检索结果-内蒙古大学图书馆

8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Saraf, Renuka Kolhe, Gopalkrushna Badjate, Soham Gadekar, Rohit Joshi, Pranjali Pune Institute of Computer Technology Computer Engineering Department Pune India

ISBN: (纸本)9798350391770

PDF's form an integral part in day to day life of every individual. Researchers, scientists, teachers and students need to read and go through tons of PDF in order to gain information. This is an overhead for the people. In past there have been multiple attempts to get the information from the PDF's by directly asking the question. But most of the approaches are limited to single PDF and the accuracy is a concern with respect to the context in which answer is generated. In this paper we propose an approach that used Mistral 7B model to generate the answers to the questions asked by the user on multiple PDF's uploaded at once. We also evaluate our model against the existing approaches to answer generation and demonstrate a better approach to solve the problem. Upon analysis we arrive at a conclusion that Mistral-7B outperforms other LLMs for this task. By incorporating prompt lookup technique, the response time was improved by approximately 80%. © 2024 IEEE.

关键词： Query languages

来源：评论

学校读者我要写书评

暂无评论

Subjective Answer Grader using Semantic Similarity and Keyword Matching 9

Subjective Answer Grader using Semantic Similarity and Keywo...

引用

9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Kripalani, Sanchit Lipare, Tejas Kadam, Dipali Pune Institute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350394474

Subjective exams and tests have been a staple metric of student evaluation. These tests require students to give brief theoretical responses to the questions. Evaluation of subjective responses is based on the presence of certain facts, keywords, phrases that need to be mentioned. Traditional assessment of these tests requires an examiner to affirm the understanding of concepts by students through their answers. The human-dependant evaluation, however, is tedious, time consuming and is rarely impartial. The time constraints often mean that the examiner is unable to provide a detailed feedback to each and every student. This calls for the need to develop an objective automated grader system, which can investigate and analyze the student performances by considering different facets of learning based on semantic similarity. The enhanced BERT model, which has been fine-tuned, is utilized to capture the semantic characteristics found in the student's response. In order to achieve this, the model compares the student's response with the model answer and generates a model score based on the comparison. This model score is boosted through a keyword matching algorithm, which emphasizes the factual information contained within the student's response. These final similarity scores are employed to offer comprehensive feedback that illuminates the specific areas in which the student's comprehension may be lacking. Finally, the proposed approach is compared to other renowned algorithms like Cosine Similarity, Word Mover's Distance, Paraphrase-mpnet, and Multi-qa-mpnet. The proposed system yields an experimental Mean Absolute Error (MAE) of 2.11, which is significantly better as compared to other algorithms. © 2024 IEEE.

关键词： Grading

来源：评论

学校读者我要写书评

暂无评论

Ensemble activation enabled deep classifier for Alzheimer's disease detection in the blockchain-enabled distributed edge environment

引用

International Journal of Information technology (Singapore) 2024年 1-18页

作者： Mandawkar, Umakant Diwan, Tausif Department of Computer Science and Engineering Indian Institute of Information Technology Nagpur India Department of Computer Engineering SVKM’s Institute of Technology Maharashtra Dhule India

Alzheimer's disease (AD) is the most well-known cause of dementia that affects memory. Alzheimer's patients have a neurodegenerative disorder that results in the loss of many brain functions. Today’s researchers have determined that the key to enhancing patient care and treatment is early disease diagnosis. Traditional techniques for diagnosing AD are difficult ineffective, and take a lot of time to learn and execute. In this research, a disease detection system is developed using the proposed tawny flamingo-based federated deep CNN (Convolutional Neural Networks) (TF based-deep CNN) which can manage accurate patient diagnosis in dispersed contexts with blockchain support. The collected data is maintained and structured in a blockchain-based database, which upholds a high level of data security and increases the detection accuracy. After the medical professional receives the data, disease detection is done using the proposed federated learning framework, which makes it feasible to manage remote data effectively. The features are then extracted from the segments created by the CUF-GW (Cuttlefish-GreyWolf) algorithm. The detection is then carried out by the proposed ensemble activation-based deep CNN classifier, with the ensemble process being assisted by the TF (Tawny Flamingo) approach. According to AD accomplishments, at TP (Training Percentage) 80, acc (Accuracy), sen (Sensitivity), and spe (Specificity) are respectively 99.19%, 99.99%, and 98.87%;similarly, based on the k-fold 10, acc, sen, and spe are respectively 99.45%, 99.99%, and 98.45%. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Alzheimer's disease CUF-GW algorithm Deep CNN Federated learning TF

来源：评论

学校读者我要写书评

暂无评论

Automated Answer and Diagram Scoring in the STEM domain: A literature review 5

Automated Answer and Diagram Scoring in the STEM domain: A l...

