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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Jha, Avinash Singh, Shresthi Sharma, Shamneesh Batra, Isha Malik, Arun Muzzaffar, Saira Nirsanametla, Yadaiah School of Computer Science and Engineering Lovely Professional University Punjab India Delivery and Student Success upGrad Education Private Limited upGrad Campus Bangalore India School of Computer Science Taylor's University Subang Jaya Malaysia Lovely Professional University Division of Research and Development Phagwara144411 India

ISBN: (纸本)9798350353266

Predicting traffic flow is a crucial component of the intelligent transportation system. This is a precise projection of the volume of traffic in a specific area at a specific future period. Researching traffic forecasts is beneficial in reducing traffic and facilitating safer, more affordable travel. Although classic models make use of shallow networks, the number of cars has grown exponentially in recent years, making these traditional machine learning techniques inapplicable to contemporary situations. We discuss some of the most recent developments in deep learning for traffic flow prediction in our article. The Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Restricted Boltzmann Machines (RBM), and Stacked Auto are among the many deep learning designs. Encoder (SAE). These deep learning models gradually extract higher level information from raw input by using numerous layers. The most recent deep learning models created to address this specific issue are examined, and because transport networks are complicated, this review also informs the reader about the aspects that affect these models and which models perform best in particular situations. © 2024 IEEE.

关键词： Convolutional neural networks

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Milling Force Prediction of Titanium Alloy Based on Support Vector Machine and Ant Colony Optimization

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2021年第2期48卷 1-12页

作者： Zhang, Xiaoxia Yu, Ziqiao Hu, Yinyin Yang, Jiao A Professor of School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan11041 China A Master Student of School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China

Milling force prediction of titanium alloy plays an important role in titanium alloy milling process. In the article, the milling process of titanium alloy materials is analyzed, and this material quality is affected directly by the milling force. Support vector machine (SVM) has shown prominent performance for many real-world problems. However, SVM computational complexity increases, as the number of samples increases. How to effectively apply it to massive datasets is still a serious challenge. Although there are some literatures on parameter optimization techniques of SVM regression, the performance of this model still needs to be further studied and improved. We present a new milling force predicting model, namely ACO&SVM, which integrate SVM regression with ACO algorithm to enhance the prediction accuracy of milling force for mill process of titanium alloy. The primary innovation feature of hybrid model is to apply the ACO to automatically determine parameters of SVM regression model. In addition, as being applied to discrete optimization, to solve continuous optimization problems, ACO algorithm needs to transform continuous variables into discrete ones by discretizing of continuous variables. The results have shown that proposed ACO&SVM model yields better prediction accuracy, and thus it can be widely applied to the fields of material processing optimization. © 2021. All Rights Reserved.

关键词： Titanium alloys

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Real Time Feature Selection Based Behavioral Anomaly Detection Using Machine Learning

SSRN

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SSRN 2024年

作者： Kolar, Shubham Shrivastava, Gyan Chandra Gupta, Anuj Kumar Parihar, Sarwar Shedpure, Yash Yadav, Nitish Kumar Malhotra, Atul School of Computer Science and Engineering Student Lovely Professional University Phagwara India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

The increasing use of E-learning platforms have created an issue of monotony which results in the drowsiness and inattentiveness among the learners. The drowsiness can lead to reduced attention, poor performance, and lack of interest in the learners. This research attempts to address this issue and its impact on student behavior during the online learning. Firstly, various existing drowsiness detection methods have been studied in order to identify the current status of research in the area. Then, an optimal drowsiness detection mechanism has been proposed which uses two deep learning algorithms namely Haar cascade algorithm and Convolutional neural network algorithm in combination to detect the drowsiness in the subjects. The experimental results proved the effectiveness of the proposed mechanism by detecting drowsiness among the subjects with an accuracy of 95 percent. © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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A New Hybrid Cuckoo Search for the Resources-Constrained Project Scheduling Problem

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2021年第2期48卷 1-12页

作者： Shen, Xin Zhang, Xiaoxia Yu, Ziqiao X. Shen is a Master Student of School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China X. X. Zhang is a Professor of School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan11041 China A Master Student of School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China

Resource-constrained project scheduling problem (RCPSP) is a kind of representative practical engineering problem, the purpose is to schedule activities in the project through rational use of limited resources in the minimum time. This paper proposes an improved hybrid cuckoo algorithm (CS&SA) to solve the RCPSP problem. Firstly, the individual elements are randomly coded into priority vectors, and the population is decoded using serial scheduling to convert the individual into a set of task scheduling sequences. Secondly, the Levy flight is redesigned to change the algorithm from random walk to adaptive as the population fitness changes. Then, this article adds three neighborhood update techniques to meet the update requirements of the algorithm at different stages. Finally, in order to prevent the algorithm from falling into a local optimum, this paper introduces a simulated annealing strategy to allow the algorithm to accept some individuals with poor quality with a certain probability in each iteration. In the testing part, this paper firstly tests the effectiveness and optimization of three different-scale examples in the classic example library PSPLIB for RCPSP problems. Among them, the average error of the CS&SA algorithm in the small-scale J30 is 0.25%, and the average error in the medium-scale J60 is 11.21%, and the average error of the large-scale J120 is 19.83%. Then, by comparing other intelligent optimization algorithms, it is proved that NCS&SA is superior to other algorithms in optimization and accuracy. © 2021. All Rights Reserved.

