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Activity Recognition in Video Frames for Enhancing Human-Robot Collaboration: A Machine Learning Perspective 3

Activity Recognition in Video Frames for Enhancing Human-Rob...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Chaubey, Ved Prakash Sharma, Shamneesh Kumar, Aman Malik, Arun Malik, Praveen Batra, Isha UpGrad Education Private Limited Delivery and Student Success UpGrad Campus Karnataka Bangalore India School of Computer Science & Engineering Lovely Professional University Punjab India School of Electronics and Communication Engineering Lovely Professional University Punjab India

ISBN: (纸本)9798350330724

Human activity recognition is a prominent area of investigation within computer vision research. Its potential applications span diverse fields such as security monitoring, healthcare, and human-computer interaction. The identification and classification of human activity through the utilization of machine learning techniques has yielded a plethora of proposed models within this dynamic field of scientific inquiry. By employing diverse kinetic models that are linked to the learning of spatial or temporal features, the characteristics of a collection of image or video data are extracted. The primary aim of this model is to identify and classify ongoing activities. This objective has been successfully achieved by various deep-layer trained models that have been implemented in this field. To fulfill the specified criteria, the proposed approach utilizes the Long Recurrent Convolutional Network (LRCN) and Long Short-Term Memory (LSTM) convolutional neural network architectures. The implementation of Vision-Based Human Activity Recognition utilizes the 3D Convolutional Neural Network (CNN) model. The model undergoes training utilizing the UCF-50 dataset. The classification of the photos by the model is contingent upon the action being performed. The model will receive videos as input, which will undergo appropriate pre-processing before being classified based on the activity. The vision-based human activity identification system offers several advantages, including but not limited to accuracy, cost-effectiveness, and user-friendliness. © 2023 IEEE.

关键词： Neural network models

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A Comprehensive Review for Optimizing the Design of Cluster Head Selection to Enhance the Network Lifetime in WSN

A Comprehensive Review for Optimizing the Design of Cluster ...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： S Bhuvaneswari A Abirami R P Suvetha Sri R Srinithy R Swathi Faculty of dept. of Computer Science and Engineering Easwari Engineering College Chennai India Student of dept. of Computer Science and Engineering Easwari Engineering College Chennai India

Large numbers of cooperative sensor nodes make up wireless sensor networks (WSNs). Sensors are the integral component in WSN IoT. Sensors separate and organize in the clusters by similar characteristics. A cluster head is in charge of gathering information from the nodes in every cluster and transmitting it to its base station (BS). Because every node are powered by batteries, lessens energy usage and elongating lifetime of network are crucial concerns for WSN. The primary goals of this analysis are to address issues that previous writers have not addressed and to provide beginning researchers with a fundamental understanding of how to choose cluster head (CH) based on various methodologies. To designate a node as CH, some enhancing parameters are chosen, such as residual energy, packet transmission rate, throughput, distance between BS and Cluster Node (CN), and betweenness centrality. Data packets are transferred using the optimized protocol to reduce delay time and increase transmission rate after many CH are chosen. Analysis of the many literatures was done. An Optimization Algorithm is needed to enhance network lifetime from this analysis. A comparison of various algorithm based on the features and difficulties from the analysis is done. There are many drawbacks, such as a cluster head is chosen at random, data transmission rate is not considered, no proper formation of clusters, importance for load and temperature is not given, stability is not taken into account. Thus, there is a need for optimized algorithm to overcome all the mentioned issues.

关键词： Wireless communication Wireless sensor networks Temperature distribution Clustering algorithms Throughput Data models Delays

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Research on students' Utilization of Artificial Intelligence Based on Random Forest Model and PCA-K-means Algorithm 24

Research on Students' Utilization of Artificial Intelligence...

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2024 International Symposium on Artificial Intelligence for Education, ISAIE 2024

作者： Liu, Can Xu, Siyi Chen, Yuxuan Wang, Zixiang Chao, Lu Lin, Yang Yan, Hao College of Information Engineering Sichuan Agriculture University Sichuan Ya'an China Chongqing University University of Cincinnati Joint Coop Institute Chongqing University Chongqing China School of Management Science and Engineering Anhui University of Finance and Economics Bengbu China College of Engineering and Computer Science Syracuse University Syracuse United States Department of Student Affairs Inner Mongolia Vocational College of Architecture Inner Mongolia Hohhot China Engineering and Applied Science University of Pennsylvania Shanghai China

