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2023 International Conference on Recent Advances in science and engineering technology, ICRASET 2023

作者： Al Ahmad, Mahmoud Diab, Maisa Patra, Sudhansu Shekhar University of Kalamoon College of Engineering It Department Damascus Suburb Syria Arab Private University for Science and Technology College of Informatics Engineering It Department Hama Syria Kiit Deemed to Be University School of Computer Applications Bhubaneswar India

ISBN: (纸本)9798350306927

The OpenFlow protocol facilitates the communication between the control and forwarding planes within a software-defined networking (SDN) framework. In SDN, the control plane is housed in a distinct entity known as the OpenFlow controller, which assumes the responsibility of making high-level determinations regarding the forwarding of network traffic. With the OpenFlow controller, network administrators can centrally program and manage network behavior, incorporating functionalities like traffic engineering, load balancing, QoS enforcement, security policies, and more. By segregating the control plane from the data plane, SDN utilizing OpenFlow empowers flexible network management, automation, and programmability. Through the utilization of the OpenFlow protocol, the OpenFlow controller interacts with the forwarding plane, typically implemented in network switches or routers. This approach offers a centralized perspective of the network and grants dynamic control over the behavior of network devices by installing flow entries into their forwarding tables. In this research, we use a single-controller N-policy finite buffer waiting queue model to investigate the system performance metrics. Numerical examples and different indices are presented to illustrate the proposed model. © 2023 IEEE.

关键词： Controllers

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Fractal Fractional Order Operators in Computational Techniques for Mathematical Models in Epidemiology

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Computer Modeling in engineering & sciences 2024年第2期138卷 1385-1403页

作者： Muhammad Farman Ali Akgül Mir Sajjad Hashemi Liliana Guran Amelia Bucur Institue of Mathematics Khwaja Fareed University of Enginnering and Information TechnologyRaheem Yar KhanPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Department of Mathematics Art and Science FacultySiirt UniversitySiirt56100Turkey Department of Mathematics Mathematics Research CenterNear East UniversityNicosiaTurkey Department of Computer Engineering Biruni UniversityIstanbul34010Turkey Department of Hospitality Services Babes-Bolyai UniversityCluj-Napoca400174Romania Department of Pharmaceutical Sciences Vasile Goldi竤Western University of AradArad310025Romania Department of Mathematics and Informatics Faculty of SciencesLucian Blaga University of SibiuSibiu550012Romania Department of Electronics and Communication Engineering Saveetha School of EngineeringSIMATSChennaiTamilnaduIndia

New fractional operators, the COVID-19 model has been studied in this paper. By using different numericaltechniques and the time fractional parameters, the mechanical characteristics of the fractional order model areidentified. The uniqueness and existence have been established. Themodel’sUlam-Hyers stability analysis has beenfound. In order to justify the theoretical results, numerical simulations are carried out for the presented methodin the range of fractional order to show the implications of fractional and fractal *** applied very effectivenumerical techniques to obtain the solutions of themodel and simulations. Also, we present conditions of existencefor a solution to the proposed epidemicmodel and to calculate the reproduction number in certain state conditionsof the analyzed dynamic system. COVID-19 fractional order model for the case of Wuhan, China, is offered foranalysis with simulations in order to determine the possible efficacy of Coronavirus disease transmission in theCommunity. For this reason, we employed the COVID-19 fractal fractional derivative model in the example ofWuhan, China, with the given beginning conditions. In conclusion, again the mathematical models with fractionaloperators can facilitate the improvement of decision-making for measures to be taken in the management of anepidemic situation.

关键词： COVID-19 model fractal-fractional operator Ulam-Hyers stability existence and uniqueness numerical simulation

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Assessment of Musculoskeletal Loads in Three Types of Body Posture According to Dolphens 21st

Assessment of Musculoskeletal Loads in Three Types of Body ...

