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22nd International Conference on Hybrid Intelligent Systems, HIS 2022 and the 18th International Conference on information Assurance and Security, IAS 2022

作者： Rassam, Murad A. Department of Information Technology College of Computer Qassim University Buraydah Saudi Arabia Faculty of Engineering and Information Technology Taiz University Taiz6803 Yemen

ISBN: (纸本)9783031274084

Anomalous data detection is an important task for ensuring the quality of data in many real-world applications. Medical healthcare services are one such application where Wireless Body Area Networks (WBAN) is used to track human health situations. Such tracking is achieved by collecting and monitoring the basic physiological vital signs and making them available to the healthcare givers to assess the criticality status of patients, especially in Intensive care units (ICU). Various anomaly detection approaches have been proposed for detecting anomalies collected in WBAN such as statistical, machine learning and deep learning techniques. However, the lack of ground truth data made the job of training such models a difficulty in supervised settings. In this paper, an Isolation Forest-based anomaly detection approach for WBAN (iForestBAN-AD) model is proposed. The iForest technique is fully unsupervised and does not employ any distance measure or density function like most existing techniques and rather detects anomalies based on the concept of isolation. To evaluate the proposed approach, experiments on data samples from real world physiological network records (Physionet) were conducted. The results show the viability of the proposed approach as it achieves around 95% AUC and outperforms many of the existing baseline unsupervised techniques on multivariate dataset samples. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

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Transfer Learning for Chest X-rays Diagnosis Using Dipper Throated Algorithm

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computers, Materials & Continua 2022年第11期73卷 2371-2387页

作者： Hussah Nasser AlEisa El-Sayed M.El-kenawy Amel Ali Alhussan Mohamed Saber Abdelaziz A.Abdelhamid Doaa Sami Khafaga Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Electronics and Communications Engineering Department Faculty of EngineeringDelta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

Most children and elderly people worldwide die from pneumonia,which is a contagious illness that causes lung *** diagnosing pneumonia from chest X-ray images,many deep learning models have been put *** goal of this research is to develop an effective and strong approach for detecting and categorizing pneumonia *** varying the deep learning approach,three pre-trained models,GoogLeNet,ResNet18,and DenseNet121,are employed in this research to extract the main features of pneumonia and normal *** addition,the binary dipper throated optimization(DTO)algorithm is utilized to select the most significant features,which are then fed to the K-nearest neighbor(KNN)classifier for getting the final classification *** guarantee the best performance of KNN,its main parameter(K)is optimized using the continuous DTO *** test the proposed approach,six evaluation metrics were employed namely,positive and negative predictive values,accuracy,specificity,sensitivity,and ***,the proposed approach is compared with other traditional approaches,and the findings confirmed the superiority of the proposed approach in terms of all the evaluation *** minimum accuracy achieved by the proposed approach is(98.5%),and the maximum accuracy is(99.8%)when different test cases are included in the evaluation experiments.

关键词： Metaheuristic pneumonia dipper throated optimization KNN

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Scheduling Tool for Deterministic Communication in Distributed Real-Time Systems 10

Scheduling Tool for Deterministic Communication in Distribut...

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10th Workshop on Software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2023

作者： Mach, Jan Tašková, Valéria Kohútka, Lukáš Institute of Computer Engineering and Applied Informatics Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava Slovakia Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava Slovakia

A network with critical data streams, where the timing of incoming and outgoing data is a necessity, is called a deterministic network. These networks are mostly used in association with real-time systems that use periodic hard-real-time data which need to be scheduled. The algorithm proposed in this paper is focused on achieving the lowest possible latency within the specified network topology depending on the physical parameters of the network and the periodic requirements of the communicating nodes. The algorithm allows to add priorities to the data flow between nodes and also the option to specify if preemption is allowed within the network, since not all devices support it. The result is a schedule of hard-real-time data communication within a specified network topology where all requirements of communicating nodes are met if such a solution is possible. © 2023 Copyright for this paper by its authors.

关键词： deadline deterministic scheduling Ethernet scheduling algorithm time-sensitive networking

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Performance Evaluation of Machine Learning Approaches in Detecting IoT-Botnet Attacks

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第19期17卷 136-146页

作者： Aljammal, Ashraf H. Qawasmeh, Ahmad Mughaid, Ala Taamneh, Salah Wedyan, Fadi I. Obiedat, Mamoon Department of Computer Science and Applications Prince Al Hussein bin Abdullah II Faculty of Information Technology The Hashemite University Zarqa Jordan Department of Information Technology Prince Al Hussein bin Abdullah II Faculty of Information Technology The Hashemite University Zarqa Jordan Department of Engineering Computing and Mathematical Sciences Lewis University RomeovilleIL United States

