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4th International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Hussain, Khalid Ullah Sheikh, Md Amin Jazri, Husin Bin Muzzaffar, Saira Chowdhury, Sohini Kavita School of Computing and Informatics Albukhary International University Kedah Alor Setar Malaysia School of Computer Science Taylor's University Faculty of Innovation and Technology Subang Jaya Malaysia Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India Chandigarh Group of Colleges Jhanjeri Department of Computer Science and Engineering Punjab Mohali140307 India

ISBN: (纸本)9798350353266

The multi-hopping mechanism features in the wireless network environment, in which all nodes contain the information of link and connectivity in any infrastructure less manner. When mobile devices either jump from their original network or change their location without describing any fashion, it is named mobile ad hoc network MANET. To ensure the up to date information related to the location and to provide the routing traffic of the node, numerous protocols have been used to remain efficient in achieving this for instance, AODV, DSR, DSDV etc. While we, the peer-to-peer networking, have challenges in many places, security is something that warrants attention more than any other things. The signal corruption hampers data transmission in bedwire networks when the attacks take place. This kind of activity can touch anyone including the layers lying above the physical and below the network layer. In the current discussion we developed, proposition of diversified, efficient and secure AI-based X-AODV routing protocol. We have investigated our proposed protocol against the recent modified two protocols which are SAODV and ARAN. The outcome of simulation reveals that our proposed routing protocol takes place in successfully performing of attack detection and is also the best when it comes to providing security and performance. The Simulation outcomes exhibit the Networks' performance improvement. © 2024 IEEE.

关键词： Network layers

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Cryptocurrency trading:a comprehensive survey

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Financial Innovation 2022年第1期8卷 404-462页

作者： Fan Fang Carmine Ventre Michail Basios Leslie Kanthan David Martinez-Rego Fan Wu Lingbo Li Department of Informatics Faculty of NaturalMathematical&Engineering ScienceKing’s College LondonStrandLondon WC2R 2LSUK Turing Intelligence Technology LimitedLondonUK

In recent years,the tendency of the number of financial institutions to include crypto-currencies in their portfolios has *** are the first pure digital assets to be included by asset *** they have some commonalities with more traditional assets,they have their own separate nature and their behaviour as an asset is still in the process of being *** is therefore important to summarise existing research papers and results on cryptocurrency trading,including available trading platforms,trading signals,trading strategy research and risk *** paper provides a comprehensive survey of cryptocurrency trading research,by covering 146 research papers on various aspects of cryptocurrency trading(e.g.,cryptocurrency trading systems,bubble and extreme condition,prediction of volatility and return,crypto-assets portfolio construction and crypto-assets,technical trading and others).This paper also analyses datasets,research trends and distribution among research objects(contents/properties)and technologies,concluding with some promising opportunities that remain open in cryptocurrency trading.

关键词： Trading Cryptocurrency Machine learning Econometrics

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The Advanced Encryption Standard with a Dynamic S-Box Using the RC4 Key Schedule 4

The Advanced Encryption Standard with a Dynamic S-Box Using ...

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4th IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Altigani, Abdelrahman Ghalib, Muhammad Rukunuddin Elshoush, Huwaida T. Elsadig, Muawia A. Alrefai, Nashat Naserelden, Shiraz Higher Colleges of Technology Computer Information Science Al Ain United Arab Emirates School of Engineering & Computing De Monfort University Dubai United Arab Emirates University of Khartoum Faculty of Mathematical Sciences and Informatics Computer Science Department Khartoum Sudan College of Computer Science and Information Technology Imam Abdulrahman bin Faisal University Dammam Saudi Arabia College of Science Imam Abdulrahman Bin Faisal University Basic and Applied Scientific Research Center Imam Abdulrahman Bin Faisal University Department of Mathematics Dammam Saudi Arabia Faculty of Science Universiti Teknologi Malaysia Department of Mathematical Sciences Johor Malaysia

ISBN: (纸本)9798350395914

Since the inception of secure communication, safeguarding data confidentiality has been paramount. With the increased reliance on technology, the need for robust security mechanisms is more critical than ever. This paper explores confidentiality in cybersecurity, focusing on symmetric encryption algorithms. While symmetric encryption, particularly the Advanced Encryption Standard (AES), is in wide use, concerns arise regarding its static nature. Therefore, the paper introduces a dynamic cipher, augmenting AES security by introducing a key-dependent S-Box. By leveraging the RC4 key schedule, the proposed cipher generates unique S-Boxes for each encryption key, significantly increasing security against non-generic attacks. Theoretical analysis demonstrates the proposed cipher is capable of resisting brute force, linear cryptanalysis, and differential cryptanalysis. Furthermore, its adaptability to existing AES implementations ensures practicality and ease of adoption. © 2024 IEEE.

