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1st International Conference on Advances in Computing Research, ACR’23

作者： Bekhit, Mahmoud Fathalla, Ahmed Eldesouky, Esraa Salah, Ahmad Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Kent Institute Australia Melbourne Australia Department of Mathematics Faculty of Science Suez Canal University Ismailia Egypt Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia College of Computers and Informatics Zagazig University Zagazig Egypt College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman

ISBN: (纸本)9783031337420

With the rising acceptance of virtual network functions (VNFs) as a replacement for traditional network functions, the optimal placement of VNFs has become a crucial task for ensuring constant performance within constrained resources. This research investigates the challenge of mapping and scheduling VNFs as a multi-objective optimization problem. The state-of-the-art methods have focused on optimizing the mapping or scheduling of VNFs while considering one or two objectives continuously. In this study, we aim to optimize two objectives: minimizing the link capacity cost and maximizing the average resource utilization of each virtual machine. To address this problem, an adaptation of the NSGA-III optimization algorithm is proposed and thoroughly evaluated through a set of experiments. In terms of objective values, the results outlined that the proposed method outperformed the other methods of comparison by a significant margin. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mapping

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Metaheuristics Algorithm for Tuning of PID Controller of Mobile Robot System

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computers, Materials & Continua 2022年第8期72卷 3481-3492页

作者： Harsh Goud Prakash Chandra Sharma Kashif Nisar Muhammad Reazul Haque Ag.Asri Ag.Ibrahim Narendra Singh Yadav Pankaj Swarnkar Manoj Gupta Laxmi Chand Indian Institute of Information Technology NagpurIndia School of Computing&Information Technology Manipal UniversityJaipurRajasthanIndia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu88400SabahMalaysia Faculty of Computing&Informatics Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Maulana Azad National Institute of Technology BhopalIndia Department of Electronics and Communication Engineering JECRC UniversityJaipurRajasthanIndia Department of Computer Science and Engineering Hanyang UniversitySeongdong-guSeoul04763Korea

Robots in the medical industry are becoming more common in daily life because of various advantages such as quick response,less human interference,high dependability,improved hygiene,and reduced aging *** is why,in recent years,robotic aid has emerged as a blossoming solution to many challenges in the medical *** this manuscript,meta-heuristics(MH)algorithms,specifically the Firefly Algorithm(FF)and Genetic Algorithm(GA),are applied to tune PID controller constraints such as Proportional gain Kp Integral gain Ki and Derivative gain *** controller is used to control Mobile Robot System(MRS)at the required set *** FF arrangements are made based on various pre-analysis.A detailed simulation study indicates that the proposed PID controller tuned with Firefly Algorithm(FF-PID)for MRSis beneficial and suitable to achieve desired closed-loop system *** FF is touted as providing an easy,reliable,and efficient tuning technique for PID *** most suitable ideal performance is accomplished with FF-PID,according to the display in the time ***,the observed response is compared to those received by applying GA and conventional off-line tuning *** comparison of all tuning methods exhibits supremacy of FF-PID tuning of the given nonlinear Mobile Robot System than GA-PID tuning and conventional controller.

关键词： Metaheuristic algorithm genetic algorithm PID controller mobile robot system firefly algorithm

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Predicting Chemical Reaction Product by Graph Transformer

Predicting Chemical Reaction Product by Graph Transformer

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computer Science, computer Engineering, & Applied Computing (CSCE), Congress in

作者： Shunya Makino Nobuaki Yasuo Masakazu Sekijima Department of Computer Science Tokyo Institute of Technology Yokohama Japan Academy for Convergence of Materials and Informatics (TAC-MI) Tokyo Institute of Technology Yokohama Japan

The substantial cost and prolonged duration associ-ated with drug discovery present significant challenges within the pharmaceutical industry. A potential solution to alleviate these issues is molecular translation, a process where an input molecule is converted into another molecule following specific rules or conditions. In this study, we introduce a novel machine learning technique for predicting chemical reaction outcomes: the molecular graph translation method, employing a transformer architecture designed for efficient chemical reaction product prediction. We assessed the performance of our proposed method using the USPTO dataset for chemical reaction product prediction. Our results demonstrated an accuracy of 86.5%. Although this accuracy was slightly lower than that of the Molecular Transformer (a string-based method using a similar architecture), our approach surpassed both the Seq2Seq and other graph-based methods in terms of performance.

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Optimising Random Forest Machine Learning Algorithms for User VR Experience Prediction Based on Iterative Local Search-Sparrow Search Algorithm 6

Optimising Random Forest Machine Learning Algorithms for Use...

