检索结果-内蒙古大学图书馆

AI Enabled Convolutional Neural Networks to Detect Brain Tumors 1

3rd International Conference on Universal Threats in Expert applications and Solutions, UNI-TEAS 2024

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kothari, Paras Rao, Ankur School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering and Applications GLA University UP Mathura India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Department of Computer Applications Geetanjali Institute of Technical Studies Rajasthan Udaipur India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

In the early stages of a person’s existence, it is common practice to screen for the presence of brain tumors. Nevertheless, in the current day, it has been improved using various AI computations. Along these same lines, in order to diagnose a patient with a brain tumor, we take into consideration the data of patients, such as MRI scans of the patient’s brain. Our main concern at this point is determining whether or not the tumor is present in the patient’s brain. For a patient to have a healthy lifespan, it is essential for the tumor to be identified at the earliest possible stage. During this phase of the project, we use a calculation called a convolutional neural network to determine the level of severity, which provides accurate results. The division of tumors is an essential and challenging task that falls within the purview of clinical image production. This is due to the fact that human-aided manual characterization might result in erroneous anticipations and discoveries. We proposed the use of a convolutional neural network as a means of differentiating cancers from 2D appealing resonance brain imaging (MRI). The exploratory study was carried out on a dataset that included tumors with a wide range of dimensions, localizations, configurations, and picture forces. Convolutional neural networks, which are built using Keras and Tensorflow, were chosen for usage by our team because, in comparison to regular neural networks, they provide a much-improved presentation. During the course of our investigation, CNN demonstrated an accuracy of 98.73%. Because of its great speculation capacity and quickness, the CNN design that was only recently developed has the potential to be employed as an effective decision-aid device for radiologists working in clinical diagnostics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

A Semi-supervised Approach of Cluster-Based Topic Modeling for Effective Tweet Hashtag Recommendation

引用

SN computer science 2024年第7期5卷 1-14页

作者： Pattanayak, Pradipta Kumar Tripathy, Rudra M. Padhy, Sudarsan Research Scholar BPUT(Rourkela) Department of Computer Science and Engineering Silicon University Patia Odisha Bhubaneswar 751024 India School of Computer Science and Engineering XIM University Harirajpur Odisha Bhubaneswar 752050 India Institute of Mathematics and Applications Andharua Odisha Bhubaneswar 751029 India

Twitter has emerged as a significant source of data to be used for text summarization, Topic Modeling, document clustering, information retrieval, sentiment analysis, etc. Using hashtags, Twitter users may categorize their tweets as hashtags provide the essential meta-information in connecting tweets to the underlying themes. However, the majority of tweets do not have hashtags, which makes it challenging to search for a particular theme. The proposed model is designed to recommend appropriate hashtags for tweets by considering their categorization into sports, politics, health, or technology. In this paper, we proposed a novel heuristic for recommending hashtags of tweets. Taking 20,000 tweets, which includes 5000 tweets from each of the specified four topics along with their respective hashtags. These hashtags were manually assigned by a group of experts, which were subsequently excluded during the topic modeling process. The basic data-cleaning technique is applied to clean and tokenize the tweets. Then Word2Vec technique is used to vectorize the tokens which captured the semantic meaning of the words in the tweets and overcomes the data sparsity issues. The dimension of the data is reduced using Singular Value Decomposition (SVD) followed by t-SNE (t-distributed Stochastic Neighbor Embedding). The reduced data is divided into four clusters and a semi-supervised method is introduced to link these clusters to the aforementioned topics, which eventually helped to produce the hashtag for a list of tweets. On comparison of our results with existing techniques, it is observed that our model performance is better with respect to the metrics: precision, recall, and F1-score. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Gibbs sampling Dirichlet mixture model Linear Dirchlet allocation Singular value decomposition Topic modeling Word2Vec

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Methodology for Software Development and IT Team Analysis in Manufacturing 1

引用

3rd International Conference on Universal Threats in Expert applications and Solutions, UNI-TEAS 2024

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kothari, Paras Sheikh, Anisha School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering & Applications GLA University UP Mathura India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Department of Computer Applications Geetanjali Institute of Technical Studies Rajasthan Udaipur India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

