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An Optimized Algorithm for Renewable Energy Forecasting Based on Machine Learning

Intelligent Automation & Soft Computing 2023年第1期35卷 755-767页

作者： Ziad M.Ali Ahmed M.Galal Salem Alkhalaf Imran Khan College of Engineering at Wadi Addawaser Prince Sattam bin Abdulaziz UniversityWadi Aldawaser11991Saudi Arabia Electrical Engineering Department Aswan Faculty of EngineeringAswan UniversityAswan81542Egypt Production Engineering and Mechanical Design Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer College of Science and Arts in Ar RassQassim UniversityAr RassSaudi Arabia Department of Electrical Engineering University of Engineering and TechnologyPeshawar814Pakistan

The large-scale application of renewable energy power generation technology brings new challenges to the operation of traditional power grids andenergy management on the load side. Microgrid can effectively solve this problemby using its regulation and flexibility, and is considered to be an ideal *** traditional method of computing total transfer capability is difficult due tothe central integration of wind farms. As a result, the differential evolutionextreme learning machine is offered as a data mining approach for extractingoperating rules for the total transfer capability of tie-lines in wind-integratedpower systems. K-medoids clustering under the two-dimensional “wind power-load consumption” feature space is used to define representative operational scenarios initially. Then, using stochastic sampling and repetitive power flow, aknowledge base for total transfer capability operating rule mining is ***, a novel method is used to filter redundant characteristics and find featuresthat are closely associated to the total transfer capability in order to decrease theultra-high dimensionality of operational features. Finally, by feeding the trainingdata into the proposed algorithm, the total transfer capability operation rules arederived from the knowledge base. It can be seen that, the proposed algorithmcan optimize the system performance with good accuracy and generality, according to numerical data.

关键词： Load forecasting distribution network machine learning renewable energy

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A Secure and Efficient Scheme for Mutual Authentication for Integrity of Exchanged Data in IoMT 28

A Secure and Efficient Scheme for Mutual Authentication for ...

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28th International computer Conference, computer Society of Iran, CSICC 2023

作者： Faraji, Mahmoud Reza Shahriari, Hamid Nikooghadam, Mahdi Amirkabir University of Technology Tehran Polytechnic Faculty of Computer Engineering Computer Science Department Tehran Iran

ISBN: (纸本)9798350338195

The emergence of pandemic diseases like Covid-19 in recent years has made it more important for Internet of Medical Things (IoMT) environments to build contact between patients and doctors in order to control their health state. Patients will be able to send their healthcare data to the cloud server of the medical service provider in remote medical environments through sensors connected to their smart devices, such as watches or smartphones. However, patients' worries surrounding their data privacy protection are still present. In order to ensure the security and privacy of patients' healthcare data in remote medical environments, a number of different schemes have been proposed by researchers. However, these schemes have not been able to take all security requirements into account. Consequently, in this study, we have proposed a secure and effective protocol to safeguard the privacy of patients' medical data when it is sent to the server. This protocol entails two components: mutual authentication of the patient and the server of the medical service provider, as well as the integrity of the exchanged data. Also, our scheme satisfies security requirements and is resistant to well-known attacks. Following this, we used the Scyther tool to formally analyze our proposed scheme. The results showed that the scheme is secure, and in the section on performance analysis, we demonstrated that the proposed scheme performs better than comparable schemes. © 2023 IEEE.

关键词： Authentication

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Metasurfaces for next-generation wireless communication systems

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National science Review 2023年第8期10卷 17-19页

作者： Younes Ra'di Nikita Nefedkin Petar Popovski Andrea Alù Department of Electrical Engineering and Computer Science Syracuse University Photonics Initiative Advanced Science Research Center City University of New York Department of Electronic Systems Aalborg University Physics Program Graduate Center City University of New York

The pace of development in the world of 5G communication systems has proven to be much more demanding than previous generations, with 5G-Advanced seemingly around the corner [1]. Extensive research is already underway to structure the next generation of wireless systems(i.e. 6G), which may potentially enable an unprecedented level of human–machine interaction [2].

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Coverless Image Steganography System Based on Maze Game Generation

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Intelligent Automation & Soft Computing 2023年第11期38卷 125-138页

作者： Al Hussien Seddik Saad Mohammed S.Reda GamalM.Behery Ahmed A.El-harby Mohammed Baz Mohamed Abouhawwash Ahmed Ismail Ebada Department of computer science Faculty of ScienceMinia UniversityShalabyMiniaEgypt Department of computer science Faculty of Computers and Artificial IntelligenceDamietta UniversityNew DamiettaDamiettaEgypt Department of Computer Engineering College of Computer and Information TechnologyTaif UniversityP.O.Box 11099Taif21994Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)College of EngineeringMichigan State UniversityEast LansingMI48824USA Department of Information Systems Faculty of Computers and Artificial IntelligenceDamietta UniversityNew DamiettaDamiettaEgypt

