检索结果-内蒙古大学图书馆

2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Raj Kumar, V.S. Kumaresan, T. Mythily, V. Raghavendran, N. Bannari Amman Institute of Technology Department of Artificial Intelligence and Data science Sathyamangalam India Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India Nandha Engineering College Department of Computer Science and Engineering Erode India Rmk college of Engineering and Technology Department of Artificial Intelligence and Data science Chennai India

ISBN: (纸本)9798350368413

Twitter plays an important role in understanding the consumer sentiment about the products. The advanced analytics and Natural Language Processing (NLP) are used to extract actionable insights from this data and useful for a business to improve features and address issues on time for better customer satisfaction and brand loyalty. Most of the current models suffer from a lack of effective ways to handle subtle sentiment analysis because of a lack of contextual understanding and proper feature extraction. Most traditional models cannot combine and optimize features that lead to obtaining sentiments. This leads to overfitting and problems caused by local minima. The proposed Emotion-Enhanced BERT (EE-BERT) model is an extension of the standard BERT to learn contextual embeddings in user sentiment. In this work, data preprocessing includes text cleaning, tokenization, dealing with emojis and emoticons, and lemmatization with the best quality data. The proposed work is basically a dual-stream architecture extended with butterfly optimizer. First, the advanced tokenization and semantic understanding techniques is used for extracting textual features. Second, the emotion-enhanced features extracted through emotion-hash vectorization to enhance the model's contextual awareness in social media data. This provides a unified framework that combines the feature extraction methods to enhance the accuracy in sentiment analysis. It demonstrated with an impressive accuracy of 98.2% on sentiment in posts and comments with 0.15 losses. The proposed work is free from overfitting and local minima in product quality enhancement. © 2024 IEEE.

关键词： Tweets

来源：评论

学校读者我要写书评

暂无评论

An Algorithm to Impute Missing Values in Medical Datasets to Predict the Risk of Diseases in Patients 2

An Algorithm to Impute Missing Values in Medical Datasets to...

引用

2nd Edition of IEEE Delhi Section Owned Conference, DELCON 2023

作者： Bhagat, Hutashan Vishal Singh, Manminder Sant Longowal Institute of Engineering and Technology Department of Computer Science and Engineering Sangrur India

ISBN: (纸本)9798350322064

The advent of Internet of Things (IoT) revolutionizes the healthcare sector by altering how medical equipment and humans interact while providing healthcare solutions. Applications of IoT in healthcare are advantageous to patients, their families, physicians, hospitals, and insurance companies. Before the IoT, patients could only speak with doctors physically, over the phone, or by text. There wasn't a practical way for medical staff or facilities to continuously assess patients' health and provide guidance. Remote monitoring in the healthcare industry is made possible by utilizing sensor devices that help physicians to deliver superlative care to their patients. Sensor devices play a crucial role in the remote monitoring of patients. However, the failure of network or sensor devices may result in data with missing values. This incomplete data cannot be utilized to diagnose a patient or to make effective predictions of the disease in early diagnosis. To ensure the effective diagnosis and predict the risk of diseases in the patient, this paper proposed a novel ReMiss (Removing Missingness in medical datasets) algorithm to impute the missing values present in incomplete medical datasets. The proposed ReMiss algorithm is a data-partitioning based missing values imputation technique that partitions the datasets into complete and incomplete subsets to predict the missing values efficiently. The adjusted coefficient of determination, RMSEs and classification accuracy are the performance metrics used to evaluate the performance of the proposed ReMiss algorithm with the existing imputation techniques. The proposed ReMiss algorithm obtained an average classification accuracy of 93.53%. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

An Improved Real-Time Sign Language Recognition using Transfer Learning 2

An Improved Real-Time Sign Language Recognition using Transf...

