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Designing an AI driven intelligent Tutorial System 5

Designing an AI driven intelligent Tutorial System

引用

5th IEEE International Conference on Recent Trends in computer Science and Technology, ICRTCST 2024

作者： Alam, Shamsher Kumar, Surjeet Khursheed, Zeeshan Mahato, Hemant Kumar Bashar, Shadan Suman, Anjali R.V.S College of Engineering & Technology Computer Science and Engineering Jamshedpur India Government Engineering College Sheikhpura Computer Science and Engineering Bihar India Jio Platform Limited DGM Mumbai India NSHM Master of Computer Application Durgapur India R.V.S College of Engineering & Technology Master of Computer Application Jamshedpur India R.V.S College of Engineering & Technology Computer Science & Engineering Student Jamshedpur India

ISBN: (纸本)9798350351378

With the fast-evolving field of education, there is an increase in the demand for intelligent tutorial systems that can adapt to the diverse learning needs of individual students. This research paper explores the design and implementation of an AI-driven Intelligent Tutorial System (ITS) aimed at enhancing the personalized learning experience. The system leverages advanced machine learning algorithms to dynamically adjust instructional content, pace, and assessments based on real-time analysis of individual student performance and preferences. The paper delves into the key components of the proposed ITS, including adaptive learning models, natural language processing capabilities for interactive communication, and data-driven decision-making mechanisms. Emphasis is placed on the seamless integration of AI technologies to create an intuitive and responsive educational *** considerations in the development and deployment of the intelligent tutorial system are discussed, addressing issues related to data privacy, algorithmic bias, and the role of human oversight. The paper also highlights the collaborative potential between AI and human educators, emphasizing the system's capacity to support rather than replace teaching professionals. Through a combination of theoretical frameworks and practical examples, the research examines the potential impact of the AI-driven ITS on student engagement, learning outcomes, and the overall educational experience. Case studies and pilot implementations are presented to illustrate the system's efficacy in diverse educational *** research contributes to the ongoing discourse on AI in education by providing insights into the design principles, implementation challenges, and ethical considerations associated with developing an intelligent tutorial system. The findings aim to inform educators, policymakers, and technologists about the potential of AI to revolutionize personalized learning and contribute to the ong

关键词： Students

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Performance Prediction Method of Laser Device Energy Component Based on Neural Network 3rd

Performance Prediction Method of Laser Device Energy Compone...

引用

3rd International Symposium on Automation, Mechanical and Design engineering, SAMDE 2022

作者： Yun, Tianyou Wang, Xiaoli Zhou, Xiaowei An, Baoran Liu, Jin Ni, Zhigao Institute of Computer Application China Academy of Engineering Physics Sichuan Mianyang China

ISBN: (纸本)9783031400698

Aiming at the specific problems faced by the traditional circuit simulation model in the performance prediction of energy assembly of laser devices, such as the difficulty of accurate calculation of xenon lamp coefficient and additional impedance, and the difficulty of accurate modeling of device performance degradation process, a regression analysis and prediction method based on neural network is proposed. This method does not depend on the physical elements such as circuit structure, device type and electrical parameters of energy assembly. Using the powerful nonlinear approximation ability of neural network, the complex mapping relationship between working voltage and output pulse current is fitted based on the measured data. The test results show that the prediction accuracy of this method is better than the traditional circuit simulation model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Circuit simulation

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An Improved Designing of Neuroprosthetics Arm Using LDA

An Improved Designing of Neuroprosthetics Arm Using LDA

引用

2023 IEEE World Conference on Applied Intelligence and Computing, AIC 2023

作者： Singh, Anuj Purohit, Kamlesh Chandra Kumar, M. Anand Sharma, Sachin Department of Computer Science and Engineering Dehradun248002 India Department of Computer Application Dehradun248002 India

