检索结果-内蒙古大学图书馆

An Approach for Maintaining Structural Uniformity of Multiword Expressions 4

An Approach for Maintaining Structural Uniformity of Multiwo...

4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Barman, Abhijit Saha, Diganta Pal, Alok Ranjan Jadavpur University Department of Computer Science and Engineering Kolkata India Cem Department of Computer Science and Engineering Kolaghat India

ISBN: (纸本)9798350343670

In this work, a Minimum Edit Distance (MED)-based approach for lexical uniformity of a Multiword Expression (MWE) in Bengali text is described. MWE can take several different forms where there are blank spaces or hyphens ('-'), separating the constituent words. For Bengali MWEs, accuracy in recognition, classification, and sensory tagging tasks decreases by 5% because of the diverse architectures of MWEs. Ensuring structural consistency is a crucial preprocessing effort to construct a reliable system for the syntactic and semantic processing of Bengali MWEs. An F1-Score of 90.58% is measured as the algorithm's performance. © 2024 IEEE.

关键词： Bi-gram Minimum Edit Distance Multiword Expression (MWE) Natural Language Processing (NLP) Tokenization

来源：评论

学校读者我要写书评

暂无评论

Heart disease Prediction Using Data Mining Techniques 1

Heart disease Prediction Using Data Mining Techniques

引用

1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Sharma, Shubang Singh, Gurinder Negi, Poonam Chandigarh University Department of Computer Science and Engineering India Uttaranchal University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350373042

Heart sickness is known as one of the leading causes of loss of life in the globe. Medical tools and various hospital programs have a large amount of clinical information. Therefore, understanding heart data is critical to improving predictive accuracy. In wildcat analysis, 10 feature selection strategies, namely, ANOVA, Chi-square, aggregated data, Help, advanced characteristic selection, characteristic selection background, full feature choice, algorithmic characteristic removal, Lasso retreat, Ridge retreat, and 6 stages, namely, fence tree, random forest, vector support machine, K-neighbor, providing retrospect, and mathematician naive Bayes, using Cleveland database cardiopathy. 88.52%, 91.30% accuracy, 80.76% sensitivity, and 85-f-measure. 71% according to the selected tree category. © 2024 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Automated Synthesis of Distributed Code from Sequential Snippets Using Deep Learning

Automated Synthesis of Distributed Code from Sequential Snip...

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Sanjel, Arun Khanal, Bikram Rivas, Pablo Speegle, Greg Baylor University School of Engineering and Computer Science Department of Computer Science United States

ISBN: (纸本)9798350362480

Processing big data poses a significant challenge when transitioning from sequential to distributed code, primarily due to the extensive scale at which data is handled. This complexity arises from both syntax and semantic differences between the codes. Unfortunately, current methods are inefficient and require more effective automated solutions. To address this problem, we utilized the Transformer-based BERT model because of its exceptional capability to understand and capture deep contextual relationships in large datasets. Our method involved creating two comprehensive datasets containing 10k and 100k sequential code snippets paired with their corresponding PySpark API calls. We optimized the BERT model by fine-tuning it to predict distributed API calls for previously unseen sequential snippets. For the 10k dataset, the model demonstrated robustness, achieving a training accuracy of 99.36%, a test accuracy of 99.7%, a Balanced Accuracy of 99.78%, and an F1 Score of 0.997. In contrast, the 100k dataset metrics were equally impressive, with a training accuracy of 89.48%, a test accuracy of 99.99%, a Balanced Accuracy of 99.98%, and an F1 Score of 0.999. Our work demonstrates the feasibility of automating the sequential-to-distributed code transition with notable precision. © 2024 IEEE.

关键词： Large datasets

来源：评论

学校读者我要写书评

暂无评论

Local Binary Pattern (LBP) and Transfer Learning Based Approach to Classify Lung and Colon Cancer

引用

SN computer science 2024年第6期5卷 783页

作者： Singh, Onkar Singh, Koushlendra Kumar Department of Computer Science and Engineering Manipal University Jaipur Machine Vision and Intelligence Lab Department of Computer Science and Engineering National Institute of Technology Jamshedpur

Several genetic disorders and other metabolic abnormalities work together to generate the lethal disease known as cancer. Today’s most contributing factors to mortality and disability in patients are lung and colon cancer. A World Health Organization (WHO) 2020 report listed cancer as one of the leading causes of mortality globally. About 2.735 million of these fatalities were caused by lung and colon cancer combined. A critical component of the patient’s treatment is the diagnosis of lung cancer by histopathology. Therefore, one of the leading research priorities, mostly in the domain of biomedical health information systems, is the identification and categorization of lung and colon cancer. The present article encompasses the Local Binary Pattern (LBP) and transfer learning-based approaches to classify lung as well as colon cancer. LBP has been used for extracting features, and transfer learning has been used for the classification of lung and colon cancer. Histopathological (LC25000) lung and colon datasets are used to validate the proposed methodology. The results of the proposed method have also been compared with different existing methods and reported in the article. Our proposed method has an average accuracy of 99.00% and a F1 score of 99.2%, whereas precision and recall have 99.4% for lung and colon cancer detection. The results of the investigation demonstrate that our suggested technique greatly outperforms current models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Histopathology images Local binary pattern (LBP) Lung cancer Transfer learning

