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Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection system

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Multimedia Tools and Applications 2024年第21期83卷 59887-59913页

作者： Abualigah, Laith Ahmed, Saba Hussein Almomani, Mohammad H. Zitar, Raed Abu Alsoud, Anas Ratib Abuhaija, Belal Hanandeh, Essam Said Jia, Heming Elminaam, Diaa Salama Abd Elaziz, Mohamed Abd Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Department of Mathematics Facility of Science The Hashemite University P.O box 330127 Zarqa13133 Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi Abu Dhabi United Arab Emirates Department of Computer Science Wenzhou-Kean University Wenzhou China Department of Computer Information System Zarqa University P.O. Box 13132 Zarqa Jordan School of Information Engineering Sanming University Sanming365004 China Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Benha12311 Egypt Computer Science Department Faculty of Computer Science Misr International University Cairo12585 Egypt Faculty of Computer Science & Engineering Galala University Suze435611 Egypt Ajman University Ajman346 United Arab Emirates Department of Mathematics Faculty of Science Zagazig University Zagazig44519 Egypt

With the ever-expanding ubiquity of the Internet, wireless networks have permeated every facet of modern life, escalating concerns surrounding network security for users. Consequently, the demand for a robust Intrusion Detection system (IDS) has surged. The IDS serves as a critical bastion within the security framework, a significance further magnified in wireless networks where intrusions may stem from the deluge of sensor data. This influx of data, however, inevitably taxes the efficiency and computational speed of IDS. To address these limitations, numerous strategies for enhancing IDS performance have been posited by researchers. This paper introduces a novel feature selection method grounded in Support Vector Machine (SVM) and harnessing the innovative modified Aquila Optimizer (mAO) for Intrusion Detection systems in Wireless Sensor Networks. To evaluate the efficacy of our approach, we employed the KDD'99 dataset for testing and benchmarking against established methods. Multiple performance metrics, including accuracy, detection rate, false alarm rate, feature count, and execution time, were utilized for assessment. Our comparative analysis reveals the superiority of the proposed method, with standout results in terms of feature reduction, detection accuracy, and false alarm mitigation, yielding significant improvements of 11%, 98.76%, and 0.02%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Network security

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Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

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computers, Materials & Continua 2023年第2期74卷 3151-3166页

作者： Marwa Obayya Nadhem NEMRI Lubna A.Alharbi Mohamed K.Nour Mrim M.Alnfiai Mohammed Abdullah Al-Hagery Nermin M.Salem Mesfer Al Duhayyim Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science and Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia

With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has *** evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based *** Electrocardiogram(ECG)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in *** to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from *** such scenario computer-assisted automated diagnostic tools are important for classification of ECG *** current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECGSignal Classification(IBADL-BECGC)*** accomplish this,the proposed IBADL-BECGC model initially pre-processes the input ***,IBADL-BECGC model applies NasNet model to derive the features from test ECG *** addition,Improved Bat Algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet ***,Extreme Learning Machine(ELM)classification algorithm is executed to perform ECG classification *** presented IBADL-BECGC model was experimentally validated utilizing benchmark *** comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.

关键词： Data science ECG signals improved bat algorithm deep learning biomedical data data classification machine learning

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The Emergence of Artificial Intelligence in Education and its Impact on Individual Literacy in Higher Education 7

The Emergence of Artificial Intelligence in Education and it...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Farkas, Imre Kovari, Attila Rajcsanyi-Molnar, Monika Institute of Computer Engineering University of Dunaujvaros Department of Computer Systems and Control Technology Dunaujvaros Hungary Eszterházy Károly Catholic University Institute of Digital Technology Faculty of Computer Science Eger Hungary Institute of Electronics and Communication Systems Kandó Kalmán Faculty of Electrical Engineering Obuda University Budapest Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemét Hungary Institute of Social Sciences University of Dunaujvaros Department of Economics and Management Dunaujvaros Hungary

ISBN: (纸本)9798331531119

In the last three years, a new partner has emerged for teachers and educators in the field of education. The mushrooming of applications based on large language models has greatly shaped the educational development fields of the present era. The use of various AI applications has become commonplace among students and teachers alike. Many questions are being raised by researchers in this field. What does the rapid development of AI applications bring to the field of pedagogy? Should the products of such applications be compared with human performance? The form presented in this thesis seeks to answer similar questions. What is the impact of frequent use of these applications on the literacy level of individuals? In which areas do students tend to use these applications? The results indicate that half of the students believe their literacy levels will decrease with the mass emergence of AI applications, while 36% feel it will not change. However, 88 % of students have already dealt with AI-based applications, with 64 % using them several times or more, suggesting a high level of integration into their educational processes. © 2024 IEEE.

