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2024 International Conference on Horizons of Information Technology and engineering, HITE 2024

作者： Hassan, Sajida Rasheed, Nadia Qureshi, Muhammmad Ali The Islamia University of Bahawalpur Computer Systems Engineering Department Pakistan The Islamia University of Bahawalpur Department of Information and Communication Pakistan

ISBN: (纸本)9798331516055

Neural networks outperform all machine-learning models in image classification, generation, and translation. One weakness of such models is that, unlike humans, they cannot learn multiple tasks sequentially. Our study suggests a workable way to train these models sequentially by safeguarding the weights crucial for earlier jobs. This paper proposes a new regularization algorithm for lifelong learning. The algorithm is based on factorized rotation, elastic weight consolidation, and principal component analysis. First, network reparameterization is incorporated by factorized rotation of parameter space. Then, the Fisher information matrix used in elastic weight consolidation identifies important directions in parameter space for each task. After that, Principal Component Analysis (PCA) is used to reduce the dimensions of the Fisher information matrix (FIM) by selectively regularizing weight updates only in unimportant directions for previously learned tasks. Extensive experimentation shows that this proposed algorithm outperforms existing regularization methods for continual learning. Our proposed algorithm performs very well in continuous learning for classification tasks on the MNIST dataset. it is 1% higher than Synaptic Intelligence(SI), 2% higher than best performing regularization-based algorithm Elastic Weight Consolidation(EWC), 1% higher than REWC(Rotated EWC) and 3% higher than Memory Aware-Synapses(MAS). © 2024 IEEE.

关键词： Fisher information matrix

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Data Secure Storage Mechanism for IIoT Based on Blockchain

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computers, Materials & Continua 2024年第3期78卷 4029-4048页

作者： Jin Wang Guoshu Huang R.Simon Sherratt Ding Huang Jia Ni School of Computer Science and Mathematics Fujian University of TechnologyFuzhou350118China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China School of Systems Engineering The University of ReadingReadingRG66AYUK School of Computer Nanjing University of Information Science and TechnologyNanjing210044China

With the development of Industry 4.0 and big data technology,the Industrial Internet of Things(IIoT)is hampered by inherent issues such as privacy,security,and fault tolerance,which pose certain challenges to the rapid development of *** technology has immutability,decentralization,and autonomy,which can greatly improve the inherent defects of the *** the traditional blockchain,data is stored in a Merkle *** data continues to grow,the scale of proofs used to validate it grows,threatening the efficiency,security,and reliability of blockchain-based ***,this paper first analyzes the inefficiency of the traditional blockchain structure in verifying the integrity and correctness of *** solve this problem,a new Vector Commitment(VC)structure,Partition Vector Commitment(PVC),is proposed by improving the traditional VC ***,this paper uses PVC instead of the Merkle tree to store big data generated by *** can improve the efficiency of traditional VC in the process of commitment and ***,this paper uses PVC to build a blockchain-based IIoT data security storage mechanism and carries out a comparative analysis of *** mechanism can greatly reduce communication loss and maximize the rational use of storage space,which is of great significance for maintaining the security and stability of blockchain-based IIoT.

关键词： Blockchain IIoT data storage cryptographic commitment

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Few-shot transfer learning for wearable IMU-based human activity recognition

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Neural Computing and Applications 2024年第18期36卷 10811-10823页

作者： Ganesha, H.S. Gupta, Rinki Gupta, Sindhu Hak Rajan, Sreeraman The Department of Electronics and Communication Engineering Amity University UP Noida201313 India The Amity Centre for Artificial Intelligence Amity University UP Noida201313 India The Department of Systems and Computer Engineering Carleton University Ottawa Canada

Deep learning has proven to be highly effective for human activity recognition (HAR) when large amount of labelled data is available for the target task. However, training a deep learning model to generalize well on a new task with just-few observations of labelled data is an active area of research. In this paper, a novel few-shot transfer learning (FSTL) approach is proposed for classification of human activities using just few instances (shots) of the data obtained from a wearable system assembled to collect inertial sensor data for different human activities, performed by two users. First, a deep learning model is trained on a large publicly available HAR dataset. The model parameters of such a model are then fine-tuned using the Reptile algorithm to determine the optimal initial parameter set using which, the model will classify activities with just few-shots of data from the target task. The proposed FSTL approach yields an average classification accuracy of 74.86 ± 0.71% and 79.20 ± 1.05% for 3-way, 5-shot classification of new activities performed by a single user and same set of activities performed by a new user, respectively. When the pre-trained weights are used as the initial weights in the Reptile algorithm, the generalization ability of the model improves by about 10% for 3-way, 5-shot classification as compared to using few-shot learning without parameter transfer. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Inertial navigation systems

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Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

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Neural Computing and Applications 2025年第1期37卷 263-281页

作者： Sekhar, J.C. Josephson, P. Joel Chinnasamy, A. Maheswari, M. Sankar, S. Kalangi, Ruth Ramya Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Malla Reddy Engineering College Telangana Hyderabad India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India

Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a

关键词： Principal component analysis

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Atmospheric Attenuation at mmWave below 57GHz Based on Measurement in Tropical Region 16

Atmospheric Attenuation at mmWave below 57GHz Based on Measu...

