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1st International Conference of Intelligent Methods, systems and Applications, IMSA 2023

作者： Montasser, Reem Kadry Ismael, Osama El-Kholy, Moataz October University for Modern Sciences and Arts Faculty of Computer Science Software Engineering Department Giza Egypt Cairo University Faculty of Computers and Artificial Intelligence Information Systems Department Giza Egypt October University for Modern Sciences and Arts Faculty of Dentistry Oral Pathology Department Giza Egypt

ISBN: (纸本)9798350335569

Due to its simplicity of usage across a variety of applications, the K-Nearest Neighbor algorithm is usually utilized as a classification approach. The K-Nearest Neighbor algorithm's accuracy is greatly impacted by the occurrence of multidimensional and outlier data. This research uses a novel hybrid technique known as Particle Optimized Scored K-Nearest Neighbor (POSKNN) to build a model to predict the emergence of dental fluorosis. The proposed novel approach is implemented in two phases: the first phase helps in the resolution of multidimensional data by performing selection of feature using the Modified Particle Swarm Optimization algorithm, and the second phase helps in the resolution of the presence of outliers by making use of the results of the first phase and applying the newly proposed scored KNN technique to them. The suggested approach was tested using a real-world dataset acquired from three dentistry clinics in Egypt's El Baharia Oasis, which has 56 attributes, two classes, and 328 records. 10-fold cross validation was implemented to the model. The experimental evaluation was divided into two parts;evaluation for modified particle swarm optimization, evaluation for scored K nearest neighbor, and the hybrid particle optimized scored K nearest neighbor. The experiment's results demonstrated that the suggested approach outperformed the traditional KNN and the modified KNN. POSKNN achieved an accuracy of 94.5% when K value is 11. © 2023 IEEE.

关键词： Nearest neighbor search

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AraDS: Arabic Datasets for Text Mining Approaches

AraDS: Arabic Datasets for Text Mining Approaches

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Smart Computing and Application (ICSCA), International Conference on

作者： Wael M.S. Yafooz Abdullah Alsaeedi Abdel-Hamid M. Emara Computer Science Department College of Computer Science and Engineering Taibah University Medina Department of Computers and Systems Engineering Faculty of Engineering Al-Azhar University Cairo Egypt

With the digital transformation, life started depending on the digital world. Hence, there is a massive amount of unstructured textual data produced and accumulated faster. Such data used in many applications such sentiment analysis, topic modeling, summarization, classifications, and clustering. However, researcher's wastes time and effort in collecting data and constructing a dataset to examine and evaluate their models or algorithms. In the Universal Language of English, there are many benchmark datasets. The Arabic language lacks datasets in many domains. This paper introduces an Arabic dataset called AraDS. AraDS consist of three Arabic datasets namely; Arabic Dataset on Herbal Treatments for Diabetes (ADHTD), Arabic Multi-Classification Dataset (AMCD), and the Arabic Sentiment Dataset - Khat (ASDK). These datasets were collected from social media platforms such as YouTube. It contains user-generated comments and video metadata. AraDS publicly available dataset. The assessment of the annotation process has been carried out and evaluated using four methods. They are Cohen's kappa, three Arabic native speaker annotators, accuracy, and F-measure. AMCD and ASDK datasets are balanced, whereas ADHTD is an imbalanced dataset. Two datasets are sided with binary classes and the third one is a multi-class dataset. These datasets can be beneficial for many data, text mining, and sentiment analysis researchers to apply methods and algorithms.

关键词： Text mining Sentiment analysis Analytical models Video on demand Social networking (online) Video description Digital transformation

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Thermal Simulation for Magnetic Coupler of Wireless Power Transfer Electric Vehicles by Using Heat Sink and Thermoelectric Cooler

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Progress In Electromagnetics Research M 2024年 126卷 89-98页

作者： Farooq, Umar Shafique, Shahryar Asif, Muhammad Arslan, Muhammad Wongjom, Poramed Ullah, Rizwan Zhilenkov, Anton Mobayen, Saleh Pijitrojana, Wanchai Electrical and Computer Engineering Department Thammasat School of Engineering Thammasat University Rangsit Campus Klong Luang Pathum Thani 12120 Thailand Ministry of IT & Telecommunications Islamabad Pakistan Department of Electrical Engineering University of Science and Technology Main Campus Bannu23200 Pakistan Department of Mechanical Engineering University of Engineering and Technology Lahore Pakistan Division of Physics Faculty of Science and Technology Thammasat University Rangsit Campus Klongluang Pathum Thani 12120 Thailand Department of Electrical Engineering Chulalongkorn University Bangkok10330 Thailand Department of Cyber-Physical Systems St. Petersburg State Marine Technical University Saint-Petersburg190121 Russia Graduate School of Intelligent Data Science National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou640301 Taiwan

