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2024 International Conference on Advances in Electrical and Communication Technologies, ICAECOT 2024

作者： Cheriet, Nadhir Belazoui, Abdelouahab University of Batna 2 Department of Computer Science Batna Algeria University of Batna 2 Lamie Laboratory Department of Computer Science Batna Algeria

ISBN: (纸本)9798350353754

In recent years, technology-enhanced learning has affected the whole educational process by introducing innovative learner-centric tools;among them are educational recommender systems. It helps to access educational resources on the web in a flexible, suggestion-based way. This paper focuses on improving the accuracy of recommendations by proposing a novel hybrid approach based on the aggregation of several techniques and models. The proposed approach is grounded in the federated learning model, which involves replicating servers running different recommendation techniques and setting up a coordination mechanism to achieve consensus among them. Finally, it's obvious that the proposed approach holds promise for overcoming the main recommendation hurdles, such as cold starts and data sparsity, and consequently improving its accuracy. © 2024 IEEE.

关键词： Federated learning

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Sentiment Analysis of Algerian Dialect Using Machine Learning and Deep Learning with Word2vec

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Informatica (Slovenia) 2022年第6期46卷 67-78页

作者： Mazari, Ahmed Cherif Djeffal, Abdelhamid LSEA Laboratory Mathematics and Computer Science Department University of Médéa Algeria LESIA Laboratory Computer Science Department University of Biskra Algeria

This paper deals with the issue of sentiment analysis on dialectal comments extracted from social media. These comments concern the Algerian spoken language, written in Arabic and/or Latin characters, which could be either Modern Standard Arabic, French or local dialect. This complexity gives rise to a large number of text processing issues. The contributions of this work are fourfold. First, the building of the Algerian dialect sentiment dataset of 11760 comments collected from diverse social media platforms. Second, the creation of the Skip-Gram and CBOW models by word2vec from a corpus containing 466424 comments, these latter are used to enhance the sentiment dataset by semantically similar words. Third, the proposal of a set of preprocessing steps adapted to deal with dialectal texts. Finally, implementation and testing of different machine learning classifiers (SVM, Naive Bayes via its three variants (Bernoulli NB, Gaussian NB and Multinomial NB)) and two deep learning architectures (CNN, RNN) to evaluate and compare the dataset in original version, in a transcribed to Latin character version and then in a semantically-enhanced version by word2vec models. Experiments reach performances of sentiment classifiers applied on "dataset transcribed to Latin characters" of accuracies = (MNB:84.21%, CNN:64.11%) and on "transcribed dataset and enhanced by word2vec models" of accuracies = (SVM:83.70%, RNN:65.21%). © 2022 Slovene Society Informatika. All rights reserved.

关键词： Sentiment analysis

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SDSL: Safe Drive Safe Live Application for IoV 4

SDSL: Safe Drive Safe Live Application for IoV

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4th International Conference on Embedded and Distributed Systems, EDiS 2024

作者： Djaidi, Malika Zerrouki, Hayet Bentitraoui, Mohamed Moncef University of Sciences and Technology Department of Computer Science Algiers Algeria University of Sciences and Technology Department of Computer Science Riima Laboratory Algiers Algeria

ISBN: (纸本)9798331519872

The Internet of Vehicles can include systems that monitor the health of drivers in real-time, detect signs of fatigue or impairment, and provide alerts to ensure safe driving conditions. With the increase in road accidents, attention is given to healthcare management in the IoV. In addition, the health of individuals, particularly drivers, can be the cause of a road accident. This is why we are interested in the detection of anomalies, in particular cardiovascular problems. In this paper, we propose a new system called Safe Drive Safe Live (SDSL) that is able to detect healthcare anomalies in any vehicle and manage them rapidly. The purpose of SDSL is to save drivers' lives and avoid accidents. The efficiency of the proposed system is evaluated through various simulations using a real map. The simulation results show that the proposed scheme is effective, useful, and feasible. © 2024 IEEE.

关键词： Highway accidents

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A Method based on Multi-agent Systems and Passive Replication Technique for Predicting Failures in Cloud Computing

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Recent Advances in computer science and Communications 2023年第1期16卷 18-32页

作者： Hioual, Ouided Hioual, Ouassila Hemam, Sofiane Mounine Maifi, Lyes Mathematics and Computer Science Department Science and Technology Abbes Laghrour University Khenchela Algeria Mathematics and Computer Science Department Science and Technology Abbes Laghrour University Lire laboratory of Constantine 2 Khenchela Algeria Algiers Algeria