ISBN: (纸本)9798350310061

Neuroprosthetics have experienced a remarkable evolution in recent times, particularly with the advent of intelligent neuroprosthetics that leverages artificial intelligence (AI) technology. These devices have the potential to improve both the input and output of neurological systems, allowing individuals to control prosthetic limbs with their thoughts and perform everyday tasks such as picking and placing objects. the key to this innovative technology lies in the ability to collect brain signals through electroencephalography (EEG) devices, which can be then deciphered and utilized by the mechatronic components of the prosthetic limb. However, effectively separating relevant signals from irrelevant ones has been a significant challenge for the design and development of neuroprosthetics, as these signals fall into three distinct categories of brain communication. In this research paper, we explore the utilization of split signals in neuroprosthetics using LDA and how this varies from person to person depending on their mental state. Incorporating LDA methods into these devices seems to be the most viable solution for society to build a system that can efficiently differentiate and utilize brain signals, bringing us one step closer to unlocking the full potential of neuroprosthetics. It is found that with the adaptation of the LDA approach, 91% of the results are properly categorized and are best among the existing literature available to date. © 2023 IEEE.

关键词： Electroencephalography

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Transforming Agriculture with Cloud Computing: Data Processing and Analysis Innovations 3

Transforming Agriculture with Cloud Computing: Data Processi...

引用

3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Mohammed, Shehu Singh, Arun Kaur, Harpreet Lovely Professional University Department of Computer Application Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Lovely Professional University School of Computer Science and Engineering Phagwara India

ISBN: (纸本)9798331515911

Cloud computing is a game-changer in modern agriculture, providing new forms to access enormous data analysis. In this paper, we investigate how cloud computing solutions are being integrated with agricultural practices to improve the efficiency, scalability and availability in managing data pertaining to agriculture. Farming operations generate massive amounts of data from a multitude of sources like sensors, drones, and satellite imagery., the performance bottleneck becomes the traditional methods with which the data are being processed. So the common factor in all these problems lies with Cloud computing itself, it has a scalable infrastructure that provides reliable analysis and Real-time processing of large data sets throwing light on an array of questions people have about various aspects. In this paper, we explore the major innovations in cloud computing that power precise agriculture and examine how machine learning algorithms, data fusion methods, and distributed processing platforms are utilized. It also discusses cloud-based solutions, which can reduce costs of the use of resources, crop yield prediction, and risk management to weather/climate and pests. Cloud computing opens up a world of monitoring, analysis, and optimization to help provide higher yields more efficiently while maintaining a commitment to sustainable agriculture. Challenges and considerations in deploying cloud computing in agriculture, e.g., data security risks, privacy concerns, and the need for stable internet connection for rural environments are also articulated in the paper. The results of this project underscore the vital importance of cloud technology in redefining the agri-future and providing a robust platform for smarter and more sustainable agriculture. © 2025 IEEE.

关键词： agricultural data processing Cloud computing data analysis machine learning precision agriculture sustainable farming

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Real-Time Face Mask Detection in Video Streams Using Deep Learning Technique 4

Real-Time Face Mask Detection in Video Streams Using Deep Le...

引用

4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Asha, V. New Horizon College of Engineering Department of Master of Computer Application Bengaluru India

ISBN: (纸本)9798350300888

Face masks are necessary during the worldwide pandemic to prevent the transmission of infectious diseases. This research proposes a deep learning-based system for detecting face masks in live video feeds in real-time. The system's goal is to automatically determine if people on a video broadcast are hiding their faces behind masks. To do this, a deep convolutional neural network architecture is sued and is trained on a huge dataset of annotated photos that include both masked and unmasked faces. The existing systems struggle to handle large-scale deployment and real-time processing of video streams for face mask detection. The network was built to learn features that reliably identify masks or no masks on faces. The transfer learning method is employed for fine-tuning in order to enhance the network's ability to generalize. Further, this study employs a powerful detection pipeline that uses hardware acceleration and parallel processing approaches to deal with the real-time nature of video streams. Since the channel processes video frames in real-time, it can be used in places where fast detection is critical, such as hospitals, airports, and other public buildings. To measure the proposed system's efficacy, large trials are run over various video datasets, each with its unique combination of circumstances and camera angles. The findings show that the proposed Convolutional neural network method is effective, comparing with Dataset-1 and Dataset-2 with real-time processing speed and good accuracy. Solution is accurate 95% to 99% and efficient than the current techniques. © 2023 IEEE.