ISBN: (纸本)9798400707100

This study delves into the impact of artificial intelligence on students' *** paper pioneers the use of k-means classification to reduce the dimensionality of features before the classification process, which minimizes redundant information and computational overhead. This step is not commonly found in prior ***, a random forest model is established with "whether AI assistance was used to complete the paper" as the output, and various features as inputs. The analysis of the predictive accuracy when changing input features reveals the degree of influence of each feature on using AI to complete the paper. After feature selection, the remaining 6 features can still effectively predict the use of AI to complete the paper, indicating the representativeness of the extracted indicators. Subsequently, the PCA-K-means algorithm is employed to classify the use of artificial intelligence. PCA algorithm is used for dimensionality reduction of the extracted features to better characterize students' use of AI. Three representative variables are selected, and the contribution of each feature is computed based on the factor loading matrix. Finally, the K-means algorithm is used to classify students based on the three-dimensional features, showing that students can be effectively classified into 2 categories. The results indicate that the majority of students support the use of AI technology to enhance their abilities, although some students may only intend to use AI to cope with exams and similar tasks. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Dimensionality reduction

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Decoding Sentiments: Harnessing the Power of NLP for Comparative Analysis of ML Algorithms

Decoding Sentiments: Harnessing the Power of NLP for Compara...

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International Conference on Computing and Machine Learning, CML 2024

作者： Varma, Nadimpallli Madana Kailash Harshini, Marisetti Madhan Mohan, R. Arora, Gagandeep Singal, Swati Student Department of Artificial Intelligence & Machine Learning Vardhaman College of Engineering Telangana Hyderabad501286 India Department of Computer Science and Engineering Faculty of Engineering Annamalai University Chidambaram India Head of the Department Professor Department of Artificial Intelligence & Machine Learning Vardhaman College of Engineering Telangana Hyderabad501286 India Sharda University Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819765874

Machine learning algorithms have become pervasive in diverse applications, revolutionizing various domains. However, the abundance of algorithms, each designed for specific purposes, poses a challenge for both novice users and experts in selecting the most suitable model. This research addresses this issue through a comprehensive analysis, leveraging Natural Language Processing (NLP) techniques and the powerful Sentiment decoding libraries. Researcher’s comments from published papers were analyzed using the various approaches, presenting a novel approach that maps algorithm scores based on adjectives. Results indicate that Support Vector Machine consistently outperforms other algorithms, providing valuable insights for practitioners. Our research contributes to a systematic evaluation framework, aiding researchers in algorithm selection. This paper presents a study focused on sentiment analysis, aimed at identifying the most effective machine learning algorithm among various contenders. Traditional conclusions regarding the superiority of specific algorithms are often based on their performance on specific datasets or tasks, which may not provide a comprehensive comparison due to the diverse nature of algorithms. To address this, a standard dataset encompassing the advantages and disadvantages of all algorithms is utilized. Three approaches employing VADER, TextBlob, and transformers are employed for sentiment analysis. Results consistently highlight SVM as the top-performing algorithm, followed by Associative analysis. This standardized conclusion contributes to the broader understanding of sentiment analysis within the realm of machine learning algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Smart Cities: Transforming Urban Living with IoT

Smart Cities: Transforming Urban Living with IoT

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： Niveditha J D R Kumar Raja UM Ashwin Kumar Post Graduate Student dept of Computer Science and Engineering Reva University Bangalore India

The Internet of Things (IoT) is being used by smart cities to harness the power of the IoT and build more efficient and sustainable urban settings as they quickly adapt to the challenges of urbanization. This study examines the idea of “smart cities” and the different fields where Internet of Things-based technologies are being used to improve urban living. The article discusses several IoT use cases and applications for smart cities, including smart energy management, smart transportation, smart healthcare, and smart public safety. Discussions also cover the advantages and difficulties of IoT-based smart city initiatives, such as the necessity for reliable infrastructure, data privacy and security concerns, and cooperative strategies. The essay also emphasizes how crucial a favorable policy and regulatory framework is to the deployment of IoT-based smart city projects. The article's conclusion discusses potential paths for further research as well as how the Internet of Things may change urban living in the years to come

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Automated Emotion Recognition from Facial Expressions using Convolutional Neural Network 5

Automated Emotion Recognition from Facial Expressions using ...

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5th International Conference on Data Analytics for Business and Industry, ICDABI 2024

作者： Hussain, Syed Aman Reddy, Nareddy Yashwanth Srivardhan, Junnuthula Sharma, Ajay Sharma, Shamneesh Gochhait, Saikat Lovely Professional University School of Computer Science and Engineering Punjab Phagwara India upGrad Education Private Limited Delivery and Student Success Karnataka Bangalore India International Deemed University Constituent of Symbiosis Symbiosis Institute of Digital and Telecom Management Pune India

ISBN: (纸本)9798350368710

In the era of digital communication, it is more about being able to detect emotions than anything else if we are to maintain this complicated and demanding human society. Emotion recognition research is pulling out all the stops, resulting in all manner of methods to recognize human feelings. Emotions are often transmitted through words and body language, and facial expressions play an important part in communicating them. In this paper, the use of a Convolutional Neural Network (CNN) is proposed as a real-time emotion recognition system based on machine learning algorithm, and the system is able to process video feeds. The classifier breaks emotions down into seven categories, with an accuracy of about 81 percent. In addition, the research extends to the use of various image datasets to improve emotion forecasting accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Implementing a Serious Traffic Game to Improve Children’s Road Awareness Skills

Implementing a Serious Traffic Game to Improve Children’s R...

