检索结果-内蒙古大学图书馆

12th International Workshop Modelling and Reasoning in Context, MRC 2021

作者： Kutt, Krzysztof Zuchowska, Laura Bobek, Szymon Nalepa, Grzegorz J. Institute of Applied Computer Science Jagiellonian University Kraków Poland Department of Applied Computer Science AGH University of Science and Technology Kraków Poland

The paper provides insights into two main threads of analysis of the BIRAFFE2 dataset concerning the associations between personality and physiological signals and concerning the game logs' generation and processing. Alongside the presentation of results, we propose the generation of event-marked maps as an important step in the exploratory analysis of game data. The paper concludes with a set of guidelines for using games as a context-rich experimental environment. © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词：

来源：评论

学校读者我要写书评

暂无评论

Bartle taxonomy-based game for affective and personality computing research 12

Bartle taxonomy-based game for affective and personality com...

引用

12th International Workshop Modelling and Reasoning in Context, MRC 2021

作者： Żuchowska, Laura Kutt, Krzysztof Nalepa, Grzegorz J. Department of Applied Computer Science AGH University of Science and Technology Kraków Poland Institute of Applied Computer Science Jagiellonian University Kraków Poland

The paper presents the design of a game that will serve as a research environment in the BIRAFFE series experiment planned for autumn 2021, which uses affective and personality computing methods to develop methods for interacting with intelligent assistants. A key aspect is grounding the game design on the taxonomy of player types designed by Bartle. This will allow for an investigation of hypotheses concerning the characteristics of particular types of players or their stability in response to emotionally-charged stimuli occurring during the game. © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Taxonomies

来源：评论

学校读者我要写书评

暂无评论

Detecting Spam Email with Machine Learning Optimized with Harris Hawks optimizer (HHO) Algorithm

Detecting Spam Email with Machine Learning Optimized with Ha...

引用

13th International Conference on Ambient Systems, Networks and Technologies, ANT 2022 / 5th International Conference on Emerging Data and Industry 4.0, EDI40 2022

作者： Mashaleh, Ashraf S. Binti Ibrahim, Noor Farizah Al-Betar, Mohammed Azmi Mustafa, Hossam M.J. Yaseen, Qussai M. School of Computer Sciences Universiti Sains Malaysia Penang Malaysia Computer Center Al-Balqa Applied University Al-Salt Jordan Ajman University Ajman United Arab Emirates Department of Information Technology Al-Huson University College Al-Balqa Applied University Al-Huson Jordan IS and CS Department Faculty of Computer Science and Informatics Amman Arab University Amman Jordan Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

Email spam has been a big issue in recent years. As the percentage of internet users grows, so does the number of spam emails. Technologies are being used for illegitimate and immoral activities, such as phishing and robbery. As a consequence, it is essential to identify fraudulent spammers by employing machine learning techniques. This paper presents a novel spam classification technique that integrates the Harris Hawks optimizer (HHO) algorithm with the k-Nearest Neighbors algorithm (k-NN). The Harris Hawks Optimization (HHO) algorithm is a new metaheuristic algorithm motivated by Harris' Hawks' cooperative relations and surprises pounce pursue technique in nature. According to empirical results on the dataset, the suggested model can handle high-dimensional data (Spambase). The suggested model's spam detection accuracy is compared to the numerous algorithms, including the Binary Dragonfly Algorithm (BDA), Equilibrium Optimizer (EO), Teaching-Learning-based Optimization, Seagull Optimization Algorithm (SO), and Marine Predators Algorithm (MPA). We found that the proposed technique trumps the other spam detection techniques investigated in this study in terms of classification accuracy. The experimental results showed that the proposed technique accuracy reaches %94.3. © 2022 Elsevier B.V.. All rights reserved.

关键词： email spam detection Harris Hawks optimizer (HHO) k-NN Machine learning

来源：评论

学校读者我要写书评

暂无评论

Robust AI-Enabled Electronic Components Authentication and Anti -Counterfeiting 9

Robust AI-Enabled Electronic Components Authentication and A...