Botnets are today recognized as one of the most advanced vulnerability threats. Botnets control a huge percentage of network traffic and PCs. They have the ability to remotely control PCs (zombie machines) by their creator (BotMaster) via Command and Control (C&C) framework. They are the keys to a variety of Internet attacks such as spams, DDOS, and spreading malwares. This study proposes a number of machine learning techniques for detecting botnet assaults via IoT networks to help researchers in choosing the suitable ML algorithm for their applications. Using the BoT-IoT dataset, six different machine learning methods were evaluated: REPTree, RandomTree, RandomForest, J48, metaBagging, and Naive Bayes. Several measures, including accuracy, TPR, FPR, and many more, have been used to evaluate the algorithms’ performance. The six algorithms were evaluated using three different testing situations. Scenario-1 tested the algorithms utilizing all of the parameters presented in the BoT-IoT dataset, scenario-2 used the IG feature reduction approach, and scenario-3 used extracted features from the attacker’s received packets. The results revealed that the assessed algorithms performed well in all three cases with slight differences. © 2023 by the authors of this article. Published under CC-BY.

关键词： Internet of things

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Impact of Data Poisoning Attack on the Performance of Machine Learning Models

Impact of Data Poisoning Attack on the Performance of Machin...

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International Conference on Data Science and Network engineering, ICDSNE 2023

作者： Das, Dipan Roy, Sharmistha Sahoo, Bibhudatta Department of Computer Science & Engineering NIT Rourkela Rourkela India Faculty of Computing and Information Technology Usha Martin University Ranchi India

ISBN: (纸本)9789819967544

The twenty-first century has witnessed widespread adoption of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). These techniques have provided reliable solutions in various areas, including statistics, information theory, and mathematics. Given the prevalence of ML techniques, there exist various adversaries which question the robustness of ML models. Adversaries aim to manipulate models to their advantage, reducing their performance and accuracy. Data poisoning attack is one such adversary in which the attacker manipulates models by introducing specially crafted poisoned data into the training dataset. This paper presents the performance analysis of different machine learning models with and without the influence of data poisoning attack to predict the probability of diabetes and its effect on accuracy and precision. It has been observed that the SVM (RBF) classifier performs best on clean data, while the KNN classifier is highly affected by data poisoning, with a lesser impact on the SVM (Linear kernel) classifier. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reliability analysis

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Steering Angle Prediction and Algorithm Performance Testing in Autonomous Driving Simulators 66

Steering Angle Prediction and Algorithm Performance Testing ...

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66th International Symposium ELMAR, ELMAR 2024

作者： Benja, Borna Mijic, David Vranjes, Mario Grbic, Ratko TTTech Auto Central and Eastern Europe Osijek Croatia Computer Science and Information Technology Osijek Faculty of Electrical Engineering Osijek Croatia

ISBN: (纸本)9798350375411

The majority of traffic accidents are caused by human error. To counteract that, Advanced Driver-Assistance Systems (ADAS) are being developed and installed in vehicles. One of those systems is the Lane Keeping Assist System (LKAS), which helps to keep the vehicle centered in the corresponding lane. The problem of calculating a steering angle that is to be applied to the steering wheel to keep the vehicle in the corresponding lane is called steering angle prediction (SAP). In this paper, a novel SAP neural network (NN) architecture is presented. It is based on Shifted Windows (Swin) Transformer architecture and uses an image from the vehicle's front-view camera to predict the steering angle. Two new datasets for the training and testing of the NN were created. The datasets consist of the vehicle's front-view camera images and the corresponding steering wheel angles from two different autonomous driving simulators, CARLA and MetaDrive. A variety of weather and road conditions are present in the datasets, including clear, rainy, and cloudy weather and two-, four-, and six-lane roads with various road markings (white and yellow, solid and dashed lines). The presented SAP solution has shown promising results on created test sets and in simulated test environments. © 2024 IEEE.

关键词： Advanced driver assistance systems

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Comparative Study of Single-Input and Dual-Input PSS in Multi-machine System 32nd

Comparative Study of Single-Input and Dual-Input PSS in Mult...

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32nd International Conference on Organization and technology of Maintenance, OTO 2023

作者： Košorog, Tomislav Mehmedović, Muharem Marić, Predrag Kljajić, Ružica Faculty of Electrical Engineering Computer Science and Information Technology Osijek University of Osijek Kneza Trpimira 2B Osijek Croatia

ISBN: (纸本)9783031514937

To meet growing demand for electricity, power systems are operating near their operating limits. Due to technical limitations, the transmission lines are close to their transmission capacity and with different dynamic characteristics interaction can occur between the parts of a single power system or between interconnected power systems. Such interactions can result in low-frequency oscillations that further limit the transmission capacity of the system. Low-frequency oscillations can be reduced by increasing the damping torque of the synchronous generators. The most efficient way to increase damping torque is to implement power system stabilizers in the generator excitation circuit. In this paper, single-input and dual-input PSS are implemented in IEEE 14-Bus test system and their influence on the small-signal stability is compared. PSS location is determined by participation analysis, while tuning of the PSS is performed using the pole placement method. For the different tuning parameters, the system performance was analyzed in both time-domain and S-domain for single-input and dual-input PSSs implementations. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electric power system interconnection

