检索结果-内蒙古大学图书馆

computing and Data Analytics (ICCDA), International Conference on

作者： Suresh Palarimath Pyingkodi Maran Wilfred Blessing N. R S. J Kavitha Cibi. A Subitha G. Sutherlin College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Applications Kongu Engineering College Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai India Data & Analytics / Visualization - Delivery Virtusa Consulting Services Private Limited Chennai India Department of Computer Science and Engineering Stella Mary's College of Engineering Kanyakumari India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

The growing occurrence of cybersecurity hazards, such as Android zero-day vulnerabilities, poses substantial difficulties because they are unattended and imperceptible. With the increasing popularity of Android smartphones, hackers are taking advantage of these weaknesses to quickly spread advanced malware. Conventional detection approaches frequently prove inadequate due to the absence of shown criteria for emergent threats. In order to address this crucial problem, this article presents the Zero-Vuln method, an advanced technique specifically developed to identify and categorize previously unidentified malware on Android platforms. Zero-Vuln combines sophisticated supervised deep learning methods with zero-shot learning algorithms to accurately detect zero-day threats. Through the utilization of comprehensive datasets, the method attains an average classification metric score of 84.22%, showcasing exceptional accuracy, precision, and recall. This advanced solution significantly improves the ability to identify and handle Android zero-day threats, representing a major progress in cybersecurity research and implementation. The Zero-Vuln strategy provides a strong and adaptable solution for the constantly changing mobile security risks, representing a notable advancement in the sector.

关键词： Measurement Instruments Zero shot learning Malware Mobile security Hazards Reliability Protection Smart phones Research and development

来源：评论

学校读者我要写书评

暂无评论

Text Clustering on CCSI System using Canopy and K-Means Algorithm

Text Clustering on CCSI System using Canopy and K-Means Algo...

引用

International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： D. Akila S. R. Raja Jeyalaksshmi S M. Revathi Farzeen Ashfaq A. A. Khan Department of Computer Applications Saveetha College of Liberal Arts & Sciences SIMATS Deemed University Chennai India Centre for Open and Digital Education Hindustan Institute of Tech. & Science Chennai India Department of IT Vels Institute of Science Technology and Advanced Studies Chennai India Department of Computer Science Meas Institute of IT Chennai India School of Computer Science Taylor's University Subang Jaya Malaysia FIT Negara Brunei Darussalam

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Text mining programs often offer numerous options for processing textual data. The inclusion of extensive meta-data-such as links to other websites, titles, authors, and publication dates-can enrich text analysis and aid in text grouping. However, this side information, or “metadata,” can also introduce noise if not handled correctly. When used indiscriminately for clustering, this noise can degrade the quality of the resulting clusters. To address this challenge, we propose a methodology that leverages a Feature Selection procedure to identify and utilize metadata that enhances clustering effectiveness. The methodology, known as Co-Clustering with Side Information (C-CSI), is designed to maximize the benefits of relevant metadata while mitigating the effects of irrelevant or noisy data. C-CSI is implemented using datamining technologies, specifically focusing on Co-Clustering, which involves clustering both rows and columns of a matrix. In our study, we employed the canopy and k-means algorithms to perform the clustering tasks. This approach ensures that the metadata used in clustering is both relevant and beneficial, improving the overall quality of the clustering results

关键词： Text mining Text analysis Noise Clustering algorithms Metadata Prediction algorithms Market research Noise measurement Indexes Standards

来源：评论

学校读者我要写书评

暂无评论

AI Literacy: Finding Common Threads between Education, Design, Policy, and Explainability

AI Literacy: Finding Common Threads between Education, Desig...

引用

Extended Abstracts of the 2023 CHI Conference on Human Factors in computing Systems, CHI EA 2023

作者： Long, Duri Roberts, Jessica Magerko, Brian Holstein, Kenneth Dipaola, Daniella Martin, Fred Department of Communication Studies Northwestern University United States School of Interactive Computing Georgia Tech United States School of Literature Media and Communication Georgia Tech United States Human-Computer Interaction Institute Carnegie Mellon University United States Personal Robots Group Mit Media Lab United States Computer Science University of Massachusetts Lowell United States

