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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Ayalew, Amogne Andualem Bitew, Gizachew Salau, Ayodeji Olalekan Tegegne, Melaku Lake Gupta, Shashi Kant Israr, Mohammad Tegegne, Tesfa Assegie, Tsehay Admassu Mizan-Tepi University School of Computing and Informatics Department of Information Technology Tepi Ethiopia Mizan Tepi University School of Computing Computer Science Department Mizan-Tepi Ethiopia Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Injibara University College of Engineering and Technology Department of Information Technology Injibara Ethiopia Eudoxia Research University Department of Computer Science and Engineering United States Maryam Abacha American University of Nigeria Hotoro Gra Kano State Nigeria Bahir Dar University Faculty of Computing Bahir Dar Institute of Technology Bahir Dara Ethiopia Injibara University College of Engineering and Technology Department of Computer Science Injibara Ethiopia

ISBN: (纸本)9798350364699

Named entity recognition has emerged as a critical step in recognizing, classifying, and extracting the most significant information from unstructured text without human intervention. It is used in information retrieval, conversation systems, machine translation, data mining, and information extraction. However, the Awngi language lacks a NER system due to inadequate datasets. This study combines feature extraction techniques with supervised machine learning algorithms, including support vector machines, Naïve Bayes, and conditional random fields with a grid search algorithm parameter optimization. These supervised machine-learning models often have various hyperparameters that must be tuned to optimize the model's performance by automatically selecting the parameter. Furthermore, the dataset used for training and testing consists of 20,193 annotated Awngi tokens, collected from Amhara Media Corporation, Awngi Telegram, and Facebook pages. The proposed SVM with grid search algorithm approach provided a promising result in ANER with an accuracy of 92% compared with Naïve Bayes, conditional random field, and decision-tree supervised machine learning models. Finally, the experiment results show that the proposed parameter tuning-based model can detect and categorize Awingi-named entities and outperforms the performance accuracy without parameter adjustment. © 2024 IEEE.

关键词： Support vector machines

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When Does Sora Show:The Beginning of TAO to Imaginative Intelligence and Scenarios engineering

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 809-815页

作者： Fei-Yue Wang Qinghai Miao Lingxi Li Qinghua Ni Xuan Li Juanjuan Li Lili Fan Yonglin Tian Qing-Long Han School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Department of Electrical and Computer Engineering Indiana University-Purdue University IndianapolisIndianapolisIN 46202USA Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Virtual Reality Fundamental Research Laboratory Department of Mathematics and TheoriesPeng Cheng LaboratoryShenzhen 518000China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

DURING our discussion at workshops for writing“What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence”[1],we had expected the next milestone for Artificial Intelligence(AI)would be in the direction of Imaginative Intelligence(II),i.e.,something similar to automatic wordsto-videos generation or intelligent digital movies/theater technology that could be used for conducting new“Artificiofactual Experiments”[2]to replace conventional“Counterfactual Experiments”in scientific research and technical development for both natural and social studies[2]-[6].Now we have OpenAI’s Sora,so soon,but this is not the final,actually far away,and it is just the beginning.

关键词： something Intelligence replace

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Deep Reinforcement Learning for Resource Management in Blockchain-Enabled Federated Learning Network

IEEE Networking Letters

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IEEE Networking Letters 2022年第3期4卷 137-141页

作者： Hieu, Nguyen Quang Tran, The Anh Nguyen, Cong Luong Niyato, Dusit Kim, Dong In Elmroth, Erik University of Technology Sydney School of Electrical and Data Engineering SydneyNSW2007 Australia Nanyang Technological University School of Computer Science and Engineering Jurong West Singapore Phenikaa University Faculty of Computer Science Hanoi10000 Viet Nam Sungkyunkwan University Department of Electrical and Computer Engineering Seoul16419 Korea Republic of Umeå University Department of Computing Science Umeå901 87 Sweden

Blockchain-enabled Federated Learning (BFL) enables model updates to be stored in blockchain in a reliable manner. However, one problem is the increase of the training latency due to the mining process. Moreover, mobile devices have energy and CPU constraints. Therefore, the machine learning model owner (MLMO) needs to decide the data and energy that the mobile devices use for the training and determine the block generation rate to minimize the system latency and mining cost while achieving the target accuracy. Under the uncertainty of BFL, we propose to use deep reinforcement learning to find the optimal decisions for the MLMO. © 2019 IEEE.

