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arXiv 2025年

作者： Rehannara Beegum, T. Idris, Mohd Yamani Idna Ayub, Mohamad Nizam Bin Shehadeh, Hisham A. Ali, Usman Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Information Technology Al-Huson University College Al-Balqa Applied University Al-Huson Jordan Department of Computer and Software Technology University of Swat KPK Pakistan

This paper proposes a new hybrid algorithm, combining FA, SSO, and the N-R method to accelerate convergence towards global optima, named the Hybrid Firefly Algorithm and Sperm Swarm Optimization with Newton-Raphson (HFASSON). The performance of HFASSON is evaluated using 23 benchmark functions from the CEC 2017 suite, tested in 30, 50, and 100 dimensions. A statistical comparison is performed to assess the effectiveness of HFASSON against FA, SSO, HFASSO, and five hybrid algorithms: Water Cycle Moth Flame Optimization (WCMFO), Hybrid Particle Swarm Optimization and Genetic Algorithm (HPSOGA), Hybrid Sperm Swarm Optimization and Gravitational Search Algorithm (HSSOGSA), Grey Wolf and Cuckoo Search Algorithm (GWOCS), and Hybrid Firefly Genetic Algorithm (FAGA). Results from the Friedman rank test show the superior performance of HFASSON. Additionally, HFASSON is applied to Cognitive Radio Vehicular Ad-hoc Networks (CR-VANET), outperforming basic CR-VANET in spectrum utilization. These findings demonstrate HFASSON’s efficiency in wireless network applications. © 2025, CC BY.

关键词： Vehicular ad hoc networks

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Design Of Car Parking Availability and Capacity system Using ESP32-Cam and NodeMCU ESP8266

Design Of Car Parking Availability and Capacity System Using...

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International Conference on Informatics and Computing (ICIC)

作者： Arya Dwitama Eri Prasetyo Wibowo Nur Sultan Salahuddin Missa Lamsani Department Of Computer System Gunadarma University Bekasi Indonesia Department Of Information Technology Gunadarma University Depok Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

The utilization of this parking system is made to increase the efficiency and convenience of motorists in finding parking spaces. This parking system is designed to find out the availability and capacity of car parking lots so that motorists can find out the position of the car park when they want to park a car that can be accessed via Telegram on a Smartphone. In the design of this tool uses input in the form of Infrared sensors that function to detect and determine the capacity of the parking lot and use the OV2640 camera module on the ESP32-Cam as monitoring the car park. In addition, the components used in this tool design include ESP32-Cam and NodeMCU ESP8266 as a place for data processing and servo motors that function to open the automatic doorstop. This tool works if there is a car entering or leaving and then detected by the Infrared sensor entering or leaving, the doorstop on the servo will open automatically. This tool is also equipped with a MySQL database to store data, so that when the incoming infrared sensor detects an incoming car, the data will be stored in the database, allowing accurate recording in monitoring the number of incoming vehicles.

关键词： Accuracy Databases Infrared sensors Data processing Cameras Car parking Recording Servomotors Informatics Monitoring

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FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures 23

FlexiFed: Personalized Federated Learning for Edge Clients w...

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2023 World Wide Web Conference, WWW 2023

作者： Wang, Kaibin He, Qiang Chen, Feifei Chen, Chunyang Huang, Faliang Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Information Technology Monash University Australia Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of the world's web traffic, making a great data source for various machine learning (ML) applications, particularly federated learning (FL) which offers a promising solution to privacy-preserving ML feeding on these data. FL allows edge mobile and WoT devices to train a shared global ML model under the orchestration of a central parameter server. In the real world, due to resource heterogeneity, these edge devices often train different versions of models (e.g., VGG-16 and VGG-19) or different ML models (e.g., VGG and ResNet) for the same ML task (e.g., computer vision and speech recognition). Existing FL schemes have assumed that participating edge devices share a common model architecture, and thus cannot facilitate FL across edge devices with heterogeneous ML model architectures. We explored this architecture heterogeneity challenge and found that FL can and should accommodate these edge devices to improve model accuracy and accelerate model training. This paper presents our findings and FlexiFed, a novel scheme for FL across edge devices with heterogeneous model architectures, and three model aggregation strategies for accommodating architecture heterogeneity under FlexiFed. Experiments with four widely-used ML models on four public datasets demonstrate 1) the usefulness of FlexiFed;and 2) that compared with the state-of-the-art FL scheme, FlexiFed improves model accuracy by 2.6%-9.7% and accelerates model convergence by 1.24 × -4.04 ×. © 2023 ACM.

