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21st International Learning and Technology Conference, L and T 2024

作者： Hussein, Aziza I. Alamoudi, Nora Ali, Mirna Zakaria, Ahmed Mourad Mabrook, M. Effat University College of Engineering Energy and Technology Research Center Saudi Arabia Effat University Electrical and Computer Engineering Department Jeddah Saudi Arabia Telecom Egypt Telecommunication Engineer Asyut Egypt Beni-Suef University Faculty of Navigation Science & Space Technology Space Communication Dept. Egypt

ISBN: (纸本)9798350393569

Recently, the research emphasis has shifted towards 5G due to its potential to accommodate the increasing demand for data traffic, extensive interconnectivity of devices, and the emergence of numerous novel applications. Given the inadequacy of speed provided by spectrum resources in the lower frequency bands of 5G, a novel sub-generation utilizing millimeter wave frequencies has been introduced. The new sub-generation is called mm-wave 5G and provides higher bandwidth for faster speed and higher capacity. RF system architecture, circuits, and antenna innovation will be required to provide the requisite speed and capacity. In this paper the design challenges and trade-offs in RF front-end circuits and receiver sub-systems are discussed. Moreover, the massive multiple-input multiple-output (MIMO) techniques are examined. In addition, design, and simulation of key components in 5G mm-wave receiver, including the design of linear phased array antenna receiver and an analog-to-digital converter (ADC) is presented. Sigma-Delta (ΣΔM) Converters are a type of ADC with a technique that involves oversampling and shaping quantization noise to achieve a higher resolution which make it a best choice to be used in the design of the mm wave 5G communication applications. The proposed ΣΔM uses multistage architecture to provide high-order noise shaping and high resolution. The simulation results shows that the designed Cascade 2-2 MASH ΣΔ achieve a signal to noise ratio of 110(resolution of 16 bits). © 2024 IEEE.

关键词： Antenna phased arrays

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Grundy Packing Coloring of Graphs

arXiv

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arXiv 2024年

作者： Gözüpek, Didem Peterin, Iztok Department of Computer Engineering Gebze Technical University Turkey Faculty of Electrical Engineering and Computer Science University of Maribor Slovenia Institute of Mathematics Physics and Mechanics Ljubljana Slovenia

A map c: V (G) → {1, . . ., k} of a graph G is a packing k-coloring if every two different vertices of the same color i ∈ {1, . . ., k} are at distance more than i. The packing chromatic number χρ(G) of G is the smallest integer k such that there exists a packing k-coloring. In this paper we introduce the notion of Grundy packing chromatic number, analogous to the Grundy chromatic number of a graph. We first present a polynomial-time algorithm that is based on a greedy approach and gives a packing coloring of G. We then define the Grundy packing chromatic number Γρ(G) of a graph G as the maximum value that this algorithm yields in a graph G. We present several properties of Γρ(G), provide results on the complexity of the problem as well as bounds and some exact results for Γρ(G).MSC Codes 05C15, 05C12, 05C69 Copyright © 2024, The Authors. All rights reserved.

关键词： Coloring

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Digital Technology in Health Education Trend Mapping through Bibliometrics Analysis 4

Digital Technology in Health Education Trend Mapping through...

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4th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Maulana, Fairuz Iqbal Lestari, Dian Widartha, Vandha Pradwiyasma Haidar, Nirwana Dirgantara, Wahyu School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Airlangga University Faculty of Medicine Surabaya60132 Indonesia Telkom University Information System of Industrial Engineering Faculty Bandung Indonesia University of Madura Informatics Engineering Study Program Pamekasan Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9781665473279

This study examines the mapping of research data on digital technology in the field of health education using bibliometric analysis method. Data was collected by identifying keywords in the Scopus database and sorting them out to sort the studies from the last 10 years (2012-2021). Through this step, a total of 1482 documents were obtained (articles, journals, proceedings, books, and others). The data is then processed using the VOSViewer instrument to obtain a visualization of the mapping analysis. This study also analyzes the network types of authors and co-authors through the VOSViewer instrument. The result of this study indicates that the most document types published within ten years are Articles (66.5%), Review (22.9%), Conference Paper (4.2%), and others. The most studied subjects are Medicine (55.7%), Nursing (9.0%), Health Professions (8.2%), Social sciences (7.8%), engineering (3.9%), computer science (2.6%), Environmental science (2.6%), Biochemistry-Genetics and Molecular Biology (2.2%), Psychology (1.6%), and Dentistry (1.3%). This study offers a written communication process and the nature and direction of developing descriptive means of counting and analyzing the various phases of communication as well as recognizing the authorship and direction of its symptoms in documents on the subject of digital technology in the health sector. © 2022 IEEE.

关键词： Mapping

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Multimodal Learning for Integrated Sensing and Communication Networks

Multimodal Learning for Integrated Sensing and Communication...