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16th IEEE Malaysia International Conference on communication, MICC 2023

作者： Li, Lu Sali, Aduwati Wali, Sangin Qahtan Universiti Putra Malaysia Faculty of Engineering Department of Computer and Communication Systems Engineering Selangor Serdang43400 Malaysia

ISBN: (纸本)9798350304343

According to ITU-R P.676-12, a modified prediction model of mmWave atmospheric attenuation suitable for tropical areas is proposed in this paper, named AA-mmWave. Combined with Malaysia's air humidity, temperature, and atmospheric pressure measured data in 2020 and 2021 via sensors, the mmWave atmospheric attenuation characteristic curve below 57GHz is simulated and calculated. The results show that the attenuation rate generally increases with frequency, especially from 55GHz to 57GHz, which increases until reaching the highest point of 5.937dB/km. Additionally, the atmospheric attenuation rate of a mmWave is inversely proportional to temperature and pressure. The lowest value of attenuation rate at 32°C achieves 0.3157 dB/km. Finally, there is a proportional correlation between water vapour density and mmWave atmospheric attenuation rate. This finding has essential and indelible reference value for developing mmWave transmission technology in the tropical region. © 2023 IEEE.

关键词： Atmospheric pressure

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Team Radiant or Dire? Comparing the SVM and k-NN Classifiers upon a DotA2 Matches Dataset 27

Team Radiant or Dire? Comparing the SVM and k-NN Classifiers...

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27th Pan-Hellenic Conference on Progress in Computing and Informatics with International Participation, PCI 2023

作者： Maratsi, Maria Ioanna Alexopoulos, Charalampos Charalabidis, Yannis Department of Information and Communication Systems Engineering University of the Aegean Greece Stockholm University Department of Computer and Systems Sciences Sweden

ISBN: (纸本)9798400716263

Match outcome prediction efforts for DotA2 have been ongoing almost since the time the game was released, in 2013. A considerable number of studies have suggested different methods aiming to approach this challenging topic in closer proximity, however, due to the complexity and plethora of parameters affecting the result, there is still room for further researching the algorithmic performance in order to choose appropriate models and achieve higher accuracy in prediction. Different combinations of classifiers have already been introduced and examined. Within the scope of this paper is to experiment with the Support Vector Machine (SVM) and k-Nearest Neighbors classifiers and apply them to compare their class predictive ability (victory for team Radiant or Dire) on a DotA2 dataset consisting of game features, among others team selection, character selection and game mode. The results of this small-case study showed that neither k-NN (for various k values) nor SVM managed to achieve a significant level of predictive accuracy. In addition, regardless of minor differences in the results, McNemar's test proved that their predictive performance is in fact equal, given a set significance level α. © 2023 Owner/Author.

关键词： Support vector machines

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A QR Code Used for Personal Information Based on Multi-Layer Encryption System

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第9期17卷 44-56页

作者： Al Dallal, Haroon Rashid Hammood Al Mukhtar, Wijdan Noaman Marzoog Department Infocommunication Technologies and Communication Systems Saratov State Technical University Saratov Russia Department of Computer Engineering and Information Technology Razi University Kermanshah Iran

Protecting and concealing sensitive data in the modern world is challenging. Due to insufficient protection and privacy, it is feasible for critical information to be fabricated. This led to a significant financial loss for someone. The intended recipient must be trusted with sensitive information and be able to independently authenticate the accuracy of the information by independently checking the specifics. There are several driving the rise in QR codes used for information transmission. Due to their enormous capacity for storing information, QR codes are vital for dissemination. However, most currently deployed QR code solutions employ insecure data formats and never employ encryption. Secure QR Code is data protection and data concealing available technology. The Quick Response (QR) code is widely used and accessible without extensive technical training. Now, the user data stored in a QR code is effectively public knowledge and occasionally even illegally used. To address the abovementioned issues, this study's authors propose a novel QR code encryption system. Using the image's mathematical processing method, we may apply the equivalence class principle to the ordered equations of the two-dimensional code, producing the desired cryptographic result. This method exploits the unique visual properties of the QR code. Only a QR code reader can decode the code's useful information, which is too complex for standard reading methods. It will be utilised to address issues in speedy business client data protection Security, commodity anticounterfeiting, and bicycle sharing QR codes. © 2023, International Journal of Interactive Mobile Technologies. All Rights Reserved.

