检索结果-内蒙古大学图书馆

5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Deshpande, Himani Chaudhari, Dhawal Sarode, Tanmay Kamath, Ayush Thadomal Shahani Engineering College Department of Artificial Intelligence & Data Science Mumbai India Thadomal Shahani Engineering College Department of Computer Science Mumbai India Science Thadomal Shahani Engineering College Department of Computer Mumbai India

ISBN: (纸本)9798350379945

Atthe forefront of the Artificial Intelligence Revolution is the Generative AI domain which is making splashes in generation of new content from existing Large Language Models. Large Language Models (LLMs) are flexible, effective tools with many uses. On the other hand, they can be tricked by devious provocation. This work explores the detection of fraudulent prompts intended to produce false or damaging outputs using Long Short-Term Memory (LSTM) networks. During this study we analysed the LTSM model against a neural network model. We found a 91.69% accuracy for the LTSM model and a 92.52% accuracy for the simple neural network. However, the simple neural network had a higher F1 score of 0.73 compared to the LTSM score of 0.69. We found that the standard neural model is more efficient at identifying harmful user prompts. Our research highlights the need for taking into account various architectures of models to reduce the risk of malicious prompting in language learning models. © 2024 IEEE.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4354-4363页

作者： Roy, Ayush Mohiuddin, S.K. Sarkar, Ram Jadavpur University Department of Electrical Engineering Kolkata700032 India Asutosh College Department of Computer Science Kolkata700026 India Jadavpur University Department of Computer Science and Engineering Kolkata700032 India

The process of modifying digital images has been made significantly easier by the availability of several image editing software. However, in a variety of contexts, including journalism, judicial processes, and historical documentation, the authenticity of images is of utmost importance. In particular, copy-move forgery is a distinct type of image manipulation, where a portion of an image is copied and pasted into another area of the same image, creating a fictitious or altered version of the original. In this research, we present a lightweight MultiResUnet architecture with the similarity-based positional attention module (SPAM) attention module for copy-move forgery detection (CMFD). By using a similarity measure across the patches of the features, this attention module identifies the patches, where a forged region is present. The lightweight network also aids in resource-efficient training and transforms the model into one that can be used in real time. We have employed four commonly used but extremely difficult CMFD datasets, namely CoMoFoD, COVERAGE, CASIA v2, and MICC-F600, to assess the effectiveness of our model. The proposed model significantly lowers false positives, thereby improving the pixel-level accuracy and dependability of CMFD tools. © 2020 IEEE.

关键词： Real time systems

来源：评论

学校读者我要写书评

暂无评论

Cryptocurrency Forecasting using Financial Indicators 15

Cryptocurrency Forecasting using Financial Indicators

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Deb, Sagar Sankhe, Atharva Dalvi, Indrajit Vartak, Pooja Dwarkadas J. Sanghvi College of Engineering Department of Computer Engineering and Data Science Mumbai India

ISBN: (纸本)9798350370249

With the rise of digital financial systems, cryptocurrencies have become a prime example of blockchain's potential. This paper presents a deep learning approach targeting the time series data of Bitcoin, which is known for its high volatility. By implementing a long short-term memory (LSTM) model, this study aims to capture the underlying trends in cryptocurrency pricing for future price prediction. © 2024 IEEE.

关键词： Decentralized finance

来源：评论

学校读者我要写书评

暂无评论

Automated Adversarial-Attack Removal with SafetyNet Using ADGIT 10

Automated Adversarial-Attack Removal with SafetyNet Using AD...

引用

10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Halim, Steven Anandi, Arvio Devin, Vyos Agung Santoso Gunawan, Alexander Eka Setiawan, Karli School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350363104

In the realm of artificial intelligence (AI), a notable challenge has surfaced: adversarial attacks, these attacks involve altering input data to mislead AI models. Developing defensive measures against adversarial attacks is necessary for a more reliable AI system to protect its users from potential harm. In response to the risk of adversarial attacks, this study aims to mitigate the risk of such attacks, especially in image classification tasks, by proposing Adversarial Detection Guided Input Transformation (ADGIT) as an architecture designed to handle such attacks. In this study, the author will experiment with creating such architecture and measure the quality of the proposed technique. ADGIT works by utilizing SafetyNet as an adversarial detector to detect and cleanse adversarial attacks. The author concludes that the proposed technique could improve robustness against adversarial attacks increasing consistent prediction accuracy from 43% to 60% and reconstructing adversarial input images to be more similar to their unperturbed version, with reconstructed images' PSNR score on 0.007 perturbation increased from 43.1182 to 89.0999. The technique proposed could be used as a new defensive measure and improving robustness against adversarial attacks. Although ADGIT is capable of handling adversarial samples, ADGIT has a drawback in performance speed due to the extra preprocessing step. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Secure UPI: Machine Learning-Driven Fraud Detection System for UPI Transactions 2

