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检索条件"任意字段=International Conference on Computer Technology and Development"

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Design of Deep Learning Models for the Identification of Harmful Attack Activities in the Industrial Internet of Things (IIOT) 10

Design of Deep Learning Models for the Identification of Har...

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10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and computer Engineering, UPCON 2023

作者： Rajesh, M. Vincent, Rajiv Kathuria, Sakshi Jamalpur, Bhavana Durgam, Thirupathi Jaiswal, Tarun School of Computer Science and Engineering Vellore Institute of Technology Chennai India K. R. Mangalam University University Computer Science and Engineering Haryana Gurugram India School of Computer Science and Artificial Intelligence Sr University Telangana Warangal India Ece India National Institute of Technology Department. of Computer Application C.G. Raipur India

ISBN: (纸本)9798350382471

Traditional industrial processes benefit greatly from the Industrial Internet of Things (IIoT), which increases efficiency as well as productivity. However, as IIoT becomes more connected, critical infrastructure is exposed to a variety of cyber risks. In order to accurately identify dangerous attack behaviors within IIoT systems, the present research focuses on the development and execution of deep learning models, combining recurrent neural networks (RNNs) including artificial neural networks (ANNs). The approach uses Python-based visualization techniques, model creation, and data pretreatment. By proactively identifying and mitigating cyber risks, our research seeks to strengthen IIoT security and ensure the resilience and honesty of industrial operations in a world that is becoming more linked. © 2023 IEEE.

关键词： Artificial Neural Networks (ANNs) Attack Identification Cyber Threat Detection Deep Learning Models IIoT Security Industrial Cyber security Recurrent Neural Networks (RNNs)

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Touchless User Interface for Sketching Using Hand Gesture Recognition 2

Touchless User Interface for Sketching Using Hand Gesture Re...

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2nd IEEE international conference on Futuristic Technologies, INCOFT 2023

作者： Babu, Tharunaa Shoban Fathima, G. Shubhakar, B.C. Kumaresan, Bhuvanesh Adhiyamaan College of Engineering Department of Computer Science and Engineering Hosur India Srm Institute of Science and Technology Department of Computer Science and Engineering Ramapuram India

ISBN: (纸本)9798350308846

Artificial intelligence (AI) technologies have a significant impact on developments in research and creative processes. With the quick development of computer vision, the extensive need for interaction with machines and humans is growing. Hand gesture recognition is often used in intelligent furniture, robot control and other meaningful use gestures using hand can be represented to give meaningful data or information. With current development in computing, the traditional writing and teaching methods are insufficient because it still uses mouse, keyboard, and touch pens. This paper proposes a solution by creating a Touchless User Interface (TUI). TUI uses hand gestures to sketch on the air without using the keyboard or mouse which is captured and reflected on the screen. The app developed for the same consists of tools such as circles, rectangles, lines, freehand drawing, and eraser which can be selected and drawn in the air, the system captures the respective shape or structure and reflects it on the screen. Human hands may be split from a complex background and identified in real time which is done using convolutional neural network. Thus, touchless user interface is developed with experimental accuracy of 98.48%. © 2023 IEEE.

关键词： Artificial Intelligence computer Vision Hand gesture recognition Touchless User Interface

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Optimising Random Forest Machine Learning Algorithms for User VR Experience Prediction Based on Iterative Local Search-Sparrow Search Algorithm 6

Optimising Random Forest Machine Learning Algorithms for Use...

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6th international conference on Communications, Information System and computer Engineering, CISCE 2024

作者： Tang, Xirui Li, Feiyang Cao, Zinan Yu, Qixuan Gong, Yulu College of Computer Sciences Northeastern University BostonMA02115 United States University of Illinois Urbana-Champaign Department of Computer Science ChampaignIL61820 United States The University of Tokyo Department of General Systems Studies Tokyo113-8654 Japan College of Computing Georgia Institute of Technology AtlantaGA30332-0280 United States School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ86011-4092 United States