ISBN: (纸本)9781450394222

Fostering public AI literacy has been a growing area of interest at CHI for several years, and a substantial community is forming around issues such as teaching children how to build and program AI systems, designing learning experiences to broaden public understanding of AI, developing explainable AI systems, understanding how novices make sense of AI, and exploring the relationship between public policy, ethics, and AI literacy. Previous workshops related to AI literacy have been held at other conferences (e.g., SIGCSE, AAAI) that have been mostly focused on bringing together researchers and educators interested in AI education in K-12 classroom environments, an important subfield of this area. Our workshop seeks to cast a wider net that encompasses both HCI research related to introducing AI in K-12 education and also HCI research that is concerned with issues of AI literacy more broadly, including adult education, interactions with AI in the workplace, understanding how users make sense of and learn about AI systems, research on developing explainable AI (XAI) for non-expert users, and public policy issues related to AI literacy. © 2023 Owner/Author.

关键词： Public policy

来源：评论

学校读者我要写书评

暂无评论

Leveraging Deep Learning Algorithms for Improved Detection and Mitigation of Market Volatility

Leveraging Deep Learning Algorithms for Improved Detection a...

引用

International Conference on computing and Networking Technology (ICCNT)

作者： V. Vishwa Priya K Jose Reena K Umamageswari B. Gayathri P Tamil Selvi Department of Computer Science Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai India Department of Computer Science with Artificial Intelligence SDNB Vaishnav College for Women (Autonomous) Chrompet Chennai Tamil Nadu India Valliammal College For Women Anna Nagar East Chennai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The field of finance faces significant challenges in predicting and managing market volatility, given its unpredictable nature and potential for significant negative impacts on financial institutions and the economy as a whole. Traditional methods for detecting and mitigating market volatility, such as statistical models and expert judgment, often fall short in accurately predicting and responding to market changes. Leveraging deep learning algorithms offers a promising solution for improving the detection and mitigation of market volatility. These advanced artificial intelligence techniques are able to integrate a vast amount of data and complex non-linear relationships to make more accurate predictions and identify patterns in market behavior. By utilizing deep learning algorithms, financial institutions can make more informed and timely decisions to protect against market volatility and minimize its impact on their business. This can lead to more efficient and stable financial markets, benefiting both individual investors and the overall economy.

关键词： Deep learning Accuracy Prevention and mitigation Heuristic algorithms Biological system modeling Predictive models Prediction algorithms Reliability Portfolios Investment

来源：评论

学校读者我要写书评

暂无评论

Editorial:Special Topic on Federated Learning for IoT and Edge computing

引用

ZTE Communications 2022年第3期20卷 1-2页

作者： PAN Yi CUI Laizhong CAI Zhipeng LI Wei Faculty of Computer Science and Control Engineering at Shenzhen Institute of Advanced Technology Chinese Academy of SciencesChina the Academy of the United Nations Sciences and Technology Organization Ukrainian Academy of Engineering Sciences American Institute for Medical and Biological Engineering Institute of Engineering Technology the College of Computer Science and Software Engineering at Shenzhen University China Department of Computer Science and the College of Business at Georgia State University USA the Center for Distributed and High Performance Computing School of Computer ScienceThe University of Sydney the Australia-China Joint Research Center on Energy Informatics and Demand Response Technologies

Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of *** is challenging and infeasible to transfer an... 详细信息

关键词： cloud generating data.

来源：评论

学校读者我要写书评

暂无评论

CNN-OSBO Encoder-Decoder Architecture for Drug-Target Interaction (DTI) Prediction of Covid-19 Targets

CNN-OSBO Encoder-Decoder Architecture for Drug-Target Intera...

引用

International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： K. Nandhini G. Thailambal Department of Computer Science Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai Tamil Nadu India

ISBN: (纸本)9781665482721

Drug Target Interaction (DTI) prediction is an important factor is drug discovery and repositioning (DDR) since it detects the response of a drug over a target protein. The Coronavirus disease 2019 (COVID-19) disease created groups of deadly pneumonia with clinical appearance mostly similar to SARS-CoV. The precise diagnosis of COVID-19 clinical outcome is more challenging, since the diseases has various forms with varying structures. So predicting the interactions between various drugs with the SARS-CoV target protein is very crucial need in these days, which may leads to discovery of new drugs for the deadly disease. Recently, Deep learning (DL) techniques have been applied by the researches for DTI prediction. Since CNN is one of the major DL models which has the ability to create predictive feature vectors or embeddings, CNN-OSBO encoder-decoder architecture for DTI prediction of Covid-19 targets has been designed Given the input drug and Covid-19 target pair of data, they are fed into the Convolution Neural Networks (CNN) with Opposition based Satin Bowerbird Optimizer (OSBO) encoder modules, separately. Here OSBO is utilized for regulating the hyper parameters (HPs) of CNN layers. Both the encoded data are then embedded to create a binding module. Finally the CNN Decoder module predicts the interaction of drugs over the Covid-19 targets by returning an affinity or interaction score. Experimental results state that DTI prediction using CNN+OSBO achieves better accuracy results when compared with the existing techniques.

