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The Emerging Internet Congestion Control Paradigms 11

The Emerging Internet Congestion Control Paradigms

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11th Mediterranean conference on Embedded Computing (MECO) / 3rd Summer School on Cyber-Physical + Systems and Internet of Things (CPS and IoT)

作者： Tafa, Zhilbert Milutinovic, Veljko Univ Business & Technol Prishtina Kosovo Univ Indiana Bloomington IN USA

ISBN: (纸本)9781665468282

This paper presents a survey on the emerging approaches to the end-to-end congestion control (EECC) in modern Internet. The actual mechanisms are inefficient in the operational contexts of complex and dynamic communication systems. The emerging learning-based approaches employ machine learning (ML) to either improve the existing schemes or to completely remodel congestion control from scratch. We classify ML algorithms according to the application area within the context of congestion control, and describe key classes of applications. We also discuss research and engineering issues regarding their implementation and their potential to be included within new operating systems.

关键词： congestion control computer networks machine learning reinforcement learning

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Data Urbanity: Smart City Evolution Through IoT and Data Science 3

Data Urbanity: Smart City Evolution Through IoT and Data Sci...

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3rd international conference on Innovative Mechanisms for Industry applications, ICIMIA 2023

作者： Singh, Ajay Kumar, Manni Chandigarh University Department.of Computer Science and Engineering Punjab Mohali India

ISBN: (纸本)9798350343632

The rapid increase in population density in urban cities has created a demand for suitable infrastructure and services to meet the needs of urban residents. To address this demand, urban planners and developers are turning to data science for assistance in creating smart city solutions. The Internet of Things (IoT) has played a significant role in the application of data science in urban planning, as IoT devices such as sensors and smart devices provide vast amounts of data that can be used to gain insights into the operations of the city and make data-driven decisions to enhance its productivity, efficiency, and overall quality of life. This paper provides a general overview of data science in urban planning for smart city development. It highlights the importance of data science in urban planning and the role it plays in creating smarter, more effective cities. By applying data analytics, machine learning, and other data science approaches, urban planners can gain insights into various aspects of urban life, including traffic patterns, energy usage, and public safety. These insights can then be used to develop strategies that improve traffic flow, reduce energy waste, and enhance overall quality of life in cities. The study aims to describe the function of data science in urban planning, examine case studies and best practices, discuss the challenges and limitations associated with applying data science in urban planning, and provide suggestions for further research and advancements in the field. By investigating how data science can be effectively used in urban planning for smart city development, this study contributes to the growing body of knowledge on creating more sustainable and livable cities. © 2023 IEEE.

关键词： Artificial intelligence Biomarkers cardiovascular disease Computed Tomography (CT) Data analysis Deep learning Diagnostic methods Early detection Electrocardiogram (ECG/EKG) Health informatics heart disease machine learning Magnetic Resonance Imaging (MRI) Medical imaging Predictive modeling Remote monitoring Risk assessment Signal processing Ultrasound Wearable devices

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Frontiers in Artificial Intelligence and applications 3

Frontiers in Artificial Intelligence and Applications

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3rd international conference on Computation, Automation and Knowledge Management, ICCAKM 2022

作者： Yadav, Jaya Shukla, Shruti Sharma, Kanika Soni, Nupur Agarwal, Shruti Pathak, Prabhash Chandra Babasaheb Bhimrao Ambedkar University Department of Information Technology Lucknow India Babu Banarasi das University Department of Computer Application Lucknow India Mangalmay Institute of Management and Technology Department of Computer Application Greater Noida India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Lucknow India

ISBN: (数字)9781665453196

ISBN: (纸本)9781665453196

The industrial revolution has been the main cause ever since tremendous technological advancement was observed. The ubiquitous deployment of recent information and communication technologies (ICT), namely Artificial Intelligence (AI), Internet of Things (IoT), and Blockchain technology, is hastening the world's industrial and technological transformation. This technical aggrandizement enhances the working culture and has a favorable impact on the workplace, as per the progressivist perspective. The breakneck pace of technological advancement, as well as AI, has enabled humans to replace manual labor in various industries. As being a domain of science and technology, AI develops machines and programs for computers that are intelligent and can accomplish tasks that would normally require human intelligence abilities. This paper mainly explores the frontiers of artificial intelligence and its applications in various fields. The AI Frontiers promulgate methodical concepts that are peer-reviewed cutting-edge research on the disruptive technological revolution of Artificial Intelligence. Additionally, some key viewpoints in the field of AI have been listed along with the main frontiers, including machine learning (ML), Deep learning (DL), Fuzzy Logic (FL), Natural Language Processor (NLP), and Genetic Algorithm (GA). Furthermore, this paper discussed some common AI applications and a briefing about the current scenario in the worldwide market for artificial intelligence. © 2022 IEEE.

