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24th International Congress on Acoustics, ICA 2022

作者： Feriani, Sophia Julia Jeong, Cheol-Ho Engsig-Karup, Allan Peter Pind, Finnur Acoustic Technology Department of Electrical and Photonics Engineering Technical University of Denmark Denmark Scientific Computing Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Treble Technologies Iceland

Urban noise pollution is a harmful environmental stressor for city inhabitants and its impact has been proven to be harmful for short- and long-term health. Having accurate models to predict and analyze the noise in ever-growing cities is therefore crucial. If we focus on the scale of city streets, several wave-based numerical methods have been developed and optimized to predict the sound propagation in urban street canyons. Some methods are more suited than others to incorporate relevant meteorological effects such as wind profile, temperature gradient and air absorption into their model. However, integrating those phenomena comes at a certain computational cost. Assessing in advance the relevant meteorological effects worth taking into account and only considering those effects in the method would possibly reduce the computational burden of the modelling. This paper aims to summarize previous modelling efforts, key effects and relate these to established computational methods for accounting meteorological conditions inside an urban street canyon. This overview seeks to identify current limitations in computational modelling with the overall aim to improve next-generation simulations using advanced wave-based computing methods. © ICA *** rights reserved

关键词： DGFEM Environmental noise Meteorological effects Urban Street canyon

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Fire Hawk Optimization based Multi-objective Dynamic Load Balancing in Cloud computing 2

Fire Hawk Optimization based Multi-objective Dynamic Load Ba...

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2nd International Conference on Distributed computing and Optimization Techniques, ICDCOT 2024

作者： Kumari, K. Aruna Soujanya, Thota Alsalami, Zaid Rohini, I. Bala Dhandayuthapani, V. Srkr Engineering College Department of Cse Chinaamiram Bhimavaram India School of Agricultural Sciences Sr University Telangana Warangal India The Islamic University Najaf Iraq Malla Reddy Engineering College for Women Dept of Eee Telangana India College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Branch Department of It Oman

ISBN: (纸本)9798350382952

Load Balancing (LB) of the tasks on Cloud computing (CC) is the significant characteristics of distribution of the resources from data center. LB has to be taken out in such a way that every Virtual Machines (VMs) must have balanced the load to attain an optimal utilization of its capabilities. As an outcome, the LB of tasks on VM is examined as a significant thing of task scheduling in the cloud. The LB in cloud faced various challenges like fault tolerance as well as data security. In this research, Fire Hawk Optimization (FHO) algorithm is proposed for the dynamic LB in cloud environment. The aim of this research is to enhance the system performance and it considers the multi-objective functions named Makespan, Resource Utilization (RU), Response Time (RT), Degree of Imbalance as well as throughput. The experimental results proven that the proposed FHO-based LB had obtained the 2246, 6.7, 134.23, 141 and 5.5 in the number of tasks of 1500 using nakespan, RT, RU, DOI and throughput. These results show better enhancement when compared to the previous approaches like Particle Swarm Optimization (PSO), Q-learning Modified PSO (QMPSO) and Binary JAYA algorithm. © 2024 IEEE.

关键词： Cloud computing

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Analyzing video footage from security cameras using the Amped FIVE professional tool specifically for the identification of vehicle license plates

Analyzing video footage from security cameras using the Ampe...

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computing, Networking, Telecommunications & Engineering Sciences Applications (CoNTESA), International Conference on

作者： Mišel Gvozdanović Damir Delija Domagoj Tuličić Goran Sirovatka Marinko Žagar Krunoslav Bilić Department of Informatics and Computing Zagreb University of Applied Science Zagreb Croatia Department of Computer Systems and Networks University of Applied Sciences in Information Technology Zagreb Croatia

This paper describes problems of improving security cameras’ video footage for forensic investigation. When processing records, one encounters problems with low resolution, poor lighting, or excessive distance of a key object in an image or video. Using the Amped FIVE Professional tool, forensic scientists create videos and analyze and process the record itself using various filters. Record quality demands a choice of video filters. The use and selection of filters show the improvement of the record itself to the level to which useful information can be obtained in this case of vehicle license plates. The presented roadmap can be used as the base for further video forensic research.