The trend of digital information transformation has become a topic of *** data are threatening;thus,protecting such data from attackers is considered an essential ***,a new methodology for data concealing has been suggested by researchers called coverless *** steganography can be accomplished either by building an image database to match its image subblocks with the secret message to obtain the stego image or by generating an *** paper proposes a coverless image steganography system based on pure image generation using secret message bits with a capacity higher than the other traditional *** system uses the secret message to generate the stego image in the form of one of the Intelligence Quotient(IQ)games,the ***,a full grid is generated with several specific rows and columns determined from the number of bits of the secret ***,these bits are fed to the full grid to form the maze game stego ***,the generated maze game stego image is sent to the *** experimental results,using the Bit Error Rate(BER),were conducted,and confirmed the strength of this system represented by a high capacity,perfect performance,robustness,and stronger hiding system compared with existing coverless steganography systems.

关键词： Coverless data hiding digital image steganography intelligence quotient games maze game

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Signature Based Identification using Locality-Sensitive Hashing for IoT Devices 2

Signature Based Identification using Locality-Sensitive Hash...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Singh, Amit Sindhu Madhuri, G. Teerthanker Mahaveer University College of Computing Science & It Department of Computing Science & It Moradabad India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

On current days, the growing applications in Internet of Things (IoT) is increasing in various real-time sectors as smart city applications (air pollution, atmospheric noise canceling and traffic flow management). Now, these technologies are combined to provide the comfort configuration for each application. However, malware / virus are highly possible to extract the device software or hardware information in IoT. All sensed information about the environment or any event is stored in these devices which are hacked by the malicious attackers. To overcome these adversities, we introduced the concept of locality sensitive hash (LSH) for IoT device trustworthiness identification. The main objective of our proposed system is to allow secure transfer of data from the resource constrained devices. In this project, the resource constrained devices are the wireless sensor nodes. The experimental analysis confirmed that our proposed approach is suitable for the application or environment with resource constrained devices. Also, the time, energy, latency and packet overhead is considerably reduced than the existing systems considered for this project. © 2023 IEEE.

关键词： Internet of things

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A Brain-computer Interface Augmented Reality Framework with Auto-Adaptive SSVEP Recognition 2

A Brain-Computer Interface Augmented Reality Framework with ...

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2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2023

作者： Mustafa, Yasmine Elmahallawy, Mohamed Luo, Tie Eldawlatly, Seif Missouri University of Science and Technology Computer Science Department MO United States German University Digital Media Engineering and Technology Department Cairo Egypt Ain Shams University Computer and Systems Engineering Department Egypt The American University Computer Science and Engineering Department Cairo Egypt

ISBN: (纸本)9798350300802

Brain-computer Interface (BCI) initially gained attention for developing applications that aid physically impaired individuals. Recently, the idea of integrating BCI with Augmented Reality (AR) emerged, which uses BCI not only to enhance the quality of life for individuals with disabilities but also to develop mainstream applications for healthy users. One commonly used BCI signal pattern is the Steady-state Visually-evoked Potential (SSVEP), which captures the brain's response to flickering visual stimuli. SSVEP-based BCI-AR applications enable users to express their needs/wants by simply looking at corresponding command options. However, individuals are different in brain signals and thus require per-subject SSVEP recognition. Moreover, muscle movements and eye blinks interfere with brain signals, and thus subjects are required to remain still during BCI experiments, which limits AR engagement. In this paper, we (1) propose a simple adaptive ensemble classification system that handles the inter-subject variability, (2) present a simple BCI-AR framework that supports the development of a wide range of SSVEP-based BCI-AR applications, and (3) evaluate the performance of our ensemble algorithm in an SSVEP-based BCI-AR application with head rotations which has demonstrated robustness to the movement interference. Our testing on multiple subjects achieved a mean accuracy of 80% on a PC and 77% using the HoloLens AR headset, both of which surpass previous studies that incorporate individual classifiers and head movements. In addition, our visual stimulation time is 5 seconds which is relatively short. The statistically significant results show that our ensemble classification approach outperforms individual classifiers in SSVEP-based BCIs. © 2023 IEEE.

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Deep Learning Approach for Automated Skin Disease Diagnosis 1

Deep Learning Approach for Automated Skin Disease Diagnosis

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Prashanth, B.S. Puneeth, B.H. Manoj Kumar, M.V. Bhavana, R.K. Arshitha, K. Santhosh, S. Puneeth, S.P. Sneha, H.R. Nitte Meenakshi Institute of Technology Department of Information Science & Engineering Karnataka Bangalore560064 India Bapuji Institute of Engineering and Technology Department of Computer Science and Business Systems Karnataka Davanagere577004 India Bapuji Institute of Engineering and Technology Department of Information Science and Engineering Karnataka Davanagere577004 India

