检索结果-内蒙古大学图书馆

2nd IEEE International Conference on Integrated intelligence and Communication Systems, ICIICS 2024

作者： Pawar, Priyanka Pramod Kumar, Deepak Meesala, Mohan Kumar Pareek, Piyush Kumar Addula, Santosh Reddy Shwetha, K.S. Department of Information Technology University of the Cumberlands WilliamsburgKY United States Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bangalore India

ISBN: (纸本)9798331504960

Due to its decentralised, accessible, and secured structure, blockchain-a framework that has historically been viewed with suspicion-has developed into a groundbreaking breakthrough. Reliable automated scripting and static data records are also a part of this system. The intricacy and expertise required to operate with blockchain technology is one of its disadvantages. machine learning (ML) frameworks are the key fixing this problem. It has the potential to make Blockchain more user-friendly. In this research paper, we reviewed the current state of blockchain-based cloud computing, machine learning, and safe data exchange. In this study, we suggest using a blockchain technology to provide a safe space where health entities can share medical information transparently. A modified advanced encryption standard (MAES) based blockchain encryption model is developed by us. This method is based on engineering and AI principles, and its main objective is to design useful artefacts that improve their respective domains. © 2024 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Improving Accuracy in Network Intrusion Detection via machine learning Algorithms: An In-Depth Examination

Improving Accuracy in Network Intrusion Detection via Machin...

引用

2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Suriya Prakash, J. Trishlaa, S. Soniya, R. Lakshmanan, V. Kiran, S. Department of Cse Ramnagara India Rajarajeshwari College of Engineering Department of Artificial Intelligence and Machine Learning Bengaluru India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Nitte Meenakshi Institute of Technology Department of Mathematics Bengaluru India

ISBN: (纸本)9798350375442

Intrusion detection is essential for securing computer networks by monitoring and analyzing activity to detect and respond to unauthorized access and malicious behavior. By examining network traffic and system logs, intrusion detection systems (IDS) help protect data integrity and availability, enabling swift responses to potential security incidents. In an ever-evolving cyber threat landscape, IDS plays a critical role in maintaining network security. This research investigates the performance of various machine learning algorithms for classification tasks using a dataset from Kyoto collected on December 31st, 2015. The algorithms explored include Naive Bayes, K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), AdaBoost, and CatBoost. Training and testing the model with different data splits (80/20, 70/30, 60/40) revealed that AdaBoost, LGBM, XGBoost, Decision Tree, Gradient Boosting, and Random Forest consistently delivered the highest accuracy. The study concludes that selecting the appropriate algorithms and data splits is crucial for enhancing IDS effectiveness, providing valuable insights for future research. Explore the code behind this study at https://***/Trishlaa07/Kyoto_2015_Dec_day31 © 2024 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

Decoding Sentiments: A Comprehensive Exploration of Real-Time Emotion Recognition from Text Through Deep learning

Decoding Sentiments: A Comprehensive Exploration of Real-Tim...

引用

International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： G. Sowmya R. Akhilesh Aurangabadkar Rohan Garikapati Kausthub Rao Atul Kumar Nayak Koppolu Midhilesh Computer Science Engineering Artificial intelligence and Machine learning MLR Institute of Technology Hyderabad India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Emotional state of a person can be found out through various modalities such as using facial expressions, voice of the person, text and so on. Our study is solely based on the emotion extracted through the text. Past research focused on categorizing texts into sentiments like positive, negative, or neutral, our novel approach shifts to predicting specific emotional states like angry, sad or happy. Our study covers the previous researches done in this field which are ranging from deep learning models, natural language processing, and machine learning models. Recurrent neural networks and its variants are mainly studied for recognizing the emotion in text. The integration of emotion detection in the customer feedback enhances organizational insights, enabling proactive measures to elevate customer satisfaction.