关键词： Students

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Analysis of integration of IoMT with blockchain: issues, challenges and solutions

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Discover Internet of Things 2024年第1期4卷 1-36页

作者： Mazhar, Tehseen Shah, Syed Faisal Abbas Inam, Syed Azeem Awotunde, Joseph Bamidele Saeed, Mamoon M. Hamam, Habib Department of Computer Science Virtual University of Pakistan Lahore55150 Pakistan Department of Artificial Intelligence and Mathematical Sciences Sindh Madressatul Islam University Karachi74000 Pakistan Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Sana’a Yemen Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Electrical and Electronic Engineering Science University of Johannesburg Auckland Park Johannesburg2006 South Africa Av. Grandes Ecoles LibrevilleBP 1989 Gabon Bridges for Academic Excellence Spectrum Tunis Tunisia

The incorporation of Artificial Intelligence (AI) into the fields of Neurosurgery and Neurology has transformed the landscape of the healthcare industry. The present study describes seven dimensions of AI that have transformed the way of providing care, diagnosing, and treating patients. It has exhibited unparalleled accuracy in analyzing complex medical imaging data and expediting precise diagnoses of neurological conditions. It has also enabled personalized treatment plans by harnessing patient-specific data and genetic information, promising more effective therapies. For instance, AI-powered surgical robots have brought precision and remote capabilities to neurosurgical procedures, reducing human error. In AI, machine learning models predict disease progression, optimizing resource allocation and patient care, whereas wearable devices with AI provide continuous neurological monitoring, and enable early intervention for chronic conditions. It has also accelerated drug discovery by analyzing vast datasets, potentially leading to breakthrough therapies. Chatbots and virtual assistants powered by AI, enhance patient engagement and adherence to treatment plans. It holds promise in further personalization of care, augmented decision-making, earlier intervention, and the development of groundbreaking treatments. The present study mainly focuses on the incorporation of blockchain technology and provides a reasonable understanding of the associated issues and challenges along with its solutions. It will allow AI and healthcare professionals to advance the field and contribute towards the improvement of an individual's well-being when facing neurological challenges. © The Author(s) 2024.

关键词： Neurosurgery

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The Impact of the Digital Transformation of Education on Literacy 7

The Impact of the Digital Transformation of Education on Lit...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Farkas, Imre Kovari, Attila Rajcsanyi-Molnar, Monika Institute of Computer Engineering University of Dunaujvaros Department of Computer Systems and Control Technology Dunaujvaros Hungary Eszterházy Károly Catholic University Institute of Digital Technology Faculty of Computer Science Eger Hungary Obuda University Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemét Hungary Institute of Social Sciences University of Dunaujvaros Department of Economics and Management Dunaujvaros Hungary

ISBN: (纸本)9798331531119

During the COVID19 epidemic, people of all ages from all walks of life around the world have become inevitably familiar with and almost dependent on the digital tools of the age and the opportunities they offer. A change had to take place very quickly that was unprecedented. This process has also affected education. During the time of the reading lock-in, there was a proliferation of digital curricular content. The second time around, online education came to the fore, thanks to less or well-functioning online platforms, and has since become part of everyday teaching. What is the effectiveness and usefulness of sharing knowledge in this way? Is literacy still needed at all? Can online and hybrid (a controlled mix of face-to-face and online) education replace the well-established contact and face-to-face education? The study show that the respondents believe that online education has had an impact on literacy and that it is perceived to have a positive impact. The results indicate that online education has a positive impact on the literacy levels of students, as it facilitates knowledge acquisition and enhances digital literacy skills. © 2024 IEEE.

关键词： digital education digital literacy digital-informatics literacy

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Single Block Encoder-Decoder Transformer Model for Multi-Step Traffic Flow Forecasting 6

Single Block Encoder-Decoder Transformer Model for Multi-Ste...