引用

9th International Conference on science, technology, Engineering and Mathematics, ICONSTEM 2024

作者： Banu, E. Afreen Priyanka, R. Thiruramanathan, P. Senthilnathan, T. Vithya, V.T. Vinoth, K. Saveetha School of Engineering Simats Department of Computer Science Engineering Tamil Nadu Chennai602105 India S.A Engineering College Department of Computer Science Engineering Tamil Nadu Chennai600077 India Ramco Institute of Technology Department of Physics Tamil Nadu Rajapalayam626117 India Sri Venkateswara College of Engineering Department of Applied Physics Tamil Nadu Sriperumbudur602117 India Rmk College of Engineering And Technology Department of Computer Science Engineering Tamil Nadu Puduvoyal601206 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science And Technology Department of Electrical And Electronics Engineering Tamil Nadu Chennai600062 India

ISBN: (纸本)9798350365092

In consumer electronics, aircraft, health care, and defense, fake electronic parts compromise system security and reliability. This study presents an AI-powered electrical component authentication and anti-counterfeiting system for efficiency and robustness. To prevent counterfeit electronic components, a multi-layered defense uses cutting-edge authentication and AI algorithms. Deep learning models for picture identification, spectroscopic analysis, and other cutting-edge component physical attribute evaluation. A decentralized blockchain platform can record the whole supply chain from producers to consumers. Smart contracts automate and enforce quality standards, eliminating many counterfeiting layers. An Integrated sensors and triggers detect physical tampering, alarm, and authenticate. Tracking and reporting component movements in real time permits quick response to suspicious activity. Stakeholders can easily validate components with AI algorithms via web or mobile apps. NFC, RFID, and QR code authentication is simple. To prevent counterfeiting, the framework specifies a stringent electrical component validation method. Advanced AI and physical and digital security measures provide comprehensive electrical component security throughout their lifespan. © 2024 IEEE.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

Heterogeneous data analysis of hypertrophic cardiomyopathy to prioritize important genes 26

Heterogeneous data analysis of hypertrophic cardiomyopathy t...

引用

26th International computer science and Engineering Conference, ICSEC 2022

作者： Janyasupab, Panisa Suratanee, Apichat Plaimas, Kitiporn Chulalongkorn University Faculty of Science Department of Mathematics and Computer Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok Thailand Center Department of Mathematics and Computer Science Faculty of Science Bangkok Thailand

ISBN: (纸本)9781665491983

Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease that is often caused by abnormal genes in the heart muscle. The identification of HCM-related genes is one of the crucial tasks to prevent and treat the patient. Gene expression analysis is a direct approach to screen for a gene with a higher or lower expression level in the HCM cell than in the normal cell. Microarray and RNA-Seq technology are used for measuring transcription levels. Both techniques have different advantages to obtain gene expression data. The integration of microarray and RNA-Seq data has already been effectively used to identify disease biomarkers. The ranking method is an interesting technique and is mostly used for ranking players or teams in sports. Each method has different strengths and can be appropriately applied to integrate various data and used to prioritize the importance genes. In this work, six ranking techniques to integrate microarray and RNA-Seq data were applied to prioritize the HCM-related genes. The performance reveals that the ranking method is also a well-suited technique in this task, and it turns out that the PageRank technique yields the best performance. © 2022 IEEE.

关键词： RNA

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Farm Irrigation: A Smart IoT-based Solution for Water Conservation in Agriculture 3

Revolutionizing Farm Irrigation: A Smart IoT-based Solution ...

引用

3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Mahalakshmi, G. Pandiaraja, P. Apirajitha, P.S. Punitha, S. Soujanya, T. Stephan, Thompson Anna University Department of Information Science and Technology Chennai600025 India M. Kumarasamy College of Engineering Department of Computer Science and Engineering Tamil Nadu Karur639113 India Department of Computer Science Engineering Tamil Nadu Chennai602117 India Graphic Era Deemed to Be University Department of Computer Science Engineering Uttarakhand Dehradun248002 India School of Agriculture SR University Telangana Warangal506371 India M. S. Ramaiah University of Applied Sciences Department of Computer Science and Engineering Karnataka Bangalore560054 India

ISBN: (纸本)9798350303360

Agriculture motor and pipe control system deploys innovative technology-based IoT platform to reduced water wastage in the agriculture field. Mobile application interfaced with the microcontroller. A mobile application is developed for the user to control a motor in smart way. The user will control motor in android application. There are many options provided by motor on and off, the user switches on the motor either automatically or manually. This operation performed in a motor can be operated and connected to an android application. In a microcontroller, a timer can be set based on user's desire. Here, the motor will operate based on option user can control, the microcontroller checks moisture and temperature present in the land, based on that water will flow to the land. Another option is that when the farm contains high yield crops when this motor will run three times longer than the other to increase water flow. The main motive of this proposed system is to provides a smart motor for farmers to reduce water flow in farming land. Thus, the objective of this work which is to check both moisture and temperature level of the land based on that water will flows. Hence it can reduce water wastage and manpower. © 2023 IEEE.

