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Exceptional Points of Degeneracy Induced by Time-Periodic Modulation 17

Exceptional Points of Degeneracy Induced by Time-Periodic Mo...

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17th International Congress on Artificial Materials for Novel Wave Phenomena, Metamaterials 2023

作者： Rouhi, K. Nikzamir, A. Figotin, A. Capolino, F. University of California Dept. of Electrical Engineering and Computer Science IrvineCA92697 United States University of California Dept. of Mathematics IrvineCA92697 United States

ISBN: (纸本)9798350332445

An exceptional point of degeneracy (EPD) is a point in parameter space where at least two eigenvalues and eigenvectors of a system coalesce. We explore the occurrence of EPD in electronic, electromagnetic and mechanical time-varying systems. Remarkably, when using time variation of a system component, EPDs exist in a single resonator and they can be found by just tuning the modulation frequency. We demonstrate EPDs of the second order in linear-time-periodic (LTP) systems and offer examples of their applications, such as sensing and power harvesting. © 2023 IEEE.

关键词： Time varying systems

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Multinomial Naive Bayes Based Machine Learning Analysis of Twitter Sentiment 2

Multinomial Naive Bayes Based Machine Learning Analysis of T...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Saravanan, U.K.Balaji Vijay, M. Shreedhar, T. Rajasekar, G. Yashwanth, R. Shakthipriya, P. Sona College of Technology Information Technology Salem India Vmkv Enginerring College Dept. of Computer Science and Engineering Salem India

ISBN: (纸本)9798350347579

Sentiment analysis identifies and categorizes thoughts and feelings expressed in the source text. Social media uses tweets, status updates, and blog columns to generate sensitive data. SA on user-generated data helps understand crowd opinion. Due to slang and spelling errors, Twitter sentiment analysis (TSA) is much more complex than general sentiment analysis. Every day, millions of tweets are generated on various topics. Machine learning algorithms can be used to solve this problem-the proposed machine learning algorithm includes preprocessing, weight calculation, feature extraction, and classification. First, we collected a Kaggle dataset from Twitter sentiment analysis. In the second step, the data preprocessing steps mainly include stop words, emoji, and repeated characters in words and URLs due to their low importance. Then, we used tokenization, normalization, stemming, and lemmatization to achieve better results. In the third step, we computed weights using word2vec to represent the tweets. In the fourth step, we use a feature extraction method consisting of TF-IDF and word embedding to check based on word and noun counts. Finally, TSA can be used to explore sentiment using Multinomial Naïve Bayes (MNB) and Maximum Entropy classification. The proposed machine learning algorithm achieves better results with TSA in terms of recognition accuracy, recall, precision and F-measure. © 2023 IEEE.

关键词： Sentiment analysis

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Ethical and Practical Implications of Implementing Facial Recognition Technology for Attendance Control in Educational Institutions and Businesses 1

Ethical and Practical Implications of Implementing Facial Re...

引用

1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kurup, Ravindra Muzalda, Devendra Sharma, Vinod Hudda, Yashika Singh, Vijay Vir Princy, Benila Madhav University Dept. of Humanities and S.Science Pindwara India SLP College Dept of CSE Gwalior India Chandigarh University Dept of Computer Science Engineering Chandigarh India Lingayas Vidyapeeth Dept. of Mathematics Faridabad India Meenakshi A.H.E.R Dept. of English Chennai India

ISBN: (纸本)9798350356816

Facial recognition technology has garnered significant attention recently as a potential solution for attendance management in educational institutions and companies. This study proposes a facial recognition attendance system designed to address limitations of existing systems and navigate the ethical and legal considerations associated with the technology. To ensure accuracy, security, and user acceptance, the proposed system integrates advanced facial recognition algorithms, strict data protection protocols, and intuitive interfaces. The methodology encompasses a thorough literature review, primary data collection through surveys and interviews, system design, testing, and data analysis to evaluate the feasibility, efficacy, and ethical implications of deploying facial recognition for attendance tracking. The experimental setup includes cameras, laptops, servers, facial recognition algorithms, attendance management software, and a diverse collection of facial images. Analysis of the collected data aims to assess the system's accuracy and effectiveness. The study's findings will offer insights into the potential benefits and challenges of adopting facial recognition technology for attendance management in educational and corporate settings, along with recommendations for future research and implementation. © 2024 IEEE.