关键词： Ciphertext

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THE DYNAMIC PROBLEM OF AN ELASTICALLY SUPPORTED BEAM ANALYSIS USING NEURAL ARCHITECTURE SEARCH WITH AUTOMATIC DIFFERENTIATION 9

THE DYNAMIC PROBLEM OF AN ELASTICALLY SUPPORTED BEAM ANALYSI...

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9th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake engineering, COMPDYN 2023

作者： Demertzis, Konstantinos S. Morfidis, Konstantinos E. Kostinakis, Konstantinos G. Iliadis, Lazaros S. School of Science & Technology Informatics Studies Hellenic Open University Greece Terma Dasylliou Thessaloniki55535 Greece Department of Civil Engineering Aristotle University of Thessaloniki Aristotle University Campus Thessaloniki54124 Greece School of Engineering Department of Civil Engineering Faculty of Mathematics Programming and General Courses Democritus University of Thrace Kimmeria Xanthi Greece

The equations of motion for linear structures considered as continuous systems are generally non-homogeneous linear partial differential equations. Their solution is functions of time and spatial variables. The traditional analysis approach involves solving the eigenvalue problem. This yields the eigenvalues and eigenvectors, which can be used to construct the general solution of the differential equations. This approach has been widely used in mechanics and has proven effective in solving many engineering problems. In recent years, machine learning methods, particularly neural networks, have also been used to solve differential equations. These methods can learn the equation's solution directly from data without the need for explicit analytical expressions. This makes them particularly useful for complex problems that may need analytical solutions or for problems where obtaining analytical solutions takes time and effort. This paper compares the performance of these two approaches for a specific problem of the vibration of the Euler-Bernoulli beam on a Winkler-type elastic foundation subjected to a moving load. The machine learning approach learns the solution directly from data generated by solving the differential equation using a Neural Architecture Search (NAS) with Automatic Differentiation (AD) method (NASAD). The paper provides insights into the relative strengths and weaknesses of each method and highlights the potential of machine learning to solve complex problems with high accuracy and low computational sources. © 2023 COMPDYN Proceedings. All rights reserved

关键词： Equations of motion

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Enhance Object Tracking on Augmented Reality Markerless using FAST Corner Detection 5

Enhance Object Tracking on Augmented Reality Markerless usin...

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5th IEEE International Conference on Information technology, Information Systems and Electrical engineering, ICITISEE 2021

作者： Nurhadi Winanto, Eko Arip Saparudin Dinamika Bangsa University Faculty Of Engineering Sriwijaya University Dept. Of Informatics Engineering Indonesia University Of Technology Malaysia School Of Computing Malaysia Telkom University Faculty Of Computer Science Dept. Of Informatics Engineering Indonesia

ISBN: (纸本)9781665401968

Markerless-based is the method in Augmented Reality (AR) to augmentation 3D objects in the frame. One of the challenges in markerless AR is to extract features as input data training on markerless AR. One of algorithm for feature extraction of object target is FAST corner detection in image processing. This paper uses FAST corner detection for feature extraction on Augmented Reality Markerless based with the aim to improve performance of augmentation objects in AR. The User Defined Target and Extended Tracking are to improve convenience and reliability of the AR. The aim is can trace 3D objects even the 3D object invisible. The results show that maximum environment for the best augmentation is intensity in of 80-190 cd. © 2021 IEEE.

关键词： Augmented reality

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Efficient Resource Management in IoTs Using Evolutionary and Swarm Intelligence Algorithms 7

Efficient Resource Management in IoTs Using Evolutionary and...

