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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Ravichandran, M. Laxmikant, Kumar Muthu, Annamalai Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India University of Technology & Applied Science Department of Electrical and Electronics Engineering Nizwa Oman

ISBN: (纸本)9798350308167

In the realm of real-time applications such as autonomous driving and surveillance, efficient vehicle detection stands as a paramount concern. The revolutionary You Only Look Once (YOLO) framework, renowned for its rapid object recognition, has reshaped this landscape. This study delves into the fusion of real-time vehicle detection and YOLO v8, optimizing its architecture for swift and precise identification. Empirical validation underscores the model's prowess, achieving a remarkable accuracy of 97.9% on mAP50 and 91.3% on mAP50-95. This achievement not only attests to the model's accuracy but also reaffirms its real-time processing capabilities. The implications resonate across transportation safety and urban planning, promising transformative potential. This research underscores the power of YOLO v8 in intelligent object detection, poised to reshape and enhance various domains. © 2023 IEEE.

关键词： mAP50 mAP50-95 Object Detection Vehicle Detection YOLO Framework

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An Empirical Investigation of Docker Sockets for Privilege Escalation and Defensive Strategies 5

An Empirical Investigation of Docker Sockets for Privilege E...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Rajyashree, R. Mathi, Senthilkumar Saravanan, G. Sakthivel, M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Department of Artificial Intelligence and Data Science Erode Sengunthar Engineering College Erode India Department of Computer Science and Engineering Erode Sengunthar Engineering College Erode India

Cloud-based infrastructures often leverage virtualization, but its implementation can be expensive. Traditional coding methods can lead to issues when transitioning code from one computing environment to another. In response, the container paradigm emerged to offer cost-effective and agile delivery. Containers differ from full machine virtualization by compactly encapsulating the entire software and its dependencies. Leveraging containers, developers can create more secure and efficient applications. Docker, a prominent containerization platform, facilitates the execution of docker images. The Docker Hub serves as a popular repository for various images. Given the importance of application security, especially in the face of threats like malware, ransomware, and data breaches, ensuring robust security is imperative. The paper investigates vulnerabilities within Docker containers and proposes defensive strategies to mitigate potential breaches. In addition, it investigates attacks involving Docker sockets and suggests preventive measures for non-root users. © 2024 The Authors. Published by Elsevier B.V.

关键词： Containers

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Enhancing Energy Efficiency with a Dynamic Trust Measurement Scheme in Power Distribution Network

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computers, Materials & Continua 2024年第3期78卷 3909-3927页

作者： Yilei Wang Xin Sun Guiping Zheng Ahmar Rashid Sami Ullah Hisham Alasmary Muhammad Waqas Zhejiang Electric-Power Corporation Research Institute Zhejiang310014China Faculty of Information Technology Beijing University of TechnologyBeijing100124China Beijing Trusty Cloud Technology Co. Ltd.Beijing100022China Department of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and EngineeringTopi23640Pakistan Department of Computer Science Shaheed Benazir Bhutto UniversitySheringalUpper Dir18050Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia School of Computing and Mathematical Science Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUK School of Engineering Edith Cowan UniversityPerth6027Australia

The application of Intelligent Internet of Things(IIoT)in constructing distribution station areas strongly supports platform transformation,upgrade,and intelligent *** sensing layer of IIoT comprises the edge convergence layer and the end sensing layer,with the former using intelligent fusion terminals for real-time data collection and ***,the influx of multiple low-voltage in the smart grid raises higher demands for the performance,energy efficiency,and response speed of the substation fusion ***,it brings significant security risks to the entire distribution substation,posing a major challenge to the smart *** response to these challenges,a proposed dynamic and energy-efficient trust measurement scheme for smart grids aims to address these *** scheme begins by establishing a hierarchical trust measurement model,elucidating the trust relationships among smart IoT *** then incorporates multidimensional measurement factors,encompassing static environmental factors,dynamic behaviors,and energy *** comprehensive approach reduces the impact of subjective factors on trust ***,the scheme incorporates a detection process designed for identifying malicious low-voltage end sensing units,ensuring the prompt identification and elimination of any malicious ***,in turn,enhances the security and reliability of the smart grid *** effectiveness of the proposed scheme in pinpointing malicious nodes has been demonstrated through simulation ***,the scheme outperforms established trust metric models in terms of energy efficiency,showcasing its significant contribution to the field.