In challenging operational environments such as underground buildings beneath roadways, reliability and performance of wireless power transfer (WPT) systems for electric vehicles (EVs) heavily hinge on the operating temperature of the magnetic couplers. Addressing this, this study introduces a novel approach employing heat sink and thermoelectric cooler technologies to mitigate temperature rise in magnetic couplers, which is particularly crucial for high-power applications. Utilizing ANSYS simulation, the study evaluates a WPT high-power application coil model with a total output power of 2 KW and an 18 cm air gap, with a 3.5 cm adjacent alignment to enhance thermal performance on both transmitter and receiver sides. Results demonstrate significant thermal enhancement, reducing the temperature of coils from 63◦ C to 54◦ C solely with the heat sink and further down to 48◦ C with the combined implementation of both heat sink and thermoelectric cooler. These measures effectively dissipate heat from the coils into the surrounding air, ensuring system efficiency and stability while facilitating functionality of system components. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Heat sinks

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Influence of Local Lag on Reaction Force in Networked Virtual Environment with Haptic Sense

Influence of Local Lag on Reaction Force in Networked Virtua...

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computer Applications (ICCA), IEEE Conference on

作者： Khaing Zar Win Yutaka Ishibashi Khaing Htet Win Faculty of Computer Science University of Computer Studies Yangon Yangon Myanmar Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan Faculty of Computer Systems and Technologies University of Computer Studies Yangon Yangon Myanmar

ISBN: (数字)9798350353624

ISBN: (纸本)9798350353631

This paper investigates the influence of local lag on the reaction forces in a networked virtual maze system incorporating haptic feedback by experiment. In the experiment, participants move a box from the starting position to the target point by using Raising method with haptic device. We measure the average operation time and the average of average reaction force for three different moving velocities and three distinct local lags. Experimental results reveal that as the moving velocity increases and the local lag becomes higher, the two measures become larger. Thus, we illustrate that there exists a strong relationship among the average operation time, the average of average reaction force, moving velocity, and local lag, through multiple regression analysis.

关键词： Force measurement Force Virtual environments computer applications Particle measurements Control systems Time measurement

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Real-Time Data-Driven Maintenance Logistics: A Public-Private Collaboration

Real-Time Data-Driven Maintenance Logistics: A Public-Privat...

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2024 Commit2Data, C2D 2024

作者： van Jaarsveld, Willem Bliek, Laurens de Weerdt, Mathijs Kapodistria, Stella Pronk, Verus Verleijsdonk, Peter Voorberg, Simon Akçay, Alp da Costa, Paulo Eshuis, Rik Kaymak, Uzay van Houtum, Geert-Jan Verwer, Sicco Zhang, Yingqian Department of Industrial Engineering and Innovation Sciences Eindhoven University of Technology Netherlands Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Philips Research Laboratories Eindhoven Netherlands Department of Information Systems Supply Chain Management and Decision Analysis NEOMA Business School Rouen France Etsy Inc. Dublin Ireland Jheronimus Academy of Data Science Eindhoven University of Technology Netherlands

ISBN: (纸本)9783959773515

The project "Real-time data-driven maintenance logistics" was initiated with the purpose of bringing innovations in data-driven decision making to maintenance logistics, by bringing problem owners in the form of three innovative companies together with researchers at two leading knowledge institutions. This paper reviews innovations in three related areas: How the innovations were inspired by practice, how they materialized, and how the results impact practice. © Willem van Jaarsveld, Alp Akçay, Laurens Bliek, Paulo da Costa, Mathijs de Weerdt, Rik Eshuis, Stella Kapodistria, Uzay Kaymak, Verus Pronk, Geert-Jan van Houtum, Peter Verleijsdonk, Sicco Verwer, Simon Voorberg, and Yingqian Zhang.

关键词： Scheduled maintenance

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Route to Clean Cities: Waste Collection Optimization with UGVs

Route to Clean Cities: Waste Collection Optimization with UG...