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Gupta, Keshav Dev Cengiz, Korhan Awasthi, Charu Ramini, Siva Akash Chatterjee, Shyamapriya Pavithra, M. Poornima College of Engineering Department of Computer Science Engineering Jaipur India University of Fujairah College of Information Technology United Arab Emirates Istinye University Department of Computer Engineering Istanbul Turkey University of Hradec Kralove Faculty of Informatics and Management Department of Information Technologies Hradec Kralove500 03 Czech Republic JSS Academy of Technical Education Department of Information Technology Noida India Andhra Pradesh Amaravati India Narula Institute of Technology Department of Information Technology West Bengal Kolkata India Jansons Institute of Technology Karumathampatti Department of Computer Science and Engineering Coimbatore641659 India

ISBN: (纸本)9798350383065

The proliferation of the Internet of Things (IoT) has ushered in a transformative era of connected devices, emphasizing the critical need for effective resource management. This study introduces an innovative approach that harnesses Evolutionary and Swarm Intelligence algorithms for IoT Task Scheduling, addressing resource optimization challenges. The approach offers superior resource utilization through advanced optimization techniques, reducing energy consumption and enhancing efficiency. Furthermore, it significantly reduces task scheduling execution time, enabling prompt decision-making in dynamic IoT environments. This results in improved task allocation quality, meeting application-specific requirements, and prioritizing critical tasks. Notably, the approach excels in minimizing completion time, making it particularly advantageous for real-time IoT applications. This research contributes to the advancement of IoT resource management, offering an adaptive, efficient, and intelligent solution with broad applicability. In the evolving landscape of IoT, the findings presented here lay a solid foundation for future research and practical implementations, ultimately promoting more responsive, sustainable, and intelligent IoT ecosystems. © 2023 IEEE.

关键词： Swarm intelligence

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Recommender Selection System for the Game Using Bonferroni Mean Based TOPSIS

Recommender Selection System for the Game Using Bonferroni M...

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International Conference on Electrical, Electronics and Information engineering (ICEEIE)

作者： Wildah Nourina Alfiani Fresy Nugroho Puspa Miladin Safitri A. Basid Firma Sahrul Bahtiar Fachrul Kurniawan Anang Kukuh Adisusilo Yunifa Miftachul Arif Informatics Engineering Faculty of Science and Technology Universitas Islam Negeri Maulana Malik Ibrahim Malang Indonesia Library and Information Science Faculty of Science and Technology Universitas Islam Negeri Maulana Malik Ibrahim Malang Indonesia Departement of Informatics Universitas Wijaya Kusuma Surabaya Surabaya Indonesia

In this paper, a recommendation system is applied to the motor selection menu in the game “Blar Rising”. This recommendation feature is provided in the selection menu to replace the commonly used randomize feature in many games. The recommendation feature takes into account each criterion of the available motors. The determination of weights is done using the ROC method, which considers the level of importance of the criteria. Therefore, in the game, there is a selection of tracks used to differentiate the importance level for weight determination. From the results obtained, in this case, both the TOPSIS and TOPSIS-BM methods exhibit good performance in making decisions. In its implementation, Bonferroni Mean (BM) is used to generalize the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. This can be observed from the difference in the results for each alternative. The TOPSIS-BM method yields similar results for each alternative, while the original TOPSIS method shows significant differences. Generalization makes the results more stable and capable of handling situations where criteria have extreme values. Usability testing across five aspects yielded an overall score of 3.43, with individual ratings for learnability (2.95), efficiency (3.87), memorability (3.67), error (3.20), and satisfaction (3.45). This suggests favorable usability acceptance among players for the recommendation feature.

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Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach

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Computers, Materials & Continua 2022年第9期72卷 5117-5134页

作者： Hiam Alquran Wan Azani Mustafa Isam Abu Qasmieh Yasmeen Mohd Yacob Mohammed Alsalatie Yazan Al-Issa Ali Mohammad Alqudah Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid22110Jordan Department of Biomedical Systems and Informatics Engineering Yarmouk University 556Irbid21163Jordan Faculty of Electrical Engineering Technology Campus Pauh PutraUniversiti Malaysia Perlis02000ArauPerlisMalaysia Advanced Computing Centre of Excellence(CoE)Universiti Malaysia Perlis(UniMAP)02000ArauPerlisMalaysia The Institute of Biomedical Technology King Hussein Medical CenterRoyal Jordanian Medical ServiceAmman11855Jordan Department of Computer Engineering Yarmouk UniversityIrbid21163Jordan

