in the field of Biotechnology.

关键词： Liver tumor diagnose ResUNet CNNs segmentation

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Enhancing Robustness of Time Series Predictions with Deep Learning Techniques 2

Enhancing Robustness of Time Series Predictions with Deep Le...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Nithya, M. Sharma, Neeraj Bhuvaneshwari, K.S. Aravind, A.R. Rathi, Snehal R. Daivagna, Umesh.M. Department of Computer Science and Information Technology Karnataka Bangalore India Vivekananda Global University Department of Electrical Engineering Jaipur India Department of Artificial Intelligence and Data Science Karpagam College of Engineering Coimbatore641032 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Atlas SkillTech University Department of UGDX Maharastra Mumbai India

ISBN: (纸本)9798350308846

The use of deep studying techniques for making time collection predictions has multiplied whole, because of the truth that these techniques have proven to have excellent effects on obligations, which include type, object detection, and herbal language processing. This paper looks at how deep mastering methods can enhance the robustness and accuracy of time collection predictions. The authors explore various processes using extended short-term reminiscence networks, characteristic engineering, and switch learning to make more correct and robust predictions. They also examine the consequences of including noise in the dataset and combining deep getting to know architectures with different devicegaining knowledge of algorithms. Effects show that the deep getting-to-know techniques can lessen prediction mistakes, even if the facts are noisy or incomplete while outperforming conventional strategies. In conclusion, the authors indicate that those deeply gaining systems knowledge can make more fantastic and accurate time collection predictions. © 2023 IEEE.

关键词： Accurate Collection Greater Knowledge Strategies

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A Review of Machine Learning Algorithms in Diabetes Management 4

A Review of Machine Learning Algorithms in Diabetes Manageme...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Gayaki, Unnati Daronde, Subodh Tale, Abhay Barhate, Aditya Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha India Faculty of Engineering and Technology Department of Biomedical Engineering Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India

ISBN: (纸本)9798331523923

Diabetes is a prevalent and chronic disease affecting millions worldwide, posing significant challenges in its management and treatment. This review article aims to explore the current and potential future roles of machine learning (ML) in diabetes management, focusing on progress, applications, and challenges. It discusses various ML techniques, including support vector machines (SVM), logistic regression, decision trees, ensemble methods, and advanced deep learning approaches such as long short-term memory (LSTM) networks and convolutional neural networks (CNN). These methods have demonstrated benefits in analytics, patient monitoring, decision support systems, personalised treatment, and patient engagement. However, significant challenges persist, such as computational complexity, data quality, limited interoperability of systems, and ethical concerns regarding AI use in healthcare. The article addresses these challenges by fostering collaboration between AI implementers and health care professionals. This collaboration is essential to develop long-term, legally compliant, and solution-oriented frameworks that enhance patient care and diabetes management and pave the way for more efficient and ethical AI applications in healthcare. © 2025 IEEE.

关键词： Support vector machines

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computer Vision Based Automatic Margin Computation Model for Digital Document Images

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SN computer science 2023年第3期4卷 1-15页

作者： Guha, Abhijit Samanta, Debabrata Sengar, Sandeep Singh Department of Data Science CHRIST (Deemed to be) University Karnataka Bengaluru India R &D Scientist First American India Private Limited Karnataka Bengaluru India Department of Computational Information Technology Rochester Institute of Technology Germia Campus Prishtina Kosovo 10000 Department of Computer Science Cardiff Metropolitan University Cardiff CF5 2YB United Kingdom

Margin, in typography, is described as the space between the text content and the document edges and is often essential information for the consumer of the document, digital or physical. In the present age of digital disruption, it is customary to store and retrieve documents digitally and retrieve information automatically from the documents when necessary. Margin is one such non-textual information that becomes important for some business processes, and the demand for computing margins algorithmically mounts to facilitate RPA. We propose a computer vision-based text localization model, utilizing classical DIP techniques such as smoothing, thresholding, and morphological transformation to programmatically compute the top, left, right, and bottom margins within a digital document image. The proposed model has been experimented with different noise filters and structural elements of various shapes and size to finalize the bilateral filter and lines and structural elements for the removal of noises most commonly occurring due to scans. The proposed model is targeted towards text document images and not the natural scene images. Hence, the existing benchmark models developed for text localization in natural scene images have not performed with the expected accuracy. The model is validated with 485 document images of a real-time business process of a reputed TI company. The results show that 91.34 % of the document images have conferred more than 90 % IoU value which is well beyond the accuracy range determined by the company for that specific process. © 2023, Crown.

关键词： computer vision Digital image processing Margin detection Text localization

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