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Work in Progress: Congestion Control in mmWave Fluctuating Scenarios in 5G-A/6G 10

Work in Progress: Congestion Control in mmWave Fluctuating S...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Sandoval, Jorge Ignacio Cespedes, Sandra University of Chile Department of Electrical Engineering Santiago Chile Concordia University Dept. of Computer Science and Software Eng Montreal Canada

ISBN: (纸本)9798350373011

The development of the next generation of cellular networks, 5G-Advanced and 6G (5G-A/6G), offers higher speeds, sub-millisecond latency, and providing wider coverage, which are key to meeting the requirements of new and demanding applications. As such, there are several challenges in managing and leveraging this opportunity to ensure that channel conditions do not impact the application due to mobility and obstructions. 5G introduces the use of millimeter wave (mmWave), and the commercial deployment of this technology brings to light the issues related to propagation and mobility in high-frequency bands, and their impact on applications has begun to be studied by exploring the response of the congestion control algorithms (CCAs) in representative scenarios. However, the highly variable channel conditions in mmWave require that they be defined as fluctuating bandwidth scenarios. It is necessary to determine how the transport layer can take advantage of the maximum available capacity and not affect the applications, and whether the network promises are fulfilled. This work aims to design and evaluate a CCA capable of efficiently adapting to the fluctuating bandwidth of extreme condition scenarios of 5G-A/6G mobile networks and to compare it with the state-of-the-art algorithms. As a first result, a set of simulation-based evaluation using 5GLENA was discussed. A combination of CCAs and different radio link control (RLC) buffer sizes allows us to highlight the importance of defining appropriate metrics and including convergence times as part of the evaluation performance of the algorithms. Algorithms such as HighSpeed and New Reno are among the slowest to converge, while BBR is the fastest. The analysis and characterization of the algorithms will allow us to define the requirements for designing an Machine Learning (ML) based algorithm with optimal performance and better convergence times. © 2024 IEEE.

关键词： Radio links

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An Effective Detection of Brain Tumor Detection Using ResNet-50 Model

An Effective Detection of Brain Tumor Detection Using ResNet...

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2024 International Conference on Advancement in Renewable Energy and Intelligent Systems, AREIS 2024

作者： Raji, V. Maheswari, S. Dharinya, S. Sree Chinniah, P. Kavin, K.S. Raja, Kiruba Thangam S.K.P. Engineering College Dept. of Information Technology Tiruvannamalai India Tamilnadu Erode India School of Computer Science Engineering and Information Systems Vit University Dept. of Software and Systems Engineering Vellore India ST. Joseph College of Engineering Dept. of Ece Tamilnadu Sriperumbudur India Government College of Engineering Dept. of Eee Tamil Nadu Tirunelveli India Vit University Dept. of Software and Systems Engineering Vellore India

ISBN: (纸本)9798350387230

Rapid proliferation of aberrant neurons in brain tumors significantly damages organ function or causes death, endangering adult health. These tumors vary widely in terms of their locations, sizes, and textures. Finding malignant tumors requires the use of Magnetic Resonance Imaging (MRI). However, physically diagnosing brain tumors is a timeconsuming and potentially unpredictable technique. Cropping and resizing to fixed dimensions ensures consistency before submitting brain MRI images for analysis using deep learning models such as modified ResNet-50. This study uses the modified ResNet-50 model, a deep Convolutional Neural Network (CNN) renowned for its accuracy and resilience in image recognition tasks, to provide an efficient method for brain tumor diagnosis. Initially trained on Image Net, the modified ResNet-50 model further improved using transfer learning on many annotated brain MRI scan datasets. By adding variation to the dataset, data augmentation makes the model more robust and helps it to generalize. Deeper networks are trained without sacrificing performance to the model's architecture, which addresses the vanishing gradient problem. This work classification model implemented using Python software, which facilitated the development, training, and evaluation processes, allowing for an analysis of their performance. The optimized model outperformed conventional Machine Learning (ML) techniques, demonstrating over 94% accuracy in tumor identification and classification tests. With the potential to enhance patient outcomes and diagnostic efficiency, the integration of modified ResNet-50 in brain tumor identification offers a significant development towards accurate and dependable automated diagnostic systems. The accuracy of the modified Resnet-50 is 94%, and the specificity comparison of the modified ResNet-50 is 93.5%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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PLCD: Policy Learning for Capped Service Mobility Downtime 32