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Sports Events Recognition Using Multi Features and Deep Belief Network

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Computers, Materials & Continua 2024年第10期81卷 309-326页

作者： Bayan Alabdullah Muhammad Tayyab Yahay AlQahtani Naif Al Mudawi Asaad Algarni Ahmad Jalal Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science Applied CollegeKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

In the modern era of a growing population,it is arduous for humans to monitor every aspect of sports,events occurring around us,and scenarios or *** recognition of different types of sports and events has increasingly incorporated the use of machine learning and artificial *** research focuses on detecting and recognizing events in sequential photos characterized by several factors,including the size,location,and position of people’s body parts in those pictures,and the influence around those *** approaches utilized,here are feature descriptors such as MSER(Maximally Stable Extremal Regions),SIFT(Scale-Invariant Feature Transform),and DOF(degree of freedom)between the joint points are applied to the skeleton ***,for the same purposes,other features such as BRISK(Binary Robust Invariant Scalable Keypoints),ORB(Oriented FAST and Rotated BRIEF),and HOG(Histogram of Oriented Gradients)are applied on full body or *** integration of these techniques increases the discriminative nature of characteristics retrieved in the identification process of the event,hence improving the efficiency and reliability of the entire *** extracted features are passed to the early fusion and DBscan for feature fusion and *** deep belief,network is employed for *** results demonstrate a separate experiment’s detection average recognition rate of 87%in the HMDB51 video database and 89%in the YouTube database,showing a better perspective than the current methods in sports and event identification.

关键词： Machine learning silhouettes extremal regions joint points scalable keypoints

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Optimizing Secure Multi-User ISAC Systems With STAR-RIS: A Deep Reinforcement Learning Approach for 6G Networks

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IEEE Access 2025年 13卷 31472-31484页

作者： Kamal, Mian Muhammad Zain Ul Abideen, Syed Al-Khasawneh, M.A. Alabrah, Amerah Sohail Ahmed Larik, Raja Irfan Marwat, Muhammad Southeast University School of Electronic Science and Engineering Jiangning Jiangsu Nanjing211189 China Qingdao University College of Computer Science and Technology Qingdao266071 China Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19111 Jordan Skyline University College School of Computing University City Sharjah Sharjah United Arab Emirates King Saud University College of Computer and Information Science Department of Information Systems Riyadh11543 Saudi Arabia Ilma University Department of Computer Science Sindh Karachi75190 Pakistan University of Science and Technology Bannu Department of Software Engineering Bannu28100 Pakistan

The rapid evolution of wireless communication technologies and the increasing demand for multi-functional systems have led to the emergence of integrated sensing and communication (ISAC) as a key enabler for future 6G networks. Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have recently garnered significant attention for their ability to enhance signal coverage and improve system efficiency. This paper investigates a STAR-RIS-assisted ISAC system designed to secure communication for multiple legitimate users (LUs) while safeguarding against multiple eavesdroppers (Eves). By jointly optimizing the base station (BS) transmit beamforming, STAR-RIS transmission and reflection coefficients, and receive filters, the proposed framework aims to maximize the long-term average secrecy rate for all LUs. Constraints are imposed to ensure minimum echo signal-to-noise ratios (SNRs) for sensing and meet the achievable rate requirements of LUs. To address the inherent complexity of this non-convex problem, two deep reinforcement learning (DRL) algorithms are proposed. Numerical results demonstrate that the proposed system achieves significant improvements in secrecy rate compared to conventional RIS setups. This work provides a scalable and efficient approach for secure multi-user ISAC systems, making it highly relevant for future 6G networks, smart cities, and IoT applications. © 2025 The Authors.

关键词： Deep reinforcement learning

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PPLN-based Optical Parametric Amplifiers for Continuous-variable Optical Quantum computing

PPLN-based Optical Parametric Amplifiers for Continuous-vari...

