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Modified Elite Opposition-Based Artificial Hummingbird Algorithm for Designing FOPID Controlled Cruise Control System

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Intelligent Automation & Soft Computing 2023年第11期38卷 169-183页

作者： Laith Abualigah Serdar Ekinci Davut Izci Raed Abu Zitar Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq25113Jordan Department of Electrical and Computer Engineering Lebanese American UniversityByblos13-5053Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia School of Engineering and Technology Sunway University MalaysiaPetaling Jaya27500Malaysia Department of Computer Engineering Batman UniversityBatman72100Turkey MEU Research Unit Middle East UniversityAmmanJordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu DhabiAbu DhabiUnited Arab Emirates

Efficient speed controllers for dynamic driving tasks in autonomous vehicles are crucial for ensuring safety and *** study proposes a novel approach for designing a fractional order proportional-integral-derivative(FOPID)controller that utilizes a modified elite opposition-based artificial hummingbird algorithm(m-AHA)for optimal parameter *** approach outperforms existing optimization techniques on benchmark functions,and we demonstrate its effectiveness in controlling cruise control systems with increased flexibility and *** study contributes to the advancement of autonomous vehicle technology by introducing a novel and efficient method for FOPID controller design that can enhance the driving experience while ensuring safety and *** highlight the significance of our findings by demonstrating how our approach can improve the performance,safety,and reliability of autonomous *** study’s contributions are particularly relevant in the context of the growing demand for autonomous vehicles and the need for advanced control techniques to ensure their safe *** research provides a promising avenue for further research and development in this area.

关键词： Cruise control system FOPID controller artificial hummingbird algorithm elite opposition-based learning

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Research on Information Big Data Prediction System Based on Support Vector Machine Integration

Research on Information Big Data Prediction System Based on ...

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Control, Electronics and computer technology (ICCECT), IEEE International Conference on

作者： Changjiang Huang Liuchun Zhan College of Computer Science Guangzhou College of Applied Science and Technology Guangdong Guangzhou China

In this paper, combined with K-nearest neighbor classification and support vector machine classification, a predictive model on customer churn is designed. The goal of the model is to reduce the loss of innovative customers to the collaborative product innovation process and the enterprise. At the same time, the system can effectively identify innovative customers with a tendency to churn in collaborative product innovation. The model first collects historical samples of mobile customer churn, removes some useless samples through preprocessing, then classifies the mobile customer type according to the SVM algorithm, and finally predicts the churn rate of specific mobile customers. Experimental results show the effectiveness and feasibility of the prediction model. The model has high accuracy and strong generalization ability.

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Modified Sine Cosine Optimization with Adaptive Deep Belief Network for Movie Review Classification

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Intelligent Automation & Soft Computing 2023年第7期37卷 283-300页

作者： Hala J.Alshahrani Abdulbaset Gaddah Ehab S.Alnuzaili Mesfer Al Duhayyim Heba Mohsen Ishfaq Yaseen Amgad Atta Abdelmageed Gouse Pasha Mohammed Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of English College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer63311Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Aflaj16828Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Sentiment analysis(SA)is a growing field at the intersection of computer science and computational linguistics that endeavors to automati-cally identify the sentiment presented in *** linguistics aims to describe the fundamental methods utilized in the formation of computer methods for understanding natural *** is classified as a negative or positive assessment articulated through *** can be commonly used for the movie review classification that involves the automatic determination that a review posted online(of a movie)can be negative or positive toward the thing that has been *** learning(DL)is becoming a powerful machine learning(ML)method for dealing with the increasing demand for precise *** this motivation,this study designs a computational intelligence enabled modified sine cosine optimization with a adaptive deep belief network for movie review classification(MSCADBN-MVC)*** major intention of the MSCADBN-MVC technique is focused on the identification of sentiments that exist in the movie review ***,the MSCADBN-MVC model follows data pre-processing and the word2vec word embedding *** the classification of sentiments that exist in the movie reviews,the ADBN model is utilized in this *** last,the hyperparameter tuning of the ADBN model is carried out using the MSCA technique,which integrates the Levy flight concepts into the standard sine cosine algorithm(SCA).In order to demonstrate the significant performance of the MSCADBN-MVC model,a wide-ranging experimental analysis is performed on three different *** comprehensive study highlighted the enhancements of the MSCADBN-MVC model in the movie review classification process with maximum accuracy of 88.93%.