关键词： IIoT trusted measure energy efficient

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Exploring the Cut-Off Between Energy Balance and Delay in In-Network Data Aggregation (I-NDA) for Wireless Sensor Networks 2

Exploring the Cut-Off Between Energy Balance and Delay in In...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kumar, Anuj Kant Agrawal, Krishna School of Computing Science & Engineering Galgotias University Greater Noida India Ajay Kumar Garg Engineering College Department of Computer Science Engineering Ghaziabad India School of Computing Science & Engineering Galgotias University Yamuna Expressway Gautam buddh Nagar Greater Noida India

ISBN: (纸本)9798350371055

In WSN, data aggregation is an important part of the data collection process. It is used to reduce network traffic and reduce energy consumption. However, this process comes with a cut-off between energy balance and delay. This cut-off is due to the competing interests of the nodes in the network: the sensors require energy to transmit data, while the sinks require low delays in data collection. In order to maintain energy balance and reduce delay, the system must select suitable methods of data aggregation that are both energy-efficient and low-delay. To reduce energy consumption, the system can use energy-conserving data aggregation protocols, such as clustering and tree-structured routing. To reduce delay, they system can use low-delay data aggregation techniques, such as time synchronization and distributed aggregation. By understanding the cut-off between energy balance and delay in I-NDA, it is possible to make informed decisions on the selection of suitable protocols and techniques to ensure energy-efficiency and low-delay data collection. It is important to select the right set of protocols and techniques to balance the efficiency and delay parameters. © 2024 IEEE.

关键词： clustering cut-off data networks sensor traffic wireless

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Optimized Biometric Key Management System for Enhanced Security 11th

Optimized Biometric Key Management System for Enhanced Secu...

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11th International Conference on Intelligent computing and Applications, ICRTC 2023

作者： Kaur, Prabhjot Kumar, Nitin School of Computing DIT University Uttarakhand India Department of Computer Science and Engineering National Institute of Technology Uttarakhand India Department of Computer Science and Engineering PEC Deemed to be University Chandigarh India

ISBN: (纸本)9789819717231

Biometric cryptosystems are security tools that use an individual’s distinctive physical or behavioral characteristics to secure sensitive data. Any biometric cryptosystem’s effectiveness is dependent on how well the features are extracted. The suggested solution uses an evolutionary algorithm to first optimize the retrieved attributes. Second, fuzzy vault is used to safeguard these optimized features. The fuzzy vault system is a well-known biometric cryptography device that creates a vault by using a polynomial equation, protecting the biometric information. However, if an attacker has access to the polynomial equation, the security of the fuzzy vault scheme may be jeopardized. The shuffled frog leaping algorithm (SFLA) method has been suggested as a way to improve the security of the fuzzy vault technique in order to overcome this drawback. The SFLA is a meta-heuristic optimization method that resolves challenging optimization issues by simulating the social behavior of frogs. This meta-heuristic technique is used in this paper’s suggested strategy to produce and secure optimized points. The experiments are performed onn AMI and MMU2 databases. The results achieve higher levels of performance compared to the traditional schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Optimization

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Combustion-assisted low-temperature ZrO_(2)/SnO_(2) films for high-performance flexible thin film transistors

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npj Flexible Electronics 2024年第1期8卷 132-142页

作者： Bongho Jang Junil Kim Jieun Lee Geuntae Park Gyuwon Yang Jaewon Jang Hyuk-Jun Kwon Department of Electrical Engineering and Computer Science DGISTDaeguSouth Korea School of Electronic and Electrical Engineering Kyungpook National UniversityDaeguSouth Korea School of Electronics Engineering Kyungpook National UniversityDaeguSouth Korea Convergence Research Advanced Centre for Olfaction DGISTDaeguSouth Korea