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Electrical engineering (ICEENG), International Conference on

作者： Farah Ahmed Hoor Amr Marwan Khalil Maryam Mohamed Youssef Amir Ziad Yasser Jessica Magdy Omar M. Shehata Mechatronics Engineering Dept. Faculty of Engineering and Materials Science German University in Cairo Computer Science Dept. Faculty of Media Engineering and Technology German University in Cairo Multi-Robot Systems (MRS) Research Group Cairo

ISBN: (数字)9798331519018

ISBN: (纸本)9798331519025

Efficient waste collection is crucial to effective urban management, with far-reaching implications for environmental sustainability, public health, and overall quality of life in cities. As urban populations continue to grow, the demand for optimized waste management systems becomes increasingly critical. This study addresses the challenge of designing efficient waste collection routes using metaheuristic optimization techniques. Specifically, four different algorithms are employed to minimize route lengths, reduce operational costs, and improve the scalability of waste collection systems. These algorithms are rigorously evaluated in a variety of test configurations that simulate real-world scenarios. The results of the experiments reveal the strengths and limitations of each algorithm, providing valuable insights into their applicability for real-world waste management systems.

关键词： Waste management Electrical engineering Costs Smart cities Scalability Metaheuristics Green products Computational efficiency Artificial intelligence Public healthcare

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A Review of Multi-omics and Machine Learning: Advancements and Applications in Cancer Research

A Review of Multi-omics and Machine Learning: Advancements a...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Hazem Ayman Madonna Bassem Malak Madkour Mariam Fawzi Nada Amr Youmna Yousry Ziad Elgayar Ashraf Abdelraouf Department of Computer Engineering and Software Systems Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Computer Science Misr International University Cairo Egypt

This comprehensive review starts with diving into the progress and real-world applications of combining multi-omics data analysis with machine learning techniques in cancer research. Multi-omics involves examining various biological data types like genomics, transcriptomics, proteomics, and metabolomics together to enhance our understanding of complex biological systems. By merging machine learning with multiomics data, we highlight the advantages for cancer studies, the deeper insights they yield and increased performance and results. Furthermore, we explore existing literature that showcases the integration of multi-omics and machine learning in cancer research. As part of our study, we conduct an experiment utilizing a multiomics dataset to predict the survival of breast cancer patients. We compare three distinct machine learning methods-ensemble, DeepProg, and DCAP-for survival prediction and conclude that despite the ensemble method that increased the prediction results of DeepProg over DCAP in multi-model setting, but the primitive capacity for DCAP is better in single model setting and achieves higher accuracy than DeepProg with noticeable margin 0.628 to 0.57 on C-Index metric, which strongly recommends using Denoising Autoencoder as the base for dimensionality reduction over the vanilla Autoencoder. Another empirical results conclude that using gaussian mixture model with diagonal covariance matrix for Clustering, which is used in DeepProg, might hinder the process for identifying reasonable clusters due to the assumption of no or zero correlation between different features which might not hold true in our problem.

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Robust Sensor Fusion Algorithms for IoT

Robust Sensor Fusion Algorithms for IoT

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Subramanian, Sakthitharan Lokeshwari, Gandrakoti Jagadeesan, S. Leela, S. Pratheep, V.G. Venkatasudhahar, Murugesan School of Computing Faculty of Engineering and Technology Srm Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai603203 India Geethanjali College of Engineering and Technology Department of Cybersecurity Telangana Hyderabad501301 India Department of Computer Science and Engineering Tamil Nadu Erode638052 India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamil Nadu Coimbatore641202 India Kongu Engineering College Department of Mechatronics Engineering Tamil Nadu Perundurai638060 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Mechanical Engineering Tamil Nadu Chennai600062 India

ISBN: (纸本)9798331543617

Sensor fusion is a critical component of the IoT ecosystem, as it combines a variety of data streams from multiple sensors to enhance the accuracy, reliability, and real-time decision-making. This research concentrates on the development of robust sensor fusion algorithms that are capable of effectively managing the heterogeneity, noise, and uncertainty that are inherent in data from Internet of Things (IoT) sensors. Our primary focus is on the ability of these algorithmic methodologies to manage data from multi-modal sensors. We investigate algorithms that are based on deep learning, Bayesian inference, and Kalman filtering. The proposed solutions aim to improve the performance of systems in sectors that are influenced by the Internet of Things (IoT), including healthcare, smart cities, industrial automation, and autonomous cars. This investigation investigates the scalability, energy efficiency, and computational burden of the Internet of Things (IoT) in the context of limited resources. Through simulations and practical applications, we demonstrate the enhancement of data precision, defect tolerance, and overall system dependability by these robust sensor fusion techniques. © 2024 IEEE.