关键词： Accuracy Attendance Management Data Analysis Diverse Facial Image Facial Recognition

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Enhancing Weather Forecasting Accuracy: Integrating Machine Learning Algorithms with Numerical Weather Prediction Models 1

Enhancing Weather Forecasting Accuracy: Integrating Machine ...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sharma, Madhuri Singh, Rana Pratap Sharma, Usha Arivukkodi, R. Roy, Ranjeet Kumar Verma, Kartik Dept. of Life Sciences CHRI Campus India Madhav University Dept. of Humanities and S.Science Pindwara India Meenakshi A.H.E.R Dept. of Computer Applications Chennai India Lingayas Vidyapeeth University Dept. of Mathematics Faridabad India Chandigarh University Dept. of Com. Science Engineering Chandigarh India

ISBN: (纸本)9798350356816

Many industries, including agriculture, transportation, & energy, heavily rely on weather forecasts. Earlier weather forecasting techniques were built on numerical weather prediction models, which use physical equations to simulate atmospheric conditions. However, these models have limitations due to measurement uncertainty & the complexity of atmospheric systems. Machine learning techniques have emerged as a promising advancement in weather forecasting. This paper introduces a novel method for weather forecasting using Naive Bayes & Chi-Square algorithms. The theoretical foundations of these algorithms & their application in weather forecasting are discussed. Experimental results on real weather datasets demonstrate the effectiveness of the proposed method. © 2024 IEEE.

关键词： Prediction models

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Identify Initial Nodes for Spreader in A New Diffusion Model Based on Topology (DBT) in Social Networks 9

Identify Initial Nodes for Spreader in A New Diffusion Model...

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9th International Conference on Web Research, ICWR 2023

作者： Khosravi, Ehsan Yazdanifar, Fatemeh Salimi, Nikoo Beni, Hamid Ahmadi Sharif University of Technology Dept. of Computer Engineering Tehran Iran Amirkabir University of Technology Dept. of Mathematics and Computer Science Tehran Iran Engineering and Naval Architecture University of Genoa Dept. of the Electrical Electronics Electronics and Telecommunication Genoa Italy Azarbaijan Shahid Madani University Dept. of Software Engineering Tabriz Iran

ISBN: (纸本)9798350399691

With the development of science and technology through social networks, the process of diffusion has become a controversial subject. In social networks, the process of diffusion is transmitted from one individual to another. The influence maximization problem, in which at least k nodes are chosen as the initial nodes of the spreader in the smallest amount of time, is one of the most critical aspects of the diffusion process. Furthermore, the initial nodes in the social network should generate the most influence on the other nodes in the network. However, in the algorithms proposed to solve the problem of influence maximization, the real-world factors which affect the influence probability in diffusion models are ignored for example, the influence probability is determined randomly. This problem causes influence maximization algorithms based on diffusion models to face the challenge of time-consuming Monte Carlo simulation. So, the RIM algorithm presented in this article which this method selects the initial nodes of the spreader according on their local reachability. In addition, Due to privacy in social networks, complete information about people such as gender, education, etc. is not available. For this reason, in the RIM algorithm, using the graph structure in social networks, the influence probability is calculated in the new DBT diffusion model. In addition, the RIM algorithm exhibits stable performance in the selection of the spreader's initial nodes in terms of influence spread, and execution time. © 2023 IEEE.