We developed high-performance flexible oxide thin-film transistors(TFTs)using SnO_(2) semiconductor and high-k ZrO_(2) dielectric,both formed through combustion-assisted sol-gel *** method involves the exothermic reaction of fuels and oxidizers to produce high-quality oxide films without extensive external *** combustion ZrO_(2) films were revealed to have an amorphous structure with a higher proportion of oxygen corresponding to the oxide network,which contributes to the low leakage current and frequency-independent dielectric *** ZrO_(2)/SnO_(2) TFTs fabricated on flexible substrates using combustion synthesis exhibited excellent electrical characteristics,including a field-effectmobility of 26.16 cm^(2)/Vs,a subthreshold swing of 0.125 V/dec,and an on/off current ratio of 1.13×10^(6) at a low operating voltage of 3 ***,we demonstrated flexible ZrO_(2)/SnO_(2) TFTs with robust mechanical stability,capable of withstanding 5000 cycles of bending tests at a bending radius of 2.5 mm,achieved by scaling down the device dimensions.

关键词： film high dielectric

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Deep Learning for Wind Speed Forecasting Using Bi-LSTM with Selected Features

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Intelligent Automation & Soft computing 2023年第3期35卷 3829-3844页

作者： Siva Sankari Subbiah Senthil Kumar Paramasivan Karmel Arockiasamy Saminathan Senthivel Muthamilselvan Thangavel Department of Computer Science and Engineering Sreenivasa Institute of Technology and Management StudiesChittoor517127India School of Information Technology&Engineering Vellore Institute of TechnologyVellore632014India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India School of Computing College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India

Wind speed forecasting is important for wind energy *** the modern era,the increase in energy demand can be managed effectively by fore-casting the wind speed *** main objective of this research is to improve the performance of wind speed forecasting by handling uncertainty,the curse of dimensionality,overﬁtting and non-linearity *** curse of dimensionality and overﬁtting issues are handled by using Boruta feature *** uncertainty and the non-linearity issues are addressed by using the deep learning based Bi-directional Long Short Term Memory(Bi-LSTM).In this paper,Bi-LSTM with Boruta feature selection named BFS-Bi-LSTM is proposed to improve the performance of wind speed *** model identiﬁes relevant features for wind speed forecasting from the meteorological features using Boruta wrapper feature selection(BFS).Followed by Bi-LSTM predicts the wind speed by considering the wind speed from the past and future time *** proposed BFS-Bi-LSTM model is compared against Multilayer perceptron(MLP),MLP with Boruta(BFS-MLP),Long Short Term Memory(LSTM),LSTM with Boruta(BFS-LSTM)and Bi-LSTM in terms of Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Square Error(MSE)and *** BFS-Bi-LSTM surpassed other models by producing RMSE of 0.784,MAE of 0.530,MSE of 0.615 and R2 of *** experimental result shows that the BFS-Bi-LSTM produced better forecasting results compared to others.

关键词： Bi-directional long short term memory boruta feature selection deep learning machine learning wind speed forecasting

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Machine Learning-Based Detection of Blood Cancer from Microscopic Images: A Comparative Study 9

Machine Learning-Based Detection of Blood Cancer from Micros...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Manoj, Milan Sreekumar, Vishnu Reghuram, Surya Anjali, T. Ramlal, Nandakishor Prabhu Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India Altair Engineering Ab Stockholm Sweden

ISBN: (纸本)9798350377972

Early and accurate detection of blood cancer is critical for improving patient outcomes. This article is proposed to develop and evaluate the effectiveness of using machine learning techniques for automation in blood cancer detection based on microscopic images of blood cells. A comparison will be conducted using three of the most used models: Support Vector Machine, Decision Tree, and Random Forest on their capability in the classification process of distinguishing blood cells from benign and malig-nant groups. The methodology is divided into the following steps: image preprocessing, extraction of color histogram and texture features through the Gray-Level Co-occurrence Matrix (GLCM), and optimization of hyperparameters for model training. Evaluation metrics used are accuracy, precision, recall, and F1-score. Random Forest has been seen to perform with a high accuracy of 99.08% with all the categories showing robust classification. It was followed by SVM with an accuracy of 96.15 % and Decision Tree with 94.92%. The results present Random Forest as a valuable resource for the automated diagnosis of blood cancer. This paper reiterates the promise that machine learning holds in boosting diagnostic efficiency and encourages future studies into more advanced approaches such as deep learning for further improvement in clinical applications. © 2024 IEEE.

