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arXiv 2024年

作者： Salimnejad, Mehrdad Pappas, Nikolaos Kountouris, Marios The Department of Computer and Information Science Linköping University Sweden The Communication Systems Dept. EURECOM France The Department of Computer Science and Artificial Intelligence University of Granada Spain

In this paper, we address the problem of timely delivery of status update packets in a real-time communication system, where a transmitter sends status updates generated by a source to a receiver over an unreliable channel. The timestamps of transmitted and received packets are measured using separate clocks located at the transmitter and receiver, respectively. To account for possible clock drift between these two clocks, we consider both deterministic and probabilistic drift scenarios. We analyze the system's performance regarding the Age of Information (AoI) and derive closed-form expressions for the distribution and the average AoI under both clock drift models. Additionally, we explore the impact of key system parameters on the average AoI through analytical and numerical results. Copyright © 2024, The Authors. All rights reserved.

关键词： Transmitters

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A Novel Dual Temporal Channel Convolutional Network with Exponential Distribution Optimizer for Satellite Image Classification with Google Earth Engine 4

A Novel Dual Temporal Channel Convolutional Network with Exp...

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4th International Conference on Ubiquitous Computing and Intelligent Information systems, ICUIS 2024

作者： Ragavendiran, S. D. Prabu Gowthami, N.R. Gayathri, C. Ramya, D. Padmapriya, D. Aparna, S.V. Suji Builders Engineering College Department of Computer Science and Engineering Tamil Nadu Tirupur638108 India Annamacharya University Department of Civil Engineering Andhra Pradesh Rajampet516126 India Mahendra Institute of Technology Tamil Nadu Namakkal637503 India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamil Nadu Coimbatore641202 India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai600123 India Department of Computer Science and Engineering CMR Technical Campus Kandlakoya Medchal Telangana Hyderabad501401 India

ISBN: (纸本)9798331529635

Classification of satellite images using Google Earth Engine entails identifying features on the satellite images to classify the land cover in a particular region through the use of deep learning algorithms. Such a process contributes to monitoring the environment and geographical distribution of resources, planning of cities, and management of resources. However, in many existing approaches, some challenges are worth noting. To address these challenges, this study introduces a new approach, the Dual Temporal Channel Convolutional Network with Exponential Distribution Optimizer (ANDualTCCNet+EDO) for satellite image classification using Google Earth Engine. This approach uses satellite imagery from the Landsat 8 Surface Reflectance dataset, with the input images preprocessed through the Adaptive Tri-Plateau Limit Tri-Histogram Equalization Algorithm (ATri-PLTri-HEA) technique. Following preprocessing, features are extracted from the images using the DenseNet201 method. Then, classification is carried out by A Novel Dual Temporal Channel Convolutional Network (ANDualTCCNet), optimized by the Exponential Distribution Optimizer (EDO) for precise satellite image classification in Google Earth Engine (GEE). The ANDualTCCNet+EDO method is implemented in Python. The proposed method was evaluated using the Landsat 8 Surface Reflectance dataset, achieving a high accuracy of 99.8%, surpassing existing methods. The results presented in this paper show that the use of this method allows for the accurate classification of satellite images in Google Earth Engine. The outcomes of the proposed method improve the method of satellite image classification in Google Earth Engine. This model captures both short- and long-duration temporal characteristics, therefore enhancing spatial-temporal feature acquisition compared to the previous method used in the classification of land cover, especially for temporal dynamics of land covers with different seasonal changes. © 2024 IEEE.

关键词： Equalizers

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CancerAI: A Deep Learning Framework for Ovarian Cancer Prediction 5

CancerAI: A Deep Learning Framework for Ovarian Cancer Predi...