ISBN: (纸本)9798350353174

In this paper, an improved method for VR user experience prediction is investigated by introducing a sparrow search algorithm and a random forest algorithm improved by an iterative local search-optimised sparrow search algorithm. The study firstly conducted a statistical analysis of the data, and then trained and tested using the traditional random forest model, the random forest model improved by the sparrow search algorithm, and the random forest algorithm improved based on the iterative local search-sparrow search algorithm, respectively. The results show that the traditional random forest model has a prediction accuracy of 93% on the training set but only 73.3% on the test set, which is poor in generalisation;whereas the model improved by the sparrow search algorithm has a prediction accuracy of 94% on the test set, which is improved compared with the traditional model. What is more noteworthy is that the improved model based on the iterative local search-sparrow search algorithm achieves 100% accuracy on both the training and test sets, which is significantly better than the other two methods. These research results provide new ideas and methods for VR user experience prediction, especially the improved model based on the iterative local search-sparrow search algorithm performs well and is able to more accurately predict and classify the user's VR experience. In the future, the application of this method in other fields can be further explored, and its effectiveness can be verified through real cases to promote the development of AI technology in the field of user experience. © 2024 IEEE.

关键词： Prediction models

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7th international Russian Automation conference, RusAutoCon 2024

7th International Russian Automation Conference, RusAutoCon ...

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7th international Russian Automation conference, RusAutoCon 2024

ISBN: (纸本)9783031824937

The proceedings contain 37 papers. The special focus in this conference is on Russian Automation. The topics include: Mapping and Path Planning Methods for Highly Automated Vehicles in Agriculture;morphological Analysis and Synthesis Features of Technological Processes;a Modern Method of Wireless Control of Unguarded Railway Crossings;duties and Obligations of the Railway Staff Concerned When the Microprocessorized Contactless Controlling Gauge Device Signal Is Triggered;integration of Automated Management Systems for Enterprises’ Transport and Technical Services;development of an Automation Module for Planning Trajectories for Painting Aircraft Fuselage Elements;identification of Metal Sheets in the Flow, Based on the Marking Imprint, Using Neural Networks;study of the Performance of Adaptive Sensor Networks for Collecting and Processing Thermoelectric Data;development and Research of a Cartographic Model for Municipal Planning as the Basis of an Intelligent Geoinformation System;BIM Visual Programming of Bionic Architecture Construction Using Dynamo and Revit;development of a Model and Algorithms for Trigger Control of Technological Processes of Resource Provision;system for Statistical Assessment of Means of Controlling Engineers’ Qualification for Information Support to Manage the Recruiting Process in Industry;smart Enumeration technology;comparative Analysis of C-Band Conical Horn Antenna Sparse Structures Characteristics at Different Frequencies;software Implementation of Heuristic Methods of Optimization and Integration into a Cloud Service;computer System for Modeling Fluid Flow Around Bodies and Its Potential in Industry;the Effect of Regular and Irregular Segmentations on Characteristics and Charge Distribution Densities of Microstrip Lines;reinforcement Learning with External Teacher for Building Energy Management.

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Harnessing Programmable Logic for Quaternion Multiplication 20th

Harnessing Programmable Logic for Quaternion Multiplication

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20th EAI international conference on computer Science and Education in computer Science, CSECS 2024

作者： Gorbounov, Yassen Chen, Hao New Bulgarian University Sofia Bulgaria China University of Mining and Technology Xuzhou221000 China

ISBN: (纸本)9783031843112

Quaternions are four-dimensional hyper-complex numbers discovered by Sir William Hamilton in the 19th century. Compared to Euler angles, quaternions allow combining rotations in three-dimensional space and help overcome the problem of not being able to rotate about an axis regardless of rotation about other axes. They find numerous applications in various fields such as mechatronics, computer graphics and signal processing. However, the computational complexity of quaternion multiplication limits its effectiveness in real-time applications. To solve this problem, the paper proposes a hardware-oriented solution based on a programmable logic device (PLD). The design structure is based on the parallel processing and reconfiguration capabilities of the PLD, which can significantly improve the quaternion multiplication performance compared to traditional implementations based on a purely software approach. The paper covers the elaborated multiplier architecture and discusses its advantages in terms of performance and resources used. The experimental work performed highlights the flexibility of the approach used and demonstrates its effectiveness in accelerating quaternion multiplication, making it suitable for real-time applications. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： computer circuits

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Enhancing Security with Machine Learning-based Finger-Vein Biometric Authentication System 5

Enhancing Security with Machine Learning-based Finger-Vein B...