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2024 Conference on Lasers and Electro-Optics/Pacific Rim, CLEO-PR 2024

作者： Umeki, Takeshi Kashiwazaki, Takahiro Inoue, Asuka Endo, Mamoru Furusawa, Akira NTT Device Technology Labs NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan

We review the capabilities of optical parametric amplifiers based on periodically poled LiNbO3 (PPLN) waveguides for optical quantum computing. Specifically, we discuss the generation of squeezed and non-Gaussian states of light over a broad bandwidth and high-speed real-time detection of the quadrature-phase amplitude by using phase-sensitive amplifiers for continuous-variable optical quantum signal processing. © 2024 The Author(s)

关键词： Optical parametric amplifiers

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Human Gait Recognition Using Deep Learning and Improved Ant Colony Optimization

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Computers, Materials & Continua 2022年第2期70卷 2113-2130页

作者： Awais Khan Muhammad Attique Khan Muhammad Younus Javed Majed Alhaisoni Usman Tariq Seifedine Kadry Jung-In Choi Yunyoung Nam Department of Computer Science HITEC University TaxilaTaxila47080Pakistan College of Computer Science and Engineering University of Ha’ilHa’ilSaudi Arabia College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Applied Artificial Intelligence Ajou UniversitySuwonKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Human gait recognition(HGR)has received a lot of attention in the last decade as an alternative biometric *** main challenges in gait recognition are the change in in-person view angle and covariant *** major covariant factors are walking while carrying a bag and walking while wearing a *** learning is a new machine learning technique that is gaining *** techniques for HGR based on deep learning are presented in the *** requirement of an efficient framework is always required for correct and quick gait *** proposed a fully automated deep learning and improved ant colony optimization(IACO)framework for HGR using video sequences in this *** proposed framework consists of four primary *** the first step,the database is normalized in a video *** the second step,two pre-trained models named ResNet101 and InceptionV3 are selected andmodified according to the dataset’s *** that,we trained both modified models using transfer learning and extracted the *** IACO algorithm is used to improve the extracted *** is used to select the best features,which are then passed to the Cubic SVM for final *** cubic SVM employs a multiclass *** experiment was carried out on three angles(0,18,and 180)of the CASIA B dataset,and the accuracy was 95.2,93.9,and 98.2 percent,respectively.A comparison with existing techniques is also performed,and the proposed method outperforms in terms of accuracy and computational time.

关键词： Gait recognition deep learning transfer learning features optimization classification

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A multi-objective grey wolf optimizer for energy planning problem in smart home using renewable energy systems

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Sustainable Operations and Computers 2024年第1期5卷 88-101页

作者： Makhadmeh, Sharif Naser Al-Betar, Mohammed Azmi Al-Obeidat, Feras Alomari, Osama Ahmad Abasi, Ammar Kamal Tubishat, Mohammad Elgamal, Zenab Alomoush, Waleed Department of Data Science and Artificial Intelligence University of Petra Amman11196 Jordan College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Information Technology Al-Huson University College Al-Balqa Applied University P.O. Box 50 Al-Huson Irbid Jordan Department of Computer Science and Information Technology College of Engineering Abu Dhabi University Abu Dhabi United Arab Emirates College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates School of Computing Skyline University College Sharjah United Arab Emirates School of Information Technology Skyline University College Sharjah P.O. Box 1797 United Arab Emirates Abu Dhabi United Arab Emirates

This paper presents the energy planning problem (EPP) as an optimization problem to find the optimal schedules to minimize energy consumption costs and demand and enhance users’ comfort levels. The grey wolf optimizer (GWO), One of the most powerful optimization methods, is adjusted and adapted to address EPP optimally and achieve its objectives efficiently. The GWO is adapted due to its high performance in addressing NP-complex hard problems like the EPP, where it contains efficient and dynamic parameters that enhance its exploration and exploitation capabilities, particularly for large search spaces. In addition, new energy and real-world resources based on solar renewable energy systems (RESs) are combined with the proposed GWO to enhance its performance and ensure the optimisation of EPP objectives. Furthermore, EPP is presented as a multi-objective planning problem to optimize all objectives simultaneously. To efficiently investigate the proposed method performance, the results obtained by the GWO with the RESs are compared in three stages: comparison with original methods without RESs, comparison with methods using RESs, and comparison with state-of-the-art. The obtained results proved the robust performance of the proposed method in handling EPP and optimizing its objectives. © 2024 The Author(s)

关键词： Multiobjective optimization

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Comparative Analysis of Differential Privacy Implementations on Synthetic Data 15

Comparative Analysis of Differential Privacy Implementations...

