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International Conference for Convergence of technology (I2CT)

作者： Kirti Wanjale Araddhana Arvind Deshmukh Jyoti Chetan Vanikar Alpana Prashant Adsul Shailesh P. Bendale Department Of Computer Engineering Vishwakarma Institute of Information Technology Symbiosis Skills and Professional University Pune Pune Maharashtra India Department of Computer Science & Information Technology (Cyber Security) Symbiosis Skill and Professional University Kiwale Pune Department of Computer Engineering Dr. D.Y. Patil College of Engineering and Innovation Affiliated to Savitribai Phule Pune University Department of Computer Engineering NBN Sinhgad School of Engineering Pune

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Blink detection, a crucial part of facial movement analysis, is applied in a number of fields, including human-computer interaction, driver monitoring, and healthcare. However, automatic blink recognition presents unique obstacles due to variability in blink rates and ambient conditions. This work proposes a novel real-time method to robustly and accurately detect eye blinks in video sequences using OpenCV and the Modified Eye Aspect Ratio (Modified EAR) as a crucial component. The suggested methodology's automatic facial landmark detectors are initially trained on a range of real-world datasets. When it comes to adapting to shifting surroundings, such as shifts in illumination, expressions on faces, and head orientations, these detectors are incredibly versatile. Our method carefully calculates the locations of facial landmarks with every frame in the video sequence, making it possible to calculate the vertical distance between the eyelids with accuracy. This metric, which takes the form of the Modified EAR, serves as a single scalar quantity that represents the degree of eye closure or openness. Our experimentation's empirical results highlight the facial landmarks' remarkable precision in reliably assessing the dynamics of eye opening and closure. Blinks are detected by applying a threshold value to the Modified EAR, which enables the detection of blink patterns that take place within a short temporal window. Our approach not only detects blinks efficiently but also outperforms current state-of-the-art methods, as demonstrated by the results obtained from a large and thorough dataset.

关键词： Video sequences Lighting Ear Detectors Medical services Real-time systems Reliability

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A Proposed Approach for Risk Planning and Management

A Proposed Approach for Risk Planning and Management

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Information technology (ACIT)

作者： Mohammed N. Al Refai Yazan Al-Smadi Adai Al-Momani Zeyad M. Jamhawi Ahmed Ali Otoom Issa Atoum Ahid Yaseen Jafar Ababneh Mohammad Kanan Software Engineering Dep. Zarqa University Zarqa Jordan Department of Computer Science Zarqa University Zarqa Jordan Department of Computer Science Applied Science Private University Amman Jordan Department of Cyber Security Applied Science Private University Amman Jordan Software Engineering Department Faculty of Information Technology Philadelphia University Jordan Department of Cyber Security Zarqa University Zarqa Jordan Dep. of Industry Engineering University of Business and Technelogy Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Project Risk management is the process of identifying, evaluating, avoiding, or reducing risks. Where there is no software project without risks existence are natural in the context of project planning and management. This study focuses on proposing an enhanced risk management approach where testing phases were added to make the risk management process more effective by proposing solutions to many mistakes and challenges that occur through dealing with risks. The proposed approach focuses on dealing with risks that cannot be easily defined, more accurate risk evaluation, planning, and managing risk coordinated with a contingency plan.

关键词： Technological innovation Accuracy Contingency management Process control Software Planning Risk management Information technology Testing

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Automatic Fish Species Identification Using Convolutional Neural Networks (CNN)

Automatic Fish Species Identification Using Convolutional Ne...

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Sustainable Expert Systems (ICSES), International Conference on

作者： I. Mettildha Mary T. Ragunthar M. Priyadharsini H Shyam Krishnaa M K Sujit Department of Computer Science and Engineering(Cyber Security) Dr.N.G.P Institute of Technology Coimbatore Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Information Security School of Computer Science and Engineering (SCOPE) Vellore Institute Technology Vellore Tamil Nadu India Siemens Healthineers Bangalore Atheniac Bangalore

