检索结果-内蒙古大学图书馆

4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Govinda Rajulu, G. Santhoshkumar, R. Venkatesan, D. Kalvikkarasi, S. Santosh Kumar Patra, P. St. Martin's Engineering College Department of Computer Science and Design India St. Martin's Engineering College Department of Artificial Intelligence and Data Science India St. Martin's Engineering College Department of Artificial Intelligence and Machine Learning Department of CSE India Podhagai College of Engineering & Technology Professor in Ece Department India St. Martin's Engineering College India

ISBN: (纸本)9781665474368

The research of Intrusion Detection Systems is known to be among the most challenging aspects of Cloud Security. In the context of the cloud, security breaches such as denial of service, scanning, injection of malicious code, viruses, worms, and the breaking of passwords are becoming increasingly commonplace. In the event that these attacks are not stopped in a timely manner, the company's reputation may be put in jeopardy, and it may sustain financial harm. As a consequence of this, it is essential to safeguard the cloud against dangers of this kind. In this research, we provide solutions to defend cloud systems by incorporating some of the most successful methods of intrusion detection. Within IDS, our primary focus has been placed on two critical aspects: the effectiveness of the detection techniques and the pace of detection. In order to overcome these obstacles, we suggest utilising machine learning in conjunction with parallelization. Approaches based on machine learning gain information from the data itself, hence diminishing the significance of the human expert. In order to identify intrusions, statistical models, safe system approaches, neural networks, and a variety of other methods are utilised. The purpose of this study is to improve the accuracy of intrusion detection in cloud computing by presenting a new approach to intrusion detection that is based on existing intrusion detection systems and the various architectures that those systems employ. © 2022 IEEE.

关键词： Cloud computing architecture

来源：评论

学校读者我要写书评

暂无评论

A Review on the Applications of machine learning Algorithms for Mental Stress Detection

A Review on the Applications of Machine Learning Algorithms ...

引用

Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Renuka Motghare Shivam Bhoyar Prateek Verma Abhay Tale Aditya Barhate Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

Stress, a widespread issue affecting both individual well-being and societal productivity, has attracted significant research interest. Defined as a state of mental or emotional strain, stress arises from various factors such as work, relationships, and health concerns, manifesting physically, emotionally, and behaviorally. This paper provides a review of machine learning (ML) techniques to detect mental stress using physiological signals like ECG, GSR, and EEG. Through a review of existing studies, the paper highlights the progress made in this area and the challenges that remain, particularly concerning data quality, algorithm accuracy, and the practical deployment of stress monitoring devices. The study underscores the potential for real-time monitoring and personalized interventions, especially in daily activities. Future research should focus on enhancing model robustness, integrating multiple data sources, and addressing real-world applicability. This paper emphasizes the importance of early stress identification and personalized treatment to improve mental health outcomes

关键词： Accuracy machine learning algorithms Reviews Soft sensors Human factors Mental health Transforms Speech recognition Real-time systems Strain

来源：评论

学校读者我要写书评

暂无评论

Band selection in Hyperspectral Images using information similarity ranking

Band selection in Hyperspectral Images using information sim...

引用

machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS), International Conference on

作者： T. Hitendra Sarma R. Dharma Reddy K. Mrudula K. Rammohan Rao Sankalp Dhondi Murali Kanthi Department of Information Technology Vasavi College of Engineering Hyderabad India Department of Mathematics GNITS Hyderabad India Machine Learning Engineer Brane Enterprises Pvt. Ltd. Hyderabad India Department of Data Science CMR Technical Campus Hyderabad India

ISBN: (数字)9798350389678

ISBN: (纸本)9798350389685

Band selection in hyperspectral images is crucial for reducing redundant features and improving subsequent tasks like classification and segmentation. This article presents a novel band ranking method based on information similarity measures from the concepts of information theory. We introduce the Normalized Mutual Information for similarity and Variation of Information for dissimilarity to create a ranking scheme based on the affinity of a band to become a cluster center. We explore hybrid schemes combining structural and informational attributes. Supervised approaches using label-wise similarities using ground truth supervision are also investigated. Evaluation against state-of-the-art band selection algorithms on popular datasets, using both traditional machine learning and Deep learning models, demonstrates superior results. This study underscores the significance of informational and structural attributes in unsupervised band selection and the potential for further enhancement through supervised similarity with ground truth information.