关键词： COVID-19 Drugs Proteins Pulmonary diseases Neural networks Predictive models Prediction algorithms

来源：评论

学校读者我要写书评

暂无评论

Geological Time Series Analysis for Spatial Seismic Forecasting in Japan

Geological Time Series Analysis for Spatial Seismic Forecast...

引用

Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Prerit Sharma Bhawna Saxena Rhythm Rajat Khandelwal Adwitiya Sinha Department of Computer Science and Engineering & Information Technology Jaypee Institute of Information Technology Noida Uttar Pradesh India Natural & Applied Sciences TERI School of Advanced Studies Delhi India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

Earthquakes pose significant threats to lives and infrastructure worldwide, underscoring the critical need for effective prediction models. Our study represents a meticulous examination of seismic data sourced from the United States Geological Survey, encompassing earthquake occurrences in Japan over five years. Employing a refined zoning approach based on historical seismicity, we tailored our analysis to account for regional variations in earthquake frequency and intensity. By integrating these discoveries into the ARIMA model, we aim to fortify earthquake prediction frameworks, thereby fostering more effective disaster mitigation strategies on a global scale. Furthermore, our analysis using SMA, EMA, and ARIMA models demonstrated that the EMA model provided superior performance. Leveraging the predictive capabilities of the EMA model, we identified twelve geographic coordinates in Japan with a higher likelihood of future earthquakes. This study underscores the potential of EMA in earthquake prediction and provides a foundational approach for integrating machine learning with geospatial analysis for natural disaster forecasting.

关键词： Surveys Analytical models Geology Disasters Earthquakes Time series analysis Machine learning Predictive models Data models Forecasting

来源：评论

学校读者我要写书评

暂无评论

IoT based Monitoring of Mushroom

IoT based Monitoring of Mushroom

引用

Sustainable computing and Data Communication Systems (ICSCDS), International Conference on

作者： J. Lysa Eben Chamandeep Kaur Mathews T Thelly Parimita Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Chennai India Department of Computer Science and Information Technology Jazan University Jazan Saudi Arabia Department of Botany Kuriakose Elias College Mannanam India Agricultural Engineering Sam Higginbottom University of Agriculture Technology & Sciences Prayagraj India

Most of India’s population relies on farming for their livelihood. Every year brings new advances in agricultural technology. The use of wireless sensors in contemporary agriculture is crucial. The employment of wireless sensors across a wide range of agricultural applications has a profoundly favourable effect on crop productivity and economic efficiency. The mushroom business is still relatively young and modest compared to other sectors of India’s agricultural market. The white button mushroom is India’s most commercially significant fungus and is a global favourite. It may be grown anywhere with a suitable climate and soil, but most of the world’s supply comes from North India in the winter. For mycelia to flourish, the ideal temperature range is between 22 and 25 degrees Celsius. In contrast, the sweet spot for developing fruit bodies is between 14 and 18 degrees Celsius with high relative humidity. Equipment for maintaining a steady temperature and pasteurising the mushrooms, should be present in the rooms used for growing. The primary objective of this study is to automate the mushroom production plant and keep track of the crop room’s environmental state to reduce the amount of human care required for the mushroom plant. The Internet of Things has also modernized agriculture industries, creating an environmental monitoring and regulating system that is used in the mushroom farm in this work. Using the Speak internet platform, users may monitor ecological conditions in a mushroom farm on their Android mobile, including the ambient temperature, relative humidity, carbon dioxide level, and light intensity. The Arduino prototype platform is employed as the sensors station’s controller, ensuring that the plant parameters are within the user-specified range. Using a pair of low-power ESP8266 as Wi-Fi modems, the current parameter status is relayed to a distant monitoring station. The integrated Arduino uses the programming environment to write the controller’s cod

关键词： Temperature measurement Temperature sensors Wireless communication Temperature distribution Wireless sensor networks Soil moisture Crops

来源：评论

学校读者我要写书评

暂无评论

An Effective Diagnosis of Alzheimer’s Disease with the Use of Deep Learning based CNN Model

An Effective Diagnosis of Alzheimer’s Disease with the Use ...