引用

5th IEEE International Conference for Emerging technology, INCET 2024

作者： Kulkarni, Aditya Endait, Sharvi Ghatage, Ruturaj Patil, Rajlaxmi Kale, Geetanjali Department of Computer Engineering Pune Institute of Computer Technology Pune India

ISBN: (纸本)9798350361155

Automated scoring of answers and diagrams in the STEM (Science, technology, engineering, and Mathematics) domain has gathered significant attention in recent years due to its potential to revolutionize assessment and feedback processes. This survey paper presents an exhaustive analysis of the automated multimodal scoring methods for answers and diagrams applied within the STEM educational context. It delves into the diverse techniques, applications, challenges, and prospects within this burgeoning field. The survey highlights the evolving technologies and tools, offering insights into the platforms and systems that have been developed to automate the evaluation of answers and diagrams in STEM subjects. Additionally, this survey provides a valuable resource for educators, researchers, and policymakers seeking a comprehensive understanding of the current landscape of automated multimodal scoring in STEM education. It aims to inform discussions on the development and implementation of effective assessment strategies in STEM fields and the role of technology in enhancing educational outcomes. © 2024 IEEE.

关键词： Data integration

来源：评论

学校读者我要写书评

暂无评论

An Empirical Survey on the Classification Strategies for Automated Alzheimer’s Disease

引用

SN computer Science 2024年第8期5卷 1140页

The early Alzheimer’s disease (AD) classification is highly important as this neuro-degenerative disease causes severe problems particularly, loss of memory among the patients. In addition, classifying Normal Control (NC), Mild Cognitive Impairment (MCI) and AD in time assists the patient to undergo treatments in the initial period of the disease. Hence, it is necessary to develop a trust worthy categorization model to classify the patients with or without AD. One of principal modalities commonly used for AD classification is the magnetic resonance imaging (MRI), the analysis of which achieved improved classification performance in categorizing the stages of AD. Hence, this review paper presents the detailed survey of 55 research articles showing different strategies to classify AD. The two important categories of the research papers are the methods using machine learning strategies, and the methods using deep learning strategies. The papers are classified based on various modalities, datasets, performance indices, and the software tools used for implementation. In addition to this, various research gaps and the challenges associated with the existing works of AD classification are discussed. In addition, this review provides the future scope for researchers with the analysis of research issues seen in the literary works. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Alzheimer’s disease Deep learning and machine learning Magnetic resonance imaging Mild cognitive impairment

来源：评论

学校读者我要写书评

暂无评论

Exploiting Retina Biometric Fused with Encoded Hash for Designing Watermarked Convolutional Hardware IP Against Piracy

引用

SN computer Science 2024年第8期5卷 1-17页

作者： Chaurasia, Rahul Sengupta, Anirban Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal Bhopal India Department of Computer Science and Engineering Indian Institute of Technology Indore India

The convolution layer in a convolutional neural network (CNN) is highly computationally intensive. It is crucial to design reusable low-cost hardware IP for convolutional layer for enabling hardware-based feature extraction. However, the involvement of fake IP vendor/untrustworthy broker in the integrated circuit (IC) supply chain, makes these IPs susceptible to the threat of piracy. The proposed approach presents high- level synthesis (HLS) driven watermarking methodology for designing low-cost and secure convolutional hardware IP. The presented watermarking approach employs complier-driven high-level transformation and exploits retinal signature fused with the encoded hash for piracy detective countermeasure. The proposed approach, therefore, firstly performs compiler-driven high-level transformation in order to optimize the design latency, followed by embedding the watermark of an authentic IP vendor. The generated watermark in the form of encoded hardware watermarking constraints (digital evidence) is covertly embedded into the resulting optimized design during the register allocation module of HLS. The proposed approach achieves the following: (i) optimized and secure design for convolutional hardware IP, (ii) robust detection of pirated IP at zero design cost overhead, (iii) significantly lower probability of coincidence (in the range of 1.3E−06 to 1.2E−09) indicating stronger digital evidence and higher tamper tolerance (in the range of 2.64E+460 to 9.60E+698) than recent approaches. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional layer Encoded hash Hardware security HLS IP design Retina biometrics

来源：评论

学校读者我要写书评

暂无评论

ML based Web Application Firewall for Signature and Anomaly Detection Using Feature Extraction 15

ML based Web Application Firewall for Signature and Anomaly ...