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6th International Conference on Communications, Information System and computer Engineering, CISCE 2024

作者： Tang, Xirui Li, Feiyang Cao, Zinan Yu, Qixuan Gong, Yulu College of Computer Sciences Northeastern University BostonMA02115 United States University of Illinois Urbana-Champaign Department of Computer Science ChampaignIL61820 United States The University of Tokyo Department of General Systems Studies Tokyo113-8654 Japan College of Computing Georgia Institute of Technology AtlantaGA30332-0280 United States School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ86011-4092 United States

ISBN: (纸本)9798350353174

In this paper, an improved method for VR user experience prediction is investigated by introducing a sparrow search algorithm and a random forest algorithm improved by an iterative local search-optimised sparrow search algorithm. The study firstly conducted a statistical analysis of the data, and then trained and tested using the traditional random forest model, the random forest model improved by the sparrow search algorithm, and the random forest algorithm improved based on the iterative local search-sparrow search algorithm, respectively. The results show that the traditional random forest model has a prediction accuracy of 93% on the training set but only 73.3% on the test set, which is poor in generalisation;whereas the model improved by the sparrow search algorithm has a prediction accuracy of 94% on the test set, which is improved compared with the traditional model. What is more noteworthy is that the improved model based on the iterative local search-sparrow search algorithm achieves 100% accuracy on both the training and test sets, which is significantly better than the other two methods. These research results provide new ideas and methods for VR user experience prediction, especially the improved model based on the iterative local search-sparrow search algorithm performs well and is able to more accurately predict and classify the user's VR experience. In the future, the application of this method in other fields can be further explored, and its effectiveness can be verified through real cases to promote the development of AI technology in the field of user experience. © 2024 IEEE.

关键词： Prediction models

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HealthChain: A Blockchain-aided Federated Healthcare Management System

HealthChain: A Blockchain-aided Federated Healthcare Managem...

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IEEE International Conference on Blockchain and Cryptocurrency (ICBC)

作者： Arnab Mukherjee Raju Halder Joydeep Chandra Shailesh Shrivastava Department of Computer Science and Engineering Indian Institute of Technology Patna Bihta India National Informatics Center Patna India

Privacy and integrity of medical records while preserving and sharing them within any healthcare system is of high importance. Recently, India's Ayushman Bharat Digital Mission (ABDM) aims to address various issues of the existing healthcare systems and proposes a federated healthcare system to bring all the players under a common umbrella. However, it falls short on various aspects, including transparency, auditability, and traceability of activities and information within the system. In this proposal, we aim to bridge these existing gaps and propose an extension of the ABDM proposal by incorporating a blockchain layer instrumented with additional features such as traceable consent management, auditable access logs, etc., while keeping in mind its performance and scalability. We present a proof of concept of our proposal based on Hyperledger Fabric and related backend technologies and manifest experimental results to reinforce the practicality of our proposal.

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Gene regulatory accelerators on cloud FPGA

Gene regulatory accelerators on cloud FPGA

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作者： Costa Penha, Jeronimo Bragança, Lucas Canesche, Michael Ribeiro, Dener Nacif, José Augusto M. Ferreira, Ricardo S. Informatics Department Universidade Federal de Viçosa Viçosa Brazil Science and Technology Institute UFV-Florestal Campus Universidade Federal de Viçosa Viçosa Brazil Computer Science Department Universidade Federal de Minas Gerais Belo Horizonte Brazil

Gene regulatory networks (GRN) are dynamic models in time and space. These models are used to predict diseases and in drugs research. GRN models are discrete, and Boolean graphs can efficiently represent them. However, GRN algorithms explore a large solution space with high computational complexity. This work proposes efficient FPGA-based accelerators to implement two GRN algorithms: attractor computation and Derrida plot. Nevertheless, FPGA accelerator design and deployment are still a challenge. This work presents an accelerator design framework for AWS Amazon FPGA cloud. The framework simplifies the software (SW) and hardware (HW) generation for GRN accelerators. The user provides a high-level model for the Boolean GRN, and our tool automatically creates AWS-ready-to-deploy software and hardware components. For the attractor and the Derrida plot computation, the proposed FPGA accelerators are on average (Figure presented.) and (Figure presented.) faster than a V100 GPU. © 2023 John Wiley & Sons, Ltd.

关键词： Field programmable gate arrays (FPGA)

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Alapana Generation using Finite State Machines and Generative Adversarial Networks

Alapana Generation using Finite State Machines and Generativ...

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International Research Conference on Smart Computing and Systems Engineering (SCSE)

作者： Vithushigan Jayatharan Dileeka Alwis Computer Science and Engineering University of Westminster London UK Department of Computing Informatics Institute of Technology Colombo Sri Lanka

The recent advancements in deep learning techniques and computational power have promoted the development of novel approaches for music generation. In this study, generating alapana, an improvisational form of Carnatic music was proposed, by leveraging Generative Adversarial Networks (GANs) and Finite State Machines (FSM). The goal is to create melodious alapana sequences that follow a given input Raga, ensuring continuity and coherence throughout the generated musical piece. The proposed approach incorporates Carnatic music theory rules into the generation process to enhance the structural coherence of the generated alapana. Additionally, various hyperparameter settings were explored to achieve the best performance. The Fréchet Audio Distance, Percentage of Correct Pitches and the Subjective evaluation through human listeners are the evaluation metrics of this approach. The result of this study demonstrates the potential of using GANs and FSM for generating continuous and pleasing alapana sequences in Carnatic music, contributing to the growing body of research in computational music generation.

