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ProctorXam - Online Exam Proctoring Tool 1

ProctorXam - Online Exam Proctoring Tool

引用

1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Ashwini, S. Bhuvan Kumar, M.S. Chandra Sai, D. Sathya, V. Atria Institute of Technology Dept. of Computer Science & Engineering Bangalore India

ISBN: (数字)9798350371802

ISBN: (纸本)9798350371802

Robust exam monitoring solutions are now essential in light of the most notable Covid-19 outbreak and the increasing inclination towards virtual learning. The automated method for supervising online exams that is suggested in this paper is intended to lessen the difficulties associated with exam cheating. With the assistance of our system's clever face detection algorithms, instructors may efficiently identify suspicious conduct by monitoring students' web activity and camera feed in real-time. Unlike earlier systems, ours provides improved accuracy and functionality, guaranteeing the authenticity of online tests. In light of the difficulties presented by the epidemic, this research attempts to offer a workable option for academic institutions and people wishing to administer fair and secure online exams. © 2024 IEEE.

关键词： Face recognition

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Hybrid RNN-CNN model for predicting stock market trends 1

Hybrid RNN-CNN model for predicting stock market trends

引用

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

作者： Bhanujyothi, H.C. Jacob, I Jeena GITAM Deemed to be University Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350356816

Stock market prediction has become a challenging task in today's world of valuable and best investment. Simple models cannot do stock market prediction to predict future values with high accuracy. The stock market has becoming more complex and volatile, necessitating accurate models that understand its complex, nonlinear dynamics. This paper creates a model to forecast future price of stock market combining Convolutional Neural Network (CNN) and Long-Short Term Memory model (LSTM). Both CNN and LSTM are used for detecting spatial and temporal patterns. Tesla Datasets is used for model implementation. This paper uses Mean Squared Error (MSE) measures as metrics to demonstrate the model's enhanced performance. displays the expected value, compares it to the actual values, and illustrates the potential improvement of the epochs on this model. © 2024 IEEE.

关键词： Convolutional neural networks

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An Advanced computer Vision Model for Aiding Automobile in Traffic Sign Classification 1

An Advanced Computer Vision Model for Aiding Automobile in T...

引用

1st IEEE International Conference on Computing and Data science, ICCDS 2024

作者： Vignesh, Veera Satya Pavan Prajith, Kukkadapu Sai Gokul, Goddati Parthav Adarsh, Cherukuri Rajareddy, Goluguri N. V. GITAM Deemed to be University Dept. of Computer Science & Engineering Visakhapatnam India

ISBN: (纸本)9798350365337

Accurate traffic sign recognition is crucial for autonomous vehicles. This paper proposes a novel deep learning approach for sign classification that addresses limitations in training data. Our method employs a three-step process: strategic data augmentation with rotation limitations for realistic variations, feature extraction using parallel atrous convolution layers with varying dilation rates to capture multi-scale information, and robust feature map generation through output concatenation. This approach achieves a remarkable accuracy of 94.65%, surpassing the performance of established pre-trained models like VGG-16, ResNet-50, and AlexNet. This research contributes a significant advancement in real-world traffic sign classification in autonomous vehicles. © 2024 IEEE.

关键词： computer vision

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Multilingual Approaches to Named Entity Recognition 3

Multilingual Approaches to Named Entity Recognition

引用

3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Savaram, Padmaja Tabassum, Suhana Bandu, Sasidhar Lakshitha, B. College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Dept. of Computer Science KSA Al-kharj Saudi Arabia University College of Engineering Osmania University Dept. of Computer Science and Engineering Hyderabad India Prince Sattam Bin Abdulaziz University Dept. of English Al-Kharj Saudi Arabia Vnrvjiet Jntu Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331518592

Named Entity Recognition (NER) represents a fundamental operation within Natural Language Processing (NLP), focused on the extraction and classification of specific entities embedded in textual data. Given the rising linguistic diversity present in digital communications, there is an escalating need for NER systems to be proficient in identifying and categorizing entities across a spectrum of languages. However, developing NER models for resource-poor languages presents significant challenges due to limited labeled data and linguistic resources. This paper examines methodologies for enhancing the ability of NLP models to perform NER across diverse languages by transferring knowledge from high-resource languages to low-resource languages. We delve into advanced approaches such as cross-lingual transfer learning, multilingual embeddings, and cross-lingual model adaptation. Cross-lingual transfer learning utilizes pre-trained models from high-resource languages to initialize NER systems for low-resource languages, thereby facilitating the effective transfer of linguistic knowledge across language boundaries. Multilingual embeddings provide a shared representation space for words across languages, facilitating the transfer of linguistic knowledge. Additionally, cross-lingual model adaptation techniques aim to adapt existing NER models to new languages through fine-tuning or domain adaptation. By enhancing the generalizability of NER models through cross-lingual knowledge transfer, we enable these models to perform effectively across diverse linguistic contexts, including both resource-rich and resource-poor languages. These advancements contribute to broader accessibility and applicability of NER technology across languages and cultures, facilitating more inclusive and comprehensive language processing applications. © 2024 IEEE.