关键词： Speech recognition

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Time Series Data Association Mining With High-Frequency Data Interference 24

Time Series Data Association Mining With High-Frequency Data...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhu, Rongjing Li, Peng Liu, Yu Deng, Miao School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China

ISBN: (纸本)9798350319934

Time series data mining is an important problem in data mining. It discovers the intrinsic correlation rules by analyzing a large amount of time series data. However, high-frequency data may exist in the dataset. High-frequency data is the data that occurs frequently in time series data, which will affect the mining of association rules. Traditional time series association mining tends to classify high-frequency data as correlated data because they co-occur too many times. In this paper, we propose the list mining framework to mine the association data by list co-occurrence to achieve the mining of massive data. We divide it into three phases, list generation phase, matrix generation phase, and rule generation phase. First, we create data lists based on time windows. Second, we create the initial count matrix, update the count matrix based on the list, and propose a GCPM algorithm to generate a conditional probability matrix based on the count matrix. Third, we propose a List Mining (LM) algorithm to generate the initial rules based on the conditional probability matrix and optimize the initial rules to get the final association rules. In addition, we weigh the rules according to the list mining method and propose a Weighted List Mining (WLM) algorithm to filter rules by reducing the weight of frequent item interference. The experimental results show that LM and WLM can effectively improve the accuracy of mining results. © 2022 IEEE.

关键词： Data mining

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Find Edge Map for Medical Images Based on Texture Characteristics

Find Edge Map for Medical Images Based on Texture Characteri...

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2021 International Conference on Communication and information technology, ICICT 2021

作者： Almola, Sahera A. S. University of Basrah College of Computer Science and Information Technology Department of Information System Basra Iraq

ISBN: (纸本)9781665439145

Finding the edges of the images is an important process, especially medical images, to access the image data accurately to find the objects in the image, especially in the analysis and diagnosis processes Detecting edges in noise images is still a very difficult process. Several methods have been introduced to solve the problem, such as Sobel, Prewitt, and canny, but it is still difficult to process noisy images. The current research presents a proposed system designed to detect edges in a medical image and has been applied to medical MRI images. The proposed system consists of two phases, the first phase is the initial processing phase and this process aims to prepare the image for the processors, the second stage of the proposed system, it is the edge detection stage. The edges of the images entered into the system are detected by finding an edge map, and depending on the texture characteristics of the image, as the method used for edge detection is the Laws method and according to the following steps: 1. Create two-dimensional filters.2. The process of circumventing images and laws filters. The efficiency and effectiveness of the proposed system were analyzed through the results of experiments that showed that the performance of the system is very good and gives excellent and accurate results, by comparing the results of the proposed system with traditional methods of edge detection. Results like sobel, prewitt and canny showed that the proposed system gives a better and faster performance. © 2021 IEEE.

