检索结果-内蒙古大学图书馆

14th International Conference on COMmunication Systems and NETworkS, COMSNETS 2022

作者： Ghoshal, Arnab Kumar Das, Nabanita Das, Soham Asutosh College Department of Computer Science Kolkata India Indian Statistical Institute Advanced Computing and Microelectronics Unit Kolkata India Microsoft Corporation RedmondWA United States

ISBN: (纸本)9781665421041

Many real-life networks such as social networks, biological networks, citation networks etc., are temporally evolving by nature. In general, the evolution takes place gradually with time. But occasionally, the nodes may exhibit anomalous behavior that is to be detected and reported in real time. In this work, for anomaly detection in evolutionary networks, an action based approach is followed taking into account the network topology. The network is represented as a temporal stream of weighted graphs, where edge weights stand for frequency or power of actions. A new definition of anomaly score is introduced for nodes based on its action history. To reduce the search space, the community structure of the graph is exploited. It has been shown that only the nodes having multiple community membership are enough to detect anomaly in a temporally evolving network with overlapping community structure. To the best of our knowledge, this is the first work to exploit the overlapping community structure of an evolutionary network to identify the graph snapshots containing anomalous events. Experimental studies on both synthetic and real-world evolutionary networks show that the proposed technique achieves almost 16% improvement in F-Score compared to the state-of-the-art algorithms. Moreover, parallel implementation of the proposed technique results nearly 10× speedup making it suitable for real time computation of anomaly in evolutionary networks. © 2022 IEEE.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

CrypToth: Cryptic Pocket Detection through Mixed-Solvent Molecular Dynamics Simulations-Based Topological Data Analysis

引用

Journal of Chemical Information and Modeling 2025年第11期65卷 5567-5575页

作者： Koseki, Jun Motono, Chie Yanagisawa, Keisuke Kudo, Genki Yoshino, Ryunosuke Hirokawa, Takatsugu Imai, Kenichiro Cellular and Molecular Biotechnology Research Institute National Institute of Advanced Industrial Science and Technology (AIST) Tokyo 135-0064 Japan Integrated Research Center for Self-Care Technology (irc-sct) National Institute of Advanced Industrial Science and Technology (AIST) Tokyo 135-0064 Japan Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL) National Institute of Advanced Industrial Science and Technology (AIST) Waseda University Shinjuku-ku Tokyo 169-8555 Japan Department of Computer Science School of Computing Institute of Science Tokyo Tokyo 152-8550 Japan Middle Molecule IT-based Drug Discovery Laboratory (MIDL) Institute of Science Tokyo Tokyo 152-8550 Japan Physics Department Graduate School of Pure and Applied Sciences University of Tsukuba Ibaraki 305-8571 Japan Division of Biomedical Science Faculty of Medicine University of Tsukuba Ibaraki 305-8575 Japan Transborder Medical Research Center University of Tsukuba Ibaraki 305-8577 Japan Global Research and Development Center for Business By Quantum-AI Technology (G-QuAT) National Institute of Advanced Industrial Science and Technology (AIST) Ibaraki 305-8560 Japan

Some functional proteins undergo conformational changes to expose hidden binding sites when a binding molecule approaches their surface. Such binding sites are called cryptic sites and are important targets for drug discovery. However, it is still difficult to correctly predict cryptic sites. Therefore, we introduce an advanced method, CrypToth, for the precise identification of cryptic sites utilizing the topological data analysis such as persistent homology method. This method integrates topological data analysis and mixed-solvent molecular dynamics (MSMD) simulations. To identify hotspots corresponding to cryptic sites, we conducted MSMD simulations using six probes with different chemical properties: dimethyl ether, benzene, phenol, methyl imidazole, acetonitrile, and ethylene glycol. Subsequently, we applied our topological data analysis method to rank hotspots based on the possibility of harboring cryptic sites. Evaluation of CrypToth using nine target proteins containing well-defined cryptic sites revealed its superior performance compared with recent machine-learning methods. As a result, in seven of nine cases, hotspots associated with cryptic sites were ranked the highest. CrypToth can explore hotspots on the protein surface favorable to ligand binding using MSMD simulations with six different probes and then identify hotspots corresponding to cryptic sites by assessing the protein’s conformational variability using the topological data analysis. This synergistic approach facilitates the prediction of cryptic sites with a high accuracy. © 2025 The Authors. Published by American Chemical Society.