关键词： Embeddings

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Seamless Service Evolution: Enhancing Customer Satisfaction Using AWS-Driven AI Chatbots for Restaurant Ordering 4

Seamless Service Evolution: Enhancing Customer Satisfaction ...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Ali, Mohammed Jaffer Vyshnavi, M.R.V. Kumar, Konda V.S. Harshith Vishnubhatla, Sravya Rajagopal, Shinu M. Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331540364

In the current restaurant business environment, factors have emerged in the manner in which consumers are attended to and the ways orders are placed. As good as these traditional systems may be, they do not offer the optimum flexibility and consumer convergence that today's client demands for fast and convenient services. Recent approaches using only basic form of automation and rule-based chatbots do offer some enhancements but lack adequate capabilities to deal with huge volumes of orders, custom recommendations and scalability according to demand. Further, the existing systems have problems with scalability, versatility to different customers' trends and needs, and real-time interactions. To overcome these challenges our work, will utilize modern advances in artificial intelligence and cloud technology. The proposed system uses Amazon Lex for NLU, A WS Lambda for processing multiple booking requests simultaneously without any intervention from the users and Amazon Polly for making interaction more interactive with personalized audio. In addition, Amazon Rekognition and A WS S3 for images will be used for analysis of images and proper storage of data respectively, enhancing the customer satisfaction by recommending products based on a past order. All these technologies integrated with A WS ecosystem in our solution will provide convenience, efficiency and passion to restaurant business proprietors to fulfill the increasing demands of modern customer. The system also supports linkage with some commonly used applications such as WhatsApp, guaranteeing that the customers receive timely updates. Thus, this solution is designed to create an improved status of restaurant ordering and eliminate the problematic experiences placed in current schemes. © 2024 IEEE.

关键词： Scalability

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Cardiovascular Disease Prediction Utilizing Machine Learning and Feature Selection with Clonal Selection Algorithm 5

Cardiovascular Disease Prediction Utilizing Machine Learning...

引用

5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Rahman, Wahidur Aneek, Riasaad Haque Moinuddin, Md. Sakib, Md. Shadman Iqbal, Md. Sadiq Rahman, Mohammad Motiur Uttara University Dept. of Computer Science and Engineering Uttara Dhaka Bangladesh Z.H. Sikder University of Science and Technology Dept. of Computer Science and Engineering Bhedarganj Shariatpur Bangladesh University of Information Technology and Sciences Dept. of Computer Science and Engineering Dhaka Bangladesh Bangladesh University Dept. of Computer Science and Engineering Adabor Bangladesh Mawlana Bhashani Science and Technology University Dept. of Computer Science and Engineering Tangail Bangladesh

ISBN: (纸本)9798350394290

A substantial and rising number of patients suffer from cardiovascular diseases, including heart attacks, heart failure, and other related illnesses. This case surge places increasing pressure on healthcare professionals and administrators while patients grapple with growing medical costs. To address these challenges, an automated system is necessary within the healthcare sector. In this paper, we present a cardiovascular health monitoring system that incorporates Machine Learning techniques. The study employed feature selection techniques on a secondary dataset. The study has used a nature-inspired technique, namely the Clonal Selection Algorithm (CSA) and Maximum Relevance Minimum Redundancy (mRMR) technique, to identify the most prominent feature in the detection of cardio-vascular illness. This approach was combined with a collection of Machine Learning classifiers. A group of experimental data has been listed to assess the suggested model's effectiveness. The research findings indicated a maximum accuracy rate of 100% when employing the proposed algorithms and orientations. The study also discusses the performance analysis for CSA and mRMR using a set of performance evaluation matrices. Based on the obtained results, it can be inferred that the suggested model will likely exhibit a high level of effectiveness in identifying cardiovascular diseases. © 2023 IEEE.

关键词： Feature Selection

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Voice Mate: Voice Controlled Chessboard for Visually Impaired

Voice Mate: Voice Controlled Chessboard for Visually Impaire...