关键词： Textures

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Deep Complex-valued Convolutional Learning for Waveform OFDM Receiver Design

Deep Complex-valued Convolutional Learning for Waveform OFDM...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Ji, Jiequ Tran, Nam Phuong Xiong, Zehui Zhu, Kun Quek, Tony Q. S. Singapore University of Technology Design Department of Information System and Design Singapore College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China

ISBN: (纸本)9798350368369

Orthogonal frequency division multiplexing (OFD-M) has been widely used in modern communication networks. Notice that OFDM typically relies on (inverse) Discrete Fourier Transform (DFT/IDFT) for processing its waveforms. In this context, we propose a deep learning-based OFDM receiver that uses a deep complex-valued convolutional neural network (DC-CNN) to recover the information bit stream from synchronized time-domain signals without relying on DFT/IDFT. Specifically, a learned linear transform is designed to utilize the cyclic prefix (CP) of OFDM waveforms instead of DFT/IDFT, which presents the ability of DCCNN for complex communication waveforms. To improve the convergence of the training model for the DCCNN-based receiver, a novel transfer learning scheme is developed to train channel equalization and demodulation in two phases. In addition, both the DCCNN equalizer and DCCNN demodulator are trained and tested at different SNRs for Rayleigh fading and noise, and a mixed multiple fading channel model with various delay spreads is utilized to smooth the training loss. Simulation results suggest that our developed DCCNN channel estimator outperforms conventional estimators such as least square (LS), linear minimum mean square error (LMMSE) and low-rank approximation of LMMSE (ALMMSE) in multipath Rayleigh fading models with varying Doppler spreads and delay spreads. © 2025 IEEE.

关键词： Orthogonal frequency division multiplexing

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Duration-Aware Task Assignment for Heterogeneous Mobility Users in Crowdsensing 25

Duration-Aware Task Assignment for Heterogeneous Mobility Us...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Liu, Zhiyuan Li, Peng Huang, Weiyi Wang, Jiahu Nie, Lei Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing Real-Time Industrial System China

ISBN: (纸本)9781665405270

This paper investigates the duration-aware task assignment problem for heterogeneous mobility users in MCS (DTAH problem). There are two types of users in MCS, vehicle users and pedestrian users. The vehicle user gets to the task fast, but the task duration is short. Pedestrian user meets the task duration limit, but it takes a long time to reach the task. Given a set of tasks and two types of users, each user, and each task has a duration limit. The DTAH problem is how to assign tasks to maximize the utility of the system. We prove that the DTAH problem is NP-hard by reducing the weighted maximum set coverage problem to the DTAH problem. Then, we solve the problem from a pedestrian perspective and a vehicle perspective. From the pedestrian user perspective, we propose a pedestrian user task assignment (PTA) algorithm based on the Kuhn-Munkres algorithm. From the vehicle user perspective, we propose a greedy vehicle user task assignment (VTA) algorithm. We prove that the VTA algorithm can obtain the approximate ratio of 1-1/e to the optimal value. Finally, we design the heterogeneous user task assignment (HTA) algorithm based on these PTA and VTA algorithms. Extensive experiments have proved that our proposed HTA algorithm achieves more efficient performance than other comparison algorithms. © 2022 IEEE.

关键词： Combinatorial optimization

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Prediction-Based Thunderstorm Path Recovery Method Using CNN-BiLSTM

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Intelligent Automation & Soft Computing 2023年第8期37卷 1637-1654页

作者： Xu Yang Ling Zhuang Yuqiang Sun Wenjie Zhang School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6G 2R3Canada CCCC Highway Consultants Co. Ltd.Beijing100101China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing210044China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing100101China

The loss of three-dimensional atmospheric electric field(3DAEF)data has a negative impact on thunderstorm *** paper proposes a method for thunderstorm point charge path *** on the relation-ship between a point charge and 3DAEF,we derive corresponding localization formulae by establishing a point charge localization ***,point charge movement paths are obtained after fitting time series localization ***,AEF data losses make it difficult to fit and visualize ***,using available AEF data without loss as input,we design a hybrid model combining the convolutional neural network(CNN)and bi-directional long short-term memory(BiLSTM)to predict and recover the lost *** paths are not present during sunny weather,we propose an extreme gradient boosting(XGBoost)model combined with a stacked autoencoder(SAE)to further determine the weather conditions of the recovered ***,historical AEF data of known weathers are input into SAE-XGBoost to obtain the distribution of predicted values(PVs).With threshold adjustments to reduce the negative effects of invalid PVs on SAE-XGBoost,PV intervals corresponding to different weathers are *** recovered AEF is then input into the fixed SAE-XGBoost *** paths need to be fitted is determined by the interval to which the output PV *** results confirm that the proposed method can effectively recover point charge paths,with a maximum path deviation of approximately 0.018 km and a determination coefficient of 94.17%.This method provides a valid reference for visual thunderstorm monitoring.