The IT team working in the manufacturing industry may use this paper to learn about the results of the recently established hybrid software development model with the aid of this paper. The IT team’s needs, difficulties, working practices, past working strategies, and the client’s expectations in the manufacturing firm all play a role in determining how the hybrid model is implemented. The IT team was able to accomplish goals such as on-time delivery with quality improvement, more accuracy, reduced maintenance cycle, the involvement of clients, reduced development time, and improved defect removal efficiency by utilizing a new model and adopting appropriate features from Traditional and Agile methodologies. This article presents a guideline on prior outcomes for the IT team at a manufacturing business and results after using a hybrid approach. The results of deploying a hybrid software development paradigm inside an in-house IT team that works in the manufacturing business are the subject of this research, which explores those results. This study intends to evaluate the effects of combining conventional and agile approaches for software development by thoroughly examining software development practices and determining how successful this combination is. The research investigates the subsequent enhancements in project delivery deadlines, product quality, and team cooperation. The findings provide light on the possible advantages and drawbacks of using a hybrid method. The results give organizations who are looking to optimize their software development processes in a manufacturing setting with useful insights that will eventually contribute to increased efficiency and competitiveness in the sector. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Efficiency

来源：评论

学校读者我要写书评

暂无评论

Exploring Human Gesture Recognition: Bridging Communication Gaps Through Dynamic Signal Analysis 3rd

Exploring Human Gesture Recognition: Bridging Communication ...

引用

3rd International Conference on Universal Threats in Expert applications and Solutions, UNI-TEAS 2024

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kothari, Paras Sheikh, Anisha School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering & Applications GLA University UP Mathura India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Applications Geetanjali Institute of Technical Studies Rajasthan Udaipur India

ISBN: (纸本)9789819751457

The ability of humans to communicate more effectively via the use of gestures is widely recognised as one of the most important aspects of human interaction. Because of the inextricable link that exists between body language and successful communication, there has been a significant amount of study conducted in the domains of computer vision and human–computer interaction (HCI). Signal recognition is one of the most important aspects of this study. Signal recognition attempts to analyse and decode the information that is transmitted by human motions. These movements include gestures such as hand and facial expressions. Signal recognition is a key bridge that enables people with varying skills to successfully communicate and connect with others. This is particularly true for those who have trouble understanding speech. There is a possibility that algorithms developed for signal detection can help bridge the communication gap and make interactions more welcoming to all parties involved. These attempts constitute a big step forward in the direction of making interactions with computers seem more natural and more analogous to communication between humans. Static signals and dynamic signals are the two basic types that may be distinguished within the field of signal recognition. These critical frames act as turning points, summing together the most important aspects of the meaning sent by the signal. The purpose of this study is to investigate the area of dynamic signal identification and key frame extraction in order to shed light on the advantages and disadvantages associated with both of these processes. By investigating these facets, our ultimate goal is to get a more profound comprehension of the complexities involved in interpreting dynamic gestures and the possible uses of these gestures. This understanding will add to the continuing attempts to make interactions between humans and computers more natural, inclusive, and human-like, which will eventually improve th

关键词： Gesture recognition

来源：评论

学校读者我要写书评

暂无评论

OHDLL:Optimized Hybrid Deep Learning Model for Classification of Leukaemia images

OHDLL:Optimized Hybrid Deep Learning Model for Classificatio...

引用

2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management, IC-RVITM 2023

作者： Saju, Binju Asha, V. Tressa, Neethu Suhas, R.A. Jagesh, Shah Yash Sandhya, V. Adi Shankara Institute of Engineering & Technology Department of Computer Science - Ai Engineering Kerala Kalady India Master of Computer Applications New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9798350306088

A serious haematological cancer that causes mortality and morbidity in people of all ages is leukaemia. The haematologists examine bone marrow and blood to determine Leukaemia. Techniques for manual blood testing that have been in use for a long period are frequently slow and produce less reliable diagnoses. The study proposes a hybrid model to classify leukaemia cell images. Initially, images are pre-processed which includes Image resizing, Gray scale conversion and noise removal using a combination of Weiner filter and Median filter. Further, images are enhanced using Gaussian Mixture based histogram equalization. Features of the images are extracted using Dense Convolutional neural network. An optimized ResNet101 model with improved Aquila optimization is used for classification. Performance of the models is compared with other studies in literature. The proposed model has obtained an F1 score of 98.98%, an accuracy of 98.20%, sensitivity of 98.9%, specificity of 98.1%, and precision of 98%. © 2023 IEEE.