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5th International Conference on Electronics and Sustainable communication systems, ICESC 2024

作者： Revathy, G. Maheswari, P. Uma Madhavi, S. Vijay Anand, P. Begum, R. Vajubunnisa Jasmin, H. Department of Computer Science and Engineering Chennai India K. Ramakrishnan College of Engineering Department of Computer Science and Business Systems Trichy India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Information Technology Chennai India Cmr Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Justice Basheer Ahmed Sayeed College for Women Affiliated by University of Madras Department of Electronics and Communication Science Teynampet Chennai India

ISBN: (纸本)9798350379945

Ovarian cancer is the fifth most common cause of death from cancer in women. This is mainly because it is often diagnosed at a late stage, as the earliest symptoms are unclear and inconsistent. Existing diagnostic techniques, such as biomarkers, biopsies, and imaging tests, have notable drawbacks such as reliance on subjective interpretation, inconsistency across different observers, and time-consuming testing procedures. This research study introduces an innovative deep learning framework that employs a convolutional neural network (CNN) algorithm to accurately predict and diagnose ovarian cancer, thereby overcoming the existing constraints. The CNN was trained using a dataset of histopathology images. The dataset was partitioned into test and training subsets to enhance the model's performance. The algorithm demonstrated an impressive accuracy rate of 96%, successfully detecting 97.12% of malignant instances and precisely categorizing 95.02% of healthy cells. This method effectively mitigates the challenges related to human expert evaluation, including elevated rates of misclassification and variability across different observers, while also reducing the time required for analysis. The results underscore the capability of this CNN-based technique to offer a more precise, effective, and dependable strategy for forecasting and detecting ovarian cancer. Subsequent investigations will prioritize the integration of new breakthroughs in deep learning to further amplify the efficacy of the suggested approach. © 2024 IEEE.

关键词： Lung cancer

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An Optimal Text Watermarking Method for Sensitive Detecting of Illegal Tampering Attacks

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computers, Materials & Continua 2022年第3期70卷 5583-5600页

作者： Anwer Mustafa Hilal Fahd N.Al-Wesabi Mohammed Alamgeer Manar Ahmed Hamza Mohammad Mahzari Murad A.Almekhlafi Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Information Systems King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Department of English College of Science&HumanitiesPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Faculty of Engineering Department of Electrical EngineeringCommunication EngineeringSana’a UniversityYemen

Due to the rapid increase in the exchange of text information via internet networks,the security and authenticity of digital content have become a major research *** main challenges faced by researchers are how to hide the information within the text to use it later for authentication and attacks tampering detection without effects on the meaning and size of the given digital *** this paper,an efficient text-based watermarking method has been proposed for detecting the illegal tampering attacks on theArabic text transmitted online via an Internet *** this purpose,the accuracy of tampering detection and watermark robustness has been improved of the proposed method as compared with the existing *** the proposed method,both embedding and extracting of the watermark are logically implemented,which causes no change in the digital *** is achieved by using the third level and alphanumeric strategy of the Markov model as a text analysis technique for analyzing the Arabic contents to obtain its features which are considered as the digital *** digital watermark will be used later to detecting any tampering of illegal attack on the received Arabic *** extensive set of experiments using four data sets of varying lengths proves the effectiveness of our approach in terms of detection accuracy,robustness,and effectiveness under multiple random locations of the common tampering attacks.

关键词： Text analysis text-watermarking tampering detection text authentication

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Towards Rehabilitation at Home After Total Knee Replacement

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Tsinghua science and Technology 2021年第6期26卷 791-799页

作者： Wenbing Zhao Shunkun Yang Xiong Luo Department of Electrical Engineering and Computer Science Cleveland State UniversityClevelandOH 44115USA School of Reliability and Systems Engineering Beihang UniversityBeijing 100191China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China

In this paper,we present the design and implementation of an avatar-based interactive system that facilitates rehabilitation for people who have received total knee replacement *** system empowers patients to carry out exercises prescribed by a clinician at the home settings more *** system helps improve accountability for both patients and *** primary sensing modality is the Microsoft Kinect sensor,which is a depth camera that comes with a Software Development Kit(SDK).The SDK provides access to 3-dimensional skeleton joint positions to software developers,which significantly reduces the challenges in developing accurate motion tracking systems,especially for use at ***,the Kinect sensor is not wellequipped to track foot orientation and its subtle *** overcome this issue,we augment the system with a commercial off-the-shelf Inertial Measurement Unit(IMU).The two sensing modalities are integrated where the Kinect serves as the primary sensing modality and the IMU is used for exercises where Kinect fails to produce accurate *** this pilot study,we experiment with four rehabilitation exercises,namely,quad set,side-lying hip abduction,straight raise leg,and ankle *** Kinect is used to assess the first three exercises,and the IMU is used to assess the ankle pump exercise.