引用

2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Goel, Lipika Karthik, N.V.S. Pavan Naidu, Madasu Jashwanth Sinha, Pradyumna Thota, Anand Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9781665491990

For decades, many researchers have been working on finding efficient solutions to implement sign language recognition systems through various technologies. Sign languages are visual languages that communicate through the hand, facial expressions, as well as body movements. Sign language is the bridge that connects us to the world of those who have impaired hearing or verbal ability. It allows them to understand the world around them through visual descriptions and, as a result, contribute to society. Thus, It strikes our mind to bridge the gap between the hearing impaired and normal people & make communication easier. Many researchers faced many challenges such as long training times to train a deep neural network model such as CNN or RNN, huge datasets of millions of images, high computational power requirements, the usage of expensive sensory devices, etc. Hence, the proposed method in this paper uses a technique known as 'Transfer learning' where we use a high-accuracy pre-trained model and fine-tune its final layer parameters according to our task that is, Sign Language recognition which significantly reduces the training time and requires a small set of data of less than 20 images per gesture. Thus in this paper, we created a custom dataset generated using the system webcam, labeled data using the LabelImg tool, used Tensorflow Object detection API and downloaded a pre-trained model named 'SSD MobinetNet V2' and applied Transfer Learning. Challenges faced in the project were issues with the correct labeling of images, a few installation errors, and lighting and background issues in a few detections. Overall, the method proposed is highly efficient in saving a lot of training time, providing high accuracy, and using fewer images for training. © 2023 IEEE.

关键词： Visual languages

来源：评论

学校读者我要写书评

暂无评论

Firefighter Skill Advancement through IoT-Enabled Virtual Reality and CNN-Based Training 11

Firefighter Skill Advancement through IoT-Enabled Virtual Re...

引用

11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Asha, P. Muthalakshmi, M. Jeslin, J Gnana Nithisha, J. Vijayakumar, S. Srinivasan, C. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Veltech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Biomedical Engineering Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Biomedical Engineering Tamil Nadu Namakkal India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350350357

To maintain the safety and efficacy of firefighters in various circumstances, modern firefighting necessitates constantly improving skills and training techniques. Utilizing the Internet of Things (IoT), virtual reality (VR), and convolutional neural networks (CNN), this paper details a novel method for training firefighters. The proposed system collects real-time data on ambient variables, equipment state, and firefighter biometrics via integrating IoT sensors into firefighting equipment and training settings. Using this information, it can develop lifelike VR training simulations of difficult and potentially dangerous scenarios. To make the training settings more realistic and malleable, CNN-based algorithms are used to assess the data. The capacity to simulate a wide variety of firefighting situations, customize training difficulty depending on individual and team performance, and provide instant feedback and performance metrics to trainees are all major benefits of this method. The method also allows teachers to check in and evaluate their learners remotely, improving instruction quality. An IoT-enabled VR and CNN-based training technique has shown promising preliminary results in pilot trials, suggesting it might greatly enhance firefighter competence, situational awareness, and decision-making ability. Because of this, it has the potential to completely alter the way firefighters are informed and prepared for the ever-changing dangers users may encounter on the job. © 2024 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Neural Network Approaches for Long Document Classification 2022

Hierarchical Neural Network Approaches for Long Document Cla...

引用

14th International Conference on Machine Learning and Computing, ICMLC 2022

作者： Khandve, Snehal Ishwar Wagh, Vedangi Kishor Wani, Apurva Dinesh Joshi, Isha Mandar Joshi, Raviraj Bhuminand Computer Engineering Department Pune Institute of Computer Technology India Department of Computer Science and Engineering Indian Institute of Technology Madras India

ISBN: (纸本)9781450395700

Text classification algorithms investigate the intricate relationships between words or phrases and attempt to deduce the document's interpretation. In the last few years, these algorithms have progressed tremendously. Transformer architecture and sentence encoders have proven to give superior results on natural language processing tasks. But a major limitation of these architectures is their applicability for text no longer than a few hundred words. In this paper, we explore hierarchical transfer learning approaches for long document classification. We employ pre-trained Universal Sentence Encoder (USE) and Bidirectional Encoder Representations from Transformers (BERT) in a hierarchical setup to capture better representations efficiently. Our proposed models are conceptually simple where we divide the input data into chunks and then pass this through base models of BERT and USE. Then output representation for each chunk is then propagated through a shallow neural network comprising of LSTMs or CNNs for classifying the text data. These extensions are evaluated on 6 benchmark datasets. We show that USE + CNN/LSTM performs better than its stand-alone baseline. Whereas the BERT + CNN/LSTM performs on par with its stand-alone counterpart. However, the hierarchical BERT models are still desirable as it avoids the quadratic complexity of the attention mechanism in BERT. Along with the hierarchical approaches, this work also provides a comparison of different deep learning algorithms like USE, BERT, HAN, Longformer, and BigBird for long document classification. The Longformer approach consistently performs well on most of the datasets. © 2022 ACM.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

