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International Conference on electrical, Communication, and computer engineering (ICECCE)

作者： Afaq Ahmed Bhatti Fawad Zaman Syes Muzahir Abbas Gridsada Phanomchoeng Shafqat Ullah Khan Athar Waseem Department of Electrical & Computer Engineering COMSATS University Islamabad Pakistan School of Engineering Faculty of Science and Engineering Macquarie University Sydney NSW Australia Department of Mechanical Engineering Chaulalongkorn University Bangkok Thailand Department of Electronics University of Buner Buner Pakistan Department of Electrical Engineering International Islamic University Islamabad Pakistan

Planar antenna arrays are considered as the one of the important parts in the applications like broadcasting, sonar and radar communication. In order to produce the directed beam of desired angles, the planar antenna array is used with large number of antenna elements. In the presence of faulty element(s) the resultant radiation pattern is distorted with high magnitude side lobes and reduced gain. The process of detection is discussed widely in linear arrays but for the planar arrays this study is limited. This study developed the process of the detection of the faulty antenna(s) in the planar array using a detection algorithm by comparing the weights of array with faulty antennas with the weights of arrays with healthy antennas. The analysis was varied for different orientations of 4 × 4 and 6 × 6 arrays with single and multiple element failures. The detection algorithm was found to be efficient in detecting the faulty elements with great accuracy.

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Sparrow Search Optimization with Deep Belief Network based Wind Power Prediction Model

Sparrow Search Optimization with Deep Belief Network based W...

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Aruna, M. Narayana, Patel Badari Kumar, S. Narasimha Walid, Abul Ala Patra, Jyoti Prasad Kumar, B. Suresh Nmit Department of Eee Karnataka Bangalore India Mahatma Gandhi Institute of Technology Department of Mechanical Engineering TS Hyderabad India Chaitanya Bharati Institute of Technology Mechanical Engineering Department Gandipet Hyderabad India Department of Computer Science and Engineering Khulna9203 Bangladesh Odisha University of Technology and Research Outr Mahalaxmi Vihar Ghatikia Techno Campus Faculty Electrical Odisha Bhubaneswar India Chaitanya Bharathi Institute of Technology Department of Eee Telangana Hyderabad India

ISBN: (纸本)9781665474511

Wind power is a clear feature of intermittent, nonstationary, and difficult fluctuations, making it challenging for achieving consistent wind power generation. Assuming the restricted nature of typical energy resources and the developing difficulties of environmental problems, several countries are starting with developing novel energy resources which are considered for environmental and renewable safety. Amongst the several novel energy resources, wind energy was abundant, doesn't cause pollution, has minimum cost, and does not deplete. Accurate wind power predictive is enhance the reliability and safety of power grid function. Therefore, this study presents a sparrow search optimization with deep belief network for wind power prediction (SSODBN-WPP) technique. The SSODBN-WPP technique follows a two stage process namely prediction and parameter tuning. At the initial stage, the SSODBN-WPP technique employs DBN method for wind power prediction. Next, the SSO algorithm is used to adjust the core hyperparameters of the DBN algorithm. The efficacy of the SSODBN-WPP method is tested on a comprehensive set of simulations that take place on wind power dataset. A comparison study of the SSODBN-WPP technique reported its betterment over other predictive approaches. © 2023 IEEE.

关键词： Wind power

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Understanding Bulk-Bitwise Processing In-Memory Through Database Analytics

arXiv

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arXiv 2022年

作者： Perach, Ben Ronen, Ronny Kimelfeld, Benny Kvatinsky, Shahar The Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering Technion – Israel Institute of Technology Haifa Israel Taub Faculty of Computer Science Technion – Israel Institute of Technology Haifa Israel

Bulk-bitwise processing-in-memory (PIM), where large bitwise operations are performed in parallel by the memory array itself, is an emerging form of computation with the potential to mitigate the memory wall problem. This paper examines the capabilities of bulk-bitwise PIM by constructing PIMDB, a fully-digital system based on memristive stateful logic, utilizing and focusing on in-memory bulk-bitwise operations, designed to accelerate a real-life workload: analytical processing of relational databases. We introduce a host processor programming model to support bulk-bitwise PIM in virtual memory, develop techniques to efficiently perform in-memory filtering and aggregation operations, and adapt the application data set into the memory. To understand bulk-bitwise PIM, we compare it to an equivalent in-memory database on the same host system. We show that bulk-bitwise PIM substantially lowers the number of required memory read operations, thus accelerating TPC-H filter operations by 1.6×–18× and full queries by 56×–608×, while reducing the energy consumption by 1.7×–18.6× and 0.81×–12× for these benchmarks, respectively. Our extensive evaluation uses the gem5 full-system simulation environment. The simulations also evaluate cell endurance, showing that the required endurance is within the range of existing endurance of RRAM devices. © 2022, CC BY.

