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arXiv 2024年

作者： Acharya, Atithi Yalovetzky, Romina Minssen, Pierre Chakrabarti, Shouvanik Shaydulin, Ruslan Raymond, Rudy Sun, Yue Herman, Dylan Andrist, Ruben S. Salton, Grant Schuetz, Martin J.A. Katzgraber, Helmut G. Pistoia, Marco Global Technology Applied Research JPMorganChase New YorkNY10017 United States Amazon Quantum Solutions Lab SeattleWA98170 United States AWS Center for Quantum Computing PasadenaCA91125 United States California Institute of Technology PasadenaCA United States

Industrially relevant constrained optimization problems, such as portfolio optimization and portfolio rebalancing, are often intractable or difficult to solve exactly. In this work, we propose and benchmark a decomposition pipeline targeting portfolio optimization and rebalancing problems with constraints. The pipeline decomposes the optimization problem into constrained subproblems, which are then solved separately and aggregated to give a final result. Our pipeline includes three main components: preprocessing of correlation matrices based on random matrix theory, modified spectral clustering based on Newman’s algorithm, and risk rebalancing. Our empirical results show that our pipeline consistently decomposes real-world portfolio optimization problems into subproblems with a size reduction of approximately 80%. Since subproblems are then solved independently, our pipeline drastically reduces the total computation time for state-of-the-art solvers. Moreover, by decomposing large problems into several smaller subproblems, the pipeline enables the use of near-term quantum devices as solvers, providing a path toward practical utility of quantum computers in portfolio optimization. Copyright © 2024, The Authors. All rights reserved.

关键词： Constrained optimization

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Improving Existing VANETs by Incorporating UAVS Using Ant Colony and PSO Algorithms

Improving Existing VANETs by Incorporating UAVS Using Ant Co...

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Data Engineering and Communication Systems (ICDECS), International Conference on

作者： S Thangam Aniket Dixit Alok Kumar Karn Arnav Thakur Cg Arunbalaji J Jesy Janet Kumari A. Saleem Raja Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India IT Department College of Computing and Information Sciences University of Technology and Applied Sciences-Shinas Sultanate of Oman

Vehicular Ad Hoc Networks (VANETs) are a type of wireless communication network that enable vehicles to communicate with each other and with roadside infrastructure in a peer-to-peer fashion; despite their vast applications, they come with several shortcomings. The key challenges in their real-world implementation include scalability, connectivity, and coverage issues. Unmanned Aerial Vehicles (UAVs), also known as drones, can complement existing VANETs in several ways to enhance their functionality and address some of their limitations. This paper studies the incorporation of UAVs in VANETs to overcome the challenges faced by the present networks. The approach is based on the dynamic deployment of UAVs in the most optimal positions, found by utilizing Particle Swarm Optimization (PSO) and Ant Colony algorithms which analyze the vehicle density, and previous coverage information in the network. The deployment of UAVs is intended to provide a seamless network coverage for ground vehicles. The impact of dynamic UAV mobility in communicating VANETs is comparatively studied. The simulation is done using Network Simulator-3 (NS3) simulator to evaluate the performance of 4 VANET protocols, AODV, DSR, OLSR, and DSDV, in terms of packet delivery ratio (PDR), Average End-to-End Delay, Throughput, Average Throughput per Packet, Packet Drop Rate, and Normalized Routing Load after incorporating the proposed modification designs. The paper concludes that the incorporation of Ant Colony is better suited to enhance VANETs than PSO.

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Krishi-Konect: Enhancing Agriculture with Crop Care Solutions

Krishi-Konect: Enhancing Agriculture with Crop Care Solution...

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Networking and Communications (ICNWC), International Conference on

作者： Prashant Tarushi Garg Shaji. K.A. Theodore Sivakumar M. Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur. Chennai India College of Computing and Information Sciences University of Technology and Applied Sciences Sultanate of Oman

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

Agricultural disease detection using convolutional neural networks enhances crop yield by providing early and accurate identification of diseases, mitigating crop losses and boosting productivity. While traditional machine learning techniques are still relevant, deep learning, particularly convolutional neural networks, demonstrates superior performance in image-based disease detection and classification tasks, contingent upon adequate data and computational *** objective of this research is to investigate and evaluate the effectiveness of deep learning techniques, particularly convolutional neural networks (CNNs), in the detection and classification of plant diseases using image-based analysis.