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5th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Babu, G. Prakash Aswal, Upendra Singh Sindhu, M. Upadhye, Vijay Jagdish Natrayan, L. Patil, Harshal Acharya Institute of Technology Department of Computer Science and Engineering Karnataka Bangalore560 107 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India R.M.K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Thiruvallur601 206 India Parul Institute of Applied Sciences Research and Development Cell Parul University Department of Microbiology Gujarat Waghodia391760 India SIMATS Saveetha School of Engineering Department of Mechanical Engineering Tamil Nadu Chennai602105 India Computer Science and Engineering Symbiosis Institute of Technology Maharashtra Pune412 115 India

ISBN: (纸本)9798350395235

Biometric authentication systems have gained popularity as the necessity for secure individual identification has expanded. Iris, hand geometry, fingerprints, retina, vein patterns on the fingers and palms, and voice patterns are all common biometric features. Biometric recognition systems based on finger-veins are becoming more common due to their numerous advantages, including low danger of duplication or fraud, minimum invasiveness, and excellent identification accuracy. Motivated by these advantages, our research looks into the subject of finger-vein authentication. Similar to identification, exploration of hidden patterns within data is essential for effective finger-vein recognition. The study employs two feature extraction approaches, the Local Binary Pattern (LBP) and the Gabor filter, as well as two machine learning models, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Support Vector Machine (SVM). Using finger-vein images from the SDUMLA HMT database, this study evaluates four distinct feature extraction and ML model combinations. The simulation results highlight the excellent accuracy of 97.72%, False Acceptance Rate (FAR) of 3.1%, and False Rejection Rate (FRR) of 1.41% attained by LBP with ANFIS. In addition to performance measurements, the study examines the amount of time required for each task. © 2024 IEEE.

关键词： Local binary pattern

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Explainable Multimodal Sentiment Analysis on Bengali Memes 26

Explainable Multimodal Sentiment Analysis on Bengali Memes

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26th international conference on computer and Information technology, ICCIT 2023

作者： Elahi, Kazi Toufique Binte Rahman, Tasnuva Shahriar, Shakil Sarker, Samir Saha Joy, Sajib Kumar Muhammad Shah, Faisal Ahsanullah University of Science and Technology Department of Computer Science and Engineering Dhaka1208 Bangladesh

ISBN: (纸本)9798350359015

Memes have become a distinctive and effective form of communication in the digital era, attracting online communities and cutting across cultural barriers. Even though memes are frequently linked with humor, they have an amazing capacity to convey a wide range of emotions, including happiness, sarcasm, frustration, and more. Understanding and interpreting the sentiment underlying memes has become crucial in the age of information. Previous research has explored text-based, image-based, and multimodal approaches, leading to the development of models like CAPSAN and PromptHate for detecting various meme categories. However, the study of low-resource languages like Bengali memes remains scarce, with limited availability of publicly accessible datasets. A recent contribution includes the introduction of the MemoSen dataset. However, the achieved accuracy is notably low, and the dataset suffers from imbalanced distribution. In this study, we employed a multimodal approach using ResNet50 and BanglishBERT and achieved a satisfactory result of 0.71 weighted F1-score, performed comparison with unimodal approaches, and interpreted behaviors of the models using explainable artificial intelligence (XAI) techniques. © 2023 IEEE.

关键词： Deep Learning Explainable AI Memes Multimodal Sentiment Analysis

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Design and Implementation of a Web-Based Application for Managing Agricultural Health Products 4

Design and Implementation of a Web-Based Application for Man...

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4th international conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Oluwagbemi, Olugbenga O. Onyeka, Emebo Ishieka, Ikashi S. Wieclaw, Anna Azeta, Ambrose A. Middlesex University Department of Computer Science London United Kingdom Virginia Tech University Department of Computer Science Blacksburg United States Namibia University of Science and Technology Department of Software Engineering Windhoek Namibia