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15th IEEE Annual computing and Communication Workshop and Conference, CCWC 2025

作者： Said, Huwida E. Mahmoud, Qusay H. Goyal, Mandeep Hashim, Faiza College of Technological Innovation Zayed University Department of Computing and Applied Technolgy Dubai United Arab Emirates Ontario Tech University Department of Electrical Computer and Software Engineering OshawaONL1G 0C6 Canada College of Information Technology Uae University Department of Computer Science and Software Engineering AI-Ain United Arab Emirates

ISBN: (纸本)9798331507695

Differential privacy offers a promising solution to balance data utility and user privacy. This paper compares two prominent differential privacy tools-PyDP and IBM's diffprivlib-that are applied to a synthetic dataset with medical attributes. We evaluate these tools based on their effectiveness in maintaining data privacy while preserving the statistical integrity of the data. Our results reveal that PyDP provides synthetic data that closely matches real-world data, making it appropriate for tasks requiring accuracy while striking a better balance between data utility and privacy. However, IBM's diffprivlib is more suitable for privacy-critical applications due to its stronger privacy guarantees, but at the cost of increased noise and decreased data utility. This paper contributes to the practical understanding of implementing differential privacy in machine learning and software applications and enhances the tools available for developers in sensitive data environments. © 2025 IEEE.

关键词： Data integrity

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Image Based Feature Separation Using RBM Tech with ADBN Tech for Accurate Fruit Classification 5

Image Based Feature Separation Using RBM Tech with ADBN Tech...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Revathi, V. Kavin, Balasubramanian Prabhu Thirumalraj, Arunadevi Gangadevi, E. Balusamy, Balamurugan Gite, Shilpa New Horizon College of Engineering Department of Applied Sciences - Physics Ring Road Bellandur Post Bengaluru India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Chengalpattu Tamil Nadu Kattankulathur603 203 India K.Ramakrishnan College of Technology Department of Computer Science Engineering Tamil Nadu Trichy India Loyola College Department of Computer Science Chennai600034 India Delhi India Symbiosis Institute of Technology Pune Aiml Department Pune412115 India

ISBN: (纸本)9798350383522

One of the fastest-growing subfields of computer vision and picture categorization is fruit identification. The fruit industry could start using a standardised categorization system so that shoppers can easily distinguish between different types and quality levels of fruit. From a health perspective, fruit quality is essential. The task of classifying fruits is crucial in many commercial contexts. A supermarket cashier may use a fruit categorization system to better recognise the varieties of fruit and their associated costs. It might also be used to determine whether or not a given kind of fruit is suitable for a person's diet. The rising use of intelligent imaging equipment may be directly attributed to the proliferation and development of AI software. Deep learning methods, such as CNN, are now being used by researchers for picture categorization. CNN's superior performance may be achieved without using any manually-crafted characteristics, in contrast to the older methods. For automated categorization, it employs a plethora of filters that strip relevant visual information. Classifying fruits accurately is a challenge in the horticulture sector and calls for the knowledge of an expert. For accurately and quantitatively analysing fruits, the currently mentioned categorization techniques fall short. Therefore, it is beneficial to review the latest suggestions for categorizing fruits. To solve this problem, we need a computerised system that can automatically sort fruits into their respective categories. Deep learning models, such as the Restricted Boltzmann Machine (RBM) and the Adam based Deep Belief Network (DBN), are used in this assignment to categorise fruits according to the attributes that have been determined to be optimum and generated. The proposed approach has been shown to have effective accuracy and quantitative analysis outcomes, and this is just the beginning of this realisation. In addition, the suggested scheme's relatively strong computational mo

关键词： Deep Belief Network Fruits Classification Fruits Grading Handcrafted features Restricted Boltzmann Machine

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