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3rd International Conference on Intelligent Data Science Technologies and Applications, IDSTA 2022

作者： Alsmadi, Izzat Al-Ahmad, Bilal Alsmadi, Mohammad Department of Computing and Cyber Security Texas A&m San Antonio United States The University of Jordan Computer Information Systems Department Aqaba Jordan Applied Science University Information Technology Amman Jordan

ISBN: (数字)9781665499606

ISBN: (纸本)9781665499606

Detection of malware and security attacks is a complex process that can vary in its details and analysis activities. As part of the detection process, malware scanners try to categorize a malware once it is detected under one of the known malware categories (e.g. worms, spywares, viruses, etc.). However, many studies and researches indicate problems with scanners categorizing or identifying a particular malware under more than one malware category. This paper, and several others, show that machine learning can be used for malware detection especially with ensemble base prediction methods. In this paper, we evaluated several custom-built ensemble models. We focused on multi-label malware classification as individual or classical classifiers showed low accuracy in such *** paper showed that recent machine models such as ensemble and deep learning can be used for malware detection with better performance in comparison with classical models. This is very critical in such a dynamic and yet important detection systems where challenges such as the detection of unknown or zero-day malware will continue to exist and evolve. © 2022 IEEE.

关键词： Scanning

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An Effective Analysis of Proficient Two Level Security Contraptions For Loading Data In Cloud

An Effective Analysis of Proficient Two Level Security Contr...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： G. Arulkumaran Santhosh Jayagopalan Thanga Mariappan L P. Balamurugan School of Computing and Information Technology REVA University Bengaluru Department of Computing British Applied College Umm Al Quwain UAE Department of Smart Computing School of Information Technology and Engineering Vellore Institute of Technology Vellore Vellore Dist Tamil Nadu India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu Dist Tamil Nadu India

One of the cornerstones of the future generation of computing is cloud computing, a form of internet-based computing. It allows for the instantaneous allocation of Internet-accessible resources whenever they are needed. Services such as software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS) are made available to people and businesses through cloud computing. By giving customers a low-cost, scalable, and financially-independent place to store their data, cloud storage services have seen rapid revenue growth in recent years. An interoperable cloud computing system is built on open architectures and interfaces that support both private and public cloud services. Many businesses today are making the switch to the cloud because of the numerous advantages it offers in terms of data storage, retrieval, management, and availability. While cloud computing is receiving a lot of attention, it is imperative that security problems be addressed first. This study proposes a crypto-stegno mechanism in the cloud as a solution to cloud security problems. This study proposes a practical method for ensuring data privacy and security in cloud storage. In order to obtain their own public and private key, users must first register their information in the cloud and come up with a unique username and password. Cryptography, a means of concealing information and sending information in an unreadable format, is employed in the next level of security. Secure data transmission and storage on the web, electronic commerce, digital media privacy, and ATM transfer are all dependent on cryptography to some degree. Repudiation, integrity, authenticity, and confidentiality are all possible using today's cryptographic methods. Signcryption is a lightweight cryptographic system that combines encryption and signatures in a logical way to achieve higher security with less computing time.

关键词： Cloud computing Data privacy Technological innovation Software as a service Platform as a service Switches Signal processing

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Towards Swarm Intelligence Ontology for Formal Description of Metaheuristics Algorithms

Towards Swarm Intelligence Ontology for Formal Description o...

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

作者： Domagoj Tuličić Marko Horvat Sandra Lovrenčić Department of Computer System and Networks University of Applied Science in Information Technology Zagreb Croatia Department of Applied Computing University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia Department of Theoretical and Applied Foundations of Information Sciences University of Zagreb Faculty of Organization and Informatics Varaždin Croatia

Swarm intelligence is a class of nature-inspired metaheuristic algorithms, that is specifically derived from biological systems in nature with an emphasis on their social interactions. These algorithms have been primarily developed to solve complex combinatorial optimization problems and are extremely popular among researchers in metaheuristic algorithms. This is evident from the numerous published scientific papers describing algorithms inspired by biological systems, ranging from insects such as ants and bees to the largest animals on the planet like whales. All these algorithms share some common concepts and relationships that can be explicitly identified, named, and formalized. These shared concepts and relationships can be organized and represented through the construction of an ontology. The goal of this paper is to present a formal ontology that standardizes vocabulary, as well as a set of rules enabling precise communication and reasoning about these algorithms. Ultimately, this will improve our understanding, analysis, research processes, and utilization of these computational approaches.