关键词： rehabilitation physical therapy total knee replacement avatar virtual reality repetition count range of motion

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Advancements in Lung Cancer Diagnosis: A Comprehensive Study on the Role of PCA, LDA, and t-SNE in Deep Learning Frameworks 1

Advancements in Lung Cancer Diagnosis: A Comprehensive Study...

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1st Asian Conference on communication and networks, ASIANComNet 2024

作者： Vikas, B. Makkapati, Satya Sukumar Bogireddy, Srinivasa Rao Balamurugan, K.S. Deepa, M. Koneru Lakshmaiah Education Foundation Bowrampet Department of Computer Science and Engineering Telangana Hyderabad500043 India Acharya Nagarjuna University Department of Computer Science and Engineering A.P Guntur District India Horizon Systems Inc PhoenixAZ United States Karpaga Vinayaga College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India Sri Shakthi Institute of Engineering and Technology Department of IT Coimbatore India

ISBN: (纸本)9798350367003

In the ever-evolving domain of medical imaging, the integration of deep learning techniques holds the promise of transformative advancements. This research delved into the potential of employing data transfer within deep learning architectures for the automated detection of three distinct lung cancer types. Leveraging sophisticated methodologies like linear discriminant analysis (LDA), t-SNE, and PCA, the study aimed to enhance accuracy and efficiency in detecting malignancies from lung CT scan images. On rigorous evaluation, the models demonstrated compelling accuracy rates: salivary gland-type lung tumors at 9 0. 5%, pleomorphic (spindle/giant cell) carcinoma at 88.2%, and primary pulmonary sarcomas at 91.3%. Additionally, ROC curve analysis further highlighted the robust discriminative capability of the models across varied decision thresholds. The promising results accentuate the potential of integrating data transfer techniques with deep learning in a clinical setting. This research not only exhibits a significant stride in lung cancer detection but also paves the path for further innovations in automated medical image analysis. © 2024 IEEE.

关键词： Lung cancer

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Overview of AI4DT&CP@IJCAI 2024: The Second Workshop on AI for Digital Twins and Cyber-Physical Applications 2

Overview of AI4DT&CP@IJCAI 2024: The Second Workshop on AI f...

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2nd Workshop on AI for Digital Twins and Cyber-Physical Applications, AI4DT and CP 2024

作者： Lombardo, Gianfranco Theusch, Felix Picone, Marco Recupero, Diego Reforgiato Vizzari, Giuseppe Department of Engineering and Architecture University of Parma Italy Germany Department of Sciences and Methods for Engineering University of Modena and Reggio Emilia Italy Department of Mathematics and Computer Science the University of Cagliari Italy Department of Informatics Systems and Communication Università degli Studi di Milano-Bicocca Milano Italy

The 2024 Workshop on AI for Digital Twins and Cyber-Physical Applications (AI4DT&CP 2024) is at its second edition, held in conjunction with IJCAI 2024: the 33rd International Joint Conference on Artificial Intelligence in Jeju (South Korea). The workshop seeks to gather experts in Artificial Intelligence, Digital Twin technology, and Cyber-Physical systems to explore the latest innovations and best practices in applying AI-driven digital twins across various cyber-physical services and applications. The discussions will cover recent trends, research projects, and emerging developments in Digital Twins and Artificial Intelligence, focusing on how these advancements address cyber-physical applications from diverse perspectives. © 2024 Copyright for this paper by its authors.

关键词： Digital twin

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Optimizing Healthcare Delivery through CloudBased Clinical Decision Support systems

Optimizing Healthcare Delivery through CloudBased Clinical D...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Ebenezar, U. Samson Jenila Vennila, G. Balakrishnan, T. Suresh Krishnan, Prabhakar Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Mohan Babu University School of Computing Department of Ai & Ml Andhra Pradesh Tirupati India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-campus India