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Accurate identification of fish species is critical for applications in fisheries management., biodiversity monitoring., and conservation efforts. Conventional manual identification techniques take a lot of time and are prone to mistakes., particularly when working with big datasets and species that have similar physical characteristics. Automating tasks related to species identification has shown promise in recent breakthroughs in machine learning., especially in deep learning techniques like Convolutional Neural Networks (CNNs) and transfer learning. However., these systems face several challenges., including high variability in fish appearances., limited availability of labelled datasets., and the computational overhead required for real-time classification. This research study presents a fish species classification system using a transfer learning approach based on MobileNetV2. MobileNetV2., known for its efficiency and lightweight architecture., is fine-tuned on a custom fish dataset to enhance species identification accuracy while minimizing computational requirements. To increase the model's resilience and ability to generalise to new species., data augmentation approaches are used. The system is designed to deliver high classification accuracy while maintaining computational efficiency., making it suitable for deployment in real-world applications with limited resources. Experimental results demonstrate that the proposed system achieves significant improvements in accuracy compared to traditional methods., while also addressing the challenges of dataset variability and overfitting.

关键词： Accuracy Transfer learning Fish Real-time systems Computational efficiency Convolutional neural networks Object recognition Monitoring Resilience Overfitting

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Enhanced Encrypted Traffic Classification Through Packet-Flow Dual Views

Enhanced Encrypted Traffic Classification Through Packet-Flo...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Feng, Beibei Wang, Qi Yijing, Wang Li, Feifan Xu, Xiaolin Zang, Tianning Ma, Jingrun Coordination Center of China National Computer Network Emergency Response Technical Team Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China China Assets Cybersecurity Technology Co. Ltd. Beijing China Zhongguancun Laboratory Beijing China

ISBN: (纸本)9798350368369

In the field of cybersecurity, encrypted traffic classification is of paramount importance, serving as a crucial technology for ensuring data privacy and defending against cyber attacks. Existing ETC (Encrypted Traffic Classification) methods commonly face three challenges in real-world Mobile Internet environments, particularly pertinent to the multimedia context: 1) singular entity traffic information, 2) fixed-size feature filters in learning models, and 3) reliance on traditional encryption protocols. To address these, we introduce the innovative Packet-Flow Dual Views (PFDV) framework. PFDV aims to push the performance boundaries of ETC tasks in multimedia traffic scenarios by examining encrypted payloads separately from the packet and flow views. More specifically, PFDV employs a dual attention framework, comprising both payload attention and gram attention, to accurately identify nuanced differences in network traffic. Such an approach underscores granular packet-level details while offering a comprehensive flow view with packet positional data, striving for an optimal balance in multimedia traffic analysis. PFDV demonstrated superior performance across diverse tasks, achieving accuracy rates and F1 scores significantly higher than competing methods, with results of up to 98.34% accuracy and 95.69% F1 score in multimedia traffic classification. Furthermore, ablation studies demonstrate each component in PFDV contributes significantly to the overall enhancement of ETC performance in the model. © 2025 IEEE.

关键词： cyber attacks

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Generalized Simultaneous Perturbation-Based Gradient Search With Reduced Estimator Bias

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IEEE Transactions on Automatic Control 2025年第7期70卷 4687-4702页

作者： Pachalyl, Soumen Bhatnagarz, Shalabh Prashanth, L.A. TCS Research Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai India Indian Institute of Science Department of Computer Science and Automation The Robert Bosch Centre for Cyber Physical Systems Bangalore India Indian Institute of Technology Madras Department of Computer Science and Engineering Chennai India

We present a family of generalized simultaneous perturbation-based gradient search (GSPGS) estimators that use noisy function measurements. The number of function measurements required by each estimator is guided by the desired level of accuracy. We first present in detail unbalanced generalized simultaneous perturbation stochastic approximation (GSPSA) estimators and later present the balanced versions (B-GSPSA) of these. We extend this idea further and present the generalized smoothed functional (GSF) and generalized random directions stochastic approximation (GRDSA) estimators, respectively, as well as their balanced variants. We show that estimators within any specified class requiring more number of function measurements result in lower estimator bias. We present a detailed analysis of both the asymptotic and non-asymptotic convergence of the resulting stochastic approximation schemes. We further present a series of experimental results with the various GSPGS estimators on the Rastrigin and quadratic function objectives. Our experiments are seen to validate our theoretical findings. © 1963-2012 IEEE.