关键词： Binding sites

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence-based detection system for hazardous liquid metal fire 8

Artificial intelligence-based detection system for hazardous...

引用

8th International Conference on computing for Sustainable Global Development, INDIACom 2021

作者： Sankarasubramanian, Praveen Ganesh, E.N. Vels Institute of Science Technology Advanced Studies Department of Computer Science and Engineering Chennai India School of Engineering Vels Institute of Science Technology Advanced Studies Chennai India

ISBN: (纸本)9789380544434

Liquid metals are commonly used in chemical industries and nuclear reactors. Since liquid metals may be hazardous, they should be handled very carefully. Careless handling might cause an adverse effect and even disasters. Corrosion and pressure can deteriorate the structure that handles the liquid metals. Leakage of liquid metals can result in ecological disasters and can lead to a humanitarian crisis. Early warning systems, detection of the accident, and prompt steps taken after the incident are the three important phases of monitoring. Continuous monitoring and timely detection of risk reduce the impact caused by the leakage of liquid metal. At present, industries have sensors-based detection. This paper proposes an enhanced version of the existing system. Here, continuous monitoring uses sensors, the Internet of things (IoT), and an artificial intelligence-based system. In this paper, the conventional system is integrated with AI to identify indoor and open-air fire situations. This paper discusses different data collected and investigated data from the videos, sensors, other monitoring systems. And the false-positive results are reduced by using the proposed methodology. © 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth.

关键词： Accident prevention

来源：评论

学校读者我要写书评

暂无评论

Compiling a Highly Accurate Bilingual Lexicon by Combining Different Approaches

Compiling a Highly Accurate Bilingual Lexicon by Combining D...

引用

2022 Globalex Workshop on Linked Lexicography, GWLL 2022

作者： Steingrímsson, Steinþór O’Brien, Luke Ingimundarson, Finnur Loftsson, Hrafn Way, Andy Department of Computer Science Reykjavik University Iceland The Árni Magnússon Institute for Icelandic Studies Iceland ADAPT Centre School of Computing Dublin City University Ireland

ISBN: (纸本)9791095546924

Bilingual lexicons can be generated automatically using a wide variety of approaches. We perform a rigorous manual evaluation of four different methods: word alignments on different types of bilingual data, pivoting, machine translation and cross-lingual word embeddings. We investigate how the different setups perform using publicly available data for the English-Icelandic language pair, doing separate evaluations for each method, dataset and confidence class where it can be calculated. The results are validated by human experts, working with a random sample from all our experiments. By combining the most promising approaches and data sets, using confidence scores calculated from the data and the results of manually evaluating samples from our manual evaluation as indicators, we are able to induce lists of translations with a very high acceptance rate. We show how multiple different combinations generate lists with well over 90% acceptance rate, substantially exceeding the results for each individual approach, while still generating reasonably large candidate lists. All manually evaluated equivalence pairs are published in a new lexicon of over 232,000 pairs under an open license. © European Language Resources Association (ELRA)

关键词： Machine translation

来源：评论

学校读者我要写书评

暂无评论

A Comparison of COVID-19 Detection using Deep Learning Methods

A Comparison of COVID-19 Detection using Deep Learning Metho...

引用

Electronics and Renewable Systems (ICEARS), International Conference on

作者： S. Pradeep Kumar Suganiya Murugan B. Rubini Electrical and Electronics Engineering Vels Institute of Science Technology and Advanced Studies Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

The recognition of covid-19 is major confront in today’s world, specified as sudden increase in spreading of the disease. Hence, identifying this infection in earlier phase facilitates medicinal fields such as doctors, nurses and lab reporters. This article introduces a novel deep learning technique especially Convolutional Neural Network (CNN) by analyzing features in chest input images. Moreover, this proposed Convolutional Neural Network detects the covid-19 disease under several layers and finally performs binary classification that categorizes input images into covid 19 and non-covid patients. Finally, comparisons had made among all models to predict which model diagnose the disease accurately. To evaluate the overall model performance in detection and classification of covid disease, metrics criterias precision, recall and F1-score are evaluated. Validation analysis were completed for quantify the outcomes via performance measures for each model. This proposed comparison attains maximum accuracy of 100% along with least loss as 0.04 that might diminish human inaccuracy in identification procedure.