关键词： Smart agriculture Productivity Deep learning Plant diseases Plants (biology) Crops Soil

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Enhancing ML model efficiency for DDoS attack detection on web servers in resource-constrained environments 9

Enhancing ML model efficiency for DDoS attack detection on w...

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9th International Conference on Digital Technologies in Education, Science and Industry, DTESI 2024

作者： Qassim, Qais Saif Askarbekova, Nessibeli College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri Ibri Oman International Information Technology University 34/1 Manas St. Almaty050000 Kazakhstan

Distributed Denial of Service (DDoS) attacks, particularly High Orbit Ion Cannon (HOIC) attacks, present a significant threat to web servers. This study focuses on enhancing feature selection and model efficiency to ensure accurate detection while minimizing computational resources. The study aims to demonstrate that effective DDoS detection can be achieved with fewer instances and a limited number of key features. This approach not only optimizes the computational resources required for model training but also highlights the potential for deploying efficient and scalable detection systems in real-world environments where data processing capacity may be constrained. The selected features and resampled datasets form the basis for developing and evaluating machine learning models capable of accurately identifying HOIC attacks across varying dataset sizes. © 2023 Copyright for this paper by its authors.

关键词： Intrusion detection

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A Robust Model for Translating Arabic Sign Language into Spoken Arabic Using Deep Learning

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Intelligent Automation & Soft computing 2023年第8期37卷 2037-2057页

作者： Khalid M.O.Nahar Ammar Almomani Nahlah Shatnawi Mohammad Alauthman Department of Computer Sciences Faculty of Information Technology and Computer SciencesYarmouk University-Irbid21163Jordan School of Computing Skyline University CollegeSharjahP.O.Box 1797United Arab Emirates IT-Department-Al-Huson University College Al-Balqa Applied UniversityP.O.Box 50IrbidJordan Department of Information Security Faculty of Information TechnologyUniversity of PetraAmmanJordan

This study presents a novel and innovative approach to auto-matically translating Arabic Sign Language(ATSL)into spoken *** proposed solution utilizes a deep learning-based classification approach and the transfer learning technique to retrain 12 image recognition *** image-based translation method maps sign language gestures to corre-sponding letters or words using distance measures and classification as a machine learning *** results show that the proposed model is more accurate and faster than traditional image-based models in classifying Arabic-language signs,with a translation accuracy of 93.7%.This research makes a significant contribution to the field of *** offers a practical solution for improving communication for individuals with special needs,such as the deaf and mute *** work demonstrates the potential of deep learning techniques in translating sign language into natural language and highlights the importance of ATSL in facilitating communication for individuals with disabilities.

关键词： Sign language deep learning transfer learning machine learning automatic translation of sign language natural language processing Arabic sign language

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A Platform for Automated Solar Data Analysis Using Machine Learning 9

A Platform for Automated Solar Data Analysis Using Machine L...

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9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022

作者： Younis, Ola Tashtoush, Yahya Alomari, Mohammad Darwish, Omar Department of Computer Science University of Liverpool Liverpool United Kingdom Computer Science Department Jordan University of Science and Technology Jordan Applied Science Research Center Applied Science Private University Jordan Information Security and Applied Computing Eastern Michigan University Ypsilanti United States

ISBN: (纸本)9798350320480

This paper presents a computer platform for the automated analysis of associations among different solar events and activities. This computer tool enables the advanced learning by implementing many associations' algorithms to analyze years of solar catalogues data and to study the associations among solar flares, eruptive filaments per prominences and Coronal Mass Ejections (CMEs). The aim is to combine all solar data catalogues in one dynamic space weather database that can be easily used in the analysis of solar activities and features. The computer tool identifies patterns of associations and provides numerical representations that can be used as inputs to the machine learning algorithms to provide computerized learning rules that can be developed in the future within the context of a real-time prediction system. © 2022 IEEE.