关键词： Stochastic systems

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Multi-party privacy-preserving decision tree training with a privileged party

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Science China(Information Sciences) 2024年第8期67卷 253-268页

作者： Yiwen TONG Qi FENG Min LUO Debiao HE Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Shanghai Technology Innovation Centre of Distributed Privacy-Preserving Artificial Intelligence Matrix Elements Technologies Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center Qilu University of Technology(Shandong Academy of Sciences)

Currently, a decision tree is the most commonly used data mining algorithm for classification tasks. While a significant number of studies have investigated privacy-preserving decision trees, the methods proposed in these studies often have shortcomings in terms of data privacy breach or efficiency. Additionally,these methods typically only apply to symmetric frameworks, which consist of two or more parties with equal privilege, and are not suitable for asymmetric scenarios where parties have unequal privilege. In this paper,we propose SecureCART, a three-party privacy-preserving decision tree training scheme with a privileged party. We adopt the existing pMPL framework and design novel secure interactive protocols for division,comparison, and asymmetric multiplication. Compared to similar schemes, our division protocol is 93.5–560.4× faster, with the communication overhead reduced by over 90%; further, our multiplication protocol is approximately 1.5× faster, with the communication overhead reduced by around 20%. Our comparison protocol based on function secret sharing maintains good performance when adapted to pMPL. Based on the proposed secure protocols, we implement SecureCART in C++ and analyze its performance using three real-world datasets in both LAN and WAN environments. he experimental results indicate that SecureCART is significantly faster than similar schemes proposed in past studies, and that the loss of accuracy while using SecureCART remains within an acceptable range.

关键词： privacy protection decision trees secure multi-party computation secret sharing privileged party

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Intelligent Poultry Health Management Based on Improved YOLOv8 Algorithm

Intelligent Poultry Health Management Based on Improved YOLO...

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2024 IEEE International Conference on Agrosystem engineering, technology and Applications, AGRETA 2024

作者： Yajie, Liu Md Johar, Md Gapar Hajamydeen, Asif Iqbal Raya, Lilysuriazna School of Graduate Studies Management and Science University Shah Alam Malaysia Software Engineering and Digital Innovation Centre Management and Science University Shah Alam Malaysia Artificial Intelligence and Cyber Security Centre Management and Science University Shah Alam Malaysia Management & Science University Department of Engineering & Technology Shah Alam Malaysia

ISBN: (纸本)9798350373660

Poultry production plays a critical role in the development of the global economy. Meanwhile, it also raises food safety concerns for humans. Poultry disease prevention and control are important for farmers to ensure food safety and economic profits. In this study, an abnormal poultry behavior detection network was proposed and developed based on an improved YOLOv8 deep learning algorithm, leveraging the advantages of modern technologies to enhance the efficiency of non-invasive monitoring for poultry health management. The YOLOv8 model was improved by combining the Alterable Kernel Convolution (AKConv) computational block and the Efficient Multiscale Attention (EMA) mechanism block. Based on experimental results, a 99.2% detection precision of the proposed improved YOLOv8 model was achieved, significantly better than other typical deep learning algorithms, such as Retinanet, YOLOv3, YOLOv5, YOLOv8, and YOLOv9. This provides effective technical support for poultry health management by detecting abnormal behaviors in poultry. © 2024 IEEE.

关键词： attention mechanism behavior detection deep learning poultry health YOLOV8

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Comparative Evaluation of Deep Learning Models in Alzheimer’s Disease Diagnosis

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Procedia computer Science 2025年 258卷 2352-2361页

作者： Leena Arya Yogesh Kumar Sharma Smitha Nayak Janaki Sivakumar Assistant Professor Computing Department Muscat College Oman Associate Professor Programme leader Computer security and forensics Department of Computer Science and Creative Technologies Global College of Engineering and Technology Muscat Oman