关键词： Computation theory

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3-sCHSL: Three-Stage Cyclic Hybrid SFS and L-SHADE Algorithm for Single Objective Optimization

3-sCHSL: Three-Stage Cyclic Hybrid SFS and L-SHADE Algorithm...

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Congress on Evolutionary Computation

作者： Heba Abdel-Nabi Mostafa Z. Ali Arafat Awajan Rami Alazrai Mohammad I. Daoud Ponnuthurai N. Suganthan Robert Reynolds Department of Computer Science Princess Sumaya University for Technology Amman Jordan Faculty of Computer & Information Technology Jordan University of Science & Technology Irbid Jordan Computer Science Department Mutah University Karak Jordan Department of Computer Engineering German Jordanian University Amman Jordan School of Electrical & Electronic Engineering Nanyang Technological University Singapore KINDI Center for Computing Research College of Engineering Qatar University Doha Qatar College of Engineering Wayne State University Detroit USA

This paper proposes a novel hybridization of two metaheuristic algorithms to solve the real-parameter single objective numerical optimization problems. The proposed Three-stage Cyclic Hybrid SFS and L-SHADE (3-sCHSL) algorithm integrates the high-level interactions of Stochastic Fractal Search (SFS) and L-SHADE algorithms in a single framework. Furthermore, a guided control population initialization strategy is injected in 3-sCHSL to address the stagnation and diversity loss issues as the evolution process evolves. The performance of the proposed algorithm is tested under different complexity levels of different transformations of the CEC 2021 benchmark suite with dimension 20. The experimental results demonstrated the efficiency and competitiveness of the proposed algorithm against recent state-of-the-art algorithms. In addition, 3-sCHSL achieved a superior performance when evaluating two engineering design problems.

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FMCW Radar-Based Enhancing Moving Target Positioning Accuracy in Spectrogram Mapping

FMCW Radar-Based Enhancing Moving Target Positioning Accurac...

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International Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

作者： Nattakarn Shutimarrungson Seksan Eiadkaew Akkarat Boonpoonga Krit Athikulwongse Suramate Chalermwisutkul Department of Business Computer Mahasarakham Business School Mahasarakham University Maha Sarakham Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering Research Center of Innovation Digital and Electromagnetic Technology (iDEMT) King Mongkut's University of Technology North Bangkok Bangkok Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Pathumthani Thailand Communication and Smart System Engineering The Sirindhorn International Thai- German Graduate School of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

In this paper, we propose a methodology aimed to enhancing the accuracy of moving target positioning using FMCW radar within spectrogram mapping. Utilizing a 10 GHz operating frequency, the FMCW radar effectively detects moving targets. However, the interpretation of intermediate frequency (IF) signals posed challenges due to multiple reflections and resulting ambiguous distances between objects. For solving this problem, we propose two steps of solution: employing an enveloping method and normalization of the IF signal. This process can extract maximum amplitude peaks specific to the target. Experimental validation involved detecting a drone moving away from the radar. Comparative analysis pre and post signal processing in spectrogram terms demonstrate the efficacy of our technique. Furthermore, verification of the detection range of radar was conducted using the GSM-015 GNSS speed meter. Results confirm the accuracy of the positioning capabilities of radar, emphasizing the effectiveness of the proposed approach.

关键词： Visualization Accuracy Refining Radar detection Radar Radar antennas Reflection

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Bayesian Optimization with Stacked Sparse Autoencoder based Cryptocurrency Price Prediction Model 5

Bayesian Optimization with Stacked Sparse Autoencoder based ...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Shaikh, Irfan Abdul Karim Krishna, P. Vamsi Biswal, Swagat Gourav Kumar, A. Sasi Baranidharan, S. Singh, Kamlesh Faculty of Business Bahrain Polytechnic Isa Town Bahrain School of Management Malla Reddy University Telangana Hyderabad India Odisha University of Tecnology and Research Department of Electrical Engineering Orissa Bhubaneswar India Institute of Engineering and Technology Srinivas University Mentor-IT-Inurture Education Solutions Pvt Ltd Bangalore Department of Cloud Technology and Data Science Karnataka Dakshina Kannada India Department of Mba and Research Center Karnataka Bangalore India Graphic Era Hill University Department of Computer Science and Engineering Dehradun Campus Dehradun India