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6th IEEE Symposium on computers and Informatics, ISCI 2024

作者： Omar, Mas Yakub, Fitri Talip, Mohamad Sofian Abu Maslan, Mohd Nazmin Sinha, Vijay Kumar Muljono, Muljono Universiti Teknologi Malaysia Wee Laboratory Malaysia-Japan International Institute of Technology Kuala Lumpur Malaysia Universiti Teknologi Malaysia Malaysia-Japan International Institute of Technology Department of Electronic System Engineering Kuala Lumpur Malaysia University of Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur Malaysia Universiti Teknikal Malaysia Faculty of Industrial and Manufacturing Technology and Engineering Melaka Malaysia Chitkara University School of Engineering & Technology Department of Computer Science & Engineering Himachal Pradesh India Universitas Dian Nuswantoro Faculty of Computer Science Semarang Indonesia

ISBN: (纸本)9798350353853

Accurate traffic flow forecasting is crucial for managing and planning urban transportation systems. Despite the widespread use of sequence modelling models like Long Short-Term Memory (LSTM) for this purpose, the potential of Transformer models remains underexplored. This is particularly true for the simplest form of a single block encoder-decoder Transformer model, which can be finely tuned through optimised hyperparameters. This paper examines the performance of a singular horizon-step forecasting method for multi-step traffic flow forecasting using a proposed Single Block Encoder-Decoder Transformer model optimised with a Grid Search algorithm. Results demonstrate that this model can enhance forecasting accuracy compared to the state-of-the-art LSTM model typically used for multi-step forecasting. The model effectively captures long-range temporal dependencies within a single road traffic flow dataset. It was tested on hourly traffic flow data to forecast the next 24 hours for the I5-North freeway in California, sourced from the Caltrans Performance Measurement system. The optimal configuration included an embedding dimension of 32, a feed-forward dimension of 128, and 8 attention heads. Results show a significant improvement, with a 4.7% reduction in Root Mean Squared Error compared to an LSTM model with two hidden layers of 100 neurons each, showcasing the potential of Single Block Encoder-Decoder Transformer models for real-world traffic prediction applications. © 2024 IEEE.

关键词： Mean square error

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Frequency Domain Adaptive Learning Algorithm for Thoracic Electrical Bioimpedance Enhancement

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computers, Materials & Continua 2022年第9期72卷 5713-5726页

作者： Md Zia Ur Rahman S.Rooban P.Rohini M.V.S.Ramprasad Pradeep Vinaik Kodavanti Department of Electronics and Communication Engineering Koneru Lakshmaiah Education FoundationK L UniversityVaddeswaramGuntur522502Andhra PradeshIndia Department of Computer Science and Engineering Faculty of Science and TechnologyICFAI Foundation for Higher EducationHyderabad500082TelanganaIndia Department of Electrical Electronics and Communication EngineeringGITAM Institute of TechnologyGITAM Deemed to be UniversityVisakhapatnam530045Andhra PradeshIndia

The Thoracic Electrical Bioimpedance(TEB)helps to determine the stroke volume during cardiac *** measuring cardiac signal it is contaminated with *** commonly encountered artifacts are Baseline wander(BW)and Muscle artifact(MA),these are physiological and *** the nature of these artifacts is random,adaptive filtering is needed than conventional fixed coefficient filtering *** address this,a new block based adaptive learning scheme is proposed to remove artifacts from TEB signals in clinical *** proposed block least mean square(BLMS)algorithm is mathematically normalized with reference to data and *** normalization leads,block normalized LMS(BNLMS)and block error normalized LMS(BENLMS)*** adaptive artifact cancellers are developed in both time and frequency domains and applied on real TEB quantities contaminated with physiological *** ability of these techniques is measured by calculating signal to noise ratio improvement(SNRI),Excess Mean Square Error(EMSE),and Misadjustment(Mad).Among the considered algorithms,the frequency domain version of BENLMS algorithm removes the physiological artifacts effectively then the other counter ***,this adaptive artifact canceller is suitable for real time applications like wearable,remove health care monitoring units.