Secure UPI: Machine Learning-Driven Fraud Detection System f...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rani, Rupa Alam, Adnan Javed, Abdul Ajay Kumar Garg Engineering College Ghaziabad Department of Computer Science & Engineering U.P. Ghaziabad India Banasthali Vidyapith Department of Computer Science & Engineering Rajasthan India Abes Institute of Technology Department of Computer Science & Engineering-Data Science U.P. Ghaziabad India

ISBN: (纸本)9798350371055

Secure UPI specializes in developing an advanced fraud detection gadget the usage of the effective XGBoost device getting to know set of rules to create an advanced fraud identity device. XGBoost is a properly-proper alternative for enhancing the precision of fraud detection fashions because of its reputation for dealing with tricky datasets with efficiency and its music record of success throughout multiple industries. In order to extract pertinent records, like transaction quantity, frequency, and place, our approach preprocesses UPI transaction facts. This article makes use of a labelled dataset to train the XGBoost version in order that we may also take gain of its sturdy prediction talents and capacity to handle imbalanced datasets. To help create a system that is less difficult to apprehend and use, feature importance evaluation is used to discover essential symptoms of feasible fraud. After training, the model is covered right into a real-time UPI transaction tracking device, in which it maintains an eye fixed out for any suspicious traits in incoming transactions. In order to lessen the results of fraudulent activity, the system is constructed with 98.2 % accuracy to send out instant notifications and take preventive steps. This challenge allows in improving UPI transaction security and advancing economic era are accomplished through demonstrating the performance of machine learning in fraud detection. © 2024 IEEE.

关键词： Dimensionality Reduction Fraud Detection Imbalanced datasets Machine Learning PCA SMOTE UPI XGBoost

来源：评论

学校读者我要写书评

暂无评论

Adaptive Flowing Traffic Prediction in Contention Random Access for Optimizing Virtual/Physical Resource in B5G/5G New Radio and Core Network

引用

IEEE Transactions on Network science and engineering 2024年第2期11卷 1934-1946页

作者： Chang, Ben-Jye Lin, Yu-Ting National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu City64002 Taiwan

For differentiating and customizing different classes of traffic and virtualizing physical resources of networks and machines, B5G/5G specifies several novel mechanisms, including VNF, SDN, Service Function Chaining, Network Slicing, MEC, etc. In B5G/5G, the out-of-band contention based random access using PHY subframe preambles is specified before data transmissions. However, B5G/5G requires dynamically efficient approaches for realizing real 5G, including randomly contending limited sharing channel preambles, dynamically determining flow slicing of SFC, dynamically determining PM/VNFi/VNFc resources, while minimizing carrying cost and energy consumption via multi-link/multi-path 5G network. This paper thus proposes the efficient Adaptive Slice Flowing Prediction in Contention Random Access for Optimizing Virtual/Physical Resource in 5G (APROR) consisting of two phases: Dynamic Flow Random Contention of 5G (dFRC) and Adaptive Real-time Predictive Flow data Rate of diverse types of slicing (aRPF). Numerical results show APROR outperforms all approaches in performance metrics: contention collision probability, access delay, E2E delay, loss, throughput, etc. Consequently, several contributions are definitely achieved: 1) to dynamically determine the preamble configuration modes and dynamic flow backoff, 2) to adaptively differentiate collision domains for different types of flows and minimize collision probability, and 3) to determine the optimal Virtual/Physical Resource achieving optimal SFC corresponding to network slices. © 2013 IEEE.

关键词： Real time systems

来源：评论

学校读者我要写书评

暂无评论

Enhanced GRU-BiLSTM Technique for Crop Yield Prediction

引用

Multimedia Tools and Applications 2024年第41期83卷 89003-89028页

作者： Vashisht, Swati Kumar, Praveen Trivedi, Munesh Chandra Computer Science & Engineering Amity University Uttar Pradesh Noida201301 India Department of Computer Engineering Astana IT University Astana Kazakhstan Computer Science & Engineering NIT Agartala Tripura Agartala799046 India

Agriculture is the major source of food and significantly contributes to Indian employment, and the economy is intricately tied to the outcomes of crop management, where the final yield and market prices play crucial roles. The final yield and the market price completely determined the outcome of crop management or agriculture in India. Real-time observation emerges as a critical determinant of overall crop production success. Recognizing the significance of insightful analysis and precise crop yield predictions for effective farming practices, this study proposes an enhanced model to address the imperative of accurate yield forecasting. The pre-processing steps of the proposed model include Min-Max normalization, deletion of irrelevant data, and addition of missing values. The pre-processed data is then subjected to feature extraction using an Improved Shearlet transform (IST). After feature extraction, feature selection is done using an Enhanced multi-objective Grey Wolf optimization (EMGWO) technique. Finally, the prediction is made using an enhanced Gate Recurrent Unit with a Bidirectional LSTM (GRU-BiLSTM) model. This enhanced the accuracy (97%), precision (93%), recall (97.25%) and F-measure (95.14%) of agricultural yield predictions. Various measures related to errors, such as RMSE, MSE, MAE, MedAE, R2 and MSLE, are compared for the proposed model and other existing techniques. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Enhanced U-Net with Transformer-Driven Encoder for Medical Image Segmentation

Enhanced U-Net with Transformer-Driven Encoder for Medical I...