关键词： Deep learning Image segmentation machine learning algorithms Clustering algorithms Task analysis Mutual information machine intelligence

来源：评论

学校读者我要写书评

暂无评论

ECTSum: A New Benchmark dataset For Bullet Point Summarization of Long Earnings Call Transcripts

arXiv

引用

arXiv 2022年

作者： Mukherjee, Rajdeep Bohra, Abhinav Banerjee, Akash Sharma, Soumya Hegde, Manjunath Shaikh, Afreen Shrivastava, Shivani Dasgupta, Koustuv Ganguly, Niloy Ghosh, Saptarshi Goyal, Pawan Department of Computer Science and Engineering IIT Kharagpur India Goldman Sachs Data Science and Machine Learning Group India Leibniz University of Hannover Germany

Despite tremendous progress in automatic summarization, state-of-the-art methods are predominantly trained to excel in summarizing short newswire articles, or documents with strong layout biases such as scientific articles or govt reports. Efficient techniques to summarize financial documents, including facts and figures, have largely been unexplored, majorly due to the unavailability of suitable datasets. Here, we present ECTSum, a new dataset with transcripts of earnings calls (ECTs), hosted by public companies, as documents, and short experts-written telegram-style bullet point summaries derived from corresponding Reuters articles. ECTs are long unstructured documents without any prescribed length limit or format. We benchmark ECTSum with state-of-the-art summarizers across various metrics evaluating the content quality and factual consistency of the generated summaries. Finally, we present a simple-yet-effective approach, ECT-BPS, to generate a set of bullet points that precisely capture the important facts discussed in the calls. Copyright © 2022, The Authors. All rights reserved.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

Investigation of Red Fox Algorithm for Solving Task Assignment Problem in Heterogeneous Unmanned Aerial Vehicle Swarm

Investigation of Red Fox Algorithm for Solving Task Assignme...

引用

Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： K. Manaswitha Titus Issac Salaja Silas J Sebastian Terance Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

In recent years, there have been significant advancements in Unmanned Aerial Vehicles (UAVs), leading to their integration into everyday life. UAVs are constrained by limited energy, communication, and localization. The various challenges in UAV research include localization, routing, navigation, and task assignment. Recently, UAV swarms have been employed across a wide range of applications. This study focuses on solving task assignment problems within UAV swarms, considering the unique multi-objective resource constraints of UAVs. The work explores the feasibility of adopting the Red Fox Algorithm to address the task assignment problem in UAV swarms. The investigation examines two primary input parameters: Task Energy Demand and distance. The evaluation includes energy utilization and shortest distance as performance metrics. Notably, the proposed approach outperforms the Particle Swarm Optimization Algorithm (PSO), resulting in a 17.47% lower energy utilization while maintaining a 17.83% reduction in the shortest distance travelled. By leveraging the Red Fox Algorithm, the work aims to enhance the efficiency and effectiveness of task allocation in UAV swarms, contributing to their broader adoption across various applications.

关键词： Location awareness Measurement Navigation Autonomous aerial vehicles Routing Resource management Internet of Things

来源：评论

学校读者我要写书评

暂无评论

Review of machine learning and Deep learning algorithms for Personality traits classification

Review of Machine Learning and Deep Learning algorithms for ...