ISBN: (纸本)9798350376517

Skin diseases has been a significant challenge in healthcare, necessitating accurate and timely diagnosis for effective treatment. This paper presents research on the use of Convolutional Neural Networks (CNNs) for the categorization of skin illnesses. CNNs are a viable tool for automated skin disease detection since they successfully leverage the deep learning capabilities in image-based tasks. The research begins with a detailed review of existing methodologies and datasets in the domain of skin disease classification. Subsequently, we propose a novel CNN architecture tailored to address the unique challenges posed by dermatological images, such as variability in lesion size, color, and texture. The proposed model is trained and evaluated on a diverse and extensive dataset, ensuring its robustness and generalization capabilities across various skin conditions. The study delves into the interpretability of the CNN model, providing insights into the features learned during the training process. This analysis contributes to the understanding of how deep learning models make decisions in the context of dermatological diagnosis, enhancing the trustworthiness and acceptance of such automated systems in clinical settings. Furthermore, the paper investigates the impact of data augmentation techniques on model performance, addressing the scarcity of labeled dermatological datasets. By increasing the training data, we explore strategies to improve the model's ability to generalize to unseen cases, thereby enhancing its practical utility. Experimental results are presented and compared with state-of-the-art approaches, proving the viability of the suggested CNN architecture. © 2024 IEEE.

关键词： Convolutional neural networks

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Semi-Automated Video Archiving Using Object Detection, Tracking and Ontological Annotation 4

Semi-Automated Video Archiving Using Object Detection, Track...

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4th International Conference on Artificial Intelligence, Robotics and Control, AIRC 2023

作者： Ayman, Shehab Eldeen Hussein, Walid Karam, Omar H. The British University in Egypt Faculty of Informatics and Computer Science Department of Software Engineering Cairo Egypt The British University in Egypt Faculty of Informatics and Computer Science Department of Computer Science Cairo Egypt

ISBN: (纸本)9798350348248

In light of this unmistakable exponential data expansion, visual media archiving must be rethought. Human generated meta-data might not be sufficient for efficient data retrieval. Object detection and object recognition enable the generation of computer-generated meta data that improve the storage and retrieval of data. We present a semi-automated approach for preserving video footage that combines object identification and ontologies. This significantly cuts down on search time, data scrubbing by providing only the relevant information making for more efficient queries. © 2023 IEEE.

关键词： computer vision

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A variable structure model of neurons’ application in computer vision

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Multimedia Tools and Applications 2025年 1-11页

作者： Sridi, Alaeddine Attia, Mouna Bouallegue, Kais Electronics and Micro-Electronics Laboratory Faculty of Sciences of Monastir University of Monastir Monastir Tunisia The Higher Institute of Computer Science and Communication Technologies of Hammam Sousse University of Sousse Sousse Tunisia Laboratory of Computer Science Parallelism and Intelligent Communicating Systems Faculty of Sciences of Tunisia University of Tunis ELManar Tunis Tunisia Department of Electrical Engineering Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Sousse Tunisia

The neuron doctrine defines the neuron as the basic unit of the nervous system, which drives the dynamic behavior of our organs. This has led to neurons becoming the focus of modern neuroscience research and to the rise of neurocomputing, in which we try to digitally simulate human behavior. The goal being to better understand the human nervous system and gain more control over neuron-related diseases, helping doctors establish faster and more reliable medical diagnoses. In this context, we aim to simulate eye diseases and vision problems by convolving input images using a new class of neural networks based on a variable structure model of neurons presented in [1]. In this work, we focus on recreating the common convolution filters as a first step. This is the first time in neurocomputing literature that dendritic neurons have been used for dynamic convolution kernels generation and eye diseases simulation. We start with a presentation of our upgrades to the model’s flexibility and accuracy. Then, we propose a novel kernel generator algorithm to convolve the input images. Finally, we show its promising results in creating the outline, pixelization, and night convolution kernels, as well as control some metrics, including brightness and data retention. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Neurons

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Next Generation Memristor Reservoir Computing 3

Next Generation Memristor Reservoir Computing

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3rd IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2024

作者： Nikiruy, K. Ivanov, T. Ziegler, M. Rossetti, D. Corinto, F. Ascoli, A. Tetzlaff, R. Demirkol, A.S. Schmitt, N. Micro-and Nanoelectronic Systems Tu Ilmenau Department of Electrical Engineering and Information Technology Ilmenau Germany Turin Italy Institute of Circuits and Systems Tu Dresden Faculty of Electrical and Computer Engineering Dresden Germany

ISBN: (纸本)9798350378009

The nonlinear transformation used in reservoir computing can be effectively replaced by nonlinear vector autoregression (NVAR) for data prediction. In such a method, also known as next generation reservoir computing (NGRC), the input signal consists of a linear part, including several previous data points, and their nonlinear combinations. Here we show that the application of this method to a network with memristive weights (memristors) can be used to predict signals, depending on the nature of the nonlinear functions and the number of memristors. The network allows an accurate prediction of chaotic time series of Mackey-Glass and Duffing oscillators. © 2024 IEEE.

关键词： Memristors

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