Background: Cloud computing refers to the computing capacities of remote computers, where the user has considerable computing power without having to own power units. The probability of failures, which can occur during execution, increases in the number of nodes. Since failures cannot be completely avoided, one solution is to use failure tolerance mechanisms. Predicting failures has become a major task for engineers and software developers, as failure increases resource usage costs. Objectives and Methods: This paper presents a hybrid method of predicting failures in a cloudcomputing environment based on the passive replication technique and multi-agent systems. It detects failures, improves the average response time and minimizes lost time. This method makes it possible to efficiently and transparently guarantee the continuity of cloud computing services in the presence of failures. Results: The results show that the proposed method performs well in the presence of failures, improves the response time and minimizes the additional costs caused by the failures. Conclusion: This paper proposes a hybrid method of predicting failures in cloud-computing based on the passive replication technique and multi-agent systems to detect failures and minimize lost time. The replication technique works by duplicating some system components, which are deployed simultaneously across different resources. This technique aims to make the system robust, increase availability and guarantee the execution of jobs. In addition, it is suitable for long-running tasks. © 2023 Bentham science Publishers.

关键词： Multi agent systems

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Combined deep convolutional neural networks for abnormality classification in wireless capsule endoscopy images

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Multimedia Tools and Applications 2025年 1-29页

作者： Garbaz, Anass Lafraxo, Samira Charfi, Said El Ansari, Mohamed Koutti, Lahcen Salihoun, Mouna LabSIV Department of Computer Science Faculty of Sciences Ibn Zohr University Agadir Morocco Informatics and Applications Laboratory Department of Computer Science Faculty of Sciences My Ismail University Meknes Morocco Faculty of Medicine and Pharmacy of Rabat Mohammed V University Rabat Morocco

A digestive disease is a condition that affects the gastrointestinal (GI) tract, ranging from mild to severe. Common issues include heartburn, cancer, irritable bowel syndrome, and lactose intolerance. Additionally, other intestinal concerns such as bleeding, polyps, celiac disease, Crohn’s disease, ulcers, diverticulitis, small bowel syndrome, and intestinal ischemia represent further digestive illnesses. Wireless capsule endoscopy (WCE) is a device designed for endoscopic exploration that employs a compact camera to capture images of the GI tract as it passes through. WCE significantly aids in the identification of GI tract disorders. However, due to the capsule’s rapid transit and the potential for blurry, ambiguous images, abnormalities in certain regions of the intestine may be overlooked. Moreover, frames can become hazy if obstructed by food or stool waste. In this research, we propose a method for detecting abnormalities in WCE images to address these limitations. To achieve high classification performance through the extraction of both high-level and low-level features, we employ a deep CNN that integrates the DenseNet121 model with a residual block. Experimental studies demonstrate that our proposed method outperforms state-of-the-art techniques on two publicly available WCE datasets, one focused on bleeding and the other on various abnormalities. The accuracy of the proposed architecture is 96.96% for the Kvasir-Capsule Dataset and 99.50% for the MICCAI 2017 dataset. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Endoscopy

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Three-dimensional Target Detection Algorithm for Dangerous Goods in CT Security Inspection

Three-dimensional Target Detection Algorithm for Dangerous G...

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2023 International Conference on Algorithm, Imaging Processing, and Machine vision, AIPMV 2023

作者： He, Jingze Guo, Yao Song, Qing Department of Computer Science and Technology Tsinghua University Beijing China Pattern Recognition and Intelligent Vision Laboratory Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781510672444

In this paper, a 3D dangerous goods detection method based on RetinaNet is proposed. This method uses the bidirectional feature pyramid network structure of RetinaNet to extract multi-scale features from point cloud data and trains the system using Focal Loss function to achieve fast and accurate detection of dangerous goods. In addition, in order to improve the detection accuracy, this paper also introduces the 3D region proposal network (3D RPN) and non-maximum suppression (NMS) algorithm. The experimental results show that the proposed method performs well on our self-built CT dataset, with high accuracy and low false positive rate, and is suitable for dangerous goods detection tasks in practical scenarios. © 2024 SPIE.

关键词： computerized tomography

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A survey on text-line segmentation process in historical Arab manuscripts 6

A survey on text-line segmentation process in historical Ara...

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6th International Hybrid Conference on Informatics and Applied Mathematics, IAM 2023

作者： Djaghbellou, Soumia Abdelouahab, Attia Abderraouf, Bouziane Department of Computer Science University of Bordj Bou Arreridj Algeria MSE Laboratory Department of Computer Science University of Bordj Bou Arreridj Algeria

The segmentation process entails dividing or decomposing the entire document image into segments or lines. This technique serves as a fundamental step in developing any writing or optical character recognition system. However, numerous existing segmentation schemes encounter challenges when dealing with specific script styles, like ancient or historical Arabic writing found in ancient manuscripts. which possesses unique characteristics. These characteristics include inclined text lines, overlapping letters, diacritic marks, decorative elements, variable letter forms, and ligatures (combinations of two or more letters merged to form a single connected shape). Thus, in this paper we present a thorough survey of the field. The survey is composed of two segments. The first segment provides a concise overview of the historical Arabic documents. The second, which serves as the primary segment, focuses on the crucial step of handwritten document recognition, specifically segmentation. A detailed and systematic overview of the various approaches to segmentation, including different levels, employed for extracting handwritten Arabic text-lines, is outlined. Subsequently, a literature study is conducted to review and analyze proposed works in this area. © 2023 Copyright for this paper by its authors.