Alzheimer’s Disease (AD) is an ever-evolving neurodegenerative confusion that influences the neurons of the mind in a manner that hinders the patients’ ability to solve problems and remember issues and that requires timely and proper identification to enable appropriate management and treatment. Based on MRI medical images, deep learning models have become one of the most effective approaches for analyzing the signs of the early stages of AD. This research, therefore, carries out a comparative analysis of five deep learning models such as Convolutional Neural Networks (CNNs) like AlexNet, VGGNet, and ResNet, as well as Long Short-Term Memory (LSTM) networks and 3D Convolutional Neural Networks (3D-CNNs) in the finding of Alzheimer’s Sickness from medical imaging data. Each model’s arrangement parameters, for example, layer configurations, learning rate, batch size, optimizer, number of epochs, loss function, applying techniques for regularization, activation functions, and the input dimensions, using pre-trained weights, were carefully initialized to yield a proper comparison between the models. Diagnostic performances were evaluated utilizing precision, responsiveness(recall), specificity, and region under the receiver operating characteristic curve (AUC-ROC). ResNet outperformed other profound learning models in diagnosing Alzheimer’s Disease from MRI scans with a precision of 0.91 and AUC-ROC of 0.95. At the same time, VGGNet, AlexNet, LSTM, and 3D-CNNs demonstrated varying strengths and limitations in balancing accuracy, computational efficiency, and feature extraction.

关键词： Artificial Intelligence (AI) Cognitive Impairment Data Augmentation Diagnosis Accuracy Medical Imaging

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Research on Concurrency Control in Database Systems 12th

Research on Concurrency Control in Database Systems

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12th International Conference on computer engineering and Networks, CENet 2022

作者： Shen, Pengxu Qian, Hanwei Department of Computer Information and Cyber Security Jiangsu Police Institute Nanjing210031 China Engineering Research Center of Electronic Data Forensics Analysis Jiangsu Nanjing China Key Laboratory of Digital Forensics Department of Public Security of Jiangsu Province Nanjing China

ISBN: (纸本)9789811969003

In a database system, if the degree of concurrency is high, using a concurrency control algorithm alone will reduce the performance of the system, and the process of selecting a concurrency control algorithm will virtually improve the knowledge threshold of users. In order to overcome this limitation, this paper proposes a hybrid concurrency control algorithm, which is called cluster based concurrency control algorithm. It creatively puts forward the concept of transaction working set, uses the minimum hash algorithm to calculate the Jaccard similarity between different transaction working sets to measure the conflict rate between different transactions, and uses this as a standard to place transactions in different clusters, Transactions in the same cluster adopt pessimistic concurrency control algorithm, and transactions in different clusters adopt optimistic concurrency control algorithm. The clustering based concurrency control algorithm combines the traditional pessimistic concurrency control algorithm with the optimistic concurrency control algorithm to obtain the advantages of the two algorithms and alleviate the performance bottleneck of the two algorithms. Finally, through simple experiments, it is proved that the concurrency control algorithm based on clustering is indeed better than the traditional pessimistic and optimistic concurrency control algorithms. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Concurrency control

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security Revisited: Honeytokens meet Google Authenticator 7

Security Revisited: Honeytokens meet Google Authenticator

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7th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2022

作者： Papaspirou, Vassilis Papathanasaki, Maria Maglaras, Leandros Kantzavelou, Ioanna Douligeris, Christos Ferrag, Mohamed Amine Janicke, Helge University of West Attica Department of Informatics and Computer Engineering Athens Greece University of Thessaly Department of Cs and Telecommunications Lamia Greece Cyber Technology Institute De Montfort University Leicester United Kingdom University of Piraeus Department of Informatics Piraeus Greece Guelma University Department of Computer Science Guelma Algeria Cyber Security CRC Edith Cowan University Perth Perth6027 Australia

ISBN: (数字)9798350398588

ISBN: (纸本)9798350398588

In contrast to the rapid advancement and evolution of technology, computer systems rely significantly on passwords to maintain their security for an unreasonably long time. Although the introduction of two or more factors improved existing authentication procedures and gave rise to new multi-factor authentication schemes, more advanced and focused assaults have revealed they are also sensitive and ineffective to assure only authorized access to systems. This study suggests a revolutionary two-factor authentication mechanism that uses QR codes and honeytokens. In the current implementation, honeywords and Google authenticator work together. In order to speed up the identification process, the honeywords technique is utilized as a second factor on a different computing device. By using a stronger and more effective authentication mechanism than a single password mechanism or a common two-factor authentication scheme, the novel and straightforward approach presented here intends to add additional layers of security and protection into a system, thereby increasing its security. © 2022 IEEE.

关键词： Authentication

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