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Encrypted Biometric Search: A Deep Learning Approach to Scalable and Secure Cross-Border Data Exchange

Encrypted Biometric Search: A Deep Learning Approach to Scal...

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IEEE International Conference on Big Data

作者： Kyriaki Miniadou Asterios Leonidis Georgios Th. Papadopoulos Constantine Stephanidis Institute of Computer Science (ICS) Foundation for Research and Technology - Hellas (FORTH) Heraklion Greece Department of Informatics and Telematics Harokopio University of Athens Athens Greece

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Cross-border collaboration among Law Enforcement Agencies is essential for effective and timely suspect identification, especially when the availability of biometric data varies between agencies. This paper presents a scalable and secure approach for multimodal biometric identification across multiple jurisdictions. Our approach allows Law Enforcement Agencies to combine biometric modalities -facial images, fingerprints, and voice samples- and compare them with collaborating agencies, improving the overall accuracy and effectiveness of suspect identification. By leveraging deep learning models for indexing and comparison, efficient data retrieval was achieved without compromising privacy or security. To ensure the protection of sensitive biometric data, our approach incorporates advanced encryption mechanisms, including Homomorphic Encryption for secure computations and Advanced Encryption Standard (AES encryption) for safeguarding biometric information. Its decentralised architecture allows each Law Enforcement Agency to maintain independent instances of the Deep Learning Indexer and Comparator, minimising risks associated with centralising sensitive data and supporting seamless collaboration between agencies. This approach not only improves the accuracy of suspect identification but also enhances operational efficiency by allowing Law Enforcement Agencies to query and share biometric data securely across borders.

关键词： Biometrics Deep learning Data privacy Accuracy Law enforcement Computational modeling Biological system modeling Collaboration computer architecture Biometric identification

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Education Tools for Teaching Classical Ciphers

Education Tools for Teaching Classical Ciphers

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International Conference on Digital Technologies (DT)

作者： Marek Klimo Eugen Antal Miroslav Kvassay Institute of Computer Science and Mathematics Slovak University of Technology in Bratislava Slovakia Department of Informatics University of Žilina Žilina Slovakia

In this work, we focus on supporting online education of courses about Classical Ciphers and promoting historical cryptography to the general public. For this purpose, we designed and developed new modules in 2020 and integrated them into our ongoing HCPortal project. We developed an education module (Angular application) with which we can present a wide range of supporting applications regarding cryptography and cryptanalysis of historical ciphers and visualize their necessary parts. In addition, we also developed a special Virtual museum (using A-Frame technology) to promote public interest in ciphers using modern technologies. These modules have undergone significant change in recent years. They have been expanded with new content, and the structure of the Educational module has also changed. We have included various Chinese and Japanese historical ciphers into these modules due to the lack of availability of resources that specifically address this area of research. We are presenting the new content added to the HCPortal, focusing on these Chinese and Japanese ciphers.

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Analysis of Virtual Reality Based on the Internet of Things on Human Psychology ‘Internet of Thoughts’ (IoThs) for Rich Content Extraction Applied Natural Language Processing and Deep Learning 20th

Analysis of Virtual Reality Based on the Internet of Things ...

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20th International Conference on Smart Living and Public Health, ICOST 2023

作者： Mah, Pascal Muam Skalna, Iwona Pelech-Pilichowski, Tomasz Derlecki, Tomasz Nasr, Mahmoud Munyeshuri, Eric Amuzang, Gilly Njoh Itoe, Micheal Blake Somaah Tah, Ning Frida Department of Technical Informatics and Telecommunications AGH University of Science and Technology Krakow Poland Department of Business Informatics and Management Engineering AGH University of Science and Technology Krakow Poland Institute of Computer Science AGH University of Science and Technology Krakow Poland Department of Economics and Management Industry AGH University of Science and Technology Krakow Poland Department of Biomedical Engineering AGH University of Science and Technology Krakow Poland Department of Engineering Mathematics and Physics Faculty of Engineering and Technology Future University Cairo Egypt Department of Production Engineering and Logistics Enterprise Silesian University of Technology Gliwice Poland Department of Logistics Management Silesian University of Technology Gliwice Poland

ISBN: (纸本)9783031439490

In the past decade, a lot of challenges to access, assess, and to acquire the needed technological opportunities to teach computers what naturally comes from the human brain and to understand how we naturally react when we rely on technology. The ability to document human thoughts, reactions and behavior to computers has led to the coming of NLP, AI, Dl, & ML. Aim to understand the influence of IoT on humans with the use of DL to achieve content correctness and accuracy with virtual technology. Studies show that the way we think, react, and do the things we think "Internet of thoughts" reflect our personality. The way we think determines the way we react and the way we do things are based on how we think. technology advancement has reinforced a lot of changes in humans which makes humans vulnerable to personal content exposure misappropriation due to the continuously changing nature of humanity and language. The study uses NLP, DL and behavior-oriented drive and influential function and results show that IoT based on VR influences human psychology "Internet of Thoughts". © 2023, The Author(s).

关键词： Internet of things

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