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18th IEEE International Conference on Vehicular Electronics and Safety, ICVES 2024

作者： Bhushan, Shourya Chaturvedi, Manish Iitram Department of Electrical and Computer Science Engineering Ahmedabad India

ISBN: (纸本)9798350387278

Emergency vehicles face difficulty in navigating through traffic and locating the shortest route to the incident site. In the developing countries or under disaster conditions when limited communication and Intelligent Transportation System (ITS) infrastructure is available, the situation becomes more challenging. This paper addresses these challenges by leveraging the unique sound generated by emergency vehicles to detect the direction of the emergency vehicles. We use the Mel-frequency cepstral coefficients (MFCC) and a Convolutional Neural Network (CNN) stacked with Long Short-Term Memory (LSTM) layers to accurately detect the movement direction of outgoing emergency vehicles at a junction. MFCC facilitates effective feature extraction from audio signals, while the CNN-LSTM architecture enables robust temporal and spatial pattern recognition. The information can be used to adapt the traffic lights at the downstream junction to enable the uninterrupted movement of the emergency vehicle through the junction. The experiment results demonstrate 98.56% accuracy in detecting movement direction of an emergency vehicle. The solution can improve emergency response under resource constrained environment and in turn improve public safety and well-being. © 2024 IEEE.

关键词： Convolutional neural networks

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Efficiency and Reliability in Student Attendance: A Trifecta of QR Codes, Face Recognition, and Geolocation

Efficiency and Reliability in Student Attendance: A Trifecta...

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2024 IEEE Students Conference on engineering and Systems, SCES 2024

作者： Singh, Garima Rastogi, Vansh Singh, Amandeep Nath, Monika Kumari Bhadula, Shuchi Dehradun Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350374711

In recent years, the need for attendance monitoring has increased drastically. The most used method is using pen and paper. Several other methods have been developed for attendance monitoring and attendance can be monitored with the help of various techniques including face recognition, fingerprint verification and capturing retina scans. However, it has been noticed that there are significant differences among the performances of these methods. In this work, we have combined several functionalities to solve the problems with the traditional systems. We have incorporated a face recognition technique with QR code verification and monitoring the geo location of the student to solve the problem of buddy punching. By combining these three verification aspects, a dependable, and user-friendly solution for attendance monitoring can be deployed. It will eventually contribute to the growth of AMSs in educational, business, and organizational settings. © 2024 IEEE.

关键词： Students

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A Comparative Analysis of Card Sorting and IDM Approaches in e-Tourism Website Design 26

A Comparative Analysis of Card Sorting and IDM Approaches in...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Rahman, Raiyan Islam, Muhammad Nazrul Ibn Aziz, Faisal Hossain, Md Zahid Afroze, Sumaiya Suha, Sayma Alam United International University Department of Computer Science and Engineering Dhaka Bangladesh Department of Computer Science and Engineering Dhaka1216 Bangladesh Bangladesh Army International University of Science and Technology Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350359015

While Interactive Dialogue Model (IDM) and card sorting are popular methods for designing websites, research is continuously being done on which method is best suited for the design of multichannel or web-based applications, based on the complexity and feature set of the application to be developed. In this research, we explore which design method performs better for websites with fairly complex navigational structures with multiple levels of page traversal. To explore, the IDM and Card Sorting methods were meticulously followed starting from early designs to fully implemented prototypes e-Tourism websites for a group of hotels or resorts. Then, both websites were evaluated following a between-subject experimental approach to assess and compare their usability. The study showed that although the navigational design with IDM may be a bit more complex and may require more clicks for completing some tasks, it far outperformed the card sorting design for our case in all significant parameters. Overall, IDM gives a better outcome in the usability and satisfaction for all users, thus pointing to the fact that IDM would be the better way to design complex structured websites like an e-Tourism website. © 2023 IEEE.