PLCD: Policy Learning for Capped Service Mobility Downtime

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32nd International Conference on computer Communications and Networks, ICCCN 2023

作者： Mwasinga, Lusungu J. Raza, Syed M. Le, Duc-Tai Kim, Moonseong Choo, Hyunseung Sungkyunkwan University Dept. of Computer Science and Engineering Suwon Korea Republic of Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of Seoul Theological University Dept. of It Convergence Software Bucheon Korea Republic of

ISBN: (纸本)9798350336184

Service mobility in Multi-access Edge Computing (MEC) paradigm is necessary to provide ultra-Reliable Low Latency Communications for the erratically roaming MEC users. It involves relocation of containerized application services to a strategically selected optimal edge host. During relocation, service containers are unavailable (downtime), resulting in the interruption of ongoing user sessions and increased operational expenses for the network operator. Prolonged service downtime degrades perceived quality of experience for users, and this study handles this problem by proposing a downtime-aware Policy Learning based Capped Downtime (PLCD) service mobility strategy. It exploits Deep Actor-Critic prowess for effectively deciding when and where to relocate a containerized application service while taking user mobility and MEC server resource fluctuations into account. Efficacy of the proposed PLCD strategy is confirmed through simulation experiments, and results indicate over 90% average reduction in service downtime comparing to a baseline scheme. © 2023 IEEE.

关键词： Reinforcement learning

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The Early Detection of Dementia Disease Using Machine Learning Approach

The Early Detection of Dementia Disease Using Machine Learni...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Kabir, Md. Shariar Khanom, Jakia Bhuiyan, Md. Atik Tumpa, Zerin Nasrin Rabby, S.K. Fazlee Bilgaiyan, Saurabh Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh Daffodil International University Dept. of Software Engineering Dhaka Bangladesh Kalinga Institute of Industrial Technology School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350348217

Early dementia detection is a crucial but challenging task in Bangladesh. Often, dementia is not recognized until it is too late to receive effective care. This results in part from a lack of knowledge about the illness and its signs and symptoms. Recent improvements in machine learning algorithms, however, may change this. In a recent study, we developed a model that can identify early dementia in Bangladesh using machine learning algorithms. This research paper proposed an efficient machine learning-based approach for early detection of dementia disease A dataset of 199 people with dementia and 175 healthy controls was used to develop the model. In 96% of the cases, the algorithm correctly identified dementia. This is a significant accomplishment that could revolutionize Bangladesh's dementia detection process. For patients to get the care they require, early dementia detection is essential. This study offers a proof-of-concept for the use of machine learning in dementia early detection & The results of this study suggest that machine learning models can be used as a powerful tool for early detection of dementia. © 2023 IEEE.

关键词： Machine learning

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Establishing a Correlation of Clinical Characteristics with the Level of Tumor Mutation Burden in Urothelial Bladder Carcinoma

Establishing a Correlation of Clinical Characteristics with ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Alshomali, Lujain Khorma, Rania Al-Refai, Abdullah Alkhateeb, Abedalrhman Princess Sumaya University for Technology Software Engineering Dept. Amman Jordan Lakehead University Computer Science Dept. Thunder Bay Canada

ISBN: (纸本)9798350337488

Bladder cancer is a complex disease and one of the most lethal types of cancer. Recently, some malignancies, including bladder carcinomas, have shown better results with immunotherapy using immune checkpoint inhibitors. Tumor mutational burden (TMB) is a potential biomarker for predicting tumor behavior and immunotherapy response as an outcome. Publicly available clinical data from the bladder cancer of TCGA project is used to analyze correlations of clinical variables with an increased tumor mutation burden (TMB) number compared with those with a lower number of mutations. The threshold for the high mutation burden in the analysis was set at 10 mutations (Mut) per Megabase (Mb). The Chi-Square test (χ2) was used to compare categorical data. The Chi-Square 'Best first' method was used to find a correlation between clinical variables and TMB, then compared with the p-value of significance (p © 2023 IEEE.

关键词： biomarkers Chi-Square clinical characteristics Tumor mutational burden urothelial bladder carcinoma

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Sinhala Sign Language Learning System for Hearing Impaired Community 4

Sinhala Sign Language Learning System for Hearing Impaired C...