A Novel Private Encryption Model in IoT Under Cloud Computing Domain

A Novel Private Encryption Model in IoT Under Cloud Computin...

引用

Proceedings of the International Conference on Intelligent Systems and Network, ICISN 2023

作者： Yadala, Sucharitha Pundru, Chandra Shaker Reddy Solanki, Vijender Kumar Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology TS Hyderabad India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Department of Computer Science and Engineering CMR Institute of Technology Hyderabad India

ISBN: (纸本)9789819947249

The presence of the Internet of Things (IoT) works with the assortment and diffusion of metropolitan information data. In any case, it can release clients’ very own security data in brilliant urban areas. Many works have been done and different security systems identifying with the precautions of distributed computing have been implemented from copious points of view. Be that as it may, they don’t propose a quantitative way to deal with dissecting and assessing protection and security in distributed computing frameworks. Accordingly, we propose another private data encryption strategy in IoT under distributed computing climate. Below IoT, as per the properties and securing time, protection data can be isolated into numerous subspaces. Because of the stream figure instrument, we plan an encryption framework model of data assortment. In the subspace, the security data is encoded and moved to the transfer hub. Subsequent to encoding, they are divided and rebuilt. At long last, we use stream figure and dual-key calculation to finish opportunity nondestructive change among plaintext and ciphertext to guarantee the trustworthiness of the encoded private data. Test outcomes illustrate that the presented strategy requires some investment in the encryption and unscrambling measure, which has superior ciphertext transformation yield impact and experiences fewer organization assaults in a similar encryption time. As far as computation cost, the proposed strategy diminishes by roughly 11%. Furthermore, it has greater security and works on the security and integrity of the protection data assortment procedure. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cryptography

来源：评论

学校读者我要写书评

暂无评论

Intrusion Detection System Using Machine Learning

Intrusion Detection System Using Machine Learning

引用

2023 International Conference on computer Communication and informatics, ICCCI 2023

作者： Kiran, Ajmeera Prakash, S. Wilson Kumar, B Anand Likhitha Sameeratmaja, Tammana Charan, Ungarala Satya Surya Ram MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering India

ISBN: (纸本)9798350348217

The use of computers and the internet has spread rapidly over the course of the past few decades. Every day, more and more people are coming to rely heavily on the internet. When it comes to the field of information security, the subject of security is one that is becoming an increasingly important focus. It is vital to design a powerful intrusion detection system in order to prevent computer hackers and other intruders from effectively getting into computer networks or systems. This can be accomplished by: (IDS). The danger and attack detection capabilities of the computer system are built into the intrusion detection system. Abuse has occurred and can be used to identify invasions when there is a deviation between a preset pattern of intrusion and an observed pattern of intrusion. An intrusion detection system (IDS) is a piece of hardware (or software) that is used to generate reports for a Management Station as well as monitor network and/or system activities for unethical behaviour or policy violations. In the current study, an approach known as machine learning is suggested as a possible paradigm for the development of a network intrusion detection system. The results of the experiment show that the strategy that was suggested improves the capability of intrusion detection. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

A Simple Algorithm to Secure Data Dissemination in Wireless Sensor Network 1

引用

1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023

作者： Sonai, Veeramani Bharathi, Indira Department of Computer Science and Engineering School of Engineering Shiv Nadar University Chennai Tamilnadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Chennai India

