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1st International Conference on Software, systems and Information technology, SSITCON 2024

作者： Kavitha, K.R. Almusawi, Muntather Vybhavi, Gummagatta Yajaman Edwin Prabhakar, P.B. Saranya, N. Naga Sona College of Technology Department of Electronics and Communication Engineering Salem India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Department of Computer Science and Engineering Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India

ISBN: (纸本)9798350352931

This research explores basketball match predictions within the field of sports analytics utilized Deep Learning (DL) technique to analyse the outcomes of National Basketball Association (NBA) games based on player performance and team statistics. The challenge in predicting NBA games outcomes and performance analysis various factor, as the high dimensionality of the data and uncertainty surrounding player due to difficult to predictions of basketball game. This paper, proposed Autoencoder with Gated Recurrent Unit (AE-GRU) algorithm to efficiently analyse and predict NBA game outcomes. The data was initially collected from NBA games between 2009 - 2021, focusing on five teams and pre-processing involved min-max normalization to uniformly scale the feature vectors. The Principal Component Analysis (PCA) utilized for feature selection, effectively reducing the high dimensionality of the NBA game data across various teams. The AE-GRU technique then captured the complex feature of basketball game data and handled uncertainty data and enhances performance. The performance of DL model evaluated the various metrics such as accuracy, precision, recall and f1-score metrics on NBA game. © 2024 IEEE.

关键词： Deep learning

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A three-level QR code sharing scheme based on SLIC and Hamming code

A three-level QR code sharing scheme based on SLIC and Hammi...

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2022 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2022

作者： Wu, Wei Zhang, Lina Zhang, Jiehui Cui, Chenyu Zhang, Xiaoyu Liu, Miao Xi'an University of Science and Technology College of Computer Science and Technology Xi'an China Shaanxi Normal University College of Computer Science Xi'an China

ISBN: (数字)9781665469722

ISBN: (纸本)9781665469722

QR code is a typical two-dimensional code tag consisting of square symbols, which is widely used in social life due to the advantages of high readability and large storage capacity. However, its security performance is low, which has caused widespread concern among scholars in various industries. This paper proposes a three-level QR code scheme based on superpixel segmentation in response. Public information can be scanned directly from shares using any standard QR code scanner. Moreover, secret information can only be obtained by the dissimilarity of shares. The reconstructed secret image in this paper retains the main features of the embedded image, and the scheme is resistant to deformation, high load, and robust because it retains the advantages of visual ciphers and QR codes. Experimental results and analysis show this scheme has many advantages over other schemes. © 2022 IEEE.

关键词： Resistance Visualization Codes Signal processing algorithms Symbols QR codes Feature extraction

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Hybrid Optimization Algorithm Based on QUATRE and ABC Algorithms 4th

Hybrid Optimization Algorithm Based on QUATRE and ABC Algori...

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4th International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2021

作者： Zhang, Xin Tang, Linlin Chu, Shu-Chuan Weng, Shaowei Pan, Jeng-Shyang College of Computer Science and Mathematics Fujian University of Technology Fuzhou China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China College of Computer Science and Technology Harbin Institute of Technology Shenzhen China College of Science and Engineering Flinders University Bedford Park Australia

ISBN: (纸本)9789811640384

Artificial bee colony optimization algorithm (ABC) is an optimization algorithm based on swarm intelligence which is obtained by observing the behavior of bees looking for nectar and sharing food information with bees in the hive. QUasi-Affine TRansformation Evolutionary (QUATRE) is an algorithm that uses quasi-affine transformation as an evolution method because ABC has the shortcoming of weak ability to develop new nectar sources, and QUATRE has weak search ability but strong development ability, so this paper combines these two algorithms to a certain extent and proposes an improved artificial bee colony optimization algorithm (QUA-ABC). QUA-ABC is inspired by the location update formula in QUATRE and proposes a new location update formula suitable for ABC. In this study, experiments were conducted using the internationally used CEC2013 data set. The optimization accuracy and convergence speed of QUA-ABC were compared with the original ABC. The results show that the QUA-ABC algorithm has stronger capabilities and better performance. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Swarm intelligence

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Pseudoinverse Learning Algorithm As a Unified Framework For Normalization In Deep Learning 13

Pseudoinverse Learning Algorithm As a Unified Framework For ...