关键词： Agriculture

来源：评论

学校读者我要写书评

暂无评论

Exploring and Preprocessing of Crunchbase Data for Accurate Startup Success Prediction 3

Exploring and Preprocessing of Crunchbase Data for Accurate ...

引用

3rd Zimbabwe Conference on Information Communication and technology, ZCICT 2024

作者： Misra, Ajai Kumar Jat, Dharm Singh Mishra, Durgesh Kumar Computer Science Namibia University of Science and Technology Windhoek Namibia SCSIT Symbiosis University of Applied Sciences Indore India

ISBN: (纸本)9798331520335

Startups are the backbone of the country's economy. The growth of startups is supporting the country by providing jobs and also directly generating revenue for the government. On the other hand, the failure of startups is impacting the economy's growth. Therefore, we need to explore and study the factors of startup success and failure. However, there is very fewer source of information about the startup's activity available openly, but the databases like Crunchbase are collecting and providing data about the startups for conducting study. In this paper, we are focusing on Crunchbase data for exploring the essential facts and attributes which are significantly contributing to startup success and failure. Additionally, we also proposed a machine learning model to accurately predict startup success and failure by utilizing the concluded attributes. The aim of the proposed model is to identify how the funding influences the operational status of the company. Thus, the Crunchbase dataset is considered where 66368 companies' data are used for evaluation and experiment. According to the conducted experiments we have found that venture capital financing is the main source of getting funds, 63.99% of companies are able to secure only the first round of funding, and less than 1 % of companies are able to get funding more than 10 times. The combination of the proposed preprocessing technique and learning algorithms provides highly accurate results and is able to predict 80% success and failure. © 2024 IEEE.

关键词： Supervised learning

来源：评论

学校读者我要写书评

暂无评论

Sand Cat Swarm Optimization with Deep Transfer Learning for Skin Cancer Classification

引用

computer Systems science & Engineering 2023年第11期47卷 2079-2095页

作者： C.S.S.Anupama Saud Yonbawi G.Jose Moses E.Laxmi Lydia Seifedine Kadry Jungeun Kim Department of Electronics and Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Computer Science and Engineering University Institute of Engineering and Technology(UIET)Guru Nanak UniversityHyderabadIndia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Skin cancer is one of the most dangerous *** of the high melanoma death rate,skin cancer is divided into non-melanoma and *** dermatologist finds it difficult to identify skin cancer from dermoscopy images of skin ***,pathology and biopsy examinations are required for cancer *** studies have formulated computer-based systems for detecting skin cancer from skin lesion *** recent advancements in hardware and software technologies,deep learning(DL)has developed as a potential technique for feature ***,this study develops a new sand cat swarm optimization with a deep transfer learning method for skin cancer detection and classification(SCSODTL-SCC)*** major intention of the SCSODTL-SCC model lies in the recognition and classification of different types of skin cancer on dermoscopic ***,Dull razor approach-related hair removal and median filtering-based noise elimination are ***,the U2Net segmentation approach is employed for detecting infected lesion regions in dermoscopic ***,the NASNetLarge-based feature extractor with a hybrid deep belief network(DBN)model is used for ***,the classification performance can be improved by the SCSO algorithm for the hyperparameter tuning process,showing the novelty of the *** simulation values of the SCSODTL-SCC model are scrutinized on the benchmark skin lesion *** comparative results assured that the SCSODTL-SCC model had shown maximum skin cancer classification performance in different measures.

