检索结果-内蒙古大学图书馆

An optimized framework for implementation of smart waste collection and management system in smart cities using IoT based deep learning approach

引用

International Journal of information Technology (Singapore) 2024年第8期16卷 5033-5040页

作者： William, P. Patil, Jaikumar M. Panda, Sunita Venugopal, Anita Vidyullatha, Pellakuri Kumar, Nellore Manoj Jandwani, Aman Department of Information Technology Sanjivani College of Engineering SPPU Department of Computer Science and Engineering Shri Sant Gajanan Maharaj College of Engineering SGBAU Department of Electrical Electronics and Communication Engineering GITAM School of Technology IT Unit Dhofar University Department: Computer Science and Engineering Koneru Lakshmaiah Education Foundation Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Research Task

Nation’s rapid urbanization growth and economic development, waste generation has significantly increased. An important environmental concern on a global scale is waste management. In a majority of the world’s nations, including India’s metropolitan centers, organic waste management is a major problem. Therefore, it is necessary to create a productive system that will either eliminate or significantly decrease this issue. It will assist us in effectively maintaining a hygienic environment, and natural atmosphere. The implementation of smart cities requires an effective garbage collection system. In the given paper, we propose a smart waste collection and management (SWCM) system centered on the Internet of Things (IoT) architecture. A deep learning (DL) approach for classifying waste is being used. The proposed framework includes the use of smart bins for the collection of garbage, the categorization of that waste into several groups, and the management of smart waste. For this study, data from smart garbage cans were gathered. The data was normalized during preprocessing. The efficacy of the proposed system is assessed and compared with conventional as well as existing recent studies based on performance metrics such as classification accuracy (%) and power consumption (%). The results of the study reveal that the proposed approach is impactful in the smart collection and management of waste. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： And hygienic environment Deep learning Internet of things (IoT) Smart cities Smart waste

来源：评论

学校读者我要写书评

暂无评论

RHC-Based Systematic Design of a Wide-Angle Periodic Coded THz Reflectarray 11

RHC-Based Systematic Design of a Wide-Angle Periodic Coded T...

引用

11th International Symposium on Telecommunication, IST 2024

作者： Yousefi, Pezhman Khajeh, Ali Yahaghi, Alireza Hosseini, Keyhan Polytechnic University of Bari Department of Electrical and Information Engineering Via Orabona 4 Bari70125 Italy Shiraz University School of Electrical and Computer Engineering Shiraz71946 Iran University of Kurdistan Faculty of Engineering Department of Electronics and Communication Engineering Kurdistan Sanandaj66177 Iran

ISBN: (纸本)9798350356250

A reflectarray designed by Random Hill Climbing (RHC) optimization process is demonstrated and simulated. On the basis of top layer effect on reflection phase and exploiting the relation between phase distribution and the direction of the reflected beam, six pixelated subcells are optimized for two incident angle waves simultaneously. The far field plot shows that for beams of incident angles 19.471° and 41.81° and in the precincts of them the reflectarray deflects the incident waves into reflections close to normal to the reflectarray surface. This shows a proximate independency of incident wave angle for the reflectarray in a certain angular range. The proposed cells provide 342° reflection phase range. Also, the reflection loss of the cell for the incident angles 19.471° and 41.81° are -1.9 dB and -1.4 dB, respectively. The pattern gain and fractional bandwidth for a finite reflectarray of size 300 λ x 150 λ are reported to be 19.44 dB and 25% for the incident angle 19.471°, and 19.17 dB and 30% for the incident angle 41.81°. © 2024 IEEE.

关键词： Reflection

来源：评论

学校读者我要写书评

暂无评论

Optimizing Digital Marketing Strategies Through Search Engine Optimization 2

Optimizing Digital Marketing Strategies Through Search Engin...