关键词： Computational linguistics movie review analysis sentiment analysis sentiment classification deep learning

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Digital-Twin Enabled Time Ahead Resource Allocation for Integrated Fiber-Wireless Connected Vehicular Network

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Journal of Communications and Information Networks 2024年第3期9卷 296-308页

作者： Akshita Gupta Saurabh Jaiswal Martin Maier Vivek Ashok Bohara Anand Srivastava Wirocomm Research Group Department of Electronics&Communication EngineeringIndraprastha Institute of Information Technology Delhi(IIITD)New Delhi 110020India Department of Computer Science&Applied Mathematics Indraprastha Institute of Information Technology Delhi(IIITD)New Delhi 110020India Optical Zeitgeist Laboratory Institute National de la Recherche ScientifiqueMontreal H5A 1K6Canada

The digital twin(DT)is envisaged as a catalyst for pioneering ecosystems of service provision within an immersive environment born from the convergence of virtual and physical ***,DT could enhance the performance of edge-intelligent connected vehicular networks by allocating network resources efficiently based on the key performance indicators(KPIs)of vehicular data ***,this work addresses the key challenge of computation and spectrum resource allocation for vehicular *** allocate the optimal resource allocation,we subdivided the problem into:traffic classification,collective learning,and resource allocation *** order to do so,this paper concentrates on two crucial vehicular applications:brake application and lane-change *** utilize a random forest model to collectively learn vehicular data traffic in the upcoming time ***,a time-ahead resource allocation algorithm is proposed to improve the quality of service(QoS)by intelligently offloading vehicular data traffic to a DT-based integrated fiber-wireless(Fi-Wi)connected vehicular *** evaluate the performance of the resource allocation strategy in terms of resources required by the network alongside the packet loss *** was observed that there was a 44.74%increase in cost as the total computation resources increased from F=50 to 100 GHz,whereas the PLR of the network decreased by 71.43%.

关键词： connected vehicles digital twin edgeintelligence fiber-wireless machine learning resource allocation

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Forgive and forget: Self-stabilizing swarms in spite of Byzantine robots

Forgive and forget: Self-stabilizing swarms in spite of Byza...

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作者： Ashkenazi, Yotam Dolev, Shlomi Kamei, Sayaka Ooshita, Fukuhito Wada, Koichi Department of Computer Science Ben-Gurion University of the Negev Beersheba Israel Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Japan Graduate School of Science and Technology Nara Institute of Science and Technology Nara Japan Department of Applied Informatics Faculty of Science and Engineering Hosei University Tokyo Japan

In this article, we consider the case in which a swarm of robots collaborates in a mission, where a few of the robots behave maliciously. These malicious Byzantine robots may be temporally or constantly controlled by an adversary. The scope is synchronized full information robot operations, where a robot that does not follow the program/policy of the swarm is immediately identified and can be remembered as Byzantine. As robots may be suspected of being Byzantine due to benign temporal malfunctions, it is imperative to forgive and forget, otherwise, a robot cannot assume collaborative actions with any other robot in the swarm. Still, remembering for a while may facilitate a policy of surrounding, isolating and freezing the movement of the misbehaving robots, by several robots, allowing the rest to perform the swarm task with no intervention. We demonstrate the need to periodically forgive and forget to realize swarm several tasks including patrolling/cleaning in the presence of possible Byzantine robots. The policy for achieving the task consists of blocking the movement of the Byzantine robot(s) by some of the robots, while the rest patrol/clean the plane. © 2020 John Wiley & Sons, Ltd.

关键词： Robots

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Addressing the Rising Challenge of Malware Innovative Detection and Mitigation Techniques

Addressing the Rising Challenge of Malware Innovative Detect...

