检索结果-内蒙古大学图书馆

13th IEEE International Conference on engineering Education, ICEED 2024

作者： Yin Sim, Serena Shing Lau, Bee Theng Amirul Islam, Fakir M Ming Than, Joel Chia Swinburne University of Technology Faculty of Engineering Computing and Science Kuching Malaysia School of Health Sciences Swinburne University of Technology Melbourne Australia

ISBN: (纸本)9798350367416

The rapid evolution of artificial intelligence (AI) in the Fourth Industrial Revolution (IR 4.0) has increased the demand for AI talents globally, yet there remains a shortage, including in Malaysia. It has brought attention to the need to foster AI literacy among the younger generation through education, an area where research studies could be improved in developing countries, especially Malaysia. The current research aims to design and develop an intervention, an AI curriculum tailored for secondary school students in Malaysia, especially in Sarawak. The paper begins with a literature review, highlighting the current state of AI education in secondary schools globally and identifying the need for AI education. The methodology employs a design-based research (DBR) approach, allowing for iterative refinement of the intervention. A combination of quantitative and qualitative methods will evaluate the effectiveness of the intervention. The study targets lower secondary school students from the urban divisions in Sarawak, selecting 383 students for the baseline recruitment and 180 students for the intervention through a multistage cluster random sampling method. The study expects to improve the students' perceived competence, attitude, and motivation towards AI through the intervention. © 2024 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

NeuroAI: A Cascaded CNN Model for High-Accuracy Autism Detection Using Neuroimaging Data 4

NeuroAI: A Cascaded CNN Model for High-Accuracy Autism Detec...

引用

4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Hema, V. Gowri, S. Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331508845

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that impacts social communication, behavior, and cognitive functions. Early detection of autism is crucial for timely intervention, which can significantly improve developmental outcomes for individuals. This paper presents a novel methodology for detecting Autism Spectrum Disorder (ASD) using a Cascaded Convolutional Neural Network (CCNN) model. The proposed CCNN model processes neuroimaging data to detect patterns indicative of autism. The dataset used comprises 6,214 samples from the Autism Brain Imaging Data Exchange (ABIDE). Key preprocessing steps include resizing images to 224x224 pixels, noise reduction using a Median Filter, image cropping to focus on the region of interest (ROI), and contrast enhancement using histogram equalization. The CCNN architecture involves multiple convolutional layers, followed by max-pooling and fully connected layers, culminating in a Softmax layer for classification. The model is trained using the Adam optimizer with binary cross-entropy loss, achieving an accuracy of 93.19% on the test set, outperforming traditional models such as SVM, Random Forest, and Logistic Regression. © 2024 IEEE.

关键词： Median filters

来源：评论

学校读者我要写书评

暂无评论

ML-AgriCare: Enhancing Crop Yield Through Machine Learning-Based Crop Predictor, Fertilizer Recommender, and Plant Disease Detector 1

ML-AgriCare: Enhancing Crop Yield Through Machine Learning-B...

引用

1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Verma, Aman Vohra, Palisha Gayathri, V.M. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Networking and Communication Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350377002

'ML-AgriCare: Enhancing Crop Yield through Machine Learning-based Crop Predictor, Fertilizer Recommender, and Plant Disease Predictor' introduces an integrated ML system revolutionizing agricultural practices. It encompasses three vital functionalities: Crop Prediction, Fertilizer Recommendation, and Plant Disease Detection. Through historical data analysis, weather patterns, and soil characteristics, Crop Prediction suggests suitable crops for specific regions, optimizing planting choices and minimizing risks. The Fertilizer Recommender provides tailored suggestions based on crop-fertilizer interactions, maximizing nutrient uptake and crop health. The Plant Disease Detector identifies diseases early, enabling targeted treatments, reducing crop losses, and promoting better ***-AgriCare fosters data-driven agriculture, promoting sustainable practices, and empowering farmers to make informed decisions, thus elevating crop yield and ensuring a resilient and prosperous agricultural future. © 2024 IEEE.

关键词： Data Mapping and Hardcoding Extreme Gradient Boosting Image Classification Resnet-9 Soil Nutrient Classification Support Vector Machine

来源：评论

学校读者我要写书评

暂无评论

Streamlined U-Net for MRI Brain Tumor Segmentation with Streamlit Visualization Interface

Streamlined U-Net for MRI Brain Tumor Segmentation with Stre...