关键词： Thunderstorm point charge atmospheric electric field(AEF) recovery

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Research on low energy task allocation and scheduling algorithm based on imprecise heterogeneous multi-core technology

Research on low energy task allocation and scheduling algori...

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2022 IEEE International Conference on systems, Man, and Cybernetics, SMC 2022

作者： Gu, Haonan Wu, Jing Hu, Wei Ma, Tianao Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System College of Computer Science and Technology Wuhan China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

In heterogeneous embedded systems, we try to find a new non real-time scheduling algorithm, which can not only meet the reliability requirements, but also achieve low energy consumption. 'Resource reservation time deterministic cyclic scheduling (RRTDCS)' algorithm is a kind of scheduling for offline cache pre-allocation. This scheduling algorithm combines time reservation and priority strategy. The algorithm can dynamically adjust according to different task sets. It can also adjust its own scheduling process to adapt to different scheduling environments. The periodic cyclic strategy enables low power consumption in task allocation. In the experimental part, we compared the algorithm proposed in this paper with the current mainstream real-time scheduling scheme. Experimental results show that RRTDCS algorithm has better performance. © 2022 IEEE.

关键词： Scheduling

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Genetic Testing of Huntington's Disease by Facial Examination of a Mankind using AI Programs 3

Genetic Testing of Huntington's Disease by Facial Examinatio...

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3rd International Conference on Innovations in computer Science and Software Engineering, ICONICS 2022

作者： Atruba Feroze, S. Faiza Nasim, S. NED University of Engineering and Technology Dept.of Computer and Information System Engineering Karachi Pakistan NED University of Engineering and Technology Dept. of Computer Science and I.T NEDUET Karachi Pakistan

ISBN: (纸本)9781665461412

This Genetic disorder is considered as one of the major challenges faced by Medical Sciences as it is inherited by humans from one generation to another. With an increasing number of natives of the World at a higher rate, it causes a major impact on information society socially and economically during their lifetime. The citizens of Pakistan and Indian sub continents are facing genomic disease in probably high frequency as compared to other continents. Occurrence of congenital diseases in Pakistan results in higher percentages of unusual genetic diseases especially in children. After analyzing all the current scenarios, some goals and objectives were settled in order to facilitate and give general awareness to people. The objectives of systematic review are evaluation of diagnostic accuracy of AI for the identification of genome contributions to the pathogenesis of Huntington's disease through facial probe AI to accurately identify genetic disorders. The narrative review is performed in Ned University. In this proceeding sequence, 30 cases of both Normal and Genetically affected Patients were documented in which some certain sorts of tests were performed by using facial recognition. For this FR test, at first a combo list of information of all six emotions of a human being i.e. (Happy, Sorrow, Amazed, Fear, Anger, Revulsion) is gathered in the form of mutated images, which was then later be arranged into a pseudo-random order. Now with the help of Vision technology we have applied some CV Techniques for the extraction of large pattern sets of features. Lastly the purpose of ML and DL techniques is to receive and compare those morphed image data from the Facial Database in order to identify each of six different emotions of HDNormal Person. This whole procedure is conducted in real time. Hence in this way we may recognize the HD genetic disorder among the people in an efficient way. The detailed procedure is explained in the Methodology section of this narrative revi

关键词： Genome

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