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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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Arabic Sentiment Analysis of Food Delivery Services Reviews

Arabic Sentiment Analysis of Food Delivery Services Reviews

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Mustafa, Dheya Khabour, Safaa M. Shatnawi, Ahmed S. Taqieddin, Eyad The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan Jordan University of Science & Technology Dept. of Software Engineering Irbid21110 Jordan School of Computing and Informatics Dept. of Cyber Security Al-Hussein Technical University Amman Jordan

ISBN: (纸本)9798350335590

Customer reviews on online platforms have grown to become an important source of insight into a company's performance. Food delivery services (FDS) companies aim to effectively use customers' feedback to identify areas for improvement of customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on FDS-related reviews in Arabic, exploiting natural language processing, and machine learning techniques to extract information that reflects customers' subjective viewpoints, recognize their feelings, and determine polarity in the FDS domain. This work begins with collecting the FDS dataset (Talabat), and then extracting and creating a dialects lexicon for Arabic dialects, followed by walking the reader through detailed steps of cleaning and pre-processing a manually annotated dataset. Finally, we examined classification algorithms including Decision Tree (DT), and Support Vector Machine (SVM). We achieved a maximum accuracy of about 82% using the SVM classifier. © 2023 IEEE.

关键词： Sentiment analysis

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Food Volume Prediction and Classification using Multi-Task Learning

Food Volume Prediction and Classification using Multi-Task L...

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Artificial Intelligence (WSAI), World Symposium on

作者： Yuita Arum Sari Atsushi Nakazawa Akio Gofuku Graduate School of Interdisciplinary Science and Engineering in Health Systems Okayama University Okayama Japan Faculty of Computer Science Brawijaya University Malang Indonesia

ISBN: (数字)9798350374162

ISBN: (纸本)9798350374179

Food volume prediction is critical for calculating calories in food analysis and nutritional evaluation. However, obtaining data and measuring volume from the 3D sensor camera concurrently is time-consuming. Additionally, high computing power is necessary to compute the volume. Therefore, machine learning techniques are employed to extract specific data collected from the data collection process for learning purposes to save time. In this study, we employ Multi-Task Learning to predict volume and categorize food due to their interdependence simultaneously. We evaluate several backbones and find that ResNet50 attains the best performance with an MAE of 4.87 for volume prediction and 100% food classification accuracy. Our proposed method showcases outstanding results, aided by an efficient volume measurement methodology,

关键词： Three-dimensional displays Accuracy Volume measurement Learning (artificial intelligence) Machine learning Data collection Multitasking Cameras Data mining Residual neural networks

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Special Issue on Robotics and Mechatronics Technology for Aerial Robots

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Journal of Robotics and Mechatronics 2024年第5期36卷 999-1000页

作者： Ohtake, Hiroshi Suzuki, Satoshi Takesue, Naoyuki Miwa, Masafumi Fujikawa, Taro Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Iizuka Fukuoka820-8502 Japan Faculty of Engineering Chiba University 1-33 Yayoi-cho Inage-ku Chiba Chiba263-8522 Japan Graduate School of Systems Design Tokyo Metropolitan University 6-6 Asahigaoka Hino Tokyo191-0065 Japan Graduate School of Technology Industrial and Social Sciences Tokushima University 2-1 Minamijosanjima-cho Tokushima Tokushima770-8506 Japan Graduate School of Science and Technology for Future Life Tokyo Denki University 5 Senju Asahi-cho Adachi-ku Tokyo120-8551 Japan

Aerial robot technologies, such as drones and unmanned aerial vehicles, are rapidly advancing in various domains. These robots now possess enhanced autonomous flying abilities and use sensor technology, Global Navigation Satellite System (GNSS), Light Detection and Ranging (LiDAR) system, and cameras to avoid obstacles, automatically land, and track flights. Evolving technologies in airspace management and route optimization ensure that flights are efficient and safe. Aerial robots have a wide range of applications, such as agriculture, disaster response, environmental monitoring, and inspection. These devices excel at precise data collection thanks to their high-resolution cameras and high-precision sensors. Swarm robotics, which allows collaboration between multiple aerial and ground robots, is also progressing. This enables quick coverage of large areas, which benefits disaster relief and land management. In addition, research and development of flying robots modeled after flying animals is underway. The evolution of aerial robots results in their efficient use in a variety of fields and new *** special issue includes the latest research papers and development reports on aerial robotic technology from the aforementioned perspectives. We hope that this special issue will pique researchers’ and engineers’ interest in aerial robots, thereby encouraging additional research and development. Finally, we would like to express our gratitude to all authors, all reviewers, the editorial board of the Journal of Robotics and Mechatronics, and Fuji Technology Press Ltd.