ISBN: (纸本)9798350353266

Caring for the health of food crops and plants can help boost food production and eradicate hunger according to the second United Nations (UN) Sustainable development Goal (SDG). The procurement of agricultural health products such as fertilizers, fungicides, pesticides, and herbicides are a major problem for an average Nigerian farmer. This results in low crop yield and negatively impacts on food production which eventually causes hunger in the land. Existing methods of procuring these health products have majorly been through offline platforms such as agricultural stores and other relevant stores. This has its disadvantages such as inaccessible roads to stores where such products can be obtained, varying prices, health products out of stock, ignorance in the application of such products. These barriers deter farmers from procuring these health products. In this paper, a survey was conducted on the needs and problems of farmers. The results of the survey informed the design and implementation of an agricultural health Informatics web application for health product management for food crops. The application was designed using unified modelling language class diagram and implemented using Python. SQLite3 serves as the backend database. The agricultural informatics solution will help farmers in Nigeria to easily procure health products for food crops and get the products delivered without stress and worry when fully deployed. This will boost food production, thus eradicating hunger;it will also help in exporting food crops to other countries. © 2024 IEEE.

关键词： Agriculture AI Application Chatbot Farmers Health Informatics

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Design of Free-Focused: Screen-Time Tracker and Task Management Browser Extension 12

Design of Free-Focused: Screen-Time Tracker and Task Managem...

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12th IEEE international conference on Communication Systems and Network Technologies, CSNT 2023

作者： Pradhan, Nawodit Giri, Roshan Raman Venkataramana, Tathapudi Pandey, Bishwajeet Jain Deemed-to-be University Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9781665462617

Since the industrial revolution, the world has been seeking new advancements in technologies. The global movement in the advancement of technology since 2008 brought in the advent of Information technology and Web development globally. Today, we aim to make everything more and more advanced, convenient, and at our fingertips. Every technology and software today focused on decreasing human effort. The development of the web browser and the Internet has played a vital role in the all-around IT technology development. In this vast system of services, a browser extension plays a small role in managing the browser. A browser extension is like a mini-website with a graphical user interface which allows the user to change the functionalities of a browser. The base idea is to change or modify the browser's functionality, providing a better user experience. This screen time tracker and task management browser extension enables the user to get the status of their screen time and access a platform that manages all their tasks. It helps boost the productivity of an individual. The main objective of Free Focused is to ease the social and organizational complications that may occur in one's work life. © 2023 IEEE.

关键词： Graphical user interfaces

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Neuromorphic Computing Architectures for EnergyEfficient Edge Devices in Autonomous Vehicles 3

Neuromorphic Computing Architectures for EnergyEfficient Edg...

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3rd IEEE international conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Pradhan, Rahul Gummadi, Annapurna Tanusha, P. Sowmya, Gandla Vaitheeshwari, S. Ponni Valavan, M. Gla University Department of Computer Science and Engineering Uttar Pradesh Mathura India Hyderabad India Kl University Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Nri Institute of Technology Department of Computer Science and Engineering Andhra Pradesh Vijayawada India Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Computer Science Chennai Avadi India K. Ramakrishnan College of Engineering Department of Ai & Ds Tamil Nadu Trichy India

ISBN: (纸本)9798350361186

Autonomous vehicles hold the promise of significantly reducing road accidents caused by human error, such as distracted driving or impaired judgment. However, the widespread adoption of autonomous vehicles faces challenges, including regulatory frameworks, liability concerns, ethical considerations, and the need for comprehensive infrastructure updates. This research focuses on the development of neuromorphic computing architectures tailored for energyefficient edge devices in autonomous vehicles. The significance of this study lies in addressing the escalating computational demands of real-time data processing in autonomous driving scenarios while mitigating the inherent power constraints of edge computing environments. The conventional approaches in autonomous vehicle computing, often centralized and powerintensive, pose challenges in terms of energy consumption and real-time responsiveness. The proposed neuromorphic computing architectures leverage spiking neural networks and event-driven processing to emulate the brain's efficiency, offering a novel solution to enhance both computational performance and energy efficiency. The research addresses prior issues related to the power-hungry nature of traditional computing paradigms in autonomous vehicles and emphasizes the need for more sustainable and responsive edge computing solutions. The novelty of this research lies in the integration of neuromorphic principles into edge devices, enabling onboard processing that is not only energy-efficient but also well-suited for the sporadic and dynamic nature of real-world driving environments. This study paves the way for advancements in autonomous vehicle technology, promising enhanced energy efficiency, reduced latency, and improved overall system performance. © 2024 IEEE.

关键词： Energy efficiency

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