关键词： Genetic algorithms

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A survey on deep learning for surgical planning 3

A survey on deep learning for surgical planning

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3rd international conference on Signal Processing and machine learning, CONF-SPML 2023

作者： Diao, Xinran Beijing Institute of Technology Room 418 JingYuanA Liangxiang East Street Fangshan District Beijing China

Deep learning-based surgical planning is currently a promising area of research. Unlike the traditional way of surgical planning through computer systems that assist physicians, deep learning methods enhance the robustness and accuracy of surgical planning systems through a data-driven approach with the great advantage it possesses in processing images. In this work, we have put together a review that presents the application of deep learning in three separate aspects of surgical planning systems, namely, surgical scene understanding, surgical scene reconstruction and automated assessment of surgical skills. This article covers multiple scopes, such as semantic segmentation, depth estimation, SLAM systems, etc. We present the relevance of these three applications for the current surgical and medical field and show how the problem can be solved by the techniques in the mentioned topics. We hope that this work will link emerging research results in the field of deep learning and surgical planning, and provide guidance to future researchers using deep learning techniques for surgical planning when it comes to understanding feasible approaches to related problems. © Published under licence by IOP Publishing Ltd.

关键词： Semantics

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Design and Simulation of Intelligent Matching Algorithm for Online Primary School Mathematics Curriculum Based on Deep learning 3rd

Design and Simulation of Intelligent Matching Algorithm for ...

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3rd EAI international conference on application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Mo, Yi Tang, Jishen Chang, Xunyun Wu, Dong Hechi University Guangxi Yizhou546300 China Foreign Languages Department Sichuan Vocational and Technical College Sichuan Suining629000 China

ISBN: (纸本)9783031631382

With the continuous development of new information technologies such as big data, cloud computing, and Internet of Things, people's access to knowledge and information has undergone profound changes. New concepts such as machine learning, wisdom education, and educational big data are changing the traditional educational ecology, teaching form, and learning methods, and at the same time, promoting the deep development of educational modernization to informationization. People's needs in the fields of artificial intelligence such as information retrieval, automatic question and answer, dialogue system, etc. begin to appear, and intelligent matching algorithms are needed to meet the high demand of users. Text matching algorithm is the core problem in natural language processing technology. In the traditional text matching field, the dimension disaster of text representation and data sparseness have affected the development of natural language processing field. It is necessary for the times and students’ development to carry out subject teaching under the guidance of deep learning, which is of great significance. Unit review class is an important class type of primary school mathematics, which is of great value to students’ development, but there are many problems in actual teaching. This paper presents a multi-view collaborative teaching network based on sparse interactive data. The matching network is mainly composed of two parts, a text-based matching model and a relationship-based matching model. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Big data

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Optimized Deep learning Model for Lung Cancer Prediction Using ANN Algorithm 3

Optimized Deep Learning Model for Lung Cancer Prediction Usi...

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3rd international conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Rawat, Deepak Meenakshi Pawar, Lokesh Bathla, Gaurav Kant, Ravi Chandigarh University Department of Mathematics Mohali India Chandigarh University Department of Computer Science and Engineering Mohali India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Cancer is disease in which there is unnatural growth of abnormal cells that infest healthy cells in the body. Lung cancer invokes imbalance in cells which affects the lungs. Prediction of lung cancer is very necessary at the early stages especially in populated and less income countries. Specialists use clinical traditional procedures such as blood test and other therapies. Artificial intelligence era has started, now with the help of machine learning algorithm and deep learning algorithms it is possible to develop computer aided diagnosis mechanism. This research study has considered four machine learning algorithms such as Bayes Net, Naive Bayes, Decision Tree, Random Forest and one deep learning algorithm Artificial Neural Network to predict lung cancer at early stages. For this purpose, state of art parameters are measured for different algorithms and evaluated on lung cancer dataset. Accuracy as a prime parameter is evaluated and compared for all-the five algorithms. As per experimental result, Artificial Neural Network is best learning algorithm with accuracy of 92.23%. Further accuracy in Artificial Neural Network is evaluated for one, two and three hidden layers and compared. Artificial Neural Network with one layer has highest accuracy in experiment conducted. © 2022 IEEE.

关键词： Decision trees

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Utilizing machine learning for Advanced Natural Language Processing and Sentiment Analysis in Social Media Platforms 1

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3rd international conference on Mechanical and Energy Technologies, ICMET 2023

作者： Manoharan, Geetha Jyothi, B. Swarna Bhat, Romica Acharjee, Purnendu Bikash Sharma, Ankush Rawal, Priyanka School of Business SR University Telangana Warangal India Rajeev Gandhi Memorial College of Engineering and Technology Nandyala India Amity School of Communication Amity University Kolkata Kolkata India Computer Science CHRIST University Pune Lavasa Campus Lavasa India Jagran Lakecity Business School Faculty of Management and Commerce Jagran Lakecity University Madhya Pradesh Bhopal India Faculty of Management and Commerce Jagran Lakecity University Madhya Pradesh Bhopal India

