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IEEE International Conference on Communications (ICC)

作者： Ramy Mohamed Marios Avgeris Aris Leivadeas Ioannis Lambadaris Department of Systems and Computer Engineering Carleton University Ottawa Canada Department of Software and IT Engineering École de technologie sup érieure Montreal Canada

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In this paper we address the challenge of efficiently deploying Virtual Network Functions (VNFs) in network infrastructures. This is particularly crucial when facing resource fragmentation, where available resources are not fully utilized due to the fluctuating allocation and deallocation of virtual network requests. Traditional optimization techniques often fall short in managing the dynamic complexities of VNF placement. To overcome this, we introduce a novel online VNF placement strategy using Deep Reinforcement Learning (DRL) combined with a Reward Constrained Policy Optimization (RCPO). This method leverages the flexibility of DRL and the constraint integration capacity of RCPO, ensuring compliance with performance and resource limitations while minimizing resource fragmentation. The results demonstrate that our DRL-based method surpasses existing methods, resulting in more effective resource management and less resource fragmentation.

关键词： Metalearning Adaptive learning Costs Transfer learning Complex networks Deep reinforcement learning Complexity theory

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Unified Approach Integrating Machine Learning Algorithms for Detection of e-Mail Phishing Attacks

Unified Approach Integrating Machine Learning Algorithms for...

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Computing, Sciences and Communications (ICCSC), International Conference on

作者： Abeer Mathur Abhinav Sudhakar Dubey Pranav Singh Mahara Usha Devi Gandhi Department of Software Systems School of Computer Science & Engineering Vellore Institute of Technology Vellore India Department of Information Technology School of Computer Science Engineering & Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

With the ever-rising risk of phishing attacks, which capitalize on vulnerable human behavior in the contemporary digital space, requires new cybersecurity methods. This literary work contributes to the solution by novel incorporation of three techniques: Support Vector Machine (SVM), Natural Language Processing (NLP) and Random Forest (RF). Utilizing NLP for deep analysis of text in phishing e-mails, SVM for strong categorization and Random Forest's decision making by group helps in improving detection. This combination of language and pattern analysis along with group learning builds a fortified system. This approach differs from prior work on a variety of factors such as reliability, security, integrity, scalability, interpretability, transparency, and robustness. Thorough evaluation on a reliable dataset aimed to demonstrate the method's effectiveness, which resulted in 98.7 % of accuracy in detection. This majorly aims at showcasing the potential of ensemble techniques to combat against cyber-attacks like phishing for laying a strong foundation for a secure digital environment.

关键词： Support vector machines Radio frequency Phishing Scalability Forestry Natural language processing Vectors Robustness Electronic mail Random forests

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Deep Learning-Based Anomaly Detection in 5G Cellular Networks

Deep Learning-Based Anomaly Detection in 5G Cellular Network...

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International Conference on Information, Intelligence, systems and Applications (IISA)

作者： Umar Farooq Aroosa Hameed Aris Leivadeas Ioannis Lambadaris Department of Software and IT Engineering École de technologie supérieure Montreal Canada Department of Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

The telecommunication industry saw a dramatic shift with the introduction of 5G technologies. This new cellular generation brought lightening speed, massive capacity, and better connectivity. Despite the many introduced opportunities, several challenges emerge at the same time, especially in the field of performance assurance. An example of such challenges is the rapid identification of network anomalies, which is critical for maintaining network performance and ensuring user satisfaction. Traditional techniques of detecting anomalies have fallen short given the unique operating requirements of 5G networks. To address this issue, this paper presents a novel technique applying deep learning for the detection of cellular network anomalies. Specifically, we leverage and exploit the capabilities of several Long-Short-Term-Memory (LSTM) and Artificial Neural Networks (ANN) flavors, such as LSTM with ANN and Bidirection-LSTM (BiLSTM) with ANN, to excel at identifying network anomalies for the preservation and efficiency of 5G networks. Our work includes extensive experimentation and the attained results show that our proposed models are overwhelmingly adapting to preserving the integrity of the network in the fast-paced, ever-changing realm of cellular networks.