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Adaptive Weighted Low-Rank Sparse Representation for Multi-View Clustering

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IEEE Access 2023年 11卷 60681-60692页

作者： Khan, Mohammad Ahmar Khan, Ghufran Ahmad Khan, Jalaluddin Anwar, Taushif Ashraf, Zubair Atoum, Ibrahim A. Ahmad, Naved Shahid, Mohammad Ishrat, Mohammad Alghamdi, Abdulrahman Abdullah Dhofar University College of Commerce and Business Administration Salalah211 Oman Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur522502 India Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China GLA University Department of Computer Engineering and Applications Uttar Pradesh Mathura281406 India AlMaarefa University College of Applied Sciences Department of Computer Science and Information Systems Riyadh13731 Saudi Arabia National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei10607 Taiwan Shaqra University College of Computing and Information Technology Shaqra11961 Saudi Arabia

Ongoing researches on multiple view data are showing competitive behavior in the machine learning field. Multi-view clustering has gained widespread acceptance for managing multi-view data and improves clustering efficiency. Large dimensionality in data from various views has recently drawn a lot of interest from researchers. How to efficiently learns the appropriate lower dimensional subspace which can manage the valuable information from the diverse views is challenging and considerable issue. To concentrate on the mentioned issue, we asserted a novel clustering approach for multiple view data through low-rank representation. We consider the importance of each view by assigning the weight control factor. We combine consensus representation with the degree of disagreement among lower rank matrices. The single objective function unifies all factors. Furthermore, we give the efficient solution to update the variable and to optimized the objective function through the Augmented Lagrange's Multiplier strategy. Real-world datasets are utilized in this study to exemplify the efficiency of the introduced technique, and it is contemplated to preceding algorithms to demonstrate its superiority. © 2013 IEEE.

关键词： Data mining

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Polynomial Reduction Methods and their Impact on QAOA Circuits

arXiv

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arXiv 2024年

作者： Schmidbauer, Lukas Wintersperger, Karen Lobe, Elisabeth Mauerer, Wolfgang Technical University of Applied Sciences Regensburg Regensburg Germany Siemens AG Technology Munich Germany Institute of Software Technology Department High-Performance Computing Braunschweig Germany Technical University of Applied Sciences Regensburg Siemens AG Technology Regensburg Munich Germany

Abstraction layers are of paramount importance in software architecture, as they shield the higher-level formulation of payload computations from lower-level details. Since quantum computing (QC) introduces many such details that are often unaccustomed to computer scientists, an obvious desideratum is to devise appropriate abstraction layers for QC. For discrete optimisation, one such abstraction is to cast problems in quadratic unconstrained binary optimisation (QUBO) form, which is amenable to a variety of quantum approaches. However, different mathematically equivalent forms can lead to different behaviour on quantum hardware, ranging from ease of mapping onto qubits to performance scalability. In this work, we show how using higher-order problem formulations (that provide better expressivity in modelling optimisation tasks than plain QUBO formulations) and their automatic transformation into QUBO form can be used to leverage such differences to prioritise between different desired non-functional properties for quantum optimisation. Based on a practically relevant use-case and a graph-theoretic analysis, we evaluate how different transformation approaches influence widely used quantum performance metrics (circuit depth, gates count, gate distribution, qubit scaling), and also consider the classical computational efforts required to perform the transformations, as they influence possibilities for achieving future quantum advantage. Furthermore, we establish more general properties and invariants of the transformation methods. Our quantitative study shows that the approach allows us to satisfy different trade-offs, and suggests various possibilities for the future construction of general-purpose abstractions and automatic generation of useful quantum circuits from high-level problem descriptions. © 2024, CC BY.