引用

International Conference on ent computing and Control Systems (ICICCS)

作者： K C Sharmili G P Suja E. Pandian Md. Abul Ala Walid Sripriya Arunachalam G.Charles Babu Computer Science Stella Maris College 17 Cathedral Road Chennai Computer science Muslim Arts College Thiruvithancode Master of Computer Application Sri Muthukumaran Institute of Technology Mangadu Chennai Department of Computer Science and Engineering Khulna University of Engineering & Technology (KUET) Khulna Computer Applications-PG School of Computing Sciences VELS Institute of Science Technology & Advanced Studies - VISTAS Velan Nagar P.V. Vaithiyalingam Road Pallavaram Chennai Department of Computer Science and Engineering GRIET Hyderabad

In recent years, Alzheimer’s disease has become a major health concern. Over 45 million individuals worldwide suffer from this illness. Alzheimer’s disease is a neurodegenerative illness of unidentified etiology and process that causes cognitive deterioration, and it mostly affects the elderly. Dementia, the gradual and irreversible loss of brain cells, is the leading cause of Alzheimer’s disease. Those with this illness lost the ability to think, read, and do other basic tasks. A machine learning system may aid in this situation by making accurate predictions about the onset of illness. Dementia diagnosis in a broad patient population is the major objective. This research presents the findings and analysis of many Machine Learning models for identifying dementia. To alleviate this obstacle and help in the diagnosis of AD will create a deep-learning architecture that uses stacked auto-encoders and a softmax output layer. The proposed technique can analyze numerous classes in a single setting, unlike earlier procedures, and only needs a small number of labeled training samples and basic domain expertise to get started. In the present studies, a substantial improvement in performance when it came to classifying all diagnostic subgroups. Using the proposed technique can run these time series through a Convolutional Neural Network (CNN) and ResNet50 model to extract the distinguishing patterns across stages. With an F1-score of 0.99 and an accuracy of 99.91 percent, the CNN-based technique outperformed the standard feature-based classifiers by a significant margin.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Sound Tagging in Infant-centric Home Soundscapes

arXiv

引用

arXiv 2024年

作者： Khan, Mohammad Nur Hossain Li, Jialu McElwain, Nancy L. Hasegawa-Johnson, Mark Islam, Bashima Department of Electrical and Computer Engineering Worcester Polytechnic Institute United States Department of Electrical and Computer Engineering University of Illinois United States Department of Human Development and Family Studies University of Illinois United States Beckman Institute for Advanced Science and Technology University of Illinois United States

Certain environmental noises have been associated with negative developmental outcomes for infants and young children. Though classifying or tagging sound events in a domestic environment is an active research area, previous studies focused on data collected from a non-stationary microphone placed in the environment or from the perspective of adults. Further, many of these works ignore infants or young children in the environment or have data collected from only a single family where noise from the fixed sound source can be moderate at the infant’s position or vice versa. Thus, despite the recent success of large pretrained models for noise event detection, the performance of these models on infant-centric noise soundscapes in the home is yet to be explored. To bridge this gap, we have collected and labeled noises in home soundscapes from 22 families in an unobtrusive manner, where the data are collected through an infant-worn recording device. In this paper, we explore the performance of a large pre-trained model (Audio Spectrogram Transformer [AST]) on our noise-conditioned infant-centric environmental data as well as publicly available home environmental datasets. Utilizing different training strategies such as resampling, utilizing public datasets, mixing public and infant-centric training sets, and data augmentation using noise and masking, we evaluate the performance of a large pre-trained model on sparse and imbalanced infant-centric data. Our results show that fine-tuning the large pre-trained model by combining our collected dataset with public datasets increases the F1-score from 0.11 (public datasets) and 0.76 (collected datasets) to 0.84 (combined datasets) and Cohen’s Kappa from 0.013 (public datasets) and 0.77 (collected datasets) to 0.83 (combined datasets) compared to only training with public or collected datasets, respectively. © 2024, CC BY.

关键词： Large datasets

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：