ISBN: (数字)9789819981298

ISBN: (纸本)9789819981281

Wireless sensor networks are heavily utilized in numerous applications in both industry and science. As the original data is transferred from the sensor node to the sink node, unauthorized individuals attempt to access it, breaching confidentiality. The first step in enhancing the security of data transmission in a wireless sensor network (WSN) is figuring out the best method for encrypting data as it moves from the sensors to the central node. In order to move encrypted data from different sensor nodes to sink nodes efficiently in wireless sensor networks, this paper introduces a simple data encryption technique. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cryptography

来源：评论

学校读者我要写书评

暂无评论

Analyzing Risk Level from RoI and VoI for Robust Data Privacy using Fuzzy Inference System 4

Analyzing Risk Level from RoI and VoI for Robust Data Privac...

引用

4th International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Rongon, Rabab Khan Akhtaruzzaman, M. Das, Krishna Sharif, Shekh Tanjil Military Institute of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh Dhaka Bangladesh Dhaka Bangladesh

ISBN: (纸本)9798331530938

In the modern digital environment, securing data privacy has become increasingly critical as organizations confront rising threats from cyber-attacks and data breaches. To safeguard sensitive information, businesses must invest in robust data protection measures, yet balancing these investments with their potential benefits remains challenging. This research addresses this issue by proposing a novel framework that integrates Return on Investment (RoI) and Value of Information (VoI) within a Fuzzy Inference System (FIS) to assess data privacy risks effectively. RoI measures the financial return relative to the cost of data protection investments, indicating the economic value derived from such measures. VoI, on the other hand, evaluates the worth of the information protected by these investments, reflecting its impact on decision-making and operational efficiency. By employing a FIS, which accommodates uncertainties and imprecise data, this framework provides a nuanced approach to risk assessment, enabling organizations to optimize their data privacy strategies. The integration of RoI and VoI within the FIS allows for a comprehensive evaluation of risk levels, facilitating more informed decision-making and improved risk management. This research aims to enhance data privacy practices by offering a methodical and adaptable tool for evaluating and balancing investments in data protection. © 2025 IEEE.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

Komodo Dragon Mlipir Algorithm-based CNN Model for Detection of Illegal Tree Cutting in Smart IoT Forest Area

引用

Recent Advances in computer Science and Communications 2024年第6期17卷 1-12页

作者： Aluvalu, Rajanikanth Sharma, Tarunika Viswanadhula, Uma Maheswari Thirumalraj, Aruna Devi Kantipudi, Maha Veera Vara Prasad Mudrakola, Swapna Department of IT Chaitanya Bharathi Institute of Technology Hyderabad India Department of Mathematics New Horizon College of Engineering Bengaluru India Department of CSE Chaitanya Bharathi Institute of Technology Hyderabad India Department of Computer Science Engineering K.Ramakrishnan College of Technology Tamil Nādu Trichy India Pune412115 India Department of CSE Matrusri Engineering College Hyderabad India

Introduction: Trees and woods are vital to preventing climate change and protecting our planet. Sadly, they are constantly being destroyed due to human activities like deforestation, fires, etc. Method: This research presents and examines an outline for using audio event categorisation to automatically detect unlawful tree-cutting activity in forests. To monitor large swaths of forest, the research team proposes using ultra-low-power, minor devices incorporating edge-computing microcontrollers and long-range wireless communication. An efficient and accurate audio classification solution based on multi-layer perceptron (MLP) and modified convolutional neural networks (M-CNN) is projected and tailored for cutting. The Komodo Dragon Mlipir Algorithm (KDMA) is used to pick the best weight for the CNN. Result: Compared to earlier efforts, the suggested system uses a computing technique to recognise deforestation-related hazards. Various preprocessing methods have been evaluated, with special attention paid to the trade-off between classification precision and computer resources, memory, and power use. Conclusion: Additionally, there have been long-range communication trials performed in natural settings. The experimental consequences demonstrate that the suggested method can notice and apprise tree-cutting occurrences through smart IoT for efficient and lucrative forest nursing. © 2024 Bentham Science Publishers.

关键词： Multilayer neural 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：