关键词： Neural networks

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Dijkstra's Algorithm with Bellman-Ford for Shortest Path Discovery in Big Data 2

Dijkstra's Algorithm with Bellman-Ford for Shortest Path Dis...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Karthikeyan, M. Sekar, Satheeshkumar Basha, H. Anwer Epsiba, P. Ganesh, E.N. Meenakshi, B. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Kattankulathur Tamil Nadu Chennai India Mphasis Corporation ChandlerAZ United States Saveetha College of Liberal Arts & Sciences Simats University Department of Computer Science Tamil Nadu Chennai India Sri Indu College of Engineering and Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Department of Electronics and Communication Engineering Avadi Tamil Nadu Chennai India Sri Sairam Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350368413

Many applications, from logistics to social networks, rely on quickly navigating the shortest pathways inside large-scale graphs. For shortest route finding in huge data settings, this study aims to compare Dijkstra's method with the Bellman-Ford algorithm. Applying these methods to large datasets will allow us to compare their speed, accuracy, and scalability. Graph density and negative weight cycles are two of the intrinsic properties of big data settings that will be used to determine which method is most suited to each. Optimal algorithm selection for given data situations is guided by insights into each algorithm's operational strengths and shortcomings, which are revealed via rigorous testing and performance metrics analysis. This comparison helps real-world applications of each method and provides valuable information for future advancements in large data processing shortest route calculation algorithms. In a study comparing Dijkstra's Algorithm and Bellman-Ford using the RandomGraph1 dataset, the results showed that for nodes 100-500, the former's ms value ranged from 5.2 to 52.3 and the latter's ms value ranged from 6.8 to 68.9. Similarly, for nodes 100-500, the MB value range for Dijkstra's Algorithm was 10.2-46.7 and for Bellman-Ford, it was 14.8-71.9. Data from a different SparseGraph1 dataset reveals that, for values between 100 and 500, Dijkstra's Algorithm has a percentage range of 98.5 to 99.8, while for Bellman-Ford, it varies from 97.2 to 99.3. © 2024 IEEE.

关键词： Graph algorithms

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Contrast enhancement of optical videoscope images

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Iran Journal of computer Science 2023年第3期6卷 261-275页

作者： Ammar, Reda Aboshosha, Sahar El-Hag, Noha A. Saifeldeen, Reda El-Shafai, Walid El-Banby, Ghada M. Ashiba, Huda I. Khalaf, Ashraf A. M. Elazm, Atef Abou El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. El-Safrawey, Amir Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Comm. Faculty of Engineering Minia University Minya Egypt Department of Management of Information Systems Higher Institute of Commercial Science El-Mahala El-Kobra Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Communications Engineering Bilbis Higher Institute of Engineering Sharqia Bilbis Egypt Security Engineering Laboratory Computer Science Department Prince Sultan University Riyadh 11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia

The videoscope (VS) images have poor quality and low contrast. Hence, in this paper, three proposed frameworks to improve the quality of VS images are presented. The first framework depends on contrast-limited adaptive histogram equalization (CLAHE) with adaptive gamma correction. The second framework depends on homomorphic processing. The third framework is based on the homomorphic enhancement with histogram equalization. The first one is superior from the entropy perspective, while the third one is superior from the contrast perspective. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Fuzzy enhancement Histogram equalization Homomorphic enhancement Spectral entropy

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Efficient Augmentation of Human Action Recognition Datasets with Warped Windows 26

Efficient Augmentation of Human Action Recognition Datasets ...

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26th International Conference on Knowledge-Based and Intelligent Information and engineering Systems, KES 2022

作者： Warchol, Dawid Oszust, Mariusz Department of Computer and Control Engineering Faculty of Electrical and Computer Engineering Rzeszów University of Technology W. Pola 2 Rzeszów35-959 Poland

In this paper, an approach to augment action recognition time series datasets, devoted to improving the accuracy of deep learning classifiers, is proposed. In the introduced method, two operators are sequentially introduced that perform linear and nonlinear modifications in the time scale of the input time series. The resulting data samples contribute to the variability within classes and allow a deep learning-based classifier to better capture their boundaries, leading to a significant improvement in the classification accuracy. The extensive experiments performed on eight publicly available action recognition datasets using the popular Bidirectional Long Short-Term Memory (BiLSTM) classifier reveal the superiority of the proposed algorithm over related approaches. © 2022 The Authors. Published by Elsevier B.V.

关键词： Time series

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MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN

MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Tiruveedula Gopi Krishna Nune Sreenivas M. Ajay Kumar Chandra Sekhar Reddy Davinder Sing Rathee Teklu Urgessa Department of Computer Science and Engineering College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia Department of Electrical and Electronics Engineering Andhra Loyala institute of Engineering and Technology Vijayawada India Department of Electrical Power and Control Engineering College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia Department of ECE College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The research presents a new efficient machine learning method to classify brain tumors because this task remains vital in fighting the high incidence of brain cancers. The proposed approach unites all its operations including image segmentation with feature extraction and classification into a single processing system. This study uses Particle Swarm Optimization (PSO) and Adaptive PSO (APSO) methodologies to optimize Locally Linear Radial Basis Function Neural Network (LLRBFNN) resulting in PSO-LLRBFNN and APSO-LLRBFNN model implementations. The research examined the optimized APSO-LLRBFNN models against Support Vector Machines (SVM) and Least Mean Squares (LMS) based LLRBFNN classifier methods. Results from experiments show that APSO-LLRBFNN gives superior outcomes with 98.93% accuracy and takes 12.23 seconds for execution, which surpasses currently available methods.