关键词： Topology

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TinyML-Based Gait Recognition for User Detection on an STM32L4 IoT Node 9

TinyML-Based Gait Recognition for User Detection on an STM32...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Hassan, Hateem Abdullah, M. Hussain, Muhammad Zunnurain Hasan, Muhammad Zulkifl Mustafa, Muzzamil Khalid, Aqsa Awan, Rimsha Hussain, Usman Khan, Zohaib Ahmed Javaid, Arslan University Lahore Department of Computer Engineering Information Technology Punjab Pakistan Bahria University Lahore Campus Dept. of Computer Science Pakistan University of Central Punjab Lahore Pakistan Faculty of Information Technology Department of Computer Science Pakistan National College of Business Administration And Economics Department of Computer Science Lahore Pakistan Information Technology University Lahore Pakistan University of Home Economics Pakistan

ISBN: (纸本)9798350394474

Gait recognition stands out as a robust biometric tool for discerning users. Yet, adapting it to devices with limited resources, such as the STM32L4 microcontroller, demands cutting-edge strategies. This research unveils an innovative TinyML methodology, leveraging a specially-designed Convolutional Neural Network (CNN). Notably, our bespoke CNN model showcased a remarkable 97.3% accuracy in pinpointing users on the STM32L4 system. © 2024 IEEE.

关键词： Convolutional neural networks

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FEZ: Flexible and Efficient Zoom-In for Ultra-Large Image Classification

FEZ: Flexible and Efficient Zoom-In for Ultra-Large Image Cl...

引用

2022 IEEE International Conference on Big Data, Big Data 2022

作者： Lu, Cheng-Han Hung, Shih-Hao National Taiwan University Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9781665480451

Mainstream deep learning algorithms are designed for typical images sizing from 224 × 224 to 600 × 600 pixels, which are relatively small compared to the whole-slide images (WSI) with billions of pixels, causing existing methods to be inefficient due to computational and memory constraints. Furthermore, most WSIs are annotated with slide-level labels, as patch-level annotations are time-consuming and have to be conducted by experienced pathologists. At the same time, the regions of interest (ROIs), e.g., where cancer, tumor, and bacteria reside, would occupy relatively small areas. Thus, we propose Flexible and Efficient Zoom-In (FEZ) as a method to precisely locate ROIs to accelerate ultra-large image classification. FEZ initially examines a low-resolution WSI with a policy network (PN) to select the potentially critical ROIs and zooms in the selected regions using medium-resolution images to select finer-grain ROIs with another PN. Eventually, FEZ loads the high-resolution patches and classifies them with a value network (VN) to make a prediction. Since FEZ only loads a few selected patches instead of the high-resolution WSI, it dramatically reduces the image loading time and the computation time needed for analyzing the patches. Experimental results show that FEZ is capable of delivering similar accuracy with higher speed than a recently proposed whole-slide training method (WST) and existing sampling methods such as Multiple Instance Learning (MIL) and CLAM. FEZ is 42× and 22× faster than MIL on the Camelyon16 and TCGA Lung Cancer datasets, respectively. With FEZ, we can reduce the classification time for ultra-large images from minutes to seconds to support interactive applications. © 2022 IEEE.

关键词： Image classification

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Students' Careers and AI: a decision-making support system for Academia

Students' Careers and AI: a decision-making support system f...

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2023 Italia Intelligenza Artificiale - Thematic Workshops, Ital-IA 2023

作者： Bertini, Flavio Dal Palù, Alessandro Formisano, Andrea Pintus, Andrea Rainieri, Sara Salvarani, Luana Dept. of Mathematical Physical and Computer Sciences Parma University Italy Dept. of Mathematics Computer Science and Physics Udine University Italy Dept. of Humanities Social Sciences and Cultural Industries Parma University Italy Dept. of Engineering and Architecture Parma University Italy