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Electrical and Information Technology (IEIT), International Conference on

作者： Azadeh Nahvi Maedeh Mosharraf Computer Engineering Student Shahid Beheshti University Tehran Iran Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran

To ensure children's safety in streets, it is crucial to teach them the basic rules of traffic. Given the risks associated with training in the real world and the ineffectiveness of traditional education in these situations, it is essential to teach road awareness through simulations and games to young traffic users. Children can be immersed in the virtual environment and feel the sense of presence in the real environment. Children can experience many risky scenarios in the virtual world and learn how to avoid them. This paper introduces a serious game to educate children about traffic laws. Seven crucial traffic regulations for children when crossing the street are presented by two scenarios. The learning process is carried out during playing in three stages: pretest, test, and posttest. Children receive feedback regarding the suitability of their traffic behavior using the coins they acquire during the game. Both of the game's scenarios were found to be effective at helping players understand traffic laws, according to the pretest and posttest results.

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Identifying Children's Personality Styles through Drawing Analysis using Machine Learning

Identifying Children's Personality Styles through Drawing An...

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Conference on Information and Knowledge Technology

作者： Maedeh Mosharraf Faezeh Banabazi Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran Computer Engineering student Shahid Beheshti University Tehran Iran

The impact of personality styles on individuals' behavior and choices is undeniable. With the advancements in datamining and machine learning techniques, various methods have been proposed to automatically identify personality styles by processing extensive data and exploring some human behavioral patterns. In this article, we examine children's personality styles by analyzing their drawings using some machine learning techniques. We employ the Murphy-Meisgeier Type Indicator for Children (MMTIC) questionnaire results to train k-nearest neighbors, random forest, and decision tree classifiers. Although we focus on a limited set of drawing features in this study, the results obtained are remarkably significant. We believe that the findings of this study will have a profound impact on early identification of children's personality styles and their decision-making throughout their lives.

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A Novel Approach of Topology Control in Underwater Sensor Networks 2

A Novel Approach of Topology Control in Underwater Sensor Ne...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Sutagundar, A.V. Halakarnimath, B.S. Bhajantri, Lokesh B. Nalini, N. Basaveshwar Engineering College Electronics & Communication Engineering Karnataka Bagalkot India S. G. Balekundri Institute of Technology Computer Science & Engineering Karnataka Belagavi India Basaveshwar Engineering College Department of Information Science and Engineering Karnataka Bagalkot India Nitte Meenakshi Institute of Technology Professor & Dean Student's Welfare Department of Computer Science & Engineering Bengaluru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

The wide spectrum of marine applications has drawn the research community to discuss the several issues of UASNs. Acoustic communication is the most relevant channel for communicating information among sensor nodes that utilize more energy to avoid network failure and render continuous connectivity. The Energy-efficient Topology Control (ETC) is proposed to discuss this issue for UASNs to overcome the coverage and connectivity by improving their lifetime. The proposed approach is divided into the topology construction and topology maintenance phase. In the construction phase, initially, each node recognizes the neighbor nodes and records the range and orientation in an internal database. After that, each node drops some nodes by applying specific conditions and forms the candidate neighbor nodes. Finally, the minimum number of sensor nodes is added to the Topology Member Set (TMS) based on the nodes' orientation, range, energy levels, and topology of the network are created. The topology's maintenance is initiated by the sink node periodically, or any sensor node can send a request message to the sink node at particular conditions. The proposed scheme's performance shows the necessity for minimal sensor nodes for seamless connection and improves coverage area. © 2022 IEEE.

关键词： Sensor nodes

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Real-Time Facial Recognition System for Secure College Bus Transport using Deep Learning Techniques

Real-Time Facial Recognition System for Secure College Bus T...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： M. N. Kavitha Saranya S S Pragatheeswari E N. Kaviya Priya S R. Ranjith N. Rahul Vanchivel Department of Computer Science and Design Kongu Engineering College Tamil Nadu India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore Tamil Nadu India Department of Computer Science and Engineering Kongu Engineering College Erode Tamil Nadu India Department of CSD UG Student Kongu Engineering College Tamil Nadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The proposed system aims to enhance student transportation security through real-time face detection and recognition. Leveraging the MTCNN framework for accurate face detection and the FaceNet model for reliable face recognition, the system ensures only authorized students can board designated buses by comparing captured faces with a pre-registered database. In case of mismatches, alerts are triggered to notify drivers and authorities. Additionally, parallel implementation of Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers improves authentication accuracy. The system's deep learning-based architecture ensures robust performance under varying environmental conditions and supports continuous learning for long-term reliability.

关键词： Support vector machines Deep learning Renewable energy sources Accuracy Face recognition Transportation Nearest neighbor methods Real-time systems Security Face detection

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