关键词： COVID-19

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Registration of CT Images of Lung Using Fireworks Algorithm 1

引用

2nd International Conference on Human-Centric Smart Computing, ICHCSC 2023

作者： Nayak, Somen Mondal, Subhodip Obaid, Ahmed J. Department of Computer Application University of Engineering and Management Rajasthan Jaipur India Department of Computer Science and Engineering University of Engineering and Management Rajasthan Jaipur India Department of Computer Science and Mathematics Department of Computer Science University of Kufa Najaf Iraq

ISBN: (数字)9789819977116

ISBN: (纸本)9789819977109

The acquisition of medical images from various modalities can provide a number of practical challenges that can be addressed through medical image registration. Intensity-based registration methods, which are used in multi-modal registration, merge multiple images with the same content into a single representation by applying image transformation. It is essential to edit photographs as best as you can. It is highly important to improve the similarity measure between the various photographs. We introduce a new method for registering lung’s CT image affected with COVID-19 in this work since the image registration process is fundamentally an improvement problem. Lung computed tomography image registration is a technique for aligning pre- and post-contrast images for analysis the effect of COVID-19 virus in the lung. A meta-heuristics-based optimization technique called FWA-IATF was explored for registering lung CT images. Following that, the FWA-IATF algorithm successfully registers the images. The results of registration based on the FWA-IATF method and PSO approach are compared. The results indicate that the FWA-IATF-based registration methods outperform the PSO-based registration approach while registering lung CT images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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Research of Equipment Combat Simulation System 3

Research of Equipment Combat Simulation System

引用

3rd International Conference on Artificial Intelligence, Virtual Reality, and Visualization, AIVRV 2023

作者： Tang, Hao Li, Pengfei Ge, Shuangquan Liu, Jianbo An, Baoran Du, Linlin Meng, Dezhuang Jiang, Xidon Computer Application Institute China Academy of Engineering Physics Mianyang621900 China

ISBN: (纸本)9781510671485

The system concept of equipment modeling and simulation and the classification of equipment simulation system are introduced. According to the division of equipment simulation elements, the key technologies of equipment simulation are summarized to 6 aspects: military equipment combat theory, combat concept modeling, battlefield environment modeling, combat behavior modeling, command and information system modeling, and data simulation results visualization. The development of equipment combat simulation is prospected from four aspects: integration of equipment combat simulation, distributed simulation, big data and intelligent simulation, and the development direction of future combat simulation system are pointed out. © 2023 SPIE.

关键词： Visualization

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Modelling and Simulation of Autonomous Decision - making Process Based on Proximal Policy Optimization 38

Modelling and Simulation of Autonomous Decision - making Pro...

引用

38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Zhou, Chen An, Baoran Huang, Weijian Yu, Bing Institute of Computer Application China Academy of Engineering Physics MianYang621900 China

ISBN: (纸本)9798350303636

The autonomous decision-making problem in the mixed process of medium-range and close-range confrontation stages of agents is studied in this paper, utilizing reinforcement learning theory. The complexity and intensity of the environment resulting from the integration of information technology and system-of-systems (SoS) support in the agent confrontation scenario, as well as the oversimplification of scenarios in traditional research on autonomous decision-making of agents, are taken into account. Firstly, a simulation platform is constructed based on the Agent-Based Modeling and Simulation (ABMS). Then, the state space, action space, and reward function are designed to adapt to the multi-stage confrontation scenario for agents. Finally, an autonomous decision-making method based on Proximal Policy Optimization (PPO) algorithm for agent confrontation is proposed. The experimental results demonstrate that the proposed decision-making algorithm can learn reasonable confrontation tactics in multi-stage confrontation scenarios, verifying the feasibility of studying the problem based on the above modeling and simulation framework. © 2023 IEEE.