关键词： COVID-19 Deep learning Training Renewable energy sources Neural networks Predictive models Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

advanced BERT and CNN-Based Computational Model for Phishing Detection in Enterprise Systems

引用

computer Modeling in Engineering & sciences 2024年第12期141卷 2165-2183页

作者： Brij B.Gupta Akshat Gaurav Varsha Arya Razaz Waheeb Attar Shavi Bansal Ahmed Alhomoud Kwok Tai Chui Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Computer Engineering Ronin InstituteMontclairNJ 07043USA Department of Business Administration Asia UniversityTaichung413Taiwan Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon College of Business Administration Management DepartmentPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Research and Innovation Insights2TechinfoJaipur302001India Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Electronic Engineering and Computer Science Hong Kong Metropolitan University(HKMU)Hong Kong518031China

Phishing attacks present a serious threat to enterprise systems,requiring advanced detection techniques to protect sensitive *** study introduces a phishing email detection framework that combines Bidirectional Encoder Representations from Transformers(BERT)for feature extraction and CNN for classification,specifically designed for enterprise information ***’s linguistic capabilities are used to extract key features from email content,which are then processed by a convolutional neural network(CNN)model optimized for phishing *** an accuracy of 97.5%,our proposed model demonstrates strong proficiency in identifying phishing *** approach represents a significant advancement in applying deep learning to cybersecurity,setting a new benchmark for email security by effectively addressing the increasing complexity of phishing attacks.

关键词： Phishing BERT convolutional neural networks email security deep learning

来源：评论

学校读者我要写书评

暂无评论

Tumor Infiltration of Microrobot using Magnetic torque and AI Technique 2

Tumor Infiltration of Microrobot using Magnetic torque and A...

引用

2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Shalini, R. Mishra, Laxmi Athulya, S. Chimankar, Abhijeet Gajanan Kandavalli, Sunanda Ratna. Keshav Kumar, K. Selvan, R. Senthamil School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Department of BCA & IT Chennai India Khwaja Moinuddin Chishti Language University Department of Electronics and Communication Engineering Uttar Pradesh India Christ Deemed to Be University Faculty of Engineering Department of Computer Science and Engineering Bangalore India Chatrapati Shivaji BCA and BCS College Department of B.Sc. Computer Science Maharashtra India Sri Padmavati Mahila Visva Vidyalayam Department of Electronics and Communication Engineering Tirupati India G. Narayanamma Institute of Technology and Science Department of Humanities and Mathematics Hyderabad India Annamacharya Institute of Technology & Sciences Department of Electronics and Communication Engineering Tirupati India

ISBN: (纸本)9798350347982

Because of their surroundings and lifestyle alternatives, human beings, these days be afflicted by a huge style of illnesses. thus, early contamination prediction will become crucial. on the other hand, primarily based just on signs, docs warfare to make correct forecasts. The most challenging issue is accurately forecasting illnesses, which is why machine learning is essential to accomplish this task. To identify concealed patterns within vast amounts of medical data, disease information is processed using data mining techniques. We evolved a extensive contamination prediction primarily based on the affected person's signs. We rent the device getting to know techniques Convolutional Neural network (CNN) and ANFIS to exactly count on sickness (adaptive community-based totally fuzzy inference machine). For an correct forecast, this trendy illness prediction considers the character's way of life picks and fitness history. ANFIS outperforms CNN's set of rules in phrases of popular infection prediction, with an accuracy price of 96.7%. additionally, CNN consumes extra memory and processing energy than ANFIS because it trains and assessments on facts from the UCI repository. The Anaconda notebook is a suitable tool for implementing Python programming as it contains a range of libraries and header files that enhance the accuracy and precision of the process. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Approximate k-Nearest Neighbor Query over Spatial Data Federation 28th

Approximate k-Nearest Neighbor Query over Spatial Data Fede...