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13th International Conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Guo, Ping Deng, Xiyan Wang, Yuping International Academic Center of Complex Systems Beijing Normal University Zhuhai China School of Computer Science and Technology Xidian University Xi’an China

ISBN: (纸本)9798331516147

In recent years, several normalization methods have been proposed in order to train neural networks, including batch normalization, layer normalization, weight normalization, and group normalization. However, it does not exist a unified framework which can unify these normalization techniques for easy using to most researchers. In this paper, we proved that most of the normalization methods can be realized in a unified framework, namely PseudoInverse Learning (PIL) algorithm. When training neural network with PIL algorithm, no other normalization methods are needed further since normalization is natural embedded in PIL algorithm. And like normalization techniques, PIL algorithm not only can speed up training deep neural networks, but also can act as a regularizer to improve generalization when utilizing low rank constraint PIL for autoencoders. Hence, our proposed unified framework can help researchers to tackle the difficult of choosing proper normalization methods, and this can greatly facilitate their research process. © 2025 IEEE.

关键词： Adversarial machine learning

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Automatic discovery of stateful variables in network protocol software based on replay analysis

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Frontiers of Information technology & Electronic Engineering 2023年第3期24卷 403-416页

作者： Jianxin HUANG Bo YU Runhao LIU Jinshu SU College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China Academy of Military Science Beijing 100091China

Network protocol software is usually characterized by complicated functions and a vast state *** this type of program,a massive number of stateful variables that are used to represent the evolution of the states and store some information about the sessions are prone to potentialﬂaws caused by violations of protocol specification requirements and program *** such variables is significant in discovering and exploiting vulnerabilities in protocol software,and still needs massive manual *** this paper,we propose a novel method that could automatically discover the use of stateful variables in network protocol *** core idea is that a stateful variable features information of the communication entities and the software states,so it will exist in the form of a global or static variable during program *** on recording and replaying a protocol program’s execution,varieties of variables in the life cycle can be tracked with the technique of dynamic *** draw up some rules from multiple dimensions by taking full advantage of the existing vulnerability knowledge to determine whether the data stored in critical memory areas have stateful *** also implement a prototype system that can discover stateful variables automatically and then perform it on nine programs in Pro FuzzBench and two complex real-world software *** the help of available open-source code,the evaluation results show that the average true positive rate(TPR)can reach 82%and the average precision can be approximately up to 96%.

关键词： Stateful variables Network protocol software Program analysis technology Network security

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Research Review and Literature Perception Towards Medicinal Plants Classification Using Deep Learning Techniques 4th

Research Review and Literature Perception Towards Medicinal ...

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4th International Conference on Advanced Computational and Communication Paradigms, ICACCP 2023

作者： Diwedi, Himanshu Kumar Misra, Anuradha Tiwari, Amod Kumar Mahmood, Atif Computer Science and Engineering Amity School of Engineering and Technology Amity University Lucknow India Computer Science and Engineering Rajkiya Engineering College Sonbhadra India Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

ISBN: (纸本)9789819942831

The pharmaceutical industry is paying more attention to medicinal plants since they are less likely to cause undesirable responses and are less expensive than current medicines. These features have attracted researchers interest in automatic medicinal plant recognition. There are several ways to improve the powerful medicinal plant classifier that can be useful in real-time classification. This work uses several reliable and effective deep-learning techniques for classifying plants based on leaf images. The recent research work is collected systematically and categorized based on their techniques. Further, the dataset details, performance measures used for evaluation, tools utilized for implementation, and chronological review of the collected works are discussed. Finally, this research is concluded with an analysis of eminent ongoing opportunities and research perspectives for improvement in this domain. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Classification (of information)

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Prediction of quality in production using optimized Hyper-parameter tuning based deep learning model

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Materials Today: Proceedings 2022年 69卷 703-709页

作者： Rajendra Kannammal, G. Sivamalar, P. Santhi, P. Vetriselvi, T. Kalpana, V. Nithya, T.M. Department of Computer Science and Engineering K Ramakrishnan College of Technology Tamilnadu Trichy India Department of Computer Science and Engineering M Kumarasamy College of Engineering Tamilnadu Karur India

Large volumes of manufacturing data may now be collected because to the growing popularity of smart Industry 4.0. Product quality may be predicted from manufacturing data acquired during production using machine learning approaches such as classification. A supply chain can benefit from eliminating uncertainty by precise forecasting at any point in the process. As a result, knowing the quality of a product batch early on can save money on recalls, packaging, and shipping. Classification methods have been extensively studied for forecasting the quality of certain manufacturing processes, but the overall obedience of production batches has not been carefully studied. Classification methods based on deep learning (Convolutional Neural Network) and optimal hyper-parameter tuning are the focus of this article, which aims to evaluate the suggested appliance production process. Existing approaches for classifying unit batches are compared to the proposed classification model in terms of several quality parameters for compliance. As a result, a model for predicting compliance quality may be built using the new method. Features and dataset knowledge are also critical in training classification models, according to this study. © 2022

关键词： Supply chains

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Energy-Proficient Adaptive Queuing Model-Based Clustering Scheme for Improving Network Lifetime in Wireless Sensor Networks 7th

Energy-Proficient Adaptive Queuing Model-Based Clustering Sc...