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21st Scientific Conference on Medical and Sport Technologies, 2024

作者： Nowakowska-Lipiec, Katarzyna Sośniak, Paulina Zadoń, Hanna Jochymczyk-Woźniak, Katarzyna Bibrowicz, Karol Szurmik, Tomasz Mitas, Andrzej W. Michnik, Robert Department of Biomechatronics Faculty of Biomedical Engineering Silesian University of Technology Roosevelta 40 Zabrze41-800 Poland Faculty of Biomedical Engineering Students Scientific Circle "Biokreatywni" Silesian University of Technology Zabrze Poland Science and Research Center of Body Posture College of Education and Therapy in Poznań Poznań Poland Faculty of Arts and Educational Science University of Silesia Cieszyn Poland Department of Medical Informatics and Artificial Intelligence Faculty of Biomedical Engineering Silesian University of Technology Zabrze Poland

ISBN: (纸本)9783031821424

This study aimed to evaluate the effect of body posture type according to the Dolphens on musculoskeletal loads. There are three types of posture: neutral, swayback and lordotic according to Dolphens classification. The study involved a group of 29 subjects. Assessment of body posture was carried out using the Posture Photographic Assessment System. The following parameters were measured for each subject: pelvic tilt angle, lumbosacral and thoracolumbar tilt, upper kyphosis tilt angle. The position of the joints and individual body segments in relation to the vertical axis, the position of the centre of mass of the body and the values of the ground reaction forces were determined. The results obtained provided the input for the modelling of the posture of each person in the AnyBody Modelling System. A full-body Standing Model was used. The model was scaled to the weight and height of each subject. The highest values of lumbar loads were observed in subjects with a kyphotic posture. For those with a kyphotic posture, the average resultant force at the L5-S1 level was 0.97 ± 0.31 BW (BW - body weight). For those with a lordotic posture it was 0.87 ± 0.27 BW and for those with a neutral posture it was 0.86 ± 0.15 BW. Subjects with a kyphotic posture had the highest values for the compressive component of the force at the L5-S1 joint (0.96 ± 0.31 BW). The highest shear force values were obtained for the lordotic posture (0.1 ± 0.04 BW). The study also assessed the force values of the abdominal and dorsal muscles. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Physiological models

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Synchronization and its slow decay in noisy oscillators with simplicial interactions

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Physical Review E 2025年第1期111卷 014223-014223页

作者： Yuichiro Marui Hiroshi Kori Department of Mathematical Informatics Graduate School of Information Science and Technology The University of Tokyo Tokyo 113-8656 Japan Department of Complexity Science and Engineering The University of Tokyo Chiba 277-8561 Japan

Previous studies on oscillator populations with two-simplex interaction have reported novel phenomena such as discontinuous desynchronization transitions and multistability of synchronized states. However, the noise effect is not well understood. Here, we study a higher-order network of noisy oscillators with generic interactions consisting of one-simplex and two types of two-simplex interactions. We observe that when a type of two-simplex interaction is dominant, synchrony is eroded and eventually disappears even for infinitesimally weak noise. Nevertheless, synchronized states may persist for extended periods, with the lifetime increasing approximately exponentially with the strength of the two-simplex interaction. When one-simplex or another type of two-simplex interaction is sufficiently strong, noise erosion is prevented, and synchronized states become persistent. A weakly nonlinear analysis reveals that as one-simplex coupling increases, the synchronized state appears supercritically or subscritically, depending on the interaction strength. Furthermore, assuming weak noise and using Kramers' rate theory, we derive a closed dynamical equation for the Kuramoto order parameter, from which the time scale of the erosion process is derived. Our study elucidates the synchronization and desynchronization of oscillator assemblies in higher-order networks and is expected to provide insights into such systems' design and control principles.

关键词： Synchronization

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Predicting Mushroom Edibility with Effective Classification and Efficient Feature Selection Techniques 3

Predicting Mushroom Edibility with Effective Classification ...

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3rd International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2023

作者： Morshed, Md. Samin Bin Ashraf, Faisal Islam, Muhammad Usama Shafi, Md. Shafiur Raihan Islamic University of Technology Department of Computer Science and Engineering Bangladesh Brac University Department of Cse Bangladesh School of Computing and Informatics Ul Lafayette United States Southeast University Department of Cse Bangladesh

ISBN: (纸本)9798350346435

Machine learning in agriculture has added a new dimension to research field through providing actionable insights for better crop yield. In our work, we have explored various types of mushrooms and utilized efficient feature selection techniques coupled with effective machine learning based classification methods to classify the edibility of mushrooms automatically. Through experimentation with nine distinct machine learning methods and twenty selected features, the result shows that our best model (k-NN) performed significantly better with an accuracy of 99% and F-1 score of 99% in comparison to other machine learning methods. Our research work provided us with valuable actionable insights to devise further scopes in research in the field of mushroom edibility predicting through machine learning. © 2023 IEEE.