引用

2024 International Conference on Computer, Electronics, Electrical engineering and their Applications, IC2E3 2024

作者： Davey, Vrishab Prasanth Kumar, Rohit Guleria, Mandeep Yeswanth, J.B. Akilandeswari, P. School of Computing SRM Institute of Science and Technology KTR India National Institute of Technology Dept. of Electrical Engineering Uttarakhand India

ISBN: (纸本)9798350388534

Brain tumor segmentation from MRI scans is a critical process in medical diagnostics, influencing both the prognosis and the strategy of treatment for patients. Traditional methods of segmentation by radiologists are not only time-intensive but also prone to human error, necessitating the development of automated and precise segmentation techniques. This research introduces an optimized U-Net convolutional neural network architecture specifically adapted for the segmentation of brain tumors from MRI data. The proposed model is designed to enhance segmentation accuracy and is trained extensively on a diverse dataset with meticulous augmentation to ensure robust generalizability. Alongside the algorithmic contributions, this study also presents an interactive visualization tool developed using streamlit, which enables clinicians to dynamically interact with both the raw and processed MRI data. The interface boasts real-time segmentation capabilities, thereby providing an accessible platform for medical professionals to validate and utilize the segmentation output. The model is better at accurately drawing the edges of tumors, as shown by the results of the experiments. This shows that it could be used in clinical settings and is an improvement over current segmentation methods. The research encapsulates not only a methodological innovation in medical imaging analysis, but also a step forward in the practical utility of such technologies in a clinical environment. © 2024 IEEE.

关键词： Clinical research

来源：评论

学校读者我要写书评

暂无评论

Magnetic Skyrmions Based One-Bit Comparator 10

Magnetic Skyrmions Based One-Bit Comparator

引用

10th IEEE International Symposium on Smart Electronic Systems, iSES 2024

作者： Shringi, Shivangi Bodapati, Srinivasu Srinivasan, Srikant School of Computing and Electrical Engineering Indian Institute of Technology Mandi Mandi India Plaksha University Chandigarh India

ISBN: (纸本)9798331533229

This paper proposes a skyrmion based 1-bit comparator implemented using a 1 × 4 demultiplexer (DMUX) and an OR-gate. A stack is composed of heavy metal (HM) and ultrathin ferromagnetic (FM) i.e., platinum (Pt) and cobalt (Co), respec-tively. The stack can exhibit a large interfacial Dzyaloshinskii-Moriya interaction (DMI), and the FM-HM interface can host stable, Néel-skyrmions at room temperature. The irradiation reduces the local anisotropy and DMI. The nonirradiated regions of the FM act as a potential barrier for skyrmions due to higher anisotropy. The proposed comparator is validated through micromagnetic simulations. © 2024 IEEE.

关键词： Anisotropy

来源：评论

学校读者我要写书评

暂无评论

Phylogenetic-Informed Generative Adversarial Network for Predicting Mutations in SARS-CoV-2 2

Phylogenetic-Informed Generative Adversarial Network for Pre...

引用

2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Chandar, Vijay Nair, Vishnu G. Rajeswari, D. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798350365269

In the pursuit of developing more effective vaccines against viruses like SARS-CoV-2, understanding their potential mutations is crucial. In order to generate protein sequences, this study presents a unique framework that synergistically blends phylogenetic data with Generative Adversarial Networks (GANs). The proposed method combines the strengths of two models: TEMPO, a transformer-based model proficient in leveraging phylogenetic data for mutation detection in SARS-CoV-2, and MutaGAN, a GAN-based model adept in generating synthetic protein sequences. The phylogenetic data from TEMPO is used in this novel design as the input for the MutaGAN generator, replacing the conventional random noise input used in GANs. The generator may now create synthetic protein sequences that are impacted by the evolutionary past included in the phylogenetic data resulting in the creation of artificial protein sequences that are more biologically significant and lifelike. The discriminator portion of the model, similar to MutaGAN, is still in charge of determining whether or not the input sequences comprise a valid parent-child pair and uses sequences produced by the first GAN as extra negative cases. This prevented the model from going too far off its planned route. Additional investigation into this fascinating confluence of bioinformatics and AI may be possible as a result of this study's conclusions. This approach has tremendous promise in many applications, such as the production of more powerful vaccinations, protein engineering, medication design, and improving our knowledge of biological systems, by forecasting how SARS-CoV-2 could change. A precision of 0.662 and an accuracy of around 0.671, together with a Matthews Correlation Coefficient (MCC) score of 0.313, show that the framework performs very well. These scores show that the framework performs better than a number of cutting-edge baseline techniques. The synthetic protein sequences generated by the framework not only bea

关键词： Coronavirus

来源：评论

学校读者我要写书评

暂无评论

Robust Multi-Dialect End-to-End ASR Model Jointly with Beam Search Threshold Pruning and LLM

引用

SN Computer Science 2025年第4期6卷 1-12页

作者： Shunmuga Priya, M.C. Karthika Renuka, D. Ashok Kumar, L. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Department of Information Technology PSG College of Technology Coimbatore India Department of Electrical and Electronics Engineering Thiagarajar College of Engineering Madurai India

