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Multi-agent based Arabic speech synthesis

International Journal of Speech Technology 2024年第1期27卷 1-17页

作者： Tebbi, Hanane Hamadouche, Maamar Faculty of Electronics and Computer Science University of Science and Technology Houari-Boumediene Bab Ezzouar Algeria

Natural Language Processing (NLP) has many applications such as Speech recognition, Speech understanding, and Speech synthesis. Several approaches have been proposed in the literature in dealing with NLP. This paper describes an ongoing research project that tackles Speech Arabic Synthesis using multi-agent system techniques. The system consists of five modules (agents): the User Interface Agent (UIA), the Facilitator Agent (FA), the Preprocessing Agent (PPA), the Orthographic and Phonetic Transcription Agent, and the Speech Generation Agent. These agents are communicating with each other to construct agent sub societies representing the user input. All the agents are cognitive, work together, and communicate with the Knowledge-Base and the Sound Segments Database to generate Arabic speech signals. We used 800 Arabic sentences and asked 10 listeners with different levels of knowledge of the Arab language to accomplish the evaluation perception process. The system presents in general a Success Rate of 86% for the set of 800 tested sentences. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2022.

关键词： Knowledge based systems

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Neutrosophic Fuzzy Power Management (NFPM): Tackling Uncertainty in Energy Harvesting for Sensor Networks

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Neutrosophic Sets and Systems 2025年 76卷 99-114页

作者： Alzubi, Musallam M. Mohamed, Mohamed A. Amer, Hanan M. Salama, A.A. Electronics and Communications Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt

This paper presents a novel approach known as Neutrosophic Fuzzy Power Management (NFPM) aimed at addressing the critical challenge of uncertain energy availability in Energy Harvesting Sensor Networks (EHWSNs). The main objective of this research is to enhance the management of energy resources within these networks, which traditional fuzzy logic methods often fail to do, leading to power failures and reduced reliability. NFPM utilizes neutrosophic logic to effectively model uncertainty by representing the degrees of truth, indeterminacy, and falsity of both harvested and residual energy levels. Through a fuzzy inference system, NFPM dynamically allocates energy budgets for each time slot based on these neutrosophic sets, resulting in more adaptive and conservative energy distribution. The results are validated through numerical examples and extensive simulations, demonstrating NFPM's superiority over traditional fuzzy logic, with significant improvements such as a 25% reduction in power failures, 95% enhanced network connectivity, a 15% increase in data transmission success rates, and overall improvements in energy efficiency and robustness to fluctuations and noise. This research establishes NFPM as a promising solution to the uncertainties inherent in EHWSNs. Future research directions include exploring the integration of NFPM with machine learning algorithms for predictive energy management, assessing its scalability in larger networks, and examining its applicability in other domains requiring energy management under uncertainty. © 2025, University of New Mexico. All rights reserved.

关键词： Budget control

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Early Parkinson's Disease Detection and Classification using Machine Learning and Neutrosophic Sets

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Neutrosophic Sets and Systems 2025年 82卷 873-886页

作者： Oudah, Hasan H. Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed Nagm, Ahmad M. Salama, Ahmed S. College of Arts Department of Journalism Ahl Al Bayt University Karbala Iraq Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Engineering and Electronics Cairo Higher Institute for Engineering Computer Science and Management New Cairo Egypt

Optimizing therapy and rehabilitation for Parkinson's disease (PD) requires early identification and precise evaluation of the illness's course. However, there is disagreement about the best way to use gait analysis to categorize the severity of motor symptoms and identify early-stage Parkinson's disease. The precision of machine learning (ML) models in identifying early and intermediate stages of Parkinson's disease was assessed in this study. Six ML models are used in this study for the prediction of PD. Different metrics are used to evaluate ML models such as accuracy, precision, recall, f1-score, and AUC score. Then we propose a multi-criteria decision-making methodology (MCDM) to evaluate the ML models and select the best one. Two MCDM methods are used such as CRITIC method to compute the criteria weights and the TOPSIS method to rank the alternatives. These methods are used under the bipolar neutrosophic sets (BNSs) to deal with uncertainty information. The results show the support vector machine is the best ML model for the prediction of PD. © 2025, University of New Mexico. All rights reserved.