关键词： Cellular networks Industries Deep learning Adaptation models Accuracy 5G mobile communication Quality of service Communications technology Sustainable development Anomaly detection

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Crop protection and disease detection using artificial intelligence and computer vision: a comprehensive review

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Multimedia Tools and Applications 2025年第7期84卷 3723-3743页

作者： Shah, Kanish Sushra, Rajat Shah, Manan Shah, Dhairya Shah, Haard Raval, Megh Prajapati, Mitul Software Engineer Walmart SunnyvaleCA United States Atmiya Vidya Mandir Gujarat Surat India Department of Chemical Engineering School of Energy Technology Pandit Deendayal Energy University Gujarat Gandhinagar India Robotics ECE Concentration Northeastern University BostonMA United States Department of Computer Science Concentration in AI Hofstra University HempsteadNY United States Department of Chemical Engineering S. S Agrawal Institute of Engineering & Technology Gujarat Navsari India R &D Engineer Span Inspection Systems Pvt. Ltd Gujarat Gandhinagar India

The technological advancements in the field of agriculture have increased to a great extent in recent years, and many techniques have evolved from other techniques. Some methods are improved or upgraded from the previous versions by implementing a new model or using better hardware devices. This has been helpful for the farmers in increasing crop productivity, and the life expectancy of crops has also increased as the diseases inside or outside the crops can be detected much earlier, and learning at an early stage helps prevent other crops. In this paper, we have presented a study where many varieties of fruits and vegetables have been taken to determine which method was used for a particular crop. By analyzing the various works carried out by the authors, it was inferred that most of the works revolved around image processing and hyperspectral imaging. Due to this, we had also included most of the papers, particularly as the models and hardware components used were much better than other works. Then, a comparative study was done where different fruits and vegetables highlighted the two main areas: the method used and the accuracy obtained. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Crops

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Online VNF Placement using Deep Reinforcement Learning and Reward Constrained Policy Optimization

Online VNF Placement using Deep Reinforcement Learning and R...

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Communications and Networking (MeditCom), IEEE International Mediterranean Conference on

ISBN: (数字)9798350309485

ISBN: (纸本)9798350309492

Appropriate deployment of Virtual Network Functions (VNFs) within the network infrastructure to optimize resource utilization, while fulfilling performance criteria, is critical to Network Function Virtualization (NFV). However, traditional optimization methods frequently struggle to deal with the dynamic and complex nature of the VNF placement problem. Therefore, this study proposes an online VNF placement approach that addresses these problems by leveraging Deep Reinforcement Learning (DRL) and Reward Constrained Policy Optimization (RCPO). This technique combines DRL’s adaptability with RCPO’s constraint incorporation capabilities, ensuring that the learned policies satisfy performance and resource restrictions while optimizing the overall VNF placement. Hence, the VNF placement problem is formulated as a constrained optimization problem and adapted to the DRL setting. We demonstrate that our proposed framework outperforms certain state-of-the-art VNF placement techniques when it comes to resource utilization and constraint satisfaction.

关键词： Metalearning Adaptive learning Costs Transfer learning Neural networks Optimization methods Deep reinforcement learning

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Determining the Critical Factors of Antenatal Care Follow-Up Using Two-Phase Machine Learning

Determining the Critical Factors of Antenatal Care Follow-Up...