关键词： Kalman filters

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Bee Colony-Reptile Search Optimization Technique for Blood Cell Cancer Detection

Bee Colony-Reptile Search Optimization Technique for Blood C...

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International Conference on computer and Automation engineering, ICCAE

作者： Aziza I. Hussein Mohammed Ali Saleh Rabab Hamed M. Aly Energy and Technology Research Center College of Engineering Effat University Jeddah Saudi Arabia Computer and Systems Engineering Department Faculty of Engineering Helwan University Egypt Computer Science Department Faculty of Computer Science Nahda University Beni Suef Egypt The Higher Institute for Management and Information Technology Minya Egypt

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

The biosystem is a crucial system grounded in classification and detection, utilizing Artificial Intelligence (AI) approaches or metaheuristic techniques. Currently, cancer of the blood cells is among the deadliest cancers in the world. Acute lymphoblastic leukemia (ALL) is a cancer of blood cells that causes excessive proliferation of lymphocytes. It is extremely time-consuming and expensive to conduct diagnostic calculations. The number of platelets in a patient's blood is computed by a platelet count. A lacking number of platelets can indicate cancer, infection, or other health problems. A patient with too many platelets is at risk for blood strokes. A single drop of blood includes tens of thousands of platelets. The main goal of this paper is how to detect the features of blood cells and classify with predicting cancer type based on platelets analysis by using Bee Colony followed by Reptile Search Optimization (BCRSO) technique. According to the results, BCRSO algorithm performed better in terms of classification efficacy and accuracy rate than other algorithms. Based on simulation results, the proposed method is more effective than previously published research for classification optimization.

关键词： Accuracy Blood platelets Simulation Metaheuristics Cells (biology) Prediction algorithms Cancer detection Classification algorithms Artificial intelligence Cancer

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Fast Learning Network Algorithm for Voice Pathology Detection and Classification

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Multimedia Tools and Applications 2025年第17期84卷 18567-18598页

作者： Albadr, Musatafa Abbas Abbood Ayob, Masri Tiun, Sabrina AL-Dhief, Fahad Taha Al-Daweri, Muataz Salam Homod, Raad Z. Abbas, Ali Hashim Department of Petroleum Project Management College of Industrial Management of Oil and Gas Basrah University for Oil and Gas Al-Basrah Iraq Faculty of Information Science and Technology CAIT Universiti Kebangsaan Malaysia Selangor Bangi Malaysia Department of Communication Engineering School of Electrical Engineering Universiti Teknologi Malaysia UTM Johor Bahru Johor Malaysia Department of Information Systems College of Economics Management & Information Systems University of Nizwa Nizwa Oman Department of Oil and Gas Engineering Basrah University for Oil and Gas Al-Basrah Iraq Department of Computer Technical Engineering College of Information Technology Imam Ja’afar Al-Sadiq University Al-Muthanna Iraq

The utilisation of ML (Machine Learning) techniques in the detection of the VP (Voice Pathology) has recently gained a lot of consideration. However, these efforts still have several drawbacks such as: I) the accuracy rates of most previous works are still not promising and need more improvement. II) The majority of the previous works have concentrated on the task of VP detection only and disregarded the task of VP classification. III) Most of the previous works have been assessed based on a single voice data only like the vowel /a/, and the other vowels (/i/, and /u/) and sentences were disregarded. IV) The majority of the previous works performance were assessed utilising a limited evaluation metrics. Recently, one of the utmost effective ML techniques is FLN (Fast Learning Network), it is an efficient technique for data classification. However, the FLN classifier has not been implemented to the problem of VP detection and classification. Therefore, this research proposes the FLN classifier with MFCC (Mel-Frequency Cepstral Coefficient) features in order to enhance the accuracy of the VP detection and classification. The FLN classifier has the ability to a) solve both binary and multiclass classification issues, b) eliminate overfitting, as well as c) operate akin to a NN (Neural Network) structure while employing the principles of a kernel-based SVM (Support Vector Machine). In this research, the SVD (Saarbrucken Voice Database) dataset was utilised to assess the FLN classifier performance in the VP detection and classification. The assessment of the proposed FLN classifier was conducted based on two phases: the first phase includes all the voice samples of the SVD dataset with the sentences and vowels (i.e., /a/, /i/, and /u/) which are pronounced in neutral, high, and low pitches. While the second phase uses the voice samples of the utmost common 3 pathology types (i.e., paralysis, polyp, and cyst) based on the vowel /a/ that is pronounced in neutral pitch. The

关键词： Classification (of information)

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