关键词： Card Sorting e-Tourism HCI Interactive Dialogue Model (IDM) Usability Testing Web Design Methods

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Subchannel selection methods for 3GPP C-V2X networks by considering vehicular mobility

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Telecommunication Systems 2024年第3期86卷 503-518页

作者： Pan, Meng-Shiuan Kao, Shao-Wei Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Department of Computer Science and Information Engineering Tamkang University New Taipei Taiwan

The 3GPP vehicle-to-everything (C-V2X) technology is a key solution to provide communication services for applications of intelligent transportation systems (ITS). According to the C-V2X specification, vehicles are allowed to communicate with each other without the help of cellular networks and can select subchannels (i.e. radio resources) by themselves. However, this design will induce subchannel collisions because of the mobility of vehicles. This work aims to relieve the aforementioned problem. We propose a cooperative subchannel selection (CoSS) scheme and a localized subchannel selection (LoSS) scheme, which consider vehicular mobility to prevent subchannel collisions. Based on the collected information, CoSS predicts possible subchannel collisions due to vehicles’ location changes. On the other hand, LoSS avoids subchannel collisions by organizing the usage of subchannels based on vehicles’ relative moving speeds. The simulation results indicate that the proposed schemes can effectively reduce subchannel collision ratio and increase packet delivery ratio for the network. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Vehicle to Everything

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Quantum Machine Learning for Rotating Machinery Prognostics and Health Management 5

Quantum Machine Learning for Rotating Machinery Prognostics ...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Tejaswi, Gaddam Prabhakar, Kunal Kunar, S.Deva VFSTR University Department of Computer Science and Engineering AP India

ISBN: (纸本)9798331530013

The study conducted by the prognostics and health management (PHM) entity inquires into the prediction of machine behavior to aid in decision-making through failure modes classification via diagnostics and estimation. This is primarily performed as a classification of the state of rotating machines using diversified The methodologies of Machine Learning (ML) and Deep Learning (DL) have garnered significant attention in contemporary research. In recent times, Quantum Computing (QC) has surfaced as a novel domain of inquiry. With applications in various fields such as optimization, artificial intelligence (AI), gaming, cybersecurity, pharmaceuticals, and the energy sector. Despite hardware limitations, QC is considered an approach to accelerate models and enhance computational power. This paper presents a QML approach for detecting defects in rolling element bearings of rotating machinery using vibration signals. The method employs a hybrid model: a classical neural network to represent a class of parameterized quantum circuits (PQCs), specifically the multi-layer perceptron (MLP). Under different configurations of the Variational Quantum Eigensolver (VQE) system, various types of spin gates and unique two-qubit gates such as CNOT, CZ, and ISWAP are implemented in each PQC configuration. The performance of these PQC configurations is evaluated using two databases of varying complexities: JNU with a failure mode of 10, and CWRRU with a failure mode of 12. Performance results show that all QML models outperform the classical MLP for the two databases considered. This indicates the promising potential of QML models in the field of PHM, despite the current fragile quantum environments. © 2024 IEEE.

关键词： Quantum electronics

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DeepFoliage: A Unified Approach for Plant Disease Detection using Deep Learning Techniques 3

DeepFoliage: A Unified Approach for Plant Disease Detection ...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Kumar, Kalyan Vardhini, Mounika Brindasri, S. Hindustan Institute of Technology and Science Chennai Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

Plant diseases pose a serious danger to crop productivity and quality, making the agricultural sector one of the main obstacles to maintaining food security. The word 'deep foliage' was coined to combine the terms 'deep' (referring to deep learning) and 'foliage', which refers to leaves or other plant life. In particular, illnesses that impact plant leaves are the main focus of this work, which aims to show how deep learning techniques can be used for plant disease analysis and diagnosis. The proposed method combines two algorithms that were trained on a varied dataset of plant pictures, including different crops and disease presentations, to create a hybrid deep learning technique. The proposed method combines two algorithms that were trained on a varied dataset of plant pictures, including different crops and disease presentations, to create a hybrid deep learning technique. With the ability to recognize and classify possible diseases in plant foliage with accuracy, the deep learning model picks up complex patterns and features. 'Deep Foliage' enables prompt intervention to lessen the impact of illnesses on agricultural productivity by offering rapid and precise diagnosis. By lowering the reliance on antiquated, labor-intensive disease detection techniques, the project seeks to promote sustainable agricultural practices. © 2024 IEEE.