ISBN: (数字)9789819727162

ISBN: (纸本)9789819727155

Social media is increasingly regarded as one of the most abundant online resources for information gathering and knowledge exchange. Among the most widely used social media sites is Twitter available today. When attempting to comprehend the information in any unknown word-based data (such as social media), natural language processing (NLP) techniques are crucial since they help remove noise from data, identify stem words, etc. It also helps with comprehension of the sentiment or semantic contents. Using social media, we apply machine learning techniques (clustering and classification) to determine the viewpoint's polarity in the information. Several classifiers and clusters, including SVM, RF, Naive Byes, and KNN, are used to detect content on social media. Sentiment analysis is the process of automatically classifying user-generated content as neutral, negative, or positive. It is possible to utilize the text, sentence, feature, or aspect as criteria to group feelings into distinct categories. This study demonstrates the application of machine learning techniques to the analysis of emotions expressed on the Twitter network. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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machine learning to Estimate Gross Loss of Jewelry for Wax Patterns 1

引用

3rd international conference on Intelligent Manufacturing and Automation, ICIMA 2022

作者： Jain, Mihir Jain, Kashish Mane, Sandip Purdue School of Industrial Engineering West LafayetteIN47906 United States Sardar Patel Institute of Technology Mumbai400058 India Dwarkadas J. Sanghvi College of Engineering Mumbai400056 India

ISBN: (数字)9789811979712

ISBN: (纸本)9789811979705

In mass manufacturing of jewelry, the gross loss is estimated before manufacturing to calculate the wax weight of the pattern that would be investment casted to make multiple identical pieces of jewelry. machine learning is a technology that is a part of AI which helps to create a model with decision-making capabilities based on a large set of user-defined data. In this paper, the authors found a way to use machine learning in the jewelry industry to estimate this crucial gross loss. Choosing a small data set of manufactured rings and via regression analysis, it was found out that there is a potential of reducing the error in estimation from ±2–3 to ±0.5 using ML algorithms from historic data and attributes collected from the CAD file during the design phase itself. To evaluate the approach’s viability, additional study must be undertaken with a larger data set. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer aided design

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Lightweight and Reliable Deep learning Model for Plant Disease Detection 3

Lightweight and Reliable Deep Learning Model for Plant Disea...

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3rd international conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Kumar, Shailender Kansal, Garvit Pandey, Jatin Singh, Harmeet Delhi Technological University Dept. of Computer Science & Engg. Delhi India

ISBN: (纸本)9798350300710

With the continued penetration of smartphones among the current populace, tasks that were previously assigned to humans can now be done by our mobile devices with better efficiency. One such use case is found in the field of agriculture, from simple tasks like transferring farming news to meticulous ones like reading soil moisture, smartphones are on a clear path to becoming indispensable companions to our farmers. Contributing to nearly 20% of India's GDP (as of 2021), agriculture and its allied activities remain a relevant and powerful area of research, one such research is on the application of smartphones in the detection of crop plant diseases. One of the largest challenges faced by the agricultural industry is that of the onslaught of plant diseases and pests, It has been estimated that around 30-40% of global crop production is lost due to disease and pest attacks, so, rapid identification of crop diseases still remains a prevalent task in many parts of the world. With the advent of better and more efficient machine learning architectures, we are now ever so closer to the future of mobile crop disease diagnosis. Although, due to the limited processing powers of smartphones, especially in the budget category, image classification via more demanding, yet accurate deep learning models still seems to be impractical. In this paper, we research and propose the use of the EfficientNet family of deep learning models for the task of detecting crop plant diseases in a mobile setting, receiving an all-time high accuracy of 98.64% with significantly fewer model parameters than the other currently available deep learning models, we believe we are on a clear path towards a more practical mobile crop disease diagnosis. © 2023 IEEE.

关键词： Smartphones

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Novel Robust Span-based Model for Event Detection 3

Novel Robust Span-based Model for Event Detection

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3rd international conference on computer, Big Data and Artificial Intelligence, ICCBDAI 2022

作者： Zhang, Hang Yuan, Xiaoguang Song, Yingyi Yang, Yuting Wang, Rui Liu, Bing Han, Cuihong Beijing Institute of Computer Technology and Application Yongding Road Beijing100854 China College of Systems Engineering National University of Defense Technology Sifangping Street Changsha410073 China

Event detection (ED) aims to identify events and their types from the given text, which is the vital subtask of event extraction. However, poor quality training corpus can seriously affect the validity of current dominating deep learning-based techniques. In order to improve the robustness of the model, this paper converts the ED task into the named entity recognition (NER) task, and uses the span-based NER framework to identify and classify event triggers. In addition, we ameliorate the framework through data cleaning strategy, hierarchical learning rate strategy, sentence-level loss function and model ensemble strategy. The experimental results suggest that our approach achieves state-of-the-art performance on the widely used Chinese dataset. Moreover, the deep analysis has confirmed the effectiveness of the improvement strategy in resisting data pollution. © Published under licence by IOP Publishing Ltd.

关键词： Deep learning

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