引用

Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Satish N Bhadane Prateek Verma Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

Nowadays, people deal with major psychological issues on a daily basis, often in the absence of a desired support system. In the field of psychology, focus is on the thoughts, feelings, behaviors, goals, and interests of individuals. The psychologist conventionally considers various types of personality traits, like the Big 5 personality traits, the Myers-Briggs Type Indicator (MBTI), the OCEAN Model (Openness, Conscientiousness, Agreeableness, Extraversion, Neuroticism), and other methods also considered for measuring personality. To forecast personality traits like extraversion, agreeableness, openness, conscientiousness, and neuroticism, consider the name "Big Five personality traits" It is a universal model to forecast these personality traits. Studies on personality prediction that identify personality disorders from user content on social media sites like Facebook, Twitter, and WhatsApp primarily rely on user behavior on these platforms. A number of popular Deep learning (DL) techniques have advanced recently, including Deep Belief Networks (DBNs), Convolution Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). Examine these methods in terms of single and multiple modalities, including psychological, audio, video, text, and image information. Researchers currently focus on predicting personality traits using single-modality text features, static facial images, audio, and video clips, including interview recordings and YouTube videos. Therefore, the current study highlights that personality prediction mostly relies on written text, audio and video datasets used in machine learning (ML) and DL models. Additional investigation is needed to resolve ethical concerns and justify the use of psychological datasets and DL models to evaluate personality traits. By combining personality traits with a psychological dataset, this study imagines a time when people everyday struggles can be effectively addressed. The integration of ML and DL in the development of p

关键词： Industries Deep learning Video on demand Recurrent neural networks Social networking (online) Reviews Psychology Sea measurements Predictive models Web sites

来源：评论

学校读者我要写书评

暂无评论

Smart Finance: An overview of Artificial Intelligence Integration in Fintech

Smart Finance: An overview of Artificial Intelligence Integr...

引用

Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Parth Dhananjay Akre Utkarsha Pacharaney Warsha Siraskar Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Yeshwantrao Chavan College of Engineering Nagpur Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The role of FinTech in advancing contemporary economies, societies, and technologies is growing. Smart FinTech is the next wave of FinTech that leverages artificial intelligence and data science methodologies to drive major advancements in fields including blockchain, cryptocurrencies, InsurTech, PayTech, BankingTech, TradeTech, LendTech, InsurTech, WealthTech, and RiskTech. An overview of these domains, as well as the artificial intelligence and data science techniques applied, is given in this paper. These techniques include augmentation, optimization, privacy-preserving processing, deep learning, federated learning, and intelligent interactions, complex system methods, mathematical approaches, knowledgeable communication, recognition and responses, and data analytics. This study offers a thorough analysis of smart financial companies, the obstacles they face, the smart FinTech environment, the artificial intelligence and data science methods that make smart FinTech possible, and potential future research areas for the artificial intelligence and data science community. To improve comprehension and analysis, the work makes use of visual aids such as figures and tables. The relevance of data quality, including data augmentation and manipulation, as well as enhancing fairness and decision-support, is emphasized in the study for financial firms. It also covers ethical and explicable FinTech, with a focus on privacy preservation, accountability, openness, and objectivity in FinTech and business designs. The potential for more research and innovation at the heart of analytics, data science, and FinTech is highlighted in the paper's conclusion.

关键词： Ethics Visualization Technological innovation Fintech Education Companies data science Regulation Risk management Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Application of the Semantic Segmentation Method in the Assessment of Forest Carbon Balance

Application of the Semantic Segmentation Method in the Asses...

引用

International Conference on Management of Large-Scale System Development (MLSD)

作者： Inna Tulokhonova Svetlana Mikhaylova Soelma Danilova Department of Software Engineering and Artificial Intelligence East Siberia State University of Technology and Management Ulan-Ude Russia Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia

This article focuses on the problem of human impact on the natural environment and its solution by machine learning methods. The concept of carbon balance is key in assessing climate change on the planet, which is especially important in connection with global warming. The main causes of global warming are associated with anthropogenic emissions of carbon dioxide as a result of agricultural production, land use and burning of fossil fuels. One of the approaches to mitigate this environmental problem is to estimate the carbon balance based on neural network semantic segmentation of forests of terrestrial ecosystems. The proposed convolutional neural network (CNN) model is based on the U-Net architecture. This architecture surpasses traditional machine learning methods in solving computer vision and pattern recognition problems, and is also quite easy to learn. The input data of the model is a set of satellite images of the forest area. The images were obtained using the Sentinel-2 satellite, which provides high spatial resolution and a large number of spectral channels. A software prototype has been developed to implement the CNN model. Training experiments and calculations on changes in the carbon balance in forests led to the conclusion that the results of the study will contribute to the balanced management of reforestation and carbon deposition.

关键词：

来源：评论

学校读者我要写书评

暂无评论

The Future of Driving: A Review on the Combination of Artificial Intelligence and Autonomous Vehicles

The Future of Driving: A Review on the Combination of Artifi...

引用

Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Nikita Zade Mayur Gawande Prateek Verma Swapnil Gundewar Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The convergence of Artificial Intelligence (AI) and Autonomous Vehicles (AV) are poised to revolutionize the automotive industry, offering safer, more efficient, and sustainable transportation solutions. This review article explores the current state of AVs, role of AI in AV, challenges and hurdles, Real world applications & development, its critical technological components, and their impact on society. This paper presents a wide array of applications of AI in AV, elaborating on the way complex sensor fusion, computer vision, and machine learning (ML) algorithms makes AVs perceive their surroundings, form critical opinions, and carry out accurate motions. AI is making real time interpretations of great quantities of data which are paving the way for safer and more effective navigation under a diversity of traffic circumstances, from sensor fusion to decision-making algorithms. The next section of this paper elaborates on the role of AI in the field of AV in perceiving, planning, and navigating through real world environment, while also talking about issues of integration and ethics. This paper further identifies its social, economic, and environmental consequences, resulting from mass use of AV. This article discusses the latest developments and opportunities in the regulatory environment, gives an overview of how AV define transportation in the near future. This paper also explores the levels of autonomy of AVs as defined by the Society of Automotive Engineers (SAE) and defines the process from driver assistance to full automation. It examines the ethical and technical considerations arising at each instant and emphasizes the key role AI continues to play in providing more autonomy while preserving security and dependability.

关键词： Ethics machine learning algorithms Reviews Navigation Decision making Sensor fusion Safety Artificial intelligence Socioeconomics Automotive engineering

来源：评论

学校读者我要写书评

暂无评论

GlobalBuildingMap — Unveiling the Mystery of Global Buildings

arXiv

引用

arXiv 2024年

作者： Zhu, Xiao Xiang Li, Qingyu Shi, Yilei Wang, Yuanyuan Stewart, Adam J. Prexl, Jonathan Chair of Data Science in Earth Observation Technical University of Munich Arcisstraße 21 Munich80333 Germany Munich Center for Machine Learning Munich80333 Germany School of Engineering and Design Technical University of Munich Arcisstraße 21 Munich80333 Germany

Understanding how buildings are distributed globally is crucial to revealing the human footprint on our home planet. This built environment affects local climate, land surface albedo, resource distribution, and many other key factors that influence well-being and human health. Despite this, quantitative and comprehensive data on the distribution and properties of buildings worldwide is lacking. To this end, by using a big data analytics approach and nearly 800,000 satellite images, we generated the highest resolution and highest accuracy building map ever created: the GlobalBuildingMap (GBM). A joint analysis of building maps and solar potentials indicates that rooftop solar energy can supply the global energy consumption need at a reasonable cost. Specifically, if solar panels were placed on the roofs of all buildings, they could supply 1.1–3.3 times — depending on the efficiency of the solar device — the global energy consumption in 2020, which is the year with the highest consumption on record. We also identified a clear geospatial correlation between building areas and key socioeconomic variables, which indicates our global building map can serve as an important input to modeling global socioeconomic needs and drivers. © 2024, CC BY-NC-ND.

关键词： data Analytics

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：