关键词： Adaptive learning artifact canceller block processing frequency domain thoracic electrical bioimpedance

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The Reliability of Information Sources: Student Perceptions and Educational Opportunities 7

The Reliability of Information Sources: Student Perceptions ...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Kollar, Anita Mihalovicsne Katona, Jozsef Obuda University Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemet Hungary Budapest Business University Faculty of Finance and Accountancy Department of Applied Quantitative Methods Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary

ISBN: (纸本)9798331531119

The modern digital age demands the skills to be able to assess information. In the current world, access to information and knowledge is abundant from different sources. Indeed, high-quality data are much needed in education and professional development, as the quality and accuracy of information directly influence the process of learning and decision-making. The study aims to investigate the perceptions of computer science students at the University of Dunaújváros related to the credibility of diverse information sources. Within a questionnaire, on a voluntary answer basis, 68 students took part, 59 males and nine females. This work is aimed at assessing the degree to which the surveyed students evaluate information sources based on a five-point Likert scale. The data shows that students find print books and textbooks as the most reliable sources;social media are the least reliable. The study also supports the importance of source credibility evaluation, for which lateral reading techniques and AI-based news source credibility identification systems can be helpful. These findings suggest the necessity of bettering the educational approaches to foster better source criticism skills in the students, especially for online content and social media. The study generally contributes to understanding the information society and helps prepare students to obtain and use reliable and accurate information in their future careers. Using AI-based tools and systems can then further support these educational efforts with robust mechanisms to evaluate information credibility in an ever more complex digital landscape. © 2024 IEEE.

关键词： Data reliability

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Investigating the Benefits of Text Mining for Information Analysis

Investigating the Benefits of Text Mining for Information An...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Singh, Vaishali Venkadeshwaran, K. Jha, Gaurav Arunadevi, R. Sharma, Yogesh K. Maheswaran, S. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka 562112 India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Kongu Engineering College Department of Electronics and Communication Engineering Tamil Nadu Erode India

ISBN: (纸本)9798350329773

Textual content mining is the manner of extracting significant information and insights from large volumes of unstructured textual information. Many businesses are beginning to recognize the capacity of text mining in gaining effective insights and presenting greater accurate statistics. This paper will talk the ability blessings of using text mining strategies in data analysis. It's going to present quite number possible applications, such as herbal language processing, sentiment evaluation, subject matter modeling, and predictive analytics. It also evaluation the numerous technical techniques and algorithms used for textual content mining, together with lateens semantic indexing, and will look at how text mining can be used to guide a range of various commercial enterprise targets. Sooner or later, the paper will address how text mining can help pick out useful styles and traits in unstructured information, and the way it may be utilized in tandem with conventional data processing methods to achieve higher effects. This paper will finish by way of analyzing the capacity challenges that may be encountered when using text mining and how ability dangers can be mitigated. © 2023 IEEE.

关键词： Semantics

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Improving Bandwidth Utilization for Underwater Fiber Optic Networks Through Intelligent Optimization Techniques

Improving Bandwidth Utilization for Underwater Fiber Optic N...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Sakhare, Nitin N. Yadav, Rakesh Kumar Rajesh, A. Hassan, Inzimam Ul Vinithra Banu, T. Maheswaran, S. Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology UP India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka 562112 India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India Kongu Engineering College Department of Electronics and Communication Engineering Tamil Nadu Erode India

ISBN: (纸本)9798350329773

Underwater Fiber Optic Networks (UFONs) are getting increasingly more important because of the emergence of the marine economic system, the enlargement of ocean exploration sports, and the need for reliable net get entry to. However, their bandwidth utilization is negative due to insufficient useful resource allocation techniques, constrained community design facts, and restricted expertise about consumer traffic styles. This paper proposes clever optimization techniques to improve the bandwidth usage of UFONs. These strategies are primarily based on gadget studying strategies along with neural networks and particle-swarm optimization. Numerous performance metrics are used to assess the proposed techniques. Simulations and experiments are performed to compare the overall performance of the proposed techniques with conventional techniques. Effects show the proposed smart optimization strategies are able to enhancing the bandwidth utilization of UFONs by means of as much as sixteen%. The proposed shrewd optimization strategies can provide significant enhancements for actual-time aid allocation, dynamic network control, and monitoring of underwater traffic styles. © 2023 IEEE.

关键词： Particle swarm optimization (PSO)

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