引用

2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Abraham, Anishamol Mammen, Agnel Soyi, Ajay Jhonson, Ben Biju, Joel Amal Jyothi College of Engineering Dept. of Computer Science Kottayam India

ISBN: (纸本)9798350382693

Traditional chess can affect people with disabilities who have physical difficulty controlling the pieces, although the concept and gameplay are closely related. This paper explores the concept of voice-controlled chessboards to address this gap and encourage participation in chess. This paper presents a method for creating voice-controlled chessboards. This method introduces the structure of the circuit board, focusing on communication skills. It explains how to use stepper motors to move the chess according to commands. The paper recognises the importance of careful observation and control to ensure correct pronunciation and good chess performance. This paper discusses possible implications and areas that require further consideration. These areas include ensuring speech accuracy, maintaining reliable status monitoring of the card, and ensuring motor control. Solving these issues through development and testing is crucial to the success of voice-controlled chessboards. The paper concludes by highlighting the importance of user testing with target users (people with disabilities) to improve usability. This project has the potential to not only make chess more accessible but also stimulate the development of new technologies for a variety of recreational games. © 2024 IEEE.

关键词： Stepping motors

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Advancing Ophthalmology through Transfer Learning and Channel-Wise Attention for Retinal Disease Classification 6

Advancing Ophthalmology through Transfer Learning and Channe...

引用

6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Islam, Sudipta Progga Alam, Ashraf Ul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350385779

The global incidence of ocular disorders, which impacts more than 2.2 billion people, requires innovative approaches to ensure early and accurate diagnosis. This paper explores the integration of artificial intelligence (AI) in ophthal-mology, focusing on retinal diseases like cataracts, glaucoma, and diabetic retinopathy. The utilization of AI enables remote examinations and consultations, providing a crucial advantage in early disease detection. This study introduces a comparative analysis of state-of-the-art models, combining pre-trained convolutional neural networks (CNNs) and incorporating a channel-wise attention mechanism for enhanced discriminative ability. The proposed hybrid model, combining EfficientNetBO and Inception V3 with channel-wise attention mechanism, exhibits a remarkable accuracy of 98.13%, outperforming standalone models and other combinations. The findings underscore the efficacy of combined architectures and the integration of attention mechanisms in advancing the classification of retinal diseases. © 2024 IEEE.

关键词： Ophthalmology

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Text-Based Sentimental Analysis to Understand User Experience Using Machine Learning Approaches 9

Text-Based Sentimental Analysis to Understand User Experienc...

引用

9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Gokulapriya, R. Sambandam, Rakoth Kandan Balamurugan, M. Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350397376

Data Analysis is turning into a driving force in every industry. It is a process in which data is analyzed in multiple ways to come to certain conclusions for the given situation. Sentiment analysis can be said to be a sub-section of data analysis where analysis is carried out on the emotions and opinions of the text. Social media has a plethora of sentiment data in various forms such as tweets, updates on the status, and so forth. Sentiment analysis on the huge volume of data can help in identifying the opinions of the general *** primary goal is to find the opinion of customers on the services of the Bangalore airport and to enhance the nature of these services according to the feedback provided. In this paper, we aim to measure customer opinion on services provided by Bangalore Airport through sentiment. Data is collected by a python-based scraper. The tweets are processed to determine whether they are of positive or negative opinion. These opinions are then analyzed to determine the factors which cause the negative opinions and the airport staff are alerted about the same. Various algorithms were used as part of the experimental analysis. LSTM produces more accuracy compared with existing approaches. © 2023 IEEE.

关键词： Python

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Building Network Digital Twins Part I: State Synchronization 3

Building Network Digital Twins Part I: State Synchronization

引用

3rd International Conference on 6G Networking, 6GNet 2024

作者： Sengendo, John Granelli, Fabrizio University of Trento Dept. of Information Engineering and Computer Science Trento Italy

ISBN: (纸本)9798350378597

The Network Digital Twin is emerging as a promising technology for future networks, including 6G, as it allows to gain deep knowledge of its Physical Twin and to perform 'what-if' analyses in a controlled environment. However, while the concept of Digital Twin reached maturity in other fields (such as manufacturing), still the application of this concept to networks presents some relevant challenges. This paper studies the problem of synchronization between the Digital and the Physical Twin, a key functionality to enable the Digital Twin to accurately replicate the behaviour of the Physical Twin. Indeed, accurate synchronization is challenging due to the amount of data to exchange and the delays due to acquisition, modeling and transmission of the related information. The paper analyzes the delay chain in the synchronization process and introduces the concept of 'negative delay' through accurate prediction in order to minimize the synchronization delay and enable real-time or 'near' real-time synchronization between the Physical and the Digital Twin. © 2024 IEEE.

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