关键词： Agriculture

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Harvesting Natural Disaster Reports from Social Media with 1D Convolutional Neural Network and Long Short-Term Memory 8

Harvesting Natural Disaster Reports from Social Media with 1...

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8th International Conference on informatics and Computing, ICIC 2023

作者： Budiman, Irwan Faisal, Mohammad Reza Nugrahadi, Dodon Turianto Muliadi Delimayanti, Mera Kartika Prastya, Septyan Eka Lambung Mangkurat University Department of Computer Science Banjarbaru Indonesia Politeknik Negeri Jakarta Computer and Informatics Engineering Department Depok Indonesia Sari Mulia University Department of Information Technology Banjarmasin Indonesia

ISBN: (纸本)9798350342604

The employment of social media platforms is progressively assuming pivotal roles in natural disaster management as early warning and monitoring systems. In emergencies, social media users can post real-time information regarding the disaster they are experiencing, along with their location. In contrast, other users can access this information, and volunteers can search for these posts to render assistance. Nonetheless, the effective utilization of social media for monitoring natural disasters remains challenging due to the lack of automated and prompt tools for identifying natural disaster reports on social media. The analysis of social media data, particularly Twitter data, heavily relies on Natural Language Processing (NLP) algorithms to classify natural disaster reports obtained from eyewitnesses. Previous studies developed a classification model utilizing a 1D Convolutional Neural Network (CNN) with feature extraction based on three word embedding techniques: Word2Vec, fastText, and Glove. Although CNN can optimize features used in the classification process, it lacks the capacity to comprehend the structure of data sequences or sequential data. Thus, this research aims to develop a combined classification model of 1D CNN and Long Short-Term Memory (LSTM). LSTM can comprehend temporal features, such as the word order in a given document. The 1D CNN + LSTM model developed in this research exhibits performance of 82.83%, 88.33%, and 81.77% for Floods, Forest Fires, and Earthquakes. The paired t-test conducted shows a significant increase with a p-value of 0.0035. This research significantly contributes to identifying the 1D CNN + LSTM model that demonstrates the best performance in classifying natural disaster reports. By exploring the effectiveness of 1D CNN + LSTM models with different word embedding techniques and levels of contamination, this research offers various insights into the optimal method for harvesting natural disaster reports from social media. Mor

关键词： Long short-term memory

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A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

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CAAI Transactions on Intelligence technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef Mohammed Dilovan Asaad Zebari Mazin Abed Mohammed Diyar Qader Zeebaree Haydar Abdulameer Marhoon Karrar Hameed Abdulkareem Seifedine Kadry Wattana Viriyasitavat Jan Nedoma Radek Martinek Department of Information Technology Lebanese French UniversityErbilIraq Department of Computer Science University of ZakhoZakhoKurdistan RegionIraq Department of Computer Science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq Department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq Department of Telecommunications VSB‐Technical University of OstravaOstravaCzech Republic Department of Cybernetics and Biomedical Engineering VSB‐Technical University of OstravaOstravaCzech Republic Department of Computer Network and Information Security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Information and Communication Technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq Department of Applied Data Science Noroff University CollegeKristiansandNorway Faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model MRI images multi‐classification

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Adversarial learning for Mirai botnet detection based on long short-term memory and XGBoost

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 153-160页

作者： Vajrobol, Vajratiya Gupta, Brij B. Gaurav, Akshat Chuang, Huan-Ming Institute of Informatics and Communication University of Delhi India International Center for AI and Cyber Security Research and Innovations. Asia University Taiwan Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan Ronin Institute MontclairNJ United States Department of Information Management National Yunlin University of Science and Technology Yunlin Taiwan Symbiosis International University Pune India Department of Electrical and Computer Engineering Lebanese American University Beirut1102 Lebanon Dehradun India