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2024 International Conference on Communication, computer sciences and Engineering, IC3SE 2024

作者： Balaram, A. Umashankari, E. Dutt, Amit Bharadwaj, Garima Revathi, V. Albawi, Ali Mlr Institute Of Technology Department Of Computer Science And Engineering Telangana Hyderabad India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India Lovely Professional University Phagwara India Amity Business School Amity University Greater Noida India New Horizon College Of Engineering Department Of Applied Sciences Bangalore India College Of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq

ISBN: (纸本)9798350366846

This research talks about Hybrid Anomaly Detection (HAD), a complex technology for finding and stopping malware. There are four different methods to apply the approach. The following are a few examples: EDR, DBA, TIS, and EML. The goal of the ablation specialists is to enhance the HAD structure in whatever way possible. With a success rate of 99.5% and only one false positive, DBA starts monitoring the process the second it starts. EML makes the system more flexible and useful by finding malware using probability estimates and weighted voting. TIS protects against new threats and motivates people to work together by sharing and prioritizing threat information. EDR is all about computer security by evaluating and reducing risks. The ablation study focuses on how well each algorithm can identify things, how flexible it is, how well it can work with others, and how safe it keeps endpoints safe. Since these algorithms work well together, the HAD framework offers a complete answer that performs better than other approaches in many ways. This study suggests a good way for everyone to work together to fight modern computer risks, which will improve cybersecurity. © 2024 IEEE.

关键词： Malware

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EuropeanLawAdvisor: An open source search engine for European laws

EuropeanLawAdvisor: An open source search engine for Europea...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Russo, Raffaele Russo, Diego Orlando, Gian Marco Romano, Antonio Riccio, Giuseppe Gatta, Valerio La Postiglione, Marco Moscato, Vincenzo University of Naples Federico II Department of Electrical Engineering and Information Technology via Claudio 21 80125 Naples Italy Northwestern University Department of Computer Science McCormick School of Engineering and Applied Science 2233 Tech Dr Evanston IL 60208 United States

ISBN: (纸本)9798350362480

Legal Artificial Intelligence has emerged as an essential field, focusing on AI technologies that facilitate various legal tasks and alleviate the workload of legal professionals. Despite advancements in Legal Artificial Intelligence, there remains a critical gap in systems that can provide both comprehensive and contextually accurate retrieval tailored to the intricate structure of EU legislation. We propose EuropeanLawAdvisor, an efficient and user-friendly legal information retrieval system designed to deliver tailored responses to legal queries. This system utilizes open-source Large Language Models within a Retrieval-Augmented Generation framework, facilitating precise and relevant information retrieval. The system employs a robust retrieval approach that integrates multi-match, k-nearest neighbors, hybrid methods, and TF-IDF search strategies across both complete documents and segmented text indexes, ensuring comprehensive retrieval for diverse query types. The implementation of the framework has demonstrated significant improvements in the accuracy and relevance of responses to EU legal queries, enhancing both the retrieval of relevant legal documents and the generation of precise responses. We show that EuropeanLawAdvisor, leveraging open-source models like Phi3-mini-3B and LLaMa-3-8B, achieves competitive Faithfulness and Relevance compared to GPT-4-Turbo. The performance gap narrows significantly in zero-shot scenarios, and our approach outperforms GPT-4-Turbo in the percentage of answered questions. We publicly release our code on GitHub: https://***/raffaele-russo/EuropeanLawAdvisor. © 2024 IEEE.

关键词： Nearest neighbor search

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Socio-cognitive Optimization of Time-delay Control Problems using Agent-based Metaheuristics 11

Socio-cognitive Optimization of Time-delay Control Problems ...

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11th IEEE International Conference on Intelligent Systems, IS 2022

作者： Nabywaniec, Mateusz Guzowski, Hubert Urbanczyk, Aleksandra Smolka, MacIej Kisiel-Dorohinicki, Marek Byrski, Aleksander Oplatkova, Zuzana Kominkova Senkerik, Roman Pekar, Libor Matusu, Radek Gazdos, Frantisek Agh University of Science and Technology Institute of Computer Science Krakow Poland Tomas Bata University in Zlín Faculty of Applied Informatics Czech Republic

ISBN: (纸本)9781665456562

Following the introduction of the socio-cognitive caste-based algorithms into the classic evolutionary metaheuristics, in this paper we focus on similar task regarding agent-based universal optimization methods. We tackle EMAS and DE algorithms and enrich them also with TOPSIS-inspired mechanism. Besides giving the details of the methods and the background, we present preliminary results after applying those techniques to solving the problem of optimization of time-delay system model. © 2022 IEEE.