This paper aims to develop a novel robust multi-dialect end-to-end ASR system with beam search threshold pruning. The efficacy of our proposed model is evaluated using word error rate (WER). Our key contributions are: (1) To develop an end-to-end ASR system using attention-based neural network architecture and analyze the effectiveness of two features such as MFCC and log mel filter bank energies on multiple speech dialect corpora including American, Britain, and Indian accents;(2) To integrate beam search threshold pruning as a decoding mechanism to reduce the decoding time (3) To conduct an experimental analysis to test the model performance and compare the results against baseline system. (4) Post processing analysis are carried out using Llama2-7B based large language model(LLM) for enhancing the performance of proposed ASR system. The proposed model significantly improves performance by 1.91% and 4.29% over clean and noisy speech in librispeech corpus. Similarly, for the Indian accented speech, the model attains an average WER of about 6.6%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Automatic speech recognition Beam search Decoding LLM Log Mel filter bank energies

来源：评论

学校读者我要写书评

暂无评论

A Deep Q-Learning Approach to Intrusion Detection and Prevention Systems: Enhancing Cybersecurity through Intelligent Adaptation 4

A Deep Q-Learning Approach to Intrusion Detection and Preven...

引用

4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Chandre, Pankaj Shinde, Manoj Bapurao Tilwant, Shruti Vilas Ghandat, Arun Bhau Shendkar, Bhagyashree D Dhotre, Prashant Mit Art Design and Technology University Mit School of Computing Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798331518981

A Deep Q-Learning approach to Intrusion Detection and Prevention Systems (IDPS) offers a cutting-edge solution for enhancing cybersecurity by leveraging intelligent machine learning models. This method dynamically adapts to evolving threats, learning optimal defense strategies through a reward-punishment system. The presented architecture integrates sensor-based traffic monitoring, feature extraction, and real-time decision-making to strengthen network resilience. The system leverages sensor nodes to monitor traffic and uses traffic analyzers to extract critical features from network data. A Deep Q-Learning model is employed to analyze these features, dynamically adjusting security policies by learning through reward and punishment mechanisms. The system interacts with network infrastructure, such as firewalls, routers, and switches, to mitigate threats in real time. Threat intelligence is continuously updated in a dedicated database, while an action executor enforces security measures and policies. This intelligent adaptation ensures proactive threat prevention and network resilience. © 2024 IEEE.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Experimental Observations and Analysis of BGP Route Flapping Dynamics 39

Experimental Observations and Analysis of BGP Route Flapping...

引用

39th International Conference on Information Networking, ICOIN 2025

作者： Cereceda, Karina But, Jason Branch, Philip Swinburne University of Technology School of Science Computing and Engineering Technologies Melbourne Australia

ISBN: (纸本)9798331506940

The Border Gateway Protocol (BGP) is responsible for updating inter-domain routing information. One of the poorly understood aspects of BGP behaviour that has a direct impact on Internet stability is route flapping, where a route is repeatedly advertised but then withdrawn. This behaviour leads to slow convergence times resulting in Autonomous Systems (ASes) having inconsistent views of the Internet. Route flapping is an important matter that needs to be addressed to ensure the stability of the Internet as it continues to grow. However, there is currently no satisfactory solution to prevent or mitigate the effects of route flapping. Before a satisfactory solution can be developed, a better understanding of how route flapping originates and propagates is needed. In this paper, we used our large network testbed to construct a network with twenty ASes. We were able to generate route flapping and demonstrated that it can be caused by a congested link between ASes. We demonstrated that the frequency of route flapping is proportional to the level of congestion. We also showed that route flaps propagate across the entire network. As part of this research, we developed an automation tool to construct medium-to-large BGP testbeds where all interconnected ASes are placed under a common out-of-band management domain, providing a single vantage point to observe the behaviour and propagation of routing flaps across affected BGP peers. As far as we are aware this is the first time route flapping has been generated and analysed using a large scale testbed of commercial equipment. However, others have carried out research using simulation and modelling. We found our results consistent with theirs. © 2025 IEEE.

关键词： BGP BGP oscillations BGP testbeds link congestion route flapping

来源：评论

学校读者我要写书评

暂无评论

Electric Power Scam Prediction Using Machine Learning Techniques 8th

Electric Power Scam Prediction Using Machine Learning Techni...

引用

8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Suresh, A. Vishnoi, Mahendra Mishra, Aditya Kumar Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur TamilNadu Chengalpattu603203 India

ISBN: (纸本)9789819765805

Electricity fraud is causing huge losses in both the public and private sectors. The proposition of this paper uses a temporarily controlled learning program to include potential theft data from non-specific data on the existence of multiple sample theft. There are two basic applications, one anticipating the use of power and the other to create a broken application and analysis space. Both are rushing for power protection in creating configuration, redesign, and functionality. Because of the significant costs of energy acquisition, as a restricted measure of energy assets, the efficient and effective use of energy assets is an important part of social and financial development in any country. This work introduces two accounting frameworks for the detection of theft of high- and low-power consumers who rely on machine learning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：