关键词： Sensitive data

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A machine learning approach for skin lesion classification on iOS: implementing and optimizing a convolutional transfer learning model with Create ML

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International Journal of computers and Applications 2024年第8期46卷 666-685页

作者： Benedek Szabo, Aron Katona, Jozsef Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Instrumentation and Automation Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Informatics GAMF Faculty of Engineering and Computer Science Kecskemét Hungary

The integration of machine learning (ML) into mobile applications presents unique challenges, particularly in resource-constrained environments such as iOS devices. Skin lesion classification is a critical task in dermatology, where accurate and efficient diagnostic tools can significantly aid in early detection of malignant lesions. This study aims to implement a machine learning-based iOS mobile application and develop a binary classification model for skin lesion images to determine whether a lesion is malignant. The research utilized Create ML to develop a convolutional neural network optimized for iOS, employing a transfer learning approach. A logistic regression model was cascaded with the convolutional neural network N to enhance classification accuracy. The model's performance was assessed through various validation metrics, ensuring its robustness and efficiency within the constraints of mobile hardware. A curated dataset from the International Skin Imaging Collaboration archive was used for training and testing. The model achieved an accuracy of 92%, a precision of 90%, a recall of 93%, and an F1-score of 91% in classifying skin lesions. These metrics validate the model's efficacy in identifying malignant lesions. Data curation involved collecting, labeling, and preparing a dataset from publicly available sources, ensuring the inclusion of diagnostically relevant features. The final model was integrated into an iOS application using Core ML and Vision frameworks. The developed application demonstrates reliable performance in classifying skin lesions with high accuracy on iOS devices. The inclusion of comprehensive performance metrics justifies the efficacy of the proposed approach. Future work will explore enhancements in model architecture and object detection capabilities to further improve diagnostic precision and application usability. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Image recognition

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Synergia: Device-Edge Server Association for ISAC-assisted Mobile Edge Computing systems

Synergia: Device-Edge Server Association for ISAC-assisted M...

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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Charatsaris, Panagiotis Penafiel, Arianna Santamaria Diamanti, Maria Tsiropoulou, Eirini Eleni Papavassiliou, Symeon National Technical University of Athens Institute of Communication and Computer Systems School of Electrical and Computer Engineering Athens Greece University of New Mexico Dept. of Electrical and Computer Engineering AlbuquerqueNM United States

ISBN: (纸本)9798350351255

The efficient operation of the unified Integrated Sensing and communication (ISAC) - Mobile Edge Computing (MEC) systems is important for enhancing data sensing, communication, and computation processes in next-generation wireless systems. Despite prior research focusing on these systems, little attention has been given to optimizing the device-edge server associations. This paper addresses this gap by introducing the novel two-stage device-edge server association Synergia framework. Firstly, representative utility functions capture the characteristics of the devices and MEC servers by jointly considering their sensing, communication, and computation characteristics. Secondly, the Estimated Synergia framework leverages the Matching Theory to rapidly determine an initial device-server matching by disregarding the devices' externalities, i.e., the matching decisions of other devices. Thirdly, the Accurate Synergia model refines and improves this matching by using the coalition formation games, while considering the devices' externalities in optimizing the utilities of both the devices and the MEC servers. Extensive numerical evaluations demonstrate the Synergia's operational efficiency and scalability, outperforming reinforcement learningbased approaches. Also, a real-world application involving car accident detection validates its applicability. © 2024 IEEE.

关键词： Mobile edge computing

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TOPMG: Trust-Based Crowdsourcing through Multilateral Bargaining Game Theory

TOPMG: Trust-Based Crowdsourcing through Multilateral Bargai...

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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Charatsaris, Panagiotis Adesokan, Adedamola Rahman, Aisha B Tsiropoulou, Eirini Eleni Papavassiliou, Symeon National Technical University of Athens Institute of Communication and Computer Systems School of Electrical and Computer Engineering Athens Greece University of New Mexico Dept. of Electrical and Computer Engineering AlbuquerqueNM United States

ISBN: (纸本)9798350351255

Crowdsourcing plays a critical role in modern information gathering and task execution, yet it faces challenges regarding the task selection and equitable monetary incentives distribution. In this paper, we introduce the TOPMG framework, which addresses these challenges by enabling the workers to select tasks based on their historically experienced monetary incentives and the platforms' trustworthiness. Specifically, the TOPMG framework utilizes a reinforcement learning approach based on the principles of Optimistic Q-learning with Upper Confidence Bound (OQ-UCB) algorithm, guiding the platform selection process by considering the workers' monetary incentives, profit, and the platforms' trustworthiness. Also, the proposed framework introduces a multilateral bargaining game to allocate the platforms' monetary incentives to the workers by prioritizing their information contribution, fairness, and the platforms' reputation. Simulation results demonstrate TOPMG's operational dynamics, scalability, and efficacy, as well as its superiority over existing methodologies. © 2024 IEEE.

关键词： Reinforcement learning

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