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International Conference on Soft Computing and Measurements (SCM)

作者： Igor A. Bessmertny Bidru C. Enkomaryam Adane L. Mamuye Faculty of Software Engineering and Computer Systems ITMO University Saint Petersburg Russia Department of Computer Science University of Gondar Gondar Ethiopia

ISBN: (数字)9798350363708

ISBN: (纸本)9798350363715

Determining the critical factors affecting antenatal visits will greatly contribute to reducing maternal and infant mortality. This study, thus attempted to construct a cluster-based predictive model to determine the critical factors of antenatal care follow-up among women in Ethiopia using two-step machine learning techniques. The 5,990 preprocessed obstetric dataset with 13 selected attributes were used that was collected from the Dabat Research Center at the University of Gondar, Ethiopia. To solve the study problem, six cluster models were developed using K-means, Expectation Maximization and Make Density clustering algorithms, which are well-suited for analyzing health datasets, providing scalability, flexibility, robustness to noise, and the ability to handle uneven cluster sizes. K-means out-formers Density based and Expectation Maximization clustering algorithms with minimum number of sum of square errors (17094) and number of iteration (3). Then, output of the K-means cluster model was used as input to segment the dataset and build a total of 21 predictive models using the tree-based J48 classifier, PART and JRip rule generators while adjusting their parameters. The model for predicting antenatal care follow-up is constructed by induction of the truncated PART rule (accuracy of 99.93%) and extracts attributes of interest such as antenatal care attendance, antenatal care location, antenatal care frequency, current vaccination, woman’s age, and HIV testing to predict antenatal care follow-up in the study area.

关键词： Machine learning algorithms Accuracy Scalability Clustering algorithms Machine learning Predictive models Prediction algorithms

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Human Tracking and Profiling for Risk Management 21

Human Tracking and Profiling for Risk Management

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21st International Conference on Advances in ICT for Emerging Regions, ICter 2021

作者： Ranjith, Vishaka Jayasekara, Anuj Ratnasooriya, Lahiru Jayasekara, Thilini Rupasinghe, Prabath Liyanapathirana, Chethana Department of Computer Science and Software Engineering Malabe Sri Lanka Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9781665466837

Infectious viruses are conveyed via respiratory droplets produced by an infected person when they speak, sneeze, or cough. So, to combat virus transmission, the World Health Organization (WHO) has imposed severe regulations such as mandatory face mask use and social segregation in public spaces. The 'Human Tracking and Profiling for Risk Management System (HTPRM)' is an online application that identifies the risk associated with failing to follow proper health practices. This proposed approach, which is divided into four components, utilizes 'You Only Look Once (YOLOv3)' to detect face-mask danger, which would be determined based on two factors: wearing the face mask properly and the type of mask (Surgical,k95, homemade, and bare). The second phase is to use OpenCV and SSD-Mobilenet to evaluate the value of a one-meter space(Social Distance) between people. The system recognizes the maximum number of individuals that can be in the vicinity of the specific hall that uses YOLO(V3) and image processing as the third procedure. In the last processing, the system identifies each person's behavior, classifies it as uncommon or not, and calculates the risk associated with each category. Finally, the system computes the overall risk and generates a warning alarm to notify the user that they are in a dangerous scenario. © 2021 IEEE.

关键词： Risk management

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Chest Radiographs Based Pneumothorax Detection Using Federated Learning

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computer systems Science & engineering 2023年第11期47卷 1775-1791页

作者： Ahmad Almadhor Arfat Ahmad Khan Chitapong Wechtaisong Iqra Yousaf Natalia Kryvinska Usman Tariq Haithem Ben Chikha Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand School of Telecommunication Engineering Suranaree University of TechnologyNakhon Ratchasima30000Thailand Department of Computer Science and Software Engineering International Islamic UniversityIslamabadPakistan Information Systems Department Faculty of ManagementComenius University in BratislavaOdbojárovBratislava440Slovakia Prince Sattam bin Abdulaziz University AlKharjSaudi Arabia