关键词： Time delay

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Solving 2 by 2 Grid Sudoku Problem using Grover's Algorithm with Intel Quantum SDK 13

Solving 2 by 2 Grid Sudoku Problem using Grover's Algorithm ...

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13th International Conference on Advanced computer Information Technologies, ACIT 2023

作者： Shinde, Tejas Gambo, Yaknan John Rasch, Kevin Liebelt, Helena Li, Rui Deggendorf Institute of Technology High Performance Computing and Quantum Computing Deggendorf Germany Quantum Computing Research Intel Inc HillsboroOR United States Deggendorf Institute of Technology Department of Applied Computer Science Deggendorf Germany

Quantum technologies are moving towards enabling real world uses and as these technologies develop. The most important thing that differs Quantum from classical computers is that Quantum computers can do 'multiple' tasks at the same time as opposed to the classical computers doing a single task at a time. This phenomenon has led to the discovery of a few algorithms that are faster than regular computers. One such algorithm is the Grover's Algorithm[l]. This algorithm has shown a few real-world applications such as Quantum Search Algorithm, Travelling salesman, etc[2]. In this paper, Grover's algorithm is introduced, and a 2 x 2 Sudoku example is solved using Intel Quantum Software Development Kit[3]. The Intel Quantum SDK is quite new and in this paper we show how a simple Grover's Problem can be implemented in the SDK. Sudoku is a popular number-based puzzle game played on a grid consisting of nine squares subdivided into smaller 3x3 grids. The objective is to fill the grid with numbers from 1 to 9, ensuring that each row, column, and 3x3 grid contains all numbers exactly once. © 2023 IEEE.

关键词： Qubits

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Comparative Analysis of Machine Learning Algorithms for Email Phishing Detection Using TF-IDF, Word2Vec, and BERT

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computers, Materials & Continua 2024年第11期81卷 3395-3412页

作者： Arar Al Tawil Laiali Almazaydeh Doaa Qawasmeh Baraah Qawasmeh Mohammad Alshinwan Khaled Elleithy Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan College of Engineering Abu Dhabi UniversityAbu DhabiAl AinP.O.Box 1790United Arab Emirates Faculty of Artificial Intelligence Al-Balqa Applied UniversitySalt19117Jordan Department of Civil and Construction Engineering Western Michigan UniversityKalamazooMI 49008USA MEU Research Unit Middle East UniversityAmman11831Jordan Department of Computer Science and Engineering University of BridgeportBridgeportCT 06604USA

Cybercriminals often use fraudulent emails and fictitious email accounts to deceive individuals into disclosing confidential information,a practice known as *** study utilizes three distinct methodologies,Term Frequency-Inverse Document Frequency,Word2Vec,and Bidirectional Encoder Representations from Transform-ers,to evaluate the effectiveness of various machine learning algorithms in detecting phishing *** study uses feature extraction methods to assess the performance of Logistic Regression,Decision Tree,Random Forest,and Multilayer Perceptron *** best results for each classifier using Term Frequency-Inverse Document Frequency were Multilayer Perceptron(Precision:0.98,Recall:0.98,F1-score:0.98,Accuracy:0.98).Word2Vec’s best results were Multilayer Perceptron(Precision:0.98,Recall:0.98,F1-score:0.98,Accuracy:0.98).The highest performance was achieved using the Bidirectional Encoder Representations from the Transformers model,with Precision,Recall,F1-score,and Accuracy all reaching *** study highlights how advanced pre-trained models,such as Bidirectional Encoder Representations from Transformers,can significantly enhance the accuracy and reliability of fraud detection systems.

关键词： Attacks email phishing machine learning security representations from transformers(BERT) text classifeir natural language processing(NLP)

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