引用

28th International Conference on Database Systems for advanced Applications, DASFAA 2023

作者： Zhang, Kaining Tong, Yongxin Shi, Yexuan Zeng, Yuxiang Xu, Yi Chen, Lei Zhou, Zimu Xu, Ke Lv, Weifeng Zheng, Zhiming State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing School of Computer Science and Engineering and Institute of Artificial Intelligence Beihang University Beijing China Department of Computer Science and Engineering HKUST Hong Kong Guangzhou China City University of Hong Kong Hong Kong

ISBN: (纸本)9783031306365

Approximate nearest neighbor query is a fundamental spatial query widely applied in many real-world applications. In the big data era, there is an increasing demand to scale these queries over a spatial data federation, which consists of multiple data owners, each holding a private, disjoint partition of the entire spatial dataset. However, it is non-trivial to enable approximate k-nearest neighbor query over a spatial data federation. This is because stringent security constraints are often imposed to protect the sensitive, privately owned data partitions, whereas naively extending prior secure query processing solutions leads to high inefficiency (e.g., 100 s per query). In this paper, we propose two novel algorithms for efficient and secure approximate k-nearest neighbor query over a spatial data federation. We theoretically analyze their communication cost and time complexity, and further prove their security guarantees and approximation bounds. Extensive experiments show that our algorithms outperform the state-of-the-art solutions with respect to the query efficiency and often yield a higher accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Nearest neighbor search

来源：评论

学校读者我要写书评

暂无评论

Detecting and Monitoring Bias for Subgroups in Breast Cancer Detection AI

arXiv

引用

arXiv 2025年

作者： Kundu, Amit Kumar Doo, Florence X. Patil, Vaishnavi Varshney, Amitabh Jaja, Joseph Department of Electrical and Computer Engineering University of Maryland United States Department of Diagnostic Radiology and Nuclear Medicine University of Maryland School of Medicine United States Department of Computer Science University of Maryland United States Institute for Health Computing University of Maryland United States University of Maryland Institute for Advanced Computer Studies United States

Automated mammography screening plays an important role in early breast cancer detection. However, current machine learning models, developed on some training datasets, may exhibit performance degradation and bias when deployed in real-world settings. In this paper, we analyze the performance of high-performing AI models on two mammography datasets—the Emory Breast Imaging Dataset (EMBED) and the RSNA 2022 challenge dataset. Specifically, we evaluate how these models perform across different subgroups, defined by six attributes, to detect potential biases using a range of classification metrics. Our analysis identifies certain subgroups that demonstrate notable underperformance, highlighting the need for ongoing monitoring of these subgroups’ performance. To address this, we adopt a monitoring method designed to detect performance drifts over time. Upon identifying a drift, this method issues an alert, which can enable timely interventions. This approach not only provides a tool for tracking the performance but also helps ensure that AI models continue to perform effectively across diverse populations. © 2025, CC BY.

关键词： Mammography

来源：评论

学校读者我要写书评

暂无评论

Optimized Hybrid Model Using Machine Learning to Combat the Prevalence of Cybercrime

Optimized Hybrid Model Using Machine Learning to Combat the ...

引用

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

作者： Gomathy M A. Vidhya School of Computing Sciences Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Tamil Nadu India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Tamil Nadu India

In this contemporary era, internet has become the mode of communication and has changed the life style of individuals with advancements in technology. Modern electronic gadgets made us more dependent on it directly or indirectly for most of people regardless of their age. The tremendous attain of the internet, the speedy unfold of mobile data, and consequently the extensive use of online forums has paved the way for online Crime. Cybercrime and victimization of female gender are excessive and it poses a serious threat to the safety of an individual as an entire. Smart devices with web connections and social media platforms support to explore huge information including objectionable materials. These offensive contents include pornography, cyberbullying, sexual abuse and threats, ferocity, gambling endanger more among ladies and youngsters on the net. These unsolicited information's has an impact to increases the vulnerability of crime against women and among younger age group children including adolescents. Cyber parental control is a method that affords a safe environment in the internet world for individual in particular women and youngsters to work in a controlled atmosphere for accessing internet with a healthy soul and mind. Considering the above context, the objective of this systematic review is two-fold, (1) To explore and understand various women related issues due to use of virtual platform, and (2) To analyze and propose an effective system for cyber parental control, the probable digital solution to reduce the vulnerability against cybercrime in specific among the feminine victim. The proposed model seeks to improve parental control by blocking offensive content. Despite the fact that pop-up messages may contain audio, video, or text, the suggested model can only handle text data. Future developments of the work could include handling multimedia data of various sizes and the ability to categorize the material. Furthermore, neural network-based algorithms

关键词：

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：