关键词： Median filters

来源：评论

学校读者我要写书评

暂无评论

IoT-Based Smart Mask Protection against the Waves of COVID-19

引用

Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 11153-11164页

作者： Goar, Vishal Sharma, Aditi Yadav, Nagendra Singh Chowdhury, Subrata Hu, Yu-Chen Govt. Engineering College Bikaner Rajasthan Bikaner India Department of Computer Science and Engineering Parul Institute of Technology Parul University Gujarat Vadodara India Department of Computer Science & Applications SVCET Engineering College Andra Pradesh Chittoor India Department of Computer Science and Information Management Providence University 200 Sec. 7 Boulevard Shalu Dist Taichung City43301 Taiwan

In the year 2020, the word "pandemic" has become quite popular. A pandemic is a disease that spreads over a wide geographical region. The massive outbreak of coronavirus popularly known as COVID-19 has halted normal life worldwide. On 11th March 2020, the World Health Organization (WHO) quoted the COVID-19 outbreak as a "Pandemic". The outbreak pattern differs widely across the globe based on the findings discovered so far;however, fever is a common and easily detectable symptom of COVID-19 and the new COVID strain. After the virus outbreak, thermal scanning is done using infrared thermometers in most public places to detect infected persons. It is time-consuming to track the body temperature of each person. Besides, close contact with infected persons can spread the virus from the infected persons to the individual performing the screening or vice-versa. In this research, we propose a device architecture capable of automatically detecting the coronavirus or new COVID strain from thermal images;the proposed architecture comprises a smart mask equipped with a thermal imaging system, which reduces human interactions. The thermal camera technology is integrated with the smart mask powered by the Internet of Things (IoT) to proactively monitor the screening procedure and obtain data based on real-time findings. Besides, the proposed system is fitted with facial recognition technology;therefore, it can also display personal information. It will automatically measure the temperature of each person who came into close contact with the infected humans or humans in public spaces, such as markets or offices. The new design is very useful in healthcare and could offer a solution to preventing the growth of the coronavirus. The presented work hasa key focus on the integration of advanced algorithms for the predictive analytics of parameters required for in-depth evaluations. The proposed work and the results are pretty effectual and performance cognizant for predictive analytic

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Search Engine for QnA using Distributed Inverted Index System 3

Search Engine for QnA using Distributed Inverted Index Syste...

引用

3rd International Conference for Innovation in Technology, INOCON 2024

作者： Dey, Snehasis Moharana, Bhimasen De, Utpal Chandra Samant, Tapaswini Behera, Trupti Mayee Banerjee, Shobhan College of Engineering Electronics & Telecommunication Engineering Bhubaneswar India Lovely Professional University Department of Computer Science & Engineering Punjab India Kalinga Institute of Industrial Technology School of Computer Applications Bhubaneswar India Kalinga Institute of Industrial Technology School of Electronics Engineering Bhubaneswar India Birla Institute of Technology and Science Department of Engineering Technology Pilani India

ISBN: (纸本)9798350381931

The internet along with its global uses has also evolved as a platform where various people can pose their questions on certain sites and get them answered by others from the same domain. Certain sites like Quora, Reddit, Yahoo Answers, etc. are sites enabling people to enhance their range over the net to find solutions to various issues or answers to various questions. But this leads to redundancy in data & hence increased memory consumption where different users can pose the same question in different ways. The same thing stands for the answers posted by different users for the same question in various ways. In this paper, we have worked on a search engine system for Question & Answers where given a question, we can find similar questions with the same semantic meaning using Elastic Search which uses an inverted index system & looks at various aspects of the formulated problem statement. © 2024 IEEE.