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4th International Informatics and software engineering Conference, IISEC 2023

作者： Idushan, K.A.S. Dilshan, P.G.T. Jayasundera, P.S. Madhushan, K.L.B. Krishara, Jenny Wijendra, Dinuka Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350318036

This research aims to develop a comprehensive system for Sinhala Sign Language (SSL) that includes a learning system, dynamic sign detection, audio/video to sign conversion, and vocal training. SSL plays a crucial role in facilitating communication for individuals who are deaf or hard of hearing in Sri Lanka. The learning system provides a platform for learning SSL and includes a text-To-sign language interpreter. The dynamic sign detection system uses computer vision techniques to identify and interpret dynamic signs accurately. The audio/video to sign conversion system bridges the gap between spoken language and SSL by converting auditory information into visual representations. The vocal training system focuses on enhancing the vocal skills of cochlear implanted children. This research contributes to the development of effective communication and language skills for SSL users. © 2023 IEEE.

关键词： Learning systems

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A Blockchain-Powered Loan Management System Enhanced with Smart Contract 1

A Blockchain-Powered Loan Management System Enhanced with Sm...

引用

1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Saleem, Muhammad Haris, Muhammad Naeem, Mehwish Abro, Abdul Basit Ahmad, Maaz Ahad, Abdul Iqra University Main Campus Dept. of Software Engineering Karachi Pakistan University Libre de Bruxelles Brussels Belgium Indus University Dept. of Computer Science Karachi Pakistan Iqra University Main Campus Dept. of Computer Science Karachi Pakistan

ISBN: (纸本)9798350373042

Most financial lending systems in use today are based on a single service platform, transactions are opaque, and the majority of system participants can be tracked back to Mechanisms for protecting data privacy are not strong enough to withstand cyberattacks. We suggest lending over the block chain (LoC), a cutting-edge finance and credit facility built on smart contracts on a block chain hyper ledger fabric, as a solution to these problems. We use the poverty level for Chinese loans as an example. We create a digital accounting model to transfer assets between decentralized and centralized ledgers and suggest smart contract closing and unlocking methods. To safeguard the privacy of data, we established digital signatures and oracles. The opening rule's and the chain rule's performance evaluation demonstrates the applicability of our system in actual loan situations. © 2024 IEEE.

关键词： Smart contract

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Transformer Models for Enhanced Natural Language Processing in Medical Records Management 4

Transformer Models for Enhanced Natural Language Processing ...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Vasani, Vaibhav Prakash Pawar, Swapnil Chandrakant Sowmya, P. Ahamad, Shahanawaj Sahu, Aradhana Talele, Gokul Somaiya Vidyavihar University Dept. of Computer Engineering Maharashtra Mumbai India Nmamit Dept. of Cse Nitte India College of Cse University of Hail Dept. of Software Engineering Hail City Saudi Arabia Rungta College of Engineering and Technology Dept. of Cse Chhattisgarh Bhilai India Accenture Pune India

ISBN: (纸本)9798350387490

The use of transformer models to enhance NLP in MR administration is investigated. Renowned for their ability to understand contextual links along with efficiently handle large datasets, transformer designs provide clear improvements in the processing along with analysis of medical language. Work improves the extraction, classification, along with summarising of data from complex medical records by use of models like BERT, GPT, along with variants. The study looks at how well these models may simplify data retrieval, increase the accuracy and relevance of CR, along with support decision making in medical environments. The results imply that the use of transformer-based NLP techniques might greatly boost the medical record administration, thereby improving patient care, accelerating procedures, along with optimising data usage. Transformer models such as BERT are used in the research approach aiming at enhancing NLP in medical record handling. Literature, preprocessing EHRs, fine-tuning and assessment along with pilot testing all plays a part. Maintaining ethical issues, a user interface is given for interaction. This method improves medical data handling effectiveness. BERT has showed promise in handling medical records in terms of enhancing identification and categorisation of medical entities found in data. Various medical entities including diseases, medications, procedures, and symptoms have been specifically tailored into the strong language paradigm. Confusion matrix performance measures reveal how well model controls medical records. Future studies could investigate combining BERT with other advanced NLP methods, extending dataset to include a wider range of medical records, using TLT in case of particular medical domains, including real time data processing and feedback systems, and investigating ethical issues along with data privacy concerns in health care. © 2024 IEEE.