关键词： Support vector machines Image segmentation Adaptation models Accuracy Magnetic resonance imaging Machine learning Feature extraction Brain modeling Data models Numerical models

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Applying Customized Convolutional Neural Network to Kidney Image Volumes for Kidney Disease Detection

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computer Systems Science & engineering 2023年第11期47卷 2119-2134页

作者： Ali Altalbe Abdul Rehman Javed Department of Computer Science Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

Kidney infection is a severe medical issue affecting individuals worldwide and increasing mortality *** Kidney Disease(CKD)is treatable during its initial phases but can become irreversible and cause renal *** the various diseases,the most prevalent kidney conditions affecting kidney function are cyst growth,kidney tumors,and *** significant challenge for the medical community is the immediate diagnosis and treatment of kidney *** failure could result from kidney disorders like tumors,stones,and cysts if not often identified and ***-assisted diagnostics are necessary to support clinicians’and specialists’medical assessments due to the rising prevalence of chronic renal illness,the lack of experts,and the rising rates of assessment and monitoring,mainly in developing *** Intelligence(AI)approaches such as machine,and deep learning has been used in literature for kidney disease detection;however,they still lack *** paper implements a deep learning-based Convolutional Neural Network(CNN)model for the classification and prognosis of kidney *** use a benchmark Computed Tomography(CT)kidney dataset for *** data is pre-processed,and then CNN extracts the features from the *** reveal that the proposed approach accurately classifies kidney disease with a considerable accuracy of 0.992%,0.994%precision,0.982%recall,and 0.987%*** study suggests using the proposed fine-tuned CNN model for kidney disease detection.

关键词： Kidney disease convolutional neural network computed tomography feature extraction deep learning machine learning

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Privacy Preserved Brain Disorder Diagnosis Using Federated Learning

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computer Systems Science & engineering 2023年第11期47卷 2187-2200页

Federated learning has recently attracted significant attention as a cutting-edge technology that enables Artificial Intelligence(AI)algorithms to utilize global learning across the data of numerous individuals while safeguarding user data *** advanced healthcare technologies have enabled the early diagnosis of various cognitive ailments like Parkinson’*** user data is frequently used to train machine learning models for healthcare systems to track the health status of *** healthcare industry faces two significant challenges:security and privacy issues and the personalization of cloud-trained AI *** paper proposes a Deep Neural Network(DNN)based approach embedded in a federated learning framework to detect and diagnose brain *** extracted the data from the database of Kay Elemetrics voice disordered and divided the data into two windows to create training models for two clients,each with different *** lessen the over-fitting aspect,every client reviewed the outcomes in three *** proposed model identifies brain disorders without jeopardizing privacy and *** results reveal that the global model achieves an accuracy of 82.82%for detecting brain disorders while preserving privacy.

关键词： Privacy preservation brain disorder detection Parkinson’s disease diagnosis federated learning healthcare machine learning

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FusionAraSA: Fusion-based Model for Accurate Arabic Sentiment Analysis

FusionAraSA: Fusion-based Model for Accurate Arabic Sentimen...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Abdulfattah E. Ba Alawi Ahmed N. Nusari Ferhat Bozkurt İbrahim Yücel Özbek Computer Engineering Department Faculty of Engineering Ataturk University Erzurum Turkey Software Engineering Department Faculty of Engineering & IT Taiz University Taiz Yemen Electrical and Electronics Engineering Department Faculty of Engineering Ataturk University Erzurum Turkey Computer and Control Engineering Department Faculty of Engineering Sana’a University Sana’a Yemen Electrical and Electronics Department Faculty of Engineering Ataturk University Erzurum Turkey

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

The Arabic language is one of the most common spoken languages. Due to the complex structure and extensive range of potential meanings of Arabic texts, it is a challenging task to perform sentiment analysis using Natural Language Processing (NLP) approaches. Traditional methods of sentiment analysis often fail to perform Arabic sentiment analysis tasks. In this study, a fusion majority voting technique was employed to enhance the performance of Arabic sentiment analysis. The proposed method integrates four models (i.e. AraBERv1, MARBERT, Modified MARBERT, and AraBERv2 ) to analyze Arabic sentiments. Then, the outputs of these four models were forwarded to a fusion stage the perform majority voting and show the corresponding identified class. This technique is called FusionAraSA which relies on integrating four models and fusion to expand the application of pre-trained models. The FusionAraSA approach shows promising performance reaching more than 95% in terms of both accuracy and F1 Score utilizing two publicly available Arabic datasets. The results obtained using FusionAraSA illustrate the exceptional performance and the feasibility of this model in Arabic sentiment analysis. Additionally, the findings highlight the remarkable potential of the fusion model and underscore the necessity for further research to unravel the intricacies of Arabic dialects. Moreover, the FusionAraSA model represents a major step towards building more complex, culturally sensitive, and technologically advanced Arabic language analysis architecture.

关键词： Sentiment analysis Analytical models Accuracy Architecture Buildings Task analysis

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