引用

2nd IEEE International Conference on Contemporary Computing and communications, InC4 2024

作者： Tatikonda, Reshmi Ponnala, Jainath Thatikonda, Ramya Yendluri, Dileep Kumar Kempanna, M. Ananthan, Bhuvanesh University of the Central Missouri Department of Computer Information Warrensburg United States Jawaharlal Technological University Department of Electronics and Communication Engineering Hyderbad India Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India PSN College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India

ISBN: (纸本)9798350383652

This article examines SEO and its significance in digital marketing. The goal is to comprehend the main elements that impact achieving high rankings in search engines and the consequences of the presence of websites. Additionally, it strives to study its influence on website visibility, organic traffic, and rank in search results. This document comprehensively examines search engine optimization techniques, including optimizing website content and external factors, optimizing textual materials, and enhancing technical factors. Moreover, it examines the connection between SEO and UX (user experience). It emphasizes the significance of maximizing user happiness and participation. The document further explores the possible future of search engine optimization and online advertising. It contains new trends, expected challenges, and advice for future investigations and real-world applications. In general, the main objective of this paper is to present valuable viewpoints regarding the realm of SEO and its importance in the digital promotion arena. © 2024 IEEE.

关键词： Websites

来源：评论

学校读者我要写书评

暂无评论

An IoT-Based CNN Model for Patients in ICU Beds During the COVID-19 Outburst

An IoT-Based CNN Model for Patients in ICU Beds During the C...

引用

2023 International Conference on Data Science and Network Security, ICDSNS 2023

作者： Parameshachari, B.D. Srinivas, Chethana Rekha, H. Harshitha, P. Deepak, R. Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India East West Institute of Technology Department of Computer Science and Engineering Bengaluru India Shridevi Institute of Engineering and Technology Department of Information Science and Engineering Tumkur India Bgs Institute of Technology Adichunchanagiri University Department of Electronics and Communication Engineering B G Nagara India

ISBN: (纸本)9798350301595

The rising number of hospital admissions is a worldwide cause for alarm, with major contributors being an ageing population, the prevalence of diseases, and the current COVID-19 epidemic. IoT provides the scalability necessary for this determination, enabling incessant and reliable health monitoring on a worldwide scale, and therefore easing this difficulty. The COVID-19 virus has quickly disseminated all across the globe. Healthcare practitioners around the world face a number of difficulties during the epidemic, one of the most significant being the management of available resources. Since disease, approximately patients will need to be self-confessed to the hospital with an immediate need for oxygen acute medical care, making it crucial to properly allocate the beds in the (ICU). Many nations' efforts to stop the spread of the deadly COVID-19 virus relied heavily on technology, especially Artificial Intelligence (AI). In this research, we modify a (CNN) to better detect the requirement for the ICU and the predicted length of stay among COVID-19 patients. A 895 patients and evaluate the CNN with Sun flower Optimization (SFO). The results of the studies disclose that the projected perfect for predicting ICU Length of Stay (LoS) achieves the maximum accuracy. It is believed that this contribution would pave the way for better resource management in healthcare institutions, ultimately leading to the saving of lives. © 2023 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Forest-based Underwater Temperature Prediction Model for IoT Devices 3

Forest-based Underwater Temperature Prediction Model for IoT...

引用

3rd IEEE Mysore Sub Section International Conference, MysuruCon 2023

作者： Mondal, Subhash Roy, Sanjoy Jeevaraj, R. Mary Jasmine, E. Ghosh, Mourina Nag, Amitava Bengaluru India Meghnad Saha Institute of Technology Information Technology Kolkata India Indian Institute of Information Technology Guwahati Electronics and Communication Engineering Guwahati India Central Institute of Technology Kokrajhar Computer Science and Engineering Kokrajhar India

ISBN: (纸本)9798350340358

IoT is a booming domain in the field of technology. The main reason for its advancements in recent years has been its applications in various domains such as the environment, healthcare, smart cities, and agriculture. Real-Time data collection and fast exchange of data allow for effective functioning. Underwater temperature detection can be more fruitful and effective when coupled with IoT. This study focuses on building a predictive model that may help Automated Underwater Vehicles (AUVs) detect the ocean bed's temperature in real-Time. The AUV embedded with IoT onboard sensors enables the exploration of submarines in an underwater environment and lives. For this purpose, six machine learning regression models are considered to be trained over a dataset containing underwater temperature records at the corresponding depths near the islands in Brazil. The standard performance metrics were utilized for the evaluation of the performance of the models. Tree-based ensemble models such as Random Forest Regressor and Decision Tree Regressor produced the best predictive results, with their R2 values being 96.13% and 94.21%, respectively. The results clearly indicate that accurate underwater temperature prediction can be achieved by implementing tree-based models. Application of this technology can help AUVs get more insight into marine life and make inferences of any anomalies regarding this whatsoever. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Leveraging Automated Methods and Advanced Neural Networks for Accurate information Extraction

Leveraging Automated Methods and Advanced Neural Networks fo...