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European Signal Processing Conference (EUSIPCO)

作者： Xiaonan Liu Tharmalingam Ratnarajah Mathini Sellathurai Yonina C. Eldar School of Engineering The University of Edinburgh UK Department of Engineering and Physical Science Heriot-Watt University Edinburgh UK Faculty of Mathematics and Computer Science Weizmann Institute of Science Israel

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Integrated sensing and communication (ISAC) is a promising technique for beyond 5G networks. In ISAC networks, the sensed environmental data may be multimodal data, which may result in high computation and communication latency due to the large size of data modalities and limited computation capability of mobile devices. To solve the problem, in this paper, we propose multimodal learning in ISAC networks. Simulation results show that the proposed multimodal learning design significantly outperforms several benchmarks without considering multimodal data sensing and communication.

关键词： Accuracy 5G mobile communication Simulation Europe Signal processing Benchmark testing Integrated sensing and communication Mobile handsets

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An Examination of Cybersecurity Threats and Authentication Systems 3

An Examination of Cybersecurity Threats and Authentication S...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Shakeel, Mohammed Lakshmana Rao, Chintala Shyam Prasad, T. Alam, Tanweer Rawat, Navneet Kavitha, R. Invertis University Faculty of Computer Applications Bareilly India Deemed to Be University School of Law Gitam Visakhapatnam India Anurag University Department of Cse Telangana Hyderabad India Islamic University of Madinah Faculty of Computer and Information Systems Department of Computer Science Madinah Saudi Arabia Graphic Era Deemed to Be University Department of Management Studies Uttarakhand Dehradun248002 India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode Sathyamangalam638401 India

ISBN: (纸本)9798350399264

Each and every nation in the globe has very crucial infrastructures that offer crucial services like internet activity, energy, banking and finance, crucial public services, transportation, and water management. For vital infrastructure based on sectors, each nation has a unique strategy. As IoT-based solutions proliferate, these crucial infrastructures now have network and Internet connectivity. Consequently, these crucial systems that are a part of information networks are equally vulnerable to online attacks. It is crucial to recognise the potential cyber-attack types, devise defence strategies, and implement various preventative measures. In the present day, it is critical to protect potential cyberattacks on these vital infrastructures. The most common attacks on crucial infrastructures are looked at in this study, especially those that have recently happened. Security precautions are also discussed with a view to reducing or preventing IP-based intrusions. The Internet of Things is the next step in machine-to-machine connectivity. IoT has made it possible for anything to be online nowadays. IoT also refers to the networking of uniquely identified, pervasive computing devices that have the ability to transmit data over a network without necessarily requiring or machine-to-human interaction. © 2023 IEEE.

关键词： Cybersecurity

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Intelligent Model to Predict Early Liver Disease using Machine Learning Technique

Intelligent Model to Predict Early Liver Disease using Machi...

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2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022

作者： Ghazal, Taher M. Rehman, Aziz Ur Saleem, Muhammad Ahmad, Munir Ahmad, Shabir Mehmood, Faisal Faculty of Information Science and Technology Ukm Network and Communication Technology Lab Center for Cyber Security Malaysia School of Computer Science NCBAandE Lahore Pakistan Gachon University Department of Computer Engineering Korea Republic of Gachon University Department of It Convergence Engineering Korea Republic of

ISBN: (纸本)9781665409209

Liver Disease (LD) is the main cause of death worldwide, affecting a large number of people. A variety of factors affect the liver, resulting in this disease. The diagnosis of this condition is both expensive and time-consuming. Machine Learning offers a lot of potential in terms of automated disease diagnosis. As a result, the purpose of this research is to assess the efficacy of various Machine Learning (ML) algorithms to lower the high cost of liver disease diagnosis through prediction. With the current rise in numerous liver disorders, it's more important than ever to detect liver disease early on. This research proposed intelligent model to predict liver disease using machine learning technique. This proposed model is more effective and comprehensive in terms of performance of 0.884 accuracy, and 0.116 miss-rate. © 2022 IEEE.

关键词： Forecasting

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Rural Automatic Healthcare Dispatch with Real-Time Remote Monitoring 2

Rural Automatic Healthcare Dispatch with Real-Time Remote Mo...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Ranganathan, Chitra Sabapathy Raman, Ramakrishnan Adhvaryu, Rachit Nandekar, Ujwal Prabhakar Srinivasan, C. Mphasis Corporation ChandlerAZ United States Maharashtra Pune India Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Gujarat Vadodara India Maharashtra Pune India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

Due to the drastically expanding use of the Internet of Things, remote monitoring of health data to provide intelligent healthcare has recently attracted much interest within the system. Health Chain is a massively-scale, blockchain-based privacy-preserving system for health data that uses encryption to provide fine-grained access control. The 24/7 Medical Counter is used in all rural areas when people have limited options to get in touch with an authorized medical professional. The proposed system is also connected to a camera, a headphone, an ultrasonic sensor, and a load cell. Additionally, set up a heart rate and temperature monitor. Utilized the medical counter and monitored from a distance. Both ends have a program installed that enables voice connection and doctor chat. Medication dispatchers provide medicine from Anytime Medical Machine (AMM) to users as doctors examine patients and write prescriptions. In order for the user to receive the precise time, they might make a request to the server. © 2023 IEEE.