关键词： Deep learning Emotion recognition Recurrent neural networks Text recognition Customer satisfaction Organizations Real-time systems Natural language processing Distance measurement Decoding

来源：评论

学校读者我要写书评

暂无评论

A Neuro-Fuzzy Predictor Trained by an Elitism artificial Electric Field Algorithm for Estimation of Compressive Strength of Concrete Structures

Informatica (Slovenia)

引用

Informatica (Slovenia) 2023年第5期47卷 41-48页

作者： Kumar Dash, Ch Sanjeev Nayak, Sarat Chandra Behera, Ajit Kumar Dehuri, Satchidananda Department of Computer Science and Engineering Silicon Institute of Technology Silicon Hills Patia Bhubaneswar751024 India Department of Artificial Intelligence Engineering and Machine Learning CMR College of Engineering and Technology Hyderabad501401 India Department of Information and Communication Technology Fakir Mohan University Vyasa Vihar Odisha Balasore756019 India

Meta-heuristic learning-based forecasting is widely acceptable vis-à-vis manual and statistical methods in estimating the compressive strength of concrete structures. However, there is always a scope for exploring an effective, automated, and accurate predictor for this domain. This article proposes an elitism artificial electric field algorithm-based neuro-fuzzy network (eAEFA+NFN) for the prediction of compressive strength of concrete structures. The elitism method helps AEFA preserve the best individuals from iteration to iteration, by directly placing best fit particles into the population for the next generation and thus strengthening the optimization capacity of AEFA. A single hidden layer neural network (SHNN) is used as the base model and its inputs are fuzzified using the Gaussian triangular membership function with a degree of membership to different classes. The optimal number of input data, hidden neurons, bias, and weights for the hidden layer are decided by eAEFA. The model is evaluated on samples from a publicly available dataset with curing ages at 3, 7, 14, and 28 days. Considering four sample series, the eAEFA+NFN produced an average MAPE of 0.092073 and ARV of 0.139731 which are better compared to others. The experimental outcomes and analysis are in favor of the eAEFA+NFN-based forecasting. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Electric fields

来源：评论

学校读者我要写书评

暂无评论

Smart Agriculture in the Era of Big Data: IoTAssisted Pest Forecasting and Resource Optimization for Sustainable Farming 2

Smart Agriculture in the Era of Big Data: IoTAssisted Pest F...

引用

2nd IEEE International Conference on Integrated intelligence and Communication Systems, ICIICS 2024

作者： Kumar, Deepak Pawar, Priyanka Pramod Meesala, Mohan Kumar Pareek, Piyush Kumar Addula, Santosh Reddy Shwetha, K.S. Department of Information Technology University of the Cumberlands WilliamsburgKY United States Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bangalore India

ISBN: (纸本)9798331504960

The goal was to facilitate quick development and revitalisation of rural areas by utilising IoT knowledge to accomplish smart agriculture against the backdrop of big data. Depleted soil fertility, increased pest attacks, and diseases are some of the climate-related risks that the agricultural sector is battling. Predicting Utilising directly sensed environmental conditions as input, a machine learning model incorporating Random Forest and the Catboost algorithm determines, based on the current environmental conditions, whether pest populations are present or not. Next, Hybrid Weighted Ant Colony Optimisation (HWACO) is used to fine-tune the Catboost algorithm. This method evaluates the machine learning model's accuracy and precision in making predictions using test data. Predictions have been made using data from 2028 to 2022, a span of five years. Weekly forecasts are produced by the pest prediction model. With high F-measure, Precision, and Recall, the weekly predictions have an overall accuracy of 93%. Over time, the precision of the pest forecast steadily rises. Predictions for the next week are made by averaging all weather data from the past seven days. For immediate defences against pests, the weekly forecasts are crucial. The findings support sustainable farming methods and food security by showing that the device can maximise crop yields while decreasing resource consumption. © 2024 IEEE.

关键词： Smart agriculture

来源：评论

学校读者我要写书评

暂无评论

Multilingual Braille and Voice Translation Model for Visually Impaired Learners

Multilingual Braille and Voice Translation Model for Visuall...