引用

2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Hemalatha, S. Deshpande, Leena A. Shrimal, Gajendra Neetha, S.S. Alam, Intekhab Karthigaikumar, P. Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Computer Science and Information Technology Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Karpagam College of Engineering Department of Electronics and Communication Engineering Coimbatore641032 India

ISBN: (纸本)9798350329773

This research investigates how computerized techniques and superior neural networks may be used to improve the accuracy of fact extraction (IE). This study uses a deep learning model to extract principles and activities from unstructured texts. The experiment uses widespread IE benchmarks to evaluate the model's capability regarding idea and event extraction accuracy. Effects show that the proposed approach rides on the faculty of deep getting to know, leveraging technique capabilities with the herbal language era. It allows for the progressed accuracy of IE over the modern-day baseline strategies. The model also achieves a precision price of 76.79% in concept extraction, with a keep-in-mind charge of 79.92%. In event extraction, the gadget reports a precision fee of 64.17% and a recall rate of 69.66%. The proposed technique is expected to be used to efficiently enhance the overall performance of IE tasks in a wide variety of programs. © 2023 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Approach for Enhancement of Underwater Images Using CLAHE and Percentile-Based Techniques 4

A Hybrid Approach for Enhancement of Underwater Images Using...

引用

4th IEEE International Conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2024

作者： Kumari, Shweta Kumar, Ravi Kumar, Manikant Kumar, Ashish Rai, Mithilesh Kumar Kumar, Vijay Information Technology Muzaffarpur Institute of Technology Muzaffarpur India Electronics and Communication Engineering Muzaffarpur Institute of Technology Muzaffarpur India Leather Technology Muzaffarpur Institute of Technology Muzaffarpur India Computer Science and Engineering Muzaffarpur Institute of Technology Muzaffarpur India

ISBN: (纸本)9798331528614

Underwater images frequently experience issues such as low contrast, uneven shading, and haziness caused by light absorption, scattering, and reflection, making it difficult to capture clear and detailed visuals. These issues hinder the extraction of meaningful information from underwater images, which is critical for applications such as marine research, underwater exploration, and surveillance. This work proposes a novel image enhancement method tailored for underwater environments. By integrating Contrast-Limited Adaptive Histogram Equalization (CLAHE) along with Percentile methodologies, proposed modal aim to significantly improve the visual quality and clarity of underwater photographs. CLAHE prevents the over-amplification of noise while adapting to both dark and bright regions of the underwater scene. The Percentile Enhancement method further refines the image by adjusting overall brightness while preserving essential details. By mapping pixel intensities to specific percentiles (e.g., 1st, 5th, 95th, and 99th), the technique enhances contrast without compromising fine image features. Proposed hybrid modal combines the strengths of CLAHE and percentile enhancement. CLAHE first improves local contrast, followed by percentile-based adjustments that enhance both global and local image features. The result is a more visually balanced and detailed representation of underwater scenes, addressing common challenges such as blurriness, low contrast, and noise. © 2024 IEEE.

关键词： Underwater photography

来源：评论

学校读者我要写书评

暂无评论

Identification of Fraudulent Transactions for Enhanced Credit Card Fraud Detection 4

Identification of Fraudulent Transactions for Enhanced Credi...