关键词： Combinatorial optimization

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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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An approach for exploiting and mitigating Log4J using Log4Shell vulnerability 3

An approach for exploiting and mitigating Log4J using Log4Sh...

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3rd International Conference on Computation, Automation and Knowledge Management, ICCAKM 2022

作者： Kaushik, Keshav Dass, Alpana Dhankhar, Ayush University of Petroleum and Energy Studies School of Computer Science Dehradun India University of Petroleum and Energy Studies Student B.Tech Computer Science and Engineering Dehradun India

ISBN: (数字)9781665453196

ISBN: (纸本)9781665453196

A new serious flaw has been discovered in log4j, a popular open-source tool used to produce logs within Java programs. The Log4Shell vulnerability, also referred as CVE-2021-44228, enables Remote Code Execution (RCE), enabling attackers to run obfuscated code on the host. A large number of programmes and businesses, including the well-known game Minecraft, employ the well-known utility log4j. Additionally, it is utilised in numerous commercial products as well as several Apache frameworks including Struts2, Kafka, Druid, and Flink. In this paper, the authors have used an approach to exploit the Log4Shell vulnerability and then provided the mitigation methods for the same. © 2022 IEEE.

关键词： Open source software

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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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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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Smart Traffic Management Solution integrating Machine Learning and Internet of Things 1

Smart Traffic Management Solution integrating Machine Learni...

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1st International Conference on Innovative engineering sciences and Technological Research, ICIESTR 2024

作者： Batra, Isha Malik, Arun Sharma, Shamneesh Gochhait, Saikat Lovely Professional University School of Computer Science and Engineering Punjab India UpGrad Education Private Limited Delivery and Student Success-upGrad Campus Bangalore India Symbiosis International Deemed University Sidtm Maharashtra Pune India Samara State Medical University Nri Russia

ISBN: (纸本)9798350348637

Increase in population and trend for luxurious lifestyle has led to huge increase in the number of vehicles. This change has further fascinated the researchers to focus on smart transportation systems. Numerous applications are offered in smart transportation systems like smart parking, detecting event of accidents, smart lights, and many more. Detecting event of accidents and informing vehicles on the move is the most prominent area of research now days. Technologies such as Internet of Things (IoT), Machine Learning (ML) can help achieve goals of smart transportation system. In this research paper, a smart architecture for accident detection and information dissemination is proposed. Initially, the data is collected using IoT sensor. After data collection, decision making is made to find out whether accident has occurred or not and the associated congestion in network using ML techniques. This information is finally disbursed to the neighboring vehicles using a secure method for data communication. The vehicles after receiving this information will decide whether to continue moving or choose some alternate path for communication. This research work emphasizes results after applying ML method and evaluates the performance of method in terms of accuracy, precision, and recall. © 2024 IEEE.

关键词： Decision making

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Next-Gen Assistive Technology: AI Applications for Deaf-Mute and Blind Communities

Next-Gen Assistive Technology: AI Applications for Deaf-Mute...

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2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2024

作者： Bharti, Tushar Singh, Shashwat Chaubey, Ved Prakash Gupta, Shivangini Sharma, Shamneesh Gochhait, Saikat School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Delivery and Student Success upGrad Education Private Limited Karnataka Bangalore India SIDTM Symbiosis International Deemed University Maharashtra Pune India NRI Samara State Medical University Russia

ISBN: (纸本)9798331533137

In today’s interconnected world, communication gaps still exist between individuals with sensory impairments, specifically the blind and deaf-mute communities. This paper introduces BridgeAI, a novel assistive technology platform designed to overcome these communication barriers by leveraging cutting-edge AI technologies. BridgeAI employs Convolutional Neural Networks (CNN) for sign language recognition, speech-to-text, and text-to-speech models tailored for communication between the blind and deaf-mute individuals. The key innovation lies in the use of a custom-built dataset, enhanced through MediaPipe for real-time hand tracking, alongside an integrated Region of Interest (ROI)based approach to improve gesture recognition accuracy. This study presents the architecture, implementation, and results of BridgeAI, which achieved a sign language recognition accuracy of 96.8%, demonstrating significant potential in improving accessibility for the blind and deaf-mute communities. ©2024 IEEE.

关键词： Assistive technology

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