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Indoor Electromagnetic Radiation Intensity Relationship to Total Energy of Household Appliances

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computers, Materials & Continua 2022年第3期70卷 5421-5435页

作者： Murad A.A.Almekhlafi Lamia Osman Widaa Fahd N.Al-Wesabi Mohammad Alamgeer Anwer Mustafa Hilal Manar Ahmed Hamza Abu Sarwar Zamani Mohammed Rizwanullah Department of Electrical Engineering–Communication Engineering Sana’a UniversityYemen Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Information Systems King Khalid UniversityMayahel AseerKSA Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The rapid technological developments in the modern era have led to increased electrical equipment in our daily lives,work,and *** this standpoint,the main objective of this study is to evaluate the potential relationship between the intensity of electromagnetic radiation and the total energy of household appliances in the living environment within the building by measuring and analyzing the strength of the electric field and the entire electromagnetic radiation flux density of electrical devices operating at frequencies(5 Hz to 1 kHz).The living room was chosen as a center for measurement at 15 homes in three different environmental regions(urban,suburbs,and open areas).The three measurement methods are(Mode 1:people in a sitting position with electrical appliances *** 2:People in a standing position with electrical appliances *** 3:People are in the upright positionwhile turning off the electrical devices)in the living *** measurement methods and their results reinforce the importance of this *** results showed that the average electric field strengthmeasured inMode 2 ismuch greater than the two methods,and we also found less electromagnetic radiation in Mode 3 than in the two *** results remain within the recommended overall exposure developed by the International Committee for the Prevention of Non-Ionizing Radiation and the International Electrotechnical Commission.

关键词： Exposure assessment electromagnetic field household appliances indoor various environments epidemiology

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Dual Stream Deep Neural Network for Joint Sperm Morphology and Motility Estimation

Dual Stream Deep Neural Network for Joint Sperm Morphology a...

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Communication systems (ECCS), European Conference on

作者： Sigit Adinugroho Atsushi Nakazawa Graduate School of Interdisciplinary Science and Engineering in Health Systems Okayama University Okayama Japan Faculty of Computer Science Brawijaya University Malang Indonesia

ISBN: (数字)9798350377927

ISBN: (纸本)9798350377934

Morphology and motility are vital features to evaluate sperm quality, which is essential for estimating the success of assisted reproduction. Current standards from the World Health Organization (WHO) suggest that human operators are needed to perform sperm quality assessments. However, human assessment is proven to be subjective and leads to variability in the results. To cope with that problem, an automated approach is needed. This study aimed to perform a joint motility and morphology estimation via a single model that has never been done before. The model takes a stack of optical flow images and a grayscale image to be processed by motion and appearance stream. The outputs from each stream are combined and further processed by a regressor to estimate the morphology and motility. The proposed approach performed better than similar methods by achieving mean average errors of $\mathbf{4. 4 9 5 \%}$ and $\mathbf{7. 8 6 5 \%}$ for morphology and motility estimation, respectively.

关键词： Standards organizations Morphology Estimation Europe Organizations Streaming media Gray-scale Quality assessment Error correction codes Optical flow

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Trends and challenges in design of embedded BCH error correction codes in multi-levels NAND flash memory devices

Memories - Materials, Devices, Circuits and Systems

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Memories - Materials, Devices, Circuits and systems 2024年 7卷

作者： Nabipour, Saeideh Javidan, Javad Drechsler, Rolf Institute of Computer Science University of Bremen Bremen Germany Faculty of Electrical and Computer Engineering University of Mohaghegh Ardabili Ardabil Iran Cyber-Physical Systems DFKI GmbH Bremen Germany

Recently, there has been a growing concern regarding the dependability of NAND flash cells, notably as the scale of their features reduces. To address this issue, implementing error correction codes (ECC) proves to be an effective solution. Among the various methods, BCH coding has gained significant interest because of its exceptional error correction capabilities. Over the last decades, there has been much research on BCH decoder design to meet the demand for reduced hardware complexities, minimized delay performance, and lower power dissipation to enable BCH decoders and their VLSI implementations to facilitate different code lengths and rates of code. This paper surveys the trends and challenges associated with BCH decoder in NAND flash memory devices, the possible solutions for overcoming of time and area overhead in architecture of BCH decoder block and an examination of the extent to which present architectures will respond to the escalating requirements on data transfer rate, bit error rate (BER) performance, power consumption, and silicon area that will be essential for the extensive acceptance of BCH code in applications that will emerge in the near future. To demonstrate the need for such solutions, we present rigorous experimental data on BCH error correction codes on various types of flash memory errors, to motivate the need for such techniques. Based on the understanding developed by the experimental characterization, we describe several area-delay efficient techniques, including three low-latency decoding strategies for implementing the BCH decoder: pipeline method, re-encoding scheme, and parallelization method, and various hardware optimization strategies for the BCH decoder, such as three area-efficient syndrome block architectures, four error locator polynomial detection algorithms, and four error position identification algorithms using the Chien search method. We investigate the increase in reliability that each of these methods brings. We also

关键词： Bit error rate

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