关键词： Diseases

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The Psychology Behind Addictive Applications in Technology 5

The Psychology Behind Addictive Applications in Technology

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Hans, Aarti Singh, Nongmeikapam Thoiba Kumar, Rohit Kumar, Anshul Choubey, Anish Suresh Chandigarh University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350383522

The proliferation of mobile applications in the digital age has introduced a captivating yet disconcerting dimension to human behavior and technology interaction. Mobile applications, ranging from social media platforms to games and productivity tools, have gained notoriety for their potential to foster addiction-like behaviors in users. This research paper delves into the intricate realm of the psychology behind addictive applications in technology, seeking to illuminate the multifaceted dynamics that underlie this contemporary challenge. Drawing on insights from psychology, cognitive science, and human-computer interaction, this study explores the fundamental questions surrounding the nature of addiction within the digital context. It investigates the cognitive and emotional factors that drive users to engage compulsively with specific applications, shedding light on the alluring mechanisms that captivate and hold user attention. Furthermore, this research uncovers the ethically charged design and marketing strategies employed by application developers to perpetuate addictive use patterns. By examining the deliberate design choices that exploit human psychology, we aim to provoke a deeper conversation about the responsible development and marketing of digital technologies. © 2024 IEEE.

关键词： human behavior human-computer interaction retention social comparison social media platforms user engagement

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Real Time Web Traffic Forecasting Using LSTM and CNN

Real Time Web Traffic Forecasting Using LSTM and CNN

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Kushwaha, Manveer Sangwan, Anamika Gupta, Keshav Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798331529833

A Real-Time Web Traffic Forecasting System Using Hybrid LSTM and CNN Paper Development of the real-time web traffic forecasting system uses a hybrid approach combining LSTM with Conventional Neural Networks (CNN) to forecast website visitor counts and user engagement metrics for better resource and server scalability and in turn user experience. Methodology This entails the utilization of machine learning techniques in a time series forecasting context, as well as data preprocessing to truthfully make short-term forecasts. The results proved that the hybrid model performs better than the traditional statistical models like ARIMA, and this has significant impacts on dynamic management of resources. For optimized management of loads on the server, real-time traffic forecasting of web traffic is necessary. The system exploits both linear and non-linear relationships of data and, hence, makes for better short-term predictions than do conventional ARIMA techniques. This paper describes the design, implementation, and evaluation of the hybrid model with the objective of proving its superiority over the complex patterns of web traffic. The results have been much better in terms of prediction, reduced system downtime, and increased efficiency in operational functioning-land on a road to resource optimization and proactive infrastructure management. © 2025 IEEE.

关键词： Prediction models

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PANDA: Persona Attributes Navigation for Detecting and Alleviating Overuse Problem in Large Language Models

PANDA: Persona Attributes Navigation for Detecting and Allev...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Kim, Jinsung Koo, Seonmin Lim, Heuiseok Department of Computer Science and Engineering Korea University Korea Republic of

ISBN: (纸本)9798891761643

In the persona-grounded dialogue (PGD) task, it is required not only to respond fluently, but also to ground the attributes according to the current conversation topic properly. However, due to their tendency to overly ground given attributes, LLMs often generate unnatural responses provoked by using attributes that deviate from the flow of the conversation or by exploiting too many attributes at once. We term this phenomenon the overuse problem of LLMs. Unfortunately, research devising precise criteria and frameworks to quantitatively verify LLMs' overuse problem is obviously insufficient. To address this issue, we propose Persona Attributes Navigation for Detecting and Alleviating the overuse problem (PANDA) framework. PANDA is the first study to quantify the persona overuse problem of LLMs by establishing clear standards of the problem and verifying various LLMs based on them. Moreover, this framework navigates us into understanding persona attributes by introducing diverse and detailed dialogue topics that consider practical conversation situations. We provide insights related to LLMs' persona attribute overuse problem through comprehensive verification and analysis with PANDA in the PGD task. Our code and resources can be found at http://***/jin62304/PANDA. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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A Review of Traditional and Neural Network Methods for Protecting Privacy in Big Data Analytics 5th

A Review of Traditional and Neural Network Methods for Prote...

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5th International Conference on Deep Learning, Artificial Intelligence and Robotics, ICDLAIR 2023

作者： Thasna, C.A. Chawla, Meenu Tiwari, Namita Department of Computer Science and Engineering MANIT Bhopal Bhopal India

ISBN: (纸本)9783031609343

Computing technology suffused the human environment and engenders copious amounts of data. An individual’s right to privacy means that they can choose what information is shared. The area of data analytics has changed as a result of big data’s introduction. If private information is not managed properly on the web, it becomes public. Data collection has become quicker because of modern technology. However, breaching people’s privacy poses an extreme concern. This study evaluates current privacy-preserving methods including traditional and neural network methods and describes the challenges of using them for heterogeneous data and how fuzzy neural networks are used in big data analytics to protect privacy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Differential privacy

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