关键词： Neurodegenerative diseases

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Evolutionary optimization of Yagi–Uda antenna design using grey wolf optimizer

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Neural Computing and Applications 2025年第6期37卷 4155-4183页

作者： Braik, Malik Sheta, Alaa Aljahdali, Sultan El-Hefnawi, Fatma Al-Hiary, Heba Elashmawi, Walaa H. Computer Science Department Al-Balqa Applied University Salt Jordan Department of Computer Science Southern Connecticut State University New HavenCT United States Computer Science Department Taif University Taif Saudi Arabia Microwave Department Electronics Research Institute Giza Egypt Department of Computer Information System Al-Balqa Applied University Salt Jordan Department of Computer Science Faculty of Computers & Informatics Suez Canal University Ismailia Egypt Department of Computer Science Faculty of Computer Science Misr International University Cairo Egypt

Meta-heuristic optimization algorithms have become widely used due to their outstanding features, such as gradient-free mechanisms, high flexibility, and great potential for avoiding local optimal solutions. This research explored the grey wolf optimizer (GWO) to find the ideal configuration for a six-element Yagi–Uda antenna. The GWO algorithm adjusted the lengths of the antenna wires and the spacings between them. The goal was to maximize the antenna’s ability to transmit signals (throughput gain). Optimal antenna selection relies on various parameters, including gain, bandwidth, impedance matching, frequency, side-lobe levels, etc. The optimization of a six-element Yagi–Uda antenna presents a challenging engineering design problem due to its multimodal and nonlinear nature. Achieving optimal performance hinges on the intricate interplay between the lengths of the constituent elements and the spacing configurations. To this end, a multiobjective function was adopted to design this antenna. The performance of several meta-heuristic algorithms, including genetic algorithms, biogeography-based optimization, simulated annealing, and grey wolf optimizer, was compared. The GWO-based approach has performed better than its competitors. This optimized antenna design based on GWO reported a gain of 14.21 decibel. Therefore, the GWO-based method optimizes antennas that can be further investigated for other antenna design problems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Simulated annealing

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Fine-Tuned Attribute Weighted Naïve Bayes with Modified Partial Instances Reduction for Gaming Disorder Classification

HighTech and Innovation Journal

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HighTech and Innovation Journal 2025年第1期6卷 67-80页

作者： Latubessy, Anastasya Wardoyo, Retantyo Musdholifah, Aina Kusrohmaniah, Sri Doctoral Program Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Informatics Engineering Faculty of Engineering Universitas Muria Kudus Indonesia Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Psychology Faculty of Psychology Universitas Gadjah Mada Yogyakarta55281 Indonesia

Fine Tuning Attribute Weighted Naïve Bayes (FTAWNB) is a reliable modified Naïve Bayes model. Even though it is able to provide high accuracy on ordinal data, this model is sensitive to outliers. To improve the performance of FTAWNB, this research modified the Partial Instances Reduction (PIR) technique to make the FTAWNB more adaptive to outliers. Nevertheless, in contrast to the original PIR technique, which substitutes missing values for data values deemed outliers, the PIR technique suggested in this study replaces data values deemed outliers using a Naïve Bayes weighting approach. The attribute values from the outlier data are replaced with the highest probability values for the attributes in the actual class. This PIR technique is referred to as modified PIR. The FTAWNB model with modified PIR has been evaluated using the Gaming Disorder dataset. Replacing the four attributes with the least amount of information resulted in accuracy gains of 99.74%, an increase of 1.53% over the FTAWNB model. The experimental result shows that adding the modified PIR technique to the FTAWNB model can handle the outlier in the data, proving it by increasing the performance in terms of accuracy, precision, and recall without pruning the dataset used. © 2025, Ital Publication. All rights reserved.