关键词： Data mining

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Harnessing computational models to uncover the role of the immune system in tuberculosis treatment

Harnessing computational models to uncover the role of the i...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Mastrostefano, Enrico Ravoni, Alessandro Onofri, Elia Tieri, Paolo Castiglione, Filippo National Research Council Institute for Applied Computing 'Mauro Picone' Rome Italy University of Roma Tre Dept. Mathematics and Physics Rome Italy Technology Innovation Institute Biotechnology Research Center Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350337488

The importance of the immune system (IS) in tuberculosis (TB) drug development is often underestimated because of the intricate nature of experiments and the specialized knowledge needed. In vitro and animal studies fall short in replicating the intricate reactions of the human IS to drugs and infections. In this study, we present our initial efforts in employing an in silico approach to comprehend how an individual's IS impacts the efficacy of therapy, particularly in managing mycobacterium tuberculosis (Mtb) infection and minimizing the risk of relapse. We employed a well-established agent-based IS simulator called C-IMMSIM. We conducted simulations to investigate the long-term outcomes of TB disease in a virtual cohort infected with Mtb over a 50-year period. Our simulations revealed that individuals with competent IS showed a high success rate in containing Mtb infection. Furthermore, to better understand the dynamic interactions between Mtb and the IS, we deliberately introduced specific IS deficiencies, thus successfully inducing short-term relapses and mortality. These results confirm the model's ability to elucidate the mechanisms underlying the interactions between Mtb and the IS. © 2023 IEEE.

关键词： C-IMMSIM epidemiological distribution in-silico modelling Mycobacterium tuberculosis pathogen-drug interaction

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Adaptive Strategies for Scalable and Heterogeneous VANET Network

Adaptive Strategies for Scalable and Heterogeneous VANET Net...

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： S Thangam Priyanshu Jha Jawed Hawari Nabin Kumar Sah Nikunj Jain J Jesy Janet Kumari A.Saleem Raja Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India IT Department College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Sultanate of Oman

Despite technological advancements, Vehicular Ad-hoc Networks (VANETs) continue to face challenges related to reliability and high-speed mobility. This study focuses on enhancing scalability, improving mobility, and addressing network heterogeneity in the context of data dissemination. Data dissemination involves the distribution or transmission of statistical data to users. Tools like NS3, SUMO utilizing OSM for realistic traffic simulations, MATLAB for Data Analysis and evaluation of various protocols like ADHOC, Dynamic Source Routing (DSR), Optimized Link State Routing Protocol (OLSR), Destination Sequenced Distance Vector Routing (DSDV). The research aimed to refine VANET simulations for effective decision making, as its primary objective data dissemination i.e. distribution or transmission of statistical data to users, forming the central focus of this investigation.

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Smart Traffic Management in Green VANETs: Ant-Inspired Routing Strategies with Local Search Optimization

Smart Traffic Management in Green VANETs: Ant-Inspired Routi...

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Recent Innovation in Smart and Sustainable technology (ICRISST), International Conference on

作者： Thangam S Jahnavi M Chandana K Dheeraj K J Jesy Janet Kumari A Saleem Raja Dept. Of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India IT Department College of Computing and Information Sciences University of Technology and Applied Sciences-Shinas Sultanate of Oman

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309157

Green Vehicular Ad-hoc Networks (VANETs) are gaining significance in smart mobility because of growing concerns about the environment. The research introduces a novel method for managing traffic in such networks by combining ant-inspired routing methods with local search optimization approaches. The solution, which draws inspiration from the efficient path-finding abilities of ants, facilitates decentralized communication and sharing of traffic data among cars. It allows rapid adaptation to changing traffic circumstances and environmental elements. Local search optimization enhances routing decisions by considering the near vicinity of each vehicle. Extensive simulations demonstrate notable enhancements in traffic efficiency, decreased congestion, and improved energy conservation as compared to conventional routing methods. The suggested solutions also enhance the development of environmentally friendly and enduring transportation networks.

关键词： Technological innovation Costs Green products Energy conservation Vehicular ad hoc networks Transportation Routing Sustainable development Optimization Pattern matching

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BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving Engineering Design Problems

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Computer Modeling in Engineering & Sciences 2024年第10期141卷 219-265页

作者： Farouq Zitouni Saad Harous Abdulaziz S.Almazyad Ali Wagdy Mohamed Guojiang Xiong Fatima Zohra Khechiba Khadidja Â Kherchouche Department of Computer Science and Information Technology Kasdi Merbah UniversityP.O.Box 30000OuarglaAlgeria Department of Computer Science College of Computing and InformaticsUniversity of SharjahP.O.Box 27272SharjahUnited Arab Emirates Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization *** approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization *** this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)*** proposed hybrid algorithm will be referred to as *** this fusion,the BHJO algorithm aims to leverage the strengths of each *** this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and *** meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization *** addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid ***,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem ***,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation *** rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedd

关键词： Global optimization hybridization of metaheuristics beluga whale optimization honey badger algorithm jellyfish search optimizer chaotic maps opposition-based learning

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