来源：评论

学校读者我要写书评

暂无评论

Analysis of facial emotion expression in eating occasions using deep learning

引用

Multimedia Tools and Applications 2023年第20期82卷 31659-31671页

作者： Yildirim, Elif Akbulut, Fatma Patlar Catal, Cagatay Department of Computer Engineering Istanbul Kültür University Istanbul Turkey Department of Computer Science and Engineering Qatar University Doha Qatar

Eating is experienced as an emotional social activity in any culture. There are factors that influence the emotions felt during food consumption. The emotion felt while eating has a significant impact on our lives and affects different health conditions such as obesity. In addition, investigating the emotion during food consumption is considered a multidisciplinary problem ranging from neuroscience to anatomy. In this study, we focus on evaluating the emotional experience of different participants during eating activities and aim to analyze them automatically using deep learning models. We propose a facial expression-based prediction model to eliminate user bias in questionnaire-based assessment systems and to minimize false entries to the system. We measured the neural, behavioral, and physical manifestations of emotions with a mobile app and recognize emotional experiences from facial expressions. In this research, we used three different situations to test whether there could be any factor other than the food that could affect a person’s mood. We asked users to watch videos, listen to music or do nothing while eating. This way we found out that not only food but also external factors play a role in emotional change. We employed three Convolutional Neural Network (CNN) architectures, fine-tuned VGG16, and Deepface to recognize emotional responses during eating. The experimental results demonstrated that the fine-tuned VGG16 provides remarkable results with an overall accuracy of 77.68% for recognizing the four emotions. This system is an alternative to today’s survey-based restaurant and food evaluation systems. © 2023, The Author(s).

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Challenges and opportunities to Enhance Buffer Management for Disaster Area in Delay Tolerant Network: A Review with Performance Analysis

Challenges and opportunities to Enhance Buffer Management fo...

引用

2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Prasad, Ajay Gurung, Sandeep Sharma, Kalpana Centre for Distance and Online Education Department of Computer Science and Engineering India Department of Computer Science and Engineering Pakyong India

ISBN: (纸本)9798350378092

A form of network called a delay-tolerant network (DTN) enables wireless nodes to communicate in settings where constant end-to-end connectivity is not ensured. In-depth discussion of buffer management inside DTNs is provided in this abstract, with an emphasis on how it might be used in disaster-prone areas. The study examines the difficulties and possibilities of enhancing buffer management techniques, particularly by adding location awareness. This research provides important insights into the performance of buffer management strategies within DTNs, particularly in disaster scenarios, by a thorough analysis of the available literature. This analysis clarifies the complex buffer management environment in DTNs by combining the results of several studies, highlighting its critical function in guaranteeing successful communication in emergency situations. The abstract emphasizes how crucial it is to use location-based information to enhance buffer management strategies. This study offers a performance analysis that aids in the creation of more reliable and effective DTN protocols for disaster management in addition to giving an overview of the present status of the field. © 2024 IEEE.

关键词： Delay tolerant networks

来源：评论

学校读者我要写书评

暂无评论

Natural Language Processing based classifier for identifying spam emails and SMS messages 3

Natural Language Processing based classifier for identifying...

引用

3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Rahman, Habibur Chowdhury, Md Tanvir Sobhani, S. M. Sabbir Nabil, Mehedi Hasan Sumon, Monjurul Islam Reza, Ahmed Wasif Emon, Md Yousuf East West University Department of Computer Science and Engineering Dhaka Bangladesh Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350384598

this research explores the development of a sophisticated classifier for screening spam emails and communications using Natural Language Processing (NLP). In today's digital age, where electronic communications continue to pour in, discriminating between beneficial and dangerous content is becoming increasingly important. The classifier uses NLP to analyze the intricacies of language used in Email/SMS and finds spam patterns. The classifier is trained on a dataset of both spam and non-spam messages using machine learning techniques, allowing it to reliably distinguish and categorize emails and SMS. This implementation's highest result for the Naïve Bayes Classifier is Bernoulli 0.98 percent. The goal is to improve the user experience by limiting exposure to irrelevant or potentially harmful content while assuring the seamless delivery of real communications. © 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

An IoT-Based Energy Conservation Smart Classroom System

引用

Intelligent Automation & Soft Computing 2023年第3期35卷 3785-3799页

作者： Talal H.Noor El-Sayed Atlam Abdulqader M.Almars Ayman Noor Amer S.Malki College of Computer Science and Engineering Taibah UniversityYanbu46421-7143MedinahKSA Department of Computer Science Faculty of ScienceTanta UniversityTantaEgypt College of Computer Science and Engineering Taibah UniversityMedinahKSA