In today's world, where digital threats are on the rise, one particularly concerning threat is the Mirai botnet. This malware is designed to infect and command a collection of Internet of Things (IoT) devices. The use of Mirai attacks has intensified in recent times, thus threatening the smooth operation of numerous devices that are connected to a network. Such attacks carry adverse consequences that include interference with services or the leakage of confidential information. To fight this growing threat, smart and flexible detection techniques are required to counter the new methods cyber attackers use. The aim of this research is to develop a resilient defense against Mirai botnet attacks. The Long Short Term Memory term (LSTM) and XGBoost combined have the best performance of 97.7% accuracy score. With this combination, the aim is to strengthen our cyber defenses against sophisticated and dynamically operating Mirai botnets to further enhance the security of our digital world. © 2024

关键词： Botnet

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Pros and Cons of Artificial Intelligence-ChatGPT Adoption in Education Settings: A Literature Review and Future Research Agendas

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IEEE engineering Management Review 2024年第3期52卷 27-42页

作者： Maita, Idria Saide, Saide Putri, Afifah Mesha Muwardi, Didi Department of Information Systems Faculty of Science and Technology State Islamic University of Sultan Syarif Kasim Riau Pekanbaru28293 Indonesia Department of Information Systems UIN Suska Riau Pekanbaru28293 Indonesia PRO Knowledge Indonesia Pekanbaru28293 Indonesia Dept. Informatics Engineering Faculty of Science and Technology Universitas Pahlawan Tuanku Tambusai Kampar28412 Indonesia Lembaga Enreach UIN Suska Riau Pekanbaru28293 Indonesia Department of Agribusiness Riau University Pekanbaru28293 Indonesia

The integration of artificial intelligence, particularly ChatGPT, in education presents both promising opportunities and notable challenges. Through a systematic review employing the PRISMA method, this article analyzed 45 references published of ChatGPTs impact on educational environments. While ChatGPT offers teachers a versatile learning tool, aiding in tasks, such as lesson planning and content generation, concerns regarding academic integrity and over-reliance on technology have emerged. Ethical considerations, including the potential for cheating in assignments and exams, highlight the need for clear guidelines and ethical frameworks to govern its use. Institutions or related organizations must address issues, such as plagiarism and data privacy, to ensure responsible integration of ChatGPT. Nurturing a growth mindset among educators and learners is crucial to effectively navigate ChatGPT integration. By aligning strategies to leverage ChatGPTs' capabilities while mitigating risks, educators, institutions, and policymakers can enhance the quality of education in an evolving technological landscape. This article contributes to a deeper understanding of ChatGPTs' implications in education, providing insights into its advantages and challenges. Informed decision making and proactive measures are essential to harness ChatGPTs potential for transformative impact while safeguarding educational integrity and ethics. © 2024 IEEE.

关键词： Knowledge management

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ρ-suppression: A Privacy Preserving Data Anonymization Method for Customer Transaction Data Publishing 7

ρ-suppression: A Privacy Preserving Data Anonymization Meth...

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7th International Conference on Information technology Systems and Innovation, ICITSI 2022

作者： Gunawan, Dedi Nugroho, Yusuf Sulistyo Al Irsyadi, Fatah Yasin Utomo, Ihsan Cahyo Andreansyah, Ilham Islam, Syful Universitas Muhammadiyah Surakarta Department of Informatics Engineering Surakarta Indonesia Noakhali Science and Technology University CSTE Department Noakhali Bangladesh

ISBN: (纸本)9781665465557

Publishing a transaction database to other parties is becoming a common way to harvest useful information from the database. Though, the database usually contains privacy concerns such as personal private itemset. Therefore, it is crucial to protect sensitive information prior to sharing the database to other parties. A solution to preserve the privacy of data subject in the transaction database is by modifying the transaction records using specific data anonymization method. Data anonymization method such as local generalization is widely adopted to solve the issue. However, the method induces excessive item loss and reduce data utility significantly. In this paper, a privacy preserving method to protect private itemsets of individual data subjects namely ρ-suppression is proposed. The method adopts cell suppression technique where personal sensitive items in transaction records are omitted. The method is distinct from an existing method, where it uses maximum term frequency normalization of the items in the database and it only suppresses items from transaction records having the privacy ration higher than that of ρ value. Experimental results indicate the proposed methods can successfully enhance the privacy protection level and has the ability to minimize the number of item loss as well as maintaining data utility level in an anonymized database. © 2022 IEEE.

关键词： Database systems

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