关键词： Attribute Weighted Classification Fine-Tune Gaming Disorder Instances Reduction Naïve Bayes

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Enhancing the efficiency of lung cancer screening: predictive models utilizing deep learning from CT scans

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Neural Computing and Applications 2025年 1-19页

作者： Tawfeek, Medhat A. Alrashdi, Ibrahim Alruwaili, Madallah Shaban, Warda M. Talaat, Fatma M. Department of Computer Science Faculty of Computers and Information Menoufia University Shebin Elkom32511 Egypt Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt

Lung cancer is the most lethal form of cancer. This paper introduces a novel framework to discern and classify pulmonary disorders such as pneumonia, tuberculosis, and lung cancer by analyzing conventional X-ray and CT scan images called lung cancer risk prediction (LCRP) model. LCRP has four modules, namely data collection and preprocessing, data augmentation module, image segmentation module, and prediction module. Actually, LCRP employs three deep learning models;sequential model, functional model, and transfer model on publicly available training datasets. Convolutional neural networks (CNNs) have emerged as a highly effective field in machine learning, particularly for image datasets in the field of biomedical applications. The primary goal is to validate these models by comparing their performance with other models in order to determine their effectiveness in addressing challenging datasets. Our research has revealed a noteworthy enhancement in the efficiency of binary and multi-class classification using mask R-CNN image segmentation. During the model training process, a combination of Adam and stochastic gradient descent dual optimizers has been used to improve performance. LCRP have outperformed current pre-trained models by minimizing training parameters, computational costs, and overhead. It introduces 98.5% accuracy, 88.7% specificity, 89% sensitivity, 89.2% precision, and 89.09% F-measure. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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Automating Data Flow Diagram Generation from User Stories Using Large Language Models

Automating Data Flow Diagram Generation from User Stories Us...

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2024 Joint International Conference on Requirements Engineering: Foundation for Software Quality Workshops, Doctoral Symposium, Posters and Tools Track, and Education and Training Track, REFSQ-JP 2024

作者： Herwanto, Guntur Budi Faculty of Computer Science University of Vienna Austria Department of Computer Science and Electronics Universitas Gadjah Mada Indonesia

Visual modeling, particularly Data Flow Diagrams (DFDs), plays an essential role in modern software development, aiding in the design, understanding, and communication of system structures and potential security and privacy threats. Despite their importance, the manual creation of visual models is time-consuming highlighting the need for automation in the generation of DFDs from user requirements. Automating the generation of DFDs presents a significant challenge, especially in accurately interpreting user requirements and abstracting them into correct and complete diagram elements. The complexity of this task is compounded by the need for semantic accuracy and the ability to facilitate visual editing for human intervention. This study explores the use of Large Language Models (LLMs) to automate DFD generation, utilizing GPT-3.5, GPT-4, Llama2, and Mixtral models. This study emphasizes human oversight and employs an open-source diagramming tool to ensure that diagrams are accurate, complete, and editable. The findings reveal GPT-4’s superior capability in generating complete DFDs, with significant progress from open-source models like Mixtral, indicating a viable path toward automated visual modeling. This approach advances scalable automation in creating visual software models, with broader implications for automating other diagram types. © 2024 Copyright © 2024 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Automation

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Resilient TCP Variant Enabling Smooth Network Updates for Software-Defined Data Center Networks

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Tsinghua science and Technology 2024年第5期29卷 1615-1632页

作者： Abdul Basit Dogar Sami Ullah Yiran Zhang Hisham Alasmary Muhammad Waqas Sheng Chen Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China and also with Department of Informatics and SystemsUniversity of Management and TechnologyLahore 54660Pakistan Department of Computer Science Shaheed Benazir Bhutto UniversitySheringalUpper Dir 18050Pakistan School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha 61421Saudi Arabia Department of Computer Engineering Faculty of Information TechnologyUniversity of BahrainSakheer 32038Bahrainand also with School of EngineeringEdith Cowan UniversityPerth WA 6027Australia School of Electronics and Computer Science University of SouthamptonSouthamptonSO171BJUK