引用

4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Gupta, Monica Gayathri, V. Narula, Rachna Kumar, Vijay Kaur, Surinder Singh, Neetu Bharati Vidyapeeth's College of Engineering Dept. of Electronics and Communication Engineering New Delhi India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering New Delhi India Bharati Vidyapeeth's College of Engineering Department of Information and Technology New Delhi India

ISBN: (纸本)9798350387490

With the flourishment of the digital era and the evolution of new techniques and technology, this paper highlights the ever-growing risk of credit card fraud. The advancing technology has provided fraudsters with new and untraceable techniques for scamming genuine users. With the increasing impact of fraud, financial institutions must use machine learning tools for scam detection to provide safety and security to their users' accounts. This paper consists of an interdisciplinary comparative analysis of ML algorithms: Random Forest Classifier, XGBoost, ADABoost, Logistic Regression, Decision Trees, and Artificial Neural Networks. The diverse selection of algorithms aims to determine the most effective algorithm with the highest accuracy score for predicting legitimate and fraudulent transactions. The methodology involves fetching data from an Excel file, training the models, testing them on 20% of the dataset, and finally obtaining results in a Word document, having a confusion matrix and accuracy of each algorithm. By comparing the results of the confusion matrix and accuracy score, the Random Forest Classifier and XGBoost demonstrate the highest accuracy scores, while Logistic Regression yields a lower accuracy score. © 2024 IEEE.

关键词： Logistic regression

来源：评论

学校读者我要写书评

暂无评论

Vision-based outdoor navigation of self-driving car using lane detection

引用

International Journal of information Technology (Singapore) 2022年第1期14卷 215-227页

作者： Kumar, Amit Saini, Tejeshwar Pandey, Pratik B. Agarwal, Apoorv Agrawal, Anand Agarwal, Basant Department of Computer Science and Engineering Indian Institute of Information Technology Kota Jaipur 302017 India Department of Electronics and Communication Engineering Indian Institute of Information Technology Kota Jaipur 302017 India

The evolution of artificial intelligence has served as the catalyst in the field of technology and is making our imaginations real. One of such creation is the birth of self- driving car (autonomous robot). In this paper, a self-driving car physical prototype based on traditional visual method of lane keeping, implemented on Raspberry Pi is proposed which is capable of maneuvering on various kind of tracks autonomously. The proposed method comprises of taking an image from a front facing dashboard camera of the car, detecting the lane from the image and analysing the deviation of the car from the road which is further used to keep the car on the track. The proposed method is implemented on a 1/10 scale car which contains Raspberry Pi 3 Model-B computer and Pi Cam Rev 1.3 for computations and processing. The testing was done without any human intervention on self-made lined track having all kind of turns. The experimental results demonstrate the effectiveness and the robustness of the self-driving car in terms of the navigation error while following the reference trajectory. © 2021, Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Autonomous robot computer vision Master slave communication Raspberry Pi Wheeled mobile robot

来源：评论

学校读者我要写书评

暂无评论

IoT and Cloud Solutions Waste-to-Energy in Hospitals for Energy Recovery from Medical Waste 10

IoT and Cloud Solutions Waste-to-Energy in Hospitals for Ene...

引用

10th International Conference on communication and Signal Processing, ICCSP 2024

作者： Thanigaivelu, P.S. Lakshmi, D. Ramasamy, Jayabharathi Kumaresh, P.S. Sumathi, G. Mathivanan, K. Srm Institute of Science and Technology Computing Technologies Chenagalpattu Chennai Tamil Nadu Kattankulathur India St. Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India K.L.N.College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Sivagangai India Kongunadu College of Engineering and Technology Department of Information Technology Tamil Nadu Trichy India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

Innovative approach to hospital waste management and energy recovery due to the Internet of Things (IoT)-powered waste-to-energy system. This system addresses the complex problems of medical waste management and renewable energy production with a single framework. The system integrates several advanced sensors and components made possible by IoT technology's power to optimize waste management and energy generation. Sensors connected to the IoT, intelligent waste containers, and radio frequency identification labels all contribute to an improved garbage collection, sorting, and processing system. While fill-level sensors and RFID technology improve garbage collection routes and save operating costs and environmental effects, real-time data collection and analysis provide exact waste monitoring. Temperature and gas sensors enable safe and effective waste processing, eliminating risks and maximizing energy recovery. Medical waste is converted into useful energy sources like syngas in the system's energy recovery phase using pyrolysis or gasification reactors. These renewable energy sources lessen the environmental impact of medical waste disposal in landfills while relieving pressure on conventional fossil fuel supplies. The environmental advantages are highlighted, with less landfill trash and reduced greenhouse gas emissions specifically highlighted. © 2024 IEEE.

关键词： Renewable energy

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：