Pneumothorax is a thoracic condition that occurs when a person’s lungs collapse,causing air to enter the pleural cavity,the area close to the lungs and chest *** most persistent disease,as well as one that necessitates particular patient care and the privacy of their health *** radiologists find it challenging to diagnose pneumothorax due to the variations in *** learning-based techniques are commonly employed to solve image categorization and segmentation ***,it is challenging to employ it in the medical field due to privacy issues and a lack of *** address this issue,a federated learning framework based on an Xception neural network model is proposed in this *** pneumothorax medical image dataset is obtained from the Kaggle *** preprocessing is performed on the used dataset to convert unstructured data into structured information to improve the model’s ***-max normalization technique is used to normalize the data,and the features are extracted from chest Xray *** dataset converts into two windows to make two clients for local model *** neural network model is trained on the dataset individually and aggregates model updates from two clients on the server *** decrease the over-fitting effect,every client analyses the results three *** 1 performed better in round 2 with a 79.0%accuracy,and client 2 performed better in round 2 with a 77.0%*** experimental result shows the effectiveness of the federated learning-based technique on a deep neural network,reaching a 79.28%accuracy while also providing privacy to the patient’s data.

关键词： Privacy preserving pneumothorax disease federated learning chest x-ray images healthcare machine learning deep learning

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Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization

Efficiently Assemble Normalization Layers and Regularization...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Khiem Le Long Ho Cuong Do Danh Le-Phuoc Kok-Seng Wong Department of Computer Science and Engineering University of Notre Dame IN USA College of Engineering and Computer Science Vin University Hanoi Vietnam Open Distributed Systems Technical University Berlin Berlin Germany

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Domain shift is a formidable issue in Machine Learning that causes a model to suffer from performance degradation when tested on unseen domains. Federated Domain Gener-alization (FedDG) attempts to train a global model using collaborative clients in a privacy-preserving manner that can generalize well to unseen clients possibly with domain shift. However, most existing FedDG methods either cause additional privacy risks of data leakage or induce signifi-cant costs in client communication and computation, which are major concerns in the Federated Learning paradigm. To circumvent these challenges, here we introduce a novel architectural method for FedDG, namely gPerXAN 1 1 https://***/lhkhiem28/gPerXAN, which relies on a normalization scheme working with a guiding regularizer: In particular, we carefully design Personalized eXplicitly Assembled Normalization to enforce client mod-els selectively filtering domain-specific features that are bi-ased towards local data while retaining discrimination of those features. Then, we incorporate a simple yet effec-tive regularizer to guide these models in directly capturing domain-invariant representations that the global model's classifier can leverage. Extensive experimental results on two benchmark datasets, i.e., PACS and Office-Home, and a real-world medical dataset, Camelyon17, indicate that our proposed method outperforms other existing methods in ad-dressing this particular problem.

关键词： Degradation Data privacy computer vision Costs Filtering Federated learning Computational modeling

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Estimating the AGV load and a battery lifetime based on the current measurement and random forest application

Estimating the AGV load and a battery lifetime based on the ...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Hanzel, Krzysztof Grzechca, Damian Ziebinski, Adam Chruszczyk, Lukas Janus, Artur Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Telecommunications and Teleinformatics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Electronics Electrical Engineering and Microelectronics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice Poland Silesian University of Technology Student of Electrical EngineeringFaculty of Electrical Engineering Gliwice Poland

ISBN: (纸本)9798350324457

The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and reliable information about power consumption and battery life estimation for autonomous guided vehicles (AGV). The authors propose a two-step algorithm. In the first step, a linear classifier was proposed. Then, the KNN classifier was tested;however, it did not give satisfactory results, so it was finally decided to use the random forest to estimate the load and PWL. The time domain current measurement is evaluated, and the beforementioned algorithms process the selected statistical measures. It has been proven that a two-step algorithm allows for achieving high accuracy. Based on the current observation, the paper is a good starting point for further investigation of the AGV because it is usually implemented in the AGV - so it does not require additional hardware. Moreover, it can lead to better energy management and increase battery lifetime. © 2023 IEEE.

关键词： AGV load estimation machine learning Prediction of energy consumption random forest

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