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7th International Conference on Advanced Computing and Intelligent Engineering, ICACIE 2022

作者： Sengathir, Janakiraman Deva Priya, M. Christy Jeba Malar, A. Jacob, Suma Sira Department of Information Technology CVR College of Engineering Mangalpally Vastunagar Telangana Hyderabad India Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamilnadu Coimbatore India Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India

ISBN: (纸本)9789819950140

Energy preservation in wireless sensor network (WSN) is vital for prolonging the network lifetime as sensors have stringent energy constraints. Reducing energy consumption is an uphill task, and process of clustering increases network lifetime to an expected level. In this context, efficient selection of Cluster Heads (CHs) facilitates effective data distribution. In this paper, Adaptive Queuing Model-based Clustering Scheme (AQMCS) is proposed for supporting maximized energy in the network with high-energy reduction in clusters through dynamic selection that mainly explores the energy incurred during the process of data packet transmission. It specifically determines the time at which packets would arrive for the objective of minimizing transmission delay. Moreover, the proposed AQMCS approach outperforms the benchmarked mechanisms by offering 19.32% improved Packet Delivery Ratio (PDR), 17.84% enhanced throughput, 16.54% minimized delay, 15.98% reduced total energy consumption, and 17.62% minimized Packet Loss Ratio (PLR) for varying number of nodes in the network. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Wireless sensor networks

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Learning-based Big Data Sharing Incentive in Mobile AIGC Networks

Learning-based Big Data Sharing Incentive in Mobile AIGC Net...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Wen, Jinbo Zhang, Yang Chen, Yulin Zhong, Weifeng Huang, Xumin Liu, Lei Niyato, Dusit Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China Guangdong University of Technology School of Automation China Xidian University School of Telecommunications Engineering China Nanyang Technological University School of Computer Science and Engineering Singapore

ISBN: (纸本)9798350351255

Rapid advancements in wireless communication have led to a dramatic upsurge in data volumes within mobile edge networks. These substantial data volumes offer opportunities for training Artificial Intelligence-Generated Content (AIGC) models to possess strong prediction and decision-making capabilities. AIGC represents an innovative approach that utilizes sophisticated generative AI algorithms to automatically generate diverse content based on user inputs. Leveraging mobile edge networks, mobile AIGC networks enable customized and real-time AIGC services for users by deploying AIGC models on edge devices. Nonetheless, several challenges hinder the provision of high-quality AIGC services, including issues related to the quality of sensing data for AIGC model training and the establishment of incentives for big data sharing from mobile devices to edge devices amidst information asymmetry. In this paper, we initially define a Quality of Data (QoD) metric based on the age of information to quantify the quality of sensing data. Subsequently, we propose a contract theoretic model aimed at motivating mobile devices for big data sharing. Furthermore, we employ a Proximal Policy Optimization (PPO) algorithm to determine the optimal contract. Numerical results demonstrate the efficacy and reliability of the proposed PPO-based contract model. © 2024 IEEE.

关键词： age of information big data sharing contract theory Mobile AIGC networks proximal policy optimization

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Deep Reinforcement Learning-Based Distributed Collaborative Service Caching in Edge Computing 12

Deep Reinforcement Learning-Based Distributed Collaborative ...

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12th International Conference on Advanced Cloud and Big Data, CBD 2024

作者： Yang, Wei Chu, Yi Chen, Chao Jiao, Shengming Xu, Xiaolong Xue, Shengjun School of Software Nanjing University of Information Science and Technology Nanjing China Jiangsu Meteorological Information Center Nanjing China School of Computer Science and Technology Silicon Lake College Suzhou China

ISBN: (纸本)9798331511074

As the Internet of Things (IoT) continues to evolve, smart devices increasingly depend on edge computing for services characterized by low latency and high efficiency. The rapid proliferation of smart devices and the diversification of service requests impose greater demands on the quality of edge service provision. Traditional caching strategies based on service request volumes, which neglect the geographic distribution of devices and the randomness of requests, fail to meet the dual requirements of accuracy and response speed, resulting in inefficient resource allocation and caching. To tackle these challenges, this paper introduces ECPG, a novel distributed collaborative service caching strategy leveraging deep reinforcement learning. By modeling the service caching issue as a Markov Decision Process (MDP) and optimizing it through the Deep Deterministic Policy Gradient (DDPG) algorithm, this strategy facilitates independent yet collaborative caching decisions by devices based on real-time data and environmental conditions. This approach significantly improves service response times and overall system efficiency. Experimental results demonstrate ECPG's superior performance in reducing service latency and increasing cache hit rates, underscoring its practicality and efficacy in contemporary edge computing environments. © 2024 IEEE.

关键词： Deep reinforcement learning

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