ISBN: (纸本)9781665474672

Digital currency is a way of currency utilized in the digital world namely electronic devices or digital forms. Many terms are alternative words for digital currency such as cyber cash, digital money, and electronic money. Cryptocurrency is a type of asset that has developed due to the progression of financial technology and it has made a tremendous chance for research workers. Cryptocurrency price prediction is challenging because of the dynamism and price volatility. The electronic economy is severely hazardous and should be advanced with greater caution, to minimize or avoid the risk that occurs in this case. Therefore, this study develops a new Bayesian optimization with Stacked Sparse Autoencoder based Cryptocurrency Price Prediction (BOSSAE-CPP) model. The major intention of the BOSSALCPP technique lies in the effectual prediction of cryptocurrency prices. To attain this, the BOSSAE-CPP technique exploits SSAE model for price prediction process. Moreover, the BO technique is used to optimally choose the hyperparameter values of the SSAE model and results in enhanced predictive outcomes. To deliberate the enhanced outcomes of the BOSSALCPP technique, extensive experimentation study is made. The comparison study highlighted the improved performance of the BOSSAE-CPP technique. © 2023 IEEE.

关键词： Electronic money

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Robust Attacks Detection Model for Internet of Flying Things based on Generative Adversarial Network (GAN) and Adversarial Training

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IEEE Internet of Things Journal 2025年

作者： Gaber, Tarek Ali, Tarek Nicho, Mathew Torky, Mohamed Engineering and Environment University of Salford School of Science Manchester United Kingdom Suez Canal University Faculty of Computers and Informatics Ismailia Egypt Manchester Metropolitan University Department of Computing and Mathematics Manchester United Kingdom Rabdan Academy Research and Innovation Centre Abu Dhabi United Arab Emirates University of Queensland School of Electrical Engineering and Computer Science Australia Egyptian Russian University-Badr City Faculty of Artificial Intelligence Cairo Egypt

The Internet of Flying Things (IoFT) holds significant promise in fields like disaster management and surveillance. However, it is increasingly vulnerable to cyberattacks that can compromise the confidentiality, integrity, and availability (CIA) of sensitive data. Despite the growing interest in proposing Intrusion Detection Systems (IDSs) for IoFT networks, current literature faces key limitations, particularly the shortage of publicly available IoFT datasets with diverse attacks, and the fact that existing IDSs lack robustness against sophisticated adversarial machine learning attacks. This paper is the first study to address these limitations by proposing a more resilient and accurate IDS tailored for IoFT networks (RIDS-IoFT). We introduce a novel IDS that leverages Generative Adversarial Networks (GANs) to generate a hybrid dataset that combines real IoFT traffic data with GAN-generated adversarial attacks, addressing the dataset diversity issue. Additionally, we introduce an innovative adversarial training method to enhance the system’s defense against evolving threats, such as Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), and Carlini & Wagner (C&W) attacks. The proposed RIDS-IoFT was evaluated using four machine learning models, Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and Logistic Regression (LR), on two datasets: ECU-IoFT and CICIDS2018. The IDS’s performance was assessed based on its ability to detect both traditional and adversarial attacks. The results show that the Random Forest model achieved the highest detection accuracy, up to 96.5%, demonstrating superior performance across both real and hybrid datasets. The proposed RIDS-IoFT not only enhances detection accuracy but also strengthens resilience against adversarial threats, making it suitable for resource-constrained IoFT environments. In conclusion, this study presents a comprehensive approach to securing IoFT networks by combining real and synthetic d

关键词： Generative adversarial networks

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Chatbots: Security, privacy, data protection, and social aspects

Chatbots: Security, privacy, data protection, and social asp...

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作者： Hasal, Martin Nowaková, Jana Ahmed Saghair, Khalifa Abdulla, Hussam Snášel, Václav Ogiela, Lidia IT4Innovations VSB—Technical University of Ostrava Ostrava Czech Republic Department of Computer Science Faculty of Electrical Engineering and Computer Science VSB—Technical University of Ostrava Ostrava Czech Republic Pedagogical University of Krakow Krakow Poland

Chatbots are artificial communication systems becoming increasingly popular and not all their security questions are clearly solved. People use chatbots for assistance in shopping, bank communication, meal delivery, healthcare, cars, and many other actions. However, it brings an additional security risk and creates serious security challenges which have to be handled. Understanding the underlying problems requires defining the crucial steps in the techniques used to design chatbots related to security. There are many factors increasing security threats and vulnerabilities. All of them are comprehensively studied, and security practices to decrease security weaknesses are presented. Modern chatbots are no longer rule-based models, but they employ modern natural language and machine learning techniques. Such techniques learn from a conversation, which can contain personal information. The paper discusses circumstances under which such data can be used and how chatbots treat them. Many chatbots operate on a social/messaging platform, which has their terms and conditions about data. The paper aims to present a comprehensive study of security aspects in communication with chatbots. The article could open a discussion and highlight the problems of data storage and usage obtained from the communication user—chatbot and propose some standards to protect the user. © 2021 The Authors. Concurrency and Computation: Practice and Experience published by John Wiley & Sons Ltd.