关键词： Records management

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Computational Model of Swarm Algorithm for Optimizing Process of Keywords Extraction from Text Information Presented as Graph 7

Computational Model of Swarm Algorithm for Optimizing Proces...

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7th International Conference on Information Technologies in engineering Education, Inforino 2024

作者： Kravchenko, Yury Kuliev, Elmar Kravchenko, Daniil Rodzin, Sergey Pavlov, Nikolay Rodzina, Lada Southern Federal University Dept. of Computer Aided Design Taganrog Russia Southern Federal University Department of Software Engineering Taganrog Russia

ISBN: (纸本)9798350371369

The article describes the scientific problem of optimizing the processes of acquiring knowledge when processing textual information presented in the form of a graph, based on the development of a modified bioinspired algorithm for extracting keywords. The solution proposed by the authors differs from the known ones by introducing new attractive operators and behavioral patterns of bacterial agents into the computational model of the bacterial swarm algorithm. This approach allows you to balance the ratio of the speed of convergence of the algorithm with the diversification of the search space for solutions. The article also presents an analytical study of the features of two types of parsing: constituency parsing and dependency parsing. As part of the study, algorithms for syntactic parsing of dependencies between elements of text information and a graph-based dependency parsing algorithm are described. The formulation of the problem of keyword extraction is given. The task of achieving a balance between accuracy and speed of a linguistic parser is an open topic that requires further research due to the importance of this problem for improving the efficiency of text analysis, especially in applications that require real-time accuracy. To increase the efficiency of solving the problem, the authors propose a modified bioinspired algorithm for extracting keywords. The computational experiment carried out allows us to conclude that the modified bacterial optimization method proposed by the authors is in a leading position, which confirms the need to continue research into the applicability of bioinspired methods for solving problems of acquiring knowledge when processing text information presented in the form of a graph. This article is focused on the direction of study in the master's program "Informatics and computer Science", as well as the scientific specialty of postgraduate training "Artificial Intelligence and Machine Learning". © 2024 IEEE.

关键词： Text processing

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EMD and VMD in Pre-Movement EEG Signal Analysis: A Hybrid Mode Selection to Classify Upper Limb Complex Movements Using Statistical Features 20

EMD and VMD in Pre-Movement EEG Signal Analysis: A Hybrid Mo...

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20th IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2023

作者： Khalid, Beenish Hassan, Ali Munir, Ehsan Ullah Niazi, Imran Khan Dept. of Computer and Software Engineering Islamabad Pakistan Comsats University Islamabad Dept. of Computer Science Pakistan College of Chiropractic Centre of Chiropractic Research Auckland New Zealand

ISBN: (纸本)9798350331110

Electroencephalogram (EEG) signals, inherently non-stationary and non-linear, present significant challenges in their processing and interpretation. This paper presents a hybrid mode selection approach using two advanced decomposition methods: Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD), to analyze these signals, targeting their application in the classification of upper limb complex movements for enhanced prosthetic limb control and rehabilitation therapy assessment. Using optimized statistical features extracted from selected modes, Intrinsic mode functions (IMFs) via EMD and modes via VMD, we seek to better distinguish neural activities in pre-movement EEG signal. Our methodology involves the following two strategies: straightforward extraction of statistical features from modes yielded by EMD and VMD;a genetic algorithm (GA) feature selection technique to select the most optimal set from these statistical features. These derived features train machine learning (ML) classifiers to differentiate limb movements. The results, derived from proprietary dataset from Aalborg University, Denmark, comprising five distinct upper limb movements, demonstrate the effectiveness of our hybrid approach. The usage of EMD and VMD significantly enhanced the discriminatory power of the extracted features, leading to improved classification performance. Furthermore, our hybrid approach yielded classification accuracies of 93.1 % and 95.6% with EMD and VMD respectively when the K-NN classifier was deployed with a 10-fold cross-validation. K-NN classifier outperformed traditional ML classifiers in terms of computational time, highlighting its potential as lightweight yet robust algorithm for classification of complex movements. The primary goal is to present and validate a hybrid mode (IMFs/modes) selection approach through EMD and VMD to analyze EEG signals associated with upper limb complex movements. © 2023 IEEE.

关键词： Empirical mode decomposition

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