关键词： Anytime Medical Machine Internet of Things Rural areas sensors Telehealth

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Cotton Leaf Diseases Recognition Using Deep Learning and Genetic Algorithm

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computers, Materials & Continua 2021年第12期69卷 2917-2932页

作者： Muhammad Rizwan Latif Muhamamd Attique Khan Muhammad Younus Javed Haris Masood Usman Tariq Yunyoung Nam Seifedine Kadry Department of Computer Science HITEC University TaxilaTaxilaPakistan Wah Engineering College University of WahWah Cantt47080Pakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway

Globally,Pakistan ranks 4th in cotton production,6th as an importer of raw cotton,and 3rd in cotton *** 10%of GDP and 55%of the country’s foreign exchange earnings depend on cotton *** 1.5 million people in Pakistan are engaged in the cotton value ***,several diseases such as Mildew,Leaf Spot,and Soreshine affect cotton *** diagnosis is not a good solution due to several factors such as high cost and unavailability of an ***,it is essential to develop an automated technique that can accurately detect and recognize these diseases at their early *** this study,a new technique is proposed using deep learning architecture with serially fused features and the best feature *** proposed architecture consists of the following steps:(a)a self-collected dataset of cotton diseases is prepared and labeled by an expert;(b)data augmentation is performed on the collected dataset to increase the number of images for better training at the earlier step;(c)a pre-trained deep learning model named ResNet101 is employed and trained through a transfer learning approach;(d)features are computed from the third and fourth last layers and serially combined into one matrix;(e)a genetic algorithm is applied to the combined matrix to select the best points for further *** final recognition,a Cubic SVM approach was utilized and validated on a prepared *** the newly prepared dataset,the highest achieved accuracy was 98.8%using Cubic SVM,which shows the perfection of the proposed framework..

关键词： Plants diseases data augmentation deep learning features fusion features selection

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Knowledge Graph-Based Effective Cardiovascular Disease Treatment Using BEHRT: A Deep Learning Approach with EHR Integration

Knowledge Graph-Based Effective Cardiovascular Disease Treat...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Dhyaneshwar K M Arthi Sri S Srikanth B K Namasivaya Naveen S Rhevathi M Department of Computer Science and Medical Engineering Sri Ramachandra Faculty of Engineering and Technology (SRET) Sri Ramachandra Institute of Higher Education and Research Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Integrating deep learning into healthcare has significantly improved clinical predictions, personalized interventions, and patient outcomes. The ability of BEHRT (Bidirectional Encoder Representations from Transformers for Electronic Health Records) to model temporal dependencies and capture complex relationships from patient medical records makes it particularly effective for predicting disease progression and outcomes. By encoding sequential information from longitudinal electronic health records (EHRs), BEHRT can analyse a patient's health trajectory over time, enabling more accurate early disease detection and personalized treatment strategies. Its transformer-based architecture allows for understanding patient data, including diagnosis codes, treatments, and other clinical features, which are critical for tailored interventions. In this study, the MIMIC-III dataset was used, and the application of BEHRTs to the cardiovascular disease subset was demonstrated. This approach showed a significant advancement in clinical predictions. The model effectively processed the large, unstructured, and semi-structured data, identifying key trends in disease progression and enhancing its ability to predict future health outcomes for patients with cardiovascular conditions. Additionally, we used a knowledge graph to represent the model’s insights into cardiovascular disease treatment and medication pathways. This graph highlighted key relationships between treatments and patient outcomes, serving as a valuable tool for clinical decision-making. BEHRT’s scalability and precision in handling vast amounts of structured health data underscore its potential to improve diagnosis, treatment planning, and overall patient care. Its application in cardiovascular disease showcases its broader utility across multiple medical domains, contributing to better healthcare outcomes and more personalized interventions.

关键词： Deep learning Scalability MIMICs Knowledge graphs Predictive models Transformers Trajectory Planning Cardiovascular diseases Medical diagnostic imaging

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A Comparison and Evaluation of Named Entity Recognition Methods in Thai Traditional Medicine Texts

A Comparison and Evaluation of Named Entity Recognition Meth...

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13th International Electrical engineering Congress, iEECON 2025

作者： Phungjununt, Warodom Lee, Wilaiporn Prayote, Akara Pipanmekaporn, Luepol Srisomboon, Kanabadee King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Science Bangkok10800 Thailand

ISBN: (纸本)9798331543952

The knowledge extraction and transliteration processes for the Thai traditional medicine documents requires experts to extract information from the textbook and spend a plenty of time. To reduce the complexity of these processes, the named entity recognition (NER) from the machine learning principle is applied to extract the key information from the document. In this work, three NER models including, CRFSuite, Bi-LSTM-CRF, and pre-trained model (WangchanBERTa) are considered the accuracy of each model of NER for the Thai traditional documents. After the testing process, the experimental results show that CRFSuite model is the most computationally efficient model and deliver high precision and recall. Moreover, CRFSuite model has the highest scores of each entity. While the accuracy of the WangchanBERTa model is the highest and faster convergence in the training process. Although the results from the Bi-LSTM-CRF model provide moderate accuracy, the Bi-LSTM-CRF can detect the common entities higher than other models. © 2025 IEEE.

关键词： Natural language processing systems

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