ISBN: (纸本)9798350373783

The experiment aims at evaluating how four machine learning algorithms work: Decision Tree, Random Forest, SVM, and Neural Network, in a task, of binary classification. Models were evaluated using comprehensive metrics such as accuracy, precision, recall, F-score and AUC that revealed a good diagnostic performance. Models selected for the experiment and parameters tested on real datasets show steady changes in performance. The Neural Network model remained on top, achieving accuracy, precision, recall, F-score and AUC-ROC of 91.8%, 92.1%, 91.3%, 91.7% and 0.93 respectively. Random Forest displayed a competitive accordance along with the accuracy of 88.6%, precision of 89.2%, recall of 88.7%, and the F-score of 89.0%, and the AUC of 0.91. From the results, Support Vector Machine and Decision Tree models posted lower accuracy, recall, and fscores compared to the Neural Network and Random Forest models in most cases. These reveal the need for the improvement of the suitable machine learning model that comes from specific task needs as well as the consideration of each one of factors including accuracy, interpretability, and computational efficiency. Extensive and development of the model may result in more comprehensive understanding of how the model leads classifying of real-world applications such as medical diagnoses, fraud detection and target marketing. © 2024 IEEE.

关键词： Random forests

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Energy-Efficient Near-Field Wideband Beamforming with Circular Arrays

Energy-Efficient Near-Field Wideband Beamforming with Circul...

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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Guo, Yunhui Zhang, Yang Wang, Zhaolin Liu, Yuanwei Ding, Zhiguo Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Khalifa University 6G Center Department of Computer and Communication Engineering Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350351255

The beamforming performance of the uniform circular array (UCA) in near-field wideband communication systems is investigated. Firstly, the unique beam squint effect in near-field wideband UCA systems is analyzed in both the distance and angular domains. It is demonstrated that the generated beams at different frequencies are focused at different locations, resulting in significant beamforming loss. To alleviate this unique beam squint effect and facilitate beamfocusing, an energy-efficient beamforming scheme based on the true-time delay (TTD) architecture is proposed. Specifically, the phase shifters (PSs) and the time delay of TTDs are designed based on the analytical formula for beamforming gain. Additionally, the minimum number of TTDs required to achieve a predetermined beamforming gain while minimizing energy consumption is quantified. Numerical results show that the proposed beamforming scheme effectively eliminates the near-field beam squint and outperforms the conventional schemes in terms of spectral efficiency and energy efficiency. © 2024 IEEE.

关键词： Near field communication

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Hybrid Deep Learning based Attack Detection and Classification Model on IoT Environment 5

Hybrid Deep Learning based Attack Detection and Classificati...

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5th IEEE International Conference on Electrical, computer and communication Technologies, ICECCT 2023

作者： Lal, Jaya Dipti Ayoub, Shahnawaz Hakim, Prashant D. Prabagar, S. Dwivedi, Vijay Kumar Tiwari, Mohit Shri G S Institute of Technology and Science Department of Electronics and Communication Engineering Madhya Pradesh Indore India Shri Venkateshwara University Department of Computer Science Uttar PradeshGujrala Amroha India Thakur College of Science and Commerce Department of Information Technology Maharashtra Mumbai India School of Computer Science and Engineering & Information Science Presidency University Networks and IoT Lab Department of Information Science Engineering Karnataka Bengaluru India Vishwavidyalaya Engineering College Department of Mathematics Chhattisgarh Ambikapur India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665493604

The Internet of Things (IoT) is becoming an active research area because of its largescale challenges and implementation. But security is the major concern while seeing the dramatic expansion in its applications and size. It is challenging to independently put security mechanism in all the IoT devices and upgrade it according to newer threats. Furthermore, machine learning (ML) techniques could better apply the massive quantity of data produced by IoT devices. Thus, several Deep Learning (DL) based algorithms were introduced for detecting attacks in IoT. Therefore, this study develops a galactic swarm optimization with Deep Learning based Attack Detection and Classification (GSODL-ADC) Model in IoT Environment. The presented GSODL-ADC technique concentrates on the identification of attacks in the IoT environment. The presented GSODL-ADC technique utilizes deep autoencoder (DAE) as a classifier model which properly recognizes the attacks in the IoT environment. Followed by this, the GSO approach is utilized for the optimum hyperparameter adjustments of the DAE model, which leads to enhanced attack detection efficacy. The experimental evaluation of the GSODL-ADC algorithm is tested against benchmark dataset. The obtained experimental values signify the betterment of the GSODL-ADC technique for attack recognition purposes. © 2023 IEEE.

关键词： Internet of things

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