关键词： Search engines

来源：评论

学校读者我要写书评

暂无评论

Skin Cancer Classification Using Convolutional Neural Network with DWT Features 14

Skin Cancer Classification Using Convolutional Neural Networ...

引用

14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Pandiyan, M. Sivakumar, S. Nayak, Soumya Ranjan Srm Institute of Science and Technology Faculty of Science and Humanities Department of Computer Applications KTR Campus Chennai Kattankulathur603203 India School of Computer Engineering Kiit Deemed to Be University Odisha Bhubaneswar751024 India

ISBN: (纸本)9798350344837

The universal climate change and the ultraviolet radiation from the sun damage the skin cells. The damaged cells that could not be repaired will develop into skin cancer. The worldwide skin cancer resides in the top three positions of leading death cancer. Over the recent decades the death ratio of skin cancer is increasing. Thus, prediction and classification of the disease at early stages is mandatory for the patients survival. The artificial intelligence techniques used to automate the system that detects the skin lesions. The deep learning based convolutional neural network (CNN) approaches evidence on an automatic recognition of skin cancer more accurately. In this proposed method, the Discrete Wavelet Transformation (DWT) based features extraction techniques employed with CNN for skin cancer classification. The DWT method is used to extract the principal features from the skin cancer images after those optimal features are fed into CNN with softmax function, used to classify different types of skin cancers. This proposed method classifies different types of skin cancer more accurately. To analyze and evaluate the performance of the proposed model is tested with HAM 10000 dataset, which produces higher accuracy then the existing machine learning models. © 2024 IEEE.

关键词： Discrete wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

Efficient Skin Lesion based Classification System for Monkeypox Detection using VGG16 and Ensemble Learning 10

Efficient Skin Lesion based Classification System for Monkey...

引用

17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Pabbi, Varinder Khullar, Vikas Angurala, Mohit Ramgarhia Institute of Engineering and Technology Department of Computer Applications Phagwara India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789380544472

Monkeypox is a recently emerged disease outbreak that has impacted the health of many individuals. This study focuses on developing a skin lesion-based classification system for monkeypox disease. Many recent works suggest that the direct implementation of machine learning (ML) and deep learning (DL) approaches for this task has not resulted in high accuracy. Therefore, this paper proposes an efficient technique that employs a hybrid of VGG16-based feature engineering, class balancing, and ensemble learning for the detection of monkeypox skin lesions. Specifically, DL-based feature extraction is applied, but the classification is carried out through ensemble learning techniques. The results demonstrate that the random forest, extra tree, and histogram gradient descent methods achieved better performance in terms of different performance metrics. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Fake News Detection Using ARO and LSTM Algorithms

引用

SN computer science 2025年第1期6卷 1-11页

作者： Bhagat, Amit Dangi, Dharmendra Suman, Vaibhav Dixit, Dheeraj Kumar Sharma, Suvarna Department of Computer Applications MANIT Madhya Pradesh Bhopal India Department of Information Technology IIIT Madhya Pradesh Bhopal India Department of Computer Science & Engineering LNCT University Madhya Pradesh Bhopal India Department of Computer Science & Engineering MITS Madhya Pradesh Gwalior India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

There has been an exponential increase in social media content over the past few years due to the popularity of social media platforms. However, this has also resulted in the dissemination of intentionally false information, or fake news, which can have significant negative effects on society. As a result, detecting fake news has become an important issue that requires attention. Promoting trust in the global community is crucial to preventing the spread of false information in mass media. A study report suggests a reliable technique for identifying false news in order to remedy this. The author has thoughtfully chosen two distinct datasets: the Buzzfeed dataset, which is an assortment of news articles from Buzzfeed News, and the ISOT dataset, which is a collection of social media postings and articles from other sources. The author thoroughly analyzed a variety of content kinds and assessed the efficacy of various approaches and methods for processing and categorizing them using the datasets. The study found that these datasets have accuracy rates of 99% and 97.8% respectively. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： ARO algorithm Fake news detection LSTM Pre-processing

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：