In the peculiar realm of higher education, some of the challenges of Public Administration, in terms of quality assurance and data intelligence, can be addressed thanks to the complex ecosystem based on the careers of students and their engagement with the host academia. University governance, ranging from the university Rector and Quality Assurance committee to single heads of degree courses, needs to rely on quantitative and unbiased measures when designing and planning actions. This paper reports on an ongoing project started at Parma University in 2019, that has multiple goals: (1) to collect various sources of students' career-related raw data and to and provide simple access to aggregated analyses through a web portal;(2) to offer an AI based synthesis, in form of automatically generated reports in natural language;(3) to analyze data to detect and predict potential issues (e.g., students drop-out, classes attendance, graduation time estimations, blockages in the career) that can be promptly highlighted, for immediate intervention. As opposed to the majority of academic analytics implementations, particular care is devoted to minimizing ethics and privacy issues and adhering to explainable AI principles in the generation of synthetic explanations of charts and reports. The results of lines of research (2) and (3) will be integrated in the portal (1) that is currently deployed at Parma University. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Quality assurance

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An Improved Dual Authentication Techniques for Secure Data Transmission in VANETs

An Improved Dual Authentication Techniques for Secure Data T...

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2023 International Conference on information Technology and Computing, ICITCOM 2023

作者： Cahyadi, Eko Fajar Khair, Fauza Amri Ginting, Jafaruddin Gusti Ariyadi, Tamsir Hwang, Min-Shiang Institut Teknologi Telkom Purwokerto Dept. of Telecommunication Engineering Purwokerto53147 Indonesia Universitas Bina Darma Dept. of Computer Engineering Palembang30266 Indonesia Asia University China Medical University Dept. of Computer Science & Information Engineering Department of Medical Research Taichung Taiwan

ISBN: (纸本)9798350359633

Research related to user authentication techniques in vehicular ad hoc networks (VANETs) has received significant attention in the last decade. However, its security and privacy still become a central issue that is widely open to discussion. In 2016, Vijayakumar et al. published dual authentication and key management techniques for secure data transmission in VANETs. The scheme aims to provide a high level of security on the vehicle side to prevent unauthorized vehicles from entering the VANETs effectively. It relies on the fingerprint and hashes code HC for the authentication process. The authors claimed that the mechanism could withstand the replaying attack by appending it with an updated timestamp and the packet's transmission. However, this paper shows that their scheme suffered a denial of service (DoS) attack due to misplaced timestamps. So, we propose an improvement of their scheme to withstand the attacks. © 2023 IEEE.

关键词： Authentication

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Car Price Prediction using Machine Learning 9

Car Price Prediction using Machine Learning

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Ahmad, Muhammad Farooq, Muhammad Ali Hussain, Muhammad Zunnurain Hasan, Muhammad Zulkifl Mustafa, Muzzamil Khalid, Aqsa Awan, Rimsha Hussain, Usman Khan, Zohaib Ahmed Javaid, Arslan University Lahore Department of Computer Engineering Information Technology Punjab Pakistan Bahria University Lahore Campus Zunnurain Dept. of Computer Science Pakistan University of Central Punjab Lahore Faculty of Information Technology Department of Computer Science Pakistan National College of Business Administration And Economics Department of Computer Science Lahore Pakistan Information Technology University Lahore Pakistan University of Home Economics Pakistan

ISBN: (纸本)9798350394474

Predicting car prices involves determining a vehicle's market worth based on various attributes like brand, model, year of manufacture, mileage, and overall state. This prediction holds significant value for the auto industry, aiding both prospective buyers and sellers in understanding pricing and making educated decisions regarding vehicle transactions. Machine learning techniques, trained on historical datasets sourced from online automotive marketplaces, dealerships, and auction platforms, are adept.at forecasting the prices of both new and pre-owned cars, using their specifications. Noteworthy machine learning models applied include Linear Regression, AdaBoostRegressor, Lasso Regression, and Ridge Regression. Integrating more diverse data points, such as customer reviews, prevailing market dynamics, and regional factors, can further refine the accuracy of these prediction models. In recent trials with the provided dataset, the Lasso Regression and Ridge Regression models stood out, delivering an impressive accuracy rate nearing 90%. © 2024 IEEE.

关键词： Forecasting

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