引用

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

作者： Dhote, Sanjana Magdum, Aditya Singh, Siddharth Raigar, Dipika Pune Institute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

In today's digital landscape, web applications have become indispensable tools for businesses, facilitating access to vital data and services. However, this ubiquity also exposes them to a myriad of cyber threats, ranging from SQL injection to cross-site scripting. Traditional Web Application Firewalls (WAFs), while essential, often struggle with rule-based detection methods, leading to issues like false positives and an inability to adapt swiftly to emerging threats. This paper introduces an advanced Web Application Firewall (WAF) fortified with Machine Learning (ML) techniques, specifically a Random Forest classifier. The proposed WAF achieved nearly 1 0 0% accuracy in classifying requests as normal or anomalous and 8 9. 5% accuracy in distinguishing between different attack types. Furthermore, it significantly reduces computational time compared to traditional WAFs. By harnessing ML, the proposed WAF enhances web application security, effectively identifying and mitigating attacks while minimizing false positives, showcasing a practical ML-driven solution for safeguarding online assets against evolving cyber threats. © 2024 IEEE.

关键词： Anomaly detection Cross-Site Scripting (XSS) Cybersecurity Deep Learning (DL) Distributed Denial of Service (DDoS) False negatives False positives Machine Learning (ML) Rule-based detection mechanisms Signature-based filtering Structured Query Language (SQL) injection Web Application Firewall (WAF) Web Application Security

来源：评论

学校读者我要写书评

暂无评论

Mining user's navigation structure by filtering impurity nodes for generating relevant predictions

引用

International Journal of Cognitive Computing in engineering 2023年第1期4卷 248-258页

作者： Jindal, Honey Sardana, Neetu Vidyarthi, Ankit Gupta, Deepak Mahmud, Mufti Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India Department of Computer Science and Engineering Maharaja Agrasen Institute of Technology Delhi India United Kingdom

Web Navigation Prediction (WNP) has been popularly used for finding future probable web pages. Obtaining relevant information from a large web is challenging, as its size is growing with every second. Web data may contain irrelevant noise, and thus cleaning such data is essential. In this paper, we emphasize the identification and elimination of noisy information from user-navigated web pages. The pruning of user browsed sessions by removing noisy webpages and their relations can help in the development of high-performing prediction models with fewer prediction errors. To minimize the prediction errors, we have proposed four pruning models namely PM3ER, PM3EN, PM3GI, and PM3EP which remove noisy web pages and their relations from the model consisting of varied simple and complex navigations. The study reveals that PM3ER is effective in prediction for websites with complicated structures resulting in complex navigations, and PM3EN is effective in predicting websites with a simple tree-like structure resulting in simple navigations. PM3ER has shown an improvement in predictions by up to 3% whereas PM3EN has attained improvement up to 5.45%. © 2023

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

ML Based Ultra Processed Food Categorization with Limited Data 8

ML Based Ultra Processed Food Categorization with Limited Da...

引用

8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Karpe, Sushil Kulkarni, Laukika Patil, Anushree Joshi, Pranjali Pune Institute of Computer Technology Computer Engineering Department Pune India

ISBN: (纸本)9798350391770

Ultra processed foods have permeated modern diets, raising concerns about their health implications. This study introduces a pioneering approach utilizing ML algorithms notably Random Forest and XGBoost, to analyze packaged food products and categorize them based on their healthiness levels. Data sourced from users included extensive nutritional information of various packaged food items. Leveraging the collected data from our responses Random Forest, XGBoost and Naive Bayes Classifiers were implemented to classify the products into the categories defined by Nutri-Score FOP Labelling system which classifies the food products into 5 categories from A (being most preferable) TO E (being worst preferable) based on the score they achieve depending on their nutritional content. We are able to obtain valuable insights for consumers and regulatory bodies facilitating informed dietary choices and addressing the challenges faced with awareness of ultra-processed foods and their consumption. © 2024 IEEE.

关键词： Food ingredients

来源：评论

学校读者我要写书评

暂无评论

An intrusion detection system using ranked feature bagging

引用

International Journal of Information technology (Singapore) 2024年第2期16卷 1213-1219页

作者： Azhagiri, M. Rajesh, A. Karthik, S. Raja, K. Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering Jain University Bangalore India Department of IT College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

An Intrusion Detection System monitors the network for any malicious attacks. It is an ideal tool for protecting extensive business networks from any kind of attack. In this paper, an Intrusion Detection System using Bagging and ranked features has been proposed. The sample datasets for training the ensemble of classifiers in the bagging approach are obtained by generating subsets of the features ranked according to their importance in the classification process. OneR algorithm has been used to rank the features. The decision tree has been used as the base classifier in the bagging approach. The proposed system was able to predict attacks with an accuracy of 99.71% with a minimal number of features and outperformed other similar approaches discussed in the literature. © The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Bagging CIC-IDS 2017 dataset Decision tree Ensemble Feature selection Intrusion detection system OneR feature selection

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：