关键词： Image segmentation

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Hybrid Flow Shop with Setup Times Scheduling Problem

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computer Systems science & Engineering 2023年第1期44卷 563-577页

作者： Mahdi Jemmali Lotfi Hidri Department of Computer Science and Information College of Science at ZulfiMajmaah UniversityMajmaah11952Saudi Arabia Mars Laboratory University of SousseSousseTunisia Department of Computer Science Higher Institute of Computer Science and Mathematics of MonastirMonastir UniversityMonastir5000Tunisia Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The two-stage hybridflow shop problem under setup times is addressed in this *** problem is *** the other hand,the studied problem is modeling different real-life applications especially in manufacturing and high *** this kind of problem requires the development of adapted *** this context,a metaheuristic using the genetic algorithm and three heuristics are proposed in this *** approximate solutions are using the optimal solution of the parallel machines under release and delivery ***,these solutions are iterative procedures focusing each time on a particular stage where a parallel machines problem is called to be *** general solution is then a concatenation of all the solutions in each *** addition,three lower bounds based on the relaxation method are *** lower bounds present a means to evaluate the efficiency of the developed algorithms throughout the measurement of the relative *** experimental result is discussed to evaluate the performance of the developed *** total,8960 instances are implemented and tested to show the results given by the proposed lower bounds and *** indicators are given to compare between *** results illustrated in this paper show the performance of the developed algorithms in terms of gap and running time.

关键词： Hybridﬂow shop genetic algorithm setup times heuristics lower bound

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A Novel Ensemble Learning Approach for Diabetes Prediction in Imbalanced Datasets 13

A Novel Ensemble Learning Approach for Diabetes Prediction i...

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13th International Conference on Research in ComputIng at Feminine, RIF 2024

作者： Boughareb, Djalila Bouteldja, Said Seridi, Hamid Department of Computer Science LabStic Laboratory University 8 May 1945 Guelma Algeria Department of Computer Science University of 8 May 1945 Guelma Algeria

The incapacity of the body to effectively make or use insulin results in diabetes, a chronic illness. Over time, this illness may cause harm to the kidneys, blood vessels, heart, eyes, nerves, and kidneys. Timely treatment is essential to stop the progression of diabetes and requires early detection. We provide a hybrid machine learning strategy in this work that predicts diabetes by combining two strong algorithms. XGBoost (eXtreme Gradient Boosting)-based voting classifier and bagging classifier are the two main components of our system. We evaluated our model using three distinct datasets: the Pima Indian diabetes dataset (PIDD), its extended version, and the Frankfurt Hospital Germany Diabetes Dataset (FHGDD). In comparison to individual algorithms (XGBoost, Bagging with Decision Tree) and other ensemble methods (Voting Classifier, HM-Bag Moov Voting Classifier, XGBoost+ Data feature stitching, and Soft), our experimental results show that our proposed approach achieved higher accuracy of 92.7%, precision of 97.1%, recall of 81.7%, and an F1 score of 88.7%%. Therefore, our results imply that the hybrid machine learning approach that has been suggested can be a dependable tool for the early diagnosis of diabetes, resulting in better patient outcomes and more prompt and efficient therapies. © 2024 Copyright for this paper by its authors.

关键词： Bagging Decision Tree Diabetes Prediction Ensemble Learning FHGDD Machine Learning Pima XGBoost

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Building Automatic Speech Recognition Systems for Moroccan Dialect: A Phoneme-Based Approach

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SN computer science 2024年第6期5卷 721页

作者： Ezzine, Abderrahim Laaidi, Naouar Zealouk, Ouissam Satori, Hassan Department of Computer Science and Mathematics Faculty of Sciences Dhar Mahraz Sidi Mohamed Ben Abbdallah University Laboratory of Computer Science Signals Automation and Cognition (LISAC)

Building efficient acoustic models for dialects is a major challenge in Automatic Speech Recognition (ASR) systems. In this paper, we investigate the Moroccan Fessi dialect speech recognition system based on phoneme modeling. We employed a combined approach, including the Hidden Markov Model (HMM) and the Gaussian Mixture Model (GMM). Also, the ASR dialect specificity was analysed, including phonemes nature and phonetic inventory. Our results show the best performance was found by using 3 HMM and 4 GMM configurations, achieving an accuracy of 97.33%. Additionally, we observed that the digits containing voiced pharyngeal phonemes, particularly the phoneme /ʕ/, achieved the highest recognition rate, while words containing the phoneme /s/ exhibited multiple substitutions. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： HMM-GMM In-house corpus Machine learning Moroccan dialect Phoneme modeling Speech recognition

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