Network updates have become increasingly prevalent since the broad adoption of software-defined networks(SDNs)in data *** TCP designs,including cutting-edge TCP variants DCTCP,CUBIC,and BBR,however,are not resilient to network updates that provoke flow *** this paper,we first demonstrate that popular TCP implementations perform inadequately in the presence of frequent and inconsistent network updates,because inconsistent and frequent network updates result in out-of-order packets and packet drops induced via transitory congestion and lead to serious performance *** look into the causes and propose a network update-friendly TCP(NUFTCP),which is an extension of the DCTCP variant,as a *** are used to assess the proposed *** findings reveal that NUFTCP can more effectively manage the problems of out-of-order packets and packet drops triggered in network updates,and it outperforms DCTCP considerably.

关键词： software defined data center networks network updates DCTCP out-of-order packets packet drop SDN

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A comprehensive survey on weed and crop classification using machine learning and deep learning

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Artificial Intelligence in Agriculture 2024年第3期13卷 45-63页

作者： Faisal Dharma Adhinata Wahyono Raden Sumiharto Department of Computer Science and Electronics Faculty of Mathematic and Natural SciencesUniversitas Gadjah MadaYogyakartaIndonesia Faculty of Informatics Institut Teknologi Telkom PurwokertoCentral JavaIndonesia

Machine learning and deep learning are subsets of Artificial Intelligence that have revolutionized object detection and classification in images or *** technology plays a crucial role in facilitating the transition from conventional to precision agriculture,particularly in the context of weed *** agriculture,which previously relied on manual efforts,has now embraced the use of smart devices for more efficient weed ***,several challenges are associated with weed detection,including the visual similarity between weed and crop,occlusion and lighting effects,as well as the need for early-stage weed ***,this study aimed to provide a comprehensive review of the application of both traditional machine learning and deep learning,as well as the combination of the two methods,for weed detection across different crop *** results of this review show the advantages and disadvantages of using machine learning and deep ***,deep learning produced superior accuracy compared to machine learning under various *** learning required the selection of the right combination of features to achieve high accuracy in classifyingweed and crop,particularly under conditions consisting of lighting and early growth ***,a precise segmentation stage would be required in cases of *** learning had the advantage of achieving real-time processing by producing smaller models than deep learning,thereby eliminating the need for additional ***,the development of GPU technology is currently rapid,so researchers are more often using deep learning for more accurate weed identification.

关键词： Deep learning Machine learning Precision agriculture Weed control Weed detection

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Throughput Prediction Using LS-SVM For 4g-LTE Network and Beyond

Throughput Prediction Using LS-SVM For 4g-LTE Network and Be...

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2024 International Conference on Telecommunications and Intelligent Systems, ICTIS 2024

作者： Hamdani, Mostefa Sahnoune, Ahmed Merzougui, Mokhtar Douara, Abdelmalek Khatemi, Fouad University El Wancharissi Laboratory of Electronics Computer Science and Applied Mathematics Faculty of Science and Technology Department of Mathematics and Computer Science Algeria University El Wancharissi Laboratory of Electronics Computer Science and Applied Mathematics Faculty of Science and Technology Department of Science and Technology Algeria

ISBN: (纸本)9798331527396

The integration of Artificial Intelligence(AI) with Long-Term Evolution (LTE) networks offers substantial potential for improving communication infrastructure. By harnessing AI algorithms, it is possible to dynamically optimize network parameters, proactively predict and mitigate equipment failures, and enhance Quality of Service (QoS) by prioritizing critical services through real-time analysis of key performance indicators. This study focuses on predicting network throughput using Least Square Support Vector Machine (LS-SVM). The predictive models developed can trigger adaptive network reconfigurations, ensuring that the network responds promptly to changing conditions. Simulation results reveal that the integration of LS-SVM significantly improves prediction accuracy, enabling more effective implementation of self-organizing network strategies in real-time. © 2024 IEEE.

关键词： Long Term Evolution (LTE)

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