关键词： agent-based modeling and simulation autonomous decision-making proximal policy optimization reinforcement learning

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An Enhanced Deep Learning Algorithms for Image Recognition and Plant Leaf Disease Detection 2

An Enhanced Deep Learning Algorithms for Image Recognition a...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Asha, V. New Horizon College of Engineering Department of Master of Computer Application Bengaluru India

ISBN: (纸本)9798350325799

Object recognition in photographs has only been possible with the advent of modern deep learning (DL) techniques. Following its success in other fields, DL techniques are increasingly being used to a broad variety of agricultural operations. Due to its intrinsic complexity, image-based plant disease detection is difficult to solve. Targeted management relies on more than just detection and requires accurate identification of each species. An automated disease detection system aids the farmer by providing a speedy and accurate diagnosis of plant illness. Accelerating agricultural diagnostics requires the development of an automated system for identifying plant leaf diseases. Using machine learning and image processing, the authors of this study outline a strategy for detecting leaf blight. A photo of a leaf may be fed into this framework. In a first processing stage, noise is eliminated from the leaf images. The mean filter may be used to get rid of noise. Histogram equalisation is a method for enhancing image quality. Segmentation is a technique used in photography to break a large image down into more manageable sections. This helps to establish the picture's limits. The image is broken up using the BIRCH (Balance Iterative Reducing and Clustering utilising Hierarchies) technique. For this purpose of feature extraction, fine grained classification model, a convolutional neural network is used. After that, the pictures are categorized using a variety of methods, one of which is RCNN (Region based Convolutional neural network). Using this approach, this study discovered the split ratio, computational time, and as a result RCNN has resulted in a high accuracy of 0.98 when detecting the leaf compared to other algorithms. © 2023 IEEE.

关键词： Iterative methods

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Disease Recognition in Plant Leaves Using CNN-Based Algorithm

Disease Recognition in Plant Leaves Using CNN-Based Algorith...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Voditel, Preeti Gurjar, Aparna Kadoo, Pranjali Dahake, Tanaya Shri Ramdeababa Callege of Engineering and Management Department of Computer Application Nagpur India

ISBN: (纸本)9798350300857

In the agricultural sector, the scourge of plant diseases casts a long shadow, leading to substantial financial losses and threatening both crop quality and quantity. With global food demands on the rise and the expansion of crop cultivation to meet these needs, the urgency for effective, scalable, and prompt plant disease detection methods cannot be overstated. Traditional approaches reliant on manual visual inspections are fraught with labor intensiveness, time constraints, and inherent limitations, necessitating innovative solutions harnessing the capabilities of modern technology. The central goal of this system is to introduce a pragmatic, efficient, and adaptable approach that capitalizes on recent breakthroughs in neural network-based image analysis, with a specific focus on the transformative potential of Convolutional Neural Networks (CNNs). The proposed system encompasses several critical phases, commencing with image acquisition, followed by meticulous pre-processing, feature extraction, and culminating in classification via neural networks. What sets this system apart is its versatility, as it can be trained with a spectrum of algorithms, offering flexibility in adapting to various agricultural settings and unique plant disease scenarios. The outcomes resulting from the deployment of this multi-step plant disease detection system hold the promise of reshaping agriculture. It delivers timely and dependable insights into crop health, empowering farmers to make well-informed decisions and enabling the precision implementation of disease control strategies. By optimizing crop management practices through early disease identification, this system has the potential not only to mitigate the financial repercussions of diseases but also to bolster overall agricultural productivity. However, it is imperative to acknowledge that further research is indispensable. Such research should address potential limitations, fine-tune the system, and enhance its accuracy and

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