引用

2024 International Conference on Brain computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： Palaniappan, Ramya Surendran, Suseendran Balakrishnan, Saranya Madanapalle Institute of Technology & Science Department of Computer Science & Engineering Madanapalle India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India KGiSL Institute of Technology Department of Artificial Intelligence and Data science Coimbatore India

ISBN: (纸本)9798331540067

The automatic segmentation of tumours or lesions from magnetic resonance imaging (MRI) pictures is a critical but difficult task in clinical settings, sometimes necessitating laborious and time-consuming techniques. Deep learning has made a significant advancement in the medical imaging field in recent years. U-Net has emerged as the predominant model used for segmenting medical images. Although the standard U-Net framework has shown excellent performance in segmenting multimodal medical pictures, it has been noticed that it lags in certain aspects when dealing with tough datasets. We propose specific improvements to the state-of-the-art U-Net model. We have introduced attention block in the skipped connections to highlight relevant features. Additionally, we have incorporated transformers in the encoding path to capture temporal dependencies in medical images. In this research work, we have introduced an innovative architecture that has the potential to replace the U-Net model for segmenting regions of interest (ROI) in medical images. This study utilizes a total of 1760 samples obtained from the ISBI 2015 and PH2 medical repositories. The experimental findings have demonstrated that our proposed model has obtained highly encouraging results of 78.02 Jaccard coefficient and IoU of 65.14. In addition, we conducted tests and compared the performance of the proposed model with the classical U-Net model using the same dataset. As a result, we observed a relative improvement in the performance of both 2.36 and 1.85, respectively. The proposed technology will function as a sophisticated toolkit for radiologists to precisely detect and diagnose anomalous tumour cells. © 2024 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Cosmetics Recommendation System Based on Ingredients Using Cosine Similarity 7

Cosmetics Recommendation System Based on Ingredients Using C...

引用

7th International Conference on data science and Its Applications, ICoDSA 2024

作者： Alisha Firdaus, Sekar Dwi Nurmuthia, Laksmi Imtiyaz, Raina Agung Santoso Gunawan, Alexander Eka Setiawan, Karli School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350365351

In the rapidly evolving beauty industry, consumers are often bombarded with an overwhelming array of skincare brands and products, making the quest for the perfect skincare regimen a daunting task. This saturation of the market not only confuses consumers but also poses the risk of resource wastage and potential skin damage due to incompatible ingredient combinations. To mitigate these challenges, our research presents an innovative recommendation system designed to streamline the product selection process. Utilizing the principle of cosine similarity, our methodology involves a detailed analysis of the ingredients contained in various skincare products. A quantitative foundation for evaluating ingredient lists of various skincare products is provided by cosine similarity, a mathematical metric that evaluates the similarity between two non-zero vectors by computing the cosine of the angle between them. Our algorithm generates customized product recommendations by thoroughly comprehending the intricate interactions among different constituents. This bespoke approach simplifies the decision-making process for consumers, enabling them to make well-informed choices that cater to their unique skin health needs. The effectiveness of our recommendation system is validated through comprehensive user feedback, demonstrating its potential to redefine the paradigm of personalized skincare recommendations within the beauty industry. Through providing customers with critical information and encouraging a culture of knowledgeable choice, we see a time when customized skincare products will not only increase customer satisfaction but also brand loyalty, which will be a big step toward the democratization of customized skincare. © 2024 IEEE.

关键词： Skin care products

来源：评论

学校读者我要写书评

暂无评论

Kernel-based Actor-Critic Learning Framework for Autonomous Brain Control on Trajectory

引用

IEEE Transactions on Cognitive and Developmental Systems 2024年第3期17卷 554-563页

作者： Song, Zhiwei Zhang, Xiang Chen, Shuhang Tan, Jieyuan Wang, Yiwen Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong University of Science and Technology Department of Chemical and Biological Engineering Hong Kong Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Department of Chemical and Biological Engineering Hong Kong

Reinforcement learning (RL)-based Brain-Machine Interfaces (BMIs) hold promise for restoring motor functions in paralyzed individuals. These interfaces interpret neural activity to control external devices through trial-and-error. In brain control (BC) tasks, subjects control the device continuously moving in space by imagining their own limb movement, in which the subject can change direction at any position before reaching the target. Such multi-step BC tasks span a large space both in neural state and over a sequence of movements. However, conventional RL decoders face challenges in efficient exploration and limited guidance from delayed rewards. In this paper, we propose a kernel-based actor-critic learning framework for multi-step BC tasks. Our framework integrates continuous trajectory control (actor) and internal continuous state value estimation (critic) from medial Prefrontal Cortex (mPFC) activity. We evaluate our algorithm's performance in a BC three-lever discrimination task using data from two rats, comparing it to a kernel RL decoder with internal binary rewards and delayed external rewards. Experimental results show that our approach achieves faster convergence, shorter target-acquisition time, and shorter distances to targets. These findings highlight the potential of our algorithm for clinical applications in multi-step brain control tasks. © 2016 IEEE.

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：