With the increase of energy consumption worldwide in several domains such as industry,education,and transportation,several technologies played an inﬂuential role in energy conservation such as the Internet of Things(IoT).In this article,we describe the design and implementation of an IoT-based energy conser-vation smart classroom system that contributes to energy conservation in the edu-cation *** proposed system not only allows the user to access and control IoT devices(e.g.,lights,projectors,and air conditions)in real-time,it also has the capability to aggregate the estimated energy consumption of an IoT device,the smart classroom,and the building based on the energy consumption and cost model that we ***,the proposed model aggregates the estimated energy cost according to the Saudi Electricity Company(SEC)***,the model aggregates in real-time the estimated energy conservation percentage and estimated money-saving percentage compared to data collected when the system wasn't *** feasibility and beneﬁts of our system have been validated on a real-world scenario which is a classroom in the college of computer science and engineering,Taibah University,Yanbu *** results of the experimental studies are promising in energy conservation and cost-saving when using our proposed system.

关键词： Energy consumption energy conservation energy cost Internet of Things(IoT) smart classroom

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Analysis of Hate Speech Detection and Recognition Utilising Advanced Machine Learning Algorithms and Techniques 3

A Comprehensive Analysis of Hate Speech Detection and Recogn...

引用

3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Mannepalli, Praveen Kumar Sharma, Saurabh Rajput, Neha Kalamdhad, Sanjay Chandigarh University Department of Computer Science & Engineering Mohali India Chandigarh University Department of Computer Science and Engineering Mohali India Chandigarh University Department of Computer Science and Engineering Punjab India St. Vincent Pallotti College of Engineering and Technology Department of Computer Science & Business System Nagpur India

ISBN: (纸本)9798331542375

Social networking sites have come into demand as a way of sharing ideas and opinions with other people. But unfortunately, people tend to misuse social media to write nasty comments. Victims' lives might be negatively impacted by behaviours that are abusive and humiliating. Social networks give people a great tool to share information on ideas on multiple topics and opinions. However, these platforms are often abused to announce toxic and offensive comments, which eventually negatively affect victims and demographics. This study offers a thorough method for identifying hate speech (HS) on social media by utilising cutting-edge ML algorithms. A publicly available Twitter dataset containing 159,571 tweets classified into six categories, including toxic and identity hatred, was utilised. The methodology comprises data collection, conditioning, and categorisation that employs BiLSTM, which is particularly appropriate for the tasks demanding contextual sensitivity. These operations include symbol removing, stemming and stop-word eliminating, feature extraction using TF-IDF, and using the Bag-of-Words model. For classification of the data between hate and non-hate, the dataset is then split into training and testing datasets and the BiLSTM model used to provide classifications for the text. The evaluation of the BiLSTM model demonstrates superior performance, achieving an accuracy86%, a precision90%, a recall86%, and an F1-score88% • A comparison between the model and other models such as SVM and CNN reveals the strength of the BiLSTM in contextual classification tasks. The results demonstrate that BiLSTM can tackle problems with hate speech detection, providing a strong answer to the problem of harmful content on social media and helping to make the internet a better place for everyone. © 2025 IEEE.

关键词： Tweets

来源：评论

学校读者我要写书评

暂无评论

A Novel and Optimized Collaborative Diversity-Driven Routing Mechanism in MANETs

引用

SN computer science 2024年第1期5卷 22页

作者： Malyadri, Neelam Ramakrishna, M. Department of Computer Science and Engineering Vemana Institute of Technology VTU Belagavi India School of Computer Science and Engineering REVA University Karnataka Bengaluru India Department of Computer Science and Engineering Vemana Institute of Technology Karnataka Bengaluru India

Wireless networks such as MANETs present unique challenges due to their dynamic and decentralized nature. Efficient routing protocols are essential for achieving reliable and robust communication in such networks. In this research, we propose a novel routing mechanism called collaborative diversity-driven routing (CollabNet) that integrates collaborative diversity to enhance the routing process in ad hoc networks. CollabNet utilizes a collaborative approach to address the challenges posed by dynamic network environments. CollabNet incorporates collaborative power control, adaptive power allocation, and energy-efficient routing to automatically adjust the dynamic power of nodes. By promoting collaborative behavior and optimizing power levels. It fosters collaborative behavior among nodes by establishing cooperation groups and leveraging multiple relay nodes. Through collaborative multipath diversity CollabNet facilitates the transmission of packets toward the destination via multiple paths within the transmission range. This approach increases the probability of successful packet delivery by establishing redundant connections. By promoting collaboration among nodes, CollabNet improves the reliability and connectivity of wireless ad hoc networks. The mechanism leverages the strength of collaborative diversity to enhance the overall performance of routing protocols. Extensive simulations demonstrate that CollabNet outperforms traditional routing protocols in terms of network connectivity, reliability, and efficiency. The proposed mechanism offers a promising solution for efficient routing in MANETs contributing to the advancement of wireless communication in dynamic ad hoc network environments. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Collaborative diversity Collaborative diversity-driven routing MANETs Routing protocols Wireless ad hoc networks

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：