关键词： Digital storage

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ICGNN: Graph Neural Network Enabled Scalable Beamforming for MISO Interference Channels

arXiv

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arXiv 2025年

作者： He, Changpeng Lu, Yang Ai, Bo Dobre, Octavia A. Ding, Zhiguo Niyato, Dusit School of Computer Science and Technology Beijing Jiaotong University Beijing100044 China School of Electronics and Information Engineering Beijing Jiaotong University Beijing100044 China Faculty of Engineering and Applied Science Memorial University St. John’sNLA1C 5S7 Canada Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi127788 United Arab Emirates College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

This paper investigates the graph neural network (GNN)-enabled beamforming design for interference channels. We propose a model termed interference channel GNN (ICGNN) to solve a quality-of-service constrained energy efficiency maximization problem. The ICGNN is two-stage, where the direction and power parts of beamforming vectors are learned separately but trained jointly via unsupervised learning. By formulating the dimensionality of features independent of the transceiver pairs, the ICGNN is scalable with the number of transceiver pairs. Besides, to improve the performance of the ICGNN, the hybrid maximum ratio transmission and zero-forcing scheme reduces the output ports, the feature enhancement module unifies the two types of links into one type, the subgraph representation enhances the message passing efficiency, and the multi-head attention and residual connection facilitate the feature extracting. Furthermore, we present the over-the-air distributed implementation of the ICGNN. Ablation studies validate the effectiveness of key components in the ICGNN. Numerical results also demonstrate the capability of ICGNN in achieving near-optimal performance with an average inference time less than 0.1 ms. The scalability of ICGNN for unseen problem sizes is evaluated and enhanced by transfer learning with limited fine-tuning cost. The results of the centralized and distributed implementations of ICGNN are illustrated. © 2025, CC BY.

关键词： Graph neural networks

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Arithmetic Optimization Algorithm with Machine Learning based Smart Irrigation System in IoT Environment 5

Arithmetic Optimization Algorithm with Machine Learning base...

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5th IEEE International Conference on electrical, computer and Communication Technologies, ICECCT 2023

作者： Aruna, M. Badari Narayana, Patel Kumar, S. Narasimha Ala Walid, Md. Abul Prasad Patra, Jyoti Kumar, B. Suresh NMIT Department of EEE Karnataka Bangalore India Mahatma Gandhi Institute of Technology Department of Mechanical Engineering TS Hyderabad India Chaitanya Bharati Institute of Technology Gandipet Mechanical Engineering Department Hyderabad India Department of Computer Science and Engineering Khulna9203 Bangladesh Faculty Electrical Odisha University of Technology and Research OUTR Mahalaxmi Vihar Ghatikia Techno Campus Odisha Bhubaneswar India Chaitanya Bharathi Institute of Technology Department of EEE Telangana Hyderabad India

ISBN: (纸本)9781665493604

An integration of new technologies like agro-hydroinformatics and computational intelligence, and information technology (IT) roles a vital play in developing a sustainable precision irrigation method with the effectual management of sensed data assuming plants, weather, and soil. Machine learning (ML) is a fast-developing technology for precision irrigation schemes, because of their capability to simulate human decision-making but also resolve the multi-variable, non-linear, and time-variant problems affecting irrigation management. An essential drive of ML is for providing data from preceding experiences and statistical data to machines towards it is carried out their allocated task of resolving a particular problem. Therefore, this study develops an Arithmetic Optimization Algorithm with Machine Learning based Smart irrigation system (AOAML-SIS) technique in IoT environment. Initially, the AOAML-SIS technique exploits the IoT sensors for data collection process. For determining the need for irrigation, the AOALM-SIS technique utilizes multilayer perceptron (MLP) classification model. Since the MLP parameters play a vital role, the parameter tuning process is carried out by the AOA algorithm. The experimental assessment of the AOAML-SIS technique takes place on agriculture data. The simulation values inferred the effectual irrigation classification efficacy of the AOAML-SIS technique over other models. © 2023 IEEE.

关键词： Machine learning

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