关键词： Timing circuits

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A Dynamic Approach for Handling UAV in Crowded Environment Using Deep Q-Networks 4

A Dynamic Approach for Handling UAV in Crowded Environment U...

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4th IEEE Global Conference for Advancement in technology, GCAT 2023

作者： Upadhyay, Yash Gahlawat, Muskan Nivetha, S. Rana, Saurabh Natarajan, B. Gupta, Pratik Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai601103 India Department of Mathematics Chandigarh University India Department of Computer Science and Engineering RVS College of Engineering & Technology Coimbatore641402 India Amrita Vishwa Vidyapeetham Chennai601103 India Department of Applied Mathematics Gautam Buddha University Noida India

ISBN: (纸本)9798350305258

The demand for wireless connections is rising, and the advent of mobile internet has sparked a hunt for fresh, creative ways to expand the capacity and reach of cellular networks. One strategy to provide ground mobile users with long-term communication coverage is to employ Unmanned Aerial vehicles (UAV) as mobile base stations. The proposed method incorporates a Deep Reinforcement Learning (DRL) method to address the challenges that persist with traditional approaches. We introduced Deep Q-Learning with an attention mechanism-based multi-agent model to develop an energy-efficient control system for UAVs in cellular networks. Due to the proposed strategy's dynamic nature, it efficiently handles the crowded environment by resolving scalability and mobility issues. The researchers evaluated the proposed model using the benchmark dataset, producing promising results. Further, the researchers tested the proposed model using the sum rate metric to analyze the capability of the proposed algorithm to provide a reasonable throughput rate. © 2023 IEEE.

关键词： Cellular Communication Convolutional Neural Network Deep Q-Networks Deep Reinforcement Learning Deployment Optimization Energy Consumption Optimum Transport Target Network Unmanned Aerial Vehicle

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Assessment Process in the Light of GenAI: Prohibiting Vs Endorsing

Assessment Process in the Light of GenAI: Prohibiting Vs End...

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IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)

作者： Bashar Barmada Abha Chitalia Eltahir Kabbar School of Computing Electrical and Applied Technology Unitec Auckland New Zealand Department of Teaching and Learning (Te Puna Ako) Unitec Auckland New Zealand

ISBN: (数字)9798350376234

ISBN: (纸本)9798350376241

Generative AI has become inevitable technology in the current era. Many businesses have started to rely on it in their daily activities. On the other hand, others are sceptical of allowing a full use of GenAI, especially in the academic sector, as it may jeopardise students achieving the learning outcomes. Many academic providers including ones in New Zealand give their staff the choice of either allow or deny the usage of GenAI in assessment work by their students. This paper analyses these two approaches (allowing or denying), their benefits, and drawbacks. It discusses the redesign of assessment requirements and the evaluation process of students’ work to guarantee the academic integrity of the submitted work and the achievement of learning outcomes. Two examples of assessment types that are commonly used in computing discipline are discussed in detail regarding both approaches: the report, which is considered to be prone to GenAI, and the practical project, which is considered to be more resilient to GenAI. The paper will help academic staff in their decision regarding the use of GenAI in assessment work and provide guidance through each approach to achieve the learning outcomes by students with the emphasis on academic integrity.