引用

Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： G Tejesh Pruthvi R M Tharun Kumar M Pavitra Gonal Rashmi Karchi Department of CSE-Artificial Intelligence and Machine Learning Nagarjuna College of Engineering and Technology Bengaluru Karnataka India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Multilingual Braille and Voice Translation Model facilitates effective, smooth communication and learning by the visually impaired in various languages. Currently available braille translation systems are frequently single-language focused, making them less helpful in multilingual contexts. The primary feature of the model is its ability to take user inputs in one language and deliver translations quickly in the chosen language(s) both formats of Braille and voice. This function makes things more inclusive and accessible, especially for education because it enables pupils with visual impairment to gain access to such content in many languages easily. To accomplish this, the system will employ the latest NLP and IoT technologies. MQTT protocol is used as a means of communication since it supports rapid and real-time data exchanges. The MQTT adds improvements to speed and growth ability, making data transfer between the devices more efficiently. Such a system can handle many languages; the proposed method holds the accuracy of 95.02%, shows higher performance than others, hence it is a powerful tool in helping overcome language and access problems in educational institutions.

关键词： Braille Technological innovation Translation Accuracy Protocols Visual impairment Natural language processing Multilingual Usability Speech to text

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Food Identification and Waste Reduction Solution with Built-in Nutritional Tracking using machine learning 8

A Comprehensive Food Identification and Waste Reduction Solu...

引用

8th International Conference on Inventive Systems and Control, ICISC 202

作者： Jonathan, J. Benjamin, Ruth Moly Prasad, Gunji Prem Karunya Institute of Technology and Sciences Computer Science Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350386578

Food Solutions and Nutrition Checker is an innovative and comprehensive project aimed at providing users with a reliable tool to identify various foods and dishes, while also offering a custom-built database for detecting fresh and wasted food items. With this system, users can easily determine the nutritional value of different foods, making informed decisions about their dietary choices. This study incorporates advanced technology that enables the recognition of a wide range of food items. By simply capturing an image or specifying the dish, users can obtain accurate information about the food, including its ingredients and origin. This feature is particularly helpful for individuals with specific dietary needs or restrictions, allowing them to easily identify suitable menu options. Additionally, this study includes a custom-built data system that helps to identify fresh and wasted food. By analyzing factors such as expiration dates, storage conditions, and quality indicators, the proposed system can determine the freshness status of various food items. This capability promotes food waste reduction by enabling users to make informed decisions regarding the consumption or disposal of perishable goods. Moreover, this study incorporates a nutritional tracker that provides users with comprehensive information regarding the nutritional composition of different foods. This feature includes details such as calorie count, macronutrient breakdown, vitamins, and minerals, empowering users to make healthier food choices aligned with their dietary goals and requirements. Overall, the proposed model offers a holistic approach for food identification, waste reduction, and nutritional tracking. By incorporating advanced technology and a robust database, this study aims to facilitate informed decision-making and promote healthier dietary practices. © 2024 IEEE.

关键词： Food ingredients

来源：评论

学校读者我要写书评

暂无评论

Visual Computing Unified Application Using Deep learning and Computer Vision Techniques

International Journal of Interactive Mobile Technologies

引用

International Journal of Interactive Mobile Technologies 2024年第1期18卷 59-74页

作者： Sowmya, B.J. Meeradevi Seema, S. Dayananda, P. Supreeth, S. Shruthi, G. Rohith, S. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Karnataka Bengaluru India Department of Artificial Intelligence and Machine Learning Ramaiah Institute of Technology Karnataka Bengaluru India Department of Computer Science and Engineering M S Ramaiah Institute of Technology Karnataka Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal India School of Computer Science and Engineering REVA University Karnataka Bengaluru India Department of Electronics & Communication Engineering Nagarjuna College of Engineering & Technology Karnataka Bengaluru India