关键词： Diseases

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A 935-953-MHz Q-Enhanced CMOS Tunable Bandpass Filter With Ultra-Narrowband and Ultralow Power for RFID Applications

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IEEE Transactions on Microwave Theory and Techniques 2024年第10期72卷 6120-6129页

作者： Hou, Yaru Li, Kun Liu, Bin Zeng, Tiantian Liu, Yuanan Chi, Pei-Ling Yang, Tao University of Electronic Science and Technology of China School of Electronic Science and Engineering Chengdu Sichuan610054 China Beijing University of Posts and Telecommunications School of Electronic Engineering Beijing100876 China National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering Hsinchu300 Taiwan

A Q-enhanced CMOS tunable bandpass filter (BPF) with ultra-narrow bandwidth and ultralow dc power consumption for radio frequency identification (RFID) devices is proposed using 55-nm bulk CMOS technology in this article. The proposed filter is constructed by a capacitively coupled LC resonator (LCC), which consists of a 16-nH on-chip integrated inductor, fixed capacitors, and tunable varactors. nMOS cross-coupled transistors are used to compensate for the loss of the resonator and improve the loaded Q of the resonator. To achieve low power consumption, an inductor with an ultralarge value is exploited to reduce the loss of the resonator. Besides, an improved dual varactor inverse (DVI) and degenerated resistors at the source of the cross-coupled transistors with linearity and resistance adjusting, respectively, are utilized to optimize the linearity under center frequency tuning. Besides, the active circuits, the fixed capacitance, and varactors are placed inside the inductor to improve the integration. With all the proposed techniques, the presented BPF achieves a 935-953-MHz center frequency tuning range with a constant narrow bandwidth of 180 kHz, a low power consumption of 85-μW , and a core area of 0.43\times0.43 mm2, demonstrating a high-performance tunable on-chip filter using bulk CMOS technology for low-power and narrowband Internet of Things (NBIoT) applications. © 1963-2012 IEEE.

关键词： Bandpass filters

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Advanced Plant Species Classification with Interclass Similarity using a Fine-Grained computer Vision Approach

Advanced Plant Species Classification with Interclass Simila...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Suchithra Dhruthi, P.A. Somashekhar, B.M. Rani, N. Shobha Lin, Hong Amrita School Of Computing Amrita Vishwa Vidyapeetham Department Of Computer Science Mysore India Gitam School Of Technology Gitam University Department Of Computer Science And Engineering Bengaluru India University Of Houston Department Of Computer Science And Engineering Technology Houston United States

ISBN: (纸本)9798350371567

Plant species classification holds immense importance across diverse sectors such as agriculture, environmental conservation, and botanical research. Accurate identification of plant species is fundamental for effective ecological management, agricultural practices, and biodiversity preservation. In this context, the research addresses the critical task of fine-grained plant classification, specifically emphasizing vegetable and fruit plants. Recognizing the significance of visual similarity analysis in plant sciences, a robust methodology leveraging Convolutional Neural Networks (CNN) is introduced. The hybrid CNN model combines the interpretability of VGG16 with the gradient-stabilizing benefits of ResNet50's residual connections, enabling nuanced classification of 23 plant classes. Achieving an outstanding 99% accuracy in plant class prediction, the model is not only highly accurate but also interpretable. Additionally, the incorporation of cosine similarity enhances the robustness of the approach, facilitating a leaf similarity search application. This innovative feature strengthens the identification of visually similar plant images based on extracted features, significantly contributing to the advancement of plant species classification methodologies. © 2024 IEEE.

关键词： Convolutional neural networks

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