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Opening Doors and Drawers by a UR5 Robot with Force Control 1

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32nd International Conference on Organization and technology of Maintenance, OTO 2023

作者： Dukić, Jana Vulić, Lukrecia Šimundić, Valentin Pejić, Petra Cupec, Robert Faculty of Electrical Engineering Computer Science and Information Technology Osijek J. J. Strossmayer University of Osijek Osijek Croatia

ISBN: (数字)9783031514944

ISBN: (纸本)9783031514937

Robots capable of automatically opening and closing articulated objects such as doors and drawers have broad potential applications in households, healthcare facilities, and industrial environments. Drawers can be opened and closed by pulling or pushing along the axis of translation, while doors can be opened by rotating around the axis of rotation. When a robot’s motion path deviates from the ideal trajectory required for opening and closing, its action can cause various damages due to excessive lateral forces. In this paper, we address the problem of force control in robotic manipulation of doors and drawers. The goal of force control is to minimize lateral forces when opening and closing doors and drawers. At the same time, position control is performed in the direction of the ideal trajectory. A force-torque sensor attached to a robot can monitor all forces in 3D space. We conduct experiments on opening and closing the doors and drawers using a UR5 robot with and without force control, and analyze the force response while monitoring the success of the actions performed. With this study, we provide insight into the usefulness of force control in opening and closing doors and drawers by a robot. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Trajectories

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COVID-19 Classification from X-Ray Images:An Approach to Implement Federated Learning on Decentralized Dataset

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computers, Materials & Continua 2023年第5期75卷 3883-3901页

作者： Ali Akbar Siddique S.M.Umar Talha M.Aamir Abeer D.Algarni Naglaa F.Soliman Walid El-Shafai Department of Telecommunication Engineering Sir Syed University of Engineering&TechnologyKarachi75300Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

The COVID-19 pandemic has devastated our daily lives,leaving horrific repercussions in its *** to its rapid spread,it was quite difficult for medical personnel to diagnose it in such a big *** who test positive for Covid-19 are diagnosed via a nasal PCR *** comparison,polymerase chain reaction(PCR)findings take a few hours to a few *** PCR test is expensive,although the government may bear expenses in certain ***,subsets of the population resist invasive testing like ***,chest X-rays or computerized Vomography(CT)scans are preferred in most cases,and more importantly,they are non-invasive,inexpensive,and provide a faster response *** advances in Artificial Intelligence(AI),in combination with state-of-the-art methods,have allowed for the diagnosis of COVID-19 using chest *** article proposes a method for classifying COVID-19 as positive or negative on a decentralized dataset that is based on the Federated learning *** order to build a progressive global COVID-19 classification model,two edge devices are employed to train the model on their respective localized dataset,and a 3-layered custom Convolutional Neural Network(CNN)model is used in the process of training the model,which can be deployed from the *** two edge devices then communicate their learned parameter and weight to the server,where it aggregates and updates the *** proposed model is trained using an image dataset that can be found on *** are more than 13,000 X-ray images in Kaggle Database collection,from that collection 9000 images of Normal and COVID-19 positive images are *** edge node possesses a different number of images;edge node 1 has 3200 images,while edge node 2 has *** is no association between the datasets of the various nodes that are included in the *** doing it in this manner,each of the nodes will have access to a separate image collection that has no correl

关键词： Artificial intelligence deep learning federated learning COVID-19 decentralized image dataset

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Performance Analysis of the Machine Learning Algorithms for the Early Detection of Breast Carcinoma 2

Performance Analysis of the Machine Learning Algorithms for ...

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2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Vanitha, K. Karpagavalli, S.M. Mahesh, T.R. Ali, A. Althaf Sridhar, T. Anitha, K. Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Al Ameen Engineering College Department of Computer Science and Engg Tamilnadu Erode India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India Madanapalle Institute of Technology & Science Department of Computer Applications Andhra Pradesh India Alliance University Alliance College of Engineering and Design Bangalore India Csi College of Engineering Department of Information Technology Tamilnadu Ooty India

ISBN: (纸本)9798350360523

Breast Carcinoma, generally known as breast cancer, primarily affects women, though men can develop it as well. Because of the existence of breast tissue and exposure to female hormones, notably oestrogen, women are at a higher risk It's critical to diagnose breast tumors early. Several techniques based on machine learning (ML were used in this study to classify breast cancer using a dataset that was made available to the public. F-score, recall, precision, preciseness, and other performance metrics were used to evaluate these ML algorithms. Previous research and experimental findings indicate that Random Forest achieved the highest accuracy, with a remarkable accuracy rate of 99.12%. © 2024 IEEE.

关键词： Hormones

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