Vision Studio aims to utilize a diverse range of modern deep learning and computer vision principles and techniques to provide a broad array of functionalities in image and video processing. Deep learning is a distinct class of machine learning algorithms that utilize multiple layers to gradually extract more advanced features from raw input. This is beneficial when using a matrix as input for pixels in a photo or frames in a video. Computer vision is a field of artificial intelligence that teaches computers to interpret and comprehend the visual domain. The main functions implemented include deepfake creation, digital ageing (de-ageing), image animation, and deepfake detection. Deepfake creation allows users to utilize deep learning methods, particularly autoencoders, to overlay source images onto a target video. This creates a video of the source person imitating or saying things that the target person does. Digital aging utilizes generative adversarial networks (GANs) to digitally simulate the aging process of an individual. Image animation utilizes first-order motion models to create highly realistic animations from a source image and driving video. Deepfake detection is achieved by using advanced and highly efficient convolutional neural networks (CNNs), primarily employing the EfficientNet family of models. © 2024 by the authors of this article. Published under CC-BY.

关键词： Animation

来源：评论

学校读者我要写书评

暂无评论

Nurturhub: A Smart Maternal Health Monitoring Device for Post Partum Depression

Nurturhub: A Smart Maternal Health Monitoring Device for Pos...

引用

IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Kate Angela Jeanne Pereira Shreenath Acharya Rihan D S A Prajwal Bangera Pratham Kumar Rao Department of Artificial Intelligence and Machine Learning St. Joseph Engineering College Mangalore India

ISBN: (数字)9798350350593

ISBN: (纸本)9798350350609

Wearables integrated with AI and IoT platforms are used to monitor stress, maternal health, and mental illness, offering personalized insights and recommendations. The device uses sophisticated sensors, such as heart rate (HR), blood volume pulse (BVP), and electrodermal activity (EDA) sensors, to gather physiological data from expectant moms. These technologies promise enhanced healthcare outcomes through continuous data-driven interventions. The proposed work of Nurturhub, pioneers a transformative approach to pregnancy care by integrating advanced technology to prioritize the holistic well-being of expectant mothers. Through the integration of machine intelligence and sensor technology, Nurturhub provides a comprehensive solution for prenatal care, enabling individualized health insights and ongoing monitoring. Its use throughout pregnancy may lead to better mother health outcomes, early postpartum depression identification, and general well-being. The methodology employs comprehensive sensor data extraction, preprocessing, and model development, resulting in exceptional accuracy in stress classification and personalized recommendations for maternal health management. By leveraging technology and compassionate support, Nurturhub sets a new standard for comprehensive perinatal care, with future endeavors aimed at expanding to encompass additional health parameters and enhancing data security measures.

关键词： Pregnancy Heart rate Accuracy Medical services Human factors Sensors Wearable devices Monitoring Intelligent sensors Blood

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Multi-Modal and Multi-Model API for Real-Time Sign Language Translation

A Comprehensive Multi-Modal and Multi-Model API for Real-Tim...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： R.R Prianka Tanya Singh Rishabh Solanki Singh Divyesh Jaiswal M.S Rohith Department of Artificial Intelligence & Machine Learning New Horizon College of Engineering Bengaluru Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This research introduces a novel three-stream, multi-modal and multi-model architecture with a comprehensive API to advance the scalability of sign language translation systems. It integrates a sentence correction model, an emotion detection system, and a sign language recognition model. The sentence correction model ensures translations are both semantically and syntactically accurate. The emotion detection system interprets emotional nuances, which is vital for signs that look similar but have different meanings. The sign language recognition model accurately decodes gestures from video, enhancing the fidelity of translations. Preliminary results indicate a significant improvement in translation accuracy and contextual relevance, with notable reductions in semantic errors and better conveyance of emotional states. This system offers more inclusive and accessible technology, paving the way for broader communication possibilities and greatly benefiting the deaf and hard-ofhearing community.

关键词： Deep learning Sign language Emotion recognition Accuracy Scalability Data security Semantics Real-time systems Grammar Context modeling

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：