关键词： Hands Law enforcement Generative AI Education Learning (artificial intelligence) Reflection Disruptive technologies Business

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Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

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Computers, Materials & Continua 2023年第11期77卷 2579-2594页

作者： Abdennasser Dahmani Yamina Ammi Nadjem Bailek Alban Kuriqi Nadhir Al-Ansari Salah Hanini Ilhami Colak Laith Abualigah El-Sayed M.El-kenawy Department of Mechanical Engineering Faculty of Science and TechnologyGIDD Industrial Engineering and Sustainable Development LaboratoryUniversity of RelizaneBourmadiaRelizane48000Algeria Laboratory of Biomaterials and Transport Phenomena(LBMPT) University of MedeaUrban PoleMedea26000Algeria Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of TamanghassetTamanrasset10034Algeria Sustainable Development and Computer Science Laboratory Faculty of Sciences and TechnologyAhmed Draia University of AdrarAdrarAlgeria CERIS Instituto Superior TécnicoUniversidade de LisboaLisbon1049-001Portugal Civil Engineering Department University for Business and TechnologyPristina10000Kosovo Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Engineering and Architecture Faculty Nisantasi UniversityIstanbulTurkey Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia Department of Computing and Information Systems School of Engineering and TechnologySunway University MalaysiaPetaling Jaya27500Malaysia Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running *** into renewable energy sources such as solar energy is being pursued to counter *** energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs *** study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global *** hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA *** from two stations in Algeria with different climatic zones were used to develop the *** error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all ***,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.

关键词： Renewable energy energy prediction global irradiation artificial intelligence BFGS quasi-Newton training algorithm

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Performance Analysis of Routing Protocols in VANETs using OSM, SUMO, and NS2

Performance Analysis of Routing Protocols in VANETs using OS...

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Interdisciplinary Approaches in technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Thangam. S Aaditi Ananya Avvaru Aryan Tandon J Jesy Janet Kumari A Saleem Raja Department of Computer Science & Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India University of Technology and Applied Sciences-Shinas Sultanate of Oman

With growing urbanization there is a need to manage vehicle-to-vehicle communication for intelligent communication in congested traffic. The performance of the widely used routing protocols AODV, DSDV, DSR, LAR, and ZRP is examined in this research in relation to traffic circumstances on a particular highway. Utilizing OSM data, integrated with NS2 and SUMO, realistic traffic scenarios are simulated. ZRP is a hybrid protocol that handles local communication within zones and LAR bridges the gap between the nodes and zones. This increases communication efficiency. Simulation of scenarios gives a clearer idea of how the dissemination is happening and helps in performance analysis. The protocols are assessed using important performance criteria in this study by means of a methodical approach that includes data collecting, simulation, and execution. Comparison of the metrics for the protocols helps in protocol selection for given scenarios, optimization, and standardized evaluation. The specific metrics considered for performance evaluation include Packet Delivery Ratio, Average End-to-End Delay, Average Throughput, Packet Drop Rate, and Normalized Routing Load.

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Intelligent Traffic Light Solution for Green and Sustainable Smart City

Intelligent Traffic Light Solution for Green and Sustainable...

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Mediterranean Conference on Embedded computing (MECO)

作者： Omid Jafari Stanislav Kolosov Nhan Vo Asmita Thapa Magar Jukka Heikkonen Rajeev Kanth School of Engineering and Technology Savonia University of Applied Sciences Kuopio Finland Department of Computing University of Turku Turku Finland

This research aims to develop a smart traffic light system that can improve traffic flow in urban areas. The proposed system uses sensors, cameras, and software to adjust the timing of traffic signals based on real-time traffic conditions. In this study, a Raspberry Pi 4 and MATLAB software was used to build the smart traffic controller. The detection part of the system involves several steps, including removing noise and retrieving information to calculate the number of cars detected. The system then switches traffic lights based on the detected car count. The MATLAB Image Acquisition and Computer Vision toolboxes were used to obtain and analyze the video frames received from the connected cameras. The detector uses the Gaussian Mixture Model to suppress frequently occurring features and to detect abnormal features, which are then used to detect changes caused by moving objects. Morphological operations are used to remove noise from the output. Finally, the system counts the cars detected by the Foreground Detector and switches the traffic lights accordingly. The proposed approach can help reduce traffic congestion and improve the overall flow of traffic in urban areas.

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