关键词： Deep learning skin cancer dermoscopic images sand cat swarm optimization machine learning

来源：评论

学校读者我要写书评

暂无评论

Optimum sensors allocation for drones multi-target tracking under complex environment using improved prairie dog optimization

引用

Neural Computing and Applications 2024年第18期36卷 10501-10525页

作者： Zitar, Raed Abu Alhadhrami, Esra Abualigah, Laith Barbaresco, Frederic Seghrouchni, Amal ElFallah Thales/SCAI Sorbonne University Abu Dhabi United Arab Emirates Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Computer Science Department Al al-Bayt University Mafraq25113 Jordan MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon Research Center University of Tabuk Tabuk71491 Saudi Arabia Thales Land and Air Systems Paris France Mohammed VI Polytechnic University-Ai movement Rabat Morocco Sorbonne University Paris France College of Engineering Yuan Ze University Taoyuan Taiwan School of Engineering and Technology Sunway University Malaysia Petaling Jaya Malaysia Applied science research center Applied science private university Amman Jordan

This paper presents a novel hybrid optimization method to solve the resource allocation problem for multi-target multi-sensor tracking of drones. This hybrid approach, the Improved Prairie Dog Optimization Algorithm (IPDOA) with the Genetic Algorithm (GA), utilizes the strengths of both algorithms to improve the overall optimization performance. The goal is to select a set of sensors based on norms of weighted distances cost function. The norms are the Euclidean distance and the Mahalanobis distance between the drone location and the sensors. The second one depends on the predicted covariance of the tracker. The Extended Kalman Filter (EKF) is used for state estimation with proper clutter and detection models. Since we use Multi-objects to track, the Joint Probability Distribution Function (JPDA) estimates the best measurement values with a preset gating threshold. The goal is to find a sensor or minimum set of sensors that would be enough to generate high-quality tracking based on optimum resource allocation. In the experimentation simulated with Stone Soup, one radar among five radars is selected at every time step of 50-time steps for 200 tracks distributed over 20 different ground truths. The proposed IPDOA provided optimum solutions for this complex problem. The obtained solution is an optimum offline solution that is used to select one or more sensors for any future flights within the vicinity of the 5 radars. Environment and conditions are assumed to be similar in future drone flights within the radars’ defined zone. The IPDOA performance was compared with the other 8 metaheuristic optimization algorithms and the testing showed its superiority over those techniques for solving this complex problem. The proposed simulated model can find the most relevant sensor(s) capable of generating the best quality tracks based on weighted distance criteria (Euclidean and Mahalanobis). That would cut down the cost of operating extra sensors and then it would be possible to

关键词： Clutter (information theory)

来源：评论

学校读者我要写书评

暂无评论

A Comparative Performance Analysis of Machine Learning Approaches for the Early Prediction of Diabetes Disease

A Comparative Performance Analysis of Machine Learning Appro...

引用

2022 IEEE International Conference on Advances in Computing, Communication and applied Informatics, ACCAI 2022

作者： Mahesh, T.R. Vivek, V. Kumar, Vinoth V Natarajan, Rajesh Sathya, S. Kanimozhi, S. Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India University of Applied Science and Technology Shinas. C. Abdul Hakeem College of Engineering and Technology Melvisharam India

ISBN: (纸本)9781665495295

Diabetes is a chronic condition that affects a large number of people. Diabetes can be predicted early on, which can lead to better treatment. One of the main causes of this metabolic condition is a hormonal imbalance. Insulin, the hormone that regulates blood sugar, is the hormone that is affected. The condition causes the patient's body to either produce insufficient insulin or to utilize it inefficiently and effectively. Every year, the same disease is responsible for the deaths of 1.6 million people. As per the report of WHO i.e, World Health Organization, the number of diabetic patients has being increased day to day due to different parameters namely bacterial or even viral infection, toxic or even chemical contents mixed with food and so on. The aim of this article is to leverage significant features to generate a machine learning (ML) based prediction algorithm and discover the best classifier to get the best results when compared to clinical outputs. Further, this article proposes a diabetes prediction system that uses medical data and six ML algorithms namely Logistic Regression (LR), Naive Bayes (NB),Decision Tree(DT) classifier K-Nearest Neighbor (KNN), support vector classifier and Random Forest(RF) Classifiers to diagnose diabetes, the accuracy of all the models are compared and finally recommended the best model for the early prediction of the diabetes disease. © 2022 IEEE.

关键词： Insulin

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：