Cervical cancer is screened by pap smear methodology for detection and classification *** smear images of the cervical region are employed to detect and classify the abnormality of cervical *** this paper,we proposed the first system that it ables to classify the pap smear images into a seven classes *** smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images *** features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine(SVM)*** success of this proposed system in distinguishing between the levels of normal cases with 100%accuracy and 100%*** top of that,it can distinguish between normal and abnormal cases with an accuracy of 100%.The high level of abnormality is then studied and classified with a high *** the other hand,the low level of abnormality is studied separately and classified into two classes,mild and moderate dysplasia,with∼92%*** proposed system is a built-in cascading manner with five models of polynomial(SVM)*** overall accuracy in training for all cases is 100%,while the overall test for all seven classes is around 92%in the test phase and overall accuracy reaches 97.3%.The proposed system facilitates the process of detection and classification of cervical cells in pap smear images and leads to early diagnosis of cervical cancer,which may lead to an increase in the survival rate in women.

关键词： Classification deep learning machine learning pap smear images resnet101 support vector machines

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High-Level Deep Features and Convolutional Neural Network for Evaluating the Classification Performance of File Cluster Types 1

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Ali, Rabei Raad Dawd, Lahib Nidhal Mostafa, Salama A. Rachmawanto, Eko Hari Jubair, Mohammed Ahmed National University of Science and Technology Thi-Qar Nasiriyah Iraq Computer Techniques Engineering Department Dijlah University College Baghdad Iraq Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia Department of Informatics Engineering Dian Nuswantoro University 207 Imam Bonjol Street Semarang50131 Indonesia Department of Computer Technical Engineering College of Information Technology Imam Ja’afar Al-Sadiq University Al-Muthanna66002 Iraq

ISBN: (数字)9789819933150

ISBN: (纸本)9789819933143

Accurate file recovery of the corrupted or fragmented file is a very important task when missing the file system, since file recovery procedure involves methods, techniques, and tools that analyze and classify the contents of each data cluster including JPG file or non-JPG file. For classification problems, convolutional neural networks and high-deep learning have been applied to be very efficient and effective in file recovery. This study aims at finding a systematic comparison between a support vector machine (SVM) classifier method and an extreme learning machine (ELM) classifier method for classification issues. The classification methods automatically classify the files in a continuous series of data clusters based on three deep features which are rate of change (RoC), byte frequency distribution (BFD), and entropy. The methods are automatically assigning a class label of JEG file or non-JPG file for fragmented data clusters. The RABEI-2017 dataset was used for evaluating the performance of this study. The results demonstrate that the SVM method outperforms the ELM method with a high accuracy rate to classify fragmented JPG files. The accuracy of classification of the SVM method is 90.15% and of the ELM method is 96.21%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Support vector machines

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The Classification of Timor Weaving Using Artificial Neural Network

The Classification of Timor Weaving Using Artificial Neural ...

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International Conference on informatics and Computing (ICIC)

作者： Mendarissan Aritonang Adi Setiawan Krismiyati Theophilus Wellem Dept of Computer Science Faculty of Information Technology Satya Wacana Christian University Salatiga Indonesia Dept. Mathematics and Data Science Faculty of Science and Mathematics Satya Wacana Christian University Salatiga Indonesia Dept. Informatics & Computer Engineering Education Faculty of Information Technology Satya Wacana Christian University Salatiga Indonesia Department of Informatics Satya Wacana Christian University Salatiga Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

The number of woven fabric on the island of Timor is very large and varied, making it difficult to distinguish between types and origins. Many woven fabric motifs appear similar but represent different types. Therefore, this classification was used to perform pattern recognition of woven fabric in the system. The process allowed the system’s ability to be tested by applying the pattern recognition algorithm to woven images. In this context, the feature extraction method used was gray level co-occurrence matrix (GLCM), while the method used for classification was artificial neural network (ANN). The results showed an accuracy of $87.5 \%$ in the system. Finally, GUI system was developed, enabling tests to be performed on woven images for the identification of Timor Weaving image patterns.

关键词： Training Accuracy Social networking (online) Neurons Artificial neural networks Feature extraction Weaving Fabrics Pattern recognition Web sites

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